Search

The document discusses the recent International Conference on Computational Electromagnetics (CEM) held in Cannes, France in April 2023. It highlights the key areas covered in the conference, including computational techniques for solving electromagnetics problems, numerical methods, and applications of computational electromagnetics. The document also presents summaries of six selected papers from the conference, covering topics such as optimization of permanent magnet motors, reduced order modeling for electromagnetic analysis, and time reversal methods for partial discharge location. Additionally, it announces the upcoming CEM 12 conference scheduled for April 2025 in Bruges, Belgium, welcoming original contributions in the field of computational electromagnetics. [Extracted from the article]

This paper aims to develop a construction and operation cost model of wastewater treatment for the paper industry in China and explores the main factors that determine these costs. Previous models mainly involved factors relating to the treatment scale and efficiency of treatment facilities for deriving the cost function. We considered the factors more comprehensively by adding a regional variable to represent the economic development level, a corporate ownership factor to represent the plant characteristics, a subsector variable to capture pollutant characteristics, and a detailed-classification technology variable. We applied a unique data set from a national pollution source census for the model simulation. The major findings include the following: (l) Wastewater treatment costs in the paper industry are determined by scale, technology, degree of treatment, ownership, and regional factors; (2) Wastewater treatment costs show a large decreasing scale effect; (3) The current level of pollutant discharge fees is far lower than the marginal treatment costs for meeting the wastewater discharge standard. Key implications are as follows: (l) Cost characteristics and impact factors should be fully recognized when planning or making policies relating to wastewater treatment projects or technology development; (2) There is potential to reduce treatment costs by centralizing wastewater treatment via industrial parks; (3) Wastewater discharge fee rates should be increased; (4) Energy efficient technology should become the future focus of wastewater treatment. [ABSTRACT FROM AUTHOR]

Hilly terrain farmland has diverse landforms, small fields, and unevenness. Thus, realizing farmland operation mechanization and automation is a difficult problem and has become one of the current research directions to implement agricultural mechanization. Full-coverage path planning is the basis for realizing agricultural mechanization and intelligence, and traditional full-coverage path planning algorithms cannot solve the full-coverage and energy consumption optimization problem during the management of hilly farmland on three-dimensional terrain. In this paper, for the full-coverage path-planning problem of hilly terrain farmland, based on analyzing the terrain characteristics of hilly farmland and the energy consumption model of robots traveling on non-flat ground, we propose a region decomposition method oriented to special terrain and prioritize the coverage of special terrain areas. We introduce a cost function for robot movement and design a full-coverage path planning algorithm for hilly three-dimensional terrain. We set up a simulation environment to carry out simulation experiments. The experimental results show that this paper's algorithm can complete full coverage tasks in hilly terrain farmland, and compared with other algorithms, it has obvious advantages in path length, total elevation difference, and other aspects, effectively reducing the energy consumption of the coverage task. This lays a research foundation for the realization of agricultural mechanization in hilly terrain farmland. [ABSTRACT FROM AUTHOR]

This paper addresses a real life scheduling problem characterised by multi-resource operations whose completion simultaneously requires more than one (renewable) resource of different types. Such problems arise in various companies not only in manufacturing but also in services. Solving such a problem needs to address two interconnected subproblems: a sequencing subproblem and a resource-allocation subproblem if more than one resource is available in some types. The resource-allocation subproblem consists of allocating to each operation the resources it requires while the sequencing subproblem consists of determining the order in which each resource performs the operations assigned to it. This paper focuses on the resource-allocation subproblem. It generalises the basic scheduling problems which consist of determining the operations' starting or completion times for a given processing sequence for every resource. We consider a cost function taking into account the makespan, the cost of resource utilisation, and the load imbalance among the resources of the same type. We first formulate the problem into a mixed-integer linear program (MILP). To efficiently solve it, even in practical-sized instances, an exact algorithm called BD (Benders decomposition with enhancing cuts) is developed where the master problem only considers integer variables. We prove that the slave problem can be transformed into finding the longest paths in a digraph and therefore can be solved with the Bellman–Ford algorithm. To enhance the efficiency of the method, equivalent solutions are limited in the master problem. The performance of the approach is evaluated by comparing it against CPLEX, a state-of-the-art commonplace MILP solver, used to directly solve the initial MILP. The computational results demonstrate that BD provides competitive solutions in all upper and lower bounds. In particular, it improves, compared with CPLEX, the upper and lower bounds by 5.07% and 4.63%, respectively, in solving practical-sized instances. The experiment also shows that considering load balancing can make more rational use of resources and avoid adverse effects caused by excessive workload of staff and imbalanced use of equipment, which is very important in real-world production. [ABSTRACT FROM AUTHOR]

