In multiview three-dimensional (3D) displays, crosstalk is one of the most annoying artefacts degrading the quality of the 3D image. In this paper, we present a system-introduced crosstalk measurement method and derive an improved crosstalk reduction method. The proposed measurement method is applied to measure the exact crosstalk among subpixels corresponding to different view images and the obtained results are very effective for crosstalk reduction method. Furthermore, an improved crosstalk reduction method is proposed to alleviate crosstalk by searching for the optimal integral intensity values of subpixels on the synthetic image. The derived algorithm based on modified Schnorr-Euchner strategy is implemented to seek the optimal solution to this box-constrained integer least squares (BILS) problem, such that the Euclidean distance between solution and its target decreases substantially. The method we develop is applicable to both multiview 3D parallax barrier displays and multiview 3D lenticular displays. Both simulation and experimental results indicate that the derived method is capable of improving 3D image quality more effectively than the existing method on multiview 3D displays.

Energy consumption has recently become a major concern to multiprocessor computing systems, of which the primary performance goal has traditionally been reducing execution time of applications. In the context of scheduling, there have been increasing research interests on algorithms using dynamic voltage scaling (DVS), which allows processors to operate at lower voltage supply levels at the expense of sacrificing processing speed, to acquire a satisfactory trade-off between quality of schedule and energy consumption. The problem considered in this paper is to find a schedule for a workflow, which is normally a precedence constrained application, on a bounded number of heterogeneous DVS-enabled processors, so as to minimize both makespan (overall execution time of the application) and energy consumption. A fast and efficient heuristic is proposed and evaluated using simulation with two real-world applications as well as randomly generated ones.

Inventory management is a sequential decision problem that can be solved with reinforcement learning (RL). Although RL in its conventional form does not require domain knowledge, exploiting such knowledge of problem structure, usually available in inventory management, can be beneficial to improving the learning quality and speed of RL. Ruminative reinforcement learning (RRL) has been introduced recently based on this approach. RRL is motivated by how humans contemplate the consequences of their actions in trying to learn how to make a better decision. This study further investigates the issues of RRL and proposes new RRL methods applied to inventory management. Our investigation provides insight into different RRL characteristics, and our experimental results show the viability of the new methods.

Steel catenary riser (SCR) is a cost-effective riser system that is widely used in deepwater offshore oilfields development. During SCR J-lay installation, the movement of pull-head must be carefully controlled to ensure riser safety. Since the SCR installation path calculation through numerical simulation software is usually time-consuming, this paper has established a mechanical model for SCR installation by making use of homotopy analysis method (HAM) to simplify its analytical solution, and dimensional analysis was considered in making initial guess solution. Based on this analytical solution, a program within the framework of MATLAB was developed to predict the two-dimensional riser behavior during installation, and a sensitivity analysis for different values of the control variables was carried out. Engineers may efficiently optimize the installation path by the application of this technique.

This paper addresses a scheduling problem in an actual industrial environment of a baking industry where production rates have been growing every year and the need for optimized planning becomes increasingly important in order to address all the features presented by the problem. This problem contains relevant aspects of production, such as parallel production, setup time, batch production, and delivery date. We will also consider several aspects pertaining to transportation, such as the transportation capacity with different vehicles and sales production with several customers. This approach studies an atypical problem compared to those that have already been studied in literature. In order to solve the problem, we suggest two approaches: using the greedy heuristic and the genetic algorithm, which will be compared to small problems with the optimal solution solved as an integer linear programming problem, and we will present results for a real example compared with its upper bounds. The work provides us with a new mathematical formulation of scheduling problem that is not based on traveling salesman problem. It considers delivery date and the profit maximization and not the makespan minimization. And it also provides an analysis of the algorithms runtime.

