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Multi-Input and Output Radar System (MIMO) is a new type of radar system that has been the subject of much research in recent years due to its many advantages. Meanwhile, the issue of target tracking in MIMO radar systems is of great importance, and providing an efficient solution for it remains an unresolved issue. In this paper, target tracking in MIMO radar systems using optimizing Kalman interactive filter is investigated. The proposed method in this research is the MIMO radar system for the simultaneous tracking of multiple targets. In the assumed system model, the interactive multiple model (IMM) based on Extended Kalman Filter (EKF) is used to understand the effective tracking of the target. Also, the optimization of the multi-objective tracking model in MIMO radar systems has been done by the proposed method using the particle swarm optimization (PSO) algorithm. This optimization algorithm estimates the fit of each studied response based on the number of energy resources and time consumed. The efficiency of the proposed method in a simulated environment has been evaluated and its performance in tracking multiple targets has been investigated in terms of different criteria. Based on the results of these experiments, the proposed method, in addition to reducing the amount of tracking error, can be effective in reducing energy consumption and reducing the sampling period. Thus, the proposed method will reduce resource consumption in the multi-objective tracking system. [ABSTRACT FROM AUTHOR]

Purpose: One of the topics for manufacturers is to discuss the diversity of customer tastes. To manage this situation with the least change in products, multiple assembly lines make the necessary flexibility to produce the products. In multi-model assembly lines, different product types in different batches are produced and there is a setup time to prepare assembly lines between two types of products to produce another product type. This paper aims to investigate multi-model assembly lines and their sequencing, balancing, and worker assignment due to the existence of various tasks for workers according to learning and disremembering effects. Frequent changes in the product design of multi-model assembly lines according to customer demands can reduce the learning effect of workers and increase task times, while in another view, repeating tasks, particularly for products with more demands can increase the learning effect and reduce the task times. Therefore, in this study, the effects of workers' learning and disremembering multi-model assembly line balancing, sequencing, and worker assignment are investigated to minimize the number of workstations for a given cycle time not only to cover the different tastes of customers, but also indirectly minimize the costs of building stations, hiring, and employing manpower. Design/methodology/approach: In this paper, as an innovation, a mixed-integer mathematical model for multi-model assembly line balancing, sequencing, and worker assignment with different workers' skill levels and learning and disremembering rates has been developed to minimize the number of stations. Based on the nature of the multi-model, random demand for each product has been considered. After mathematical modeling, different small-sized problems have been solved by the GAMS software. Results and sensitivity analysis underlined the validity of the proposed model. Since this problem is typically NP-hard, GAMS software cannot solve medium and large-sized problems in a reasonable time. Therefore, the Krill herd optimization and Particle Swarm Optimization (PSO) algorithms have been used for medium and large-sized problems, which have not been used earlier in similar cases. The Krill herd optimization algorithm has been used as the proposed algorithm and PSO has been used as a competing algorithm. The parameters of both algorithms have been adjusted by the Taguchi method, and the best level has been selected for each parameter. Findings: 12 test problems were solved with different sizes. Results indicated that only five GAMS problems could reach the optimal solution. For better comparison of the Krill herd optimization and the particle swarm optimization algorithm, each test problem was run 30 times and minimum, maximum, and average objective function and their running times were reported. The results indicated that the objective function of both metaheuristic algorithms was the same but the Krill herd optimization algorithm can achieve optimal or near-optimal answers in less time than GAMS and the PSO algorithm declared the efficiency of the proposed algorithm in solving these problems. Research limitations/implications: One of the limitations in this research was the lack of cooperation of factories whose assembly lines were similar to the problem considered in this study, and in this regard, the real-world data was not accessible. Therefore, the standard test problems were used that existed in the famous database of assembly line balancing problems. Since the problem in this paper was new, some other required data, and different examples in different ways needed to be considered, randomly. Another limitation of using this research in a real-world situation was the challenge of exact determination of learning and disremembering rate of each worker which can be solved by using experts in the field of assessment and training. Originality/value: In this paper, a mathematical model was developed for multi-model assembly line balancing, sequencing, and worker assignment according to the learning and disremembering effect. Since the problem was NP-Hard, as well as GAMS software, two metaheuristic algorithms were applied for a similar problem, and their efficiency was compared with each other. The two-mentioned algorithms have not been used in previous studies. Both academic researchers and production managers can benefit from applying the findings of this study. [ABSTRACT FROM AUTHOR]

