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  • Articles  (1,101)
  • Hindawi  (1,101)
  • 2015-2019  (1,101)
  • 2017  (1,101)
  • Technology  (1,086)
  • Geography  (15)
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  • Articles  (1,101)
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  • 2015-2019  (1,101)
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  • 1
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    Understanding Youth Violence in Kumasi: Does Community Socialization Matter? A Cross-Sectional Study (2017)
    Hindawi
    Details
    Publication Date: 2017
    Description: Violence by young people is one of the most visible forms of social disorder in urban settlements. This study assesses the causes and consequences of youth violence in the Kumasi metropolis. The study design was a nonexperimental cross-sectional survey. A mixed method approach facilitated the random sampling of 71 young people in the Kumasi metropolis through a stratified procedure between December 2014 and November 2015. Ten (10) participants were purposively selected and enrolled in a focus group discussion. Descriptive statistics formed the basis for the analysis. This was supported with a matched discourse analysis of the emerging themes. More than half of the youth (39, 54.9%) demonstrated history of ever engaging in violence in the past one year of whom 24 (61.5%) were without formal education. The frequency of the violence perpetuation ranged from daily engagement (3, 4%) to weekly engagement in violence (12, 17%). Principally, the categories of youth violence were manifested in noise making, rape, murder, stealing, drug addiction, obscene gestures, robbery, sexual abuse, and embarrassment. Peer pressure and street survival coping approaches emerged as the pivotal factors that induced youth violence. Addressing youth violence requires an integrative framework that incorporates youth perspectives, government, chiefs, and nongovernmental organizations in Ghana, and religious bodies.
    Print ISSN: 2090-4185
    Electronic ISSN: 2090-4193
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  • 2
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    Ethnoreligious Urban Violence and Residential Mobility in Nigerian Cities: The Kaduna Experience (2017)
    Hindawi
    Details
    Publication Date: 2017
    Description: This paper seeks to examine the ethnoreligious urban violence and residential mobility in the city of Kaduna with a view to make recommendations towards ameliorating its effects by evaluating the causal factors fueling the crisis and examining the pattern and direction of the residential mobility in the city. The sources of data were both primary and secondary. The sampling technique used was purposive and random sampling from two residential districts from both the northern and southern parts of the city. A total of 1,000 questionnaires were administered within the study areas and 900 questionnaires were collected. Face-to-face interviews were conducted with major stakeholders from the two parts. The data obtained were analysed using thematic and content analysis for the qualitative data whilst the quantitative data were analysed using simple percentages. The results revealed that the factors causing the ethnoreligious urban violence and residential mobility are unemployment, social institutional breakdown, politics, and colonial impact and the pattern/direction of the residential mobility in the city of Kaduna show a clear polarization along religious lines based reactive residential mobility between the two parts of the city. Based on these results recommendations were made to assist the academia, practitioners, and policy makers.
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  • 3
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    Determinants of Growth in Multiunit Housing Demand since the Great Recession: An Age-Period-Cohort Analysis (2017)
    Hindawi
    Details
    Publication Date: 2017
    Description: Following the Great Recession (2007–2009), growth in multiunit housing starts has been exceptionally strong and sustained. In this study, we examine empirical evidence for three possible explanations, namely, the passage of Baby Boomers into senior years, the depressed economic conditions, and rising preference of recent birth cohorts for residing in urban cores. Applying Age-Period-Cohort analysis to census data on multiunit housing occupancy from 1970 to 2010, we find evidence to support the explanations that a sharp increase in demand from Millennials drawn to urban cores and retiring Baby Boomers are contributing to the growth in multiunit housing starts. The results provide weak evidence of a negative relationship between depressed economic conditions and demand for multiunit housing starts. Over the long term, demand for multiunit housing can be expected to moderate as a result of the projected aging of the population.
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  • 4
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    The Power of Place: A Case Study of Auckland’s Design Creative Industries (2017)
    Hindawi
    Details
    Publication Date: 2017
    Description: Auckland is New Zealand’s largest city which also has the largest concentration of creative sector employment and businesses in the country. This study examines the spatial logic of firms in the design creative subsector in inner Auckland to gain better understanding of the ways in which place, space, and built form determine location choices. Firms’ attributes are also analysed with the aim of ascertaining whether firms with shared characteristics have similar spatial behaviour. Despite a high degree of spatial clustering, the unequal distribution of firms across inner Auckland suggests that some areas, such as Parnell, are favoured more than others. Parnell’s unique heritage built environment was identified as a location factor that has appealed to architects, designers, and advertisers and has contributed to the formation of the area’s creative clusters. The quality of Parnell’s built environment was associated with place reputation and image branding which was an important part of creating firm’s identity. A central location and the attractiveness of an amenities-rich local environment also played a role in the decision-making process. Furthermore, the results also suggest that firms that possess common characteristics have similar spatial logic and make similar location choices.
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  • 5
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    Nature-Related Experience during Childhood in Urban and Rural Areas: The Case of Peninsular Malaysians (2017)
    Hindawi
    Details
    Publication Date: 2017
    Description: Direct experiences with nature in childhood are essential for enhancing psychological and physical development in children. However, researches on childhood nature-related experiences and their effects are largely biased toward more developed Western countries. In this study, we created a questionnaire on childhood experiences with nature and surveyed 357 adults (〉20 years old) around Kuala Lumpur, the capital of Malaysia, to determine whether younger generations had fewer nature-based experiences than older generations and whether people who grew up in urban areas had fewer experiences than those who grew up in rural areas. We found that playing in rivers or waterfalls and collecting and eating tropical fruits were the most common nature-related activities experienced in childhood. There was a minimal decline in nature-related experiences among generations. However, people who grew up in rural areas had more nature-related experiences than those who grew up in urban areas. The loss of nature areas and increase in population density may accelerate the decline in nature-related experiences in urban areas. Therefore, efforts to create urban parks and other public spaces for reconnecting urban children to nature will become increasingly important for urban planning and environmental education in tropical developing countries such as Malaysia.
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  • 6
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    Nature-Related Experience during Childhood in Urban and Rural Areas: The Case of Peninsular Malaysians (2017)
    Hindawi
    Details
    Publication Date: 2017
    Description: Direct experiences with nature in childhood are essential for enhancing psychological and physical development in children. However, researches on childhood nature-related experiences and their effects are largely biased toward more developed Western countries. In this study, we created a questionnaire on childhood experiences with nature and surveyed 357 adults (〉20 years old) around Kuala Lumpur, the capital of Malaysia, to determine whether younger generations had fewer nature-based experiences than older generations and whether people who grew up in urban areas had fewer experiences than those who grew up in rural areas. We found that playing in rivers or waterfalls and collecting and eating tropical fruits were the most common nature-related activities experienced in childhood. There was a minimal decline in nature-related experiences among generations. However, people who grew up in rural areas had more nature-related experiences than those who grew up in urban areas. The loss of nature areas and increase in population density may accelerate the decline in nature-related experiences in urban areas. Therefore, efforts to create urban parks and other public spaces for reconnecting urban children to nature will become increasingly important for urban planning and environmental education in tropical developing countries such as Malaysia.
    Print ISSN: 2090-4185
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  • 7
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    Determinants of Growth in Multiunit Housing Demand since the Great Recession: An Age-Period-Cohort Analysis (2017)
    Hindawi
    Details
    Publication Date: 2017
    Description: Following the Great Recession (2007–2009), growth in multiunit housing starts has been exceptionally strong and sustained. In this study, we examine empirical evidence for three possible explanations, namely, the passage of Baby Boomers into senior years, the depressed economic conditions, and rising preference of recent birth cohorts for residing in urban cores. Applying Age-Period-Cohort analysis to census data on multiunit housing occupancy from 1970 to 2010, we find evidence to support the explanations that a sharp increase in demand from Millennials drawn to urban cores and retiring Baby Boomers are contributing to the growth in multiunit housing starts. The results provide weak evidence of a negative relationship between depressed economic conditions and demand for multiunit housing starts. Over the long term, demand for multiunit housing can be expected to moderate as a result of the projected aging of the population.
    Print ISSN: 2090-4185
    Electronic ISSN: 2090-4193
    Topics: Architecture, Civil Engineering, Surveying , Geography
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  • 8
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    The Power of Place: A Case Study of Auckland’s Design Creative Industries (2017)
    Hindawi
    Details
    Publication Date: 2017
    Description: Auckland is New Zealand’s largest city which also has the largest concentration of creative sector employment and businesses in the country. This study examines the spatial logic of firms in the design creative subsector in inner Auckland to gain better understanding of the ways in which place, space, and built form determine location choices. Firms’ attributes are also analysed with the aim of ascertaining whether firms with shared characteristics have similar spatial behaviour. Despite a high degree of spatial clustering, the unequal distribution of firms across inner Auckland suggests that some areas, such as Parnell, are favoured more than others. Parnell’s unique heritage built environment was identified as a location factor that has appealed to architects, designers, and advertisers and has contributed to the formation of the area’s creative clusters. The quality of Parnell’s built environment was associated with place reputation and image branding which was an important part of creating firm’s identity. A central location and the attractiveness of an amenities-rich local environment also played a role in the decision-making process. Furthermore, the results also suggest that firms that possess common characteristics have similar spatial logic and make similar location choices.
    Print ISSN: 2090-4185
    Electronic ISSN: 2090-4193
    Topics: Architecture, Civil Engineering, Surveying , Geography
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  • 9
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    Understanding Youth Violence in Kumasi: Does Community Socialization Matter? A Cross-Sectional Study (2017)
    Hindawi
    Details
    Publication Date: 2017
    Description: Violence by young people is one of the most visible forms of social disorder in urban settlements. This study assesses the causes and consequences of youth violence in the Kumasi metropolis. The study design was a nonexperimental cross-sectional survey. A mixed method approach facilitated the random sampling of 71 young people in the Kumasi metropolis through a stratified procedure between December 2014 and November 2015. Ten (10) participants were purposively selected and enrolled in a focus group discussion. Descriptive statistics formed the basis for the analysis. This was supported with a matched discourse analysis of the emerging themes. More than half of the youth (39, 54.9%) demonstrated history of ever engaging in violence in the past one year of whom 24 (61.5%) were without formal education. The frequency of the violence perpetuation ranged from daily engagement (3, 4%) to weekly engagement in violence (12, 17%). Principally, the categories of youth violence were manifested in noise making, rape, murder, stealing, drug addiction, obscene gestures, robbery, sexual abuse, and embarrassment. Peer pressure and street survival coping approaches emerged as the pivotal factors that induced youth violence. Addressing youth violence requires an integrative framework that incorporates youth perspectives, government, chiefs, and nongovernmental organizations in Ghana, and religious bodies.
    Print ISSN: 2090-4185
    Electronic ISSN: 2090-4193
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  • 10
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    Ethnoreligious Urban Violence and Residential Mobility in Nigerian Cities: The Kaduna Experience (2017)
    Hindawi
    Details
    Publication Date: 2017
    Description: This paper seeks to examine the ethnoreligious urban violence and residential mobility in the city of Kaduna with a view to make recommendations towards ameliorating its effects by evaluating the causal factors fueling the crisis and examining the pattern and direction of the residential mobility in the city. The sources of data were both primary and secondary. The sampling technique used was purposive and random sampling from two residential districts from both the northern and southern parts of the city. A total of 1,000 questionnaires were administered within the study areas and 900 questionnaires were collected. Face-to-face interviews were conducted with major stakeholders from the two parts. The data obtained were analysed using thematic and content analysis for the qualitative data whilst the quantitative data were analysed using simple percentages. The results revealed that the factors causing the ethnoreligious urban violence and residential mobility are unemployment, social institutional breakdown, politics, and colonial impact and the pattern/direction of the residential mobility in the city of Kaduna show a clear polarization along religious lines based reactive residential mobility between the two parts of the city. Based on these results recommendations were made to assist the academia, practitioners, and policy makers.
