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The environmental/economic dispatch (EED) of power systems addresses the environmental pollution problems caused by power generation at the operational level, offering macroscopic control without requiring additional construction and remediation costs, garnering widespread attention in recent years. This paper undertakes a comprehensive review of existing EED models, categorizing them according to the control of atmospheric pollutants into total air pollutant control (TAPC) and control considering the spatial and temporal diffusion (STD) of atmospheric pollutants. In addition, various methods employed to address the EED problems, as well as the current state of research on multi-area EED models, are presented. Finally, this paper analyzes and summarizes the literature on existing EED models, highlighting the deficiencies of the current work and future research directions. Through these explorations, the authors find that controlling the EED model by considering TAPC is more suitable for general macro planning, whereas the EED model considering the STD of air pollutant emissions enables more precise and effective control. Summarizing such models and techniques is conducive to developing dispatch plans adapted to local conditions, which is significantly beneficial for public welfare and government management, promoting sustainable and environmentally friendly power system dispatch methods. [ABSTRACT FROM AUTHOR]

The internet holds a significant role in all aspects of our lives, and its importance continues to grow each day. Therefore, the usability of the Internet holds great significance. Low data quality and disinformation severely impact the usability of the internet. Consequently, people face challenges in obtaining accurate and clear information. In the present day, websites predominantly feature image-based content like pictures and videos, as opposed to text-based content. The classification of such content holds immense importance for search engines. As a result, the classification of web pages stands as a crucial research area for scholars. This study focuses on the classification of image-based web pages. A deep learning-based approach is proposed to categorize web pages into four main groups: tourism, machinery, music, and sports. The suggested method yielded the most favourable outcomes when utilizing the Stochastic Gradient Descent (SGD) optimization method, achieving an accuracy of 0.9737, a recall of0.9474, an Fl score of 0.9474, and an Area Under the ROC Curve (AUC) value of 0.9649. Furthermore, the utilization of Deep Learning (DL) led to achieving the most advanced results in web page classification within the existing literature, particularly on the WebScreenshots dataset. [ABSTRACT FROM AUTHOR]

Determination of the precision of the designed observations in a geodetic network referred as the Second Order Design is an essential element of the network design process. Although the precision requirements are usually of key importance, ensuring an adequate level of reliability, understood as the possibility of outliers detection can be also vital. The subject of this study is the optimization of the observations' precision distribution to get the balanced observation reliability indices. The objective of the work is to test usability of two optimization methods based on optimization algorithms, (simulated annealing and Hooke–Jeeves optimization), to solve the mentioned problem. An analytical method proposed by Amiri-Simkooei was applied as a reference. The performance of the above-mentioned methods was tested on two simulated angular-linear networks. Due to acceptable working time and the possibility of defining the boundary conditions on the final solution, the Hooke–Jeeves method appeared to be the most suitable to solve the analysed problem. [ABSTRACT FROM AUTHOR]

The problem of minimizing (maximizing) multiple extremum functionals (infinite-dimensional optimization) is considered. This problem cannot be solved by conventional gradient methods. New gradient methods with adaptive relaxation of steps in the vicinity of local extrema are proposed. The efficiency of the proposed methods is demonstrated by the example of optimizing the shape of a hydraulic gun nozzle with respect to the objective functional, which is the average force of the hydraulic pulse jet momentum acting on an obstacle. Two local maxima are found, the second of which is global; in the second maximum, the average force of the jet momentum is three times higher than in the first maximum. The corresponding nozzle shape is optimal. Conventional gradient methods have not found any maximum; i.e., they were unable to solve the problem. [ABSTRACT FROM AUTHOR]

This article discusses model and model-free approaches to solve problems of spectral ellipsometry as applied to microelectronic problems related to measuring the thicknesses and optical parameters of thin layers of dielectrics, metals, and semiconductors. Model approaches are based on the use of a priori information about the form of the Cauchy, Drude, Drude–Lorentz, and Tauc–Lorentz dispersion relation. Model-free approaches can use any smooth multiparameter functional relationship that can describe a smooth spectral curve. We can also use machine learning (ML) to implement a model-free approach, which is suited for determining the thickness of multilayer structures and their optical characteristics and can significantly increase the speed of data processing. [ABSTRACT FROM AUTHOR]

The integro-differential equation with the Cauchy kernel is used in many different technical problems, such as in circuit analysis or gas infrared radiation studies. Therefore, it is important to be able to solve this type of equation, even in an approximate way. This article compares two approaches for solving this type of equation. One of the considered methods is based on the application of the differential Taylor series, while the second approach uses selected heuristic algorithms inspired by the behavior of animals. Due to the problem domain, which is symmetric, and taking into account the form of the function appearing in this equation, we can use this symmetry in some cases. The paper also presents numerical examples illustrating how each method works and comparing the discussed approaches. [ABSTRACT FROM AUTHOR]

In this article, a plasma‐metal model is studied as an electromagnetic absorber for stealth application. Epstain distribution is considered for plasma and the electromagnetic reflection of the structure is analyzed by the Green's function volume integral equation method. The attenuation of microwaves in plasma depends on microwave frequency, plasma electron density, spatial distribution of plasma, collision frequency, and plasma thickness. Using a metaheuristic optimization method, it is possible to change the parameters simultaneously in the specified domains to obtain conditions for which microwaves can be absorbed effectively in the specified frequency. In this paper, optimized plasma parameters related to ideal attenuation in desired frequency band are determined by this method. The advantage of this method is that the intervals related to the plasma parameters are selecting according to the practical conditions and in many cases prevent the production of high volume with high density plasma to achieve effective attenuation. The proposed method can be useful for designing adjustable devices in stealth applications. Proper management of power consumption in these devices is one of the results of adjustment. [ABSTRACT FROM AUTHOR]

This review article explores the azeotropic mixtures and investigates the application of Pressure Swing Distillation (PSD) as an effective separation method. Azeotropic mixtures have compositions that result in vapor and liquid phases with the same composition, making them challenging to separate using simple distillation. The content offers a structured approach to design, optimize, and execute separation processes, emphasizing the importance of heat integration, process optimization, control strategies, economic considerations, and environmental impacts within the context of PSD. Furthermore, these findings provide valuable insights for practitioners in the selection of appropriate heat-integration, optimization methods, and balancing the trade-off between economy and controllability when selecting an appropriate separation technique by balancing the economic benefits and controllable performance of different methods. PSD is reviewed detailing thermodynamic equilibrium, selection of pressure and column sequence, heat integration, optimization techniques, control strategy, and economics. The recent progresses of PSD are evaluated to determine how different heat integration techniques are useful in achieving cost and energy savings. The review provides valuable guidance to researchers and engineers navigating the complexities of azeotropic mixture separation, with a specific focus on PSD practical applications. [ABSTRACT FROM AUTHOR]