The fundamental function of a cellular manufacturing system (CMS) is based on the definition and recognition of the type of similarity among the parts that should be produced in a planning period. Cell formation (CF) and cell/machine layouts are two major steps in implementing the CMS design. This paper aims to propose a new mathematical nonlinear programming model for cell formation that employs the rectilinear distance concept to determine layout in a continuous space. In the proposed model, the benefits of cellular layout consideration are used, and the objective function computes the cost of cell reconfiguration and the costs of intra-cell and inter-cell material handling movements. Due to its problem complexity, a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm are proposed to solve the problem. To address the efficiency of the linearized model and solution methods, the production information of a real case study is used and 30 test problems in different dimensions are presented. Design/methodology/approach: In this paper, a mathematical programming model of cell formation and cell layout has been proposed in a continuous space, using the concept of rectilinear distance. In the proposed model, production information similar to the production flow between machines, alternative process routing, cells capacity, and the inter-cell and intra-cell transportation costs has been considered. Due to the nature and complexity of the proposed model, two metaheuristic algorithms, i.e., GA and PSO have been also developed for larger problems. Findings: In this paper a real case study in the BATA company was studied and the result of configuration was illustrated. By computing the efficiency of the linearized model and solution methods, the production information of a real case study was used and 30 test problems in different dimensions were presented. Findings highlighted the high efficiency of the genetic algorithm in solving large-scale problems. Research limitations/implications: Issues such as assuming similar dimensions for machines or their constant availability are considered as the limitations of this study. For future research, the following subjects can be attractive and the present study can provide the necessary background for researchers who seek to work on such subjects: - Considering unequal dimensions for machines; in the proposed model, machines were considered as squares of equal area with unit dimension. To obtain a more appropriate schema from the space of a job shop, machines’ dimensions can be assumed as the input parameters. - Developing probabilistic models and fuzzy model factors (e.g., available machines, operation time, costs, transportation time, and demand for each part) can be considered fuzzy or probabilistic. - To make the model more realistic, production data such as setup times and holding inventory between periods can be incorporated. Also, the proposed model can be integrated with the scheduling problem. Practical implications: CMS, which is the most important application of GT, overcomes the inefficiency of traditional approaches by reducing transportation time and distance. A flow shop layout has high efficiency in a mass production system, while a job shop is a very flexible system for producing various parts. Each of these systems does not have any other benefits. The CMS is an approach between the two manufacturing systems and aims to improve flexibility and efficiency to produce manufacturing groups in different sizes. In a CMS, machines and parts assignment to cells must impose a minimum cost on the system. Due to the practical nature of the proposed mathematical model, a real case was studied in which, the production information of the BATA company was used. Originality/value: In this study, a new non-linear mixed-integer programming model was proposed which considered the simultaneous cell formation and intra-cell and inter-cell layouts in a continuous space. The model aimed to determine the cell formation and the intra and inter-cell layout concurrently in a way that the total transportation cost of parts and the reconfiguration cost of cells were minimized. The proposed model attempted at calculating the material handling costs realistically. The material handling cost was computed based on the actual location of machines and cells on the shop floor regarding the dimensions of equal-sized machines. Handling both intra/intercellular materials using batch sizes for transferring parts was taken into account in calculating the transportation cost. The transportation cost was calculated based on the rectilinear travel distance, according to the center-to-center interval among machines. [ABSTRACT FROM AUTHOR]