Passive liquid dampers have been used to effectively reduce the dynamic response of civil infrastructures subjected to earthquakes or strong winds. The design of liquid dampers for structural vibration control involves the determination of the optimal parameters. This paper presents an optimal design methodology for tuned liquid column dampers (TLCDs) based on the H_∞ control theory. A practical structure, Dalian Xinghai Financial Business Building, is used to illustrate the feasibility of the optimal procedure. The model of structure is built by the finite element method and simplified to the lumped mass model. To facilitate the design of TLCDs, the TLCD parametric optimization problem is transferred to the feedback controller design problem. Through the bounded real lemma, an optimization problem with bilinear matrix inequality (BMI) constraints is constructed to design a static output feedback H_∞ controller. Iterative linear matrix inequality method is employed and it added some value range constraints to solve the BMI problem. After the TLCD parameters are optimized, the responses of displacement and acceleration in frequency domain and time domain are compared for the structure with and without TLCD. It is validated that the TLCD with the optimized parameters can make the structure satisfy the need for safety and comfort.

A novel power and rate control system model for wireless communication networks is presented, which includes uncertainties, input constraints, and time-varying delays in both state and control input. A robust delay-dependent model predictive power and rate control method is proposed, and the state feedback control law is obtained by solving an optimization problem that is derived by using linear matrix inequality (LMI) techniques. Simulation results are given to illustrate the effectiveness of the proposed method.

During the development of water drive gas reservoirs, the phenomena of gas escaping from water and water separating out from gas will change the seepage characteristics of formation fluid. Therefore, the traditional gas-water two-phase inflow performance relationship (IPR) models are not suitable for calculating the water producing gas well inflow performance relationship in water drive gas reservoirs. Based on the basic theory of fluid mechanics in porous medium, using the principle of mass conservation, and considering the process of dissolution and volatilization of gas and water formation, this paper establishes a new mathematical model of gas-water two-phase flow. Multiobjective optimization method is used to automatically match the sample well production data in water drive gas reservoirs and then we can achieve the sample well’s productivity equation, relative permeability curve, water influx intensity, and single well controlled reserves. In addition, the influence of different production gas water ratios (GWR) and gas-soluble water coefficients on absolute open flow rate ($q_{\text{A}\text{O}\text{F}}$) is discussed. This method remedied the limitation of well testing on site and was considered to be a new way to analyze the production behaviors in water producing gas well.

It can be deduced from previous studies that there exists a research gap in assembly line sequencing optimization model for mixed-model production lines. In particular, there is a lack of studies which focus on the integration between job shop and assembly lines using fuzzy techniques. Hence, this paper is aimed at addressing the multiobjective mixed-model assembly line sequencing problem by integrating job shop and assembly production lines for factories with modular layouts. The primary goal is to minimize the make-span, setup time, and cost simultaneously in mixed-model assembly lines. Such conflicting goals arise when switching between different products. A genetic algorithm (GA) approach is used to solve this problem, in which trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data.

This study provides a dynamic model and analyzes its process that may plunge the business ecosystem into ToC (the Tragedy of the Commons). When developing the model, we have in mind some industries where the marketing competition to secure a large installed base is intense. The social commerce industry is a representative example of this type of industries, but the scope of this study is not limited to the industry. We first introduce a previous study focusing on the static Nash equilibrium, and then present an extended version of the basic model in a dynamic perspective. According to our analyses on the dynamic equilibria together with their stability, there may be a unique interior equilibrium, but it is highly likely unstable. In addition, possible (near) boundary equilibria are also unstable for a wide range of parameter values. We also conduct some numerical experiments and discover cycles as solutions to some particular instances. Since those cycles contain the ToC traps, a policy measure or regulation may need to be employed. Our approach and results will help to figure out a clue to escape from the ToC trap, thereby shedding new light on the sustainable growth of the business ecosystem, which is prone to excessive marketing competition.

Classification is an important theme in data mining. Rough sets and neural networks are the most common techniques applied in data mining problems. In order to extract useful knowledge and classify ambiguous patterns effectively, this paper presented a hybrid algorithm based on the integration of rough sets and BP neural network to construct a novel classification system. The attribution values were discretized through PSO algorithm firstly to establish a decision table. The attribution reduction algorithm and rules extraction method based on rough sets were proposed, and the flowchart of proposed approach was designed. Finally, a prototype system was developed and some simulation examples were carried out. Simulation results indicated that the proposed approach was feasible and accurate and was outperforming others.