With the presence of a large number of inversion algorithms for inverse heat transfer problems (IHTPs) and non-IHTPs, a need for review to have a holistic view is seen. An exhaustive literature review, with the motivation of selecting the inversion technique best fit for a given problem, was made for a general inverse problem. For ill-posedness, a classification of available regularization algorithms namely Tikhonov's regularization, Bayesian regularization, mollification method, Beck's sequential approach and Alifanov's iterative approach, has been provided. Inversion methods like singular value decomposition, truncated singular value decomposition, Tikhonov regularization and total variation regularization are explained. Optimization methods namely steepest descent method, conjugate gradient method, Newton method, Levenberg–Marquardt method, Lagrange method, adjoint method, function specification method, genetic algorithm, differential evolution and particle swarm optimization (PSO) are reviewed. Further, a technique based on neural networks is studied, and wavelet methods like shrinking and wavelet vaguelette decomposition are reviewed. Associated literature has also been listed, highlighting the gaps. The usability of various algorithms in IHTP, starting from the golden section search method, for retrieval of a single parameter, to the regularized versions of the inversion technique, for retrieval of multiple parameters with uncertainty, demonstrates real-life applications to fins in IHTP. An inversion algorithm capable to handle every kind of nonlinearity is sought in literature, whose absence raises the research question, "Is there a technique that works globally for every inverse problem?", is asked prior to, "What if the available techniques were not utilized to an extent that they should?" is posed. In lieu of this gap, a general comparative framework is developed, such that an efficient technique is selected, based on the total minimum error, which can be used in any field of interest. [ABSTRACT FROM AUTHOR]

This article presents a numerical strategy for actively manipulating electromagnetic (EM) fields in layered media. In particular, we develop a scheme to characterize an EM source that will generate some predetermined field patterns in prescribed disjoint exterior regions in layered media. The proposed question of specifying such an EM source is not an inverse source problem (ISP) since the existence of a solution is not guaranteed. Moreover, our problem allows for the possibility of prescribing different EM fields in mutually disjoint exterior regions. This question involves a linear inverse problem that requires solving a severely ill-posed optimization problem (i.e. suffering from possible non-existence or non-uniqueness of a solution). The forward operator is defined by expressing the EM fields as a function of the current at the source using the layered media Green's function (LMGF), accounting for the physical parameters of the layered media. This results to integral equations that are then discretized using the method of moments (MoM), yielding an ill-posed system of linear equations. Unlike in ISPs, stability with respect to data is not an issue here since no data is measured. Rather, stability with respect to input current approximation is important. To get such stable solutions, we applied two regularization methods, namely, the truncated singular value decomposition (TSVD) method and the Tikhonov regularization method with the Morozov Discrepancy Principle. We performed several numerical simulations to support the theoretical framework and analyzes, and to demonstrate the accuracy and feasibility of the proposed numerical algorithms. [ABSTRACT FROM AUTHOR]

In this work, the new application of cooperation search algorithm (CSA) is addressed to extract the unknown solid oxide fuel cell (SOFC)'s parameters supported by investigating and testing the cell steady-state and dynamic performances. Primarily, the indefinite SOFC's parameters are estimated utilizing CSA and the results are assessed and verified with compulsory comparisons. The minimum errors are obtained, CSA convergence behavior and SOFC's performance are all compared with other recognized algorithms in the literature. At last, and for more validations, the dynamic behavior of the SOFCs is studied when supplying electric vehicle (EV) operated with four DC motors at different speeds. The SOFCs and EV are represented via Simulink/MATLAB package. The EV's input current, voltage, power, and torque are traced at various acceleration and speed according to the road nature and/or driver requirements. The extensive investigations and validations prove the high efficacy of CSA capabilities in representing the SOFCs and for analyzing their steady-state and dynamic behaviors. • Cooperation Search Algorithm (CSA) is applied to define SOFC model uncertain parameters. • Verifying the results of the proposed CSA against other well-known and established algorithms. • Studying the steady state behavior of the SOFC based on obtained parameters by CSA. • The dynamic performance of SOFC is analyzed when supplying Electric Vehicle. • The results and applications showed the efficacy of CSA. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have enormous promise for the development of future transportation systems. The widespread use of EVs could negatively impact how power systems operate, particularly at the distribution level. Therefore, smart charging techniques are essential to increasing EV adoption in general. The connection between the electricity grid and the transportation network is made at electric vehicle charging stations (EVCS), and both networks will be simultaneously impacted by the operational behaviour of EVs. Therefore,  EVCS must be placed in a distribution network in the best possible way. In this paper, an efficient and smart charging approach is formulated to schedule the charging of electric vehicles so that adverse effects like an increase in peak demand for the system and the cost of charging electric vehicles are minimized. The EV  load model is formulated by considering factors such as the state of charge, trip distance travelled, and the user’s charging behaviour. The proposed electric charging schedule reduces the peak load and optimizes the cost of charging. Different types of EVs are considered based on their usage patterns for making realistic problem formulations. The smart charging technique presented in this work reduces the peak demand by 30% and the cost of charging by 50%. In addition, EVCS placement is implemented alongside distributed generation on IEEE 33 and 69 bus systems to reduce power losses and improve the voltage profile by using a metaheuristic algorithm known as Jaya Algorithm. The effectiveness of the proposed algorithm is established by comparing the results with published work. [ABSTRACT FROM AUTHOR]

This article is devoted to developing an approach for manipulating the von Neumann entropy S (ρ (t)) of an open two-qubit system with coherent control and incoherent control inducing time-dependent decoherence rates. The following goals are considered: (a) minimizing or maximizing the final entropy S (ρ (T)) ; (b) steering S (ρ (T)) to a given target value; (c) steering S (ρ (T)) to a target value and satisfying the pointwise state constraint S (ρ (t)) ≤ S ¯ for a given S ¯ ; (d) keeping S (ρ (t)) constant at a given time interval. Under the Markovian dynamics determined by a Gorini–Kossakowski–Sudarshan–Lindblad type master equation, which contains coherent and incoherent controls, one- and two-step gradient projection methods and genetic algorithm have been adapted, taking into account the specifics of the objective functionals. The corresponding numerical results are provided and discussed. [ABSTRACT FROM AUTHOR]

