Search

Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]

Owing to increasing competition, the marketing of commercial software products is a challenging task. Diverse marketing strategies are adopted to tap the huge market share. Among these, pricing and warranty are the most significant ones. The warranty policies organizations adopt usually have two dimensions: duration and effort spent during the warranty phase. In this paper, two variables are broadly taken: testing, which is an essential phase during software development, and warranty, which is a crucial component of after‐sales service. Here, we will formulate an optimization problem where returns and expenses will be influenced by price, testing resources spent, the duration for which testing is done, warranty duration and effort spent during warranty for the software product. The product sales are assumed to follow a Stochastic Bass model based on Non‐Homogeneous Poisson Process (NHPP). A free on‐site support warranty with an upper limit on duration and effort covers the 2D failure process. The numerical illustration is presented wherein profit is maximized by conjointly optimizing five variables, that is price, testing duration, testing effort, warranty duration and warranty effort. This study emphasizes on application of a popular soft computing, metaheuristic technique named genetic algorithm (GA) to solve an optimization problem for a software system. The solution to the Optimization problem is obtained using the GA tool in MATLAB. The results obtained have relevance in the industry for finding optimal values of significant variables before the release of newly developed software products to the ultimate client. The concept discussed in this paper has diverse applications in computer engineering, software and hardware industry. [ABSTRACT FROM AUTHOR]

A battery energy storage system (BESS), due to its very fast dynamic response, plays an essential role in improving the transient frequency stability of a grid. The performance of the BESS varies with the system's installation site. Hence, the optimal location of the BESS is of utmost importance for improving transient frequency stability. Therefore, this paper presents a hierarchical approach for optimizing the BESS placement to improve a grid's transient frequency stability. In most research, frequency nadir and rate of change of frequency (ROCOF) have been considered for studying frequency stability. This paper considers two more parameters, along with frequency nadir and ROCOF, to study the transient frequency stability, settling time, and decay ratio. A novel frequency stability index (FSI) using the four transient frequency parameters has been developed. After a significant disturbance in a benchmarked test system, the FSI was used to identify the optimal location of the BESS for stabilizing the frequency. It has been observed that, after a sudden generator outage, the ROCOF and the frequency nadir improve the best when the BESS is located at the bus closest to the generator experiencing the outage. However, considering the other two parameters as well, the value of the FSI is the minimum; that is, the optimum solution is when the BESS is located at the bus that is the second closest to the generator experiencing the outage. Results of similar studies validate the proposed FSI in indicating the optimal location of the BESS in improving the transient frequency behavior of the system. [ABSTRACT FROM AUTHOR]

Finding the optimum conductor's pattern for unequally spaced grounding grids can be achieved by using empirical formulas or optimization techniques. Empirical formulas give an approximate solution depending on human experience and never cover the entire search space. On the other hand, the optimization techniques generally require a significant effort and time. To overcome the previous issues, this paper presents a closed-form solution based on optimized results. The main motivation behind this formula is the comprehensive study carried out by the authors to find optimum location of grounding grid conductors over different areas. In that work, particle swarm optimization technique (PSO) has been adapted with combined integration matrix method (CIMM) to facilitate the study. In this paper, the results obtained previously were summarized in the proposed formula. The proposed formula gives the optimum design of unequally grounding grids by specifying the conductor's locations as percentage of the side length for square and rectangular shapes. As long as the side length and the number of conductors are given, the location of each conductor as a percentage of the side length can be obtained. The effect of adding driven rods and the stratified soil conditions on the results of the optimized design were investigated. The validity of the proposed closed-form solution was verified experimentally. [ABSTRACT FROM AUTHOR]

Thermo-elastic systems have a wide range of applications in various engineering fields. Traditional single-objective topology optimization (TO) design of thermos-elastic structures is difficult to achieve the combined optimum of multiple properties, and the non-periodic TO design of results are complex and difficult to fabricate. This paper introduces a multi-objective TO formulation based on density for designing thermo-elastic structures with periodic constraints, aiming to overcome the aforementioned issues. This paper assesses two competing weighted objective functions, the first function corresponds to structural and thermal compliance, while the second pertains to regional temperature and global stress. To solve the optimization issue, we utilize the p-norm function with a modified coefficient for evaluating the maximum temperature and stress values. Additionally, the adjoint variable method is employed to evaluate the sensitivity of various objectives, while the method of moving asymptotes (MMA) is used to update the design variables. Then the influence of different weight coefficients and subregion numbers regarding thermos-elastic coupled analysis is demonstrated through numerical examples. The results show that the complexity of the subregion features decreases as the number of subregions increases, and that changing both the number of subregions and the weighting coefficients results in changes in the overall structural performance. Finally, the geometric model is reconstructed using the TO results, and the structural performance is validated through the simulation of the reconstructed model. The results indicate that the TO results obtained by the method of this paper have predefined performance. [ABSTRACT FROM AUTHOR]

This paper presents a novel numerical method for the Newton seismic full-waveform inversion (FWI). The method is based on the full-space approach, where the state, adjoint state, and control variables are optimized simultaneously. Each Newton step is formulated as a PDE-constrained optimization problem, which is cast in the form of the Karush–Kuhn–Tucker (KKT) system of linear algebraic equitations. The KKT system is solved inexactly with a preconditioned Krylov solver. We introduced two preconditioners: the one based on the block-triangular factorization and its variant with an inexact block solver. The method was benchmarked against the standard truncated Newton FWI scheme on a part of the Marmousi velocity model. The algorithm demonstrated a considerable runtime reduction compared to the standard FWI. Moreover, the presented approach has a great potential for further acceleration. The central result of this paper is that it establishes the feasibility of Newton-type optimization of the KKT system in application to the seismic FWI. [ABSTRACT FROM AUTHOR]