    Print ISSN: 2090-4185
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  • 11
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    Delay Induced Hopf Bifurcation of an Epidemic Model with Graded Infection Rates for Internet Worms (2017)
    Hindawi
    Details
    Publication Date: 2017-01-01
    Description: A delayed SEIQRS worm propagation model with different infection rates for the exposed computers and the infectious computers is investigated in this paper. The results are given in terms of the local stability and Hopf bifurcation. Sufficient conditions for the local stability and the existence of Hopf bifurcation are obtained by using eigenvalue method and choosing the delay as the bifurcation parameter. In particular, the direction and the stability of the Hopf bifurcation are investigated by means of the normal form theory and center manifold theorem. Finally, a numerical example is also presented to support the obtained theoretical results.
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  • 12
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    Convolutional Shallow Features for Performance Improvement of Histogram of Oriented Gradients in Visual Object Tracking (2017)
    Hindawi
    Details
    Publication Date: 2017-01-01
    Description: Histogram of oriented gradients (HOG) is a feature descriptor typically used for object detection. For object tracking, this feature has certain drawbacks when the target object is influenced by a change in motion or size. In this paper, the use of convolutional shallow features is proposed to improve the performance of HOG feature-based object tracking. Because the proposed method works based on a correlation filter, the response maps for each feature are summed in order to obtain the final response map. The location of the target object is then predicted based on the maximum value of the optimized final response map. Further, a model update is used to overcome the change in appearance of the target object during tracking. A performance evaluation of the proposed method is obtained by using Visual Object Tracking 2015 (VOT2015) benchmark dataset and its protocols. The results are then provided based on their accuracy-robustness (AR) rank. Furthermore, through a comparison with several state-of-the-art tracking algorithms, the proposed method was shown to achieve the highest rank in terms of accuracy and a third rank for robustness. In addition, the proposed method significantly improves the robustness of HOG-based features.
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  • 13
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    A Signal Processing Algorithm Based on 2D Matched Filtering for SSAR (2017)
    Hindawi
    Details
    Publication Date: 2017-01-01
    Description: This study discusses a smart radar antenna scanning mode that combines features of both the sector-scan mode used for conventional radar and the line-scan mode used for synthetic aperture radar (SAR) and achieves an application of the synthetic aperture technique in the conventional sector-scan (mechanically scanned) radar, and we refer to this mode as sector-scan synthetic aperture radar (SSAR). The mathematical model is presented based on the principle of SSAR, and a signal processing algorithm is proposed based on the idea of two-dimensional (2D) matched filtering. The influences of the line-scan range and speed on the SSAR system are analyzed, and the solution to the problem that the target velocity is very high is given. The performance of the proposed algorithm is evaluated through computer simulations. The simulation results indicate that the proposed signal processing algorithm of SSAR can gather the signal energy of targets, thereby improving the ability to detect dim targets.
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  • 14
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    A Stochastic Programming Approach for Resilient Hub Location in Power Projection Network considering Random Hub Failures (2017)
    Hindawi
    Details
    Publication Date: 2017-01-01
    Description: Hubs are critical facilities in the power projection network. Due to the uncertainty factors such as terrorism threats, severe weather, and natural disasters, hub facilities may be disrupted randomly, which could lead to excessive cost or loss in practice. One of the most effective ways to withstand and reduce the impact of disruptions is designing more resilient networks. In this paper, a stochastic programming model is employed for the hub location problem in the presence of random hub failures. A heuristic algorithm based on Monte Carlo method and tabu search is put forward to solve the model. The proposed approach is more general if there are numbers of hubs that would fail even with different failure probability. Compared with the benchmark model, the model which takes the factor of stochastic failure of hubs into account can give a more resilient power projection network.
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  • 15
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    A Multiproduct Single-Period Inventory Management Problem under Variable Possibility Distributions (2017)
    Hindawi
    Details
    Publication Date: 2017-01-01
    Description: In multiproduct single-period inventory management problem (MSIMP), the optimal order quantity often depends on the distributions of uncertain parameters. However, the distribution information about uncertain parameters is usually partially available. To model this situation, a MSIMP is studied by credibilistic optimization method, where the uncertain demand and carbon emission are characterized by variable possibility distributions. First, the uncertain demand and carbon emission are characterized by generalized parametric interval-valued (PIV) fuzzy variables, and the analytical expressions about the mean values and second-order moments of selection variables are established. Taking second-order moment as a risk measure, a new credibilistic multiproduct single-period inventory management model is developed under mean-moment optimization criterion. Furthermore, the proposed model is converted to its equivalent deterministic model. Taking advantage of the structural characteristics of the deterministic model, a domain decomposition method is designed to find the optimal order quantities. Finally, a numerical example is provided to illustrate the efficiency of the proposed mean-moment credibilistic optimization method. The computational results demonstrate that a small perturbation of the possibility distribution can make the nominal optimal solution infeasible. In this case, the decision makers should employ the proposed credibilistic optimization method to find the optimal order quantities.
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  • 16
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    A Hybrid Short-Term Forecasting Model of Passenger Flow on High-Speed Rail considering the Impact of Train Service Frequency (2017)
    Hindawi
    Details
    Publication Date: 2017-01-01
    Description: Short-term forecasting of OD (origin to destination) passenger flow on high-speed rail (HSR) is one of the critical tasks in rail traffic management. This paper proposes a hybrid model to explore the impact of the train service frequency (TSF) of the HSR on the passenger flow. The model is composed of two parts. One is the Holt-Winters model, which takes advantage of time series characteristics of passenger flow. The other part considers the changes of TSF for the OD in different time during a day. The two models are integrated by the minimum absolute value method to generate the final hybrid model. The operational data of Beijing-Shanghai high-speed railway from 2012 to 2016 are used to verify the effectiveness of the model. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of the TSF.
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  • 17
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    A Coupled Pseudospectral-Differential Quadrature Method for a Class of Hyperbolic Telegraph Equations (2017)
    Hindawi
    Details
    Publication Date: 2017-01-01
    Description: Pseudospectral methods and differential quadrature methods are two kinds of important meshless methods, both of which have been widely used in scientific and engineering calculation. The Lagrange interpolation polynomials are used as the trial function of the two methods, and the same distribution of grid points is used. This paper points out that the differential quadrature method is a special form of the pseudospectral method. On the basis of the above, a coupled pseudospectral-differential quadrature method (PSDQM) is proposed to solve a class of hyperbolic telegraph equations. Theoretical analysis and numerical tests show that the new method has spectral precision convergence in spatial domain and has A-stability in time domain. And it is suitable for solving multidimensional telegraph equations.
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  • 18
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    Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination (2017)
    Hindawi
    Details
    Publication Date: 2017-01-01
    Description: A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of K-means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences.
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  • 19
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    Assisting in Auditing of Buffer Overflow Vulnerabilities via Machine Learning (2017)
    Hindawi
    Details
    Publication Date: 2017-01-01
    Description: Buffer overflow vulnerability is a kind of consequence in which programmers’ intentions are not implemented correctly. In this paper, a static analysis method based on machine learning is proposed to assist in auditing buffer overflow vulnerabilities. First, an extended code property graph is constructed from the source code to extract seven kinds of static attributes, which are used to describe buffer properties. After embedding these attributes into a vector space, five frequently used machine learning algorithms are employed to classify the functions into suspicious vulnerable functions and secure ones. The five classifiers reached an average recall of 83.5%, average true negative rate of 85.9%, a best recall of 96.6%, and a best true negative rate of 91.4%. Due to the imbalance of the training samples, the average precision of the classifiers is 68.9% and the average F1 score is 75.2%. When the classifiers were applied to a new program, our method could reduce the false positive to 1/12 compared to Flawfinder.
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  • 20
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    Image Regularity and Fidelity Measure with a Two-Modality Potential Function (2017)
    Hindawi
    Details
    Publication Date: 2017-01-01
    Description: We define a strictly convex smooth potential function and use it to measure the data fidelity as well as the regularity for image denoising and cartoon-texture decomposition. The new model has several advantages over the well-known ROF or TV-L2 and the TV-L1 model. First, due to the two-modality property of the new potential function, the new regularity has strong regularizing properties in all directions and thus encourages removing noise in smooth areas, while, near edges, it smoothes the edge mainly along the tangent direction and thus can well preserve the edges. Second, the new potential function is very close to the L1 norm; thus using it to measure the data fidelity makes the new model perform very well in removing impulse noise and preserving the contrast. Lastly, the proposed fidelity and regularization term is strictly convex and smooth and thus allows a unique global minimizer and it can be solved by using the steepest descent method. Numerical experiments show that the proposed model outperforms TV-L2 and TV-L1 in removing impulse noise and mixed noise. It also outperforms some state-of-the-art methods specially designed for impulse noise. Tests on cartoon-texture decomposition show that our method is effective and performs better than TV-L1.
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  • 21
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    Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center (2017)
    Hindawi
    Details
    Publication Date: 2017-01-01
    Description: With the wide deployment of cloud computing data centers, the problems of power consumption have become increasingly prominent. The dynamic energy management problem in pursuit of energy-efficiency in cloud data centers is investigated. Specifically, a dynamic energy management system model for cloud data centers is built, and this system is composed of DVS Management Module, Load Balancing Module, and Task Scheduling Module. According to Task Scheduling Module, the scheduling process is analyzed by Stochastic Petri Net, and a task-oriented resource allocation method (LET-ACO) is proposed, which optimizes the running time of the system and the energy consumption by scheduling tasks. Simulation studies confirm the effectiveness of the proposed system model. And the simulation results also show that, compared to ACO, Min-Min, and RR scheduling strategy, the proposed LET-ACO method can save up to 28%, 31%, and 40% energy consumption while meeting performance constraints.
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  • 22
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    Aeroengine Fault Diagnosis Using Optimized Elman Neural Network (2017)
    Hindawi
    Details
    Publication Date: 2017-01-01
    Description: A new Elman Neural Network (ENN) optimized by quantum-behaved adaptive particle swarm optimization (QAPSO) is introduced in this paper. According to the root mean square error, QAPSO is used to select the best weights and thresholds of the ENN in training samples. The optimized neural network is applied to aeroengine fault diagnosis and is compared with other optimized ENN, original ENN, BP, and Support Vector Machine (SVM) methods. The results show that the QAPSO-ENN is more accurate and reliable in the aeroengine fault diagnosis than the conventional neural network and other ENN methods; QAPSO-ENN has great diagnostic ability in small samples.
