ALBERT

Description: To the significant amount of pilot overhead of the interference cancelation methods in orthogonal frequency division multiplexing (OFDM) based on offset quadrature amplitude modulation (OFDM/OQAM) system, we proposed an improved interference cancelation method (ICM) for OFDM/OQAM system in this paper. In this method, we use the auxiliary pilot (AP) to eliminate the influence of the intersymbol interference on channel estimation, which can reduce the pilot overhead of OFDM/OQAM system significantly. At the same time, to improve the channel estimation performance, we analyze the source of the intrinsic interference of system and its distribution in time and frequency domain, then, we reset the interference cancelation range of AP, which can cancel more intrinsic interference for OFDM/OQAM system. According to the results of performance analysis, compared to the conventional interference cancelation methods, the proposed method performs better in terms of energy efficiency and spectral efficiency. Also, the simulation results of the proposed method show that the proposed method can outperform traditional interference cancelation methods in channel estimation performance.

Description: In this paper, post-buckling and free nonlinear vibration of microbeams resting on nonlinear elastic foundation subjected to axial force are investigated. The equations of motion of microbeams are derived by using the modified couple stress theory. Using Galerkin’s method, the equation of motion of microbeams is reduced to the nonlinear ordinary differential equation. By using the equivalent linearization in which the averaging value is calculated in a new way called the weighted averaging value, approximate analytical expressions for the nonlinear frequency of microbeams with pinned–pinned and clamped–clamped end conditions are obtained in closed-forms. Comparisons with previous solutions are showed accuracy of the present solutions. Effects of the material length scale parameter and the axial compressive force on the frequency ratios of microbeams; and effect of the material length scale parameter on the buckling load ratios of microbeams are investigated in this paper.

Description: Numerical investigations on flow and heat transfer characteristics in the heat exchanger tube with the V-wavy surface are presented. The finite volume method with the SIMPLE algorithm is selected to solve the present problem. The effects of flow attack angles (α = 15°, 20°, 25°, 30°, 35°, 40°, 45°, 50°, 55°, and 60°) and flow directions (V-tip pointing downstream known as “V-Downstream” and V-tip pointing upstream known as “V-Upstream”) for the V-wavy surface on flow and heat transfer patterns are considered for both laminar and turbulent regions. The laminar regime is studied in the range Re = 100–1200, while the turbulent region is investigated in the range Re = 3000–10,000. The mechanisms on flow and heat transfer in the test section are reported. The numerical results reveal that the V-wavy surface changes the flow structure in the test section. The vortex flow is produced by the V-wavy surface. The vortex flow disturbs the thermal boundary layer on the heat transfer surface that is the reason for heat transfer and thermal performance enhancements. The optimum flow attack angles of the V-wavy surface for laminar and turbulent regimes are concluded.

Description: This paper proposes a new evolutionary multiobjective optimization technique for a linear compliant mechanism of nanoindentation tester. The mechanism design is inspired by the elastic deformation of flexure hinge. To improve overall static performances, a multiobjective optimization design was carried out. An efficient hybrid optimization approach of central composite design (CDD), finite element method (FEM), artificial neural network (ANN), and multiobjective genetic algorithm (MOGA) is developed to solve the optimization problem. In this procedure, the CDD is used to lay out the experimental data. The FEM is developed to retrieve the quality performances. And then, the ANN is developed as black box to call the pseudo-objective functions. Unlike previous studies on multiobjective evolutionary algorithms, most of which generating only one Pareto-optimal solution, this proposed approach can generate more than three Pareto-optimal solutions. Based on the user’s real-work problem, one of the best optimal solutions is chosen. The results showed that the optimal results were found at the displacement of 330.68 μm, stress of 140.65 MPa, and safety factor of 3.6. The statistical analysis is conducted to investigate the behavior of the MOGA. The sensitivity analysis was carried out to determine the significant contribution of each factor. The results revealed that the lengths and thickness almost significantly affect both responses. It confirms that the proposed hybrid optimization approach gains high robustness and effectiveness with flexible decision maker rules to solve complex optimization engineering problems.

Description: A unique secure communication scheme that can be used for the transmission of gray-scale and color videos is presented in this paper. The proposed scheme is developed by using the Karhunen-Loéve (K-L) decomposition and the synchronization of the unified chaotic system with the hyperchaotic Chen system. First, the gray-scale or color video is represented by a set of frames. In order to reduce the data, the K-L decomposition is used to come up with data coefficients and eigenfunctions that optimally obtain the crux of the frames. Using only the most energetic eigenfunctions to approximate the original frames results in computational savings. The data coefficients corresponding to the most energetic eigenfunctions are encrypted and transmitted using a master system composed of a combination of the unified chaotic system and the hyperchaotic Chen system. At the receiver end, these coefficients are recovered and a controller of the sliding mode type is utilized forcing the master and slave systems to synchronize. Simulation results illustrate how the proposed control law is able to synchronize the master and the slave systems. In addition, a demonstration of the recovery of the original frames using the decrypted data coefficients along with the eigenfunctions of the frame is provided. The presented simulations indicate that the proposed scheme results in an excellent performance.

Description: The purpose of this note is (i) to point to a reference missing in the paper (Mayo-Maldonado et al., 2018) and (ii) to show that some of its content is covered in a more general setting by the results in that reference (Göknar, 1972).

Description: In this study, the dynamic model for the herringbone planetary gear transmission system is established by the lumped parameter method based on the system dynamics and the Lagrange equation, and the impact of the support stiffness and the torsional stiffness on dynamic characteristics is studied. The research results have a guiding significance for the design of the herringbone gear transmission system. In this model, the herringbone gear is treated as a special gear coupled by 2 opposite helical gears, where the stagger angle, comprehensive meshing error, support stiffness, support damping, and load inertia are considered in the analysis of dynamics. Moreover, the dynamic characteristic of the carrier is considered as well. By calculating the meshing force curve of the transmission system, the impact of the stagger angle, supporting stiffness, and the torsional stiffness on meshing force and load sharing coefficient is analyzed. The results show that the stagger angle has an obvious impact on load sharing coefficient while it has little impact on maximum meshing force. And the support stiffness has a more obvious impact on the dynamic characteristics of the system. The recommendary support stiffness of the system is that all of the support stiffness of the sun gear, planetary gear, ring gear, and carrier is 107 N/m. The torsional stiffness has little impact on the dynamic characteristics of transmission system, except the torsional stiffness of planetary gear, and carrier has an obvious impact on load sharing coefficient. The commercial software ADAMS carried out dynamics analysis of the transmission system to verify the necessity validity of the theoretical analysis.

Description: The existence of stress sensitivity effect made the percolation mechanism of low-permeability reservoirs significantly complex. Further, numerous reservoirs have composite stratum properties in actual development procedure. This paper based on the concept of permeability stress sensitivity presents an unsteady flow model for horizontal well taking both stress sensitivity and composite reservoir into account. Analytic solutions for the transient pressure and the rate decline behaviors are obtained by comprehensive utilization of regular perturbation method, Laplace transformation, orthogonal transformation, and Stehfest numerical inversion. The example analysis verifies that the proposed model is reliable and practical. Likewise, there is a discussion of the influence of permeability modulus and other relevant parameters on the transient pressure and the rate decline for horizontal well in stress-sensitive composite reservoir. The work of this paper improved the previous researches and provided a more accurate basis for transient flow analysis and formation evaluation of this typical reservoir.

Description: An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for an efficient and cost-effective product development and production. This paper proposes a new binary particle swarm optimization- (BPSO-) based association rule mining (BPSO-ARM) method for discovering the hidden relationships between machine capabilities and product features. In particular, BPSO-ARM does not need to predefine thresholds of minimum support and confidence, which improves its applicability in real-world industrial cases. Moreover, a novel overlapping measure indication is further proposed to eliminate those lower quality rules to further improve the applicability of BPSO-ARM. The effectiveness of BPSO-ARM is demonstrated on a benchmark case and an industrial case about the automotive part manufacturing. The performance comparison indicates that BPSO-ARM outperforms other regular methods (e.g., Apriori) for ARM. The experimental results indicate that BPSO-ARM is capable of discovering important association rules between machine capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

Description: Flexible photoelectric film is an anisotropic material. The slight change of equipment performance during processing is prone to cause deformation of the material. Therefore, it is important to predict the degradation of processing equipment performance. Since the performance degradation of flexible photoelectric film material Roll-to-Roll (R2R) processing equipment is a nonlinear process, this paper introduces an adaptive fuzzy clustering method to construct a fuzzy membership function model for calculating the performance degradation index of R2R processing equipment and studies the parameter solving method such as the AFCM division of the roller vibration data, the category center value of the fuzzy membership function, and the input data division area width. Finally, the performance degradation index calculation algorithm is designed. The roller shaft accelerated life test was carried out using self-made equipment. The test data were 1000 sets. The results showed that the root mean square eigenvalues and the kurtosis eigenvalues of the roller vibration data are sensitive to the performance degradation. The equipment performance curve described by the first and second types of performance degradation indicators was very stable in the early stage. After the group, the curve continued to decrease, and the change was more severe, indicating that the performance degradation of the equipment is more serious. In the group, the longer-lasting roller shaft was damaged, and the performance index value was about zero, which proved the correctness of the performance degradation prediction method proposed in this paper in calculating the performance degradation value of the equipment.

Description: The frequencies and amplitudes of the broken rotor bar (BRB) fault features are the basis for the accurate diagnosis of the BRB fault. However, how to accurately detect their frequency and amplitudes has always been a difficult problem for induction motor fault detection. For this problem, a new fault detection method based on an improved multiple signal classification (MUSIC) and least-squares magnitude estimation is proposed. First, since the fixed-step traversal search reduces the computational efficiency of MUSIC, a niche bare-bones particle swarm optimization (NBPSO) for multimodal peaks search is proposed to improve MUSIC, which is used to compute the frequency values of fault-related and fundamental components in stator current signal. Second, using these frequency values, a fault current signal model is established to convert the magnitude estimation problem into a linear least-squares problem. On this basis, the amplitudes and phases of fault-related and fundamental components could be estimated accurately with the singular value decomposition (SVD). A simulation signal is used to test the new method and the results show that the proposed method not only has higher frequency resolution, but also improves estimation accuracy of parameters greatly even with short data window. Finally, experiments for a real induction motor are performed, and the effectiveness and superiority of the proposed method are proved again.