Because of the unique imaging mechanism of infrared (IR) sensors, IR images commonly suffer from blurred edge details, low contrast, and poor signal-to-noise ratio. A new method is proposed in this paper to enhance IR image details so that the enhanced images can effectively inhibit image noise and improve image contrast while enhancing image details. First, for the traditional guided image filter (GIF) applied to IR image enhancement is prone to halo artifacts, this paper proposes a detail enhancement guided filter (DGIF). It mainly adds the constructed edge perception and detail regulation factors to the cost function of the GIF. Then, according to the visual characteristics of human eyes, this paper applies the detail regulation factor to the detail layer enhancement, which solves the problem of amplifying image noise using fixed gain coefficient enhancement. Finally, the enhanced detail layer is directly fused with the base layer so that the enhanced image has rich detail information. We first compare the DGIF with four guided image filters and then compare the algorithm of this paper with three traditional IR image enhancement algorithms and two IR image enhancement algorithms based on the GIF on 20 IR images. The experimental results show that the DGIF has better edge-preserving and smoothing characteristics than the four guided image filters. The mean values of quantitative evaluation of information entropy, average gradient, edge intensity, figure definition, and root-mean-square contrast of the enhanced images, respectively, achieved about 0.23%, 3.4%, 4.3%, 2.1%, and 0.17% improvement over the optimal parameter. It shows that the algorithm in this paper can effectively suppress the image noise in the detail layer while enhancing the detail information, improving the image contrast, and having a better visual effect. [ABSTRACT FROM AUTHOR]

Fierce marketing competition and varied customer requirements on products make a lot of companies transfer to the customisation strategy. However, customers may be overwhelmed by the vast assortment of products and options. This paper proposes a methodology aimed at optimising the range of customised product varieties considering synergy effects of modules and preferences of experts. The methodology outlines key steps required for a successful customised product configuration. Firstly, a questionnaire based on the Kano model is designed to identify customer preferences for product module customisation. The potential customers can be segmented based on the distance among their multidimensional preferences. Secondly, within each segmented market, the customer satisfaction function that considers module synergies is established using the quantified Kano model and coefficient matrix. Thirdly, the cost function that considers experts' preferences is established based on the prospect theory. Finally, a bi-objective optimisation model is formulated to maximise customer satisfaction and minimise cost under technical constraints. Pareto solutions are obtained by solving the model with the non-dominated sorting genetic algorithm II (NSGA-II). Modules and attributes available to customers can be determined by the solutions. An illustrative example of modular laptop computers confirms the methodology's effectiveness in optimising product configuration. [ABSTRACT FROM AUTHOR]

This paper presents a receding horizon optimal control (RHOC) method with an economic objective function for balancing the cost of resources (resource use × cost) with income through yield (yield × product price). This paper considers the two elements that determine the income through yield. The first element is yield and associated fruit development. A new, computationally viable, approach to model the income through yield is proposed and its prediction accuracy with respect to the original model is evaluated. The new approach employs a model that predicts at each time step, the future income through yield based on the assimilates partitioned to the fruits at the current time step. Simulations suggest that the assumptions made to arrive at the model for the new approach, do not significantly affect the accuracy of the predictions. The second element considered in this paper is the product price and the uncertainty inherent in its forecasts. Historical product price data are used to generate artificial product price forecasts. An uncertainty analysis, in combination with the artificial product price forecasts, showed that the product price forecast error does not considerably affect the optimised control strategy. Season-wide simulations with RHOC suggest that the product price forecast error does not considerably affect the value of the economic objective function. • A computationally viable approach to model the income through yield is proposed. • The new approach to model income through yield allows for RHOC with a short horizon. • Historical product price data are used to create artificial product price forecasts. • Product price forecast errors do not considerably affect the control strategy. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unmanned aerial vehicles (UAVs) provide efficient and flexible means for maritime emergency rescue, with path planning being a critical technology in this context. Most existing unmanned device research focuses on land-based path planning in two-dimensional planes, which fails to fully leverage the aerial advantages of UAVs and does not accurately describe offshore environments. Therefore, this paper establishes a three-dimensional offshore environmental model. The UAV's path in this environment is achieved through a novel swarm intelligence algorithm, which is based on the coati optimization algorithm (COA). New strategies are introduced to address potential issues within the COA, thereby solving the problem of UAV path planning in complex offshore environments. The proposed OCLCOA introduces a dynamic opposition-based search to address the population separation problem in the COA and incorporates a covariance search strategy to enhance its exploitation capabilities. To simulate the actual environment as closely as possible, the environmental model established in this paper considers three environmental factors: offshore flight-restricted area, island terrain, and sea winds. A corresponding cost function is designed to evaluate the path length and path deflection and quantify the impact of these three environmental factors on the UAV. Experimental results verify that the proposed algorithm effectively solves the UAV path planning problem in offshore environments. [ABSTRACT FROM AUTHOR]

The wear of a cutting tool can lead to tool failure and product quality deterioration, and thus timely maintenance of tools is crucial. Meanwhile, the wear of tools from a same population usually exhibits heterogeneous patterns. Therefore, this paper proposes a dynamic condition-based maintenance (CBM) policy for heterogeneous-wearing tools with considering the product quality deterioration caused by tool wear. The tool wear is modelled by an Inverse Gaussian (IG) process, and the wear rate is assumed to be a random variable to characterise the heterogeneity among tool wear processes. The posterior distribution of reciprocal of tool wear rate is dynamically estimated using the online wear data based on a Bayesian approach. Moreover, the impact of tool wear on product quality deterioration is modelled. The IG process is discretized into a discrete time Markov chain (DTMC). Under the frame of the DTMC, a cost function, containing product quality loss, preventive maintenance (PM) cost and corrective maintenance cost, is developed to determine the optimal PM threshold. The cost function updates dynamically with the dynamic estimation of tool wear rate and thus enables the optimal PM threshold to be dynamically revised. The effectiveness of the proposed CBM policy is demonstrated through a case study. [ABSTRACT FROM AUTHOR]