Purpose – This paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs). Design/methodology/approach – The research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation was carried out using the Scopus, ScienceDirect and Web of Science databases. The papers selection and classification took place in three steps considering only studies in English language and published in electronic journals (from 2008 to 2022). The investigation led up to the selection of 46 publications (10 presenting literature reviews and 36 proposing CSMs). Findings – The findings showed that CSMs are usually formulated using Financial Analysis, Machine Learning, Statistical Techniques, Operational Research and Data Mining Algorithms. The main databases used by the researchers were banks and the University of California, Irvine. The analyses identified 48 methods used by CSMs, the main ones being: Logistic Regression (13%), Naive Bayes (10%) and Artificial Neural Networks (7%). The authors conclude that advances in credit score studies will require new hybrid approaches capable of integrating Big Data and Deep Learning algorithms into CSMs. These algorithms should have practical issues considered consider practical issues for improving the level of adaptation and performance demanded for the CSMs. Practical implications – The results of this study might provide considerable practical implications for the application of CSMs. As it was aimed to demonstrate the application of optimisation methods, it is highly considerable that legal and ethical issues should be better adapted to CSMs. It is also suggested improvement of studies focused on micro and small companies for sales in instalment plans and commercial credit through the improvement or new CSMs. Originality/value – The economic reality surrounding credit granting has made risk management a complex decision-making issue increasingly supported by CSMs. Therefore, this paper satisfies an important gap in the literature to present an analysis of recent advances in optimisation methods applied to CSMs. The main contribution of this paper consists of presenting the evolution of the state of the art and future trends in studies aimed at proposing better CSMs. [ABSTRACT FROM AUTHOR]

This paper proposes a control strategy to achieve minimum wake-induced power losses in a wind farm. At first, the axial-induction-based wake model is developed to consider the aerodynamic wake interactions among wind turbines. To optimize the generated power of the whole wind farm, the axial induction factor of each wind turbine is calculated by the genetic algorithm. As a supervisory controller, each wind turbine's optimal axial induction factor calculated by the genetic algorithm is implemented as a setpoint of each wind turbine's internal controller. In the internal control loop, a comprehensive controller is designed to track the commanded axial induction factor. In the partial load region, the commanded axial induction factor was attained by tuning the generator torque. In the transient and full load regions, the blade pitch angle is tuned to keep the generator speed and torque at the rated values. The performance of the proposed control strategy is investigated through case studies, including three different wind speeds and a time-varying wind speed case in a 3 × 3 wind-farm layout. The simulation results show the satisfactory performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of brain tumor segmentation. In this study, handcrafted features were extracted from MRI scans that included intensity-based, texture-based, and shape-based features. In parallel, a unique CNN architecture was developed and trained to detect the features from the data automatically. The proposed hybrid method was combined with the handcrafted features and the features identified by CNN in different pathways to a new CNN. In this study, the Brain Tumor Segmentation (BraTS) challenge dataset was used to measure the performance using a variety of assessment measures, for instance, segmentation accuracy, dice score, sensitivity, and specificity. The achieved results showed that our proposed approach outperformed the traditional handcrafted feature-based and individual CNN-based methods used for brain tumor segmentation. In addition, the incorporation of handcrafted features enhanced the performance of CNN, yielding a more robust and generalizable solution. This research has significant potential for real-world clinical applications where precise and efficient brain tumor segmentation is essential. Future research directions include investigating alternative feature fusion techniques and incorporating additional imaging modalities to further improve the proposed method's performance. [ABSTRACT FROM AUTHOR]

A new approach to the fast optimization of crystal electric field (CEF) parameters to fit experimental data is presented. This approach is implemented in a lightweight Python‐based program, CrysFieldExplorer. The main novelty of the method is the development of a unique loss function, referred to as the spectrum characteristic loss (LSpectrum), which is based on the characteristic polynomial of the Hamiltonian matrix. Particle swarm optimization and a covariance matrix adaptation evolution strategy are used to find the minimum of the total loss function. It is demonstrated that CrysFieldExplorer can perform direct fitting of CEF parameters to any experimental data such as a neutron spectrum, susceptibility or magnetization measurements etc. CrysFieldExplorer can handle a large number of non‐zero CEF parameters and reveal multiple local and global minimum solutions. Crystal field theory, the loss function, and the implementation and limitations of the program are discussed within the context of two examples. [ABSTRACT FROM AUTHOR]

The data envelopment analysis is related to a non-parametric mathematical tool used to assess the relative efficiency of productive units. In different studies on productive efficiency, it is common to employ semi-parametric procedures in two stages to determine whether any exogenous factors of interest affect the performance of productive units. However, some of these procedures, particularly those based on conventional statistical inference, generate inconsistent estimates when dealing with incoherent data-generating processes. This inconsistency arises due to the efficiency scores being limited to the unit interval, and the estimated scores often exhibit serial correlation and have limited observations. To address such inconsistency, several strategies have been suggested, with the most well-known being an algorithm based on a parametric bootstrap procedure using the truncated normal distribution and its regression model. In this work, we present a modification of this algorithm that utilizes the beta distribution and its regression structure. The beta model allows for better accommodation of asymmetry in the data distribution. Our proposed algorithm introduces inferential characteristics that are superior to the original algorithm, resulting in a more statistically coherent data-generating process and improving the consistency property. We have conducted computational experiments that demonstrate the improved results achieved by our proposal. [ABSTRACT FROM AUTHOR]

The primary objective of airport management worldwide is always to make it easier to provide transportation services and minimize latency. This could be accomplished by controlling the movement of travelers through the airport's different checkpoints for passports, baggage handling, customs, and both departure and arrival lobbies. As one of the biggest passenger terminals around the world and among the most attractive destinations for visitors during the Hajj pilgrimage, this paper concentrates on enhancing the movement of travelers in the King Abdulaziz International Airport's pilgrimage station in the Kingdom of Saudi Arabia. Several optimization methods are used to better schedule the phases within the airport terminals as well as the assignment of arriving flights to vacant airport portals. These include the differential evolution algorithm (DEA), harmony search algorithm, genetic algorithm (GA), flower pollination algorithm (FPA), and black widow optimization algorithm. The findings demonstrated the potential sites for the development of airport stages, which may assist decision-makers in improving operational efficiency in the future. The simulation results showed that GA was more efficient in most of the experiments than the alternative algorithms for small population sizes in terms of the quality of the solutions obtained and the convergence rates. In contrast, DEA performed better in the larger population sizes. The outcomes also showed that FPA performed better than its rivals in identifying the optimal solution in terms of the overall duration of passenger waiting time. [ABSTRACT FROM AUTHOR]

