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Production technology known as additive manufacturing completely deviates from the conventional subtractive method. Due to its unique nature, its application could result in significant Supply Chain (SC) changes and impact the interactions between supply chain participants. This study shows the additive manufacturing applicable in an SC, considers the combination of traditional and additive manufacturing, and redesigns the SC structure. Also, this study aims to reduce operational and traditional costs and provides a new optimization model for changeable multi-level SC. Additive manufacturing is considered both a centralized and decentralized state. Additionally, this paper proposes a new Monte Carlo (MC) method combined with a Machine Learning (MCML) approach to improve the cost uncertainty accuracy compared with simple MC. For validation, the model is tested in a real case and sensitively analyzed regarding changes in the uncertainty and type of manufacturers. The results show that this hybrid model can reduce costs, MCML-based-MPL can increase the uncertainty accuracy in MC, and this model performs considerably better than only one type of traditional or additive manufacturing in SC. [ABSTRACT FROM AUTHOR]

In recent years, security incidents of website occur increasingly frequently, and this motivates us to study websites' security. Although there are many phishing detection approaches to detect phishing websites, the detection accuracy has not been desirable. In this paper, we propose a novel phishing detection model based on a novel neural network classification method. This detection model can achieve high accu-racy and has good generalization ability by design risk minimization principle. Furthermore, the training process of the novel detection model is simple and stable by Monte Carlo algorithm. Based on testing of a set of phishing and benign websites, we have noted that this novel phishing detection model achieves the best Accuracy, True-positive rate (TPR), False-positive rate (FPR), Precision, Recall, F-measure and Matthews Correlation Coefficient(MCC) comparable to other models as Naive Bayes (NB), Logistic Regression(LR), K-Nearest Neighbor (KNN), Decision Tree (DT), Linear Support Vector Machine (LSVM), Radial-Basis Support Vector Machine (RSVM) and Linear Discriminant Analysis (LDA). Furthermore, based upon experiments, we find that the proposed detection model can achieve a high Accuracy of 97.71% and a low FPR of 1.7%. It indicates that the proposed detection model is promising and can be effectively applied to phishing detection. [ABSTRACT FROM AUTHOR]

This paper deals with multi-factory parallel job scheduling in which independent factories try to satisfy market demand by cooperating with each other. In parallel job scheduling, unlike classical scheduling, jobs require a pre-specified job-dependent number of machines simultaneously when being processed. In the research, although it is assumed that the factories operate separately, in some cases, due to a large number of orders in one factory, some jobs may be sent to other factories to minimize the total completion time of jobs taking into account transportation time. In other words, in this system, it is assumed that each factory, after satisfying the demand of its region, can cooperate with other factories in order to achieve a better objective function for the production network. In the first step of the proposed approach, after associating the scheduling system with a constrained graph, a semidefinite programming rounding and a heuristic are proposed to color the graph in small and large-size instances, respectively. Finally, in the first step, it is shown that the problem under study can be reduced to a parallel machine scheduling problem. Given the heterogeny of factories, in the next step, mixed-integer linear programming, as well as two heuristic algorithms, are proposed. The comparison results of the proposed algorithm, imperialist competitive algorithm and the non-cooperative local scheduling algorithm show that the two-phase cooperative distributed algorithm is quite efficient. [ABSTRACT FROM AUTHOR]

In this contribution, order reduction of tenth-order single-machine-infinite-bus (SMIB) system is proposed with the help of matching of time moments (TMs) and Markov parameters (MPs) utilizing Routh array (RA) approximation. The unknown denominator coefficients of desired reduced-order model (ROM) are obtained by RA approximation method. The RA approximation method is simple in application. Moreover, stability of desired ROM can be ensured directly using RA approximation. The determination of numerator coefficients is done by matching of TMs and MPs of system and desired ROM. To show the effectiveness of proposed method, comparative analysis is performed. The ROMs already obtained by researchers for SMIB system using different methods are compared on the basis of time-domain specifications (TDSs). TDSs considered are rise time, settling time, undershoot, overshoot, peak and peak time. The comparative analysis considering performance error criteria (PEC) such as integral of absolute error, integral of time absolute error, integral of time-squared absolute error (IT 2 AE), integral of squared-error, integral of time-squared-error (IT 2 SE) and integral of time-squared square error (IT 2 SE) is also provided. The tabulated values of TDSs and PEC along with plots prove applicability of proposed method for order reduction of SMIB system. [ABSTRACT FROM AUTHOR]

Transferring knowledge from pre-trained deep models for downstream tasks, particularly with limited labeled samples, is a fundamental problem in computer vision research. Recent advances in large-scale, task-agnostic vision-language pre-trained models, which are learned with billions of samples, have shed new light on this problem. In this study, we investigate how to efficiently transfer aligned visual and textual knowledge for downstream visual recognition tasks. We first revisit the role of the linear classifier in the vanilla transfer learning framework, and then propose a new paradigm where the parameters of the classifier are initialized with semantic targets from the textual encoder and remain fixed during optimization. To provide a comparison, we also initialize the classifier with knowledge from various resources. In the empirical study, we demonstrate that our paradigm improves the performance and training speed of transfer learning tasks. With only minor modifications, our approach proves effective across 17 visual datasets that span three different data domains: image, video, and 3D point cloud. [ABSTRACT FROM AUTHOR]