To further improve the performance of the kernel adaptive filtering algorithm in a non-Gaussian environment, a robust kernel least mean square algorithm is proposed, and the effectiveness of the root cost function and the convergence of the algorithm is theoretically analyzed. An improved online vector quantization criterion is then applied to the proposed algorithm to suppress the linearly growing network size. Finally, the different performances of the algorithm of this paper and other kernel adaptive filtering algorithms as well as this paper's algorithm before and after quantization are compared in Mackey Glass chaotic time series as well as in system identification, confirming the superiority of the algorithm of this paper and the improved online vector quantization criterion. [ABSTRACT FROM AUTHOR]

Regarding the traditional lane-changing decision theory around the vehicle's intention to change lanes, less consideration of the behavioral interactions between vehicles, and the personalized driving preferences of different drivers, this paper proposes a dynamic game for a lane-changing decision-making method that considers human-like driving preferences. First, to match the multiperformance evaluation requirements in the lane-changing process of intelligent vehicles, a cost function including driving space, traffic efficiency, and driving comfort is constructed. Second, the analytic hierarchy process (AHP) and criteria importance through intercriteria correlation (CRITIC) methods are used to conduct subjective and objective analyses on the next generation simulation (NGSIM) traffic data set to obtain the weight coefficients of multiple performance indicators of human-like driving preferences. The effects of different driving behaviors on lane-changing intentions and performance indexes are also studied. Finally, fuzzy control theory and intelligent driver model (IDM) are used to predict the driving behavior of interacting vehicles in the target lane, and the master-slave dynamic game theory and the particle swarm optimization algorithm are used to realize the behavioral interaction between the main vehicle and surrounding vehicles and to make the optimal lane-changing decisions. The research results show that the dynamic game lane-changing decision-making method of intelligent vehicles as proposed in this paper, which considers human-like driving preferences, can effectively meet the personalized requirements of different driving behaviors on driving space and traffic efficiency in the process of lane changing and improve the safety of intelligent vehicle lane-changing driving. Practical Applications: The lane change behavior strategy of intelligent vehicle is an important component of intelligent driving technology. Accurately identifying the driving style and uncertainty factors of surrounding vehicles and making corresponding lane-change decisions to ensure the driving safety of drivers are of great significance. Based on this, this paper proposes a dynamic game lane-change decision method considering human-like driving preferences. First, the lane-changing vehicle decision model is constructed from driving space, driving efficiency, and driving comfort. Second, through the analytic hierarchy process-criteria importance through intercriteria correlation (AHP-CRITIC) method, the weight of multiple performance indicators of human-like driving preferences is obtained from the three dimensions of indicator importance, indicator conflict, and data volatility from subjective and objective perspectives. Finally, based on fuzzy theory, the vehicle driving feature coefficient is obtained by taking the headway, speed coefficient, and acceleration and deceleration speed as the output. The behavior of the surrounding vehicles is predicted by the vehicle driving feature coefficient and intelligent driver model (IDM), and the lane-change decision is optimized by the prediction information of the surrounding vehicles, and the lane-change decision information is finally output. The lane-change decision method proposed in this paper considering human-like driving preferences can help intelligent vehicles realize multiperformance index evaluation demand analysis and lane-change interaction behavior research. [ABSTRACT FROM AUTHOR]

Research has focused on multilevel inverters (MLIs) due to their use in electric vehicles, renewable energy systems, and industrial applications. This paper proposes a new design for a single-phase 21-level asymmetrical MLI for photovoltaic (PV) applications that reduces the number of components, voltage stress, and overall size and cost. Enhanced incremental maximum power point tracking (EINC-MPPT) is used in the PV standalone system to offer a fast dynamic response, track maximum power, and regulate the PV module output voltage. This paper presents a PV-boost DC–DC single-input multi-output (SIMO) converter linked to solar panels to provide supply voltage to the inverter. A level-shifted constant multicarrier sinusoidal pulse width modulation (LSCMSPWM) technique is used to produce a better-synthesized output waveform from the MLI, resulting in low total harmonic distortion (THD) and also meeting IEEE standards. The suggested MLI is simulated in MATLAB/Simulink and tested with a hardware prototype under various load conditions. It is suitable for medium-power and grid-connected renewable energy systems applications. The qualitative and quantitative parameters of the proposed MLI have been evaluated by cost function (CF), number of components, reliability, THD, and total standing voltage (TSV); these parameters are compared with the existing MLIs. [ABSTRACT FROM AUTHOR]

The application of Model-Free Predictive Control (MFPC) in power electronic systems has garnered increasing attention. In this paper, MFPC control based on replacing the classical factory model with an ultra-local model (ULM) is studied. Generally, the Integral Sliding Mode Observer (ISMO) is used to estimate the unknown part in the ULM where the non-physical factor in the ULM is selected with approximate values ​​ranging from the nominal value of the system which is contrary to the concept of MFPC control. In this research, an improved adaptive integral sliding mode observer (AISMO) based MFPC (AISMO-MFPC) is proposed to estimate this factor with the unknown function in the ULM equation. The new observer design enables the estimation of this factor based on the current error, which allows for independent prposedcontrol of the system parameters.To obtain the lowest current ripple, the concept of active vector execution time (AVET) has been incorporated into the proposed control where two vectors are selected in the sampling period to minimize the cost function instead of selecting a single vector. ULM is also used to calculate AVET which facilitates the implementation of the imposed control. The combination of the proposed AISMO-MFPC and AVET gives faster system response and reduces the current ripple and lower harmonics, especially in case of mismatch parameters. Finally, the effectiveness of the proposed control is confirmed under various conditions by the presented simulation and experimental results. [ABSTRACT FROM AUTHOR]

