ALBERT

Description: Telescoping path optimization (TPO) of single-cylinder pin-type multisection boom (SPMB) is a practical engineering problem that is valuable to investigate. This article studies the TPO problem and finds the key of TPO is to obtain the maximum retraction backmost combination. A mathematic model on the basis of the quadratic penalty function of a Hopfield neural network (HNN) is constructed. Two strategies are presented to improve the performance of TPO model: one is proportional integral derivative (PID) strategy that adaptively adjusts the parameter λ of the constrained term and the parameter of the optimization objective term by controlling the value of constraint violation and the other is efficiency factor strategy that an efficiency factor is introduced in model for prioritizing the constrained term over the objective term. Data test shows that compared with the path of boom length changing before optimization, both the number of sections that need to be moved and the total travels of cylinder can be reduced by 10%-30% after optimization. Both the PID strategy and the efficiency factor strategy achieve good optimization effects. The efficiency factor strategy is excellent at moderating the conflicts between the constrained term and the objective term; thus the generations of the valid and the optimal solutions get well improved.

Description: A belt drive and a chain drive are the main types of flexible power transmission. In the traditional belt and chain drive design process, engineers need to do a lot of rework to get a design. To solve this problem, taking the Shell Eco-Marathon vehicle as an example, the traditional design and optimization design of the transmission system are carried out. In the optimization design, component optimization and overall optimization design model of the belt and chain drive are first established. Second, the charts in the design manual are converted into formulas by using MATLAB. Finally, an optimization design model is established in Microsoft Excel, and the Excel Solver tool is used to find the optimal design result. The design method proposed in this paper can effectively determine the optimal design of transmission system and provides a new method for the processing of such problems.

Description: This paper proposes a superresolution two-dimensional (2D) direction of arrival (DOA) estimation algorithm for a rectangular array based on the optimization of the atomic norm and a series of relaxation formulations. The atomic norm of the array response describes the minimum number of sources, which is derived from the atomic norm minimization (ANM) problem. However, the resolution is restricted and high computational complexity is incurred by using ANM for 2D angle estimation. Although an improved algorithm named decoupled atomic norm minimization (DAM) has a reduced computational burden, the resolution is still relatively low in terms of angle estimation. To overcome these limitations, we propose the direct minimization of the atomic norm, which is demonstrated to be equivalent to a decoupled rank optimization problem in the positive semidefinite (PSD) form. Our goal is to solve this rank minimization problem and recover two decoupled Toeplitz matrices in which the azimuth-elevation angles of interest are encoded. Since rank minimization is an NP-hard problem, a novel sparse surrogate function is further proposed to effectively approximate the two decoupled rank functions. Then, the new optimization problem obtained through the above relaxation can be implemented via the majorization-minimization (MM) method. The proposed algorithm offers greatly improved resolution while maintaining the same computational complexity as the DAM algorithm. Moreover, it is possible to use a single snapshot for angle estimation without prior information on the number of sources, and the algorithm is robust to noise due to its iterative nature. In addition, the proposed surrogate function can achieve local convergence faster than existing functions.

Description: In this paper, we enrich and develop power-type Aczél-Vasić-Pečarić’s inequalities. First of all, we give some new versions of theorems and corollaries about Aczél-Vasić-Pečarić’s inequalities by quoting some lemmas. Moreover, in combination with Hölder’s inequality, we give some applications of the new version of Aczél-Vasić-Pečarić’s inequality and give its proof process.

Description: Aiming at the problems in which there exists collocation between services and manufacturing tasks, multiobjective cloud manufacturing service composition optimization seldom considers the synergy degree of composite cloud services and the complexity of service composition, so a novel service composition optimization approach, called improved genetic algorithm based on entropy (IGABE), is put forward. First, the mathematical expressions of service collocation degree, composition synergy degree, composition entropy, and their related influence factors of the service composition are analyzed, and their definitions and calculation methods are given. Then, a multiobjective cloud manufacturing service composition optimization mathematical model is established. Moreover, crossover and mutation operators are improved by introducing normal cloud model theory and piecewise function, and improved roulette selection method is used to perform the selection operation. And the fitness function of the proposed IGABE is designed by combining Euclidean deviation with angular deviation. Finally, the manufacturing task of a wheeled cleaning robot is exemplified to verify the correctness of the proposed multiobjective optimization model for cloud manufacturing service composition and the effectiveness of the proposed algorithm, compared with Standard Genetic Algorithm (SGA), Hybrid Genetic Algorithm (HGA), and Cloud-entropy Enhanced Genetic Algorithm (CEGA). The studied results show that IGABE converges faster than SGA and HGA and can analyze and reflect the content difference expressed by the objective functions of service composition scheme and its approximation degree to the corresponding dimensions of the ideal point vector more comprehensively than CEGA. As such, the optimal service composition obtained by IGABE algorithm can better meet the complex needs of users.

Description: The phenomenon of coordinate measuring machines has led to a significant improvement in accuracy, adaptability, and reliability for measurement jobs. The coordinate measuring machines with scanning capabilities provide the alternative to output precise acquisition at a faster rate. However, they are less accurate as compared to discrete probing systems and slower than the noncontact techniques. Therefore, the data acquisition using a scanning touch probe needs improvement, so that it can provide commendable performance both in terms of accuracy and scanning time. The determination of appropriate scanning parameters is crucial to minimize the inaccuracy and time associated with the scanning process. However, it can be demanding as well as unreliable owing to the presence of uncertainty from a multitude of factors that may influence the measurement process. The optimization of data acquisition using a scanning touch probe is a multiresponse process which involves definite uncertainties from various sources. Therefore, multioptimization tools based on grey relational analysis coupled with principal component analysis and fuzzy logic were employed to enhance the utilization of the scanning touch probe. The work described here has the objective to identify the appropriate combination of scanning factors which can simultaneously boost the accuracy and lessen the scanning time. This study demonstrates the capability and effectiveness of the uncertainty theory based optimization methods in coordinate metrology. It also suggests that the uncertainty associated with the parameter optimization can be significantly reduced using these techniques. It has also been noticed that the results from the two techniques are in accord, which corroborates their application in coordinate metrology. The result from this study can be applied to other probing systems and can be broadened to include more experiments and parameters in various scenarios as needed by the specific application.

Description: Dissolved gas-in-oil analysis (DGA) is a powerful method to diagnose and detect transformer faults. It is of profound significance for the accurate and rapid determination of the fault of the transformer and the stability of the power. In different transformer faults, the concentration of dissolved gases in oil is also inconsistent. Commonly used gases include hydrogen (H2), methane (CH4), acetylene (C2H2), ethane (C2H6), and ethylene (C2H4). This paper first combines BP neural network with improved Adaboost algorithm, then combines PNN neural network to form a series diagnosis model for transformer fault, and finally combines dissolved gas-in-oil analysis to diagnose transformer fault. The experimental results show that the accuracy of the series diagnosis model proposed in this paper is greatly improved compared with BP neural network, GA-BP neural network, PNN neural network, and BP-Adaboost.

Description: This paper presents a hybrid metaheuristic that combines estimation of distribution algorithm with tabu search (EDA-TS) for solving the max-mean dispersion problem. The proposed EDA-TS algorithm essentially alternates between an EDA procedure for search diversification and a tabu search procedure for search intensification. The designed EDA procedure maintains an elite set of high quality solutions, based on which a conditional preference probability model is built for generating new diversified solutions. The tabu search procedure uses a fast 1-flip move operator for solution improvement. Experimental results on benchmark instances with variables ranging from 500 to 5000 disclose that our EDA-TS algorithm competes favorably with state-of-the-art algorithms in the literature. Additional analysis on the parameter sensitivity and the merit of the EDA procedure as well as the search balance between intensification and diversification sheds light on the effectiveness of the algorithm.

Description: The measured temperature of a concrete pouring block depends strongly on the position of the buried thermometer. Only when the temperature measured by the thermometer accurately reflects the actual temperature of the concrete pouring block do reasonable temperature-control measures become possible. However, little research has been done on how to determine the proper position of thermometers buried in a concrete pouring block embedded with cooling pipes. To address this situation, we develop herein a method to determine the position of thermometers buried in a concrete pouring block. First, we assume that the design temperature-control process line characterizes the average-temperature history of the concrete pouring block. Under this assumption, we calculate the average-temperature history of the concrete pouring block by using the water-pipe-cooling FEM, following which the temperature history of an arbitrary point in the concrete pouring block is obtained by interpolating the shape function. Based on the average-temperature history of the concrete pouring block and the temperature history of the arbitrary point, we build a mathematical model to optimize the buried position of the thermometer and use the optimization algorithm to determine this position. By using this method, we establish finite-element models of concrete prisms with four typical water-pipe spacing cases for concrete-dam engineering and obtain the geometric position of the thermometers by using the optimization algorithm. By burying thermometers at these positions, the measured temperature should better characterize the average-temperature history of the concrete pouring block, which can provide useful information for regulating the temperature of concrete pouring blocks.

Description: In this paper, we consider a risk averse competitive firm that adopts currency futures and options for hedging purpose. Based on the assumption of unbiased markets of currency futures and options, we propose the optimal hedging model in dynamic setting. By using two-stage optimization method, we prove that it is desirable for the prudent enterprise to buy exchange rate options to hedge currency risk. Furthermore, we derive the closed-form solutions of the multiperiod hedging problem with the quadratic utility function. We investigate an empirical study incorporated into GARCH-t prediction on the efficiency of hedging with currency futures and options. The empirical results demonstrate that hedging with currency futures and options can reduce the silver export firm’s risk exposure. Profits and the effective boundaries are compared in three cases: hedging with futures and options synchronously, only with futures and without any hedge. The results of multiple comparisons among different hedging strategies show that hedging with linear and nonlinear derivatives is advisable for the export firm.

Description: Currently, it is a challenge to effectively assess the seismic performance of the high-speed railway bridge line. To figure it out, this paper discussed the applicability of the Pushover analysis in the seismic fragility of the high-speed railway bridge. As the piers are the core components to resist the earthquakes, a typical high-speed railway bridge line consisting of 22 piers was established by the finite element software OpenSees. The influences of the different pier height and sites on the fragility analysis of the pies were investigated. From the component level, the seismic performance of the high-speed railway bridge line was evaluated by the Pushover analysis. The results show that the seismic responses of the piers by the Pushover analysis are agreeable with those by the incremental dynamical analysis when the peak ground acceleration is less than 0.4g. The high piers have better seismic performance than the lower piers. The high-speed railway bridge line exhibits good seismic performance under the 7-degree design earthquake (0.15g) and the 8-degree low-level earthquake (0.10g) but may be severely damaged under the 9-degree low-level earthquake (0.40g).