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  • 23
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    Optimization Design of IGV Profile in Centrifugal Compressor (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: Variable inlet guide vane (IGV) is used to control the mass flow and generate prewhirl in centrifugal compressors. The efficient operation of IGV is limited to the range of aerodynamic characteristics of their vane profiles. In order to find out the best vane profile for IGV regulation, the modern optimization method was adopted to optimize the inlet guide vane profile. The main methodology idea was to use artificial neural network for continuous fitness evaluation and use genetic algorithm for global optimization. After optimization, the regulating performance of IGV has improved significantly, the prewhirl ability has been enhanced greatly, and the pressure loss has been reduced. The mass flow and power of compressor reduced by using the optimized guide vane at large setting angles, and the efficiency increased significantly; the flow field distribution has been improved obviously, since the nonuniform distribution of flow and flow separation phenomenon greatly weakened or even completely disappeared. The achievement of this research can effectively improve the regulation ability of IGV and the performance of compressor.
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  • 24
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    Maximum Likelihood Estimation of Model Uncertainty in Predicting Soil Nail Loads Using Default and Modified FHWA Simplified Methods (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: Accuracy evaluation of the default Federal Highway Administration (FHWA) simplified equation for prediction of maximum soil nail loads under working conditions is presented in this study using the maximum likelihood method and a large amount of measured lower and upper bound nail load data reported in the literature. Accuracy was quantitatively expressed as model bias where model bias is defined as the ratio of measured to predicted nail load. The maximum likelihood estimation was carried out assuming normal and lognormal distributions of bias. Analysis outcomes showed that, based on the collected data, the default FHWA simplified nail load equation is satisfactorily accurate on average and the spread in prediction accuracy expressed as the coefficient of variation of bias is about 30%, regardless of the distribution type. Empirical calibrations were proposed to the default FHWA simplified nail load equation for accuracy improvement. The Bayesian Information Criterion was adopted to perform a comparison of suitability between the competing normal and lognormal statistical models that were intended for description of model bias. Example of reliability-based design of soil nail walls against internal pullout limit state of nails is provided in the end to demonstrate the benefit of performing model calibration and using calibrated model for design of soil nails.
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    Cubic B-Spline Curves with Shape Parameter and Their Applications (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: The present studies on the extension of B-spline mainly focus on Bezier methods and uniform B-spline and are confined to the adjustment role of shape parameters to curves. Researchers pay little attention to nonuniform B-spline. This paper discusses deeply the extension of the quasi-uniform B-spline curves. Firstly, by introducing shape parameters in the basis function, the spline curves are defined in matrix form. Secondly, the application of the shape parameter in shape design is analyzed deeply. With shape parameters, we get another means for adjusting the curves. Meanwhile, some examples are given. Thirdly, we discuss the smooth connection between adjacent B-spline segments in detail and present the adjusting methods. Without moving the control points position, through assigning appropriate value to the shape parameter, C1 continuity of combined spline curves can be realized easily. Results show that the methods are simple and suitable for the engineering application.
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    Latest Stored Information Based Adaptive Selection Strategy for Multiobjective Evolutionary Algorithm (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: The adaptive operator selection (AOS) and the adaptive parameter control are widely used to enhance the search power in many multiobjective evolutionary algorithms. This paper proposes a novel adaptive selection strategy with bandits for the multiobjective evolutionary algorithm based on decomposition (MOEA/D), named latest stored information based adaptive selection (LSIAS). An improved upper confidence bound (UCB) method is adopted in the strategy, in which the operator usage rate and abandonment of extreme fitness improvement are introduced to improve the performance of UCB. The strategy uses a sliding window to store recent valuable information about operators, such as factors, probabilities, and efficiency. Four common used DE operators are chosen with the AOS, and two kinds of assist information on operator are selected to improve the operators search power. The operator information is updated with the help of LSIAS and the resulting algorithmic combination is called MOEA/D-LSIAS. Compared to some well-known MOEA/D variants, the LSIAS demonstrates the superior robustness and fast convergence for various multiobjective optimization problems. The comparative experiments also demonstrate improved search power of operators with different assist information on different problems.
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    Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs) management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA) and machine learning powerful algorithms such as Self-Organizing Maps (SOMs) and Random Forest (RF). We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs) from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model.
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    Denoising GPS-Based Structure Monitoring Data Using Hybrid EMD and Wavelet Packet (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: High-frequency components are often discarded for data denoising when applying pure wavelet multiscale or empirical mode decomposition (EMD) based approaches. Instead, they may raise the problem of energy leakage in vibration signals. Hybrid EMD and wavelet packet (EMD-WP) is proposed to denoise Global Positioning System- (GPS-) based structure monitoring data. First, field observables are decomposed into a collection of intrinsic mode functions (IMFs) with different characteristics. Second, high-frequency IMFs are denoised using the wavelet packet; then the monitoring data are reconstructed using the denoised IMFs together with the remaining low-frequency IMFs. Our algorithm is demonstrated on a synthetic displacement response of a 3-story frame excited by El Centro earthquake along with a set of Gaussian random white noises on different levels added. We find that the hybrid method can effectively weaken the multipath effect with low frequency and can potentially extract vibration feature. However, false modals may still exist by the rest of the noise contained in the high-frequency IMFs and when the frequency of the noise is located in the same band as that of effective vibration. Finally, real GPS observables are implemented to evaluate the efficiency of EMD-WP method in mitigating low-frequency multipath.
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    Design of Gain Scheduling Control Using State Derivative Feedback (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: In recent years, the study of systems subject to time-varying parameters has awakened the interest of many researchers. The gain scheduling control strategy guarantees a good performance for systems of this type and also is considered as the simplest to deal with problems of this nature. Moreover, the class of systems in which the state derivative signals are easier to obtain than the state signals, such as in the control for reducing vibrations in a mechanical system, has gained an important hole in control theory. Considering those ideas, we propose sufficient conditions via LMI for designing a gain scheduling controller using state derivative feedback. The D-stability methodology was used for improving the performance of the transitory response. Practical implementation in an active suspension system and comparison with other methods validates the efficiency of the proposed strategy.
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    Ballistic Target PHD Filter Based on Infrared Focal Plane Ambiguous Observation (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: Space-based early warning system, the main detection means of which is passive detection based on focal plane, is an important part of ballistic missile defense system. The focal plane is mainly composed of CCD, and its size can reach the micron level, so the pixel is often regarded as point of no area in image postprocessing. The design of traditional tracking methods is based on this, and the observation based on the focal plane is modeled as the azimuth with random noise. However, this modeling is inaccurate. In the context of space-based detection, CCD cannot be simplified as a point, and its size should be considered. And the corresponding observation cannot be treated as azimuth with random noise. In this paper, the observation based on focal plane is modeled as Unambiguously Generated Ambiguous (UGA) measurement. The PHD filter algorithm is redesigned and simplified. The simulation results show that the algorithm based on UGA measurement observation model has better tracking effect compared with that based on traditional observation model. This method provides technical support for more accurate target tracking for space-based early warning system.
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    HRR Profiling on Integrated Radar-Communication Systems Using OFDM-PCSF Signals (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: In order to improve both the transmission data rate and the range resolution simultaneously in integrated radar-communication (RadCom) systems, orthogonal frequency-division multiplexing with phase-coded and stepped-frequency (OFDM-PCSF) waveform is proposed. A corresponding high resolution range (HRR) profile generation method is also presented. We first perform OFDM-PCSF waveform design by combining the intrapulse phase coding with the interpulse stepped-frequency modulation. We then give the ambiguity function (AF) based on the presented waveforms. Then, the synthetic range profile (SRP) processing to achieve HRR performance is analyzed. Theoretical analysis and simulation results show that the proposed methods can achieve HRR profiles of the targets and high data rate transmissions, while a relative low computational complexity can be achieved.
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    A Smoothing Algorithm for a New Two-Stage Stochastic Model of Supply Chain Based on Sample Average Approximation (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: We construct a new two-stage stochastic model of supply chain with multiple factories and distributors for perishable product. By introducing a second-order stochastic dominance (SSD) constraint, we can describe the preference consistency of the risk taker while minimizing the expected cost of company. To solve this problem, we convert it into a one-stage stochastic model equivalently; then we use sample average approximation (SAA) method to approximate the expected values of the underlying random functions. A smoothing approach is proposed with which we can get the global solution and avoid introducing new variables and constraints. Meanwhile, we investigate the convergence of an optimal value from solving the transformed model and show that, with probability approaching one at exponential rate, the optimal value converges to its counterpart as the sample size increases. Numerical results show the effectiveness of the proposed algorithm and analysis.
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    Numerical Simulation of an Offset Jet in Bounded Pool with Deflection Wall (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: The k-ε turbulent model and VOF methods were used to simulate the three-dimensional turbulence jet. Numerical simulations were carried out for three different kinds of jets in a bounded pool with the deflection wall with angles of 0°, 3°, 6°, and 9°. The numerical simulation agrees well with the experimental data. The studies show that the length of the potential core zone increases with the increase of the deflection angle. The velocity distribution is consistent with the Gaussian distribution and almost not affected by the deflection angle in potential core zone. The decay rates of flow velocity in the transition zone are 1.195, 1.281, 1.439, and 1.532 corresponding to the unilateral deflection angles, 0°, 3°, 6°, and 9°, respectively. The decay rates of velocity in the transition zone are 1.928 and 2.835 corresponding to the bilateral deflection angles 3° and 6°. It is also found that the spread of velocity is stronger in the vertical direction as the deflection angles become smaller. The spread rates of velocity with unilateral deflection wall are higher than those with bilateral deflection walls in the horizontal plane in the pool.
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    Simulation of the Spatial Distribution of Hydraulic Conductivity in Porous Media through Different Methods (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: Seepage problems exist in water conservancy projects, groundwater research, and geological research, and hydraulic conductivity is an important factor that affects the seepage field. This study investigates the heterogeneity of hydraulic conductivity. Kriging methods are used to simulate the spatial distribution of hydraulic conductivity, and the application of resistivity and grain size is used to obtain hydraulic conductivity. The results agree with the experimental pumping test results, which prove that the distribution of hydraulic conductivity can be obtained economically and efficiently and in a complex and wide area.
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    Sampling Schemes by Variables Inspection for the First-Order Autoregressive Model between Linear Profiles (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: We present four new sampling schemes by variables inspection to deal with the first-order autoregressive model between linear profiles. The first plan is based on exponentially weighted moving average (EWMA) and the rest of three plans are using the resubmitted sampling, repetitive group sampling (RGS), and multiple dependent state (MDS) sampling schemes. The nonlinear optimization problem is developed to find the number of profiles and the corresponding acceptance criteria, such that the producer’s and consumer’s risk are satisfied simultaneously. The efficiency of the proposed plans is compared with the conventional single sampling plan in terms of average sample number and the probability of acceptance. The result implies that all of the proposed sampling plans are superior to the single acceptance sampling plan by variables. In addition, the EWMA method appeared to be better than the others. The applications of proposed plans are shown with the help of industrial examples taken from calibration of an optical imaging system, and tire cornering stiffness test.
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    Usability Evaluation Approach of Educational Resources Software Using Mixed Intelligent Optimization (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: Aiming at the problems of strong subjectivity and uncertain fuzziness of attribute weights in the software usability evaluation approach, an evaluation approach based on mixed intelligent optimization was proposed, which combines subjective and objective methods to measure software usability for educational resources software. Firstly, the usability evaluation index system of educational resources software was established, and the basic probability assignment was generated by the interval method from the historical sample data. Then the weight optimization problem was adapted to the smooth optimization problem by the maximum entropy function method, and the hybrid social cognitive optimization (HSCO) algorithm was introduced to solve the optimal weights of evidence. Finally, the software usability level was fused by DS evidence theory. The experimental results show that the educational resources software usability evaluation approach can objectively and truly reflect the usability of the software. It provides an efficient way to evaluate the usability of the software.