Stochastic dual dynamic programming (SDDP) is a cutting-edge methodology for addressing the challenges related to the uncertainties in the generation scheduling of hydrodominant power systems. This problem is formulated as a multistage stochastic programming problem spanning a multiyear planning horizon. To efficiently navigate this complexity, SDDP decomposes the multistage linear problem into one-period subproblems, which are iteratively solved via forward and backward steps. Recognizing SDDP's pivotal role, this paper introduces enhancements to boost its computational performance. Firstly, we propose a novel approach, decomposing the scenario tree into two-period subproblems. Although this increases the computational burden of solving larger subproblems, it also increases the quality of resulting future cost functions. Secondly, the paper explores synergy among different decompositions and parallelization techniques. This integrated approach aims to maintain speed in solving smaller one-period subproblems while capitalizing on the improved quality of future cost functions resulting from aggregating periods within the same subproblem. We use Brazilian power system data to show the proposed framework's efficacy in a long-term generation scheduling problem. The results underscore the advantages of the two-period subproblem approach, which yields superior out-of-sample performance compared with the standard SDDP when policies are computed using the same number of iterations. When comparing in-sample confidence intervals, the proposed decomposition approach demonstrates a 26% reduction compared with the conventional one-period SDDP, all within the same execution time. Furthermore, combining decomposition techniques in an asynchronous framework results in a 17% improvement in the confidence interval compared with an asynchronous strategy without this combination. In summary, this paper provides valuable insights into enhancing SDDP's computational efficiency, presenting a refined approach to scenario tree decomposition and advocating for the strategic combination of decomposition techniques and parallelization strategies. These findings hold practical implications for optimizing hydrodominant power systems, particularly in the long-term generation planning of the Brazilian power system. Practical Applications: This paper introduces novel developments aimed at enhancing the SDDP method. We introduce a novel approach to decompose the multistage scenario tree and explore the optimal combination of different decompositions through parallelization. The primary application of SDDP lies in addressing the long-term generation scheduling of hydrothermal power systems. In Brazil this problem encompasses the monthly planning operation of the power system, considering a planning horizon from 3 to 5 years, focused on minimizing the total costs of the thermal dispatch. The method's performance and the quality of future cost functions directly influence operational policies, subsequently impacting energy availability and prices. Our proposed two-period decomposition of the scenario tree promises an improved future cost function, holding the potential to improve the operation of power systems. This development opens up new avenues for discussions and applications, fostering advancements in the field of stochastic optimization for sustainable and efficient energy management. [ABSTRACT FROM AUTHOR]

This paper presents a method to design an active suspension controller for 8 × 8 armored combat vehicles, which is called corner damping control (CDC). It is assumed that the target vehicle with 8 × 8 drive mechanisms and 8 suspensions has active actuators on each suspension for vertical, roll and pitch motion control on a sprung mass. A state-space model with 22 state variables is derived from the target vehicle. With the state-space model, a linear quadratic (LQ) cost function is defined. The control objective is to reduce the vertical acceleration, pitch and roll angles of a sprung mass for ride comfort, durability and turret stabilization. To avoid full-state feedback of LQR, a static output feedback control (SOF) is selected as a control structure for CDC. The vertical velocity, roll and pitch rates of a sprung mass, and vertical velocities at each corner, are selected as a sensor output. With those sensor outputs and LQ cost function, four LQ SOF controllers are designed. To validate the effectiveness of the LQ SOF controllers, simulation is carried out on a vehicle simulation package. From the simulation results, it is shown that the proposed CDC with LQ SOF controllers with a much smaller number of sensor outputs and controller gains can reduce the vertical acceleration, pitch and roll angles of a sprung mass and, as a result, improve ride comfort, durability and turret stabilization. [ABSTRACT FROM AUTHOR]

This paper aims to analyse the dynamic response of a corrupted spacecraft rendezvous system from the perspective of attacker. The optimal data injection attack problem is formulated by constructing a tradeoff cost function in a quadratic form. First, the optimal attack strategy and associated sufficient condition for its existence are derived similar to optimal control for attacker without being detected. Breaking the assumption in most existing works, the goal of this paper is to explore the optimal attack strategy without knowing system matrices. A model free Q‐learning approach is designed with the application to solve attacker's optimization problem. Critic network and action network are used to adaptive tuning the value and action for attacker in a forward time. For a more practical situation, a model free attack strategy design is implemented only based on measured input/output data. Finally, the simulation results on the spacecraft system are presented to show the effectiveness of the proposed method for model free attack strategy design. [ABSTRACT FROM AUTHOR]