Description: As typical thermostatically controlled loads (TCL) driven by constant-speed compressor, constant-speed air-conditioners play important roles in demand-side response for their abilities of energy conversion and storage. Their great potential for load regulation can be incorporated into power system scheduling through demand response. In view of their operating characteristics, a virtual energy storage (VES) model of thermostatically controlled loads with electrical and thermal parameters is established. This model is discretized and linearized to simplify calculation. By analyzing the control function and constraints of the VES model, the control strategy of VES of constant-speed air-conditioners load with virtual charging state priority is proposed. Example analysis shows that this strategy can solve and alleviate power shortage problem of the system by participating in demand response, which provides methodological support for constant-speed compressor temperature-control load to participate in the system operation.

Description: The buckling loads of shell structures are sensitive to initial geometric imperfections. Conventional methods used to model geometric imperfections cannot determine the accuracy of buckling loads with high computational efficiency. A new computational approach based on particle swarm optimization (PSO) is proposed to obtain the lower bound of the buckling load of shell structures with geometric imperfections. The proposed approach assumes a nodal geometric position using uncertain parameters. The buckling loads of the shell structures are then optimized using the PSO-based approach. Both academic and practical numerical examples have been thoroughly investigated. Thus, the applicability and accuracy of the proposed method is critically validated.

Description: Online quality prediction helps to identify the web service quality degradation in the near future. While historical web service usage data are used for online prediction in preventive maintenance, the similarities in the usage data from multiple users invoking the same web service are ignored. To improve the service quality prediction accuracy, a multivariate time series model is built considering multiple user invocation processes. After analysing the cross-correlation and similarity of the historical web service quality data from different users, the time series model is estimated using the multivariate LSTM network and used to predict the quality data for the next few time series points. Experiments were conducted to compare the multivariate methods with the univariate methods. The results showed that the multivariate LSTM model outperformed the univariate models in both MAE and RMSE and achieved the best performance in most test cases, which proved the efficiency of our method.

Description: The problem of artificial potential function (APF) safety and obstacle avoidance guidance for autonomous rendezvous and docking of chaser spacecraft with noncooperative spacecraft is studied. The relative motion equation of the chaser and the target is established based on the line-of-sight coordinate system, the reference state is designed, and the corresponding state error is deduced. The attitude motion equation of the noncooperative target spacecraft in space is established. The safety and obstacle avoidance guidance problem of autonomous rendezvous and docking with noncooperative target is transformed into a path planning problem in a dynamic environment. The attractive potential function is designed according to the state error. In order to ensure that the chaser can safely approach the noncooperative target spacecraft, a safe corridor with ellipse cissoid is designed in the final approaching stage of autonomous rendezvous and docking. The obstacle is assumed to be a sphere with a certain radius to avoid its influence in the approach, and the obstacle potential function is designed based on the Gaussian function method. The total potential function of the system is designed according to the attractive potential function, the safe potential function, and the obstacle potential function. The total potential function of the system is modified to ensure that the reference state is the minimum of the total potential function of the system. The stability of the system is proven according to the Lyapunov stability principle, and the conditions for satisfying the monotonic decrease in the total potential function of the system are deduced. Finally, the effectiveness of the proposed method is verified by three sets of numerical simulations.

Description: Target signal extraction has a great potential for applications. To solve the problem of error extraction of target signals in the current constrained independent component analysis (cICA) method, an enhanced independent component analysis with reference (EICA-R) method is proposed. The new algorithm establishes a unified cost function, which combines the negative entropy contrast function and the distance metric function. The EICA-R method transforms the constrained optimization problem into unconstrained optimization problem to overcome the problem of threshold setting of distance metric function in constrained optimization problem. The theoretical analysis and simulation experiment show that the proposed EICA-R algorithm overcomes the problem of the error extraction of the existing algorithm and improves the reliability of the target signal extraction.

Description: A mathematical model is developed to examine the behaviors of a peristalsis flow with nanoparticles in a symmetric channel under the magnetic environment. Here, the nanofluid is electrically conducted through an external magnetic field. Thermal radiation and Joule heating effects are also retained in the present analysis. Under the lubrication approach, the reduced nonlinear systems are obtained. Then, they are solved very efficiently by means of a homotopy analysis method-based package BVPh 2.0. The influences of important physical parameters on the flow behaviors are presented. Analysis of the entropy generation is illustrated. It is found that the Brownian diffusion and the thermophoresis are the two most important nanoparticle slip mechanisms in the Jeffery fluids as well. Besides, the Hartman number, the type of the Jeffery fluid, the Brinkman number, and the thermal radiation parameter play important roles on flow behaviors. Results show that the temperature profile enhanced but the nanoparticles’ volume fraction profiles lowered with increase in the Hartman number. However, using the Jeffery nanofluid induces effect on the velocity distribution that decreases with the increase in the Jeffery fluid parameter. It is also found that the generated total entropy increases with an increase in the Brownian motion parameter but with a decrease in the thermophoresis parameter.

Description: Iris recognition is one of the most useful methods to identify or verify people in biometric recognition systems. Iris patterns contain many features that distinguish people from each other. In this paper, a novel iris recognition method is proposed based on the fusion of Fisher Linear Discriminate Analysis (FLDA) with embedding Principal Component Analysis (PCA) method. In this work, firstly we use 1D Log-Gabor to elicit the iris features from an approximation part. Secondly, we obtain an appropriate degree of clarity for the iris with fusion of FLDA/PCA to eliminate the optical reflections on the iris image. Experiments of our proposed algorithm are performed on the CASIA V1 database. The results of our proposed approach show a good performance with recognition rate up to 99.99%.

Description: A weakly compressible smoothed particle hydrodynamics (WCSPH) method was developed to model open-channel flow over rough bed. An improved boundary treatment is proposed to quantitatively characterize bed roughness based on the ghost boundary particles (GBPs). In this model, the velocities of GBPs are explicitly calculated by using evolutionary polynomial regression with a multiobjective genetic algorithm. The simulation results show that the proposed boundary treatment can well reflect the influence of wall roughness on the vertical flow structure. A fully developed open channel is established, and its flume length, processing time, and turbulent model are discussed. The mixed-length-based subparticle scale (SPS) turbulence model is adopted to simulate uniform flow in open channel, and this model is compared with the Smagorinsky-based one. For the modified WCSPH model, the results show that the calculated vertical velocity and turbulent shear stress distribution are in good agreement with experimental data and fit better than the calculations obtained from the traditional Smagorinsky-based model.

Description: Facial expression recognition plays an important role in communicating the emotions and intentions of human beings. Facial expression recognition in uncontrolled environment is more difficult as compared to that in controlled environment due to change in occlusion, illumination, and noise. In this paper, we present a new framework for effective facial expression recognition from real-time facial images. Unlike other methods which spend much time by dividing the image into blocks or whole face image, our method extracts the discriminative feature from salient face regions and then combine with texture and orientation features for better representation. Furthermore, we reduce the data dimension by selecting the highly discriminative features. The proposed framework is capable of providing high recognition accuracy rate even in the presence of occlusions, illumination, and noise. To show the robustness of the proposed framework, we used three publicly available challenging datasets. The experimental results show that the performance of the proposed framework is better than existing techniques, which indicate the considerable potential of combining geometric features with appearance-based features.

Description: This paper presents a fuzzy regression analysis method based on a general quadrilateral interval type-2 fuzzy numbers, regarding the data outlier detection. The Euclidean distance for the general quadrilateral interval type-2 fuzzy numbers is provided. In the sense of Euclidean distance, some parameter estimation laws of the type-2 fuzzy linear regression model are designed. Then, the data outlier detection-oriented parameter estimation method is proposed using the data deletion-based type-2 fuzzy regression model. Moreover, based on the fuzzy regression model, by using the root mean squared error method, an impact evaluation rule is designed for detecting data outlier. An example is finally provided to validate the presented methods.

Description: The discrete element method (DEM) and smoothed particle hydrodynamics (SPH) can be adopted to simulate the granular materials and fluid media respectively. The DEM-SPH coupling algorithm can be developed for the dynamic interaction between the two media. When the particle material is simulated by polyhedral element, a fluid-solid coupling interface would lead to the complex geometry between the granular particle and the fluid. The boundary particle method is traditionally used for the fluid-solid interface but with low computational efficiency. In this paper, the dilated polyhedral element is constructed based on Minkowski sum theory, while the contact force between the elements is calculated by Hertzian contact model. Accordingly the dilated polyhedra based DEM is established. The weakly compressible SPH is adopted to simulate the fluid medium, while the interaction on the geometrically complex fluid-solid interface is evaluated with the repulsive force model which can be determined by the contact detection between SPH particles and solid particles in geometry. This method avoids the storage and calculation of a large number of boundary particles, which can be potentially applied for the complex fluid-solid boundary. In order to improve the computational efficiency, a GPU-based parallel algorithm is employed to achieve high performance computation of SPH. The acceleration of the parallel algorithm is evaluated by the cases of dam break. The numerical simulation of the impact of dam break on cubes is implemented. The simulation results are verified with the corresponding experimental and simulation results. Therefore, the rationality and accuracy of the DEM-SPH coupling method for numerical simulation of the interaction between granular materials and fluid media are illustrated. This method is then adopted for the impact of falling rocks on underwater pipeline. The force of water and rocks on the pipeline is analyzed. This method can be further applied for real engineering problems.

Description: The durability of lightweight cellular concrete (LCC) and the corresponding assessment method are studied in this paper to improve the utilization of LCC in subgrade construction engineering. The durability assessment method is established by combining the analytic hierarchy process (AHP) with fuzzy comprehensive evaluation (FCE). The main assessment processes are as follows. Firstly, based on the physical and mechanical properties of LCC, the influencing factors are selected in terms of preliminary design, construction technology, and operation and management after completion of construction. The grading standard of influencing factors is established as well. Secondly, a multilevel assessment model with targets level, criteria level, and indexes level is established. AHP determines the effective weight of the lower level relative to the upper level. The consistency check of the judgment matrix is conducted to prove the rationality of the distribution of influencing factors’ effect weight. Thirdly, the membership function which is suitable for each influencing factor is built to calculate the membership degree. Besides, the practicality and reliability of AHP combined with FCE are demonstrated through a practical engineering case, which is the third section of a highway in Guangdong Province, China.