Fuel cell is the best suggestion to replace internal combustion engines. Fuel cell systems have no pollution and no moving parts. The efficiency of fuel cells is more than three times that of internal combustion engines. Modeling the behavior of solid oxide fuel cells has special complications, and determining its performance according to its structural characteristics is one of the required parameters to further understand the behavior of solid oxide fuel cells. In this study, a new methodology is presented for optimal parameters estimation of the solid oxide fuel cell (SOFC) model. This paper's major goal was to provide a novel, efficient method for estimating the SOFC model's unknown parameters. To achieve this, the sum of squared errors between the output voltage of the proposed model and the experimental voltage measurements should be as little as possible. To reduce the error value, this study developed a better metaheuristic algorithm dubbed the Self-adaptive Henry Gas Solubility Optimizer. The developed method was then used with a 96-cell SOFC stack, and the sensitivity analysis was carried out while using various optimization algorithms at various temperatures and pressures. When 150 data points from a temperature sensitivity analysis at five temperatures, including 625 °C, 675 °C, 725 °C, and 775 °C under constant pressure, values of 3 atm, were taken into consideration, the smallest error was 9.41 e–5 for 575 °C. For pressure variations between 1 and 5 atm at constant temperatures of 775 °C, the lowest inaccuracy was 8.21 e–3 for 1 atm. Simulation results show that the proposed approach is more effective than the other techniques as an identifying tool. [ABSTRACT FROM AUTHOR]

Three-phase distribution system normally works in unbalanced nature every time especially when it is three phases four-wire system, the system becomes more complex when DG is incorporated in an unbalanced system because the power quality decreases and may damage the equipment. This paper presents an optimal placement and sizing of Type IV i.e. induction generators in large wind farms and their amalgamation in an unbalanced distribution system with reactive support using UPQC (unified power quality conditioner) using particle swarm optimization (PSO) for loss minimization. The size of DG is determined by the penetration level and here the penetration is kept under 12% to avoid too harsh tripping of the circuit breaker. The power loss may be decreased using the iterative PSO method. On unbalanced IEEE 34 and 123 bus systems, the suggested method is examined. The simulation results demonstrate the technique's remarkable suitability and adequacy. [ABSTRACT FROM AUTHOR]

This paper introduces the randomized self-updating process (rSUP) algorithm for clustering large-scale data. rSUP is an extension of the self-updating process (SUP) algorithm, which has shown effectiveness in clustering data with characteristics such as noise, varying cluster shapes and sizes, and numerous clusters. However, SUP’s reliance on pairwise dissimilarities between data points makes it computationally inefficient for large-scale data. To address this challenge, rSUP performs location updates within randomly generated data subsets at each iteration. The Law of Large Numbers guarantees that the clustering results of rSUP converge to those of the original SUP as the partition size grows. This paper demonstrates the effectiveness and computational efficiency of rSUP in large-scale data clustering through simulations and real datasets. [ABSTRACT FROM AUTHOR]

The job shop scheduling problem (JSSP) is critical for building one smart factory regarding resource management, effective production, and intelligent supply. However, it is still very challenging due to the complex production environment. Besides, most current research only focuses on classical JSSP, while flexible JSSP (FJSSP) is more usual. This article proposes an effective method, GAILS, to deal with JSSP and FJSSP based on genetic algorithm (GA) and iterative local search (ILS). GA is used to find the approximate global solution for the JSSP instance. Each instance was encoded into machine and subtask sequences. The corresponding machine and subtasks chromosome could be obtained through serval-time gene selection, crossover, and mutation. Moreover, multi-objects, including makespan, average utilization ratio, and maximum loading, are used to choose the best chromosome to guide ILS to explore the best local path. Therefore, the proposed method has an excellent search capacity and could balance globality and diversity. To verify the proposed method's effectiveness, the authors compared it with some state-of-the-art methods on sixty-six public JSSP and FJSSP instances. The comparative analysis confirmed the proposed method's effectiveness for classical JSSP and FJSSP in makespan, average utilization ratio, and maximum loading. Primarily, it obtains optimal-like solutions for several instances and outperforms others in most instances. [ABSTRACT FROM AUTHOR]