Structural break detection is an important time series analysis task. It can be treated as a multi-objective optimization problem, in which we ought to find a time series segmentation such that time series theoretical models constructed on each segment are well-fitted and the segments are long enough to bear meaningful information. Metaheuristic optimization can help us solve this problem. This paper introduces a suite of new cost functions for the structural break detection task. We demonstrate that the new cost functions allow for achieving quantitatively better precision than the cost functions employed in the literature of this domain. We show particular advantages of each new cost function. Furthermore, the paper promotes the use of Particle Swarm Optimization (PSO) in the domain of structural break detection, which so far has relied on the Genetic Algorithm (GA). Our experiments show that PSO outperforms GA for many analysed time series examples. Last but not least, we introduce a non-trivial generalization of the top-performing state-of-the-art approach to the structural break detection problem based on the Minimum Description Length (MDL) rule with autoregressive (AR) model to MDL ARIMA (autoregressive integrated moving average) model. [ABSTRACT FROM AUTHOR]

This paper deals with the power supply queuing model M/M/∞. In this paper, we analyze total cost of power supply queuing model with infinite servers. We develop a total cost function and suject it to optimization, which consequently gives us non-linear equations. These non-linear equations are further solved by using Newton-Raphson method with the help of R-software. We also present analysis of sensitivity, tables and graphs of the model to exhibit the comprehensive interpretation of the same. [ABSTRACT FROM AUTHOR]

Effective path planning is crucial for autonomous mobile robots navigating complex environments. The "global–local" coupled path planning algorithm exhibits superior global planning capabilities and local adaptability. However, these algorithms often fail to fully realize their potential due to low efficiency and excessive constraints. To address these issues, this study introduces a simpler and more effective integration strategy. Specifically, this paper proposes using a bi-layer map and a feasible domain strategy to organically combine the D*Lite algorithm with the Dynamic Window Approach (DWA). The bi-layer map effectively reduces the number of nodes in global planning, enhancing the efficiency of the D*Lite algorithm. The feasible domain strategy decreases constraints, allowing the local algorithm DWA to utilize its local planning capabilities fully. Moreover, the cost functions of both the D*Lite algorithm and DWA have been refined, enabling the fused algorithm to cope with more complex environments. This paper conducts simulation experiments across various settings and compares our method with A_DWA, another "global–local" coupled approach, which combines A* and DWA. D_DWA significantly outperforms A_DWA in complex environments, despite a 7.43% increase in path length. It reduces the traversal of risk areas by 71.95%, accumulative risk by 80.34%, global planning time by 26.98%, and time cost by 35.61%. Additionally, D_DWA outperforms the A_Q algorithm, a coupled approach validated in real-world environments, which combines A* and Q-learning, achieving reductions of 1.34% in path length, 67.14% in traversal risk area, 78.70% in cumulative risk, 34.85% in global planning time, and 37.63% in total time cost. The results demonstrate the superiority of our proposed algorithm in complex scenarios. [ABSTRACT FROM AUTHOR]

Residential care services are under increasing pressure to lower service provision costs while maintaining quality of care. Using a translog cost function, this paper examines the relationship between cost, quality and output in England's learning disability (LD) residential care sector. It finds genuine but diminishing economies of scale in LD residential care services vis-à-vis output (i.e., care weeks). However, some variation exists: higher-quality LD residential care homes appear to have larger economies of scale than lower-quality ones. Supplementary regression analysis, examining quality-size, further finds quality (a) is negatively associated with LD care homes of six or fewer beds; (b) shows no association with homes of more than six beds. These findings enhance residential care literature and raise the possibility that, by promoting the establishment of larger high-quality care homes, cost savings may be achieved without sacrificing quality. [ABSTRACT FROM AUTHOR]

This editorial summarizes the papers published in issue 17(3) (2022). The first paper analyses the impact of knowledge spillovers on patent applications using a Tobit model. The second paper sets out an economic-theoretical model of industrial specialization patterns across cities and their impact on the spatial agglomeration of skilled workers and long-term productivity growth. The third paper analyses the price and average cost functions of a competitive industry in which firms face diseconomies of scale but enjoy economies of scale when they agglomerate. The fourth paper shows that productivity spillover effects and their endogeneity are key to understanding the productivity-compensation gap. The fifth paper studies geographical and sectoral specialization versus concentration of global supply chains. The sixth paper combines spatial autoregressive (SAR) and geographically weighted regression (GWR) models to test whether urban residents have reacted to the Covid-19 pandemic by moving out of US metropolitan centres into the suburbs. The seventh paper investigates the impact of natural disasters caused by climate change on forced outmigration flows in South and South-East Asian countries. [ABSTRACT FROM AUTHOR]

In some location scenarios where the location information of nodes cannot be mastered in advance, the anchor-free location technology is particularly important. In order to reduce the complicated calculation and eliminate the accumulated error in the traditional anchor-free location algorithm, a new anchor-free location algorithm based on transition coordinates is proposed in this paper. This algorithm is different from the traditional methods such as minimum cost function or inverse matrix. Instead, N initial coordinates are randomly generated as the starting position of the transition coordinates, and the position increment between the transition coordinates and the real coordinates of the node is constantly modified. After K iterations, the convergent position coordinates are finally infinitely close to the real position coordinates of N nodes, and the computational complexity is less than most existing algorithms. As follows, the factors that affect the performance of the algorithm are investigated in the simulation experiment, including the topology structure, positioning accuracy and the total number of nodes, etc. The results show great advantages compared with the traditional anchor-free positioning algorithm. When the topology structure of the initial coordinates changes from a square to a random graph, the number of iterations increases by 15.79%. When the positioning accuracy increased from 1% to 1‰, the number of iterations increased by 36.84%. When the number of nodes N is reduced from 9 to 4, the number of iterations is reduced by 63.16%. In addition, the algorithm can also be extended to the field of moving coordinates or three-dimensional spatial positioning, which has broad application prospects. [ABSTRACT FROM AUTHOR]