Description: In this paper, the problem of input-output finite-time control of positive switched nonlinear systems with time-varying and distributed delays is investigated. Nonlinear functions considered in this paper are located in a sector field. Firstly, the proof of the positivity of switched positive nonlinear systems with time-varying and distributed delays is given, and the concept of input-output finite-time stability ( IO-FTS) is firstly introduced. Then, by constructing multiple co-positive-type nonlinear Lyapunov functions and using the average dwell time (ADT) approach, a state feedback controller is designed and sufficient conditions are derived to guarantee the corresponding closed-loop system is IO-FTS. Such conditions can be easily solved by linear programming. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method.

Description: An urban agglomeration (shorted as UA) is a highly developed spatial form of integrated city and an important driving force for regional economic development. The network of UA mainly reflects the spatial connections and organizational structure of all cities, which is of great significance for understanding the development status of UAs and revealing their development laws. However, there are few horizontal studies comparing the network structure of China’s UAs. This study constructs the economic network of China’s eight UAs with the gravity model and explores the overall network structure and city centrality using indicators in network analysis. Then, two groups of UAs with similar network structures are compared. Finally, the association between the gravity model and empirical data is discussed. The results show that the spatial pattern of cities in UAs can be expressed by the gravity model approximately. Besides, UAs with different development levels present different spatial network structures, but the network structures cannot reflect the development levels of UAs directly. We also find that the cities with high betweenness centrality have greater development potential to be the next growth pole.

Description: Due to the equipment characteristics (for example, the crane of each span cannot transfer products directly to other spans and path has less turning points and no slash lines) in a slab library, slab transportation is mainly realized by manually operating the crane. Firstly, the grid method is used to model the slab library. Secondly, an improved ant colony algorithm is proposed. The algorithm is used to solve the path planning of the slab library crane, which is improved by integrating the turning points, filtering the candidate solutions, dynamically evaporating pheromone, setting the dynamic region, etc. Finally, the algorithm is applied to plan the crane path of the slab library. The results show that the obstacle-free optimal path with fewer turning points, no slash lines, and short paths is found automatically.

Description: The behavior of a fine-grained piezoelectric coating/substrate with multiple Griffith interface cracks under electromechanical loads is investigated. In this work, double coupled singular integral equations are proposed to solve the fracture problems. Both the singular integral equation and single-valued conditions are simplified into an algebraic equation and solved by numerical calculation. Thereby, the intensity factors of electric displacement and stress obtained are used to obtain the expression of the energy release rate. Furthermore, numerical results of the energy release rate with material parameters are demonstrated. Based on the obtained results, it could be concluded that the energy release rate is closely related to the size of the interface cracks and the mechanical-electrical loading. For a bimaterial structure, the fine-grained piezoelectric structure exhibited better material performance compared to the large one.

Description: Weapon-target assignment (WTA) is critical to command and decision making in modern battlefields and is a typical nondeterministic polynomial complete problem. To solve WTA problems with multiple optimization objectives, a multipopulation coevolution-based multiobjective particle swarm optimization (MOPSO) algorithm is proposed to realize the rapid search for the globally optimal solution. The algorithm constructs a master-slave population coevolution model. Each slave population corresponds to an objective function and is used to search for noninferior solutions. The master population receives all the noninferior solutions from the slave populations, repairs the gaps between the noninferior solutions, and generates a relatively optimal Pareto optimal solution set. In addition, to accelerate the slave populations searching for noninferior solutions and master population repairing the gaps between noninferior solutions, the particle velocity update method is improved. The simulation results show that the proposed algorithm has higher computational efficiency and achieves better solutions than existing algorithms capable of providing a good solution. The method is suitable for rapidly solving multiobjective WTA (MOWTA) problems.

Description: In this work, a controller design technique called linear algebra based controller (LABC) is presented. The controller is obtained following a systematic procedure that is summarized in this work. In addition, the influence of additive uncertainty on the tracking error is analyzed, and a solution using integrators is proposed. A mobile robot is used as a benchmark to test the performance of the proposed algorithms. In addition, implementation to other systems such as marine vessel is referenced. In this work, the design of controllers in continuous and discrete time is included and experimental and simulation results are shown in a Pioneer 3AT mobile robot. Comparisons are also shown with other controllers proposed in the literature.

Description: Cutting mixed oil in product pipelines has a great influence on the economy of the pipeline operation processes. The reasonable prediction of CDMS (the concentration distribution in the mixed segment) is important for cutting mixed segments. The classical model cannot explain the tailing phenomenon well which should not be neglected during operation processes. Based on Fick’s diffusion law, a new model for calculating the diffusion coefficient is proposed in this article, which originates from the essence of the diffusion phenomenon and considers the effects of both physical properties of oil products and the turbulence. At the same time, the dynamic fluid equilibrium model of CDMS near the pipe wall is given which has considered the adsorption effect of wall roughness. Based on these two factors, a novel numerical model for simulating the quantity of tailing oil is proposed, which is solved via the characteristic method and the finite difference method. The effects of different physical properties, as well as the adsorption, on both LFMS (the length of the front of the mixed segment) and LTMS (the length of the tail of the mixed segment), are analyzed. The comparison between the simulation results and the experimental data is utilized to validate the proposed numerical model. The simulation results show that the novel model can well describe the mixed segment tailing phenomenon and also explain the mixing essence of two miscible but dissimilar fluids in the pipeline more clearly. To sum up, this model can provide theoretical guidance for the prediction of CDMS and cutting process in practical operation processes; therefore, more economic benefit can be obtained.

Description: The variation trend, failure trajectory, probability distribution, and other information vary with time and working conditions for rolling bearing vibration performance, which makes the evaluation and prediction of the evolution process difficult for the performance reliability. In view of this, the chaos theory, grey bootstrap method, and maximum entropy method were effectively fused to propose a mathematical model for the dynamic uncertainty evaluation of rolling bearing vibration performance. After reconstructing the phase space of the vibration performance time series, four local prediction methods were applied to predict the vibration values of bearings to verify the effectiveness and validity of chaos theory. The estimated true value and estimated interval were calculated using the grey bootstrap method (GBM) and maximum entropy method. Finally, the validity of the proposed model was verified by comparing the probability that the original data fall into the estimated interval with the given confidence level. The experimental results show that the proposed method can effectively predict the variation trend and failure trajectory of the vibration performance time series so as to realize the dynamic monitoring of the evolution process for rolling bearing vibration performance online.

Description: Lithology identification is an indispensable part in geological research and petroleum engineering study. In recent years, several mathematical approaches have been used to improve the accuracy of lithology classification. Based on our earlier work that assessed machine learning models on formation lithology classification, we optimize the boosting approaches to improve the classification ability of our boosting models with the data collected from the Daniudi gas field and Hangjinqi gas field. Three boosting models, namely, AdaBoost, Gradient Tree Boosting, and eXtreme Gradient Boosting, are evaluated with 5-fold cross validation. Regularization is applied to the Gradient Tree Boosting and eXtreme Gradient Boosting to avoid overfitting. After adapting the hyperparameter tuning approach on each boosting model to optimize the parameter set, we use stacking to combine the three optimized models to improve the classification accuracy. Results suggest that the optimized stacked boosting model has better performance concerning the evaluation matrix such as precision, recall, and f1 score compared with the single optimized boosting model. Confusion matrix also shows that the stacked model has better performance in distinguishing sandstone classes.

Description: The digging resistance in a normal state is the key to excavator design and automated excavation. It is difficult to accurately predict, simulate, or directly measure the digging resistance in a normal state due to uncertainties in the soil properties and excavation parameters. In this paper, a research idea is proposed that uses the working device as the entry point to indirectly calculate the digging resistance in a normal state by measuring the motion parameters and the cylinder pressure intensity. Based on the rule of combination for spatial force systems, a method for combining and projecting the system of the digging resistance is proposed in which the system is projected as six parts, and the tangential force, normal force, and bending moment in the plane of symmetry of the working device are the objects of the solution to avoid redundant equations. Based on kinematics and dynamics models of the excavator and the force and moment equilibrium conditions of the working device, equations for the active-side calculation of the incomplete digging resistance are derived. Based on these equations, the motion parameters of the working device and data on the cylinder pressure intensity obtained by measurement are used to calculate the incomplete digging resistance. The validation scheme and process proposed use the incomplete digging resistance as the external load to obtain the simulated stress of the working device through transient analysis. The simulated stress and the measured stress corresponding to the position of the measurement point are extracted and compared. The results show that there is a difference in the size of the numerical value between the simulated and measured stress, but the variation law is highly consistent, which validates the calculation method. In this paper, an active-side calculation method is provided for the incomplete digging resistance in a normal state without considering the soil-tool interaction relationships, which lays a theoretical foundation for the study of the digging resistance characteristics in a normal state, as well as excavator design and automated excavation.

Description: In this paper a model for Nonuniform Transmission Lines for electromagnetic transient analysis that incorporates frequency dependency of electrical parameters, variation of line electrical parameters with respect to distance, and distributed excitations due to incident electromagnetic fields is presented. The model is developed using the Method of Characteristics in the actual physical domain instead of the Modal Domain; this simplifies the mathematical development and the final equations. Moreover, the equations of the resultant model are valid either for two-conductor lines or for multiconductor lines without any change; this can be an advantage when the computer programming language considers a scalar as a 1x1 matrix. The proposed numerical model is developed under the hypothesis that the dielectric surrounding the conductors is homogenous. It is shown that in this case the characteristic curves of a Nonuniform Transmission Line become straight lines. Finally, the model is validated by comparison with results obtained using the Numerical Laplace Transform method.