A method with easy operation procedure and simple calibration condition is presented in this paper to solve the base frame calibration problem for cooperative robots. It is carried out through constructing a series of handclasp configurations and recording coordinates of the contact points, respectively, in base frame of each robot. Then the rotation matrix and translation matrix between base frame of cooperative robots can be calculated which is just the calibration result for cooperative robots. Based on typical installation mode for industrial robot, the floor mounted, wall mounted and ceiling mounted, constraints between base frames of these robots are further explored. These constraints are used to improve the calibration results for base frame calibration problem. In order to validate the correctness and effectiveness of our method, experiments on two industrial robots (Motoman VA1400 and HP20) are carried out at the end of the paper. The calibration errors are less than 8 mm in most cases, which satisfies the requirement of positioning accuracy for most industrial process, such as arc welding, transporting, and cutting. These experiment results assert the correctness of our method which can be used effectively to solve the base frame calibration problem for cooperative robots in manufacturing process.

A set of designed experiments, involving the use of a pulsed Nd:YAG laser system milling 316L Stainless Steel, serve to study the laser-milling process of microcavities in the manufacture of drug-eluting stents (DES). Diameter, depth, and volume error are considered to be optimized as functions of the process parameters, which include laser intensity, pulse frequency, and scanning speed. Two different DES shapes are studied that combine semispheres and cylinders. Process inputs and outputs are defined by considering the process parameters that can be changed under industrial conditions and the industrial requirements of this manufacturing process. In total, 162 different conditions are tested in a process that is modeled with the following state-of-the-art data-mining regression techniques: Support Vector Regression, Ensembles, Artificial Neural Networks, Linear Regression, and Nearest Neighbor Regression. Ensemble regression emerged as the most suitable technique for studying this industrial problem. Specifically, Iterated Bagging ensembles with unpruned model trees outperformed the other methods in the tests. This method can predict the geometrical dimensions of the machined microcavities with relative errors related to the main average value in the range of 3 to 23%, which are considered very accurate predictions, in view of the characteristics of this innovative industrial task.

The lightweight design of the outer tie rod installed on an electrical vehicle was achieved through material selection and optimization technique. The aluminum alloy Al6082M was selected as a steel-substitute, and its structural shape was optimized by applying metamodel-based optimization. In this process, finite element analysis was performed to predict the structural responses, such as buckling resistance and fatigue life. First, for an arbitrary base design made of steel, the structural responses were calculated. Then, the design variables were defined to find a lightweight design made of Al6082M. Secondly, metamodel-based optimization based on the kriging interpolation method was applied, leading to determination of an optimum design. The suggested optimum design has the minimum weight satisfying the critical design requirement. Finally, the numerical results of the buckling resistance and fatigue life were validated, through bucking and fatigue tests.

Thousands of victims and millions of affected people are hurt by natural disasters every year. Therefore, it is essential to prepare proper response programs that consider early activities of disaster management. In this paper, a multiobjective model for distribution centers which are located and allocated periodically to the damaged areas in order to distribute relief commodities is offered. The main objectives of this model are minimizing the total costs and maximizing the least rate of the satisfaction in the sense of being fair while distributing the items. The model simultaneously determines the location of relief distribution centers and the allocation of affected areas to relief distribution centers. Furthermore, an efficient solution approach based on genetic algorithm has been developed in order to solve the proposed mathematical model. The results of genetic algorithm are compared with the results provided by simulated annealing algorithm and LINGO software. The computational results show that the proposed genetic algorithm provides relatively good solutions in a reasonable time.