Description: Crowd sensing is a human-centered sensing model. Through the cooperation of multiple nodes, an entire sensing task is completed. To improve the efficiency of sensing missions, a cost-effective set of service nodes, which is easy to fit in performing different tasks, is needed. In this paper, we propose a low-cost service node selection method based on region features, which builds on the relationship between task requirements and geographical locations. The method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster service nodes and calculate the center point of each cluster. The area then is divided into regions according to rules of Voronoi diagrams. Local feature vectors are constructed according to the historical records in each divided region. When a particular sensing task arrives, Analytic Hierarchy Process (AHP) is used to match the feature vector of each region to mission requirements to get a certain number of service nodes satisfying the characteristics. To get a lower cost output, a revised Greedy Algorithm is designed to filter the exported service nodes to get the required low-cost service nodes. Experimental results suggest that the proposed method shows promise in improving service node selection accuracy and the timeliness of finishing tasks.

Description: In this paper, a kind of BAM neural networks with leakage delays in the negative feedback terms and time-varying delays in activation functions was considered. By constructing a suitable Lyapunov function and using inequality techniques, some sufficient conditions to ensure the existence and exponential stability of antiperiodic solutions of these neural networks were derived. These conditions extend some results recently appearing in recent papers. Lastly, an example is given to show the feasibility of these conditions.

Description: Prioritizing traffic signals for trams crossing intersections without stops can increase the service punctuality and travel speed of trams, but it may also increase the delays of other vehicles at intersections. This paper presents a model on coordinated control of traffic signals among successive intersections along the tramline, taking into account driving characteristics of trams and vehicles. The objective is maximizing the valid bandwidth of vehicle green wave to reduce vehicle delays, while the trams cross intersections without stops. Linear Interactive and General Optimizer (LINGO) is applied to solve the proposed model and VISSIM simulation software is adopted to assess the solutions attained by the proposed model and the previous TRAMBAND model. Case studies show that the solutions given by the proposed model facilitate trams to go through all intersections along the tramline without stops. In comparison with the TRAMBAND model, the proposed model reduces tram delay by 13.14 s/pcu and increases the throughput of vehicles at intersections by 4.45% and reduces vehicle delays by 2.22%. Extensive simulations have verified that the performance of the proposed model is stable under different tram headways, dwell time, and traffic volumes. It is also found that the tram headway must be multiple of traffic signal cycle time to completely realize green wave control of all trams at all intersections along the tramline.

Description: Logistics plays a basic supporting role in the growth of national economy. However, tail gas, noise, and traffic congestion caused by logistics have a negative impact on the environment. An effective evaluation mechanism for sustainable development of urban logistics industry is necessary. Data envelopment analysis (DEA) is a common tool for efficiency evaluation. But, DEA has a limited effect on resource allocation in advance because it is ex-post evaluation. It requires input-output indications and the output is after-the-fact data. This defect is particularly prominent in the evaluation of ecological logistics because pollution indicators belong to ex-post output data that threaten the human environment. First prediction and then evaluation is a possible idea. In addition, DEA efficiency ranking does not have a good discrimination due to its coarse granularity. To solve the issues, combining DEA with the Bayes method, we propose an efficiency evaluation model without after-the-fact data, where an efficiency level is predicted and an evaluation value is calculated according to different investment combinations. Then, it is applied to logistics industries of Jiangsu province in China. The results show that our DEA-Bayes method has good discrimination and is easy to operate; a city with geographical advantage and environmental awareness generally gets a higher efficiency score. So the method can help decision makers to allocate resources rationally and further promote the coordinated development of logistics industry.

Description: A phase-based adaptive differential evolution (PADE) algorithm is proposed to solve the economic load dispatch (ELD) considering valve-point effects (VPE) and transmission losses. To a great extent, PADE makes up for the drawbacks of the traditional differential evolution (DE) through three improvements. First, we establish an archive of storing successful individuals to improve the quality of offspring. Second, to balance the exploring and exploiting ability of the algorithm, a phase-based mutation operation is carried out. Third, two control parameters are adaptively adjusted, which is helpful for enhancing the robustness of the algorithm. In addition, two types of repair methods of constraint handling are employed for the ELD without or with transmission losses to help PADE find feasible solutions more efficiently. A performance comparison between PADE and other DE approaches from the literature was carried out on six ELD test cases which consider a set of operating constraints including the VPE and transmission losses. Results show a competitive PADE performance in all test cases regarding the compared DE approaches. Compared to methods from the literature, the costs obtained by PADE are lower in most cases while the corresponding constraint violations reach a lower level.

Description: Over-constrained assembly of rigid parts is widely adopted in aircraft assembly to yield higher stiffness and accuracy of assembly. Unfortunately, the quantitative tolerance analysis of over-constrained assembly is challenging, subject to the coupling effect of geometrical and physical factors. Especially, gravity will affect the geometrical gaps in mechanical joints between different parts, and thus influence the deviations of assembled product. In the existing studies, the influence of gravity is not considered in the tolerance analysis of over-constrained assembly. This paper proposes a novel tolerance analysis method for over-constrained assembly of rigid parts, considering the gravity influence. This method is applied to a typical over-constrained assembly with constraints of multiple planar hole-pin-hole pairs. This type of constraints is non-linear, which makes the tolerance analysis more challenging. Firstly, the deviation propagation analysis of an over-constrained assembly is conducted. The feasibility of assembly is predicted, and for a feasible assembly, the assembly deviations are determined with the principle of minimum potential energy. Then, the statistical tolerance analysis is performed. The probabilities of assembly feasibility and quality feasibility are computed, and the distribution of assembly deviations is estimated. Two case studies are presented to show the applicability of the proposed method.

Description: Model reduction can greatly reduce complexity and difficulty of control design for spatiotemporal systems (STS) in engineering applications. Empirical eigenfunctions (EEFs) are widely used for the model reduction of spatiotemporal systems, however, truncation of higher modes may describe the behaviours of nonlinear spatiotemporal systems inaccurately. In this paper, modified EEFs are proposed and applied to model reduction of nonlinear spatiotemporal systems. Modified EEFs are obtained via modifying the weights matrix in the method of snapshots, which can be rewritten as linear combinations of initial EEFs. The coefficient matrix for combinations is computed according to the nonlinear temporal dynamics of STSs. Thus, the effects of higher modes are considered into modified EEFs with less computational requirements. The reduced model can give a more accurate description for behaviours of the system. The performance of the proposed method is further proved theoretically, and a numerical example demonstrates the effectiveness of the proposed method.

Description: The aims of this study contribute to a new hybrid model by combining ensemble empirical mode decomposition (EEMD) with multikernel function least square support vector machine (MKLSSVM) optimized by hybrid gravitation search algorithm (HGSA) for short-term wind speed prediction. In the forecasting process, EEMD is adopted to make the original wind speed data decomposed into intrinsic mode functions (IMFs) and one residual firstly. Then, partial autocorrelation function (PACF) is applied to identify the correlation between the corresponding decomposed components. Subsequently, the MKLSSVM using multikernel function of radial basis function (RBF) and polynomial (Poly) kernel function by weight coefficient is exploited as core forecasting engine to make the short-term wind speed prediction. To improve the regression performance, the binary-value GSA (BGSA) in HGSA is utilized as feature selection approach to remove the ineffective candidates and reconstruct the most relevant feature input-matrix for the forecasting engine, while real-value GSA (RGSA) makes the parameter combination optimization of MKLSSVM model. In the end, these respective decomposed subseries forecasting results are combined into the final forecasting values by aggregate calculation. Numerical results and comparable analysis illustrate the excellent performance of the EEMD-HGSA-MKLSSVM model when applied in the short-term wind speed forecasting.

Description: In this paper, a compound control scheme with linear active disturbance rejection control (LADRC) and nonlinear active disturbance rejection control (NLADRC) is designed to stabilize the speed control system of the marine engine. To deal with the high nonlinearity and the complex disturbance and noise conditions in marine engines, the advantages of both LADRC and NLADRC are employed. As the extended state observer (ESO) is affected severely by the inherent characteristics (cyclic speed fluctuation, cylinder-to-cylinder deviations, etc.) of the reciprocating engines, a cycle-detailed hybrid nonlinear engine model is adopted to analyze the impact of such characteristics. Hence, the controller can be evaluated based on the modified engine model to achieve more reliable performance. Considering the mentioned natural properties in reciprocating engines, the parameters of linear ESO (LESO), nonlinear ESO (NLESO), and the switching strategy between LADRC and NLADRC are designed. Finally, various comparative simulations are carried out to show the effectiveness of the proposed control scheme and the superiority of switching strategy. The simulation results demonstrate that the proposed control scheme has prominent control effects both under the speed tracking mode and the condition with different types and levels of load disturbance. This study also reveals that when ADRC related approaches are employed to the reciprocating engine, the impact of the inherent characteristics of such engine on the ESO should be considered well.

Description: Operational simulators have a fundamental role in space programs. During a satellite operation, these simulators are essential for validating critical manoeuvres, testing new on-board software versions, and supporting the diagnosis of anomalies. With the purpose of reusing the operational simulators, the Brazilian National Institute for Space Research has proposed a new standard for the specification of the components that must be integrated in their in-house developed simulators. The new standard describes the behaviour of satellite subsystems using cause-effect tables that relate telecommands, electrical switches, equipment working states, energy consumption, telemetries, and operating modes of the subsystem. Using this new standard as input, this work proposes an approach that merges model-based testing and model checking to verify the correct implementation of new components in the satellite simulator. The verification approach consists of extracting state machines from the cause-effect tables and used it to automatically derive a test case suite. In order to validate the proposal, we applied it to three different satellite subsystems and assessed the results obtained from the test campaigns. In all the three cases, the proposed approach identified errors in the simulator components that were not initially detected by the traditional testing approach used at the Brazilian National Institute for Space Research.