The machining distortion induced by residual stresses is always a standout manufacturing issue during the processing of complex components. Therefore, it is very necessary for us to master the generation, evaluation, and optimization methods of residual stresses. In this review, the author concludes the research on the generation mechanism, simulation, and prediction of milling residual stress in the past decades, as well as the detection methods and other auxiliary process control methods of milling residual stress. Moreover, the author sums up the problems and difficulties in the research on milling residual stress at present, and gives the prospect of the future trends. This review can provide a reference for the further research of milling residual stresses. [ABSTRACT FROM AUTHOR]

The main goal of machine learning is the creation of self-learning algorithms in many areas of human activity. It allows a replacement of a person with artificial intelligence in seeking to expand production. The theory of artificial neural networks, which have already replaced humans in many problems, remains the most well-utilized branch of machine learning. Thus, one must select appropriate neural network architectures, data processing, and advanced applied mathematics tools. A common challenge for these networks is achieving the highest accuracy in a short time. This problem is solved by modifying networks and improving data pre-processing, where accuracy increases along with training time. Bt using optimization methods, one can improve the accuracy without increasing the time. In this review, we consider all existing optimization algorithms that meet in neural networks. We present modifications of optimization algorithms of the first, second, and information-geometric order, which are related to information geometry for Fisher–Rao and Bregman metrics. These optimizers have significantly influenced the development of neural networks through geometric and probabilistic tools. We present applications of all the given optimization algorithms, considering the types of neural networks. After that, we show ways to develop optimization algorithms in further research using modern neural networks. Fractional order, bilevel, and gradient-free optimizers can replace classical gradient-based optimizers. Such approaches are induced in graph, spiking, complex-valued, quantum, and wavelet neural networks. Besides pattern recognition, time series prediction, and object detection, there are many other applications in machine learning: quantum computations, partial differential, and integrodifferential equations, and stochastic processes. [ABSTRACT FROM AUTHOR]

It is well known that thermal conductivity measurement is a challenging task, due to the weaknesses of the traditional methods, such as the high cost, complex data analysis, and limitations of sample size. Nowadays, the requirement of quality of life and tightening energy efficiency regulations of buildings promote the demand for new construction materials. However, limited by the size and inhomogeneous structure, the thermal conductivity measurement of wall samples becomes a demanding topic. Additionally, we find the thermal parameter values of the samples measured in the laboratory are different from those obtained by theoretical computation. In this paper, a novel signal-transmissive wall is designed to provide the problem solving of signal connectivity in 5G. We further propose a new thermal conductivity predictor based on the Harmony Search (HS) algorithm to estimate the thermal properties of laboratory-made wall samples. The advantages of our approach over the conventional methods are simplicity and robustness, which can be generalized to a wide range of solid samples in the laboratory measurement. [ABSTRACT FROM AUTHOR]

The approach proposed for the development of this research work was based on the integration of Distributed Energy Generation (DG) into an energy distribution network, improving the voltage profile by establishing an optimal location and sizing of DG power plants, for which the use of a heuristic optimization method known as Genetic Algorithm (GA), which has several restrictions to limit its operation and thus achieve an optimal solution to the planned optimization problem, was resorted to. The development of a power flow allowed having the voltage measurement in the bars of the network before incorporating DG, allowing to verify that the voltage fluctuations present an improvement after the incorporation one by one of each DG central defined by the GA. The systems in which the voltage measurement was performed, as well as the verification of its fluctuations with and without DG once the GA was used, were the IEEE systems of 9 and 14 busbars, the latter the one used to demonstrate the scalability of the GA to locate and dimension DG without considering the number of busbars in a system. [ABSTRACT FROM AUTHOR]

Optimal cyclic scheduling problems of wafer-residency-time-constrained dual-arm cluster tools in wafer fabrication are challenging and remain to be fully solved. Existing studies assume that all processing modules (PMs) of a required type are used to process the same type of wafers. This sometimes brings unneeded conservativeness to scheduling results, because we may be able to make a tool schedulable by reducing the number of PMs in some steps if the original one is not. In some cases, we may use fewer PMs to reach the same result if the original one is schedulable, thus saving energy and other production resources. This work selects a proper number of PMs of needed types to process wafers while ensuring the highest productivity of a wafer-residency-time-constrained dual-arm cluster tool. It proposes the necessary and sufficient conditions under which a tool is schedulable. It then develops a polynomial-complexity algorithm that finds an optimal cyclic schedule. Examples are given to show its superiority over existing ones, thus advancing this field of cluster tool scheduling greatly and helping semiconductor producers to realize the green manufacturing of wafers. [ABSTRACT FROM AUTHOR]

Metal working process is one of the main activities in mechanical manufacturing industry; it is considered as a major consumer of energy and natural resources. In material removal process, the selection of cutting parameters and cooling or cutting liquid is necessary to save energy and achieve energy efficiency as well as sustainability. During the last two decades, the number of publications in this field has rapidly increased and has shown the importance of this research area. This review paper identifies and reviews in detail a total of 166 scientific studies which exhibit original contributions to the field and address multiple energy efficiency challenges. The recently developed models of energy consumption and different materials used in the machining process are presented. Therefore, this study describes various techniques for modeling and optimizing machining operations such as turning, milling, and drilling. Modeling techniques, experimental methods, multi-objective and single-objective optimization methods, and hybrid techniques optimization are presented in a detailed manner compared to previous review papers where only energy models are discussed. It can help practitioners and researchers to select the most appropriate approach for the desired experience and to highlight the progress of these methods in terms of machining energy efficiency. Additionally, this paper provides a review of different cutting fluids adopted in machining processes. This paper assists researchers and manufacturers in making advantageous technical decisions that have substantial economics in terms of energy saving. [ABSTRACT FROM AUTHOR]