Beam pumping system which is widely used in petroleum enterprises of China is one of the most energy-consuming equipment. It is difficult to be modeled and optimized due to its complication and nonlinearity. To address this issue, a novel intelligent computing based method is proposed in this paper. It firstly employs the general regression neural network (GRNN) algorithm to obtain the best model of the beam pumping system, and secondly searches the optimal operation parameters with improved strength Pareto evolutionary algorithm (SPEA2). The inputs of GRNN include the number of punching, the maximum load, the minimum load, the effective stroke, and the computational pump efficiency, while the outputs are the electric power consumption and the oil yield. Experimental results show that there is good overlap between model estimations and unseen data. Then sixty-one sets of optimum parameters are found based on the obtained model. Also, the results show that, under the optimum parameters, more than 5.34% oil yield is obtained and more than 3.75% of electric power consumption is saved.

The large adoption of electric vehicles (EVs), hybrid renewable energy systems (HRESs), and the increasing of the loads shall bring significant challenges to the microgrid. The methodology to model microgrid with high EVs and HRESs penetrations is the key to EVs adoption assessment and optimized HRESs deployment. However, considering the complex interactions of the microgrid containing massive EVs and HRESs, any previous single modelling approaches are insufficient. Therefore in this paper, the methodology named Hierarchical Agent-based Integrated Modelling Approach (HAIMA) is proposed. With the effective integration of the agent-based modelling with other advanced modelling approaches, the proposed approach theoretically contributes to a new microgrid model hierarchically constituted by microgrid management layer, component layer, and event layer. Then the HAIMA further links the key parameters and interconnects them to achieve the interactions of the whole model. Furthermore, HAIMA practically contributes to a comprehensive microgrid operation system, through which the assessment of the proposed model and the impact of the EVs adoption are achieved. Simulations show that the proposed HAIMA methodology will be beneficial for the microgrid study and EV’s operation assessment and shall be further utilized for the energy management, electricity consumption prediction, the EV scheduling control, and HRES deployment optimization.

This paper solves the dynamic traveling salesman problem (DTSP) using dynamic Gaussian Process Regression (DGPR) method. The problem of varying correlation tour is alleviated by the nonstationary covariance function interleaved with DGPR to generate a predictive distribution for DTSP tour. This approach is conjoined with Nearest Neighbor (NN) method and the iterated local search to track dynamic optima. Experimental results were obtained on DTSP instances. The comparisons were performed with Genetic Algorithm and Simulated Annealing. The proposed approach demonstrates superiority in finding good traveling salesman problem (TSP) tour and less computational time in nonstationary conditions.

Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature) were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN) and false positive (FP) rates.

Nitrogen replacement is a key process for natural gas pipeline before it is put into operation. A computational fluid dynamic model coupled to a species-transportation model has been used to investigate the gas mixture length of nitrogen replacement in large-diameter pipeline without isolator. A series of numerical simulations are performed over a range of conditions, including pipe length and diameter, inlet rate, and inclination angle of pipe. These affecting factors are analyzed in detail in terms of volume fraction of nitrogen, the maximum gas mixture length, and gas mixture length varied with time. Gas mixture length increases over time, and the maximum gas mixture length is present at outlet of pipe. Long and large-diameter pipe and fast speed of nitrogen lead to long length of mixed gas, while large inclination angle of pipe brings about short length. Several fitting formulas have been obtained, which can predict the maximum gas mixture length in gas pipelines. The used method of fitting formula is shown in the paper by examples. The results provide effective guidance for practical operation of nitrogen replacement.

The paper addresses the problem of track-to-track association in the presence of sensor biases. In some challenging scenarios, it may be infeasible to implement bias estimation and compensation in time due to the computational intractability or weak observability about sensor biases. In this paper, we introduce the structural feature for each local track, which describes the spatial relationship with its neighboring targets. Although the absolute coordinates of local tracks from the same target are severely different in the presence of sensor biases, their structural features may be similar. As a result, instead of using the absolute kinematic states only, we employee the structural similarity to define the association cost. When there are missed detections, the structural similarity between local tracks is evaluated by solving another 2D assignment subproblem. Simulation results demonstrated the power of the proposed approach.