In the traditional predictive torque control strategy, the cost function considers two different control objectives: stator flux and torque. Therefore, it is necessary to reasonably set the weighting factor to ensure that the optimal voltage vector is accurately selected during the control process, thereby achieving better torque and flux control performance. In view of the complex tuning process caused by traditional predictive torque control setting method of weighting factor, a new weighting factor selection method for permanent magnet synchronous motor control system is proposed in this paper. The derivation process is based on the equivalent transformation between the torque–flux prediction and the voltage vector prediction. If both the cost functions of the torque–flux and voltage vector reach their minimum values, the same output voltage vector will be selected. Compared with previous studies, the algorithm proposed in this paper can find the precise location of the optimal weighting factor without changing the form of the cost function, which simplifies the parameter tuning process. Experimental results are also provided to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Forecasting sales trends is a valuable activity for companies of all types and sizes, as it enables more efficient decision making to avoid unnecessary expenses from excess inventory or, conversely, losses due to insufficient inventory to meet demand. In this paper, we designed a personalized cost function to reduce economic losses caused by the excessive acquisition of products or derived from their scarcity when needed. Moreover, we designed an LSTM network integrated with Glorot and Orthogonal initializers and dropout to forecast sales trends in a lumber mill in Tamaulipas, Mexico. To generalize and appropriately forecast the sales of the lumber mill products, we optimized the LSTM network's hyperparameters through a genetic algorithm, which was essential to explore the solution space. We evaluated our proposal in instances obtained from the historical sales of the five main products sold by the lumber mill. According to the results, we concluded that for our case study the proposed function cost and the hyperparameters optimization allowed the LSTM to forecast the direction and trend of the lumber mill's product sales despite the variability of the products. [ABSTRACT FROM AUTHOR]

This research paper focuses on addressing the challenge of infinite-time linear quadratic tracking control (LQT) for linear systems with parametric uncertainty. Traditional solutions to the LQT problem often involve using a discount factor to prevent the cost function from growing unbounded over time. However, this approach can introduce instability in the closed-loop system. To overcome this issue, this paper proposes an alternative approach using an undiscounted cost function that ensures the asymptotic stability of the uncertain closed-loop system. To design a control scheme without requiring precise knowledge of the system dynamics, reinforcement learning (RL) algorithms are employed. However, for systems with uncertain parameters that may lead to instability, the convergence of RL algorithms to a stabilising solution is not guaranteed. To address this limitation, a robust optimal control structure is developed using on-policy and off-policy reinforcement learning algorithms, resulting in a model-free controller. The effectiveness of the proposed robust optimal controller is validated through comparative simulations on an uncertain model of a DC–DC buck converter connected to a constant power load. These simulations demonstrate the advantages and benefits of the robust optimal controller in handling parametric uncertainty and ensuring stability in the control system. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The shortest path problem is fundamental in graph theory and has been studied extensively due to its practical importance. Despite this aspect, finding the shortest path between two nodes remains a significant challenge in many applications, as it often becomes complex and time consuming. This complexity becomes even more challenging when constraints make the problem non-additive, thereby increasing the difficulty of finding the optimal path. The objective of this paper is to present a broad perspective on the conventional shortest path problem. It introduces a new method to classify cost functions associated with graphs by defining distinct sets of cost functions. This classification facilitates the exploration of line graphs and an understanding of the upper bounds on the transformation sizes for these types of graphs. Based on these foundations, the paper proposes a practical methodology for solving non-additive shortest path problems. It also provides a proof of optimality and establishes an upper bound on the algorithmic cost of the proposed methodology. This study not only expands the scope of traditional shortest path problems but also highlights their computational complexity and potential solutions. [ABSTRACT FROM AUTHOR]

Metaheuristics are currently playing an increasingly important role in the tuning of industrial controllers. In particular, this paper presents the results of implementing various nature-inspired meta-heuristics for the tuning of a proportional-integral controller intended for use in a high-voltage pulse generator. This paper analyses and compares the results of tuning obtained using both classical meta-heuristics, such as simulated annealing, genetic algorithms, particle swarm optimisation, and differential evolution, and newer approaches, such as sand cat swarm optimisation and sea lion optimisation. An original, complex multi-criteria cost function is constructed in this paper for optimising and ranking nature-inspired metaheuristics for the tuning of the proportional-integral controller. The results show that sand cat swarm optimisation outperforms other optimisation approaches according to the adopted multi-criteria optimisation criterion. [ABSTRACT FROM AUTHOR]