Description: The study describes a general argument analysis technique for holomorphic and meromorphic complex functions in several variables, or simply -variable complex functions with . Argument analytic relationships for -variable complex functions with significance similar to the argument principle for one-variable ones are retrieved partially and locally. More precisely, argument analysis in -variable complex functions is carried out one-by-one in terms of each and all variables, namely, partially, so that argument-principle-like relations are established in poly-disc neighborhoods of the variable domains, namely locally. The technique is applicable graphically with loci plotting, independent of Cauchy integral contour and locus orientations; it is also numerically tractable without loci plotting via argument incremental integration. Numerical examples are included to illustrate the main results.

Description: In order to solve the multiobjective optimization problems efficiently, this paper presents a hybrid multiobjective optimization algorithm which originates from invasive weed optimization (IWO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), a popular framework for multiobjective optimization. IWO is a simple but powerful numerical stochastic optimization method inspired from colonizing weeds; it is very robust and well adapted to changes in the environment. Based on the smart and distinct features of IWO and MOEA/D, we introduce multiobjective invasive weed optimization algorithm based on decomposition, abbreviated as MOEA/D-IWO, and try to combine their excellent features in this hybrid algorithm. The efficiency of the algorithm both in convergence speed and optimality of results are compared with MOEA/D and some other popular multiobjective optimization algorithms through a big set of experiments on benchmark functions. Experimental results show the competitive performance of MOEA/D-IWO in solving these complicated multiobjective optimization problems.

Description: The study of damage characteristics of rock mass is of great significance to the analysis of rock mass structure. According to the characteristics of the microscopic unit strength of rock with random distribution, the Weibull distribution is widely used as the statistical functions of the strength of the microunit of rock to establish the damage constitutive model. The concepts of damage ratio and damage index are proposed. Damage ratio is mainly used to describe the law of damage evolution in rock. Damage index can be used to evaluate the damage degree of rock. The influence of confining pressure on distribution parameters and damage ratio is analyzed through uniaxial and triaxial compression tests of sandstone. The results show that damage ratio is an index of structural characteristics of rock damage, which can reflect the evolution characteristics of microcracks in rock under spatial stress. Critical damage ratio refers to the damage ratio corresponding to the peak stress of rock and can be used as a parameter to characterize the strength of rock for corresponding to the peak strain one to one. The critical damage ratio is linearly related to the logarithmic function of confining pressure. Its relationship is as follows: . With the increase of , the increasing trend of slows down and gradually tends to a certain value. The larger the damage index is, the more serious the damage of rock is. The smaller the damage index is, the less serious the damage of rock is. Therefore, the damage index can be used to evaluate the damage degree of rock. It will be an important direction of rock damage mechanics research to distinguish the severity of rock damage by using damage index as the limit value.

Description: Clustering is widely used in data analysis, and density-based methods are developed rapidly in the recent 10 years. Although the state-of-art density peak clustering algorithms are efficient and can detect arbitrary shape clusters, they are nonsphere type of centroid-based methods essentially. In this paper, a novel local density hierarchical clustering algorithm based on reverse nearest neighbors, RNN-LDH, is proposed. By constructing and using a reverse nearest neighbor graph, the extended core regions are found out as initial clusters. Then, a new local density metric is defined to calculate the density of each object; meanwhile, the density hierarchical relationships among the objects are built according to their densities and neighbor relations. Finally, each unclustered object is classified to one of the initial clusters or noise. Results of experiments on synthetic and real data sets show that RNN-LDH outperforms the current clustering methods based on density peak or reverse nearest neighbors.

Description: The Jacobi–Davidson iteration method is efficient for computing several eigenpairs of Hermitian matrices. Although the involved correction equation in the Jacobi–Davidson method has many developed variants, the behaviors of them are not clear for us. In this paper, we aim to explore, theoretically, the convergence property of the Jacobi–Davidson method influenced by different types of correction equations. As a by-product, we derive the optimal expansion vector, which imposed a shift-and-invert transform on a vector located in the prescribed subspace, to expand the current subspace.

Description: Mine dust is one of the most serious environmental hazards in the coal mining process. This paper introduces a numerical simulation of a novel foam generator used for dust control in coal mines. The amount of foam generated by this device significantly depends on the amount of air entrainment. Therefore, a computational fluid dynamics (CFD) method was used to study three influencing factors, namely, throat-nozzle distance, mixing throat length, and the contraction angle of the suction chamber. The predicted values by the CFD simulation proved to be in good agreement with the experimental data. The results revealed that the air entrainment reached its maximum when the ratio of throat-nozzle distance to mixing throat length was 2/3. The optimum values of the throat ratio (its length to diameter) and the contraction angle of the suction chamber were obtained at 20 and 5°, respectively. This research provides essential guidance in the geometric parameter design of the self-suction type foam generator, which has the advantage of negating the need for compressed-air pipelines and having high reliability, compared to traditional foam generators.

Description: With the rapid development of China’s economy, the construction of infrastructure has continuously improved. In the past few years, the construction of water conservancy projects has been constantly developing, and related geological disasters have become increasingly prominent. The stability of water conservancy slopes is related to whether water conservancy projects can be safely constructed and built to function safely and effectively, which has become a topic of increasing concern for geologists and related researchers. This paper selects the Jinping 1 Hydropower Station in Sichuan, China, for analysis. Four categories of evaluation factors (geological, engineering, environmental, and monitoring) and 24 subfactors (17 quantitative indicators and 7 qualitative indicators) are selected to ascertain the risk of the slope more accurately. By investigating the deficiencies of the traditional cloud model, the related concepts and computational models of a finite-area cloud model are proposed. By obtaining the characteristic parameters, the degrees of membership of the measurement samples belonging to different risk levels are further obtained. The weights of the indicators determined by the cloud processor and the weighted distance discriminant method are used to determine the final weights and achieve a final classification of the slope stability level. The research results demonstrate that the weighted distance discriminant algorithm combined with the improved finite-interval cloud model can consider the comprehensive information of each evaluation index and the degrees of mutual influence between the indicators, making the evaluation results more objective. Moreover, the proposed approach can quickly and accurately classify slope stability and deliver a prediction of the safety, thereby providing new ideas for evaluating the stability of slopes.

Description: Under the unavailability of some components of a complex system, the Hardware In the Loop (HIL) tool allows the emulation of other subsystems. When these devices are not available, a customized emulator can be developed based on the Piecewise Linear Model (PWLM) and a numerical method for solving the differential equations system. However, these implementations require the use of a Field Programmable Gate Array (FPGA) with extensive hardware resources. In this article we propose the use of switching functions for the modeling of power converters of a Hybrid Power System (HPS), allowing the reduction of hardware resources of the FPGA, and the number of steps per switching cycle is increased. The results are compared with SABER simulations and a PWLM evaluated with the Euler method.

Description: Forecasting energy data, especially the primary energy requirement, is the key part of policy-making. For those territories of different developing types, seeking a knowledge-based and dependable forecasting model is an essential prerequisite for the prosperous development of policy-making. In this paper, both autoregressive integrated moving average and backpropagation neural network models which have been proved to be very efficient in forecasting are applied to the forecasts of the primary energy consumption of three different developing types of territories. It is shown that the average relative errors between the actual data and simulated value are from 4.5% to 5.9% by the autoregressive integrated moving average and from 0.04% to 0.47% by the backpropagation neural network. Specially, this research shows that the backpropagation neural network model presents a better prediction of primary energy requirement when considering gross domestic product, population, and the particular values as predictors. Furthermore, we indicate that the single-input backpropagation neural network model can still work when the particular values have contributed most to the energy consumption.

Description: This paper discusses the inventory mechanism with backordering and with the infinite planning horizon consisting of two stages wherein the demand rate in the first stage is strictly greater than that in the second stage. To minimize the retailer’s inventory cost, we establish a lot sizing decision model. On the basis of the inventory cost analysis, we present a closed-form solution to the model and provide an optimal replenishment and stocking strategy to the retailer. The given numerical experiments show the validity of the model.

Description: The reasonable scheduling of multisensor systems to maximize combat benefits has become a research hotspot in the field of sensor management. To minimize the uncertainty in the threat level of targets and improve the survivability of sensors, a risk-based multisensor scheduling method is proposed in this paper. In this scheduling problem, the best sensors are systematically selected to observe targets for the trade-off between the threat assessment risk and the emission risk. First, the scheduling problem is modelled as a partially observable Markov decision process (POMDP) for target threat assessment. Second, the calculation methods of the threat assessment risk and the emission risk are proposed to quantify the potential loss caused by the uncertainty in the threat level of targets and the emission of sensors. Then, a nonmyopic sensor scheduling objective function is built to minimize the total risk which is the weighted sum of the threat assessment risk and the emission risk. Furthermore, to solve the high complexity computational problem in optimization, a decision tree search algorithm based on branch pruning is designed. Finally, simulations are conducted, and the results show that the proposed algorithm can significantly reduce the searching time and memory consumption in optimization compared with those of traditional algorithms, and the proposed method has a better risk control effect than the existing sensor scheduling methods.

Description: Leasing has been increasingly seen as a viable alternative to traditional business models. In this paper, we consider a manufacturer making decisions on green product design by accounting for the trade-off between traditional and environmental qualities under three business models, including a pure selling, a pure leasing, and a hybrid model with both selling and leasing. Under leasing, there exists the pooling effect that allows a manufacturer to meet consumer needs with fewer products. Since the pooling effect decreases the marginal cost of production, leasing produces positive incentives to increase product quality. However, the cannibalization effect within the product line distorts the incentives so that the pooling effect only increases the traditional quality rather than the environmental quality. As a result, leasing may have a negative impact on the average environmental quality of products. The manufacturer should make business model choices depending on some factors, including the types of markets, the usage cost, and the pooling effect. In general, when the pooling effect is strong, the manufacturer prefers a leasing or hybrid model to selling but designs products with lower environmental quality than selling. When the pooling effect is weak, the optimal decision should be made depending on the types of markets and the usage cost: in the high-end (low-end) market, the manufacturer should adopt a leasing or hybrid model only when the usage cost is high (low); the adoption of leasing or hybrid model can improve the average environmental quality.

Description: The Jordan decomposition of matrix is a typical scientific and engineering computational task, but such computation involves enormous computing resources for large matrices, which is burdensome for the resource-limited clients. Cloud computing enables computational resource-limited clients to economically outsource such problems to the cloud server. However, outsourcing Jordan decomposition of large-scale matrix to the cloud brings great security concerns and challenges since the matrices usually contain sensitive information. In this paper, we present a secure, verifiable, efficient, and privacy preserving algorithm for outsourcing Jordan decomposition of large-scale matrix. Security analysis shows that our algorithm is practically secure. Efficient verification algorithm is used to verify the results returned from the cloud.