The numerical solution of optimal control problems by direct collocation is a widely used approach. Quasi-Newton approximations of the Hessian of the Lagrangian of the resulting nonlinear program are also common practice. We illustrate that the transcribed problem is separable with respect to the primal variables and propose the application of dense quasi-Newton updates to the small diagonal blocks of the Hessian. This approach resolves memory limitations, preserves the correct sparsity pattern, and generates more accurate curvature information. The effectiveness of this improvement when applied to engineering problems is demonstrated. As an example, the fuel-optimal and emission-constrained control of a turbocharged diesel engine is considered. First results indicate a significantly faster convergence of the nonlinear program solver when the method proposed is used instead of the standard quasi-Newton approximation.

This review provides an overview of the queueing modeling issues and the related performance evaluation and optimization approaches framed in a joined manufacturing and product engineering. Such networks are represented as queueing networks. The performance of the queueing networks is evaluated using an advanced queueing network analyzer: the generalized expansion method. Secondly, different model approaches are described and optimized with regard to the key parameters in the network (e.g., buffer and server sizes, service rates, and so on).

The inspection of inhomogeneous transverse and longitudinal wall thicknesses, which determines the quality of reducing pipe during the production of seamless steel reducing pipe, is lags and difficult to establish its mechanism model. Aiming at the problems, we proposed the quality prediction model of reducing pipe based on EOS-ELM-RPLS algorithm, which taking into account the production characteristics of its time-varying, nonlinearity, rapid intermission, and data echelon distribution. Key contents such as analysis of data time interval, solving of mean value, establishment of regression model, and model online prediction were introduced and the established prediction model was used in the quality prediction and iteration control of reducing pipe. It is shown through experiment and simulation that the prediction and iteration control method based on EOS-ELM-RPLS model can effectively improve the quality of steel reducing pipe, and, moreover, its maintenance cost was low and it has good characteristics of real time, reliability, and high accuracy.

This paper considers an airline overbooking problem of a new single-leg flight with discount fare. Due to the absence of historical data of no-shows for a new flight, and various uncertain human behaviors or unexpected events which causes that a few passengers cannot board their aircraft on time, we fail to obtain the probability distribution of no-shows. In this case, the airlines have to invite some domain experts to provide belief degree of no-shows to estimate its distribution. However, human beings often overestimate unlikely events, which makes the variance of belief degree much greater than that of the frequency. If we still regard the belief degree as a subjective probability, the derived results will exceed our expectations. In order to deal with this uncertainty, the number of no-shows of new flight is assumed to be an uncertain variable in this paper. Given the chance constraint of social reputation, an overbooking model with discount fares is developed to maximize the profit rate based on uncertain programming theory. Finally, the analytic expression of the optimal booking limit is obtained through a numerical example, and the results of sensitivity analysis indicate that the optimal booking limit is affected by flight capacity, discount, confidence level, and parameters of the uncertainty distribution significantly.

This paper studies an inventory model for Weibull-distributed deterioration items with trapezoidal type demand rate, in which shortages are allowed and partially backlogging depends on the waiting time for the next replenishment. The inventory models starting with no shortage is are to be discussed, and an optimal inventory replenishment policy of the model is proposed. Finally, numerical examples are provided to illustrate the theoretical results, and a sensitivity analysis of the major parameters with respect to the optimal solution is also carried out.

A modern and civilized society is so much dependent on the use of electrical energy because it has been the most powerful vehicle for facilitating economic, industrial, and social developments. Electrical energy produced at power stations is transmitted to load centres from where it is distributed to its consumers through the use of transmission lines run from one place to another. As a result of the physical properties of the transmission medium, some of the transmitted power is lost to the surroundings. The overall effect of power losses on the system is a reduction in the quantity of power available to the consumers. An accurate knowledge of transmission losses is hinged on the ability to correctly predict the available current and voltage along transmission lines. Therefore, mathematical physics expressions depicting the evolution of current and voltage on a typical transmission line were formulated, and derived therefrom were models to predict available current and voltage, respectively, at any point on the transmission line. The predictive models evolved as explicit expressions of the space variable and they are in close agreement with empirical data and reality.