Featured Application: This paper proposes a computationally efficient algorithm for base placement in automatic multi-robot vehicle painting. The proposed algorithm incorporates the CAD model of the vehicle the manufacturer is interested in painting and the kinematic parameters of the robotic manipulators (e.g., their Denavit–Hartenberg parameters). The algorithm computes the robot's optimal fixed base positions. The base positions can subsequently be utilized by already available robotic path/motion planners. This paper investigates the problem of optimal base placement in collaborative robotic car painting. The objective of this problem is to find the optimal fixed base positions of a collection of given articulated robotic arms on the factory floor/ceiling such that the possibility of vehicle paint coverage is maximized while the possibility of robot collision avoidance is minimized. Leveraging the inherent two-dimensional geometric features of robotic car painting, we construct two types of cost functions that formally capture the notions of paint coverage maximization and collision avoidance minimization. Using these cost functions, we formulate a multi-objective optimization problem, which can be readily solved using any standard multi-objective optimizer. Our resulting optimal base placement algorithm decouples base placement from motion/trajectory planning. In particular, our computationally efficient algorithm does not require any information from motion/trajectory planners a priori or during base placement computations. Rather, it offers a hierarchical solution in the sense that its generated results can be utilized within already available robotic painting motion/trajectory planners. Our proposed solution's effectiveness is demonstrated through simulation results of multiple industrial robotic arms collaboratively painting a Ford F-150 truck. [ABSTRACT FROM AUTHOR]

Energy prices are usually determined by the marginal costs obtained by solving economic dispatch problems without considering commitment costs. Hence, generating units are compensated through uplift payments. However, uplift payments may undermine market transparency as they are not publicly disclosed. Alternatively, energy prices can be obtained from the unit commitment problem which considers commitment costs. But, due to non-convexity, prices may not monotonically increase with demand. To resolve this issue, convex hull pricing has been introduced. It is defined as the slope of the convex envelope of the total cost function over the convex hull of a unit commitment (UC) problem. Although several approaches have been developed, a relevant survey has not been found to aid the understanding of convex hull pricing from the current limited literature. This paper provides a systematic survey of convex hull pricing. It reviews, compares, and links various existing approaches, focusing on the modeling and computation of convex hull prices. Furthermore, this paper explores potential areas of improvement and future challenges due to the ongoing efforts for power system decarbonization. [ABSTRACT FROM AUTHOR]

Guided waves (GW) allow fast inspection of a large area and hence have received great interest from the structural health monitoring (SHM) community. Fiber Bragg grating (FBG) sensors offer several advantages but their use has been limited for the GW sensing due to its limited sensitivity. FBG sensors in the edge-filtering configuration have overcome this issue with sensitivity and there is a renewed interest in their use. Unfortunately, the FBG sensors and the equipment needed for interrogation is quite expensive, and hence their number is restricted. In the previous work by the authors, the number and location of the actuators was optimized for developing a SHM system with a single sensor and multiple actuators. But through the use of the phenomenon of acoustic coupling, multiple locations on the structure may be interrogated with a single FBG sensor. As a result, a sensor network with multiple sensing locations and a few actuators is feasible and cost effective. This paper develops a two-step methodology for the optimization of an actuator–sensor network harnessing the acoustic coupling ability of FBG sensors. In the first stage, the actuator–sensor network is optimized based on the application demands (coverage with at least three actuator–sensor pairs) and the cost of the instrumentation. In the second stage, an acoustic coupler network is designed to ensure high-fidelity measurements with minimal interference from other bond locations (overlap of measurements) as well as interference from features in the acoustically coupled circuit (fiber end, coupler, etc.). The non-sorting genetic algorithm (NSGA-II) is implemented for finding the optimal solution for both problems. The analytical implementation of the cost function is validated experimentally. The results show that the optimization does indeed have the potential to improve the quality of SHM while reducing the instrumentation costs significantly. [ABSTRACT FROM AUTHOR]

Currently, regardless of the algorithm used, motion planners for dealing with dynamic obstructions need to rely on high‐precision sensors and high performance processors. The requirements for hardware increase as the density of dynamic obstructions in an area becomes higher. Additionally, motion planners are more prone to errors in complex environments. The Rapidly‐exploring Random Tree (RRT ∗) algorithm only considers static obstructions and cannot effectively avoid densely populated regions of dynamic obstructions. This paper develops an improved RRT ∗ algorithm that is capable of avoiding densely populated regions of dynamic obstructions. In this algorithm, the cost function of the traditional RRT ∗ algorithm is modified based on the density of dynamic obstructions, allowing the planned path to bypass densely populated regions. The algorithm also introduces reasonable penalty terms to penalize segments that pass through densely populated regions, while maintaining asymptotic optimality of the traditional RRT ∗ algorithm. Numerical experiments reveal that the improved RRT ∗ algorithm is able to successfully avoid densely populated regions of dynamic obstructions with minimal time cost and exhibits better robustness during the path search process in comparison to the traditional RRT ∗ algorithm. Thus, the improved RRT ∗ algorithm possesses the ability to adapt to more complex areas for path planning. [ABSTRACT FROM AUTHOR]

Wastewater reuse is a proven strategy to mitigate water stress in drought-prone regions. However, this practice is still limited due to high implementation costs, regulatory hurdles, and limited public acceptance. In regions with low reclaim rates, a thorough evaluation of the potential for reuse is needed to support decision-making, focusing on opportunities that address both low-hanging fruit and high-leverage projects. This paper introduces a streamlined, data-centric methodology for assessing wastewater reuse potential, adaptable to various regional contexts. The methodology involves comprehensive data collection and processing to evaluate wastewater treatment plant (WWTP) capabilities and identify potential users, allowing the prioritisation of case studies based on demand alignment. Different treatment and distribution systems are analysed to match WWTP capabilities with user needs, considering volume, quality, and infrastructure requirements. Cost analysis incorporates capital expenditure (CAPEX), operational expenditure (OPEX) and unit costs using novel cost functions for treatment and distribution. Risk analysis adheres to WHO methodology to ensure safety and sustainability. A case study in the Lisbon and Oeste areas in Portugal validates this approach, revealing key insights into the potential and economic viability of water reuse. By comparing tariffs and costs associated with different reuse scenarios, this paper offers benchmarks for the economic feasibility of reuse projects. [ABSTRACT FROM AUTHOR]