This paper presents a performance analysis and optimization of heat exchanger fin shapes in a thermoelectric power generation system for ship waste heat recovery. It proposes a new curved fin design aimed at enhancing heat transfer efficiency and maximizing thermoelectric power generation capacity. Numerical analysis and response surface methodology (RSM) focused on 225 parameters to identify the optimal fin shape. Computational analysis using CFX was conducted on the thermoelectric module system with curved fins. The methodology was verified by comparing and analyzing CFD and PIV experimental results for a curved fin. The paper comprehensively compares the fluid dynamics of curved fins and straight fins, highlighting how curved fins improve heat exchange by creating a tip leak vortex. The results demonstrate the superiority of the curved fin design in terms of heat transfer efficiency and net power generation over the conventional straight fin design. [ABSTRACT FROM AUTHOR]

Using historical traffic flow information to accurately and real-time predict future traffic flow information is particularly important for the management and control of traffic scenarios, and the guidance and planning of traffic travel. Short-term traffic flow prediction requires a large amount of forecast data and complex changes in traffic flow in space and time. However, the current prediction model cannot meet the requirements of real-time and accuracy of short-term traffic flow prediction. This paper proposes two traffic flow prediction models based on the changing trend and correlation of short-term traffic flow in time and space, combined with deep learning related theories. Firstly, this paper takes the traffic flow of the expressway as the research object, explores the basic parameters of traffic flow-flow, speed occupancy rate in time and space distribution law and change trend, and selects the speed as the research object, analyzes its temporal and spatial characteristics and correlation to provide data support for this study. Then, this paper introduces the related theories and frameworks of deep learning, mainly including neural network theory and hyperparameter optimization theory, as well as the deep learning model design framework. Pytorch and hyperparameter optimization framework Optuna, so as to provide theoretical foundation and technical support for this research. Finally, experiments of short-term traffic prediction based on single detection point and short-term traffic prediction based on multiple detection points are carried out on the PeMS dataset. Firstly, a multi-lane spatiotemporal convolutional network model (MSCTAN) based on traffic flow prediction at a single detection point is proposed to capture the spatiotemporal correlation of multi-lane traffic at a single detection point. Spatiotemporal Convolution and Spatiotemporal Attention Network Model (MSCSAN). For the two models proposed in this paper, comparative experiments were designed, and the mean absolute error (MAE), the root mean square error (RMSE) and the mean absolute percentage error (MAPE) were used as evaluation indicators to verify their performance at a single detection point and multiple detection points. The results show that, whether it is a single detection point experiment or a multi-detection point experiment, the two models achieve the best performance compared with the comparison model. [ABSTRACT FROM AUTHOR]

This paper presents a detailed review of the existing literature on peer-to-peer (P2P) energy trading considering market architectures, trading strategies, and enabling technologies. P2P energy trading enables individual users in the electricity network to act as sellers or buyers and trade energy among each other. To facilitate the discussion on different aspects of P2P energy trading, this paper focuses on P2P market mechanisms, relevant bidding strategies, and auction models. In addition, to solve the energy management problems associated with P2P energy trading, this paper investigates widely used solution methods such as game-theoretic models, mathematical optimisation, as well as more recent machine learning techniques and evaluates them in a critical manner. The outcomes of this investigation along with the identification of the challenges and limitations will allow researchers to find suitable P2P energy trading mechanisms based on different market contexts. Moreover, the discussions on potential future research directions are expected to improve the effectiveness of P2P energy trading technologies. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive survey of optimization developments in various aspects of electric vehicles (EVs). The survey covers optimization of the battery, including thermal, electrical, and mechanical aspects. The use of advanced techniques such as generative design or origami-inspired topological design enables by additive manufacturing is discussed, along with sensitivity studies of battery performance with alternate materials and incorporating sustainability considerations. Strategies for battery charging/discharging and battery swapping are reviewed, taking into consideration factors such as operation, cost, battery performance, and range anxiety. Future research is suggested to address uncertainties in charging ecosystem design and incorporate both forward and inverse prediction capabilities, leveraging benefits for both the grid and individual vehicles. The optimization techniques for other EV components, such as motors, powertrains, tires, and chassis, are also discussed. Finally, this paper presents a review of the EV management, specifically the optimization of charging station, grid, and fleet management, including research on charging station construction, charging station operation strategies, and power system operation strategies. The need for further research on robustness, reliability, and sustainability is emphasized to justify the use of EVs in the future. [ABSTRACT FROM AUTHOR]

This review paper synthesizes the recent advancements in voltage regulation techniques for active distribution networks (ADNs), particularly in contexts with high renewable energy source (RES) penetration, using photovoltaics (PVs) as a highlighted example. It covers a comprehensive analysis of various innovative strategies and optimization algorithms aimed at mitigating voltage fluctuations, optimizing network performance, and integrating smart technologies like smart inverters and energy storage systems (ESSs). The review highlights key developments in decentralized control algorithms, multi-objective optimization techniques, and the integration of advanced technologies such as soft open points (SOPs) to enhance grid stability and efficiency. The paper categorizes these strategies into two main types: analytical methods and computational methods. In conclusion, this review underscores the critical need for advanced analytical and computational methods in the voltage regulation of ADNs with high renewable energy penetration levels, highlighting the potential for significant improvements in grid stability and efficiency. [ABSTRACT FROM AUTHOR]