Description: The dominant grouping of the discontinuities in a rock mass is crucial to investigating the rock structure and stability. The traditional grouping methods are mostly based on the discontinuity orientation. However, other discontinuity parameters nonnegligibly impact the properties of a rock mass. If two discontinuities have the same orientation but differ otherwise, their mechanical and hydraulic properties would differ. In the present study, orientation, trace length, opening degree, and undulation of discontinuities were used to develop a multi-parameter dominant discontinuity grouping method that utilizes an improved ISODATA algorithm. The developed method uses several indicators, such as the weighted Euclidean distance and standard deviation, to iteratively update the clustering centers of the discontinuities. A clustering validity index was introduced for assessment and optimization of the discontinuity grouping results, and an adaptive grouping model that considers the weight of each grouping parameter was developed. By grouping discontinuities generated by Monte Carlo stochastic simulation, defects existing in the grouping based on the orientation only were illustrated, and the rationality of the established adaptive grouping model was verified. The engineering practicability of the method was further verified by using it to group discontinuities measured in the Dongsheng Quarry in Jingyuetan, Changchun, China.

Description: Energy is the lifeline and the base of development of social economy in every country or area. However, with the increasing depletion of fossil energy, it is imperative for business managers and government policy makers to seek and utilize new energy resources for keeping the national economy development sustained, rapid, and healthy. As a new energy resource, wind energy is developing with a high speed in the world for the reasons that it is an abundant natural resource with the characteristics of renewable, low cost, and no pollution. But in reality, forecasting wind speed accurately is continuously a challenging subject in the field of the wind energy development for a long time. In this paper, a novel ensemble wind speed forecasting model based on machine leaning models and hybrid models with data preprocessing technology is proposed to forecast the wind speed effectively in the Longdong area of Loess Plateau in China; numerical results show that the ensemble model has better precision, adaptability, and robustness.

Description: Frequent time window changing disruptions result in high secondary delivery rates in the last mile delivery. With the rapid growth of parcel volumes in online shopping, the time window changing disruptions could translate to substantial delivery cost-wastes. In recent years, customer pickup (CP), a new delivery mode that allows customers to pick up their parcels from shared delivery facilities, has provided a new way to deal with such disruptions. This study proposed a disruption recovery problem with time windows change in the last mile delivery in which customers can be served through home delivery (HD) or CP. A variant variable neighborhood descent (VVND) algorithm was presented to solve the problem. Computational experiments based on a set of instances were tested, and results were compared with other heuristics in the literature, which have affirmed the competitiveness of the model and algorithm.

Description: This paper proposes a novel approach to the directional forecasting problem of short-term oil price changes. In this approach, the short-term oil price series is associated with incomplete fuzzy information, and a new fused genetic-fuzzy information distribution method is developed to process such a fuzzy incomplete information set; then a feasible coding method of multidimensional information controlling points is adopted to fit genetic-fuzzy information distribution to time series forecasting. Using the crude oil spot prices of West Texas Intermediate (WTI) and Brent as sample data, the empirical analysis results demonstrate that the novel fused genetic-fuzzy information distribution method statistically outperforms the benchmark of logistic regression model in prediction accuracy. The results indicate that this new approach is effective in direction accuracy.

Description: In order to improve the safety, efficiency, and reliability in large scale wind turbines, a great deal of statistical and machine-learning models for wind turbine health monitoring system (WTHMS) are proposed based on SCADA variables. The data-driven WTHMS have been performed widely with the attentions on predicting the failures of the wind turbine or primary components. However, the health status of wind turbine often degrades gradually rather than suddenly. Thus, the SCADA variables change continuously to the occurrence of certain faults. Inspired by the ability of recurrent neural network (RNN) in redefining the raw sensory data, we introduce a hybrid methodology that combines the analysis of variance for each sequential SCADA variable with RNN to assess the health status of wind turbine. First, each original sequence is split by different variance ranges into several categories to improve the generalized ability of the RNN. Then, the long short-term memory (LSTM) is procured on the normal running sequence to learn the gradually changing situations. Finally, a weighted assessment method incorporating the health of primary components is applied to judge the health level of the wind turbine. Experiments on real-world datasets from two wind turbines demonstrate the effectiveness and generalization of the proposed model.

Description: It is a research hot spot in cognitive electronic warfare systems to classify the electromagnetic signals of a radar or communication system according to their modulation characteristics. We construct a multilayer hybrid machine learning network for the classification of seven types of signals in different modulation. We extract the signal modulation features exploiting a set of algorithms such as time-frequency analysis, discrete Fourier transform, and instantaneous autocorrelation and accomplish automatic modulation classification using naive Bayesian and support vector machine in a hybrid manner. The parameters in the network for classification are determined automatically in the training process. The numerical simulation results indicate that the proposed network accomplishes the classification accurately.

Description: In the process of oilfield development, it is important to predict the oil and gas production. The predicted value of oil production is the amount of oil that may be obtained within a certain area over a certain period. Because of the current demand for oil and gas production prediction, a prediction model using a multi-input convolutional neural network based on AlexNet is proposed in this paper. The model predicts real oilfield data and achieves good results: increasing prediction accuracy by 17.5%, 20.8%, 11.6%, 8.9%, 6.9%, and 14.9% with respect to the backpropagation neural network, support vector machine, artificial neural network, radial basis function neural network, K-nearest neighbor, and decision tree methods, respectively. It addresses the uncertainty of oil and gas production caused by the change in parameter values during the process of petroleum exploitation and has far-reaching application significance.

Description: Recently, face datasets containing celebrities photos with facial makeup are growing at exponential rates, making their recognition very challenging. Existing face recognition methods rely on feature extraction and reference reranking to improve the performance. However face images with facial makeup carry inherent ambiguity due to artificial colors, shading, contouring, and varying skin tones, making recognition task more difficult. The problem becomes more confound as the makeup alters the bilateral size and symmetry of the certain face components such as eyes and lips affecting the distinctiveness of faces. The ambiguity becomes even worse when different days bring different facial makeup for celebrities owing to the context of interpersonal situations and current societal makeup trends. To cope with these artificial effects, we propose to use a deep convolutional neural network (dCNN) using augmented face dataset to extract discriminative features from face images containing synthetic makeup variations. The augmented dataset containing original face images and those with synthetic make up variations allows dCNN to learn face features in a variety of facial makeup. We also evaluate the role of partial and full makeup in face images to improve the recognition performance. The experimental results on two challenging face datasets show that the proposed approach can compete with the state of the art.

Description: Emerging technologies such as smart cities and unmanned vehicles all need Global Navigation Satellite Systems (GNSS) to provide high-precision positioning and navigation services. Fast and reliable carrier phase ambiguity resolution (AR) is a prerequisite for high-precision positioning. The poor satellite geometry and severe multipath effect caused by Beidou Navigation Satellite System (BDS) signal occlusion and reflection in complex environments will degrade the AR performance. In this contribution, a fast triple-frequency AR method combining Microelectromechanical System-Inertial Measurement Unit (MEMS-IMU) and BDS is proposed. First, the Extra-Wide Lane (EWL) ambiguity is fixed with the positioning parameters of MEMS-IMU instead of the pseudorange. Then, the phase noise variance of Narrow Lane (NL) observation is obtained from ambiguity-fixed EWL observation to reduce the total noise level of NL observation, and the NL ambiguity can be reliably fixed, and the BDS positioning result is obtained. Finally, the BDS positioning result is used as the posterior measurement of the extended Kalman filter to update the MEMS-IMU positioning parameters to form the coupling loop of MEMS-IMU and BDS. The data of urban road vehicle experiments were collected to verify the feasibility and effectiveness of the proposed algorithm. Results show that MEMS-IMU can speed up AR, and reduction of total noise level can significantly improve the reliability of AR.

Description: Passive bistatic radar (PBR) has attracted widespread attention for its capabilities in dealing with the threat of electronic countermeasure, stealth technology, and antiradiation missile. However, passive detection methods are limited by unknown characteristics of the uncooperative illuminators, and conventional radar signal processing algorithms cannot be conducted accurately, especially when the carrier frequency of the transmitting signal is agile and the signal-to-noise ratio (SNR) in the scattered wave of target is low. To address the above problems, this paper presents a novel weak target detection method based on probability histogram, which is then tested by a field experiment. Preliminary results indicate the feasibility of the proposed method in weak target detection.

Description: The steady-state performance of a moving-object tracking filter is theoretically analyzed, assuming the simultaneous measurement of the range and range-rate (RRM system), and the use of linear frequency modulated (LFM) waveforms (RRM-LFM filter). An efficient analytical steady-state performance index, called an RMS index, is derived for the RRM-LFM filter to clarify the steady-state range prediction errors, theoretically. Using the derived RMS index, the optimal performance of the RRM-LFM filter is analyzed. The performance variation due to the use of LFM waveforms is clarified for the RRM tracking system. The theoretical performance analysis verifies that the measured range-rate significantly improves the tracking accuracy, compared to the conventional range-only measuring LFM tracking filter. Furthermore, the quantitative relationships among the measurement accuracy, degree of target maneuvering, and steady-state range prediction errors are clarified to validate the effectiveness of the RRM-LFM filter.

Description: The availability of wearable cameras in the consumer market has motivated the users to record their daily life activities and post them on the social media. This exponential growth of egocentric videos demand to develop automated techniques to effectively summarizes the first-person video data. Egocentric videos are commonly used to record lifelogs these days due to the availability of low cost wearable cameras. However, egocentric videos are challenging to process due to the fact that placement of camera results in a video which presents great deal of variation in object appearance, illumination conditions, and movement. This paper presents an egocentric video summarization framework based on detecting important people in the video. The proposed method generates a compact summary of egocentric videos that contains information of the people whom the camera wearer interacts with. Our proposed approach focuses on identifying the interaction of camera wearer with important people. We have used AlexNet convolutional neural network to filter the key-frames (frames where camera wearer interacts closely with the people). We used five convolutional layers and two completely connected hidden layers and an output layer. Dropout regularization method is used to reduce the overfitting problem in completely connected layers. Performance of the proposed method is evaluated on UT Ego standard dataset. Experimental results signify the effectiveness of the proposed method in terms of summarizing the egocentric videos.

Description: Realizing accurate detection of an unknown radio transmitter (URT) has become a challenge problem due to its unknown parameter information. A method based on received signal strength difference (RSSD) fingerprint positioning technique and using factor graph (FG) has been successfully developed to achieve the localization of an URT. However, the RSSD-based FG model is not accurate enough to express the relationship between the RSSD and the corresponding location coordinates since the RSSD variances of reference points are different in practice. This paper proposes an enhanced RSSD-based FG algorithm using weighted least square (WLS) to effectively reduce the impact of RSSD measurement variance difference on positioning accuracy. By the use of stochastic RSSD errors between the measured value and the estimated value of the selected reference points, we utilize the error weight matrix to establish a new WLSFG model. Then, the positioning process of proposed RSSD-WLSFG algorithm is derived with the sum-product principle. In addition, the paper also explores the effects of different access point (AP) numbers and grid distances on positioning accuracy. The simulation experiment results show that the proposed algorithm can obtain the best positioning performance compared with the conventional RSSD-based K nearest neighbor (RSSD-KNN) and RSSD-FG algorithms in the case of different AP numbers and grid distances.