Very sensitive loads require the safe operation of electrical distribution networks, including hospitals, nuclear and radiation installations, industries used by divers, etc. To address this issue, the provided paper suggests an innovative method for evaluating the appropriate allocation of Distribution STATic COMpensator (DSTATCOM) to alleviate total power losses, relieve voltage deviation, and lessen capital annual price in power distribution grids (PDGs). An innovative approach, known as the modified capuchin search algorithm (mCapSA), has been introduced for the first time, which is capable of addressing several issues regarding optimal DSTATCOM allocation. Furthermore, the analytic hierarchy process method approach is suggested to generate the most suitable weighting factors for the objective function. In order to verify the feasibility of the proposed mCapSA methodology and the performance of DSTATCOM, it has been tested on two standard buses, the 33-bus PDG and the 118-bus PDG, with a load modeling case study based on real measurements and analysis of the middle Egyptian power distribution grid. The proposed mCapSA technique's accuracy is evaluated by comparing it to other 7 recent optimization algorithms including the original CapSA. Furthermore, the Wilcoxon sign rank test is used to assess the significance of the results. Based on the simulation results, it has been demonstrated that optimal DSTATCOM allocation contributes greatly to the reduction of power loss, augmentation of the voltage profile, and reduction of total annual costs. As a result of optimized DSTATCOM allocation in PDGs, distribution-level uncertainties can also be reduced. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles are useful tools for many applications. However, autonomous path planning for Unmanned Aerial Vehicles in unfamiliar environments is a challenging problem when facing a series of problems such as poor consistency, high influence by the native controller of the Unmanned Aerial Vehicles. In this paper, we investigate reinforcement learning-based autonomous local path planning methods for Unmanned Aerial Vehicles with high autonomous decision-making capability and locally high portability. We propose an autonomous local path planning algorithm based on the TD3 strategy to solve the problem of local obstacle avoidance and path planning in unfamiliar environments using autonomous decision-making of Unmanned Aerial Vehicles. The simulation results on Gazebo show that our method can effectively realize the autonomous local path planning task for Unmanned Aerial Vehicles, the success rate of path planning with our method can reach 93% under the interference of no obstacles, and 92% in the environment with obstacles. Finally, our method can be used for autonomous path planning of Unmanned Aerial Vehicles in unfamiliar environments. [ABSTRACT FROM AUTHOR]

The greatest advantages of wireless power transfer (WPT) are its absence of severe environmental hazards, its portability, and its independence from other factors. The wireless charging system for electric vehicles has a serious problem with the amount of misalignment it can tolerate. This study explores the usage of a novel multi-level boost power factor correction (PFC) rectifier with less switch count to improve the efficiency of power conversion of a 5-kW wireless electric vehicle (EV) charger. Especially in the context of wireless charging, which provides convenience and flexibility, there is a pressing need for efficient and dependable charging infrastructure to keep up with the rising demand for electric vehicles. In contrast to wired EV chargers, wireless chargers often have poorer power conversion efficiency because of losses in power semiconductor devices. An innovative multi-level boost PFC rectifier design is offered as a solution to this problem since it uses fewer switches while retaining high-performance levels. The suggested rectifier achieves much higher power conversion efficiency. In addition, power factor correction capabilities are improved, making it comply with global rules. Simpler, cheaper, and more dependable rectifiers improve the whole system. [ABSTRACT FROM AUTHOR]

Single machine infinite bus (SMIB) power system and multi-machine power system (MMPS) stability improvement by tuning of static var compensator (SVC) based controller parameters are investigated in the proposed method. The design problem is formulated as an optimization problem with a time-domain simulation-based objective function and real-coded genetic algorithm is used for searching optimal controller parameters. SMIB power system and MMPS models are developed using MATLAB's SIMULINK which incorporates SVC controller. A fault is created on the transmission line. The simulation results of SMIB power system and MMPS without SVC controller and with SVC controller are presented. The simulation results are analyzed which show that the power system becomes unstable on the occurrence of the fault if SVC controller is not used. This paper proves the effectiveness of the proposed design. Thus the proposed method enhances the power system stability. [ABSTRACT FROM AUTHOR]

Job scheduling has always been a challenging task in modern manufacturing and the most real life scheduling problems which involves multi-criteria, multi-machine environments. In this research, the single-machine scheduling problem is studied in which job processing times are controllable, namely, they may vary within a specified interval. The goal of this research is to minimize total tardiness and earliness on a single machine, simultaneously. In this context, we first propose a mathematical model for the considered problem and then a net benefit compression-net benefit expansion heuristic is presented for obtaining the set of amounts of compression and expansion of jobs processing times in a given sequence. Two meta-heuristic approaches are then employed to solve medium-to-large-sized problems as local search methods. Thereafter, we apply a hybrid method based on our heuristic as well as these two meta-heuristics in order to obtain solutions with higher quality within lesser computational time. The addressed problem is NP-hard since the single machine total tardiness problem is already NP-hard. The computational results show that our proposed heuristics can effectively solve such Just-In-Time problem with a high-quality solution. [ABSTRACT FROM AUTHOR]

Increased penetration of wind energy systems has serious concerns on power system stability. In spite of several advantages, doubly fed induction generator (DFIG) based wind energy systems are very sensitive to grid disturbances. DFIG system with conventional vector control is not robust to disturbances as it is based on PI controllers. The objective of this paper is to design a new vector control that is robust to external disturbances. To achieve this, inner current loop of the conventional vector control is replaced with sliding mode control. In order to avoid chattering effect and achieve finite time convergence, the control gains are selected based on positive semi-definite barrier function. The proposed barrier function adaptive sliding mode (BFASMC) is evaluated by testing it on a benchmark multi-machine power system model under various operating conditions. The simulated results show that the proposed method is robust to various disturbances. [ABSTRACT FROM AUTHOR]