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    Multidimensional Taylor Network Optimal Control of MIMO Nonlinear Systems without Models for Tracking by Output Feedback (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: The actual controlled objects are generally multi-input and multioutput (MIMO) nonlinear systems with imprecise models or even without models, so it is one of the hot topics in the control theory. Due to the complex internal structure, the general control methods without models tend to be based on neural networks. However, the neuron of neural networks includes the exponential function, which contributes to the complexity of calculation, making the neural network control unable to meet the real-time requirements. The newly developed multidimensional Taylor network (MTN) requires only addition and multiplication, so it is easy to realize real-time control. In the present study, the MTN approach is extended to MIMO nonlinear systems without models to realize adaptive output feedback control. The MTN controller is proposed to guarantee the stability of the closed-loop system. Our experimental results show that the output signals of the system are bounded and the tracking error goes nearly to zero. The MTN optimal controller is proven to promise far better real-time dynamic performance and robustness than the BP neural network self-adaption reconstitution controller.
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    A GRASP-Tabu Heuristic Approach to Territory Design for Pickup and Delivery Operations for Large-Scale Instances (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: We address a logistics districting problem faced by a parcel company whose operations consist of picking up and delivering packages over a service region. The districting process aims to find a partition of the service region into delivery and collection zones that may be served by a single vehicle that departs from a central depot. Criteria to be optimized are to balance workload content among the districts and to create districts of compact shape. A solution approach based on a hybrid procedure that combines elements of GRASP and Tabu Search (TS) is proposed to solve large-scale instances. Numerical experimentation is performed considering different instance sizes and types. Results show that the proposed solution approach is able to solve large-scale instances in reasonable computational times with good quality of the solutions obtained. To determine the quality of the solutions, results are compared with CPLEX solutions and with the current real solution to highlight the benefits of the proposed approach. Conclusions and recommendations for further research are provided.
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    The Modified Fourier-Ritz Approach for the Free Vibration of Functionally Graded Cylindrical, Conical, Spherical Panels and Shells of Revolution with General Boundary Condition (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: The aim of this paper is to extend the modified Fourier-Ritz approach to evaluate the free vibration of four-parameter functionally graded moderately thick cylindrical, conical, spherical panels and shells of revolution with general boundary conditions. The first-order shear deformation theory is employed to formulate the theoretical model. In the modified Fourier-Ritz approach, the admissible functions of the structure elements are expanded into the improved Fourier series which consist of two-dimensional (2D) Fourier cosine series and auxiliary functions to eliminate all the relevant discontinuities of the displacements and their derivatives at the edges regardless of boundary conditions and then solve the natural frequencies by means of the Ritz method. As one merit of this paper, the functionally graded cylindrical, conical, spherical shells are, respectively, regarded as a special functionally graded cylindrical, conical, spherical panels, and the coupling spring technology is introduced to ensure the kinematic and physical compatibility at the common meridian. The excellent accuracy and reliability of the unified computational model are compared with the results found in the literatures.
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    A Proximal Fully Parallel Splitting Method for Stable Principal Component Pursuit (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: As a special three-block separable convex programming, the stable principal component pursuit (SPCP) arises in many different disciplines, such as statistical learning, signal processing, and web data ranking. In this paper, we propose a proximal fully parallel splitting method (PFPSM) for solving SPCP, in which the resulting subproblems all admit closed-form solutions and can be solved in distributed manners. Compared with other similar algorithms in the literature, PFPSM attaches a Glowinski relaxation factor η∈3/2,2/3 to the updating formula for its Lagrange multiplier, which can be used to accelerate the convergence of the generated sequence. Under mild conditions, the global convergence of PFPSM is proved. Preliminary computational results show that the proposed algorithm works very well in practice.
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    Shear Resistance Capacity of Interface of Plate-Studs Connection between CFST Column and RC Beam (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: The combination of a concrete-filled steel tube (CFST) column and reinforced concrete (RC) beam produces a composite structural system that affords good structural performance, functionality, and workability. The effective transmission of moments and shear forces from the beam to the column is key to the full exploitation of the structural performance. The studs of the composite beam transfer the interfacial shear force between the steel beam and the concrete slab, with the web bearing most of the vertical shear force of the steel beam. In this study, the studs and vertical steel plate were welded to facilitate the transfer of the interfacial shear force between the RC beam and CFST column. Six groups of a total of 18 specimens were used to investigate the shear transfer mechanism and failure mode of the plate-studs connection, which was confirmed to effectively transmit the shear forces between the beam and column. The results of theoretical calculations were also observed to be in good agreement with the experimental measurements.
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    Safety-Guaranteed Trajectory Tracking Control for the Underactuated Hovercraft with State and Input Constraints (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: This paper develops a safety-guaranteed trajectory tracking controller for hovercraft by using a safety-guaranteed auxiliary dynamic system, an integral sliding mode control, and an adaptive neural network method. The safety-guaranteed auxiliary dynamic system is designed to implement system state and input constraints. By considering the relationship of velocity and resistance hump, the velocity of hovercraft is constrained to eliminate the effect of resistance hump and obtain better stability. And the safety limit of drift angle is well performed to guarantee the light safe maneuvers of hovercraft tracking with high velocities. In view of the natural capabilities of actuators, the control input is constrained. High nonlinearity and model uncertainties of hovercraft are approximated by employing adaptive radical basis function neural networks. The proposed controller guarantees the boundedness of all the closed-loop signals. Specifically, the tracking errors are uniformly ultimately bounded. Numerical simulations are implemented to demonstrate the efficacy of the designed controller.
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    A Bilevel Stochastic Dynamic Programming Model to Assess the Value of Information on Actual Food Quality at Wholesale Markets (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: In the fresh produce wholesale market, the market price is determined by the total demand and supply. The price is stochastic, and either wholesaler or retailer has few influence on it. In the wholesaler’s inventory decision, the price’s uncertainty plays an important role as well as the uncertainty from the demand side: the wholesaler makes his decision based on the retailer’s ordering, which is influenced by the stochastic market price and the distribution of the consumer’s demand. In addition, when at the wholesale stage, the products show a similar quality of similar appearance. With more efforts being input, the wholesaler could detect and record more additional information than that reflected from the appearance. Based on this, he can classify the quality into different levels. No experience shows how the wholesaler could use the underlying quality information and how much this information could improve his profit. To describe and explore this problem, a bilevel dynamic programming approach is employed. We evaluate different strategies of using the underlying information, show the features of the optimal policy, develop heuristics, and discuss the influence of factors such as quality and market price. We also develop the managerial principles for the practical use.
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    A Multicriteria Intelligence Aid Methodology Using MCDA, Artificial Intelligence, and Fuzzy Sets Theory (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: Intelligence is increasingly relevant today in both military and business intelligence contexts. Business executives, military, and governments have more large datasets and meet difficulties in anticipating threat/competitor future decisions. Decision anticipation is desirable because it will enhance situation understanding and then will limit the surprise effect and favor more appropriate reactions and decision-making. Generating and evaluating competitor/threat actions is a very challenging problem because of the uncertainty, incompleteness, and ambiguity associated with it. This paper extends the multicriteria decision aid (MCDA) methodology to the context of intelligence analysis and proposes the main pillars of a novel methodology called “Multicriteria Intelligence Aid” (MCIA). More specifically, this paper addresses how can we adapt MCDA to the context of intelligence analysis and how can we use existent methods and techniques from MCDA, artificial intelligence, and fuzzy sets theory to build this methodology. The paper presents the MCIA steps, which consist of (i) structuring the competitor/threat decision problem, (ii) handling imperfect data, (iii) modeling the analyst’s risk attitude, and (iv) aggregating the performance of the generated potential actions. An illustration of the methodology is provided in the military context. Results show that the novel methodology is applicable and provides interesting and valuable results.
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    An Evacuation Model for Passenger Ships That Includes the Influence of Obstacles in Cabins (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: Passenger behavior and ship environment are the key factors affecting evacuation efficiency. However, current studies ignore the interior layout of passenger ship cabins and treat the cabins as empty rooms. To investigate the influence of obstacles (e.g., tables and stools) on cabin evacuation, we propose an agent-based social force model for advanced evacuation analysis of passenger ships; this model uses a goal-driven submodel to determine a plan and an extended social force submodel to govern the movement of passengers. The extended social force submodel considers the interaction forces between the passengers, crew, and obstacles and minimises the range of these forces to improve computational efficiency. We drew the following conclusions based on a series of evacuation simulations conducted in this study: (1) the proposed model endows the passenger with the behaviors of bypassing and crossing obstacles, (2) funnel-shaped exits from cabins can improve evacuation efficiency, and (3) as the exit angle increases, the evacuation time also increases. These findings offer ship designers some insight towards increasing the safety of large passenger ships.
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    Application of Multi-Attribute Value Theory to Improve Cargo Delivery Planning in Disaster Aftermath (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: Although many Operational Research models have been applied to disaster response operations, few researchers aim at revealing Decision Makers’ goals or measuring their trade-offs. This article uses a holistic Multi-Criteria Decision Analysis (MCDA) method to elucidate what objectives to pursue and to create appropriate strategies for planning vital items delivery to victims. We propose a framework for applying a Multi-Attribute Value Theory technique and test it with a humanitarian Decision Maker. The resulting mathematical model can be used to evaluate guidelines that make on-field decisions easier, improving (or at least not compromising) their outcomes. Our contribution to the MCDA field includes the documentation of an alternative generation methodology.
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    Curve and Surface Smoothing Using a Modified Cahn-Hilliard Equation (2017)
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    Publication Date: 2017-01-01
    Description: We present a new method using the modified Cahn-Hilliard (CH) equation for smoothing piecewise linear shapes of two- and three-dimensional objects. The CH equation has good smoothing dynamics and it is coupled with a fidelity term which keeps the original given data; that is, it does not produce significant shrinkage. The modified CH equation is discretized using a linearly stable splitting scheme in time and the resulting scheme is solved by using a Fourier spectral method. We present computational results for both curve and surface smoothing problems. The computational results demonstrate that the proposed algorithm is fast and efficient.
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    An Improved Car-Following Model Accounting for Impact of Strong Wind (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: In order to investigate the effect of strong wind on dynamic characteristic of traffic flow, an improved car-following model based on the full velocity difference model is developed in this paper. Wind force is introduced as the influence factor of car-following behavior. Among three components of wind force, lift force and side force are taken into account. The linear stability analysis is carried out and the stability condition of the newly developed model is derived. Numerical analysis is made to explore the effect of strong wind on spatial-time evolution of a small perturbation. The results show that the strong wind can significantly affect the stability of traffic flow. Driving safety in strong wind is also studied by comparing the lateral force under different wind speeds with the side friction of vehicles. Finally, the fuel consumption of vehicle in strong wind condition is explored and the results show that the fuel consumption decreased with the increase of wind speed.
