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  • Open Access

    ARTICLE

    Dynamic Economic Scheduling with Self-Adaptive Uncertainty in Distribution Network Based on Deep Reinforcement Learning

    Guanfu Wang1, Yudie Sun1, Jinling Li2,3,*, Yu Jiang1, Chunhui Li1, Huanan Yu2,3, He Wang2,3, Shiqiang Li2,3

    Energy Engineering, Vol.121, No.6, pp. 1671-1695, 2024, DOI:10.32604/ee.2024.047794

    Abstract Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which are difficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamic decisions continuously. This paper proposed a dynamic economic scheduling method for distribution networks based on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distribution network is established considering the action characteristics of micro-gas turbines, and the dynamic scheduling model based on deep reinforcement learning is constructed for the new energy distribution network system with a More >

  • Open Access

    ARTICLE

    Research on the Control Strategy of Micro Wind-Hydrogen Coupled System Based on Wind Power Prediction and Hydrogen Storage System Charging/Discharging Regulation

    Yuanjun Dai, Haonan Li, Baohua Li*

    Energy Engineering, Vol.121, No.6, pp. 1607-1636, 2024, DOI:10.32604/ee.2024.047255

    Abstract This paper addresses the micro wind-hydrogen coupled system, aiming to improve the power tracking capability of micro wind farms, the regulation capability of hydrogen storage systems, and to mitigate the volatility of wind power generation. A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction, the hydrogen storage state division interval, and the daily scheduled output of wind power generation. The control strategy maximizes the power tracking capability, the regulation capability of the hydrogen storage system, and the fluctuation of the joint output of the wind-hydrogen… More >

  • Open Access

    ARTICLE

    A New Equalization Method for Lithium-Ion Battery Packs Based on CUK Converter

    Yu Zhang, Sheng Tian*, Yongkang Zhang

    Energy Engineering, Vol.121, No.6, pp. 1459-1472, 2024, DOI:10.32604/ee.2024.047247

    Abstract Aiming at the traditional CUK equalizer can only perform energy equalization between adjacent batteries, if the two single batteries that need to be equalized are far away from each other, there will be the problem of longer energy transmission path and lower equalization efficiency, this paper optimizes the CUK equalizer and optimizes its peripheral selection circuit, which can support the equalization of single batteries at any two positions. The control strategy adopts the open-circuit voltage (OVC) of the battery and the state of charge (SOC) of the battery as the equalization variables, and selects the… More > Graphic Abstract

    A New Equalization Method for Lithium-Ion Battery Packs Based on CUK Converter

  • Open Access

    ARTICLE

    Sensitivity Analysis of Electromagnetic Scattering from Dielectric Targets with Polynomial Chaos Expansion and Method of Moments

    Yujing Ma1,4, Zhongwang Wang2, Jieyuan Zhang3, Ruijin Huo1,4, Xiaohui Yuan1,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 2079-2102, 2024, DOI:10.32604/cmes.2024.048488

    Abstract In this paper, an adaptive polynomial chaos expansion method (PCE) based on the method of moments (MoM) is proposed to construct surrogate models for electromagnetic scattering and further sensitivity analysis. The MoM is applied to accurately solve the electric field integral equation (EFIE) of electromagnetic scattering from homogeneous dielectric targets. Within the bistatic radar cross section (RCS) as the research object, the adaptive PCE algorithm is devoted to selecting the appropriate order to construct the multivariate surrogate model. The corresponding sensitivity results are given by the further derivative operation, which is compared with those of More >

  • Open Access

    ARTICLE

    Development of a Three-Dimensional Multiscale Octree SBFEM for Viscoelastic Problems of Heterogeneous Materials

    Xu Xu1, Xiaoteng Wang1, Haitian Yang1, Zhenjun Yang2, Yiqian He1,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1831-1861, 2024, DOI:10.32604/cmes.2024.048199

    Abstract The multiscale method provides an effective approach for the numerical analysis of heterogeneous viscoelastic materials by reducing the degree of freedoms (DOFs). A basic framework of the Multiscale Scaled Boundary Finite Element Method (MsSBFEM) was presented in our previous works, but those works only addressed two-dimensional problems. In order to solve more realistic problems, a three-dimensional MsSBFEM is further developed in this article. In the proposed method, the octree SBFEM is used to deal with the three-dimensional calculation for numerical base functions to bridge small and large scales, the three-dimensional image-based analysis can be conveniently… More >