This review aims to exploit a study on different benchmark test functions used to evaluate the performance of Meta-Heuristic (MH) optimization techniques. The performance of the MH optimization techniques is evaluated with the different sets of mathematical benchmark test functions and various real-world engineering design problems. These benchmark test functions can help to identify the strengths and weaknesses of newly proposed MH optimization techniques. This review paper presents 215 mathematical test functions, including mathematical equations, characteristics, search space and global minima of the objective function and 57 real-world engineering design problems, including mathematical equations, constraints, and boundary conditions of the objective functions carried out from the literature. The MATLAB code references for mathematical benchmark test functions and real-world design problems, including the Congress of Evolutionary Computation (CEC) and Genetic and Evolutionary Computation Conference (GECCO) test suite, are presented in this paper. Also, the winners of CEC are highlighted with their reference papers. This paper also comprehensively reviews the literature related to benchmark test functions and real-world engineering design challenges using a bibliometric approach. This bibliometric analysis aims to analyze the number of publications, prolific authors, academic institutions, and country contributions to assess the field's growth and development. This paper will inspire researchers to innovate effective approaches for handling inequality and equality constraints. [ABSTRACT FROM AUTHOR]

Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This paper addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, it proposes an efficient deep learning approach for stress detection from facial videos. In contrast to the research on wearable devices, this paper proposes novel Hybrid Deep Learning (DL) networks for stress detection based on remote photoplethysmography (rPPG), employing (Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), 1D Convolutional Neural Network (1D-CNN)) models with hyperparameter optimisation and augmentation techniques to enhance performance. The proposed approach yields a substantial improvement in accuracy and efficiency in stress detection, achieving up to 95.83% accuracy with the UBFC-Phys dataset while maintaining excellent computational efficiency. The experimental results demonstrate the effectiveness of the proposed Hybrid DL models for rPPG-based-stress detection. [ABSTRACT FROM AUTHOR]

• A review of waste management optimization models (WM-OMs). 2. • A novel characterization method for examining and benchmarking the features WM-OMs. 3. • A pioneering guide for the selection of proper uncertainty analysis approaches. 4. • A compilation of characteristics of the papers reviewed and their data (Appendix). 5. • A perspective on the design of WM-OMs for different applications and stakeholders. 6. The transition to a circular economy through sustainable waste management (WM) follows different paths in each region, depending on its socioeconomic conditions and existing infrastructure. Mathematical optimization models are rigorous tools for informing local decision-making and identifying WM policy levers based on a variety of configurations. This review explores the pathways taken when designing WM optimization models (WM-OMs) that establish a network of waste valorization technologies. To standardize the literature review process, we propose a novel characterization method for examining, relating, and benchmarking the features of WM-OMs. After a thorough review of 58 articles published between 2015 and 2022, we assembled a comprehensive database to document the characteristics of these papers and the type of data reported in their case studies. We aim to provide a solid foundation for streamlining and enhancing future WM-OMs. Our work identifies various opportunities to improve the accuracy and reliability of WM-OMs. They include modeling thermo-chemical reactions in WM processes; considering regulatory, environmental, and political constraints; recognizing the informal sector; exploring the impact of marketing mechanisms on waste prevention and recycling; improving the traceability of case study data; specifying the rationale for uncertainty analysis (UA); and indicating the mathematical model (type, optimization algorithm, and equations). As many WM-OM authors have implemented UA without justifying their method choices, our review provides a pioneering guide for selecting the UA approach. Finally, we discuss the need for a trade-off between performance and practicality as models become more complex, making it critical to consider the specific needs of stakeholders. [ABSTRACT FROM AUTHOR]

A Hybrid Photovoltaic Thermal (PVT) system is one of the most emerging and energy-efficient technologies in the area of solar energy engineering. This review paper provides a comprehensive review of hybrid PVT systems in the context of the history of PVT, general classification, and parameter analysis. Several cell technologies with spectrum analysis are discussed to understand the application's ability and energy efficiency. Hybrid PVT concept, characteristics, and structure analysis is also discussed in this study. An extensive analysis on the classifications of hybrid PVT systems from the recent literature is also presented here. These literatures are identified based on several criteria. In order to provide a complete and energy-efficient technology, an innovative classification of the hybrid PVT system is proposed in this paper. This proposed classification is a combination and upgrade of various existing classifications mentioned in recent research studies. Parameters have a significant and unavoidable impact on the performance and efficiency of the hybrid PVT system. A brief analysis of different parameters and the optimization of the system is conducted after reviewing recent research articles. This analysis provides insights into the impact of parameter variations on the system. A novel parameter model comprising parametric and optimistic analyses is also presented in this paper. It provides a detailed parametric description that significantly affects the performance and efficiency of the hybrid PVT system. Finally, the assessment focuses on a critical analysis of the main challenges in adopting PVT technology and suggests ways to overcome these barriers. [ABSTRACT FROM AUTHOR]

Economic Dispatch Problems (EDP) refer to the process of determining the power output of generation units such that the electricity demand of the system is satisfied at a minimum cost while technical and operational constraints of the system are satisfied. This procedure is vital in the efficient energy management of electricity networks since it can ensure the reliable and efficient operation of power systems. As power systems transition from conventional to modern ones, new components and constraints are introduced to power systems, making the EDP increasingly complex. This highlights the importance of developing advanced optimization techniques that can efficiently handle these new complexities to ensure optimal operation and cost-effectiveness of power systems. This review paper provides a comprehensive exploration of the EDP, encompassing its mathematical formulation and the examination of commonly used problem formulation techniques, including single and multi-objective optimization methods. It also explores the progression of paradigms in economic dispatch, tracing the journey from traditional methods to contemporary strategies in power system management. The paper categorizes the commonly utilized techniques for solving EDP into four groups: conventional mathematical approaches, uncertainty modelling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, a predominant focus on single-case studies that limit the generalizability of findings, and the challenge of comparing research due to arbitrary system choices and formulation variations. The present paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field. [ABSTRACT FROM AUTHOR]