This paper establishes a semi-physical simulation platform on the basis of massive data analysis and computation according to the characteristics of mining speed-regulating magnetic coupling. The platform can be used to simulate various environment changes in the actual on-load application of magnetic coupling, verify the control strategy for magnetic coupling, provide simulation basis and references for on-site usage, as well as establish a mathematical model for all components of driving system of the entire belt conveyor. Through the experiment analysis, it is proved that this platform can effectively and objectively simulate the speed control situation of coupling, as well as the multi-power balance effect of the belt conveyor during soft startup of belt conveyor, thus providing practical engineering applications with strong data support. [ABSTRACT FROM AUTHOR]

novel technique for the optimal tuning of power system stabilizer (PSS) was proposed, by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC). Firstly, a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC). It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search. Secondly, the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima. The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions. The performance of the proposed MPSOC was compared to the MPSO, PSO and GA through eigenvalue analysis, nonlinear time-domain simulation and statistical tests. Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs. The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA, PSO and MPSO. [ABSTRACT FROM AUTHOR]

We interpret and explain a phenomenon in short-term swing dynamics of multi-machine power grids that we term the Coherent Swing Instability (CSI). This is an undesirable and emergent phenomenon of synchronous machines in a power grid, in which most of the machines in a sub-grid coherently lose synchronism with the rest of the grid after being subjected to a finite disturbance. We develop a minimal mathematical model of CSI for synchronous machines that are strongly coupled in a loop transmission network and weakly connected to the infinite bus. This model provides a dynamical origin of CSI: it is related to the escape from a potential well, or, more precisely, to exit across a separatrix in the dynamical system for the amplitude of the weak nonlinear mode that governs the collective motion of the machines. The linear oscillations between strongly coupled machines then act as perturbations on the nonlinear mode. Thus we reveal how the three different mode oscillations-local plant, inter-machine, and inter-area modes-interact to destabilize a power grid. Furthermore, we present a phenomenon of short-term swing dynamics in the New England (NE) 39-bus test system, which is a well-known benchmark model for power grid stability studies. Using a partial linearization of the nonlinear swing equations and the proper orthonormal decomposition, we show that CSI occurs in the NE test system, because it is a dynamical system with a nonlinear mode that is weak relative to the linear oscillatory modes. [ABSTRACT FROM AUTHOR]

In this paper, a system consisting of a network of machines with random breakdown and repair times is considered. The machines in this system can be in one of four states: operational, in repair, starved, and blocked. Failure and repair times of the machines are exponentially distributed. Previous research on multi-machine failure-prone manufacturing systems (FPMS) has focused on systems consisting of machines in series or in parallel. This paper considers a network of machines with relationship constraints. Additionally, the system under study models work in process for multiple products, intermediate and final buffers and one type of final product. The demand rate for the final commodity is constant and unmet demand is either backlogged or lost. The objective of this control problem is to find the production rates and policies of the different machines so as to minimize the long run average inventory and backlog cost. The applied control policy is the hedging point policy that is determined by factors representing the level of buffer inventory for each machine. Obtaining analytical solutions is generally impossible for such complex systems. To simultaneously control the production rates of the machines we have therefore developed a method based on a combination of stochastic optimal control theory, discrete event simulation, experimental design and automated response surface methodology (RSM). The application of an automated RSM for Network FPMS is another contribution of this paper. The model can be extended easily to systems with age-dependent failure rates, a preventive repair maintenance policy and non-exponentially distributed up and down times. [ABSTRACT FROM AUTHOR]

We introduce a novel global constraint for the total weighted completion time of activities on a single unary capacity resource. For propagating the constraint, we propose an O( n) algorithm which makes use of the preemptive mean busy time relaxation of the scheduling problem. The solution to this problem is used to test if an activity can start at each start time in its domain in solutions that respect the upper bound on the cost of the schedule. Empirical results show that the proposed global constraint significantly improves the performance of constraint-based approaches to single-machine scheduling for minimizing the total weighted completion time. We then apply the constraint to the multi-machine job shop scheduling problem with total weighted completion time. Our experiments show an order of magnitude reduction in search effort over the standard weighted-sum constraint and demonstrate that the way in which the job weights are associated with activities is important for performance. [ABSTRACT FROM AUTHOR]

We address the short-term production planning and scheduling problem coming from the glass container industry. A furnace melts the glass that is distributed to a set of parallel molding machines. Both furnace and machine idleness are not allowed. The resulting multi-machine multi-item continuous setup lotsizing problem with a common resource has sequence-dependent setup times and costs. Production losses are penalized in the objective function since we deal with a capital intensive industry. We present two mixed integer programming formulations for this problem, which are reduced to a network flow type problem. The two formulations are improved by adding valid inequalities that lead to good lower bounds. We rely on a Lagrangian decomposition based heuristic for generating good feasible solutions. We report computational experiments for randomly generated instances and for real-life data on the aforementioned problem, as well as on a discrete lotsizing and scheduling version. [ABSTRACT FROM AUTHOR]