  • Open Access

    ARTICLE

    Adaptive Network Sustainability and Defense Based on Artificial Bees Colony Optimization Algorithm for Nature Inspired Cyber Security

    Chirag Ganguli1, Shishir Kumar Shandilya2, Michal Gregus3, Oleh Basystiuk4,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 739-758, 2024, DOI:10.32604/csse.2024.042607

    Abstract Cyber Defense is becoming a major issue for every organization to keep business continuity intact. The presented paper explores the effectiveness of a meta-heuristic optimization algorithm-Artificial Bees Colony Algorithm (ABC) as an Nature Inspired Cyber Security mechanism to achieve adaptive defense. It experiments on the Denial-Of-Service attack scenarios which involves limiting the traffic flow for each node. Businesses today have adapted their service distribution models to include the use of the Internet, allowing them to effectively manage and interact with their customer data. This shift has created an increased reliance on online services to store… More >

  • Open Access

    ARTICLE

    A Reference Vector-Assisted Many-Objective Optimization Algorithm with Adaptive Niche Dominance Relation

    Fangzhen Ge1,3, Yating Wu1,*, Debao Chen2,4, Longfeng Shen1,5

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 189-211, 2024, DOI:10.32604/iasc.2024.042841

    Abstract It is still a huge challenge for traditional Pareto-dominated many-objective optimization algorithms to solve many-objective optimization problems because these algorithms hardly maintain the balance between convergence and diversity and can only find a group of solutions focused on a small area on the Pareto front, resulting in poor performance of those algorithms. For this reason, we propose a reference vector-assisted algorithm with an adaptive niche dominance relation, for short MaOEA-AR. The new dominance relation forms a niche based on the angle between candidate solutions. By comparing these solutions, the solution with the best convergence is More >

  • Open Access

    ARTICLE

    L-Smooth SVM with Distributed Adaptive Proximal Stochastic Gradient Descent with Momentum for Fast Brain Tumor Detection

    Chuandong Qin1,2, Yu Cao1,*, Liqun Meng1

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1975-1994, 2024, DOI:10.32604/cmc.2024.049228

    Abstract Brain tumors come in various types, each with distinct characteristics and treatment approaches, making manual detection a time-consuming and potentially ambiguous process. Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes. Machine learning models have become key players in automating brain tumor detection. Gradient descent methods are the mainstream algorithms for solving machine learning models. In this paper, we propose a novel distributed proximal stochastic gradient descent approach to solve the L-Smooth Support Vector Machine (SVM) classifier for brain tumor detection. Firstly, the smooth hinge loss is… More >

  • Open Access

    ARTICLE

    A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

    Tsu-Yang Wu1,2, Haonan Li2, Saru Kumari3, Chien-Ming Chen1,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 19-46, 2024, DOI:10.32604/cmc.2024.048347

    Abstract Hyperspectral image classification stands as a pivotal task within the field of remote sensing, yet achieving high-precision classification remains a significant challenge. In response to this challenge, a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm (AFLA-SCNN) is proposed. The Adaptive Fick’s Law Algorithm (AFLA) constitutes a novel metaheuristic algorithm introduced herein, encompassing three new strategies: Adaptive weight factor, Gaussian mutation, and probability update policy. With adaptive weight factor, the algorithm can adjust the weights according to the change in the number of iterations to improve the performance of the algorithm. Gaussian… More >

  • Open Access

    ARTICLE

    Collaborative Charging Scheduling in Wireless Charging Sensor Networks

    Qiuyang Wang, Zhen Xu*, Lei Yang

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1613-1630, 2024, DOI:10.32604/cmc.2024.047915

    Abstract Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides a promising solution to this problem, which is not easily affected by the external environment. In this paper, we study the recharging of sensors in wireless rechargeable sensor networks (WRSNs) by scheduling two mobile chargers (MCs) to collaboratively charge sensors. We first formulate a novel sensor charging scheduling problem with the objective of maximizing the number of surviving sensors, and further propose a collaborative charging scheduling algorithm (CCSA) for WRSNs. In the scheme, the sensors are divided into important sensors More >

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