This study presents an optimal insertion model for battery storage systems in the nodes of an electrical transmission network. The proposed model is developed through mixed integer linear programming applied to the calculation of DC power flows, considering restrictions given by the characteristics of the network and by the parameters of the generation units. The proposal's main objective is to reduce the costs of operation and non-supplied energy produced, due to needing to meet the demand fully or partially. As a case study to evaluate the proposed methodology, the IEEE 24-bar test system is used. In this base case, electrical generators that depend on different primary energy resources are modeled: hydraulic, thermal, photovoltaic, and wind, in addition to potential electrical energy storage systems. These storage systems are assigned as possible analysis scenarios through the proposed optimization technique. The study is carried out in a time horizon of 24 h per day, according to a standard demand curve. With the incorporation of optimally selected storage systems in their capacity and location, it is possible to minimize dependence on the use of fossil fuels. In addition, considerable savings are obtained by reducing generation costs, and the stability of the energy supply is guaranteed. This novel proposal presents a methodology that covers all the variables of this problem, thus guaranteeing an authentic and precise study in terms of optimization. The results obtained highlight and demonstrate the benefits of stability, continuous attention to demand, reduction in dependence on exhaustible and polluting sources, and cost reduction. [ABSTRACT FROM AUTHOR]

A multiprocessor system should be able to identify and eliminate faults in time to avoid the paralysis of a whole system. This paper proposes an improved binary bat algorithm to identify faulty processors in a multiprocessor system. Compared with most existing works based on metaheuristic algorithms, the proposed algorithm employs a random initial population and does not require transfer functions. The exclusive-OR operation in the velocity equation is used to measure the distance between two individuals in binary space. To improve population diversity and avoid local optima, the mutation operator is integrated into the position update equation. A new local search strategy is proposed to strengthen the ability of local search in binary space. Experimental results show that the proposed algorithm based on the Malek model can maintain approximately 100 % diagnostic accuracy in a small random initial population with fewer iterations and less CPU running time. [ABSTRACT FROM AUTHOR]

The relative protective devices are cascaded in a proper sequence with a proper min/max coordination time margin (CTM) to minimize the outage area of the network in case of fault condition. This manuscript addresses a new methodology based on the gorilla troops optimizer (GTO) to produce the best automated settings for overcurrent relays. In the GTO, the exploration and exploitation phases are realized using five methodologies. Three of them are used in the exploration phase and the other two in the exploitation phase. In the exploration phase, all gorillas are considered as candidate solutions and the best one is considered as the silverback gorilla. Then again, the exploitation phase comprises two steps: (i) the first one is the follow of silverback gorilla, and (ii) the second one is the competition for adult females. The latter mentioned offers an added advantage to the GTO framework to move forward steadily to global minima and to avoid trapping into local minima. Two test cases under numerous scenarios are demonstrated comprising an isolated real distribution network with distributed generations for the Agiba Petroleum company which is in the Western Desert of Egypt. The relay coordination problem is adapted as an optimization problem subject to a set of predefined constraints which is solved using the GTO including fixed and varied inverse IEC curves, in which the practical constraints including transformer phase shift and other scenarios for min/max fault conditions are dealt with. In due course, this current effort aims at proving the best strategy for achieving the smoothest coordination of overcurrent relays (OCRs), with the least obtained value of CTMs for the studied cases being established via the automated relay settings. At last, it can be pointed out that the GTO successfully dealt with this problem and was able to produce competitive answers compared to other competitors. [ABSTRACT FROM AUTHOR]

Customer rephasing is one of the most efficient techniques for balancing loads, reducing losses, and improving electricity quality. Traditionally, phase changes were performed by an operator, interrupting the supply during the process. Instead, a more recent approach proposes using phase switching devices that dynamically and imperceptibly transfer the customer load from one phase to another. Although this solution requires an extra investment related to these devices, it preserves the balance over time. This paper presents a new method to determine the minimum number of devices that should be installed and find their optimal allocation. The method is based on genetic algorithms and only uses data from the substation and a fraction of the customers, requiring less than 50% of penetration of smart meters to fix the imbalance problem. A new control algorithm for the switching devices is also proposed. It was designed to reduce the load imbalance while maintaining the number of changes per customer to a minimum, reducing device degradation. The method was validated through simulations using data from Tucumán province, Argentina. With at least 40% of the customers measured, it is possible to allocate the switches to less than 20% of customers and correctly reduce the load imbalance. [ABSTRACT FROM AUTHOR]

Complex network science is an interdisciplinary field of study based on graph theory, statistical mechanics, and data science. With the powerful tools now available in complex network theory for the study of network topology, it is obvious that complex network topology models can be applied to enhance artificial neural network models. In this paper, we provide an overview of the most important works published within the past 10 years on the topic of complex network theory-based optimization methods. This review of the most up-to-date optimized neural network systems reveals that the fusion of complex and neural networks improves both accuracy and robustness. By setting out our review findings here, we seek to promote a better understanding of basic concepts and offer a deeper insight into the various research efforts that have led to the use of complex network theory in the optimized neural networks of today. [ABSTRACT FROM AUTHOR]

Renewable energy solutions are appropriate for on-grid and off-grid applications, acting as a supporter for the utility network or rural locations without the need to develop or extend costly and difficult grid infrastructure. As a result, hybrid renewable energy sources have become a popular option for grid-connected or standalone systems. This paper examines hybrid renewable energy power production systems with a focus on energy sustainability, reliability due to irregularities, techno-economic feasibility, and being environmentally friendly. In attaining a reliable, clean, and cost-effective system, sizing optimal hybrid renewable energy sources (HRES) is a crucial challenge. The presenters went further to outline the best sizing approach that can be used in HRES, taking into consideration the key components, parameters, methods, and data. Moreover, the goal functions, constraints from design, system components, optimization software tools, and meta-heuristic algorithm methodologies were highlighted for the available studies in this timely synopsis of the state of the art. Additionally, current issues resulting from scaling HRES were also identified and discussed. The latest trends and advances in planning problems were thoroughly addressed. Finally, this paper provides suggestions for further research into the appropriate component sizing in HRES. [ABSTRACT FROM AUTHOR]

In this article, we provide a metaheuristic-based solution for stability analysis of nonlinear systems. We identify the optimal level set in the state space of these systems by combining two optimization phases. This set is in a definite negative region of the time derivative for a polynomial Lyapunov function (LF). Then, we consider a global optimization problem stated in two phases. The first phase is an external optimization to search for a definite positive LF, whose derivative is definite negative under linear matrix inequalities. The candidate LF coefficients are adjusted using a Jaya metaheuristic optimization algorithm. The second phase is an internal optimization to ensure an accurate estimate of the attraction region for each candidate LF that is optimized externally. The key idea of the algorithm is based mainly on a Jaya optimization, which provides an efficient way to characterize accurately the volume and shape of the maximal attraction domains. We conduct numerical experiments to validate the proposed approach. Two potential real-world applications are proposed. [ABSTRACT FROM AUTHOR]