Description: Flow and fracture of granular materials under external loads is a complex mechanical process, and the research on its law is still in the exploratory stage. In this paper, the flow and fracture law of granular materials is taken as the research object, and numerical algorithm compilation and program development are combined to study. Taking full advantage of the existing algorithms and developing new ones based on the existing DEM theory, a numerical simulation program for the flow and fracture of granular materials is developed. The flow and fracture process of concrete spherical granular system with diameter of 4 cm under loading rate of 70 mm/min and end of loading of 50 kN is taken as an example to verify the simulation program. At the same time, the loading experiment of the concrete spherical particle system under the same simulation conditions was also carried out. The simulation results are compared with the experimental results in three aspects: the generation location of the particle system, the relationship between the whole load and displacement, and the degree of particle breakage. The results show that the numerical simulation is in good agreement with the experimental results, which verifies the reliability of the numerical algorithm and the simulation program, and can provide support for the study of the flow and fracture process of granular materials.

Description: An evolutionary discrete firefly algorithm (EDFA) is presented herein to solve a real-world manufacturing system problem of scheduling a set of jobs on a single machine subject to nonzero release date, sequence-dependent setup time, and periodic maintenance with the objective of minimizing the maximum completion time “makespan.” To evaluate the performance of the proposed EDFA, a new mixed-integer linear programming model is also proposed for small-sized instances. Furthermore, the parameters of the EDFA are regulated using full factorial analysis. Finally, numerical experiments are performed to demonstrate the efficiency and capability of the EDFA in solving the abovementioned problem.

Description: As a novel and promising technology for 5G networks, device-to-device (D2D) communication has garnered a significant amount of research interest because of the advantages of rapid sharing and high accuracy on deliveries as well as its variety of applications and services. Big data technology offers unprecedented opportunities and poses a daunting challenge to D2D communication and sharing, where the data often contain private information concerning users or organizations and thus are at risk of being leaked. Privacy preservation is necessary for D2D services but has not been extensively studied. In this paper, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. Firstly, we provide a framework for the D2D big data sharing and analyze the threat model. Then, we propose an (a, k)-anonymity privacy-preserving framework for D2D big data deployed on MapReduce. In our privacy-preserving framework, we adopt (a, k)-anonymity as privacy-preserving model for D2D big data and use the distributed MapReduce to classify and group data for massive datasets. The results of experiments and theoretical analysis show that our privacy-preserving algorithm deployed on MapReduce is effective for D2D big data privacy protection with less information loss and computing time.

Description: This paper treats the problems of stability analysis and control synthesis of the switched inverted pendulum system with nonlinear/linear controllers. The proposed control strategy consists of switching between backstepping and linear state feedback controllers on swing-up and stabilization zones, respectively. First, the backstepping controller is implemented to guarantee the rapid convergence of the pendulum to the desired rod angle from the vertical position. Next, the state feedback is employed to stabilize and maintain the system on the upright position inherently unstable. Based on the quadratic Lyapunov approach, the switching between the two zones is analyzed in order to determine a sufficient domain in which the stability of the desired equilibrium point is justified. A real-time experimentation shows a reduction of of the samples below the classical scheme when using only the backstepping control in the entire operating region. Furthermore, the reduction percentage has become 92% in comparison with the composite linear/linear controller.

Description: In order to reduce the curse of dimensionality of massive data from SCADA (Supervisory Control and Data Acquisition) system and remove data redundancy, the grey correlation algorithm is used to extract the eigenvectors of monitoring data. The eigenvectors are used as input vectors and the monitoring variables related to the unit state as output vectors. The genetic algorithm and cross validation method are used to optimize the parameters of the support vector regression (SVR) model. A high precision prediction is carried out, and a reasonable threshold is set up to alarm the fault. The condition monitoring of the wind turbine is realized. The effectiveness of the method is verified by using the actual fault data of a wind farm.

Description: In order to reduce the investment risk, the evaluation standard of transmission line project investment planning becomes higher, which puts forward higher requirements for the reasonable level prediction of transmission line project cost. This paper combines principal component analysis (PCA) with the least squares support vector machine (LSSVM) model and establishes a point prediction model for transmission line project cost. Based on the analysis of the error of the point prediction model, the kernel density estimation (KDE) method is innovatively introduced to estimate the prediction error, and the probability density function of the error is obtained. Then, according to different confidence levels, the corresponding cost intervals are obtained, which means that the reasonable level of transmission line project cost is obtained. The results show that the coverage rate of the cost prediction interval under 85% confidence level is 88.57%. This conclusion shows that the model has high reliability and can provide a reliable basis for the evaluation of transmission line project investment planning.

Description: In this paper, a new type of piezoelectric harvester for vehicle suspension systems is designed and presented that addresses the current problems of low energy density, vibration energy dissipation, and reduced energy harvesting efficiency in current technologies. A new dual-mass, two degrees of freedom (2-DOF), suspension dynamic model for the harvester was developed for the inertial mass and the force of the energy conversion component by combining with the piezoelectric power generation model, the rotor dynamics model, and the traditional 2-DOF suspension model. The influence of factors such as vehicle speed, the parameters of the harvester, and road classification on the root mean square (RMS) of the generated electric power is discussed. The results show that the RMS increases with the increase of the speed of the vehicle, the thickness and length of piezoelectric patches and magnetic slabs, and the residual flux density of magnets and road roughness coefficient and with the decrease of the width of piezoelectric patches and magnetic slabs and the space between the stator ring and the rotator ring. In the present research, a power of up to 332.4 W was harvested. The proposed model provides a powerful reference for future studies of energy harvesting from vehicle suspension systems.

Description: The underground ventilation in coal mine comes from the ground air so that the seasonal fluctuation of atmospheric temperature inevitably has influence upon its climate. The paper obtains a simplified mathematical calculation method of the radial and axial temperature fields to furnish a supplement to air temperature prediction of coal roadway with the emphasis on the influence of seasonal characteristic of inlet airflow. The results reveal that the propagation of radial and axial temperature wave has the similar characteristic of amplitude decay and phase delay. In the radial direction, the main factor is the dimensionless annual cycle time; and in the axial direction, it is influenced by the location-related dimensionless time, the Nusselt number, the Biot number, and the dimensionless annual cycle time. In addition, for the wave amplitude, they present the negative exponential trend as the location increases, the overall shape of which resembles a folded page; for the delayed phase, they present the linear variation trend as the location increases, the overall shape of which resembles a parallelogram. In the paper, the evolution mechanism of temperature field of coal roadway under seasonal fluctuation boundary is profoundly comprehended, which has important guiding significance for the field engineer.

Description: The catenary riser such as steel catenary riser (SCR), under wave action or current action, shows a kind of rotation that acts as a rigid body along a similarly fixed axis of oscillation determined by the varying suspension and touch down point, respectively. The characteristics of acceleration of catenary riser influenced by rigid body swing integrity backwards and forwards (RBSIBF) in cross direction cannot be neglected. Based on the large deflection slender beam model, top motion of x direction, RBSIBF, and wave force model, this manuscript studies and explains effect of RBSIBF in cross direction (z direction) on riser in quantitative and qualitative perspectives. The rigid body wiggle effect can be considered by amplitude-value multiplication with the safety factor of 1.2. The calculation shows that, in terms of the overall motion pattern, the motion response in the xy plane develops gradually from the narrow amplitude wiggle in in-line direction of top region to narrow amplitude wiggle in vertical direction of bottom area. Wave load is the main effect load in cross-flow direction. Along the depth increase, the acceleration amplitude of the top hanging point area is maximum, and the amplitude decreases most strongly or violently. With the decrease of case amplitude, the structural acceleration responses of node 10th to 80th significantly reduced by about 30% and the corresponding of node 140th to 200th increased by about 15%. The most influential point of RBSIBF on acceleration is node 200th with an influence level of about 20%. When the structure mainly rotates in the xz plane, rigid body wiggle and swing are positively correlated with rotation vector diameter. The rigid body wiggle and swing increase acceleration of structure. In the rotational yz plane of the structure, rigid body wiggle and swing reduce acceleration response.

Description: This paper is concerned with the nonlinear model predictive control of the harvesting effort of a renewable resource system, namely, sustainable seafood (farmed fish), with a nonlinear state equation. A solution approach is proposed and discussed, and numerical illustrations are provided.

Description: The main objective of this article is to describe an innovative methodology of synchronous local optimization which considers the whole ship speed range being presented for a KRISO Container Ship (KCS). Parametric form approaches are adopted by employing a fairing B-spline curve in order to generate variants of the bow and stern of forms using form design parameters modified, resulting in an optimization system based on NSGA-II. The total resistance is calculated by the Rankine source panel method and the empirical formula which agrees well with the corresponding experimental data and further acquires validation with the overall error of 2.0%. Accordingly, the ship forepart and stern form have been optimized under conditions of the single design speed and whole speeds range based on the considerations of generally distributed and variable operational speeds for the operating characteristics of modern container ships synchronously. The optimized result presents well-balanced drag reduction benefits which averagely remain above 4.0% of ship resistance decrease. Compared to the traditional optimization process which is based on a specific design speed, the newly developed method is more practical and effective in both automation and integration.

Description: In this paper, we develop a novel linear singularity representation method using spatial K-neighbor block-extraction and Haar transform (BEH). Block-extraction provides a group of image blocks with similar (generally smooth) backgrounds but different image edge locations. An interblock Haar transform is then used to represent these differences, thus achieving a linear singularity representation. Next, we magnify the weak detailed coefficients of BEH to allow for image enhancement. Experimental results show that the proposed method achieves better image enhancement, compared to block-matching and 3D filtering (BM3D), nonsubsampled contourlet transform (NSCT), and guided image filtering.