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    Inverse Kinematic Solutions of Dual Redundant Camera Robot Based on Genetic Algorithm (2017)
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    Publication Date: 2017-01-01
    Description: Inverse kinematic solutions for a dual redundant camera robot in position are examined in order to alleviate operation difficulty and reduce time. The inverse kinematic algorithm is based on a basic genetic algorithm, and the genetic algorithm which is used to solve the problem of a redundant robot is mainly optimized in the joint space. On this basis, the genetic algorithm improvement strategies are studied. In this paper, a genetic algorithm with constrained 2 redundant degrees of freedom (DOF) is proposed through setting 2 parameter variables, with more flexible structure of optimization objective function and more efficient algorithm than basic genetic algorithm. Finally, the result of inverse kinematic algorithm is achieved in terms of the physical prototype.
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    Interval-Valued Hesitant Fuzzy Multiattribute Group Decision Making Based on Improved Hamacher Aggregation Operators and Continuous Entropy (2017)
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    Publication Date: 2017-01-01
    Description: Under the interval-valued hesitant fuzzy information environment, we investigate a multiattribute group decision making (MAGDM) method with continuous entropy weights and improved Hamacher information aggregation operators. Firstly, we introduce the axiomatic definition of entropy for interval-valued hesitant fuzzy elements (IVHFEs) and construct a continuous entropy formula on the basis of the continuous ordered weighted averaging (COWA) operator. Then, based on the Hamachert-norm andt-conorm, the adjusted operational laws for IVHFEs are defined. In order to aggregate interval-valued hesitant fuzzy information, some new improved interval-valued hesitant fuzzy Hamacher aggregation operators are investigated, including the improved interval-valued hesitant fuzzy Hamacher ordered weighted averaging (I-IVHFHOWA) operator and the improved interval-valued hesitant fuzzy Hamacher ordered weighted geometric (I-IVHFHOWG) operator, the desirable properties of which are discussed. In addition, the relationship among these proposed operators is analyzed in detail. Applying the continuous entropy and the proposed operators, an approach to MAGDM is developed. Finally, a numerical example for emergency operating center (EOC) selection is provided, and comparative analyses with existing methods are performed to demonstrate that the proposed approach is both valid and practical to deal with group decision making problems.
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    Data-Driven Robust Control of Unknown MIMO Nonlinear System Subject to Input Saturations and Disturbances (2017)
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    Publication Date: 2017-01-01
    Description: This paper presented a new data-driven robust control scheme for unknown nonlinear systems in the presence of input saturation and external disturbances. According to the input and output data of the nonlinear system, a recurrent neural network (RNN) data-driven model is established to reconstruct the dynamics of the nonlinear system. An adaptive output-feedback controller is developed to approximate the unknown disturbances and a novel input saturation compensation method is used to attenuate the effect of the input saturation. Under the proposed adaptive control scheme, the uniformly ultimately bounded convergence of all the signals of the closed-loop nonlinear system is guaranteed via Lyapunov analysis. The simulation results are given to show the effectiveness of the proposed data-driven robust controller.
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    A New Shooting Bouncing Ray Method for Composite Scattering from a Target above the Electrically Large Scope Sea Surface (2017)
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    Publication Date: 2017-01-01
    Description: A new shooting and bouncing ray (SBR) simulator based on the hybrid scheme of GO/PO/SDFM/EEC method is developed for the accurate prediction of composite scattering from a low altitude target above the electrically very-large-scale sea surface. It can adequately deal with the complex local electromagnetic interactions between the target and the large scope sea surface. The method is compared with the exact computational electromagnetic solver FEKO-MLFMM to validate its accuracy and efficiency. Then, it is applied to simulate the bistatic and monostatic scattering characteristics of an airplane above the electrically large sea surface at X-band, for different sea states. The results reveal the contributions from the target, sea surface, and interactions, which are of significance for radar target detection and remote sensing in real maritime environments.
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    A Problem of Particulate Contamination in an Automated Assembly Machine Successfully Solved by CFD and Simple Experiments (2017)
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    Publication Date: 2017-01-01
    Description: Assembly of hard disk drives (HDDs) needs to be done in an automated assembly machine (AAM) virtually free of particulate contamination that can cause them to malfunction. Fan filter units (FFUs) are installed above the AAM to reduce the number of suspended particles in the recirculating air flowing over and around them. At one time, several HDDs were found to be defective. To find out the root cause of this problem, computational fluid dynamics (CFD) was used to investigate the airflow over and around the AAM. It was found that the cause of the high particle counts was improper air speed from the FFUs. The optimal FFUs air speed needed to be in the range of 0.35–0.65 m/s in which the airflow would block out nearby airborne particles and purge away particles generated by the AAM effectively which would, in effect, reduce the particle counts down below the threshold level of class 100 clean room. A few available measurement tools at the factory were then used to perform validating measurements against the simulation results, and the validation was positive. This optimal speed range was implemented at the factory after which the level of contamination was reduced to an acceptable level.
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    A Parameter Estimation Algorithm for Multiple Frequency-Hopping Signals Based on Sparse Bayesian Method (2017)
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    Publication Date: 2017-01-01
    Description: Parameter estimation and network sorting for noncooperative wideband frequency-hopping (FH) signals have been essential and challenging tasks, especially in the case with little or even no prior information at all. In this paper, we propose a nearly blind estimation approach to estimate signal parameters based on sparse Bayesian reconstruction. Taking the sparsity in the spatial frequency domain of multiple FH signals into account, we propose a sparse Bayesian algorithm to estimate the spatial frequency parameters. As a result, the frequency and direction of arrival (DOA) parameters can be obtained. In order to improve the accuracy of the estimation parameters, we employ morphological filter methods to further clean the data poisoned by the noise. Moreover, our method is applicable to the wideband signal models with little prior information. We also conduct extensive numerical simulations to verify the performance of our method. Notably, the proposed method works well even in low signal-to-noise ratio (SNR) environment.
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    Event-Triggered Multitarget Formation Control for Multiagent Systems (2017)
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    Publication Date: 2017-01-01
    Description: The problems of multitarget selection and formation for multiagent systems are considered. First of all, an improved multitarget selection method based on auction algorithm is proposed such that each agent can automatically choose its target, and during the process of choosing targets, we apply an event-triggered mode to reduce the communication links between agents. Second, in view of the fact that all agents with the same target need to form a desired formation shape, we provide an event-triggered formation controller for each agent. Finally, we carry out the simulation experiment of the algorithm and the simulation results have illustrated the effectiveness of it.
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    The Snap Tension Analysis of Taut-Slack Mooring Line with tanh Method (2017)
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    Publication Date: 2017-01-01
    Description: The continuous model is introduced, and the nonlinear partial differential equations of taut-slack mooring line system are transferred to nonlinear algebraic equations through tanh method, and four solitary solutions are obtained further. At the same time, to express the results clearly, the curve surfaces of strains, displacements, and tension are plotted. The results show that there are four different solutions in the system. With the pretension increasing, the tension changes from one solitary solution to snap tension, and when the pretension is increased further, the curve converts to continuous line, until straight line, which is corresponding to the taut mooring line. In the process of increasing of pretension, the mooring line transfers from slack to taut, accompanied with tension skipping, which is reduced by the system parameters, and different combination of parameters may introduce different tension in line, and the uncertainty may cause the breakage of mooring system. The results have an agreement with experiment, which shows that the calculating method in this paper may be believable and feasible. This work may provide reference for design of mooring system.
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    Laser-Induced Color Marking of Titanium: A Modeling Study of the Interference Effect and the Impact of Protective Coating (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: Laser-induced color marking of metals, due to numerous advantages, including inter alia the high quality, resolution, durability, and noncontact methodology of surface marking, seems to be attractive for use in various applications. In this method, the resulting color is mainly evident from the interference effect. Therefore, one of the still unsolved problems on titanium is the color change after imposition of an additional layer (fingerprints, grease, etc.). In this paper, a computer simulation based on the theoretical thin layers model was presented. The results of the modeling study revealed that theoretically a thin protective coating of a known refractive index can be applied while still maintaining the target color. In this case, as a protective layer, an amphiphobic coating has been taken into consideration with its ability to resist surface contamination. The study was performed for titanium (grade 2). The model utilizes the real data derived from the spectrophotometer, as well as from the ellipsometry measurements of laser-induced samples.
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    A New Mathematical Method for Solving Cuttings Transport Problem of Horizontal Wells: Ant Colony Algorithm (2017)
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    Publication Date: 2017-01-01
    Description: Cuttings transport problem has long been recognized as one of the key difficulties in drilling horizontal wells, and the models in cuttings transport research are usually formulated with highly nonlinear equations set. When using Newton methods to solve real engineering problems with nonlinear equations set, the problems of result dependence on initial values, Jacobian matrix singularity, and variable outflow of its definition domain in iterations are three of the often-encountered difficulties. In this paper, the ant colony algorithm is applied to solve the two-layer cuttings transport model with highly nonlinear equations set. The solution-searching process of solving nonlinear equations set is transformed into an optimization process of searching the minimum value of an objective function by applying ant colony algorithm. Analyzing the results of the example, it can be concluded that ant colony algorithm can be used to solve the highly nonlinear cuttings transport model with good solution accuracy; transforming the solution-searching process of solving nonlinear equations set into an optimization process of searching the minimum value of the objective function is necessary; the real engineering problem should be simplified as much as possible to decrease the number of unknown variables and facilitate the use of ant colony algorithm.
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    The Least Squares Hermitian (Anti)reflexive Solution with the Least Norm to Matrix Equation AXB=C (2017)
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    Publication Date: 2017-01-01
    Description: For a given generalized reflection matrix J, that is, JH=J, J2=I, where JH is the conjugate transpose matrix of J, a matrix A∈Cn×n is called a Hermitian (anti)reflexive matrix with respect to J if AH=A and A=±JAJ. By using the Kronecker product, we derive the explicit expression of least squares Hermitian (anti)reflexive solution with the least norm to matrix equation AXB=C over complex field.
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    Data-Driven Optimization Framework for Nonlinear Model Predictive Control (2017)
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    Publication Date: 2017-01-01
    Description: The structure of the optimization procedure may affect the control quality of nonlinear model predictive control (MPC). In this paper, a data-driven optimization framework for nonlinear MPC is proposed, where the linguistic model is employed as the prediction model. The linguistic model consists of a series of fuzzy rules, whose antecedents are the membership functions of the input variables and the consequents are the predicted output represented by linear combinations of the input variables. The linear properties of the consequents lead to a quadratic optimization framework without online linearisation, which has analytical solution in the calculation of control sequence. Both the parameters in the antecedents and the consequents are calculated by a hybrid-learning algorithm based on plant data, and the data-driven determination of the parameters leads to an optimization framework with optimized controller parameters, which could provide higher control accuracy. Experiments are conducted in the process control of biochemical continuous sterilization, and the performance of the proposed method is compared with those of the methods of MPC based on linear model, the nonlinear MPC with neural network approximator, and MPC nonlinear with successive linearisations. The experimental results verify that the proposed framework could achieve higher control accuracy.