Summary: The underwater acoustic sensor network is a fundamental source for ocean exploration. The potential applications of underwater acoustic sensor network (UASN) include seismic imaging, disaster prevention, mine reconnaissance, pollution monitoring, exploration of natural resources, and military surveillance. To acquire accurate results, implementing all applications of underwater sensor networks requires an adequate network connection and communication technology. The precise placement of underwater sensor nodes needs to be identified in order to achieve effective communication. To accomplish the requirement, the paper presents an efficient localization algorithm to compensate the stratification effect based on an improved underwater salp swarm optimization technique (LAS‐IUSSOT). To compute the location of sensor nodes with high accuracy, the nodes are initially randomly deployed in three‐dimensional underwater acoustic sensor network (3D‐UASN). After that, the hybridization of centroid‐based localization and the ray theory technique is used, and then, the degree of coplanarity is analyzed among the underwater sensor nodes. Then, the computation of the location of unknown nodes is performed using improved underwater salp swarm optimization technique (IUSSOT) to obtain the optimized location and compensate the impact of the stratification. The comparison of the simulation results of the existing algorithm and the proposed algorithm is performed. The LAS‐IUSSOT achieves 40.46% and 28.00% accuracy in terms of localization of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. The LAS‐IUSSOT achieves 49.39% and 62.57% accuracy in terms of ranging of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. Simulation results illustrate that the proposed algorithm outperforms the existing algorithm in terms of localization and ranging accuracy in both sparse and dense regions in 3D‐UASN, root mean square error (RMSE), normalized localization error (NLE), computation time, and convergence rate. [ABSTRACT FROM AUTHOR]

This paper presents a powerful performance optimization approach for load frequency control in the off-grid mode of a delayed fuel cell microgrid with PI controller. The new approach based on stability boundary locus is used. Thanks to this, the search is only carried out in PI control parameter space keeping stable the system in the proposed optimization. The superiority of the proposed approach in terms of iteration history, fitness values, and time responses of the system is presented by comparing the results obtained. According to the results obtained, the proposed approach reaches the global minimum in a very short number of iterations and achieves this by both guaranteeing the stability of the system and providing the desired time response performances compared to the other method. • The stability of a delayed fuel cell microgrid system is a very important issue, especially in off grid mode. • A delayed fuel cell microgrid system should provide the desired quality of energy to the load. • The optimization approach provides the desired quality of energy to the load ensuring the stability of the microgrid. • Proposed optimization approach has great advantages in terms of iteration history and fitness value. [ABSTRACT FROM AUTHOR]

The necessity of employing renewable energy sources within microgrid-based Power-to-X (P2X) systems has been emphasized by the imperatives of reducing global warming and fossil fuel consumption. Due to their numerous advantages, solar and wind energy have acquired prominence in contemporary power systems. However, their intermittent nature presents challenges. To overcome these challenges, energy storage devices are utilized. Batteries, however, are plagued by issues such as high maintenance needs, self-discharge, and diminishing storage capacity with age. Consequently, investigating alternatives such as hydrogen storage has become a financially viable option, especially for high-power applications. However, the intrinsically slow dynamics of hydrogen production from electricity contrast with the rapid responses of contemporary power electronic devices. This paper presents a versatile technique for optimizing hydrogen production in the presence of stochastic fluctuations in solar irradiance and wind speed. The proposed method combines a modified version of the Symbiotic Differential Whale Optimization Algorithm (m-SDWOA) with power converters to maximize production from renewable sources and electrolyzers. Using extensive simulations to demonstrate the efficacy of the proposed method by comparing its performance to that of the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO). The results demonstrate the superior performance of the m-SDWOA-integrated method and pave the way for improved hydrogen generation in microgrid-based P2X systems. • Innovative control framework coordinates WT, PV, BESS, and HESS. • Maximizes hydrogen production while maintaining power quality. • Efficient controllers stabilize DC-bus voltage amidst WT/PV variability. • Ensures consistent, reliable power supply in microgrid. • Efficient energy management via m-SDWOA for resource optimization. [ABSTRACT FROM AUTHOR]

Sparse matrix-vector multiplication (SpMV) plays a critical role in a wide range of linear algebra computations, particularly in scientific and engineering disciplines. However, the irregular memory access patterns, extensive memory usage, high bandwidth requirements, and underutilization of parallelism hinder the computational efficiency of SpMV on GPUs. In this paper, we propose a novel approach called block-wise dynamic mixed-precision (BDMP) to address these challenges. Our methodology involves partitioning the original matrix into uniformly sized blocks, with each block's size determined by considering architectural characteristics and accuracy requirements. Additionally, we dynamically assign precision to each block using a precision selection method that takes into account the value distribution of the original sparse matrix. We develop two distinct SpMV computation algorithms for BDMP: BDMP-PBP (Precision-based partitioning) and BDMP-TCKI (Tailored compression and kernel implementation). BDMP-PBP partitions the matrix into two independent matrices for separate computations based on block precision, offering flexibility for integration with other optimization techniques. Meanwhile, BDMP-TCKI focuses on achieving significant thread-level parallelism and memory utilization by tailoring an appropriate compressed storage format and kernel implementation for each block. We compare BDMP with NVIDIA's cuSPARSE library and three state-of-the-art SpMV methods, including SELLP, MergeBase, and BalanceCSR, using matrices from the University of Florida's SuiteSparse dataset collection. BDMP-PBP and BDMP-TCKI show average speedups up to 2.64 × and 2.91 × on Turing RTX 2080Ti, and up to 2.99 × and 3.22 × on Ampere A100. The results demonstrate that BDMP enables the optimization of computation speed without compromising the precision necessary for reliable results. [ABSTRACT FROM AUTHOR]