Description: Human activity recognition (HAR) has become a popular topic in research because of its wide application. With the development of deep learning, new ideas have appeared to address HAR problems. Here, a deep network architecture using residual bidirectional long short-term memory (LSTM) is proposed. The advantages of the new network include that a bidirectional connection can concatenate the positive time direction (forward state) and the negative time direction (backward state). Second, residual connections between stacked cells act as shortcut for gradients, effectively avoiding the gradient vanishing problem. Generally, the proposed network shows improvements on both the temporal (using bidirectional cells) and the spatial (residual connections stacked) dimensions, aiming to enhance the recognition rate. When testing with the Opportunity dataset and the public domain UCI dataset, the accuracy is significantly improved compared with previous results.

Description: The effective case depth (ECD) plays an important role in the meshing strength of internal gear transmissions. Carburizing quenching heat treatment is commonly used to enhance gear strength and wear resistance. However, the different ECDs in internal and external gears caused by heat treatment significantly affect the meshing strength, causing vibration, reducing gear service life, and hastening malfunction in internal gear transmission. In this study, we conducted an investigation of different ECDs by the heat treatment of carburized gear pairs by numerical simulation with the finite element method (FEM) and experiment tests. We analyzed three different carburized layer models, with the ECD in the internal gear being greater than, less than, and equal to the ECD in the external gear. In addition, we investigated the ability to distinguish between hardness gradients in gear teeth by dividing the carburized depth into seven layers to improve modeling accuracy. Results revealed that the meshing strength of internal gear transmission could be significantly enhanced by adopting the model with the ECD in the internal gear being less than the ECD in the external gear, and moreover, the shear stress of carburized gears initially increased and then decreased along with depth direction, and the maximum value appeared in the middle of the lower surface.

Description: We construct a new opinion formation of the Deffuant-Weisbuch model with the interference of the outer noise, where there are finite agents and the evolution is discrete-time. The opinion interaction occurs by one randomly chosen pair at each time step. The difference to the original Deffuant-Weisbuch model is that communications of any selected pairs will be affected by noises. The aim of this paper is to study the robust consensus of this noisy Deffuant-Weisbuch model. We first define the noise strength as the maximum noise absolute value. We will then show that when the noise strength is less than a certain threshold, this noisy model will achieve robust consensus when is sufficiently large; next we prove that the noisy model achieves robust consensus with a positive probability; finally, we demonstrate these results and provide numerical relations among the noise strength and some model parameters.

Description: Effective control of the heat-treatment operation is essential to reducing manufacturing cost in mould manufacturing. The heat-treatment shop floor is a flow shop with parallel batch processors and incompatible jobs. The jobs differ from each other in product types, sizes, release times, and due-dates. The scheduling objective is to minimize manufacturing cost, including energy cost, subcontracting cost, and jobs’ tardiness penalties cost. A hierarchical production planning structure is proposed, which contains three decision-making levels: (1) balancing capacity and demand, (2) machining at the quenching stage, and (3) machining at the tempering stage. At the first level, the periodic rolling scheduling heuristic is proposed for the purpose of balancing the capacity and the demand in the coming period in the heat-treatment shop floor. At the second and third levels, two new look-ahead batching heuristics are proposed. An extensive computational experiment is conducted to verify the effectiveness of the proposed heuristics.

Description: Partial differential equations (PDEs) are widely used in mechanics, control processes, ecological and economic systems, chemical cycling systems, and epidemiology. Although there are some numerical methods for solving PDEs, simple and efficient methods have always been the direction that scholars strive to pursue. Based on this problem, we give the meshless barycentric interpolation collocation method (MBICM) for solving a class of PDEs. Four numerical experiments are carried out and compared with other methods; the accuracy of the numerical solution obtained by the present method is obviously improved.

Description: In view of the practical engineering problem of dynamic positioning of rotor UAV pod and the strong nonlinear problem in dynamic operation of UAV, a real-time estimation of the dynamic position of the rotor UAV pod is proposed by using the SR-UKF algorithm. This algorithm uses the nonlinear propagation of UT transform to generate the point set to maintain the mean and covariance information and thus achieves higher precision. Moreover, it uses the square root of covariance instead of covariance to participate in the recursive operation, thus improving the numerical stability of the filter and reducing the amount of computation. In this work, a pseudosatellite positioning platform was constructed in a field site in Nanjing. Based on evaluation of the space geometry of pseudosatellite base station, the accuracy of several nonlinear filtering algorithms was analyzed and evaluated using GPS RTK positioning results. It was found that the SR-UKF algorithm was the most accurate and efficient algorithm. It can meet the requirements of dynamic positioning of the rotor-wing UAV pod. The experiment results of this algorithm provide a more efficient positioning algorithm and implementation means for the actual engineering of UAV pod positioning using pseudosatellite system, which has high application value.

Description: Fuzzy sliding mode control as a robust and intelligent nonlinear control technique is proposed to control processes with severe nonlinearity and unknown models. This paper proposes a new adaptive tracking fuzzy sliding mode controller for nonlinear systems in the presence of fuzzy compensation. The main contribution of the proposed method is that the fuzzy system is used to realize the adaptive approximation of the unknown part of the model, and the fuzzy gain can be reduced effectively. The fuzzy self-adaptive rate is derived through the Lyapunov method, and the stability and convergence of the whole closed-loop system are guaranteed by adjusting the adaptive weight value. The performance of the proposed approach is evaluated for double joint rigid manipulator problems. The simulation results illustrate the effectiveness of our proposed controller.

Description: This paper studies the optimal transportation and inventory strategy for perishable items under carbon cap-and-trade and carbon tax regulations. Nonlinear optimization models are constructed to maximize the total profits per unit time when the deterioration rate satisfies a two-parameter Weibull distribution and the demand rate is a linear function with respect to the current inventory level. The existence conditions of the optimal replenishment cycles under two carbon emission policies are proved. The characteristics of the optimal replenishment cycles, maximal total profits, and carbon emissions per unit time under two carbon emission policies are compared. Finally, a numerical test is provided to illustrate the theoretical results and Taguchi method is employed to analyze the sensitivity of total profits and carbon emissions per unit time with respect to the parameters of carbon emission policies, transportation time, and Weibull parameters. The results show that parameters of carbon emission regulations have greater effect on total profits per unit time, and the transportation time has the greatest effect on carbon emissions per unit time.

Description: Advanced driver assistance systems (ADAS) have recently been thrust into the spotlight in the automotive industry as carmakers and technology companies pursue effective active safety systems and fully autonomous vehicles. Various sensors such as lidar (light detection and ranging), radar (radio detection and ranging), ultrasonic, and optical cameras are employed to provide situational awareness to vehicles in a highly dynamic environment. Radar has emerged as a primary sensor technology for both active/passive safety and comfort-advanced driver-assistance systems. Physically building and testing radar systems to demonstrate reliability is an expensive and time-consuming process. Simulation emerges as the most practical solution to designing and testing radar systems. This paper provides a complete, full physics simulation workflow for automotive radar using finite element method and asymptotic ray tracing electromagnetic solvers. The design and optimization of both transmitter and receiver antennas is presented. Antenna interaction with vehicle bumper and fascia is also investigated. A full physics-based radar scene corner case is modelled to obtain high-fidelity range-Doppler maps. Finally, this paper investigates the effects of inclined roads on late pedestrian detection and the effects of construction metal plate radar returns on false target identification. Possible solutions are suggested and validated. Results from this study show how pedestrian radar returns can be increased by over 16 dB for early detection along with a 27 dB reduction in road construction plate radar returns to suppress false target identification. Both solutions to the above corner cases can potentially save pedestrian lives and prevent future accidents.

Description: Industry 4.0 aims to ensure the future competitiveness of the manufacturing industry, where one of the major challenges faced by its implementation is the manufacturing/production system robustness (that is, able to perform in the presence of noise), as they may not be able to absorb input disruptions without bending or breaking. In this paper we propose to use the Max-Plus algebra approach to study the propagation of manufacturing disturbances (i.e., processing time variations), presenting a case study and performing a sensitivity analysis, with the idea of understanding under which conditions disturbance propagation takes place. Findings show that the impact propagation depends on where the variation source is located within the manufacturing system. Two are the main original contributions of this paper: the use of Max-Plus algebra to study the impact propagation of processing time variations and a four-step methodology to derive the equations representing the deterministic manufacturing system.

Description: A simple alternating direction method is used to solve the conic trust region subproblem of unconstrained optimization. By use of the new method, the subproblem is solved by two steps in a descent direction and its orthogonal direction, the original conic trust domain subproblem into a one-dimensional subproblem and a low-dimensional quadratic model subproblem, both of which are very easy to solve. Then the global convergence of the method under some reasonable conditions is established. Numerical experiment shows that the new method seems simple and effective.

Description: In order to meet the demand of power supply, the construction of transmission line projects is constantly advancing, and the level of cost control is constantly improving, which puts forward higher requirements for the accuracy of cost prediction. This paper proposes an intelligent cost prediction model based on least squares support vector machine (LSSVM) optimized by particle swarm optimization (PSO). Originally extracting natural, technological, and economic indexes from the perspective of cost composition, principal component analysis (PCA) is used to reduce the dimension of indexes. And PSO is innovatively introduced to optimize the parameters of LSSVM model to obtain the optimal parameters. The obtained principal component data are imported into empirical parameter LSSVM prediction model and the optimized parameter PSO-LSSVM prediction model, respectively, for modeling and prediction, and then comparing the prediction results to analyze the effect of model optimization. The results show that the absolute deviation of the optimized parameter prediction model is less than 9%. And the prediction accuracy of the optimized parameter prediction model is better than that of the empirical parameter model, which can provide a reliable basis for investment decision-making of transmission line projects.