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    Multispeed Lattice Boltzmann Model with Space-Filling Lattice for Transcritical Shallow Water Flows (2017)
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    Publication Date: 2017-01-01
    Description: Inspired by the recent success of applying multispeed lattice Boltzmann models with a non-space-filling lattice for simulating transcritical shallow water flows, the capabilities of their space-filling counterpart are investigated in this work. Firstly, two lattice models with five integer discrete velocities are derived by using the method of matching hydrodynamics moments and then tested with two typical 1D problems including the dam-break flow over flat bed and the steady flow over bump. In simulations, the derived space-filling multispeed models, together with the stream-collision scheme, demonstrate better capability in simulating flows with finite Froude number. However, the performance is worse than the non-space-filling model solved by finite difference scheme. The stream-collision scheme with second-order accuracy may be the reason since a numerical scheme with second-order accuracy is prone to numerical oscillations at discontinuities, which is worthwhile for further study.
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    On 2-Absorbing Primary Fuzzy Ideals of Commutative Rings (2017)
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    Publication Date: 2017-01-01
    Description: In this work, we give a characterization of generalizations of prime and primary fuzzy ideals by introducing 2-absorbing fuzzy ideals and 2-absorbing primary fuzzy ideals and establish relations between 2-absorbing (primary) fuzzy ideals and 2-absorbing (primary) ideals. Furthermore, we give some fundamental results concerning these notions.
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    Hybrid Differential Evolution Optimisation for Earth Observation Satellite Scheduling with Time-Dependent Earliness-Tardiness Penalties (2017)
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    Publication Date: 2017-01-01
    Description: We study the order acceptance and scheduling (OAS) problem with time-dependent earliness-tardiness penalties in a single agile earth observation satellite environment where orders are defined by their release dates, available processing time windows ranging from earliest start date to deadline, processing times, due dates, sequence-dependent setup times, and revenues. The objective is to maximise total revenue, where the revenue from an order is a piecewise linear function of its earliness and tardiness with reference to its due date. We formulate this problem as a mixed integer linear programming model and develop a novel hybrid differential evolution (DE) algorithm under self-adaptation framework to solve this problem. Compared with classical DE, hybrid DE employs two mutation operations, scaling factor adaptation and crossover probability adaptation. Computational tests indicate that the proposed algorithm outperforms classical DE in addition to two other variants of DE.
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    The Application of PSO-AFSA Method in Parameter Optimization for Underactuated Autonomous Underwater Vehicle Control (2017)
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    Publication Date: 2017-01-01
    Description: In consideration of the difficulty in determining the parameters of underactuated autonomous underwater vehicles in multi-degree-of-freedom motion control, a hybrid method that combines particle swarm optimization (PSO) with artificial fish school algorithm (AFSA) is proposed in this paper. The optimization process of the PSO-AFSA method is firstly introduced. With the control simulation models in the horizontal plane and vertical plane, the PSO-AFSA method is elaborated when applied in control parameter optimization for an underactuated autonomous underwater vehicle. Both simulation tests and field trials were carried out to prove the efficiency of the PSO-AFSA method in underactuated autonomous underwater vehicle control parameter optimization. The optimized control parameters showed admirable control quality by enabling the underactuated autonomous underwater vehicle to reach the desired states with fast convergence.
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    Energy-Based Controller Design of Stochastic Magnetic Levitation System (2017)
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    Publication Date: 2017-01-01
    Description: This paper investigates the control problem of magnetic levitation system, in which velocity feedback signal is influenced by stochastic disturbance. Firstly, single-degree-freedom magnetic levitation is regarded as an energy-transform action device. From the view of energy-balance relation, the magnetic levitation system is transformed into port-controlled Hamiltonian system model. Next, based on the Hamiltonian structure, the control law of magnetic levitation system is designed by applying Lyapunov theory. Finally, the simulation verifies the correctness of the proposed results.
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    A Study on the Optimization of Chain Supermarkets’ Distribution Route Based on the Quantum-Inspired Evolutionary Algorithm (2017)
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    Publication Date: 2017-01-01
    Description: The chain supermarket has become a major part of China’s retail industry, and the optimization of chain supermarkets’ distribution route is an important issue that needs to be considered for the distribution center, because for a chain supermarket it affects the logistics cost and the competition in the market directly. In this paper, analyzing the current distribution situation of chain supermarkets both at home and abroad and studying the quantum-inspired evolutionary algorithm (QEA), we set up the mathematical model of chain supermarkets’ distribution route and solve the optimized distribution route throughout QEA. At last, we take Hongqi Chain Supermarket in Chengdu as an example to perform the experiment and compare QEA with the genetic algorithm (GA) in the fields of the convergence, the optimal solution, the search ability, and so on. The experiment results show that the distribution route optimized by QEA behaves better than that by GA, and QEA has stronger global search ability for both a small-scale chain supermarket and a large-scale chain supermarket. Moreover, the success rate of QEA in searching routes is higher than that of GA.
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    Deformable Models for Segmentation Based on Local Analysis (2017)
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    Publication Date: 2017-01-01
    Description: Segmentation tasks in medical imaging represent an exhaustive challenge for scientists since the image acquisition nature yields issues that hamper the correct reconstruction and visualization processes. Depending on the specific image modality, we have to consider limitations such as the presence of noise, vanished edges, or high intensity differences, known, in most cases, as inhomogeneities. New algorithms in segmentation are required to provide a better performance. This paper presents a new unified approach to improve traditional segmentation methods as Active Shape Models and Chan-Vese model based on level set. The approach introduces a combination of local analysis implementations with classic segmentation algorithms that incorporates local texture information given by the Hermite transform and Local Binary Patterns. The mixture of both region-based methods and local descriptors highlights relevant regions by considering extra information which is helpful to delimit structures. We performed segmentation experiments on 2D images including midbrain in Magnetic Resonance Imaging and heart’s left ventricle endocardium in Computed Tomography. Quantitative evaluation was obtained with Dice coefficient and Hausdorff distance measures. Results display a substantial advantage over the original methods when we include our characterization schemes. We propose further research validation on different organ structures with promising results.
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    A l1 Norm Based Image Prior Combination in Multiframe Superresolution (2017)
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    Publication Date: 2017-01-01
    Description: We address the multiframe superresolution problem using the variational Bayesian method in this paper. In the variational Bayesian framework, the prior is crucial in transferring the ill-posed reconstruction problem to a well-posed one. We propose a prior combination method based on filter bank and l1 norm. Multiple filters are used in our prior model, and the corresponding combination coefficient vector can be estimated by the characteristics of the filtered image and noise. Furthermore, the local adaptive coefficients of every filter are more effective in removing noise and preserving image edges. Extensive experiments demonstrate the advantages of the proposed method.
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    Multiobjective Optimization for Fixture Locating Layout of Sheet Metal Part Using SVR and NSGA-II (2017)
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    Publication Date: 2017-01-01
    Description: Fixture plays a significant role in determining the sheet metal part (SMP) spatial position and restraining its excessive deformation in many manufacturing operations. However, it is still a difficult task to design and optimize SMP fixture locating layout at present because there exist multiple conflicting objectives and excessive computational cost of finite element analysis (FEA) during the optimization process. To this end, a new multiobjective optimization method for SMP fixture locating layout is proposed in this paper based on the support vector regression (SVR) surrogate model and the elitist nondominated sorting genetic algorithm (NSGA-II). By using ABAQUS™ Python script interface, a parametric FEA model is established. And the fixture locating layout is treated as design variables, while the overall deformation and maximum deformation of SMP under external forces are as the multiple objective functions. First, a limited number of training and testing samples are generated by combining Latin hypercube design (LHD) with FEA. Second, two SVR prediction models corresponding to the multiple objectives are established by learning from the limited training samples and are integrated as the multiobjective optimization surrogate model. Third, NSGA-II is applied to determine the Pareto optimal solutions of SMP fixture locating layout. Finally, a multiobjective optimization for fixture locating layout of an aircraft fuselage skin case is conducted to illustrate and verify the proposed method.
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    Stability and Multiscroll Attractors of Control Systems via the Abscissa (2017)
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    Publication Date: 2017-01-01
    Description: We present an approach to generate multiscroll attractors via destabilization of piecewise linear systems based on Hurwitz matrix in this paper. First we present some results about the abscissa of stability of characteristic polynomials from linear differential equations systems; that is, we consider Hurwitz polynomials. The starting point is the Gauss–Lucas theorem, we provide lower bounds for Hurwitz polynomials, and by successively decreasing the order of the derivative of the Hurwitz polynomial one obtains a sequence of lower bounds. The results are extended in a straightforward way to interval polynomials; then we apply the abscissa as a measure to destabilize Hurwitz polynomial for the generation of a family of multiscroll attractors based on a class of unstable dissipative systems (UDS) of affine linear type.
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    Mean First Passage Time of Preferential Random Walks on Complex Networks with Applications (2017)
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    Publication Date: 2017-01-01
    Description: This paper investigates, both theoretically and numerically, preferential random walks (PRW) on weighted complex networks. By using two different analytical methods, two exact expressions are derived for the mean first passage time (MFPT) between two nodes. On one hand, the MFPT is got explicitly in terms of the eigenvalues and eigenvectors of a matrix associated with the transition matrix of PRW. On the other hand, the center-product-degree (CPD) is introduced as one measure of node strength and it plays a main role in determining the scaling of the MFPT for the PRW. Comparative studies are also performed on PRW and simple random walks (SRW). Numerical simulations of random walks on paradigmatic network models confirm analytical predictions and deepen discussions in different aspects. The work may provide a comprehensive approach for exploring random walks on complex networks, especially biased random walks, which may also help to better understand and tackle some practical problems such as search and routing on networks.
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    A Fuzzy-Grey Multicriteria Decision Making Approach for Green Supplier Selection in Low-Carbon Supply Chain (2017)
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    Publication Date: 2017-01-01
    Description: Due to the increasing awareness of global warming and environmental protection, many practitioners and researchers have paid much attention to the low-carbon supply chain management in recent years. Green supplier selection is one of the most critical activities in the low-carbon supply chain management, so it is important to establish the comprehensive criteria and develop a method for green supplier selection in low-carbon supply chain. The paper proposes a fuzz-grey multicriteria decision making approach to deal with these problems. First, the paper establishes 4 main criteria and 22 subcriteria for green supplier selection. Then, a method integrating fuzzy set theory and grey relational analysis is proposed. It uses the membership function of normal distribution to compare each supplier and uses grey relation analysis to calculate the weight of each criterion and improves fuzzy comprehensive evaluation. The proposed method can make the localization of individual green supplier more objectively and more accurately in the same trade. Finally, a case study in the steel industry is presented to demonstrate the effectiveness of the proposed approach.
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    Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function (2017)
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    Publication Date: 2017-01-01
    Description: Reliability is one of the quantifiable software quality attributes. Software Reliability Growth Models (SRGMs) are used to assess the reliability achieved at different times of testing. Traditional time-based SRGMs may not be accurate enough in all situations where test effort varies with time. To overcome this lacuna, test effort was used instead of time in SRGMs. In the past, finite test effort functions were proposed, which may not be realistic as, at infinite testing time, test effort will be infinite. Hence in this paper, we propose an infinite test effort function in conjunction with a classical Nonhomogeneous Poisson Process (NHPP) model. We use Artificial Neural Network (ANN) for training the proposed model with software failure data. Here it is possible to get a large set of weights for the same model to describe the past failure data equally well. We use machine learning approach to select the appropriate set of weights for the model which will describe both the past and the future data well. We compare the performance of the proposed model with existing model using practical software failure data sets. The proposed log-power TEF based SRGM describes all types of failure data equally well and also improves the accuracy of parameter estimation more than existing TEF and can be used for software release time determination as well.