This paper provides six metaheuristic algorithms, namely Fast Cuckoo Search (FCS), Salp Swarm Algorithm (SSA), Dynamic control Cuckoo search (DCCS), Gradient-Based Optimizer (GBO), Northern Goshawk Optimization (NGO), Opposition Flow Direction Algorithm (OFDA) to efficiently solve the optimal power flow (OPF) issue. Under standard and conservative operating settings, the OPF problem is modeled utilizing a range of objectives, constraints, and formulations. Five case studies have been conducted using IEEE 30-bus and IEEE 118-bus standard test systems to evaluate the effectiveness and robustness of the proposed algorithms. A performance evaluation procedure is suggested to compare the optimization techniques' strength and resilience. A fresh comparison methodology is created to compare the proposed methodologies with other well-known methodologies. Compared to previously reported optimization algorithms in the literature, the obtained results show the potential of GBO to solve various OPF problems efficiently. [ABSTRACT FROM AUTHOR]

The drying process is a crucial stage in the thermal treatment of iron ore pellets in a straight grate, as it has a direct impact on pellet quality and energy consumption. Consequently, this paper develops a simulation model for the pellet bed drying process, which is validated through industrial data and enables the visualization of temperature and moisture distribution within the pellet bed. Based on the simulation model, an optimization model for the drying process is developed to minimize the specific energy consumption per tonne of green pellets. The constraints of the drying process are the moisture content requirements and burst temperature. A genetic algorithm is employed to optimize the parameters such as trolley speed, pellet bed thickness, air temperature, and air pressure. The results show that, with an absolute error criterion of 10 °C, the temperature calculation accuracy of the simulation model exceeds 86%, effectively describing the drying process in a straight grate sintering machine. Under yield limitations, the optimal cumulative energy consumption per tonne of green pellet is reduced by 1.50% during the drying process. Finally, a simulation and optimization system for the drying process in the straight grate is implemented in C++. This system not only generates essential data for designing the drying section in a straight grate but also lays a theoretical foundation for system optimization and optimal control. [ABSTRACT FROM AUTHOR]

Personalized laser powder bed fusion (PBF-LB/M) Ti-6Al-4V (TC4) has a broader application prospect than that of traditional casting. In this paper, the composition and corrosion resistance of the passive film formation mechanism of TC4 prepared by optimization of PBF-LB/M techniques and traditional casting were systematically studied in 0.9 wt.% NaCl at 37 °C by electrochemical technique and surface analysis. The rates of the passive film formation process, corrosion resistance and composition of TC4 show different characteristics for the different preparation processes. Although the rate of passive film formation of cast-TC4 was higher at the initial immersion, the open circuit potential was more positive, and the film thickness was larger after stabilization, those facts show no positive correlation with corrosion resistance. On the contrary, with no obvious defects on the optimized PBF-LB/M-TC4, the passive film resistance is 2.5 times more, the defect concentration is reduced by 30%, and the TiO2 content is higher than that of the cast-TC4, making the martensitic-based PBF-LB/M-TC4 exhibit excellent corrosion resistance. This also provides good technical support for the further clinical application of PBF-LB/M-TC4. [ABSTRACT FROM AUTHOR]

We give a general Lagrange multiplier rule for mathematical programming problems in a Hausdorff locally convex space. We consider infinitely many inequality and equality constraints. Our results gives in particular a generalisation of the result of Jahn (Introduction to the theory of nonlinear optimization, Springer, Berlin, 2007), replacing Fréchet-differentiability assumptions on the functions by the Gateaux-differentiability. Moreover, the closed convex cone with a nonempty interior in the constraints is replaced by a strictly general class of closed subsets introduced in the paper and called "admissible sets". Examples illustrating our results are given. [ABSTRACT FROM AUTHOR]

During the coronavirus pandemic, demand surges and supply chain disruptions have resulted in healthcare commodity shortages. Limitations in modeling approaches forced decision makers to make quick decisions with little information. This paper presents a practical optimization model to guide ordering plans for a set of healthcare commodities and populations, such as masks for health care providers, during a pandemic. We collaborated with several organizations managing inventory for healthcare commodities to identify the key challenges, decisions, and objectives they face during a pandemic. The proposed model differs from other inventory and order management models in that the optimization balances the impact of commodity substitutions with delays in meeting demand forecasts. To balance these impacts, we introduce a Healthcare Commodity Metric that quantifies the relative consequences of delay and substitutions for multiple commodities and populations that vary in criticality. To the best of our knowledge, our model is the first to balance the consequences of delays and substitutions for multiple healthcare commodities during a pandemic. The model supports an agile and collaborative decision-making process needed in a constantly changing environment. The model is agile in that it can be adapted quickly to changes in demand, supply capacities, supply costs, and lead times. The model supports collaborative decision making by estimating the impacts of operational (e.g., ordering) and strategic (e.g., budget) decisions. We present an example use case to illustrate how the model balances delay and substitution and can be used to support agile and collaborative decision making. [ABSTRACT FROM AUTHOR]