Description: The minimum spanning tree- (MST-) based clustering method can identify clusters of arbitrary shape by removing inconsistent edges. The definition of the inconsistent edges is a major issue that has to be addressed in all MST-based clustering algorithms. In this paper, we propose a novel MST-based clustering algorithm through the cluster center initialization algorithm, called cciMST. First, in order to capture the intrinsic structure of the data sets, we propose the cluster center initialization algorithm based on geodesic distance and dual densities of the points. Second, we propose and demonstrate that the inconsistent edge is located on the shortest path between the cluster centers, so we can find the inconsistent edge with the length of the edges as well as the densities of their endpoints on the shortest path. Correspondingly, we obtain two groups of clustering results. Third, we propose a novel intercluster separation by computing the distance between the points at the intersection of clusters. Furthermore, we propose a new internal clustering validation measure to select the best clustering result. The experimental results on the synthetic data sets, real data sets, and image data sets demonstrate the good performance of the proposed MST-based method.

Description: Correlation filter based trackers have received great attention in the field of visual target tracking, which have shown impressive advantages in terms of accuracy, robustness, and speed. However, there are still some challenges that exist in the correlation filter based methods, such as target scale variation and occlusion. To deal with these problems, an improved kernelized correlation filter (KCF) tracker is proposed, by employing the GM(1,1) grey model, the interval template matching method, and multiblock scheme. In addition, a strict template update strategy is presented in the proposed method to accommodate the appearance change and avoid template corruption. Finally, some experiments are conducted. The proposed method is compared with the top state-of-the-art trackers, and all the tracking algorithms are evaluated on the object tracking benchmark. The experimental results demonstrate obvious improvements of the proposed KCF-based visual tracking method.

Description: Time-varying characteristics of DC traction power supply system network structure are the key technical problem in its mathematical modeling. Based on node admittance network equation theory, this paper puts forward an optimizational mathematical modeling method of DC traction power supply system for urban mass transit, and that is to conduct automatic sequencing firstly for all the nodes on both ends of traction substation and the train according to the given rules, then arrange the nodes according to the position coordinates and save them into the arrays, and finally generate the node admittance network equation of DC traction power supply system according to the self-adaptive real-time dynamics of node arrays and input parameter documents. Simulation study indicates the rationality and correctness of optimizational mathematical modeling method of DC traction power supply system for urban mass transit.

Description: In this paper, an effective and reliable sleep circuit is proposed, which not only reduces leakage power but also shows significant reduction in ground bounce noise (GBN) in approximate full adder (FA) circuits. Four 1-bit approximate FA circuits are modified using proposed sleep circuit which uses one NMOS and one PMOS transistor. The design metrics such as average power, delay, power delay product (PDP), leakage power, and GBN are compared with nine other 1-bit FA circuits reported till date. All the comparisons are done using post-layout netlist at 45nm technology. The modified designs achieve reduction in leakage power and GBN up to 60% and 80%, respectively, as compared to the best reported approximate FA circuits. The modified approximate FA also achieves 83% reduction in leakage power as compared to conventional FA. Finally, application level metrics such as peak signal to noise ratio (PSNR) are considered to measure the performance of all the proposed approximate FAs.

Description: Source depth estimation is always a problem in underwater acoustic area, because depth estimation is a nonlinear problem. Traditional depth estimation methods use a vertical line array, which has disadvantage of poor mobility due to the size of sensor array. In order to estimate source depth with a horizontal line array, we propose a matched-mode depth estimation method based on autoregressive (AR) wavenumber estimation for a moving source in shallow water waveguides. First, we estimate the mode wavenumbers using the improved AR modal wavenumber spectrum. Second, according to the mode wavenumber estimation, we estimate the mode amplitudes by the wavenumber spectrum, which is obtained by generalized Hankel transform. Finally, we estimate source depth estimation by the peak of source depth function wherein the data mode best matches the replica mode that is calculated using a propagation model. Compared with synthetic aperture beamforming, the proposed method exhibits a better performance in source depth estimation under low signal-to-noise ratio or the small range span. The robustness of the proposed method is illustrated by simulating the performance in mismatched environment.

Description: Modeling approach for mesoscopic model of concrete depicting mass transportation and physicochemical reaction is important since there is growing demand for accuracy and computational efficiency of numerical simulation. Mesoscopic numerical simulation considering binder, aggregate, and interfacial transition zone (ITZ) generally produces huge number of DOFs, which is inapplicable for full structure. In this paper, a two-dimensional multiscale approach describing three-phase structure of concrete was discussed numerically. An effective approach generating random aggregate in polygon based on checking centroid distance and intersection of line segment was introduced. Moreover, ITZ elements were built by parallel expanding the edge of aggregates on inner side. By combining mesoscopic model including full-graded aggregate and macroscopic model, cases related to diffusivity and width of ITZ, volume fraction, and grade of aggregate were studied regarding the consideration of multiscale compensation. Result clearly showed that larger analysis model in multiscale model expanded the diffusion space of chloride ion and decreased chloride content in front of rebar. Finally, this paper addressed some noteworthy conclusions about the chloride distribution and rebar corrosion regarding the configuration of rebar diameter, concrete cover, and exposure period.

Description: An arbitrary-location pulse determination algorithm based on multipulse linear prediction coding (MP-LPC) is presented. This algorithm can determine all the amplitudes of the pulses at a time according to given pulse locations without the use of analysis-by-synthesis. This ensures that the pulses are optimal in a least-square sense, providing the theoretical foundation to improve the quality of synthesized speech. A fixed-location pulse linear prediction coding (FLP-LPC) method is proposed based on the arbitrary-location pulse determination algorithm. Simulation of the algorithm in MATLAB showed the superior quality of the speech synthesized using pulses in different locations and processed using the arbitrary-location pulse determination algorithm. The algorithm improved speech quality without affecting coding time, which was approximately 1.5% of the coding time for MP-LPC. Pulse locations in FLP-LPC are fixed and do not need to be transmitted, with only LSF, gain, and 16 pulse amplitudes requiring coding and transmission. FLP-LPC allows the generation of synthesized speech similar to G.729 coded speech at a rate of 2.5 kbps.

Description: Electromagnetic vector sensors (EMVS) have attracted growing attention in recent years. However, the mutual coupling effects in practical EMVS arrays may seriously degrade the parameter estimation performance. In order to solve this problem, a novel array configuration consisting of two parallel sparse dipole arrays is proposed. Based on the spatially rotational invariance property between the two parallel arrays and the interdipole spacing inside each array, highly accurate but ambiguous direction-cosine estimates, coarse direction-of-arrival (DOA) estimates, and polarization parameter estimation can be obtained jointly. The coarse DOA estimates are then employed to disambiguate the phase ambiguities in the fine estimates. Compared with collocated EMVS, the proposed array overcomes the mutual coupling problem. Moreover, the DOA estimation accuracy is promoted due to the sparse array aperture extension. Simulation results demonstrate the effectiveness of the proposed algorithm.

Description: Ship wakes are large, exist for a long time, and are difficult to disguise or conceal. These characteristics can be used as an important basis for ship tracking and recognition. However, distinguishing between wakes, underwater targets, and sea surface is a difficult problem that currently limits acoustic wake homing technology. To solve this problem, in this study, from the viewpoint of feature recognition, the effects of bubble radius, air volume fraction, frequency, depth, and other parameters on the group bubble were first investigated. The volume scattering intensity of acoustic wakes at different frequencies and depths was also calculated and analyzed, and the results of our theoretical calculations were verified through an experiment using a multifrequency single wave sonar dock. Subsequently, through the single frequency and multibeam sonar sea trial test, a statistical model of target characteristics with a clear physical mechanism was developed. The developed model can be utilized for the guidance and recognition of acoustic wake targets. Thus, this study lays the foundation for the practical application of acoustic wake guidance.

Description: The paper proposes a displacement-based design method for seismic retrofit of RC buildings using hysteretic dissipative braces. At first, a fully multimodal procedure based on an adaptive version of the capacity spectrum method is applied to the 3D model of the damped braced structure. Then, the properties of an idealized bilinear model are defined using the seismic characteristics of the compound system thus accounting for the frame-damped brace interaction. Finally, an iterative procedure is developed to provide an optimal distribution of dampers. The proposed method overcomes the limitations of the design procedures in the literature that generally neglect the frame-damped braces interactions. Moreover, it addresses the main issues of seismic design of damped braces: effect of force demands applied to the frame due to the damper yielding and strain hardening, higher modes contribution, effect of soft-storey irregularities, and torsion effect in asymmetric buildings. The proposed design procedure is first validated using nonlinear static and dynamic analyses of a numerical example. Then, it is implemented to a real case study of a RC school building to assess its applicability in current practice.

Description: A novel approach to estimate suspension state information and payload condition was developed in this article. A nonlinear quarter car model with air spring and damper was built. After verification of system observability and solvability, a certain coordinate transform was built to transform the nonlinear system into a linear one. Then a Kalman filter observer was applied. A sprung mass observer, which works cooperatively with suspension state information observer, was also designed. Designed dual-observer was verified under typical road profile and sprung mass disturbance. Compared with extended Kalman filter, the dual-observer showed better accuracy and robustness.

Description: The technique of pattern time delay shift coding (PDS) underwater acoustic (UWA) communication based on parametric array is presented in this paper, which is easy to be implemented and robust in the spatiotemporal variable ocean environment. The parametric array can generate low-frequency, broadband, and high-directivity beam with small-aperture. The high directivity reduces the impact of time variant characteristics of UWA channel especially multipath effects and improves the reuse rate of underwater acoustic channel at the same time. The wide bandwidth allows high rate communications. The sea trial results show that it can be employed to combat multipath propagation in shallow water and achieve very low bit error rate (BER). The theoretical research and sea trial verify the feasibility and effectiveness of the proposed UWA method.

Description: An optimization model for the complementary operation of a photovoltaic-wind-pumped storage system is built to make full use of solar and wind energy. Apart from ensuring the maximum economic benefit which is normally used as the only objective, the stable objectives of minimizing the output fluctuation and variation of load and output difference are added to form the multiobjective problems because of lack of study on access capacity of photovoltaic and wind power. The model aims to increase the power benefit and reduce the output fluctuation and variation of load and output difference under the constraints of station, output balance, and transmission limitation. In a case study, four schemes including single-objective independent operation, single-objective complementary operation, and multiobjective complementary operation are compared to discuss the effect of pumped storage station on economic objective and stable objectives. Furthermore, the opposite trend of the two objectives is proved and a compromise optimal solution is given. The results indicate that the pumped storage station can effectively increase power benefit and access capacity of photovoltaic and wind power. The study can provide references to the complementary optimization of the pumped storage station and the intermittent renewable energy.