This paper presents a procedure for designing a supplementary damping stabilizer for a static synchronous series compensator (SSSC) in multi-machine power systems. The objective is to shift the lightly damped inter-area modes toward the prescribed stability region. A lead-lag stabilizer is used to demonstrate this technique, in which a particular measure of stabilizer gain is considered as an objective function. Constraints of the problem for phase-lead and lag structures are derived. The objective function with the constraints is formed as a quadratic mathematical programming problem. For robust design, the parameters of the stabilizer are calculated under various operating conditions. Two types of SSSC-based stabilizer have been presented and designed. Numerical results including eigenvalue analysis and the nonlinear simulations on the 4- and 50-machine power systems are presented to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we study the multi-machine scheduling problem with earliness and tardiness penalties and sequence dependent setup times. This problem can be decomposed into two subproblems—sequencing and timetabling. Sequencing focuses on assigning each job to a fixed machine and determine the job sequence on each machine. We call such assignment a semi-schedule. Timetabling focuses on finding an executable schedule from the semi-schedule via idle-time insertion. Sequencing is strongly NP-hard in general. Although timetabling is polynomial-time solvable, it can become a computational bottleneck if the procedure is executed many times within a larger framework. This paper makes two contributions. We first propose a quantum improvement to the computational efficiency of the timetabling algorithm. We then apply it within a squeaky wheel optimization framework to solve the sequencing and overall problem. Finally, we demonstrate the strength of our proposed algorithms by experiments. [ABSTRACT FROM AUTHOR]

A construction method for power system transient energy function is studied in the paper, which is simple and universal, and can unify the forms of some current energy functions. A transient energy function including the induction motor model is derived using the method. The unintegrable term is dealt with to get an approximate energy function. Simulations in a 3-bus system and in the WSCC 4-generator system verify the validity of the proposed energy function. The function can be applied to direct transient stability analysis of multi-machine large power systems and provides a tool for analysis of the interaction between the generator angle stability and the load voltage stability. [ABSTRACT FROM AUTHOR]

In this paper, the Evolutionary Simulated Annealing (ESA) algorithm, its distributed implementation (dESA) and its application to two combinatorial problems are presented. ESA consists of a population, a simulated annealing operator, instead of the more usual reproduction operators used in evolutionary algorithms, and a selection operator. The implementation is based on a multi island (agent) system running on the Distributed Resource Machine (DRM), which is a novel, scalable, distributed virtual machine based on Java technology. As WAN/LAN systems are the most common multi-machine systems, dESA implementation is based on them rather than any other parallel machine. The problems tackled are well-known combinatorial optimisation problems, namely, the classical job-shop scheduling problem and the uncapacitated facility location problem. They are difficult benchmarks, widely used to measure the efficiency of metaheuristics with respect to both the quality of the solutions and the central processing unit (CPU) time spent. Both applications show that dESA solves problems finding either the optimum or a very near optimum solution within a reasonable time outperforming the recent reported approaches for each one allowing the faster solution of existing problems and the solution of larger problems. [ABSTRACT FROM AUTHOR]

Starting from normalized generators’ equations of rotor motion with respect to the center of inertia of power systems, post-fault power system dynamic is analogized as a motion of a particle with 1.0 mass in an n-dimensional Euclidean space. A rotational coordinate axis is defined for the moving particle. Transient stability of a multi-machine power system is transformed into a simple one-dimensional motion of particle on the axis. Based upon the above new idea, a new concept transient energy function (NCTEF) is proposed for transient stability assessment of power systems. Case studies on the 10-generator New England power system verified the rationality of NCTEF. [ABSTRACT FROM AUTHOR]

Large production variations caused by abnormal disturbances can significantly reduce the production capacity of a flexible manufacturing system (FMS). To prevent production delays, short-term capacity adjustment strategies can be used to augment the capacity of the FMS, such as working overtime, using alternative tools that are suited for faster processing, and producing parts outside of the FMS. We propose a mixed integer programming (MIP) model to obtain an optimal production plan for a multi-machine FMS. Our model evaluates both the FMS loading decision and the effective use of short-term capacity adjustment strategies to minimize the total part production cost. We develop an iterative procedure to solve the model that uses the Lagrangian relaxation method for finding lower bounds and a Lagrangian heuristic for obtaining feasible solutions. The procedure exploits certain special structures found in the Lagrangian multipliers which enable us to obtain good solutions to reasonably large test problems quickly. [ABSTRACT FROM AUTHOR]