This article discusses the use of analysis and forecasting methods for sales funnels to help further decision-making. A number of objective and subjective factors preventing the wide use of various sales funnel forecasting methods are described. It has been substantiated that due to a large number of external and internal factors, perfect forecasting results cannot be obtained. It has been proved that the most accurate and suitable methods for the forecasting of sales funnels are the methods included in the group of time series forecasting methods. Recommendations have been developed to improve some of the methods that significantly increase the accuracy of the forecasted data. Using the data received from different organizations, it was possible to empirically verify the accuracy of the forecast values. The obtained results of analysis and forecasting were used for testing the methods of searching the optimal scenarios of achieving the forecast indicators. [ABSTRACT FROM AUTHOR]

Global energy demand has consistently increased in recent decades, owing to the rapid population increase. Energy consumption is higher than it has ever been, and most fossil supplies are on the verge of exhaustion with the current rate of exploitation. Facing the double pressure of meeting energy demands and reducing carbon emissions, the integration of renewable energy into different aspects of the energy ecosystem has become a unified agreement for all countries globally. Hybrid renewable energy systems that integrate multiple energy sources can effectively solve this problem. In the hybrid renewable energy system, optimizing the unit size is the key to achieving efficient utilization of renewable energy. Research trends show that artificial intelligence methods are gaining attention from researchers and can provide good system optimization in the absence of long-term weather data to provide good system optimization. While different studies have presented articles in this research domain, it is important to give a comprehensive collation/summary of the research trend while highlighting key models/methods utilized in this research domain. Hence, based on the published literature, this paper provides a comprehensive bibliographical review of the current trends/status of hybrid renewable energy systems research. Key research articles published in the Web of Science between the years 2000 and 2022 in this research domain have been reviewed. This paper describes the hybrid renewable energy systems, summarizes many different energy systems in existing literature, compares the differences between various energy systems, and analyzes the physical models of different systems, as well as the optimization methods and the optimization of the systems. Further, the uncertainty of electricity generation from renewable energy sources is analyzed in the literature review and the future challenges of hybrid renewable energy systems are summarized. [ABSTRACT FROM AUTHOR]

Aiming at the key design variables of journal bearings, a novel optimization scheme is proposed to minimize oil leakage and power loss. For the first time, the inversion technology is introduced into the multi-objective optimization genetic algorithm under thermohydrodynamics. Using the hybrid optimization method (sequential quadratic programming and multi-objective optimization genetic algorithm) and the pareto optimal frontier method, the journal bearing model under the oil supply condition of oil groove (Model A) and oil hole (Model B) is optimized. More importantly, the oil leakage (QL) formula is exhaustively deduced, and good prediction results are obtained by simulating the data in literature. The optimization test results show that compared with the maximum errors (13% and 25%) of the power loss and leakage flow prediction results in literature, the maximum errors of this prediction model are 8% and 14%, respectively. In addition, compared with hybrid optimization method, the pareto optimal frontier has better advantages under inversion technology. Both methods can give good prediction results. The accuracy of this model is proved by comparing experimental data in the literature. [ABSTRACT FROM AUTHOR]

Despite the fact that the effectiveness of experiment planning methods, especially when solving applied problems, has been proven more than once, the ideas of a multifactorial experiment are very slowly being introduced into science and engineering practice. The reasons for this are: the complexity of organizing the experiment; the parameters of the systems under study are of a complex dynamic nature and are subject to significant influences of changes in environmental conditions; the apparent complexity of the planning and calculation matrix discourages researchers with insufficient mathematical background; many manuals on the application of a multifactorial experiment are written at a level inaccessible to engineers, given the training in the disciplines of mathematical and computer cycles in technical universities. The experiment occupies the main place among the methods of obtaining information about the internal relationships of phenomena in nature and technology. As the processes and phenomena under study become more complex, the costs of the equipment and the experiment increase. During the tests, a large amount of experimental data is collected that requires processing and analysis. At the same time, the duration of the analysis, comprehension of the test results and their accounting for adjusting the characteristics of new products is very significant. The systems approach involves considering all elements of an active experiment as a single system. From these positions, it is necessary to present the general properties of the experiment as an object of study and give recommendations on the choice of mathematical techniques and methods that the experimenter can use when choosing decisions during the preparation of the experiment, its conduct and processing of the results. It is very important to choose methods and tools for processing experimental data. Despite the fact that, since 2002, many works have been proving the effectiveness of using the Excel analysis package for processing experimental data, the scientific and educational literature still uses coding of variables, randomization of experiments, and the choice of critical values of the criteria for assessing the quality of mathematical models from tables. In addition, optimization methods are not used in the works for forecasting. In this paper, in order to accelerate the process of introducing effective computer processing methods into science and engineering practice, the implementation of the methodology for processing active experiment data using Excel is presented. [ABSTRACT FROM AUTHOR]

In this paper, a heuristic approach to doubly fed induction generator (DFIG) protection and low voltage ride through (LVRT) is carried out. DFIG-based wind systems are rapidly penetrating the power generation section. Despite their advantages, their direct coupling grid makes them highly sensitive to symmetrical faults. A well-known solution to this is the crowbar method of DFIG protection. This paper provides a method to determine the optimal crowbar resistance value, to ensure a strong trade-off between the rotor current and DC voltage transients. Further, since the crowbar method requires disconnection from the grid, the linear quadratic regulator (LQR) is applied to the system. This is to ensure fault ride through compliance with recent grid code requirements. The well-known PSO, as well as the recently developed African vultures optimization algorithm (AVOA), was applied to the problem. The first set of results show that for severe symmetrical voltage dips, the AVOA provides a good option for crowbar magnitude optimization, whereas PSO performed better for moderately severe dips. Secondly, when the LQR was optimized via the AVOA, it exhibited superiority over the conventional PSO-based PI controller. This superiority was in terms of rotor current transient magnitude, DC voltage transient magnitude, and reactive power steady-state ripple and were in the order of 67.5%, 20.35%, and 37.55%, respectively. When comparing the crowbar method and the LQR, it was observed that despite the LQR exhibiting superiority in terms of transient performance, the crowbar method offered a unanimously superior settling time. [ABSTRACT FROM AUTHOR]

The electric power industry is mainly responsible to ensure the high-quality, reliable, and economical operation of power systems by defining the limits and constraints of power system equipment. This paper uses the interiorpoint method to solve the nonlinear OPF problem. This method adjusts optimum values of OPF control variables, including the generator's active and reactive power output, with the objective function of minimizing total system losses. The interior-point method has been analyzed on a standard IEEE-14 bus test system using optimum power flow/unit commitment tools of DIgSILENT/Powerfactory. The analyses are conducted for alternating current (AC) power flow analysis and optimum power flow analysis, which represent Case 1 and Case 2, respectively. The results indicate that the total losses of the power system are reduced from 13.39 MW to 2.31 MW with the proposed algorithm. [ABSTRACT FROM AUTHOR]