Featured Application: This work provides a systematic way to produce a defect-free atom array of neutral atom platforms for quantum information processing. This paper aims to present an approach to address the atom rearrangement problem by developing Convolutional Neural Network (CNN) models. These models predict the coordinates for atom movements while ensuring collision-free transitions and filling all vacancies in the target array. The process begins with designing a cost function for the assignment problem that incorporates constraints to prevent collisions and guarantee vacancy filling. We then build and train CNN models using datasets for three different grid sizes: 10 × 10 ,   13 × 13 ,   and   21 × 21 . Our models achieve high accuracy in predicting atom positions, with individual position accuracies of 99.63%, 98.93%, and 97.24%, respectively, for the aforementioned grid sizes. Despite limitations in training larger models due to hardware constraints, our approach demonstrates significant improvements in speed and accuracy. The final section of the paper presents detailed accuracy results and calculation times for each model, highlighting the potential of CNN-based methods in optimizing atom rearrangement processes. [ABSTRACT FROM AUTHOR]

The genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection, and it is known for its iterative optimization capabilities in both constrained and unconstrained environments. In this paper, a novel method for GA-based dual-automatic guided vehicle (AGV)-ganged path planning is proposed to address the problem of frequent steering collisions in dual-AGV-ganged autonomous navigation. This method successfully plans global paths that are safe, collision-free, and efficient for both leader and follower AGVs. Firstly, a new ganged turning cost function was introduced based on the safe turning radius of dual-AGV-ganged systems to effectively search for selectable safe paths. Then, a dual-AGV-ganged fitness function was designed that incorporates the pose information of starting and goal points to guide the GA toward iterative optimization for smooth, efficient, and safe movement of dual AGVs. Finally, to verify the feasibility and effectiveness of the proposed algorithm, simulation experiments were conducted, and its performance was compared with traditional genetic algorithms, Astra algorithms, and Dijkstra algorithms. The results show that the proposed algorithm effectively solves the problem of frequent steering collisions, significantly shortens the path length, and improves the smoothness and safety stability of the path. Moreover, the planned paths were validated in real environments, ensuring safe paths while making more efficient use of map resources. Compared to the Dijkstra algorithm, the path length was reduced by 30.1%, further confirming the effectiveness of the method. This provides crucial technical support for the safe autonomous navigation of dual-AGV-ganged systems. [ABSTRACT FROM AUTHOR]

As enterprises increasingly rely on cloud-based Human Resource Management Systems (HRMS) deployed on the Hadoop platform, the optimization of weighting algorithms becomes imperative to enhance system efficiency. This paper addresses the complex challenge of load balancing in the cloud environment by proposing Effective Load Balancing Strategy (ELBS) a hybrid optimization approach that integrates both Genetic Algorithm (GA) and Grey Wolf Optimization (GWO). The optimization objective involves the allocation of N jobs submitted by cloud users to M processing units, each characterized by a Processing Unit Vector (PUV). The PUV encapsulates critical parameters such as Million Instructions Per Second (MIPS), execution cost α, and delay cost L. Concurrently, each job submitted by a cloud user is represented by a Job Unit Vector (JUV), considering service type, number of instructions (NIC), job arrival time (AT), and worst-case completion time (wc). The proposed hybrid GA-GWO aims to minimize a cost function ζ, incorporating weighted factors of execution cost and delay cost. The challenge lies in determining optimal weights, a task addressed by assigning user preferences or importance as weights. The hybrid algorithm iteratively evolves populations of processing units, applying genetic operators, such as crossover and mutation, along with the exploration capabilities of GWO, to efficiently explore the solution space. This research contributes a comprehensive algorithmic solution to the optimization of weighting algorithms in enterprise HRMS on the cloud and Hadoop platform. The adaptability of the hybrid ELBS to dynamic cloud environments and its efficacy in handling complex optimization problems position it as a promising tool for achieving load balancing in HRMS applications. The proposed approach provides a foundation for further empirical validation and implementation in practical enterprise settings. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of trajectory planning for autonomous vehicles operating in complex, constrained environments. The proposed method enhances the hybrid A-star algorithm through back-end optimization. An adaptive node expansion strategy is introduced to handle varying environmental complexities. By integrating Dijkstra's shortest path search, the method improves direction selection and refines the estimated cost function. Utilizing the characteristics of hybrid A-star path planning, a quadratic programming approach with designed constraints smooths discrete path points. This results in a smoothed trajectory that supports speed planning using S-curve profiles. Both simulation and experimental results demonstrate that the improved hybrid A-star search significantly boosts efficiency. The trajectory shows continuous and smooth transitions in heading angle and speed, leading to notable improvements in trajectory planning efficiency and overall comfort for autonomous vehicles in challenging environments. [ABSTRACT FROM AUTHOR]