This paper focuses on the multisynchronization problem of delayed fractional-order neural networks with parametric uncertainties. Firstly, partition space method is used to determine that each subnetwork of fractional-order neural networks has ∏ j = 1 n K j + 1 locally Mittag-Leffler stable periodic orbits or equilibrium points. Secondly, a universal impulsive controller is proposed to impose on each node except the last one, and all nodes eventually tend to the same state. By means of average impulsive interval method, linear matrix inequality (LMI) and some other inequality techniques, the sufficient conditions for the dynamical and static multisynchronization of whole systems are respectively given. Finally, two numerical examples are provided to illustrate the correctness of the theoretical results. [ABSTRACT FROM AUTHOR]

Multi-robot cooperative path planning is a significant research area in the domains of intelligent reconnaissance, transportation, and combat. The complexity of resolving multi-path conflicts in large-scale multi-robot scenarios poses a significant challenge to researchers. To address this issue, this paper proposed a universal conflict resolution mode, collision avoidance strategy in local crossing, and behavior regulation method that allows robots to take intelligent measures to avoid conflicts in scenarios with a large number of robots. Specifically, we introduced a novel algorithm, Universal Conflict Resolution and Free Crossing Emergence (UCR-FCE), that solves the conflict problem emerging in a significant number of local areas. The algorithm includes three extended multi-path resolution algorithms and a mechanism of avoiding Receptor Dodger (RD) from Noumenon Dodger (ND) to the free junction. We provided a completeness proof with Set Theory and Regional Theory to demonstrate that UCR-FCE can solve all conflict scenarios given sufficient free path nodes. Furthermore, a behavior regulation algorithm was developed to reduce the complexity of real-time path conflicts during robot motion. The proposed multi-robot cooperative intelligent planning algorithm is tested through simulation and field experiments. Results illustrate that the system can effectively refer to the traffic rules and intelligently adapt to ever-changing potential conflicts. A comparative simulation is also established to prove the effectiveness of each improvement proposed in this paper and to exhibit the superiority of the proposed method over other methods available in the literature. Results indicate that the proposed method outperforms eight comparative methods, with an absolute increase in the success planning rate of 56 % , 56 % , 44 % , 24 % , 12 % , 22 % and 18 % in large-scale multi-robot scenarios, respectively, when the number of robots in ROS-stage simulation environment reaches 400. [ABSTRACT FROM AUTHOR]

Rapid and accurate judgment of the rock mass condition is the key to guaranteeing the safety and efficiency of tunnel boring machine (TBM) tunneling. This paper proposes a method for evaluating rock mass parameters based on K-means clustering, grouping tunneling areas according to the values of TBM tunneling parameters. A dataset including rock mass and TBM tunneling data is treated by logistic normalization and principal component analysis (PCA), and large volumes of tunneling data with different features are transformed into appropriate volumes of dimensionless data. K-means clustering is used, samples are grouped according to the values of tunneling data, and the specific ranges as defined by clustering are regarded as the unified evaluated results of each group. Based on the C1 part of the Pearl Delta water resources allocation project, 100 training samples and 30 testing samples were field-collected, and the proposed method was realized by the training samples and verified by the testing samples. The evaluation accuracies of uniaxial compressive strength (UCS), and joint frequency (Jf) were 90%, and 86.7% respectively, demonstrating that the evaluation had acceptable values, and the proposed method was greatly helpful for judging rock conditions. [ABSTRACT FROM AUTHOR]

Now that the population is growing, the expenditure on basic needs of life is also increasing due to a lack of or less availability of resources. The economy consumed electricity is reaching peaks as its main fuel, coal, is decreasing day by day. Due to this, 90% of the population who are in the middle class, lower middle class, or rural areas are economically poor and are unable to bear the prices. To overcome the financial problems, many researchers have prepared various types of microgrids that generate electricity from various types of flow resources, like hydro, solar, biogas, and air current power stations, whose system is called a compound flow power system. This paper gives a combined review of various research papers that discuss some case studies and some research on various models designed on software like HOMER Pro, how microgrids become economic barriers, optimal power supply solutions with CFPS, distributed and centralized microgrid components, the technical and economic feasibility of EV charging stations, and the analysis of various combinations of power systems at various locations like Bangladesh, Canada, the Republic of Djibouti, China, Indonesia, Sierra Leone, some rural sites in India, and some developing countries. This overview provides a glimpse into the various aspects of CFPS, including fusion approaches, techno-economic analysis, simulation platforms, storage technologies, design specifications, unit sizing methodologies, and control techniques. Further research and analysis in these areas are needed to explore their applications and advancements in CFPS development. The main reason for the study is to analyze and bring various ideas and models of various researchers together on a common platform and make a combined conceptual framework for further proceedings. [ABSTRACT FROM AUTHOR]