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    Shallow-Water-Equation Model for Simulation of Earthquake-Induced Water Waves (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: A shallow-water equation (SWE) is used to simulate earthquake-induced water waves in this study. A finite-difference method is used to calculate the SWE. The model is verified against the models of Sato and of Demirel and Aydin with three kinds of seismic waves, and the numerical results of earthquake-induced water waves calculated using the proposed model are reasonable. It is also demonstrated that the proposed model is reliable. Finally, an empirical equation for the maximum water elevation of earthquake-induced water waves is developed based on the results obtained using the model, which is an improvement on former models.
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    Optimization of Roller Velocity for Quenching Machine Based on Heat Transfer Mathematical Model (2017)
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    Publication Date: 2017-01-01
    Description: During quenching process of steel plate, control parameters are important to product quality. In this work, heat transfer mathematical model has been developed for roller-type quenching machine to predict the temperature field of plate at first, and then an optimization schedule considering quenching technology and equipment limitations is developed firstly based on the heat transfer mathematical model with considering the shortest quenching time. A numerical simulation is performed during optimization process to investigate the effects of roller velocity on the temperature of representative plate. Based on the optimization method, study is also performed for different thickness of plate to obtain the corresponding roller velocity. The results show that the optimized roller velocity can be achieved for the roller-type continuous quenching machine based on the heat transfer mathematical model. With the increasing of plate’s thickness, the optimized roller velocity decreases exponentially.
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    Robust Fault Detection and Estimation in Nonlinear Systems with Unknown Constant Time-Delays (2017)
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    Publication Date: 2017-01-01
    Description: This paper studies the problem of fault detection and estimation in nonlinear time-delayed systems with unknown inputs, where the time-delays are supposed to be constant but unknown. A new fault detection filter, which can estimate online the time-delays, is first introduced. Then, a reference residual model is proposed to formulate the robust fault detection filter design problem as an H∞ model-matching problem. Furthermore, by a novel robust adaptive fault estimation algorithm, the classical assumption that the time derivative of the output error should be known is removed. In addition, applying a robust H∞ optimization control technique, sufficient conditions for the existence of the fault detection filter (FDF) are derived in terms of linear matrix inequality (LMI). Finally, simulation results are presented to illustrate the effectiveness of the proposed algorithm.
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    Sharp Refinements for the Inverse Sine Function Related to Shafer-Fink’s Inequality (2017)
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    Publication Date: 2017-01-01
    Description: The aim of our work is to give new sharp refinements of Shafer-Fink’s inequality, using suitable changes of variables.
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    A Bearingless Induction Motor Direct Torque Control and Suspension Force Control Based on Sliding Mode Variable Structure (2017)
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    Publication Date: 2017-01-01
    Description: Aiming at the problems of the large torque ripple and unstable suspension performance in traditional direct torque control (DTC) for a bearingless induction motor (BIM), a new method of DTC is proposed based on sliding mode variable structure (SMVS). The sliding mode switching surface of the torque and flux linkage controller are constructed by torque error and flux error, and the exponential reaching law is used to design the SMVS direct torque controller. On the basis of the radial suspension force mathematical model of the BIM, a radial suspension force closed-loop control method is proposed by utilizing the inverse system theory and SMVS. The simulation models of traditional DTC and the new DTC method based on SMVS of the BIM are set up in the MATLAB/Simulink toolbox. On this basis, the experiments are carried out. Simulation and experiment results showed that the stable suspension operation of the BIM can be achieved with small torque ripple and flux ripple. Besides, the dynamic response and suspension performance of the motor are improved by the proposed method.
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    Robust Course Keeping Control of a Fully Submerged Hydrofoil Vessel without Velocity Measurement: An Iterative Learning Approach (2017)
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    Publication Date: 2017-01-01
    Description: This paper proposes a novel robust output feedback control methodology for the course keeping control of a fully submerged hydrofoil vessel. Based on a sampled-data iterative learning strategy, an iterative learning observer is established for the estimation of system states and the generalized disturbances. With the state observer, a feedback linearized iterative sliding mode controller is designed for the stabilization of the lateral dynamics of the fully submerged hydrofoil vessel. The stability of the overall closed-loop system is analyzed based on Lyapunov stability theory. Comparative simulation results verify the effectiveness of the proposed control scheme and show the dominance of the disturbance rejection performance.
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    Single-Machine Scheduling with Learning Effects and Maintenance: A Methodological Note on Some Polynomial-Time Solvable Cases (2017)
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    Publication Date: 2017-01-01
    Description: This work addresses four single-machine scheduling problems (SMSPs) with learning effects and variable maintenance activity. The processing times of the jobs are simultaneously determined by a decreasing function of their corresponding scheduled positions and the sum of the processing times of the already processed jobs. Maintenance activity must start before a deadline and its duration increases with the starting time of the maintenance activity. This work proposes a polynomial-time algorithm for optimally solving two SMSPs to minimize the total completion time and the total tardiness with a common due date.
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    Optimization of Support Structures for Offshore Wind Turbines Using Genetic Algorithm with Domain-Trimming (2017)
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    Publication Date: 2017-01-01
    Description: The powerful genetic algorithm optimization technique is augmented with an innovative “domain-trimming” modification. The resulting adaptive, high-performance technique is called Genetic Algorithm with Domain-Trimming (GADT). As a proof of concept, the GADT is applied to a widely used benchmark problem. The 10-dimensional truss optimization benchmark problem has well documented global and local minima. The GADT is shown to outperform several published solutions. Subsequently, the GADT is deployed onto three-dimensional structural design optimization for offshore wind turbine supporting structures. The design problem involves complex least-weight topology as well as member size optimizations. The GADT is applied to two popular design alternatives: tripod and quadropod jackets. The two versions of the optimization problem are nonlinearly constrained where the objective function is the material weight of the supporting truss. The considered design variables are the truss members end node coordinates, as well as the cross-sectional areas of the truss members, whereas the constraints are the maximum stresses in members and the maximum displacements of the nodes. These constraints are managed via dynamically modified, nonstationary penalty functions. The structures are subject to gravity, wind, wave, and earthquake loading conditions. The results show that the GADT method is superior in finding best discovered optimal solutions.
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    Analysis and Design of Associative Memories for Memristive Neural Networks with Deviating Argument (2017)
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    Publication Date: 2017-01-01
    Description: We investigate associative memories for memristive neural networks with deviating argument. Firstly, the existence and uniqueness of the solution for memristive neural networks with deviating argument are discussed. Next, some sufficient conditions for this class of neural networks to possess invariant manifolds are obtained. In addition, a global exponential stability criterion is presented. Then, analysis and design of autoassociative memories and heteroassociative memories for memristive neural networks with deviating argument are formulated, respectively. Finally, several numerical examples are given to demonstrate the effectiveness of the obtained results.
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    Study on the Damping of the Discontinuous Deformation Analysis Based on Two-Block Model (2017)
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    Publication Date: 2017-01-01
    Description: The deformation and failure of rock mass is a process of energy dissipation; the damping of DDA is a very important and basic problem. The correctness and effectiveness of DDA rely on the appropriate values of the numeric controlling parameters like time interval, spring stiffness, and assumed maximum displacement ratio g2, and the contact using the penalty method is the core content of DDA. A mechanical model of two contact blocks loaded with the normal force acting along one side of block boundary is established to study the DDA damping problem, which involves the contact and eliminates the influence of some numeric control parameters (e.g., g2). Based on the Newmark method and the theory of DDA, the motion equations of two-block system can be established, and then the relationship of some numeric control parameters and the influence of damping can be obtained. The algorithmic damping increases with the increasing of time interval. Given a very small time interval, the spring stiffness may have no obvious effect on the algorithmic damping. The numerical results reveal that the essence of time interval influencing the open-close iteration is the fact that the algorithmic damping is mainly controlled by time interval.
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    Efficient Approximation of the Labeled Multi-Bernoulli Filter for Online Multitarget Tracking (2017)
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    Publication Date: 2017-01-01
    Description: Online tracking time-varying number of targets is a challenging issue due to measurement noise, target birth or death, and association uncertainty, especially when target number is large. In this paper, we propose an efficient approximation of the Labeled Multi-Bernoulli (LMB) filter to perform online multitarget state estimation and track maintenance efficiently. On the basis of the original LMB filer, we propose a target posterior approximation technique to use a weighted single Gaussian component representing each individual target. Moreover, we present the Gaussian mixture implementation of the proposed efficient approximation of the LMB filter under linear, Gaussian assumptions on the target dynamic model and measurement model. Numerical results verify that our proposed efficient approximation of the LMB filer achieves accurate tracking performance and runs several times faster than the original LMB filer.
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    Topology Detection for Output-Coupling Weighted Complex Dynamical Networks with Coupling and Transmission Delays (2017)
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    Publication Date: 2017-01-01
    Description: Topology detection for output-coupling weighted complex dynamical networks with two types of time delays is investigated in this paper. Different from existing literatures, coupling delay and transmission delay are simultaneously taken into account in the output-coupling network. Based on the idea of the state observer, we build the drive-response system and apply LaSalle’s invariance principle to the error dynamical system of the drive-response system. Several convergent criteria are deduced in the form of algebraic inequalities. Some numerical simulations for the complex dynamical network, with node dynamics being chaotic, are given to verify the effectiveness of the proposed scheme.
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    The Influence of Slip Boundary Condition on Casson Nanofluid Flow over a Stretching Sheet in the Presence of Viscous Dissipation and Chemical Reaction (2017)
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    Publication Date: 2017-01-01
    Description: The impacts of multiple slips with viscous dissipation on the boundary layer flow and heat transfer of a non-Newtonian nanofluid over a stretching surface have been investigated numerically. The Casson fluid model is applied to characterize the non-Newtonian fluid behavior. Physical mechanisms responsible for Brownian motion and thermophoresis with chemical reaction are accounted for in the model. The governing nonlinear boundary layer equations through appropriate transformations are reduced into a set of nonlinear ordinary differential equations, which are solved numerically using a shooting method with fourth-order Runge-Kutta integration scheme. Comparisons of the numerical method with the existing results in the literature are made and an excellent agreement is obtained. The heat transfer rate is enhanced with generative chemical reaction and concentration slip parameter, whereas the reverse trend is observed with destructive chemical reaction and thermal slip parameter. It is also noticed that the mass transfer rate is boosted with destructive chemical reaction and thermal slip parameter. Further, the opposite influence is found with generative chemical reaction and concentration slip parameter.
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    Optimal Exercise Boundary of American Fractional Lookback Option in a Mixed Jump-Diffusion Fractional Brownian Motion Environment (2017)
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    Publication Date: 2017-01-01
    Description: A new framework for pricing the American fractional lookback option is developed in the case where the stock price follows a mixed jump-diffusion fraction Brownian motion. By using Itô formula and Wick-Itô-Skorohod integral a new market pricing model is built. The fundamental solutions of stochastic parabolic partial differential equations are estimated under the condition of Merton assumptions. The explicit integral representation of early exercise premium and the critical exercise price are also given. Numerical simulation illustrates some notable features of American fractional lookback options.