Description: Since wind power is directly influenced by wind speed, long-term wind speed forecasting (WSF) plays an important role for wind farm installation. WSF is essential for controlling, energy management and scheduled wind power generation in wind farm. The proposed investigation in this paper provides 30-days-ahead WSF. Nonlinear Autoregressive (NAR) and Nonlinear Autoregressive Exogenous (NARX) Neural Network (NN) with different network settings have been used to facilitate the wind power generation. The essence of this study is that it compares the effect of activation functions (namely, tansig and logsig) in the performance of time series forecasting since activation function is the core element of any artificial neural network model. A set of wind speed data was collected from different meteorological stations in Malaysia, situated in Kuala Lumpur, Kuantan, and Melaka. The proposed activation functions tansig of NARNN and NARXNN resulted in promising outcomes in terms of very small error between actual and predicted wind speed as well as the comparison for the logsig transfer function results.

Description: The avoidance behavior of pedestrians was characterized in the present paper by simulating the movement of crowds in both unidirectional and bidirectional pedestrian flow. A phase change of alternative lane formation observed in real bidirectional pedestrian flows has been studied, where pedestrians tended to evade individuals in counterflow and simultaneously keep a certain distance from each other in the uniform pedestrian flow when the counterflow disappeared. What is more, the comparison between the effect of evading and pushing behavior on evacuation has been investigated in the room egress scenario. Additionally, the evading and overtaking behavior of fast pedestrians have also been simulated in heterogeneous crowds. The performance of the proposed model was compared to the experimental data and the results obtained using other evacuation models. Numerical results showed that both the phase change of alternative lane formation in bidirectional pedestrian flow and the effective evading behavior in unidirectional pedestrian flow were conductive to reduce the evacuation time of pedestrian crowds. Even though pushing behavior of fast pedestrians seemed to improve the flow through the wide exit, it might lead to the panic and other negative effect on the crowds, such as crowds trample. The proposed model in this paper could provide a theoretical basis for the pedestrian crowd management during emergency evacuation.

Description: With the fastest consumer demand growth, the increasing customer’s demands trend to multivarieties and small-batch and the customer requires an efficient distribution planning. How to plan the vehicle route to meet customer satisfaction of mass distribution as well as reduce the fuel consumption and emission has become a hot topic. This paper proposes a two-phase optimization method to handle the vehicle routing problem, considering the customer demands and time windows coupled with multivehicles. The first phase of the optimization method provides a fuzzy hierarchical clustering method for customer grouping. The second phase formulates the optimization en-group vehicle routing problem model and a genetic algorithm to account for vehicle routing optimization within each group so that fuel consumption and emissions are minimized. Finally, we provide some numerical examples. Results show that the two-phase optimization method and the designed algorithm are efficient.

Description: We presented the applications of entropy generation for SWCNTs and MWCNTs based on kerosene oil for Casson nanofluid flow by rotating channels. Kerosene oil has advanced thermal conductivity and exclusive features and has a lot of practical uses due to its unique behavior. That is why we have used kerosene oil as a based fluid. For the entropy generation second law of thermodynamics is applied and implemented for the nanofluid transport mechanism. In the presence of magnetic field, the effects of thermal radiations and heat source/sink on the temperature profiles are studied. The fluid flow is supposed in steady state. With the help of suitable similitude parameters, the leading equations have been transformed to a set of differential equations. The solution of the modeled problem has been carried out with the homotopic approach. The physical properties of carbon nanotubes are shown through tables. The effects of the imbedded physical parameters on the velocities, temperature, entropy generation rate, and Bejan number profiles are investigated and presented through graphs. Moreover, the impact of significant parameters on surface drag force and heat transfer rate is tabulated.

Description: In the work, an analysis of the influence of electrode inclination on the distribution of temperature in the weld overlaying has been conducted. In the analytical description of the temperature field, a volumetric heat source model with an inclined axis with respect to the direction of surfacing was adopted. In the numerical simulation, the own theoretical model of heat source, algorithm, and program performed in the Borland Delphi environment were used. In the calculation examples, different electrode inclination angles were adopted in relation to the welded plate, in the direction of surfacing, opposite to the direction of welding, and perpendicular to the weld bead.

Description: The accurate measurement of roll angular rate for high spinning projectile has long been a challenging problem. Aiming to obtain the accurate roll angular rate of high spinning projectile, a novel extraction and filter algorithm, BSCZT-KF, is proposed in this paper. Firstly, a compound angular motion model of high spinning projectile is established. According to the model, we translate the roll angular rate measurement problem into a frequency estimation problem. Then the improved CZT algorithm, BSCZT, was employed to realize an accurate estimation of the narrowband signal frequency. Combined with the peak detection method, the BSCZT-KF algorithm is presented to further enhance the frequency estimation accuracy and the real-time performance. Finally, two sets of actual flight tests were conducted to verify the effectiveness and accuracy of the algorithm. The test results show that the average error of estimated roll angular rate is about 0.095% of the maximum of roll angular rate. Compared with the existing methods, the BSCZT-KF has the highest frequency estimation accuracy for narrowband signal.

Description: This paper presents an effective partial differential equation- (PDE-) based preprocessing algorithm for automated image-based crack detection. The proposed formulation combines various relevant and multiple processes such as contrast and selective edge enhancement in addition to edge-preserving smoothing to enhance the image prior to detection. The approach is adaptive and controlled by reliable image metrics to determine the stopping time of the PDE ensuring optimum results for various images. Additionally, a simplified thresholding algorithm based on local global maximum gradient matching is used to extract the crack features from the image. The proposed scheme does not require arbitrary or manually tuned parameters nor a large dataset for training to obtain good results. Experiments indicate that the proposed approach performs better when compared to several other algorithms in the literature.

Description: This paper presents stress influence functions for uniformly distributed, time-harmonic rectangular loads within a three-dimensional, viscoelastic, isotropic full-space. The coupled differential equations relating displacements and stresses in the full-space are solved through double Fourier integral transforms in the wave number domain, in which they can be solved algebraically. The final stress fields are expressed in terms of double indefinite integrals arising from the Fourier transforms. The paper presents numerical schemes with which to integrate these functions accurately. The article presents numerical validation of the synthesized stress kernels and their behavior for high frequencies and large distances from the excitation source. The influence of damping ratio on the dynamic results is also investigated. This article is complementary to previous results of the authors in which the corresponding displacement solutions were derived. Stress influence functions, together with their displacement counterparts, are a fundamental part of many numerical methods of discretization such the boundary element method.

Description: This paper deals with the hybrid particle swarm optimization-Cuckoo Search (PSO-CS) algorithm which is capable of solving complicated nonlinear optimization problems. It combines the iterative scheme of the particle swarm optimization (PSO) algorithm and the searching strategy of the Cuckoo Search (CS) algorithm. Details of the PSO-CS algorithm are introduced; furthermore its effectiveness is validated by several mathematical test functions. It is shown that Lévy flight significantly influences the algorithm’s convergence process. In the second part of this paper, the proposed PSO-CS algorithm is applied to two different engineering problems. The first application is nonlinear parameter identification for the motor drive servo system. As a result, a precise nonlinear Hammerstein model is obtained. The second one is reactive power optimization for power systems, where the total loss of the researched IEEE 14-bus system is minimized using PSO-CS approach. Simulation and experimental results demonstrate that the hybrid optimal algorithm is capable of handling nonlinear optimization problems with multiconstraints and local optimal with better performance than PSO and CS algorithms.

Description: The store-within-a-store (SWS) arrangement is one of the most important channel structures. Taking the perspective of manufacturers, this paper extends the literature by examining the role of manufacturers’ prediction accuracy, moderated by demand uncertainty and inventory-related parameters, in determining the adoption of the SWS strategy. This study constructs a theoretical model to investigate manufacturers’ decision based on the uncertainty model. The result shows that, first, as a manufacturer’s demand forecast accuracy increases, it would be more likely to choose the SWS mode, and demand uncertainty and inventory-related parameters further strengthen this effect. Second, if the manufacturer and retailer can reach an agreement on a proper percentage of the revenues that the manufacturer pays to the retailer, the SWS mode would be a better choice for both of them. This research provides new insights for the adoption of the SWS strategy.

Description: This paper focuses on solving deadlock problems of flexible manufacturing systems (FMS) based on Petri nets theory. Precisely, one novel control transition technology is developed to solve FMS deadlock problem. This new proposed technology can not only identify the maximal saturated tokens of idle places in Petri net model (PNM) but also further reserve all original reachable markings whatever they are legal or illegal ones. In other words, once the saturated number of tokens in idle places is identified, the maximal markings of system reachability graph can then be checked. Two classical S3PR (the Systems of Simple Sequential Processes with Resources) examples are used to illustrate the proposed technology. Experimental results indicate that the proposed algorithm of control transition technology seems to be the best one among all existing algorithms.

Description: Recently, skeleton-based action recognition has become a very important topic in the field of computer vision. It is a challenging task to accurately build a human action model and precisely distinguish similar human actions. In this paper, an action (skeleton sequence) is represented as a third-order nonnegative tensor time series to capture the original spatiotemporal information of the action. As a linear dynamical system (LDS) is an efficient tool for encoding the spatiotemporal data in various disciplines, this paper proposes a nonnegative tensor-based LDS (nLDS) to model the third-order nonnegative tensor time series. Nonnegative Tucker decomposition (NTD) is utilized to estimate the parameters of the nLDS model. These parameters are used to build extended observability sequence for the action, which implies that can be considered as the feature descriptor of the action. To avoid the limitations introduced by approximating with a finite-order matrix, we represent an action as a point on infinite Grassmann manifold comprising the orthonormalized extended observability sequences. The classification task can be performed by dictionary learning and sparse coding on the infinite Grassmann manifold. The experimental results on the MSR-Action3D, UTKinect-Action, and G3D-Gaming datasets demonstrate that the proposed approach achieves a better performance in comparison with the state-of-the-art methods.

Description: Since the intricate anatomical structure of the craniomaxillofacial region and the limitation of surgical field and instrument, the current surgery is extremely of high risk and difficult to implement. The puncturing operations for biopsy, ablation, and brachytherapy have become vital method for disease diagnosis and treatment. Therefore, a craniomaxillofacial surgery robot system was developed to achieve accurate positioning of the puncture needle and automatic surgical operation. Master-salve control and “kinematic + optics” hybrid automatic motion control based on navigation system, which is proposed in order to improve the needle positioning accuracy, were implemented for different processes of the operation. In addition, the kinematic simulation, kinematic parameters identification, positioning accuracy experiment (0.56 ± 0.21 mm), and phantom experiments (1.42 ± 0.33 mm, 1.62 ± 0.26 mm, and 1.41 ± 0.30 mm for biopsy, radiofrequency, and brachytherapy of phantom experiments) were conducted to verify the feasibility of the hybrid automatic control method and evaluate the function of the surgical robot system.