This paper presents a control method based on model predictive control (MPC) to control the buck-type dynamic capacitor (DCAP) to improve power quality. In this method, the reference current consists of two distinct parts, i.e., reactive power compensator (RPC), and hai'monic current eliminator (HCE) and based on the load current and dynamically, it adapts itself to load changes. The proposed prediction model (PM) was extracted from the linearized differential equations of the DCAP. In the proposed method, vector decision variables in each cycle converter switching are updated only once during the prediction horizon, and the optimization problem related to predictive control is solved based on the particle swarm optimization (PSO) algorithm. This reduces the volume of calculations significantly. By considering the total harmonic distribution (THD) value and the power factor (PF) value, the prediction horizon is proposed. The proposed method, unlike the EHM method, has fewer control parameters and can be implemented more easily. The simulation results in MATLAB software showed the superiority of the proposed method in comparison with the EHM method on a single phase DCAP. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to design and regulate the Adaptive Neural Fuzzy Inference Systems (ANFIS) of supplier development, determine the importance of different categories of supplier development (SD), and suggest appropriate activities based on the results for the development of suppliers in the automotive industry. Design/methodology/approach: To design ANFIS, Grid Partitioning, Subtractive Clustering, and FCM have been used. Then, they have been regulated using Back Propagation (BP), Hybrid, Ant Colony Optimization (ACO), Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) methods. A five-step procedure has been used for sensitivity analysis of the supplier development categories. By comparing the results of the regulated systems, the most appropriate ones were selected. Also, based on the supplier development score in different categories separately, the supplier development score was predicted for 53 strategic suppliers in the automotive industry. By sensitivity analysis, SD-related categories were prioritized to guide automotive industry manufacturers to use SD-related activities. Findings: Findings indicated that the FCM compared to the other two methods; meta-heuristic regulation methods compared to BP and Hybrid, and ACO, DE, and GA compared to PSO led to better results. The ACO in all systems, the DE in four systems, and the GA in two categories were identified as the dominant methods, while the PSO was not dominant in any of the categories. This finding implies the priority of meta-heuristic algorithms as ACO> DE> GA> PSO, based on the data of this study. The results of the correlation between the scores of the ANFIS and the average scores of the experts show that the designed ANFIS has high accuracy. Practical implications: The results of this study will direct manufacturers' investments and direct involvement in SD. The findings encourage manufacturers and suppliers of the Iranian automotive industry first to activities related to the development of environmental capabilities and then activities related to the development of three other categories. The managers of Iran's automotive industry are suggested to apply the activities related to the development of suppliers' environmental capabilities. These activities include evaluating the supplier's environmental performance and feedback, sharing environmental information, ethics and social responsibility, obtaining environmental and social certifications, developing programs to improve the quality of life of target communities, green procurement, and environmental awareness, logistics activities Inversion, and joint efforts to improve performance are sustainable. Originality/value: This study was one of the first in-house studies to compare the results of metaheuristic algorithms compatible with ANFIS in the field of SD. In addition, in terms of implementation, it offered suitable SD activities to car manufacturers. [ABSTRACT FROM AUTHOR]

One of the most important parts of infrared search and tracking systems is image processing, which detects the targets in infrared images. In this paper, a new infrared small target detection algorithm is proposed. The proposed method uses heuristic optimization algorithms to find small targets in infrared images. In this way, the particle swarm optimization algorithm is used as one of the best heuristic optimization algorithms. The performance of the proposed algorithm is evaluated using real and simulated infrared images. These images include a variety of false response sources like high-intensity edges, cloudy background, complex sea-sky background, and a target close to high-intensity background clutter. The experimental results are compared with the four common methods of infrared small target detection. The experimental results show the effectiveness and performance of the proposed algorithm. Also, the runtime of the proposed method is comparable to other commonly used methods, and this makes it usable in real-time programs. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid model for increasing the precision of the support vector machine classifier is proposed to detect stator windings short circuit fault detection in induction motors. The proposed method consists of three different phases, wherein the first phase the statistical features of a healthy and defective data set are extracted. The principal component analysis is used to reduce the dimensions of the obtained features. Then, different SVMs are constructed based on training data sets. To achieve a better result, the parameters of the SVM are determined by the fractionalorder chaotic particle swarm optimization algorithm. Finally, a hybrid model for combining SVMs with type-2 Fuzzy logic is implemented. The proposed approach is then applied on measured stator current data for stator winding short circuit fault detection in a three-phase induction motor with 2.2kW, 50Hz, 6 Pole. The average accuracy of 98.4% of the detection of stator winding error on laboratory data under different load conditions indicates the performance and validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

One of the most important geophysical problems in exploration of mineral deposits is to estimate the depth of buried structure using observed gravity data. In this paper, we are trying to estimate mass anomaly depth by using one of the intelligence methods as Particle Swarm Optimization (PSO) with simple shapes as sphere, horizontal and vertical cylinder. In this modeling, two parameters of depth (z) and shape factor (q) were considered as particles and the maximum and minimum depth were used as the prior information. The method is tested for synthetic models with random noise. The method gives precise results for synthetic models contaminated with random noise which is quite acceptable and promising. This technique was also successfully applied to real data for mineral exploration. The applied real data belongs to an area with hilly topography located in the Fars province close to the Abadeh city where the barite deposit is under exploration. The method is used for a profile of real data that is provided from the residual anomalies and passed from the main detected positive anomaly in the area. The estimated depth from this method was 8.15 m which was in good agreement with the results obtained through Euler method and also outcrop-scale observations. [ABSTRACT FROM AUTHOR]