This paper examines an alternative approach to the centralized resource allocation model that indicates that all the units are under the control of an entity of the centralized decision maker. The proposed approach is a technique for connecting the two basic Radial CRA-BCC and Nonradial CRA-SBM models in an integrated structure called connected CRA-SBM. In the proposed model, exchanging the two parameter amounts can change the location of the analysis between the CRA-BCC and the CRA-SBM models and deal with the weaknesses inherent in such models. By remedying all the weaknesses in one model, the entire units are simply projected on the frontier line and one can obtain suitable benchmarks for each of them. In the offered model, all of the inputs and outputs, respectively, decrease and increase simultaneously. Lastly, numerical examples emphasize the significance of the offered method.

This paper studies the optimal hub routing problem of merged tasks in collaborative transportation. This problem allows all carriers’ transportation tasks to reach the destinations optionally passing through 0, 1, or 2 hubs within limited distance, while a cost discount on arcs in the hub route could be acquired after paying fixed charges. The problem arises in the application of logistics, postal services, airline transportation, and so forth. We formulate the problem as a mixed-integer programming model, and provide two heuristic approaches, respectively, based on Lagrangian relaxation and Benders decomposition. Computational experiments show that the algorithms work well.

Train operation is a complex nonlinear process; it is difficult to establish accurate mathematical model. In this paper, we design ATO speed controller based on the input and output data of the train operation. The method combines multimodeling with predictive functional control according to complicated nonlinear characteristics of the train operation. Firstly, we cluster the data sample by using fuzzy-c means algorithm. Secondly, we identify parameter of cluster model by using recursive least square algorithm with forgetting factor and then establish the local set of models of the process of train operation. Then at each sample time, we can obtain the global predictive model about the system based on the weighted indicators by designing a kind of weighting algorithm with error compensation. Thus, the predictive functional controller is designed to control the speed of the train. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm.

In offshore oil and gas engineering the pipeline abandonment and recovery is unavoidable and its mechanical analysis is necessary and important. For this problem a third-order differential equation is used as the governing equation in this paper, rather than the traditional second-order one. The mathematical model of pipeline abandonment and recovery is a moving boundary value problem, which means that it is hard to determine the length of the suspended pipeline segment. A novel technique for the handling of the moving boundary condition is proposed, which can tackle the moving boundary condition without contact analysis. Based on a traditional numerical method, the problem is solved directly by the proposed technique. The results of the presented method are in good agreement with the results of the traditional finite element method coupled with contact analysis. Finally, an approximate formula for quick calculation of the suspended pipeline length is proposed based on Buckingham’s Pi-theorem and mathematical fitting.

In the paper industry, numerous studies have explored means of optimizing order allocation and cutting trim loss. However, enterprises may not adopt the resulting solutions because some widths of the inventory exceed or are less than those required for acceptable scheduling. To ensure that the results better suit the actual requirements, we present a new decision model based on the adjustment of scheduling and limitation of inventory quantity to differentiate trim loss and inventory distribution data. Differential analysis is used to reduce data filtering and the information is valuable for decision making. A numerical example is presented to illustrate the applicability of the proposed method. The results show that our proposed method outperforms the manual method regarding scheduling quantity and trim loss.

The research question is whether the positive relationship found between supplier involvement practices and new product development performances in developed economies also holds in emerging economies. The role of supplier involvement practices in new product development performance is yet to be substantially investigated in the emerging economies (other than China). This premise was examined by distributing a survey instrument (Jayaram’s (2008) published survey instrument that has been utilised in developed economies) to Malaysian manufacturing companies. To gauge the relationship between the supplier involvement practices and new product development (NPD) project performance of 146 companies, structural equation modelling was adopted. Our findings prove that supplier involvement practices have a significant positive impact on NPD project performance in an emerging economy with respect to quality objectives, design objectives, cost objectives, and “time-to-market” objectives. Further analysis using the Bayesian Markov Chain Monte Carlo algorithm, yielding a more credible and feasible differentiation, confirmed these results (even in the case of an emerging economy) and indicated that these practices have a 28% impact on variance of NPD project performance. This considerable effect implies that supplier involvement is a must have, although further research is needed to identify the contingencies for its practices.