This paper presents the development of a framework for the parametric design of tunnels using geographic information system (GIS) mapping and building information modelling (BIM). According to the framework, a parametric representation of each system component (e.g., layers of rock mass, size of excavation, topography, fault geometry, primary lining, secondary lining, rock bolts, etc.) can be incorporated in the GIS model with high levels of detail and used for the automatic generation of numerical models for the design of tunnel construction. This parametric evaluation allows the designer to consider several levels of detail for each component, for the efficient design and process optimization in conventional tunneling. Information between parametric analysis and numerical simulations can be freely exchanged with significantly reduced computational effort while results can be exported into BIM. It is anticipated that the proposed framework could lead to a comprehensive, yet efficient analysis of complex conventional tunneling project using parametric evaluations at the early design stages. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) are currently being substantially integrated into the power grid because of the recent liberalization of the energy market and associated benefits to both the environment and the economy. A significant amount of renewable energy sources are stochastic and weather dependent, being integrated into the power system that causes a mismatch between the generation of power and the load demand, which creates major issues like variations in frequency and power transmission between the regions. So it is necessary to maintain the frequency and tie-line power variations within a defined range. This is known as automatic generation control (AGC) or load frequency control (LFC). This paper uses brown bear optimization algorithm (BOA) to tune the PID and cascaded PI-PDN controllers for LFC of the renewable integrated two-area power system. The ITAE objective function is utilized to obtain optimal gains of the PID controller. The performance of LFC with BOA-tuned PID controller is compared with other optimization techniques named grey wolf optimization technique (GWO) and particle swarm optimization (PSO) on a RES integrated two-area power system under various scenarios. Further, the effectiveness of the BOA is verified by comparing performance of the PID and cascaded PI-PDN controllers. According to simulation results, BOA offers superior performance in comparison with the GWO and PSO technique. [ABSTRACT FROM AUTHOR]

In this paper, a class of cyclic (noncyclic) operators of condensing nature are defined on Banach spaces via a pair of shifting distance functions. The best proximity point (pair) results are manifested using the concept of measure of noncompactness (MNC) for the said operators. The obtained best proximity point result is used to demonstrate existence of optimum solutions of a system of differential equations involving ψ -Hilfer fractional derivatives of complex order. [ABSTRACT FROM AUTHOR]

Using Hybrid optimization algorithms for nonlinear systems analysis is a novel approach. It is a powerful technique that uses the exploitation ability of one algorithm and the exploration ability of another algorithm, to find the best solution. Literature survey reveals that hybrid algorithms not only show quality response but also give faster convergence of error for nonlinear systems. In this paper, hybrid optimization techniques based proportional integral derivative (PID) controller is used for benchmark problems: Continuous stirred tank reactor (CSTR), Inverted pendulum and blood glucose system. Two recent hybrid algorithms: Particle swarm optimization‐Gravitational search algorithm (PSO‐GSA) and Particle swarm optimization‐Grey wolf algorithm (PSO‐GWO) are implemented to control the temperature and concentration of CSTR, pendulum angle of inverted pendulum, glucose concentration and insulin level of blood glucose system. In PID and PSOGWO algorithms, the exploration abilities of GSA and GWO combined with the exploitation ability of PSO have been used. The performance of these algorithms is then compared with individual PSO, GSA, and GWO algorithms proving their superiority. Stability is ensured using the Lyapunov approach while the robustness of the systems is checked using the parameter perturbation technique. Simulation results show substantial improvement in the performance of these systems by using these meta‐heuristic hybrid optimization techniques. A comparative analysis of these algorithms has also been done. [ABSTRACT FROM AUTHOR]

Research in neurophysiology has shown that humans are able to adapt the mechanical stiffness at the hand in order to resist disturbances. This has served as inspiration for optimising stiffness in robot arms during manipulation tasks. Endpoint stiffness is modelled in Cartesian space, as though the hand were in independent rigid body. But an arm is a series of rigid bodies connected by articulated joints. The contribution of the joints and arm configuration to the endpoint stiffness has not yet been quantified. In this paper we use mathematical optimisation to find conditions for maximum stiffness and compliance with respect to an externally applied force. By doing so, we can retroactively explain observations made about humans using these mathematically optimal conditions. We then show how this optimisation can be applied to robotic task planning and control. Experiments on a humanoid robot show similar arm posture to that observed in humans. This suggests there is an underlying physical principle by which humans optimise stiffness. We can use this to derive natural control methods for robots. [ABSTRACT FROM AUTHOR]

The inverse analysis of the deformation moduli of high arch dams based on displacement monitoring data is essential for structural safety assessment. In traditional inverse analysis methods, the deformation moduli are identified based on the single‐objective optimization and the hydrostatic component derived from the statistical model. This type of method has two main shortcomings: First, it treats the essential multi‐objective optimization problem as a single‐objective problem; second, the extracted hydrostatic component may be biased due to the multicollinearity of variables in the statistical model. This paper presents a methodology for the inverse analysis of the deformation moduli of high arch dams under a multi‐objective optimization strategy. The methodology employs empirical mode decomposition to extract the aging component from displacement monitoring data. Then, thermomechanical analysis is used to reconstruct the remaining hydrostatic and temperature components, thereby avoiding the biases encountered in solving the statistical model. The adaptive polynomial chaos expansion method is embedded in the NSGA‐III algorithm to establish and solve multi‐objective functions in the inverse analysis. Additionally, a composite decision index considering errors and test information is proposed to determine acceptable deformation moduli from the Pareto solution set. A high arch dam is selected to illustrate this methodology with static and dynamic monitoring data. The results show that the identified deformation moduli have errors of 3.8% and 7.2% in displacement and acceleration, respectively. The proposed methodology can yield deformation modulus values that are more consistent with the physical implications than those of the single‐objective optimization method. [ABSTRACT FROM AUTHOR]