Electroencephalogram signals (EEGs) show the electrical activity of brain neurons. EEG is a non-invasive method that can be used to detect abnormal brain activities. Seizure is one of these abnormal activities and is the most common manifestation of epilepsy. Spikes are the most important characteristic of the seizure prone EEG signals. By detecting spikes, it is possible to detect epileptic seizures from EEG signals. EEG signals are non-stationary signals, so the wavelet transform that has appropriate time and frequency resolution can be a good option for extracting features of EEG signals. In this paper, after the extraction process using wavelet transform, artificial neural networks (ANNs) are used to classify healthy and epileptic signals. Particle swarm optimization (PSO) is also used as a novel approach to select weights and biases of network to improve network performance. The results of the implementation of the proposed algorithm have a 96.2% accuracy, which shows acceptable performance compared to existing methods. [ABSTRACT FROM AUTHOR]

A water distribution network (WDN) may not be able to satisfy all required demands when it’s in the pressure deficit mode or under over-loaded demand conditions. Analysis of the network in this mode requires pressure dependent analysis (PDA). Unlike demand driven analysis (DDA), PDA needs an extra equation for every node to relate the nodal demand and the nodal pressure; so it should be solved with the other network’s equations simultaneously. In this paper, based on the Particle Swarm Optimization (PSO) algorithm, a decision support system has been developed by using MATLAB and EPANET for PDA simulation in WDNs. A four-loop network selected from the literature was analyzed using different scenarios and different pressure dependent functions presented by the previous investigations. The results showed that the proposed model (PSO-PDA) was as accurate as the previous ones and provided better convergence. The results of the nodes’ pressure and discharge also indicated minor differences obtained by different PDA functions. However, the differences between the results of PDA and DDA were considerable. [ABSTRACT FROM AUTHOR]

The use of the electric energy stored in batteries of electric vehicles connected to the grid (V2G) will play a significant role in the development of distribution systems in the future. Electric vehicles (EVs) are able to charge during base load hours and inject energy into the grid during peak hours. Besides, the grid-connected EVs increase the system reliability under the outages. In this paper, Particle Swarm Optimization (PSO) algorithm is employed to solve the multi-objective problem of development scheduling of electric vehicles in the distribution network. ε-constraint method is employed to solve the proposed multi-objective problem. Besides, a fuzzy decision making approach is employed to determine the most compromise solution among the Pareto solutions obtained by the solving the sub-problems generated by the ε-constraint method. Decision variables include the location, and charge and discharge capacity of the smart parking lots of the EVs. IEEE 54-bus distribution test system is employed as the studied test network. Operation costs of the distribution network are compared in both states of with or without EVs. The results demonstrate the effectiveness of the proposed method for EVs' development scheduling in the distribution network. [ABSTRACT FROM AUTHOR]

Background: Warfarin is one of the most common oral anticoagulant, which role is to prevent the clots. The dose of this medicine is very important because changes can be dangerous for patients. Diagnosis is difficult for physicians because increase and decrease in use of warfarin is so dangerous for patients. Identifying the clinical and genetic features involved in determining dose could be useful to predict using data mining techniques. The aim of this paper is to provide a convenient way to select the clinical and genetic features to determine the dose of warfarin using artificial neural networks (ANN) and evaluate it in order to predict the dose patients. Methods: This experimental study, was investigate from April to May 2014 on 552 patients in Tehran Heart Center Hospital (THC) candidates for warfarin anticoagulant therapy within the international normalized ratio (INR) therapeutic target. Factors affecting the dose include clinical characteristics and genetic extracted, and different methods of feature selection based on genetic algorithm and particle swarm optimization (PSO) and evaluation function neural networks in MATLAB (MathWorks, MA, USA), were performed. Results: Between algorithms used, particle swarm optimization algorithm accuracy was more appropriate, for the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE) were 0.0262, 0.1621 and 0.1164, respectively. Conclusion: In this article, the most important characteristics were identified using methods of feature selection and the stable dose had been predicted based on artificial neural networks. The output is acceptable and with less features, it is possible to achieve the prediction warfarin dose accurately. Since the prescribed dose for the patients is important, the output of the obtained model can be used as a decision support system. [ABSTRACT FROM AUTHOR]