Scientific communities are still motivated to create novel approaches and methodologies for early estimation of software project development efforts and testing efforts in soft computing environments due to scheduling and budgetary concerns. Therefore, the software engineering prediction problems (SEPPs) are formulated as machine learning (ML) models with the aim of addressing these issues. In such methodologies that may exhibit significant limitations and drawbacks, efficient metaheuristic approaches are essential to improving prediction performance. Accordingly, this study aims to address software test effort prediction (STP) and software development effort prediction (SEP) with the aim of maximizing prediction accuracy, which in turn minimizes overall project costs and optimizes resource allocation. To achieve this goal, we developed several ML models composed of a backpropagation neural network (BPNN). The proposed models contain the Salp Swarm Algorithm (SSA), which is utilized to replace the traditional network training method and tackle its limitations. The models also contain the great deluge (GD) local search algorithm, which is hybridized with the SSA algorithm to enhance optimization capabilities by finding more balance between exploration and exploitation. During the validation stage of this study, fourteen benchmark datasets were utilized to evaluate the developed models for each of the respective problems. The obtained results were quantified using eight performance metrics and compared across two sections. In the first section, a comparison was made between the results of the hybrid-developed model (HSSA) and those of the standard SSA algorithm and BPNN. In the second comparison, the performance of the HSSA model was compared with several contemporary techniques that are considered state-of-the-art. The evaluation shows that the HSSA performs better than related approaches in most cases for both problems. Finally, additional analysis was performed on the collected results, including examinations of statistical significance, distribution through box plots, and model convergence behavior. [ABSTRACT FROM AUTHOR]

In the last two decades, the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) has attracted extensive research interests, and it is still one of the hottest research methods to deal with multi-objective optimization problems. Considering the importance and wide applications of NSGA-II method, we believe it is the right time to provide a comprehensive survey of the research work in this area, and also to discuss the potential in the future research. The purpose of this paper is to summarize and explore the literature on NSGA-II and another version called NSGA-III, a reference-point based many-objective NSGA-II approach. In this paper, we first introduce the concept of multi-objective optimization and the foundation of NSGA-II. Then we review the family of NSGA-II and their modifications, and classify their applications in engineering community. Finally, we present several interesting open research directions of NSGA-II for multi-objective optimization. [ABSTRACT FROM AUTHOR]

The energy matrix is rapidly changing in recent years mainly due to concerns with the environment. The participation of renewable resources connected to the power system via frequency inverters is increasing, adding new challenges to the power systems operation and control. The system inertia reduction as a result of the conventional synchronous generators replacement makes the system more susceptible to frequency variations, leading to the deterioration of the system dynamic response. In this paper, a control scheme is proposed, aiming to improve the system frequency response after disturbances. The control scheme is based on the online identification of a system model. Changes in system inertia, resulting from changes in the dispatch of renewable sources and disconnection of conventional sources, are taken into account by the periodic update of the identified model. A design method is applied to update the control in order to limit frequency excursions and improve dynamic response. The impact of PMUs and transmission latency on the control performance are also evaluated. Simulations are performed in order to validate the proposed approach. [ABSTRACT FROM AUTHOR]

A formal approach to rephrasing nonlinear filtering of stochastic differential equations is the Kushner setting in applied mathematics and dynamical systems. Thanks to the ability of the Carleman linearization, the 'nonlinear' stochastic differential equation can be equivalently expressed as a finite system of 'bilinear' stochastic differential equations with the augmented state under the finite closure. Interestingly, the novelty of this paper is to embed the Carleman linearization into a stochastic evolution of the Markov process. The nonlinear swing equation is the cornerstone and lays the foundation of the power systems dynamics. To illustrate the Carleman linearization of the Markov process, this paper embeds the Carleman linearization into a nonlinear swing stochastic differential equation. Furthermore, we achieve the nonlinear swing equation filtering in the Carleman setting. Filtering in the Carleman setting has simplified algorithmic procedures. The concerning augmented state accounts for the nonlinearity as well as stochasticity. We show that filtering the nonlinear stochastic swing equation in the Carleman framework is more refined as well as sharper in contrast to the benchmark nonlinear extended Kalman filter (EKF). The Carleman filtering framework reduces approximately three times more conditional mean absolute errors as compared to the benchmark EKF method for the application of machine swing dynamics. This paper suggests the usefulness of the Carleman embedding into the stochastic differential equation to filter the concerning nonlinear stochastic differential system. This paper will interest nonlinear stochastic dynamists exploring and unfolding linearization embedding techniques to their research. [ABSTRACT FROM AUTHOR]

We consider batch scheduling on m machines to minimize the makespan. Each job has a given set of machines to be assigned. Each machine can process several jobs simultaneously as a batch, and the machines may have different batch capacities. We study two models: (i) scheduling on equal-speed batch machines under a nested processing set restriction, where the machines have the same processing speed, and (ii) scheduling on uniform batch machines under a tree-hierarchical processing set restriction, where the machines have different processing speeds. For both models we design polynomial-time approximation algorithms to solve them. The algorithms have a worst-case ratio of 2 for non-identical batch capacities and a worst-case ratio of 2 - 1 / m for identical batch capacities. [ABSTRACT FROM AUTHOR]