Positive and unlabeled learning (PU learning) is a significant binary classification task in machine learning; it focuses on training accurate classifiers using positive data and unlabeled data. Most of the works in this area are based on a two-step strategy: the first step is to identify reliable negative examples from unlabeled examples, and the second step is to construct the classifiers based on the positive examples and the identified reliable negative examples using supervised learning methods. However, these methods always underutilize the remaining unlabeled data, which limits the performance of PU learning. Furthermore, many methods require the iterative solution of the formulated quadratic programming problems to obtain the final classifier, resulting in a large computational cost. In this paper, we propose a new method called the absolute value inequality support vector machine, which applies the concept of eccentricity to select reliable negative examples from unlabeled data and then constructs a classifier based on the positive examples, the selected negative examples, and the remaining unlabeled data. In addition, we apply a hyperparameter optimization technique to automatically search and select the optimal parameter values in the proposed algorithm. Numerical experimental results on ten real-world datasets demonstrate that our method is better than the other three benchmark algorithms. [ABSTRACT FROM AUTHOR]

Summary: IoT‐assisted WSN contains various nodes, which are placed on a huge scale that increases complications. Thus, the challenges and issues of these networks fluctuate as compared to WSNs. Hence, sensor nodes are imperative unit that runs on less energy resources. Hence, devising a robust and energy‐effective protocol for increasing network lifetime is a complex task. This paper devises a novel hybrid optimization‐driven approach for selecting cluster head (CH) in IoT‐assisted WSNs. Initially, the simulation of IoT nodes is done by configuration. Thereafter, the Cluster Head selection is done using a newly devised optimization technique, namely the Taylor‐Political optimizer (Taylor‐PO). Thus, fitness is newly developed by adapting certain attributes like energy, delay, inter and intra‐cluster distance, Link Lifetime (LLT), predicted energy, and delay. Here, the multipath routing is accomplished using Tunicate Swarm gray wolf optimization (TSGWO). Thus, the proposed Taylor‐PO is offered for effective Cluster Head selection along with multipath routing using TSGWO. The proposed Taylor‐PO offered improved performance with smallest delay of 0.006 sec, highest energy of 2.368 J, highest throughput of 494.043 kbps. [ABSTRACT FROM AUTHOR]

Meta-learning is a pivotal and potentially influential machine learning approach to solve challenging problems in reinforcement learning. However, the costly hyper-parameter tuning for training stability of meta-learning is a known shortcoming and currently a hotspot of research. This paper addresses this shortcoming by introducing an online and easily trainable hyper-parameter optimization approach, called Meta Parameters Learning via Meta-Learning (MPML), to combine online hyper-parameter adjustment scheme into meta-learning algorithm, which reduces the need to tune hyper-parameters. Specifically, a basic learning rate for each training task is put forward. Besides, the proposed algorithm dynamically adapts multiple basic learning rate and a shared meta-learning rate through conducting gradient descent alongside the initial optimization steps. In addition, the sensitivity with respect to hyper-parameter choices in the proposed approach are also discussed compared with model-agnostic meta-learning method. The experimental results on reinforcement learning problems demonstrate MPML algorithm is easy to implement and delivers more highly competitive performance than existing meta-learning methods on a diverse set of challenging control tasks. [ABSTRACT FROM AUTHOR]

A machine learning‐based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication‐less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes. [ABSTRACT FROM AUTHOR]

In recent years, high-resolution traffic signal event data has provided valuable insights into understanding and managing congestion at signalized intersections. While existing applications primarily employ automated traffic signal performance monitoring (ATSPM) systems as postanalysis tools for identifying everyday congestion causes, traffic engineers are increasingly overwhelmed by the number of ATSPM-capable intersections. The workload increases extensively as the number of ATSPM-capable intersections rises mainly due to the necessity of manually checking and generating performance figures. Nonetheless, an advanced ATSPM system capable of automatically detecting time-of-day congestion bottlenecks among multiple intersections and suggesting "top intersections of interest" would significantly aid traffic managers in monitoring historical congestion and preventing future congestion occurrences. This paper introduces an efficient graphical automated congestion ranking method for capable intersections, leveraging high-resolution traffic signal event data as the basis for automated congestion ranking. To accomplish these objectives, we build upon ATSPM concepts by continuously generating ATSPM measures of effectiveness (MOEs). Utilizing continuously generated ATSPM performance measures in Frisco, Texas, over several months, we devise an efficient graphical method for ranking hourly congestion levels among the studied ATSPM-capable intersections. All intersections are assessed and ranked using a multiobjective optimization technique, the Pareto front method. The points on the Pareto front represent dominating intersections with at least one inferior performance measurement, warranting prioritized improvement. The dominating points identified from the test dataset were validated and further explained using Purdue coordination diagrams (PCD), along with another individual dataset--Wejo-connected vehicle data. The outcomes of this approach have proven the validity of the method. [ABSTRACT FROM AUTHOR]