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    A Self-Adaptive Numerical Method to Solve Convection-Dominated Diffusion Problems (2017)
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    Publication Date: 2017-01-01
    Description: Convection-dominated diffusion problems usually develop multiscaled solutions and adaptive mesh is popular to approach high resolution numerical solutions. Most adaptive mesh methods involve complex adaptive operations that not only increase algorithmic complexity but also may introduce numerical dissipation. Hence, it is motivated in this paper to develop an adaptive mesh method which is free from complex adaptive operations. The method is developed based on a range-discrete mesh, which is uniformly distributed in the value domain and has a desirable property of self-adaptivity in the spatial domain. To solve the time-dependent problem, movement of mesh points is tracked according to the governing equation, while their values are fixed. Adaptivity of the mesh points is automatically achieved during the course of solving the discretized equation. Moreover, a singular point resulting from a nonlinear diffusive term can be maintained by treating it as a special boundary condition. Serval numerical tests are performed. Residual errors are found to be independent of the magnitude of diffusive term. The proposed method can serve as a fast and accuracy tool for assessment of propagation of steep fronts in various flow problems.
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    Radar Communication Integrated Waveform Design Based on OFDM and Circular Shift Sequence (2017)
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    Publication Date: 2017-01-01
    Description: Based on the orthogonal frequency division multiplexing (OFDM) technique, an intelligent waveform is designed, which is suitable for simultaneously performing data transmission and radar sensing. In view of the inherent high peak-to-mean envelope power ratio (PMERP) and poor peak-to-side-lobe ratio (PSLR) problems in the OFDM based radar and communication (RadCom) waveform design, we propose two technologies to deal with that. To be specific, we adopt Gray code technology to reduce the PMERP and simultaneously choose an optimal cyclic sequence to improve the PSLR of RadCom waveform. In our method, the optimal cyclic sequence is dynamically generated to continuously provide the best waveform according to the change of communication data. In addition, to meet the requirements of different radar detection tasks, two simple methods are utilized to adjust the bandwidth of RadCom waveform. To verify the advantages of the designed waveform, we conduct several simulation experiments to compare with some existing RadCom waveforms. The results show that our designed RadCom waveform can simultaneously achieve lower PMERP and higher PSLR. In addition, our designed RadCom waveform has a thumbtack type fuzzy function and shows the good ability to do multitarget detection.
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    Dynamics Analysis for Hydroturbine Regulating System Based on Matrix Model (2017)
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    Publication Date: 2017-01-01
    Description: The hydraulic turbine model is the key factor which affects the analysis precision of the hydraulic turbine governing system. This paper discusses the basic principle of the hydraulic turbine matrix model and gives two methods to realize. Using the characteristic matrix to describe unit flow and torque and their relationship with the opening and unit speed, it can accurately represent the nonlinear characteristics of the turbine, effectively improve the convergence of simulation process, and meet the needs of high precision real-time simulation of power system. Through the simulation of a number of power stations, it indicates that, by analyzing the dynamic process of the hydraulic turbine regulating with 5-order matrix model, the calculation results and field test data will have good consistency, and it can better meet the needs of power system dynamic simulation.
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    Flight Time and Frequency-Optimization Model for Multiairport System Operation (2017)
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    Publication Date: 2017-01-01
    Description: This study’s goal is to reduce the number of flights and alleviate congestion in hub airports. It proposes a flight time and frequency-optimization method for multiairport systems. A flight time and frequency-optimization model for multiairport system operation is created to minimize loss of passenger trip time. A k-means clustering algorithm is designed to solve the model and calculate indexes such as flight time and frequency, passenger trip-time loss, and distribution of airplane models and quantity. The calculation results of an example in China are as follows. Under multiairport system operation mode, passenger demands are divided into 7 categories; 11 flights satisfy all passenger demands; passenger trip-time loss is 129,573 min; and the average passenger load factor is 90.1%. Under an independent operation mode, passenger demands are divided into 8 categories; 13 flights satisfy all passenger demands; passenger trip-time loss is 173,705 min; and the average passenger load factor is 87.4%. The multiairport system operation mode not only improves passenger trip efficiency but also benefits airlines by improving the passenger load factor and reducing flights. Moreover, comparative analysis of a genetic algorithm versus a clustering algorithm further proves the accuracy of the clustering algorithm.
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    Subpixel Mapping Algorithms Based on Block Structural Self-Similarity Learning (2017)
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    Publication Date: 2017-01-01
    Description: Subpixel mapping (SPM) algorithms effectively estimate the spatial distribution of different land cover classes within mixed pixels. This paper proposed a new subpixel mapping method based on image structural self-similarity learning. Image structure self-similarity refers to similar structures within the same scale or different scales in image itself or its downsampled image, which widely exists in remote sensing images. Based on the similarity of image block structure, the proposed method estimates higher spatial distribution of coarse-resolution fraction images and realizes subpixel mapping. The experimental results show that the proposed method is more accurate than existing fast subpixel mapping algorithms.
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    Clustering Mixed Data by Fast Search and Find of Density Peaks (2017)
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    Publication Date: 2017-01-01
    Description: Aiming at the mixed data composed of numerical and categorical attributes, a new unified dissimilarity metric is proposed, and based on that a new clustering algorithm is also proposed. The experiment result shows that this new method of clustering mixed data by fast search and find of density peaks is feasible and effective on the UCI datasets.
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    Design of Initial Value Control for Modified Lorenz-Stenflo System (2017)
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    Publication Date: 2017-01-01
    Description: For the sake of complexity, unpredictability, and exceeding sensitivity to initial conditions in the chaotic systems, there were many studies for information encryption of chaotic systems in recent years. Enhancing the security in information encryption of chaotic systems, an initial value control circuit for chaotic systems is proposed in this paper. By way of changing the initial value, we can change the behavior of chaotic systems and also change the key of information encryption. An analog circuit is implemented to verify the initial value control circuit design.
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    Application of Fractional Calculus to Frontal Crash Modeling (2017)
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    Publication Date: 2017-01-01
    Description: Fractional derivative viscoelastic model is used in the analysis of a frontal impact of a vehicle against a rigid obstacle. The frontal part of the vehicle is first modeled as a viscoelastic fractional rod and then it is modeled as two different viscoelastic fractional rods with a different length. In the second model also the friction is taken into account. A motion is analyzed during several phases because of both different lengths of the rods and the presence of a dry friction force in the later model. Governing systems of differential equations together with the corresponding initial conditions are derived. Parameter identification is done on the basis of the existing experimental results using the solution of a posed impact problem. What makes the problem more complex, regarding the second model, is the fact that it belongs to the class of nonsmooth fractional order systems, which require special treatment when dealing with deformation history during different motion phases.
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    Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing (2017)
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    Publication Date: 2017-01-01
    Description: This paper presents a novel method for fault diagnosis based on an improved adaptive resonance theory (ART) neural network and ensemble technique. The method consists of three stages. Firstly, the improved ART neural network is comprised of the soft competition technique based on fuzzy competitive learning (FCL) and ART based on Yu’s norm, the neural nodes in the competition layer are trained according to the degree of membership between the mode node and the input, and then fault samples are classified in turn. Secondly, with the distance evaluation technique, the optimal features are obtained from the statistical characteristics of original signals and wavelet coefficients. Finally, the optimal features are input into the neural network ensemble (NNE) based on voting method to identify the different fault categories. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the neural network ensemble can reliably classify different fault categories and the degree of faults, which has a better classification performance compared with the single neural network.
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    An Efficient Approach Based on the Gradient Definition for Solving Conditional Nonlinear Optimal Perturbation (2017)
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    Publication Date: 2017-01-01
    Description: Conditional nonlinear optimal perturbation (CNOP) has been widely applied to study the predictability of weather and climate. The classical method of solving CNOP is adjoint method, in which the gradient is obtained using the adjoint model. But some numerical models have no adjoint models implemented, and it is not realistic to develop from scratch because of the huge amount of work. The gradient can be obtained by the definition in mathematics; however, with the sharp growth of dimensions, its calculation efficiency will decrease dramatically. Therefore, the gradient is rarely obtained by the definition when solving CNOP. In this paper, an efficient approach based on the gradient definition is proposed to solve CNOP around the whole solution space and parallelized. Our approach is applied to solve CNOP in Zebiak-Cane (ZC) model, and, compared with adjoint method, which is the benchmark, our approach can obtain similar results in CNOP value and pattern aspects and higher efficiency in time consumption aspect, only 12.83 s, while adjoint method spends 15.04 s and consumes less time if more CPU cores are provided. All the experimental results show that it is feasible to solve CNOP with our approach based on the gradient definition around the whole solution space.
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    Reliability-Based Analytic Models for Fatigue Lifetime Distribution Estimation of Series Mechanical Systems under Random Load considering Strength Degradation Path Dependence (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: Time-dependent statistical characteristics of load process and strength degradation process are critical to lifetime distribution of series mechanical systems. Conventional rain-flow counting method for lifetime distribution estimation has limited practical application due to its strict requirement for statistical properties of load process. Besides, dynamic interaction between load process and strength degradation process results in strength degradation path dependence (SDPD). SDPD and failure dependence of components jointly bring considerable difficulties in prediction of system lifetime distribution and system residual lifetime distribution. To address these problems, reliability-based analytic models for estimation of whole lifetime distribution and residual lifetime distribution of series mechanical systems under random load are developed in this paper, which take the time-dependent statistical parameters of load process and strength degradation process as the input of the models. Furthermore, SDPD and failure dependence of components are taken into account in the proposed models in an explicit mathematical expression. The results show that SDPD, failure dependence of components, and initial strength dispersion have significant influences on system lifetime distribution.
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    Orderly Roulette Selection Based Ant Colony Algorithm for Hierarchical Multilabel Protein Function Prediction (2017)
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    Publication Date: 2017-01-01
    Description: Ant colony optimization (ACO) algorithms have been successfully applied to identify classification rules in data mining. This paper proposes a new ant colony optimization algorithm, named hmAntMinerorder, for the hierarchical multilabel classification problem in protein function prediction. The proposed algorithm is characterized by an orderly roulette selection strategy that distinguishes the merits of the data attributes through attributes importance ranking in classification model construction. A new pheromone update strategy is introduced to prevent the algorithm from getting trapped in local optima and thus leading to more efficient identification of classification rules. The comparison studies to other closely related algorithms on 16 publicly available datasets reveal the efficiency of the proposed algorithm.
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    Cost Sharing in the Prevention of Supply Chain Disruption (2017)
    Hindawi
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    Publication Date: 2017-01-01
    Description: We examine the influence of cost-sharing mechanism on the disruption prevention investment in a supply chain with unreliable suppliers. When a supply chain faces considerable loss following a disruption, supply chain members are motivated toward investing in manners that reduce their disruption probability. In improving supply chain reliability, the cost-sharing mechanism must be set appropriately to realize the efficiency of the disruption prevention investment. In a supply chain where the focal manufacturing company has its own subsidiary supplier and an outsourcing supplier, we analyze different forms of cost-sharing mechanisms when both suppliers confront disruption risks. Through the cost-sharing mechanisms presented in this study, supply chain members can improve their reliability via disruption prevention investments without considerably increasing the total supply chain cost. We present two concepts, the cost-sharing structure and the cost-sharing ratio, in this study. As the two key components of cost-sharing mechanism, these two elements constitute a practicable cost allocation mechanism to facilitate disruption prevention.
    Print ISSN: 1024-123X
    Electronic ISSN: 1563-5147
    Topics: Mathematics , Technology
    Published by Hindawi
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