Description: Rebate is a traditional type of promotion, and it can benefit manufacturers and retailers with expanded demands. However, the impact of leadership strategy in rebate competition on supply chain members and rebate decision is still somewhat unclear. Our paper focuses on a horizontal competition with respect to both rebate and leadership between two manufacturers selling substitutable products through a common retailer to consumers who are heterogeneous in their price sensitivity. Furthermore, we investigate the impacts of leadership strategy on profits and study rebate decision under different strategies. Our research indicates that Bertrand-Nash game benefits the retailer, but hurts manufacturers, while Stackelberg game benefits manufacturers but hurts the retailer, which shows no difference from previous studies. In addition, the sequential-move Stackelberg game could eliminate the classic prisoners’ dilemma in rebate decision, which is also influenced by fixed cost control.

Description: Fuzziness is a key concern in modern industry and, thus, its implementation in manufacturing process modeling is of high practical importance for a wide industrial audience. The scientific contribution of the present attempt lies on the fact that the assembly line balancing problem of type 2 (SALBP-2) is approached for a real manufacturing process by introducing fuzzy processing times. The main scope of this work is the solution of the SALBP-2, which is an NP-hard problem, for a real manufacturing process considering fuzziness in the processing times. Since the data obtained from realistic situations are imprecise and uncertain, the consideration of fuzziness for the solution of SALBP-2 is of great interest. Thus, real data values for the processing times are gathered and estimated with uncertainty. Then, fuzzy processing times are used for finding the optimum cycle time. The optimization tool for the solution of the fuzzy SALBP-2 is a Genetic Algorithm (GA). The validity of the proposed approach is tested on the construction process of a metallic robotic arm. The experimental results demonstrate the effectiveness and efficiency of the proposed GA in determining the optimum sequence of the tasks assigned to workstations which provides the optimum fuzzy cycle time.

Description: Object tracking is a vital topic in computer vision. Although tracking algorithms have gained great development in recent years, its robustness and accuracy still need to be improved. In this paper, to overcome single feature with poor representation ability in a complex image sequence, we put forward a multifeature integration framework, including the gray features, Histogram of Gradient (HOG), color-naming (CN), and Illumination Invariant Features (IIF), which effectively improve the robustness of object tracking. In addition, we propose a model updating strategy and introduce a skewness to measure the confidence degree of tracking result. Unlike previous tracking algorithms, we judge the relationship of skewness values between two adjacent frames to decide the updating of target appearance model to use a dynamic learning rate. This way makes our tracker further improve the robustness of tracking and effectively prevents the target drifting caused by occlusion and deformation. Extensive experiments on large-scale benchmark containing 50 image sequences show that our tracker is better than most existing excellent trackers in tracking performance and can run at average speed over 43 fps.

Description: Estimators for the parameters of the Markovian multiserver queues are presented, from samples that are the number of clients in the system at arbitrary points and their sojourn times. As estimation in queues is a recognizably difficult inferential problem, this study focuses on the estimators for the arrival rate, the service rate, and the ratio of these two rates, which is known as the traffic intensity. Simulations are performed to verify the quality of the estimations for sample sizes up to 400. This research also relates notable new insights, for example, that the maximum likelihood estimator for the traffic intensity is equivalent to its moment estimator. Some limitations of the results are presented along with a detailed numerical example and topics for future developments in this research area.

Description: In this article, we investigate a joint pricing and inventory problem for a retailer selling fresh agriproducts (FAPs) with two-period shelf lifetime in a dynamic stochastic setting, where new and old FAPs are on sale simultaneously. At the beginning of each period the retailer makes ordering decision for new FAP and sets regular and discount price for new and old inventories, respectively. After demand realization, the expired leftover is disposed and unexpired inventory is carried to the next period, continuing selling. Unmet demand of all FAPs is backordered. The objective is to maximize the total expected discount profit over the whole planning horizon. We present a price-dependent, stochastic dynamic programming model taking into account zero lead time, linear ordering costs, inventory holding, and backlogging costs, as well as disposal cost. Considering the influence of the perishability, we integrate a Multinomial Logit (MNL) choice model to describe the consumer behavior on purchasing fresh or nonfresh product. By way of the inverse of the price vector, the original formulation can be transferred to be jointly concave and tractable. Finally we characterize the optimal policy and develop effective methods to solve the problem and conduct a simple numerical illustration.

Description: The existing registration algorithms suffer from low precision and slow speed when registering a large amount of point cloud data. In this paper, we propose a point cloud registration algorithm based on feature extraction and matching; the algorithm helps alleviate problems of precision and speed. In the rough registration stage, the algorithm extracts feature points based on the judgment of retention points and bumps, which improves the speed of feature point extraction. In the registration process, FPFH features and Hausdorff distance are used to search for corresponding point pairs, and the RANSAC algorithm is used to eliminate incorrect point pairs, thereby improving the accuracy of the corresponding relationship. In the precise registration phase, the algorithm uses an improved normal distribution transformation (INDT) algorithm. Experimental results show that given a large amount of point cloud data, this algorithm has advantages in both time and precision.

Description: This article considers a two-stage assembly scheduling problem (TSASP) with batch setup times, time-dependent deterioration, and preventive maintenance activities (PMAs). The objective of this problem is to simultaneously determine the optimal component-manufacturing sequence (CMS), product-assembly sequence (PAS), number of setups, and number and position of PMAs (PPMA). First, to determine the optimal solution, a novel mixed integer linear programming model (MILP) for the proposed problem is derived. Then, a standard genetic algorithm (SGA), hybrid genetic algorithm (HGA), standard harmony search (SHS), hybrid harmony search (HHS), and harmony-search-based evolutionary algorithm (HSEA) were proposed owing to the intractability of the optimal solution for large-scale problems. SGA and SHS provide a chromosome to represent a complete solution including three decisions (CMS, PAS, and PPMA). HGA, HHS, and HSEA provide a chromosome to represent a partial solution including PAS. CMS and PPMA are found by an effective local search heuristic based on the partial solution. A computational experiment is then conducted to evaluate the impacts of the factors on the performance of the proposed algorithms.

Description: In this paper, we investigate finite-time synchronization problems of complex dynamical networks with different dimensions of nodes, which contain unknown periodically coupling structures and bounded time-varying delay. Based on finite-time stability theory, the inequality techniques, and the properties of Kronecker production of matrices, some useful finite-time synchronization criteria for complex dynamical network with unknown periodical couplings have been obtained. In addition, with proper adaptive periodical learning law designed, the unknown periodical couplings have been estimated successfully. Finally, some simulation examples are performed to verify the theoretical findings.

Description: The resonance vibration control of flexible rotor supported on active magnetic bearings (AMB) is a challenging issue in the industrial applications. This work addresses the application of robust control method to the resonance vibration control for AMB flexible rotor while passing through the critical speed. This model-based method shows great superiority to handling flexible mode vibration, which can guarantee robust stability and performance when encountering modal perturbation. First, the designed flexible rotor-AMB test rig is briefly introduced. Then the system modeling is described in detail including flexible rotor, power amplifier, displacement sensors and magnetic actuator and rotordynamics are analyzed. Model validation is carried out by sine sweeping test. Finally, the μ-synthesis controller is designed. The simulation and experimental results indicate that the designed μ-synthesis controller, which shows great robustness to modal perturbation, can effectively suppress the resonance vibration of flexible rotor and achieve supercritical operation.

Description: The collaborative filtering (CF) methods are widely used in the recommendation systems. They learn users’ interests and preferences from their historical data and then recommend the items users may like. However, the existing methods usually measure the correlation between users by calculating the coefficient of correlation, which cannot capture any latent features between users. In this paper, we proposed an algorithm based on graph. First, we transform the users’ information into vectors and use SVD method to reduce dimensions and then learn the preferences and interests of all users based on the improved kernel function and map them to the network; finally, we predict the user’s rating for the items through the Multilayer Perceptron (MLP). Compared with existing methods, on one hand, our method can discover some latent features between users by mapping users’ information to the network. On the other hand, we improve the vectors with the ratings information to the MLP method and predict the ratings for items, so we can achieve better effects for recommendation.

Description: Unbalanced supply and demand, bottleneck of transport capacity, seasonal cycle, and other factors lead to fragile supply chain of fresh agricultural products led by the platform, impeding smooth operation of the supply chain and even causing disruption risk. This paper studies the short-term and long-term vulnerability of the platform leading fresh agricultural product supply chain under the influence of logistics capital flow and information flow, defines its structure and the meaning of its vulnerability, analyzes the vulnerability of each link, and finds out the existing weak links in the supply chain through empirical research. The probability of accident is quantitatively analyzed by using the Bayesian Network. Firstly, the bow-tie model is used to identify the cause and consequence of the accident, and then it is transformed into the Bayesian Network model; then, the “Precursor Incident” information and prior probability are introduced to derive the posterior accident occurrence probability, and the probability of accident occurrence changing with time is quantitatively analyzed; finally, the dynamic risk calculation of fresh agricultural product trading center dominated by a certain platform was carried out. The results show that, with the increase of supply chain operation time and Precursor Incident, the probability of short-term supply chain vulnerability and accident risk present a significant increase trend, while the probability of long-term supply chain vulnerability and accident risk present a significant decrease trend. Therefore, it is suggested that enterprises should establish a dynamic risk evaluation system to monitor and predict the probability of event vulnerability, pay attention to “Precursor Incident,” and take measures to reduce it, such as effective integration of supply chain principal information, timely improvement of information integrated technologies, and comprehensive training on food safety and moral credibility.

Description: The size effect on the seismic performance of conventional reinforced concrete columns has been observed in terms of flexural failure and shear failure. Under earthquake loading, slender columns experience flexural failure, and short columns experience flexure-shear failure and shear failure. However, the effect of section size on the seismic performance of high-strength reinforced concrete columns under the conditions of different shear span-to-depth ratios requires further confirmation. For this purpose, six high-strength reinforced concrete columns with shear span-to-depth ratios of 2 and 4 were subjected to cyclic loading in this study. The experimental results indicated that relative nominal flexural strength, energy dissipation coefficient, factor of safety, and local factor of safety all exhibited a strong size effect by decreasing with increasing column size. Furthermore, the size effect became stronger as the shear span-to-depth ratio was increased, except for average energy dissipation coefficient. The observed changes in the factor of safety were in good agreement with the Type 2 size effect model proposed by Bažant. Thus, based on the local factor of safety and Bažant’s Type 2 model, the code equation for moment capacity of different shear span-to-depth ratios was modified to provide a consistent factor of safety regardless of column size.