Description: A fault-tolerant supervisory control method for dynamic positioning of ships with actuator failures and sensor failures is presented in this paper. Unlike the traditional fault detection and control, fault detection and fault-tolerant controller are designed as a unit in this paper through a supervisor. By introducing a nonlinear estimation error and virtual controller, the sensor failures are separated from the actuator failures in the supervisory control system. It guarantees that the detectability property and matching property of the switched system are satisfied. Firstly, a new extended state observer is designed to match the models of different actuator failures. Secondly, by introducing a virtual controller, the detectability property of the switched system is guaranteed. Finally, a nonlinear estimation error operator is used in the designing of switching logic to guarantee stability of the closed-loop system with sensor failures. When sensor failures and actuator failures occur, we show that all the states of the closed-loop system are guaranteed to be bounded. The effectiveness of the fault-tolerant control is verified by simulation experiments.

Description: Underground cavities formed by underground mining activities are a potential threat to open-pit mining activities. Longtan Village Iron Mine is located in Chengde city, Hebei province, China. The open-pit mining and underground mining of Longtan Village Iron Mine are simultaneously performed. Cavities will remain after underground mining. When mining is performed on the top of the underground cavities in the open pit, concerns arise regarding the closest distance to prevent the collapse of the cavities. (The closest distance is the safe distance between the explosive and the cavities. If the distance between the explosive and the cavities is less than the closest distance, the stability of the cavities will be affected.) The collapse will endanger the safety of the workers and equipment in the open pit. Therefore, it is necessary to estimate the stability of the underground cavities near the bench blasting. In this paper, a series of bench blast tests was performed, and the site-specific attenuation relations of the PPV (Peak particle velocity) and principal frequency of Longtan Village Iron Mine were obtained. Then, an ANASYS three-dimensional numerical model was created, and the propagation of the blast wave and the response of the multicavities were calculated by LS-DYNA. The accuracy of the simulation was verified. However, the bench blast tests do not affect the stability of the cavities. The formula to calculate the closest distance was obtained, which can be used as an approximate guide when designing the bench explosion of Longtan Village Iron Mine. Workers can mine in a safe area of the open pit, and the stability of the cavities will not be affected.

Description: This paper investigates robust controllability and observability of Boolean control networks under disturbances. Firstly, under unobservable disturbances, some sufficient conditions are obtained for robust controllability of BCNs. Then an algorithm is proposed to construct the least control sequences which drive the trajectory from a state to a given reachable state. If the disturbances are observable, by defining the order-preserving system, an efficient sufficient condition is obtained for robust controllability of BCNs. Finally, the robust observability problem is converted into an equivalent robust controllability via set controllability and is solved by using the results obtained for set controllability. Some numerical examples are presented to illustrate the obtained results.

Description: The scraper conveyor is the key conveying equipment for fully mechanized coal mining. Wear failure of the chute is the main form of failure of the scraper conveyor. In this study, the discrete element method (DEM) was combined with the wear model. The wear mechanism and wear regularity of the chute were explored by tracking the changes in the position of coal particles during the wear process. After the validation of wear simulation, a wear test of coal for different intrinsic parameters was designed. In one wear cycle, the three-body wear was about 32.84 times that of the two-body wear. In the research range, the wear of the scraper conveyor chute increased with the increase of Poisson’s ratio, shear modulus, and density of the coal. The shear modulus showed remarkable effect on the wear of the chute, followed by Poisson’s ratio and density. There existed a linear relationship between the shear modulus and wear (R2 = 0.8232). This study is expected to be used to predict the wear of the scraper conveyor chute and provide a theoretical basis for the applicability of the chute in different mines.

Description: The term double-fault networked control system means that sensor faults and actuator faults may occur simultaneously in networked control systems. The issues of modelling and an guaranteed cost fault-tolerant control in a piecewise delay method for double-fault networked control systems are investigated. The time-varying properties of sensor faults and actuator faults are modelled as two time-varying and bounded parameters. Based on the linear matrix inequality (LMI) approach, an guaranteed cost fault-tolerant controller in a piecewise delay method is proposed to guarantee the reliability and stability for the double-fault networked control systems. Simulations are included to demonstrate the theoretical results of the proposed method.

Description: Assessing the demand hazards of structures is requested in the framework of performance-based earthquake engineering. An efficient method for estimating the seismic risk of structures is proposed in this paper. The relationship between multiple limit capacities and corresponding response parameters is denoted by using a generalized multidimensional limit state equation. The limit states of different components are described as random and convex mixed variables. The seismic responses of different components are considered dependent and follow a multidimensional lognormal distribution. The mathematical formula of multidimensional demand hazards of structures is then derived through combining the seismic fragility function and the seismic hazard curve. The proposed method is used to perform the demand hazard analysis and the parameter sensitivity analysis of a multispan continuous concrete girder bridge, selecting column ductility and bearing displacement as the two-dimensional seismic response parameters obtained by Incremental Dynamic Analysis. The results demonstrate that the coefficient of variation and correlation coefficient N, which are involved in the limit state equation, have an impact on the evaluation of the demand hazards.

Description: This experimental study investigates the influence of different sizes, quantities, and axial positions of model ice on propeller hydrodynamic performance. We used particle image velocimetry measurements to analyze the characteristics of the propeller wake flow field. The measurement results show that ice blockage leads to an increase in propeller thrust, torque, and efficiency. The smaller the advance coefficient of the propeller is, the smaller the influence of model ice on propeller blockage is. As the model ice becomes thicker and the thrust and efficiency of the propeller increase, the propeller torque is smaller for low advance coefficient and higher for high advance coefficient. The wider the model ice is, the larger the thrust and torque of the propeller are. Once the model ice width exceeds the propeller diameter, the change in its width has no effect on propeller efficiency. When the propeller is blocked with model ice, the fluid velocity in the wake flow reduces in the inflow direction, and the increase in fluid velocity in the horizontal transverse direction and variation of fluid velocity in the vertical direction are related to the model ice width.

Description: This paper is concerned with the problem of constraint control for an Antilock Braking System (ABS) with time-varying asymmetric slip ratio constraints. A quarter vehicle braking model with system uncertainties and a Burckhardt’s tire model are considered. The Time-varying Asymmetric Barrier Lyapunov Function (TABLF) is embedded into the controllers for handling the time-varying asymmetric slip ratio constraint problems. Two adaptive nonlinear control methods (TABLF1 and TABLF2) based on TABLF are proposed not only to track the optimal slip ratio but also to guarantee no violation on the slip ratio constraints. Simulation results show that the proposed controllers can guarantee no violation on slip ratio constraints and avoid self-locking. In the meantime, TABLF1 controller can achieve a faster convergence rate, shorter stopping time, and shorter distance, compared to TABLF2 controller with the same control parameters.

Description: This study proposes a path-finding model for multi-target strike planning. The model evaluates three elements, i.e., the target value, the aircraft’s threat tolerance, and the battlefield threat, and optimizes the striking path by constraining the balance between mission execution and the combat survival. In order to improve the speed of the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), we use the conjugate gradient method for optimization. A Gaussian perturbation is added to the search points to make their distribution closer to the population distribution. The simulation shows that the proposed method effectively chooses its target according to the target value and the aircraft’s acceptable threat value, completes the strike on high value targets, evades threats, and verifies the feasibility and effectiveness of the multi-objective optimization model.

Description: Given that a single remote sensing image dehazing is an ill-posed problem, this is still a challenging task. In order to improve the visibility of a single hazy remote sensing multispectral image, we developed a novel and effective algorithm based on a learning framework. A linear regression model with the relevant features of haze was established. And the gradient descent method is applied to the learning model. Then a hazy image accurate transmission map is obtained by learning the coefficients of the linear model. In addition, we proposed a more effective method to estimate the atmospheric light, which can restrain the influence of highlight areas on the atmospheric light acquisition. Compared with the traditional haze removal methods, the experimental results demonstrate that the proposed algorithm can achieve better visual effect and color fidelity. Both subjective evaluation and objective assessments indicate that the proposed method achieves a better performance than the state-of-the-art methods.

Description: Peer-to-Peer (P2P) lending has attracted increasing attention recently. As an emerging micro-finance platform, P2P lending plays roles in removing intermediaries, reducing transaction costs, and increasing the benefits of both borrowers and lenders. However, for the P2P lending investment, there are two major challenges, the deficiency of loans’ historical observations about the certain borrower and the ambiguity problem of estimated loans’ distribution. In order to solve the difficulties, this paper proposes a data-driven robust model of portfolio optimization with relative entropy constraints based on an “instance-based” credit risk assessment framework. The model exploits a nonparametric kernel approach to estimate P2P loans’ expected return and risk under the condition that the historical data of the same borrower is unavailable. Furthermore, we construct a robust mean–variance optimization problem based on relative entropy method for P2P loan investment decision. Using the real-world dataset from a notable P2P lending platform, Prosper, we validate the proposed model. Empirical results reveal that our model provides better investment performances than the existing model.

Description: A novel adaptive fuzzy dynamic surface control (DSC) scheme is for the first time constructed for a larger class of (multi-input multi-output) MIMO non-affine pure-feedback systems in the presence of input saturation nonlinearity. First of all, the restrictive differentiability assumption on non-affine functions has been canceled after using the piecewise functions to reconstruct the model for non-affine nonlinear functions. Then, a novel auxiliary system with bounded compensation term is firstly introduced to deal with input saturation, and the dynamic system employed in this work designs a bounded compensation term of tangent function. Thus, we successfully relax the strictly bounded assumption of the dynamic system. Additionally, the fuzzy logic systems (FLSs) are used to approximate unknown continuous systems functions, and the minimal learning parameter (MLP) technique is exploited to simplify control design and reduce the number of adaptive parameters. Finally, two simulation examples with input saturation are given to validate the effectiveness of the developed method.