Due to the volatile nature of renewable energy sources (RES), energy storage systems (ESS) are the primary impetus for increased dependence on RES in current power networks. Because of their quick responsiveness and rapid transition from charge to discharge in less than 1 ms, batteries are necessary even in the presence of alternative storage devices. Lithium-ion batteries (LIBs) have demonstrated their viability as ESS for RES. Given the significance of LIBs, it was vital to correctly and effectively quantify their costs in energy systems. Most studies did not offer a true real-world assessment of these batteries' aging but instead averaged their performance indices, which vary substantially depending on the system's working circumstances such as temperature, voltage, state of charge, and current level. Failure to use precise values for each charging/discharging operation might result in disastrous judgments and future techno-economic issues. This paper provides a novel and highly accurate piecewise modeling of LIBs for each charging and discharging operation based on real-world operational conditions. This novel aging model is used for calculating hourly aging and its impact on energy costs. The musical chair algorithm (MCA) is used to determine the optimal parameters' values of the LIB's model and the appropriate size of autonomous hybrid RES. When compared to other optimization techniques, the findings of optimal parameter determination using MCA revealed an 80% reduction in convergence time and better accuracy. The novel proposed aging model of the batteries predicts a 60% cost share of the battery, meanwhile, the existing strategy predicts 51% only. The levelized cost of energy is $0.1291/kWh and $0.0731/kWh for the new proposed aging model and existing strategy, respectively. [ABSTRACT FROM AUTHOR]

This study introduces an asymmetric V-shaped interior permanent magnet synchronous motor (AVIPMSM) by modifying the conventional flux barrier of a V-shaped interior permanent magnet synchronous motor (VIPMSM) including optimization-based finite element analysis (FEA). Nowadays usage of VIPMSM is increasing where higher torque density with high efficiency by decreasing the torque ripple factor is required. To improve these properties, flux barriers are modified in VIPMSM by applying optimization methods. For each pole of VIPMSM, modification has been depleted based on symmetric and asymmetric barriers. It introduces the magnetic field shifting effect in the motor. After deciding the flux barrier in the rotor, an optimized method is applied for achieving the proper dimensions of each barrier to estimate the maximum torque and other performances of the motor. Here, a 24/4 pole VIPMSM is considered for design and analysis. ANSYS Maxwell platform is chosen for performance analysis of motor. In addition to using the inbuilt minimum function optimization techniques of ANSYS, genetic algorithm (GA)/particle swarm optimization (PSO) in MATLAB is applied in the proposal to optimize the flux barriers of the motor. To validate the effectiveness of the proposed method, FEA is adopted by taking into account optimized parameters. The paper also investigates the effect of flux barriers on loss factors to improve the motor efficiency. A comparative study with the benchmark VIPMSM is presented to validate the designed AVIPMSM using optimization and its results are reported. Eventually, the FEA result proves that PSO optimized model is more appropriate for the case understudies. [ABSTRACT FROM AUTHOR]

The centred simplex gradient (CSG) is a popular gradient approximation technique in derivative-free optimization. Its computation requires a perfectly symmetric set of sample points and is known to provide an accuracy of |$\mathcal {O}(\varDelta ^2)$|⁠ , where |$\varDelta $| is the radius of the sampling set. In this paper, we consider the situation where the set of sample points is not perfectly symmetric. By adapting the formula for the CSG to compensate for the misaligned points, we define a new Adapted-CSG. We study the error bounds and the numerical stability of the Adapted-CSG. We also present numerical examples to demonstrate its properties relative to each new parameter and make a comparison to an alternative method. [ABSTRACT FROM AUTHOR]

Objective: Due to the alarming rise in COVID-19 cases every day in India, numerous academics are already developing a variety of math-based estimation models to forecast the pandemic future course. This paper makes use of publicly available data to anticipate certain COVID-19 trajectories in India. Methods: We used the Auto-Regressive Integrated Moving Average Model, a time series model, to anticipate the number of COVID-19 infected cases every day soon. People need to consume nutritious food that is well-balanced and contains the right number of calories, nutrients, as well as vitamins for strong development, bearing in mind that sustaining and repairing bodily tissues is the goal while preventing unfavorable illnesses and disease. Results: Recent studies have shown that healthy eating can help lower the possibilities of developing cancer, cardiovascular disease and for COVID patients. Therefore, an optimization strategy and LINDO software was used to solve the model. This study offers an exhaustive daily meal plan created especially for hospital patients, acting as a helpful resource for school administrators. With six different food choices, the diet plan makes sure that you get all the nutrients you need each day at a reasonable price. Conclusions: The model is solved using LINDO software in the study, which shows how effective it is when compared to other heuristic techniques like biological algorithms. It is determined through thorough investigation that the chosen meals are both financially and nutritionally feasible to serve in ho spital settings. Hospital patients make up the study's participants, and each day's total cost comes to Rs109.34. This cost makes it possible to deliver meals that are minimally more costly but of higher quality, improving the patients' overall nutritional value. [ABSTRACT FROM AUTHOR]

This article develops two metaheuristics optimization techniques, Grey Wolf Optimizer (GWO) and Harris Hawks Optimization (HHO), to handle multi-objective optimal power flow (MOOPF) issues. Multi Objective GWO (MOGWO) and Multi Objective HHO (MOHHO) are the names of the developed techniques. By combining these optimization techniques with Pareto techniques, the non-dominated solution set can be obtained. These developed approaches are characterized by simplicity and have few control parameters. Fuel cost, emissions, real power losses, and voltage deviation were the four objective functions considered. The theories used to determine the best compromise solution and organize the Pareto front options are the fuzzy membership equation and the crowding distance approach, respectively. To validate and evaluate the performance of the presented techniques, two standard IEEE bus systems—30-bus and 57-bus power systems—were proposed. Bi, Tri, and Quad objective functions with 21 case studies are the types of objective functions and the scenarios that were applied in this paper. As compared to the results of the most recent optimization techniques documented in the literature, the comparative analysis results for the proposed methodologies demonstrated the superiority and robustness of MOGWO and MOHHO. [ABSTRACT FROM AUTHOR]