Description: Electronic waste recycle (e-recycling) is gaining increasing importance due to greater environmental concerns, legislation, and corporate social responsibility. A novel approach is explored for designing the e-recycling reverse logistics network (RLN) under uncertainty. The goal is to obtain a solution, i.e., increasing the storage capacity of the logistics node, to achieve optimal or near-optimal profit under the collection requirement set by the government and the investment from the enterprise. The approach comprises two parts: a matrix-based simulation model of RLN formed for the uncertainty of demand and reverse logistics collection which calculates the profit under a given candidate solution and simulated annealing (SA) algorithm that is tailored to generating solution using the output of RLN model. To increase the efficiency of the SA algorithm, network static analysis is proposed for getting the quantitative importance of each node in RLN, including the static network generation process and index design. Accordingly, the quantitative importance is applied to increase the likelihood of generating a better candidate solution in the neighborhood search of SA. Numerical experimentation is conducted to validate the RLN model as well as the efficiency of the improved SA.

Description: The girder of prestressed concrete continuous box girder bridge strengthened by a stay cable system has a very complex deformation mechanism, and it is difficult to establish accurate numerical model for prediction. For these problems, a prediction method based on the combination of ant colony algorithm and residual combination correction model is proposed. In this method, the measuring points are considered as the cities in TSP ant colony algorithm, the information function model and heuristic function model are constructed, the pheromone update mechanism and ant search mechanism are established, and the deformation prediction of the main girder based on ant colony algorithm prediction model is achieved. On this basis, in order to fit the random change process of main girder deflection better, the periodic function generated by harmonic transform and sine function are introduced to modify the predicted results, which makes up for the low precision defect of single model. The results show that compared with finite element method, unequal interval gray model (1,1) (UIGM (1,1)), one-time residual correction UIGM (1,1), Markov chain residual correction UIGM (1,1), and ant colony algorithm model, this model can reflect the space-time effect and casual fluctuation feature of the development of girder deformation better and also has higher prediction accuracy and efficiency. The mean relative error of the predictive value is 3.39%, the posterior error ratio is 0.060, and the accuracy level reaches level 1. This model provides a new way for the girder deformation prediction of bridge strengthened by the stay cable system.

Description: The development of new venture enterprise is the result of joint efforts of entrepreneurs and venture capitalists who collaborate based on complementary resources. In this paper, we analyze a venture capital incentive contracting model in which a venture capitalist interacts with an entrepreneur who is risk neutral and fairness concerned, offering him an equity contract. We solve the venture capitalist’s maximization problem in the presence of double-sided moral hazard. Our results show that fairness concerns change the structure of the optimal contract. More importantly, we show that the solution to the contract regarding the optimal share given to the entrepreneur is nonlinear and is a fixed point between 0 and 1. Further, we simulate the model under the assumption that venture project’s revenue is a Constant Elasticity of Substitution (CES) function and obtain the following results. (1) When the two efforts are complementary, the venture capitalist’s effort does not monotonically decrease in the share allocated to the entrepreneur, while the entrepreneur’s effort does not monotonically increase in his share. (2) Relative to the benchmark case where the entrepreneur is fairness neutral, the optimal equity share allocated to the fair-minded entrepreneur is larger than 1/2, and as the degree of efforts complementarity increases, the optimal equity share tends to 60%. In this scenario, for a given efforts substitution parameter, the fair-minded entrepreneur provides a higher effort level than the venture capitalist.

Description: Characterizing heterogeneous elastic property distribution of soft tissues is of great importance in disease detection. In this paper, we investigate an inverse approach to map the heterogeneous material property distribution of soft solids using harmonic motion data. To examine the feasibility of this approach, a number of numerical examples are presented. We observe that the shear modulus distribution is recovered well using harmonic motion measurements. Compared to the static inverse approach, the proposed dynamic inverse method improves the quality of the recovered shear modulus distribution significantly. We also study the influence of the uncertainty in the driving frequency on the reconstruction results and observe that the influence is not very significant in recovering the shape of the inclusion. The proposed inverse algorithm has potential to be a promising tool to diagnose diseases in clinical medicine.

Description: Subintimal angioplasty is a highly challenging technique for percutaneous treatment of chronic total occlusion (CTO) in blood vessels, and the development of predictive tools for preliminary evaluation of potential outcomes and risks could be very useful for clinicians. While finite element (FE) simulation is a well-established approach to investigating partial occlusions, its extension to CTO has not been investigated yet, because of several additional issues that have to be addressed. In this work, we discuss the implementation of a FE model to simulate the main steps of the procedure, i.e., subintimal insertion of an initially folded balloon in a false lumen, inflation from eccentric position, deflation, and extraction. The model includes key morphological features of the CTO and possibility of varying spatial distribution of material properties to account for different constituents and degree of calcification. Both homogeneous and heterogeneous CTO configurations were analyzed, comparing arterial stress state, plaque compression, and postprocedural recoil. For a peak inflation pressure of 12 bar, the degree of lumen restoration was in the range 65-80%, depending on plaque heterogeneity. After balloon extraction, homogeneous highly calcified plaques exhibited substantial recovery of original shape. For homogeneous and heterogeneous CTO, values of peak von Mises stress in the arterial wall were of the same order of magnitude (range 1-1.1 MPa) but at different locations. Results compared favorably with data reported in literature for postprocedural lumen restoration and arterial stress data, confirming potential usefulness of the approach.

Description: To solve the problem of high accuracy initial alignment of strap-down inertial navigation system (SINS) for ballistic missile, an on-line identification method of initial alignment error based on adaptive particle swarm optimization (PSO) is proposed. Firstly, a complete navigation model of SINS is established to provide the accurate model basis for subsequent numerical optimization calculation. Then setting the initial alignment error as the optimization parameter and regarding the minimum deviation between SINS and GPS output as the objective function, the error parameter optimization model is designed. At the same time, the mutation idea of genetic algorithm (GA) is introduced into the PSO; thus the adaptive PSO is adopted to identify the initial alignment error on-line. The simulation results show that it is feasible to solve the initial alignment error identification problem of SINS by intelligent optimization algorithm. Compared with the standard PSO algorithm and the GA, the adaptive PSO algorithm has the fastest convergence speed and the highest convergence precision, and the initial pitch error and the initial yaw error precision are within and the initial azimuth error precision is within . The navigation accuracy of SINS is improved effectively. Finally, the feasibility of the adaptive PSO algorithm to identify the initial alignment error is further validated based on the test data.

Description: In this paper, we propose a novel model-free trajectory tracking control for robot manipulators under complex disturbances. The proposed method utilizes time delay control (TDC) as its control framework to ensure a model-free scheme and uses adaptive nonsingular terminal sliding mode (ANTSM) to obtain high control accuracy and fast dynamic response under lumped disturbance. Thanks to the application of adaptive law, the proposed method can ensure high tracking accuracy and effective suppression of noise effect simultaneously. Stability of the closed-loop control system is proved using Lyapunov method. Finally, the effectiveness and advantages of the newly proposed TDC scheme with ANTSM dynamics are verified through several comparative simulations.

Description: For simulating the shock wave with high peak force in a short duration, a novel variable damping hydraulic shock wave simulator was developed, and it was used for simulating the cannon recoil motion. The working principle of this simulator was explained with the assistance of established mathematical model and the flow behavior in damping channel was analyzed. The shock wave characteristics curves were obtained by using the numerical computation method. The results showed that the shock wave characteristics were directly related to the sectional area of the damping channel and the damping fluid medium characteristic; the shock wave curve can be simulated by adjusting the variable damping parameters. The computational results agreed well with the theory analysis, which meant that the proposed mathematical model can be used for supplying theoretical references for the cannon recoil motion in artillery fire shock simulation test.

Description: Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.

Description: In this work the problem of chaos suppression for a class of continuous chemical reactor with chaotic dynamics is tackled via a nonlinear control strategy. The proposed controller is developed under the framework of optimal control theory, where a functional is proposed to maximize the chemical reaction rate via a proposed Lagrangian-type which contains directly the state equation of the reacting system, avoiding the problems of Lagrange, the Hamiltonian formulation, and consequently the explicit constraints to the system. This allows solving in an easier form the optimization problem in comparison with the standard methods. This procedure allows suppressing the chaotic behavior of the reacting system by stabilizing the reaction rate term by leading it to an extreme value. Numerical experiments are done in order to show the satisfactory performance of the proposed methodology.

Description: Due to inadequate designers in fast fashion industry and the development of the Internet, small-and-medium-sized garment makers have gradually turned to external talents to enhance their new product design efficiency via crowdsourcing initiative. This paper presents a new framework of crowdsourcing supply chain for fast fashion industry. First, a basic multiperiod order model is established in a crowdsourcing supply chain system, where a garment maker chooses the best one among available solutions submitted by online designers (i.e., crowdsources) in each period, then transforms it into finished product, and sells to consumers through a retailer. Second, we extend this model by integrating the factors of capital turnover, the retailer’s risk-aversion, and the garment maker’s minimum production quantity. Moreover, utilizing wholesale price, buyback, and profit-sharing scheme designs a mixed contract for coordinating crowdsourcees, retailer, and garment maker of crowdsourcing supply chain for achieving Pareto optimality. The models help the garment maker determining the optimal production quantities of crowdsourcing designed products and enable the retailer placing the optimum orders and setting the reasonable risk level. In addition, we also find that the incentive policy for crowdsourcing designers can be fulfilled by using a profit-sharing scheme with a piecewise function of order quantity instead of a linear function.