The power quality issue becomes an important concern for the electric power companies, sensitive loads and manufacturers due to deregulation in power supply systems. This paper aims to enhance the power quality features in the distribution system using novel structure of Dynamic Voltage Restorer (DVR). This costume power device can operate in its best compensation performance and functionality in case that both the control system and multilevel inverter components to be reconditioned. Toward this subject, this paper proposes three following novelties: (1) Upside–Down Asymmetrical Multi-Level Inverter (UDAMLI) topology with reduced structure to create high-step staircase sinusoidal voltage with consideration of low semiconductor switches. (2) Fractional Order Interval Type-2 Fuzzy System (FOIT2FS) for triggering angle control system to create accurate d-q modulation index. (3) Optimal design of triggering angle control system using Stochastic Fractal Search (SFS) to create required voltage reference signals. The aforementioned novelties have significantly upgraded the AC voltage synthesizer part so that DVR can accurately compensate all voltage disturbances such as voltage sag, voltage swell and voltage harmonic distortion. Three different voltage disturbances have been considered to test and analyze the compensation capability of the proposed DVR. Meanwhile, the FOIT2FS-based DVR has been compared with PID, PIαDβ, IT1FS and IT2FS to validate its accuracy and robustness. In the main, the simulation results achieved by proposed compensator and other compensators reveal the high compensation performance of FOIT2FS-based UDAMLI-DVR with trivial Total Harmonic Distortion (THD). Furthermore, it can be stated that the major findings of this paper which are related to the DVR structure are: enhancing the accuracy and compensation capability, reducing the semiconductor count and increasing the cost-effectiveness. [ABSTRACT FROM AUTHOR]

The unified power flow controller (UPFC) is a device used in power systems to control and optimize the flow of electric power. It is a flexible AC transmission system device that combines several power electronic components to provide comprehensive control over voltage, active power, and reactive power in transmission lines. This research offers an improved gray wolf optimization (IGWO)-based decoupled power control strategy (DPCS) for a six-phase UPFC (6P-UPFC) to enhance power quality. The proposed 6P-UPFC-DPCS divides its control efforts between reactive power control and active power control. This is because the UPFC can independently regulate the transmission line's voltage and phase angle by decoupling the active and reactive power flows. A fault isolation technique is provided for isolating the damaged part of the transmission line, which would increase system efficiency and dependability. By analyzing the voltage and current signals at the UPFC terminals, as well as the line impedance, it is possible to ascertain whether a problem exists. After the trouble spot has been identified, the 6P-UPFC must be adjusted to compensate for the predicted impedance of the damaged line to restore power to the system. After that, the coordinated voltage and current are managed through the virtual impedance. The PI controller's proportional and integral gains, as well as the inverter's voltage phase angle and magnitude, are optimized using the IGWO method. The performance and dependability of the control strategy are further enhanced by tuning the controller parameters for the 6P-UPFC-DPCS controller using an IGWO approach. The simulation results show that the proposed 6P-UPFC-DPCS might reduce the false setting time to 0.01 s, boost the power factor to 0.98, and lower the total harmonic distortion to 1.40%. [ABSTRACT FROM AUTHOR]

Nowadays, integration of large-scale wind farms (WFs) into power systems is experiencing rapid growth. As this rapid integration can affect transient stability significantly, employing doubly fed induction generator (DFIG)-based wind turbines, which have shown better behavior regarding system stability, has attracted much attention. This research contributes to the literature by investigating the transient stability of the power system with increasing penetration of DFIG-based WFs. In the proposed framework, the current-balance form is utilized for the network equations, and in this way, transient stability is performed using time-domain simulation. According to the simulation results, when the rate of wind power generation exceeds 0.7 per-unit, the increasing trend of the critical clearing time (CCT) is reversed and the CCT decreases greatly with the increased wind power penetration. In addition, the reactive power compensation by DFIG, the gearbox ratio, the power system strength, and DFIG parameters are comprehensively investigated as effective parameters on transient stability. Since the rated rotor speed of DFIG significantly impacts the electrical torque and machine currents, the reduction of the rated rotor speed due to the change of the gearbox ratio has been investigated as one of the effective factors to improve the transient stability. The simulation results demonstrate the effectiveness of the proposed approach in improving power system transient stability. [ABSTRACT FROM AUTHOR]

Aggregate production planning (APP) is a medium-term planning in the production system, which determines the optimal production plan in the planning horizon. To allocate the optimal production quantity to the production lines, we propose an efficiency-based APP to multi-line manufacturing systems. For that purpose, first, considering the line efficiency factors, we calculate the efficiency score of production lines with an extension of data envelopment analysis (namely DEA-AR). Pollution rate, defective product rate, production capacity, downtime, and electricity consumption are the criteria employed to calculate the efficiency of production lines. Then, using the result of DEA as a parameter, we develop a bi-objectives integer mathematical model that allocates the most production to efficient lines while minimizing total production costs considering loading constraints. To solve the proposed model, the ℇ-constraint method is employed. We evaluate the performance of the multi-line APP using a set of data collected from a plastic production factory. Results indicate that in using the proposed model, both efficiency and production costs are appropriately satisfied in the efficiency-based APP. The proposed framework is generic and provides the managers of different manufacturing organizations with a powerful tool to deal with medium-term planning by taking the line efficiency into account. [ABSTRACT FROM AUTHOR]