Many researchers have addressed problems involving time-varying (TV) general linear matrix equations (GLMEs) because of their importance in science and engineering. This research discusses and solves the topic of solving TV GLME using the zeroing neural network (ZNN) design. Five new ZNN models based on novel error functions arising from gradient-descent and Newton optimization methods are presented and compared to each other and to the standard ZNN design. Pseudoinversion is involved in four proposed ZNN models, while three of them are related to Newton's optimization method. Heterogeneous numerical examples show that all models successfully solve TV GLMEs, although their effectiveness varies and depends on the input matrix. [ABSTRACT FROM AUTHOR]

In a wind farm, the interactions between turbines caused by wakes can significantly reduce the power output of the wind farm. Cooperative control among the turbines has the potential to improve the power output. However, existing centralized power optimization methods are computationally expensive and does not scale well for large wind farms, limiting their practical use in real-time control for time-varying wind conditions and turbine configuration (with adding or maintaining of turbines). To address this problem, this paper proposes a fully distributed power optimization method for wind farms using alternating direction method of multipliers (ADMM). The proposed method allows the wind farm power output to be optimized in fully distributed manner with turbine-to-turbine message passing over a mesh network, guarantees the implemented control actions satisfy the control constraints of all turbines, and provably converges to a stationary point of the wind farm power optimization problem. Simulation results demonstrate that the proposed method can significantly reduce the computation time with hardly sacrificing the power gain compared with centralized method and thus is computationally efficient for real-time power optimization of large wind farms. [ABSTRACT FROM AUTHOR]

This paper studies distributed algorithms for (strongly convex) composite optimization problems over mesh networks, subject to quantized communications. Instead of focusing on a specific algorithmic design, a black-box model is proposed, casting linearly convergent distributed algorithms in the form of fixed-point iterates. The algorithmic model is equipped with a novel random or deterministic Biased Compression (BC) rule on the quantizer design, and a new Adaptive encoding Non-uniform Quantizer (ANQ) coupled with a communication-efficient encoding scheme, which implements the BC-rule using a finite number of bits (below machine precision). This fills a gap existing in most state-of-the-art quantization schemes, such as those based on the popular compression rule, which rely on communication of some scalar signals with negligible quantization error (in practice quantized at the machine precision). A unified communication complexity analysis is developed for the black-box model, determining the average number of bits required to reach a solution of the optimization problem within a target accuracy. It is shown that the proposed BC-rule preserves linear convergence of the unquantized algorithms, and a trade-off between convergence rate and communication cost under ANQ-based quantization is characterized. Numerical results validate our theoretical findings and show that distributed algorithms equipped with the proposed ANQ have more favorable communication cost than algorithms using state-of-the-art quantization rules. [ABSTRACT FROM AUTHOR]

We present a deterministic iterative method for synthesizing equal-amplitude aperiodic array radiating patterns with arbitrary upper bounds and are capable of scanning in a wide angular range without violating the sidelobe level constraint. The approach employs the principle of “inflating” and “deflating” the sources to reduce the excitation dynamic of a starting layout iteratively. The proposed approach is flexible since it can start from a regular grid array with variable excitation amplitudes or from a nonuniform aperiodic layout generated with any other synthesis algorithm. Several numerical simulations comparing the proposed approach to some of the algorithms available in the open literature demonstrate the excellent performance of the approach. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) frequency spectrum offers orders of magnitude greater spectrum to mitigate the severe spectrum shortage in conventional cellular bands. To overcome the high propagation loss in the mmWave band, massive multiple-input and multiple-output (MIMO) can be adopted at both transmitter and receiver to provide large beamforming gains. At the same time, hybrid architecture is applied to reduce the huge power consumption caused by devices operating at radio frequency (RF). However, because of the hybrid architecture and large number of antennas, it is hard to obtain the channel state information (CSI) which is crucial for obtaining desirable beamforming gains. Off-grid error and sparsity pattern (SP) estimation error are two main limiting factors of the performance of most existing compressive sensing (CS) based channel estimation (CE) algorithms. Off-grid error presents when the true angle does not lie on the discretized angle grid of mmWave channel in the spatial domain. In this paper, we first propose a fast Bayesian matching pursuit method with ‘virtual sparsity’ to improve the accuracy of SP estimation and name it as the improved Bayesian matching pursuit (IBMP). Then an enhanced algorithm, named off-grid IBMP (OG-IBMP), is developed to mitigate the off-grid problem, followed by a theoretical analysis of OG-IBMP. This method iteratively updates the selected grid points and updates the corresponding parameters based on the maximum a posteriori (MAP) criterion. Numerical simulations are performed to validate our theoretical analysis and evaluate the performance of the proposed method. Compared to other existing methods, the results show that our proposed OG-IBMP algorithm greatly reduces the off-grid error and significantly enhances the accuracy of the SP estimation with low computational complexity. [ABSTRACT FROM AUTHOR]

The Area under the ROC curve (AUC) is a well-known ranking metric for problems such as imbalanced learning and recommender systems. The vast majority of existing AUC-optimization-based machine learning methods only focus on binary-class cases, while leaving the multiclass cases unconsidered. In this paper, we start an early trial to consider the problem of learning multiclass scoring functions via optimizing multiclass AUC metrics. Our foundation is based on the M metric, which is a well-known multiclass extension of AUC. We first pay a revisit to this metric, showing that it could eliminate the imbalance issue from the minority class pairs. Motivated by this, we propose an empirical surrogate risk minimization framework to approximately optimize the M metric. Theoretically, we show that: (i) optimizing most of the popular differentiable surrogate losses suffices to reach the Bayes optimal scoring function asymptotically; (ii) the training framework enjoys an imbalance-aware generalization error bound, which pays more attention to the bottleneck samples of minority classes compared with the traditional $O(\sqrt{1/N})$ O (1 / N) result. Practically, to deal with the low scalability of the computational operations, we propose acceleration methods for three popular surrogate loss functions, including the exponential loss, squared loss, and hinge loss, to speed up loss and gradient evaluations. Finally, experimental results on 11 real-world datasets demonstrate the effectiveness of our proposed framework. The code is now available at https://github.com/joshuaas/Learning-with-Multiclass-AUC-Theory-and-Algorithms. [ABSTRACT FROM AUTHOR]