Description: The problem of optimal design of the statically indeterminate arch girder which constitutes the primary structural system of the arch bridge is presented. The task is to determine the optimal shape of the axis of the arch girder, as well as the optimal distribution of the cross section height, ensuring the minimum arch volume as well as fulfillment of the standard requirements. This optimisation task, with numerous control functions and constraints, is formulated as a control theory problem with maintaining the formal structure of the minimum principle and then transformed to the multipoint boundary value problem and solved by means of numerical methods. The numerical results are obtained with optimal control methods, using the Dircol software. Since the changes in the shape and cross-section of the arch affect the distribution of the dead and moving loads transferred on the girder from the bridge deck, the optimisation procedure is combined with the finite element method analysis, which together with the complexity of the multidecision arch optimisation problem accounts for the novelty of the proposed approach. The numerical analysis reveals that the optimal girder shape is the frame-arched structure, with considerable lengths of straight sections and only short arch elements, in the areas of the application of concentrated forces and moments. The presented method can be successfully extended to optimisation of structures with different static schemes and load categories taken into account.

Description: Nonlinear consolidation scenarios, based on potential type constitutive dependences—like those proposed by Juárez-Badillo—and eliminating the more restrictive hypothesis of 1+e and dz constant, were characterized by the nondimensionalization process of the governing equations, providing the independent dimensionless groups that rule the main unknowns of interest. From these, universal curves have been depicted for both the characteristic time and the average degree of consolidation. The solutions were verified by numerical simulations and successfully compared in a case study, showing the simplicity of use of the curves and the high reliability of the solutions they provide.

Description: Urban power supply network plays a vital role in maintaining the city operation. The vulnerability of power supply network in the face of events has been one of significant concerns. This study presents a new methodology and framework for “dose-response” vulnerability assessment of urban power supply network. This framework can explore the vulnerability of power supply network under two types of events: random type and intentional type. It also integrates a new metric that calculates the vulnerability of power supply network in both structural dimension and functional dimension. Taking the power supply network of a city in east China as an example, network vulnerability under different types of events was assessed, and the “dose-response” interrelationships between network performance and event scale under different types of events were thoroughly discussed. The results demonstrated that power supply network was more vulnerable to intentional events in both dimensions. For intentional events, power supply network was more vulnerable to degree-based attack than to betweenness-based attack. After that, the redundancy coefficient α of power supply network was optimized. The conclusion and some suggestions for future research were given in the end.

Description: Welding tip is an appliance for making footprint to connect the arm and head gimbal assembly (HGA) together in reflow soldering process. The welding tip is made from 3 materials: copper alloy, stainless steel, and haynes 230. It works based on Joule heating effect. The haynes 230 head tip is the area used to create a footprint. In the past, failure in the reflow soldering process of a hard disk drive factory was found resulting in defective products; therefore, a solution to resolve this problem must be researched. This article reports a solution to the aforementioned problem by using transient thermal-electric simulation to investigate the heat transfer in the welding tip and a simple experiment to verify the simulation. By using ANSYS, the simulation results revealed the temperature of welding tip. The maximum temperature was 406°C on the head tip at t=0.7s and then it rapidly decreased. The reflow soldering process failure occurred when footprint was done after 0.7s causing the temperature to be too low for melting the solder so the arm and HGA were unable to connect to each other. We proposed simple solutions and ways to improve the efficacy of the reflow soldering process; e.g., footprints should be done at 0.7s, and the welding tip’s material should be changed from haynes 230 to 556. After the factory implemented our results, the problem could truly be resolved. Not only do products have a higher quality but also miscellaneous expenses from defective products are saved.

Description: The braking process of electric locomotive is featured by short braking time, large braking power, large voltage fluctuations, etc. Faced with the problem of low utilization of braking energy and high investment cost of the current regenerative braking energy utilization systems, an energy optimization scheme is proposed in this paper by combining the control strategy for energy storage and energy optimization. The regenerative braking energy utilization system is modeled by analyzing the braking process of electric locomotive. The instantaneous absorption reference powers of the energy storage subsystem and energy feedback subsystem in braking process are obtained according to the established mathematical model. The energy storage subsystem uses super capacitor and adopts a power-current dual closed-loop control strategy. The energy feedback subsystem adopts a voltage-current dual closed-loop control strategy. Through the tracking control of the instantaneous power, a reasonable distribution of the regenerative braking energy is achieved between the energy feedback subsystem and energy storage subsystem, thereby increasing the utilization efficiency of the two subsystems. Finally, the performance of the proposed scheme is verified by simulation and experiment.

Description: This paper presents an improved model of echo state networks (ESNs) and gives the definitions of energy consumption, energy efficiency, etc. We verify the existence of redundant output synaptic connections by numerical simulations. We investigate the relationships among energy consumption, prediction step, and the sparsity of ESN. At the same time, the energy efficiency and the prediction steps are found to present the same variation trend when silencing different synapses. Thus, we propose a computationally efficient method to locate redundant output synapses based on energy efficiency of ESN. We find that the neuron states of redundant synapses can be linearly represented by the states of other neurons. We investigate the contributions of redundant and core output synapses to the performance of network prediction. For the prediction task of chaotic time series, the predictive performance of ESN is improved about hundreds of steps by silencing redundant synapses.

Description: Top-tensioned riser (TTR) is one of the most frequently used pieces of equipment in offshore petroleum engineering. At present, the research of its vortex-induced vibration (VIV) is mainly focused on numerical simulation with few analytical approaches. In this paper, the nonlinear dynamic equation of VIV about TTR is developed by introducing the third-order variable lift coefficient hydrodynamic model. The amplitude-frequency response equation under 1:1 primary resonance excitation is deduced based on the single mode discrete results. The nonlinear dynamic responses characteristics of the TTR vibration under the excitation of vortex-induced resonance are discussed. Bifurcation theory is used to study the singularity of the analytical solution of the riser system. The hysteresis and solitary solutions from the results of singularity analysis are found. Such results can provide guidance for the design and optimization of riser structural parameters.

Description: We establish the Injury Model of Wheel Flats with 10 degrees of freedom and calculate the dynamic responses of the railway vehicle system, which include different vehicle speeds and different length flats. The Hilbert envelope spectrum method based on Empirical Mode Decomposition (EMD) is proposed according to the nonstationary characteristics of axle box acceleration (ABA) signal. The vibration characteristics of the ABA are studied thoroughly. And then the effects concerning speed and flat length on the diagnosis results are analyzed. The simulation results show the amplitude corresponding to the frequency component of wheel flats raise with the increasing of the wheel flat length when the single or double wheel flats impact the track at the same vehicle speed. In other words, the longer the wheel flat is, the greater the magnitude of the decomposition result is. In the same vehicle speed, the amplitude corresponding to the frequency component of wheel flat is minimum when the two flats’ phase difference is 180°. With the same flat length (single or double wheel flats), the amplitude corresponding to the frequency components of wheel flats decreases with the increasing of the speed. This method could accurately and effectively identify the frequency of wheel flats.

Description: Many parts of platforms are expected to be replaced by unmanned systems in modern warfare. All the assets and supporting vehicles are linked to each other with a communication network, and it is called the network-centric warfare environment. Hence, it is critical when communication failure occurs during engagement in ground battlefield because this failure will directly affect overall combat effectiveness of one’s owned assets. However, research regarding communication failure issues is scarce. We herein propose a new agent-based modeling process to measure the overall combat effectiveness combined with communication success ratio, based on the terrain condition of the ground engagement. Additionally, we provide the effectiveness analysis result when a communication repeater is applied during communication failure as an alternative measure.

Description: Underwater Electric Potential is an important signal characteristic of a ship. The signal contains location information which can be used to track the ship. This research tries to study the possibility of ship tracking using Underwater Electric Potential. Aiming at the problems existing in the traditional Kalman filters under large initial errors, a new nonlinear filter is proposed. State space model of ship tracking is established; the problem existing in the ordinary Kalman filters is analyzed from the perspective of Kullbeck-Leibler Divergence; the new algorithm is proposed based on progressive Bayesian; simulations are designed. Simulation results show: it is feasible to use underwater electric potential to track the ship; the new method can effectively improve filter performance under large initial error and can effectively track the ship with preferable precision and convergence, which has great practical value.

Description: The existing positioning methods that use received signal strength indication (RSSI) and channel state information (CSI) may suffer from multipath and shadowing in a complex wireless environment, which can result in more positioning errors. This paper proposes a method for accurate multilabel positioning in the non-line-of-sight (NLOS) environment. First, the position is roughly estimated using the orthogonal variable spreading factor (OVSF-TH) algorithm, which can automatically match the signal interference. The ultra-wideband (UWB) spectral density and pulse amplitude in the time domain are used to determine the direction of the label and enhance estimation of the mobile label direction. Then, the location of the tag is obtained by triangulation, and a coordinate-based coordinate estimation method is proposed to calculate the relative displacement of multiple tags to determine the label position. Finally, by setting up a real experimental environment, the influence of the number of base stations on the accuracy and the performance of the localization method under different circumstances are analyzed. The theoretical analysis and experimental results show that the method is simple to deploy, inexpensive, and very accurate in terms of positioning, having a clearly effective indoor positioning accuracy. Compared with other existing positioning methods, this method can achieve more accurate positioning. Moreover, it has important theoretical and practical applicability because of the reliability and accuracy of indoor positioning in an NLOS environment.

Description: With regard to developing pavement performance models (PPMs), the existing state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and associated PPMs simultaneously. However, the approach does not determine optimal clustering to minimize error; that is, the number of clusters and explanatory variables are prespecified to determine the corresponding coefficients of the PPMs. In addition, existing formulations do no address issues associated with overfitting as there is no limit to include parameters in the model. In order to address this limitation, this paper proposes a mathematical program within the CLR approach to determine simultaneously an optimal number of clusters, assignment of segments into clusters, and regression coefficients for all prespecified explanatory variables required to minimize the estimation error. The Bayesian Information Criteria is proposed to limit the number of optimal clusters. A simulated annealing coupled with ordinary least squares was used to solve the mathematical program.

Description: Cylindrical fitting is an essential step in Large Process Pipeline’s measurement process, and precision of initial values of cylindrical fitting is a key element in getting a correct fitting result. In order to get well initial values, covariance matrixes of all points in cylinder’s three-dimensional laser scanning point cloud should be firstly established to estimate normals of all points, and then cylinder’s axis vector can be calculated by using least squares method. Secondly, remaining parameters’ initial values of the cylinder can be got by coordinate transformation. Finally, Levenberg-Marquardt algorithm is used in iterative optimization process to get fitting result by using the above values as initial values. Experiments demonstrate that this method can get precise initial values of cylindrical fitting and improve the accuracy and speed of cylindrical fitting.