Description: The main contribution of this study is to present a new optimal eighth-order scheme for locating zeros with multiplicity . An extensive convergence analysis is presented with the main theorem in order to demonstrate the optimal eighth-order convergence of the proposed scheme. Moreover, a local convergence study for the optimal fourth-order method defined by the first two steps of the new method is presented, allowing us to obtain the radius of the local convergence ball. Finally, numerical tests on some real-life problems, such as a Van der Waals equation of state, a conversion chemical engineering problem, and two standard academic test problems, are presented, which confirm the theoretical results established in this paper and the efficiency of this proposed iterative method. We observed from the numerical experiments that our proposed iterative methods have good values for convergence radii. Further, they not only have faster convergence towards the desired zero of the involved function but also have both smaller residual error and a smaller difference between two consecutive iterations than current existing techniques.

Description: Currently the renewable energies including wind power and photovoltaic power have been increasingly deployed in power system to achieve contamination free and environmental-friendly power production. However, due to the natural characteristics of wind and solar, both wind power and photovoltaic power contain uncertainty and randomness which may significantly impact the stability, security, and economic efficiency of the conventional power system mainly consisted by hydropower and thermal power. To deal with the issue, this paper presents a two-stage robust model which is able to achieve the optimal day-ahead dispatch strategy in the worst-case scenario of wind and photovoltaic outputs. Because of the strong interactions between the two stages, the original optimization has been decomposed into the day-ahead dispatch master problem and the additional adjustment subproblem considering the uncertainty and randomness of the wind and the photovoltaic outputs. Also, the piecewise linearization technique is employed to convert the original problem into a MILP problem. Afterward, the dualization of the additional adjustment subproblem can be obtained by using linear programming strong duality theory. Additionally, the Big-M method enables the linearization of the dual model. The interacted-iterations between the master problem and the subproblem are successfully implemented which can ultimately figure out the optimal day-ahead dispatch strategy of the power system with conventional and renewable energies.

Description: While specialized services continue to be developed in the mining industry, there has been a lack of corresponding risk management research in China. This study develops a risk assessment index framework of general contract for mine production and operational specialized service. From previous research, the risk factors for specialized mine production and operation services are identified and the index framework is set up. An index weight is established using Delphi and set-valued statistics-triangular fuzzy number method, and a fuzzy comprehensive evaluation method is applied to assess the mine production and operation risks. With the Jinjie Coal Mine of the Shendong Coal Group selected as a test case, this shows that safety risk, environmental risk, and economic risk belong to level II. Management risk, resources risk, contract risk, and technological risk belong to level I, and the total risk of Jinjie coal mine belongs to level II. The results of the case study indicated that the proposed prevention and control strategies offered a better guide for the risk management practices for the specialized services at Shendong Coal Group.

Description: In this paper, the mechanism governing the particle-fluid flow characters in the stepped pipeline is studied by the combined discrete element method (DEM) and computational fluid dynamics (CFD) model (CFD-DEM) and the two fluid model (TFM). The mechanisms governing the gas-solid flow in the horizontal stepped pipeline are investigated in terms of solid and gas velocity distributions, pressure drop, process performance, the gas-solid interaction forces, solid-solid interaction forces, and the solid-wall interaction forces. The two models successfully capture the key flow features in the stepped pipeline, such as the decrease of gas velocity, solid velocity, and pressure drop, during and after the passage of gas-solid flow through the stepped section. What is more important, the reason of the appearance of large size solid dune and pressure surge phenomena suffered in the stepped pipeline is investigated macroscopically and microscopically. The section in which the blockage problem most likely occurs in the stepped pipeline is confirmed. The pipe wall wearing problem, which is one of the most common and critical problems in pneumatic conveying system, is analysed and investigated in terms of interaction forces. It is shown that the most serious pipe wall wearing problem happened in the section which is just behind the stepped part.

Description: Sustainable procurement is an emerging theme in the construction industry across the globe. However, organizations in the construction industry often encounter impediments in improving environmental performance in construction projects, especially in procurement. Besides its other facets, procurement of construction equipment is inherited to be capital-intensive and vital for managing environmental concerns associated with built environment projects. In this regard, selection criteria in such procurement processes are generally supportive of considering cost and engineering specifications as key parameters. However, sustainability apprehensions in today’s Malaysian construction industry have mounted pressure on industry professionals to rethink their equipment acquisition strategies. The notion of green or sustainable procurement is still infancy for the Malaysian construction industry and facing challenges for embedding it in the current procurement practices. This research aims to address these apprehensions by considering six main criteria, namely, life cycle cost (LCC), performance (P), system capability (SC), operational convenience (OC), environmental impact (EI), and social benefits (SBs), and their 38 subcriteria towards procurement of sustainable construction equipment. A multicriteria-based equipment selection framework on the triple bottom line of sustainability in the context of the Malaysian construction industry has been developed and tested. The application of analytical hierarchy process (AHP) established the sustainable procurement index with a consistent sensitivity analysis results. As such, the proposed procurement index shall help decision-makers in the process of the acquisition of sustainable construction equipment in Malaysia.

Description: In this work, based on the Reynolds stress model (RSM) of the computational fluid dynamics (CFD) software Fluent and experimental method, the velocity field, pressure characteristics, split ratio, and separation efficiency of the hydrocyclone are analyzed under different gas-liquid ratios (GLRs). For the inlet velocity, the lower limit is ascertained by the flow field stability, the upper limit is largely determined by the energy consumption, and the optimum range is 4 m/s to 10 m/s. Within the optimum range, the peak value of tangential velocity increases while the GLR increases, whereas the pressure and pressure drop decrease. With the increase in the GLR, the axial velocity decreases, and the locus of zero vertical velocity shifts inward. The increase in the GLR causes more gas to collect at the vortex finder, which hinders the discharge of the solid-liquid mixture from the overflow, and the larger the GLR, the faster the decrease in the split ratio. The separation efficiency of particles with a particle size of 15 μm is increased by 6.75%, and the separation efficiency of particles with a particle size of 30 μm is increased by 0.57%. Meanwhile, the separation efficiency is increased by 2.43%, and the cut size is reduced as the GLR increases.

Description: In this paper, factors influencing the inclusion removal in high-manganese and high-aluminum steel in RH refining process were studied by numerical simulations, production practice, and metallographic experiments. A mathematical model for inclusion removal was established, and the phenomenon of inclusions mixing in RH up-leg region was verified due to fluid circulation. Removal efficiency of RH circulation time 120 s is much better than 600 s, and it was the lowest efficiency after 600 s. After 600 s circulation time, it shall not apply in production practice. The mass concentration of inclusions in practical steel was 11.64% probability error than values obtained by numerical simulation, because the numerical simulation did not consider the problem of inclusions adsorbing to the walls of refractory materials and corrosion of refractories. This work lays the foundation for the optimization and upgrading of process technology and establishes big data for full automation of RH out of furnace refining.

Description: In this paper, some fixed-point theorems are established for strongly subadditive maps on (where denotes the space of -valued continuous functions on a compact Hausdorff space and is a unital Banach algebra). Finally, the result is applied to prove the existence and uniqueness of a solution for a system of nonlinear integrodifferential equations.

Description: In order to solve the problem of how to choose a financing strategy for supply chain enterprises with financial constraints, including the manufacturer and the retailer, this paper puts forward two financing strategies in the bilateral supply chain with uncertain output and certain demand. The two financing strategies are the noncollaborative financing (finance from a bank separately (FBS)) and the collaborative financing (finance from a bank uniformly (FBU)). It derives the production order formula of the supply chain enterprises under financial constraints. Under the complete information, according to this formula, it analyzes how the bank prices the loan interest rates and finds the optimal decisions under the two financing strategies. The following results are found: (1) The manufacturer’s planned output is negatively correlated with the bank’s loan interest rate. The increased interest rates do not necessarily lead to the increased bank’s loan profits. (2) The bank’s loan profit is higher, when the supply chain enterprises choose the FBS strategy. (3) The FBU strategy does not necessarily make the profits of the manufacturer and the retailer better. It is affirmative only if some parameters in the supply chain meet certain conditions. The above-mentioned conclusions supply a policy guiding the supply chain enterprises with financial constraints to make a choice of the financing strategy.

Description: In this paper, a class of generalized Halanay inequalities is investigated. Under some assumptions, the generalized exponential stability and boundedness are discussed by means of integral inequalities. We do not require the boundedness of the time-varying delays and coefficients. In addition, our results improve some previous works. At last, three examples and simulations are given to illustrate the effectiveness of the main results.

Description: This paper takes a cross section of 6 × 7 + IWS wire rope as an example to complete the studies on a method of geometric modeling for no-joint wire rope and implementation of a 3D solid model. Frenet–Serret frame theory establishes three types of Frenet–Serret frame, which are double-helix winding straight line, single-helix winding arc, and double-helix winding arc, and it allows derivation of eight types of center line equation of wire. This paper has ascertained tangent condition of center lines of steel wires with the same name between arc and straight-line wire rope and closed condition of no-joint wire rope. It has been concluded that the length of steel wire must be an integer multiple of its lay pitch either in arc wire rope or in straight-line wire rope, and the name of wires in each wire rope must be consistent at the same time. Finally, using Pro/ENGINEER Wildfire 5.0 software generates a 3D solid model of no-joint wire rope.

Description: In the present study, the transport and deposition of solid particles to mitigate the loss circulation of fluid through a fracture transversely placed to a vertical channel is numerically investigated. These solid particles (commonly known in the industry as lost circulation materials—LCMs) are injected into the flow during the drilling operation in the petroleum industry, in hopes to control the fluid loss. The numerical simulation of the process follows a two-stage process: the first characterizes the lost circulation flow and the second the particle injection. The numerical model comprises an Eulerian–Lagrangian approach, in which the dense discrete phase model (DDPM) is combined with the discrete element method (DEM). A parametric analysis is done by varying the vertical channel Reynolds number, the particle-to-fluid density ratio, and the particle diameter. Results are shown in terms of the particle’s bed geometric characteristics, focusing on the location inside the fracture where the particles deposit, and the particle bed length, height, and time spent to fill the fracture. Also monitored are the fluid loss reduction over time and the fractured channel bottom pressure (which can be related to the fracture pressure). Results indicate that using a slow/intermediate flow velocity, associated with heavy particles with small diameters, provides the best combination for the efficient mitigation of the fluid loss process.