The mechanism of torque ripple generation by time and space harmonic magnetic fields in permanent magnet synchronous motors is investigated to obtain advanced motor designs. The general expression between the torque ripples and harmonic air-gap flux densities in the motor is derived by using Maxwell stress tensor. Both the numerical and experimental verifications of this expression are carried out. By using this expression, the difference of the torque ripple generation between the surface and interior permanent magnet synchronous motors are discussed. Based on these investigations, the shape of the rotor surface of an interior permanent magnet motor is optimized. It is clarified that specific harmonic components of thetorque ripples in interior permanent magnet synchronous motors can be reduced to be nearly zero by optimizing the rotor surface shape. [ABSTRACT FROM AUTHOR]

The optimization of dual active bridge converters (DAB) is widely studied and numerous modulation schemes are proposed to enhance the performance of a DAB converter. However, a unified description method both suitable for time and frequency domain analysis still lacks. Besides, the optimization process encounters a difficulty of the potential nonconvex feasible region restricted by the power transmission equality, making the regular convex optimization methods no longer applicable. To solve these problems, this article gave a unified description method both suitable for time and frequency domain analysis based on the standard decomposition of instantaneous electric quantities. Besides, a unified optimization method is proposed based on tangent point searching for the feasible region and optimization objective surfaces. With the proposed unified optimization method, the optimization objective can be designed arbitrarily as long as it is piecewise continuous, and smooth. An efficiency-oriented optimization objective design method is introduced considering all electric quantities as well as the soft-switching conditions. Finally, the experimental results confirm the unified optimization method and the efficiency-oriented optimization objective design method. [ABSTRACT FROM AUTHOR]

Objective: In this work, to deal with the difficulties in choosing regularization weighting parameters and low spatial resolution problems in difference electrical impedance tomography (EIT), we propose two adaptively regularized bases-expansion subspace optimization methods (AR-BE-SOMs). Methods: Firstly, an adaptive $L^{1}$ -norm based total variation (TV) regularization is introduced under the framework of BE-SOM. Secondly, besides the additive regularization method, an adaptive weighted $L^{2}$ -norm multiplicative regularization is further proposed. The regularized objective functions are optimized by conjugate gradient method, where the unknowns in both methods are updated alternatively between induced contrast current (ICC) and conductivity domain. Conclusion: Both numerical and experimental tests are conducted to validate the proposed methods, where AR-BE-SOMs show better edge-preserving, anti-noise performance, lower relative errors, and higher structure similarity indexes than BE-SOM. Significance: Unlike the common regularization techniques in EIT, the proposed regularization factors can be obtained adaptively during the optimization process. More importantly, AR-BE-SOMs perform well in reconstructions of some challenging geometry with sharp corners such as the “heart and lung” phantoms, deformation phantoms, triangles and even rectangles. It is expected that the proposed AR-BE-SOMs will find their applications for high-quality lung health monitoring and other clinical applications. [ABSTRACT FROM AUTHOR]

AutoML aims at best configuring learning systems automatically. It contains core subtasks of algorithm selection and hyper-parameter tuning. Previous approaches considered searching in the joint hyper-parameter space of all algorithms, which forms a huge but redundant space and causes an inefficient search. We tackle this issue in a cascaded algorithm selection way, which contains an upper-level process of algorithm selection and a lower-level process of hyper-parameter tuning for algorithms. While the lower-level process employs an anytime tuning approach, the upper-level process is naturally formulated as a multi-armed bandit, deciding which algorithm should be allocated one more piece of time for the lower-level tuning. To achieve the goal of finding the best configuration, we propose the Extreme-Region Upper Confidence Bound (ER-UCB) strategy. Unlike UCB bandits that maximize the mean of feedback distribution, ER-UCB maximizes the extreme-region of feedback distribution. We first consider stationary distributions and propose the ER-UCB-S algorithm that has $O(K\ln n)$ O (K ln n) regret upper bound with $K$ K arms and $n$ n trials. We then extend to non-stationary settings and propose the ER-UCB-N algorithm that has $O(Kn^\nu)$ O (K n ν) regret upper bound, where $\frac{2}{3}<\nu <1$ 2 3 < ν < 1 . Finally, empirical studies on synthetic and AutoML tasks verify the effectiveness of ER-UCB-S/N by their outperformance in corresponding settings. [ABSTRACT FROM AUTHOR]

Mechanical systems with (possibly fast) time-varying but bounded uncertainty are considered. The exact value of the boundary is unknown. All the designer can get is that the boundary values are in a (known) fuzzy set. On the basis, a robust control method is proposed, which can ensure the uniformly bounded and uniformly ultimately bounded of the controlled mechanical system. The control contains two flexibly selectable design parameters. We seek to choose the optimal parameters. For a superior performance, two fuzzy-set-based performance indices are proposed to reflect the transient performance as well as the steady-state performance. Each performance index can be influenced by two design parameters. However, influences are nonconciliatory. This poses a design dilemma: the increase of a parameter may harness one performance while inflict the other. Therefore, the “optimal” choice of the design parameters is not intuitively clear. To resolve this dilemma, the two-player Nash-based noncooperative game theory is adopted, which is a notable feature of this article. Once the problem is formulated, we show that there is always a Nash-equilibrium solution to the two-player problem. We also show how to find it. The approach is very general, which also can be extended to $n$ -player Nash game for future research. The control is applied to a compressor powered by permanent magnet synchronous motor (PMSM) as a demonstration. The resulting performance shows that this Nash-based robust control design is both practical and effective. [ABSTRACT FROM AUTHOR]

Large Power Transformers (LPT) are a major element of the modern transmission grid. The tanks of traditional LPTs are made of low carbon steel which could contribute to their overall weight. This study provides a preliminary evaluation and selection process to modernize LPT tank materials following well-established principles of advanced materials engineering. A primary objective of this study was to select an advanced material for an LPT tank to make it more resilient to potential in-service structural damage. The selection process resulted in five advanced composite materials based on glass and carbon fibers with epoxy and phenolic resins employing two different fiber architectures. These composites were further screened to identify the most suitable materials considering several environmental conditions that the tank could be exposed to in service. As part of the final materials selection analysis, the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method was employed for the final ranking of the LPT tank replacement materials. Preliminary recommendations of modifications to the composites to withstand the effects of the thermal, electrical, and electromagnetic environment of the LPT are presented. [ABSTRACT FROM AUTHOR]