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Intelligent ISSA-Based Non-Singular Terminal Sliding-Mode Control of DC-DC Boost Converter Feeding a Constant Power Load System

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ENERGIES 2023年第13期16卷 4973-4973页

作者： Liu, Lu Zeng, Yun Kunming Univ Sci & Technol Faulty Met & Energy Engn Kunming 650000 Peoples R China

In response to the issue of system oscillations in direct current (DC) microgrid systems with constant power loads (CPL), this paper proposes a non-singular terminal sliding-mode control (NTSMC) strategy based on the improved salp swarm algorithm (ISSA). Firstly, the state-feedback exact linearization technique is employed to establish a linearized model of the converter system. Then, the NTSMC based on a composite sliding-mode surface is designed to achieve rapid convergence and effectively weaken the chattering issue in traditional sliding-mode control, ensuring a constant power supply to the load. The parameters of the proposed NTSMC are optimized using the ISSA, which introduces an intelligent NTSMC. Finally, a MATLAB/Simulink simulation model is established. The simulation results show that the ISSA-based composite sliding-mode surface NTSMC system designed for DC microgrid systems with CPL exhibits high robustness and guarantees ideal steady-state characteristics and dynamic responses when input voltage fluctuations and load disturbances occur.

关键词： boost converter constant power load feedback linearization sliding mode optimization algorithm

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Machine Learning algorithms for Predicting Wear Rates on the Basis of Friction Noise

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TRIBOLOGY TRANSACTIONS 2024年第4期67卷 730-743页

作者： Zhao, Honghao Yang, Zi Zhang, Bo Xiang, Chong Guo, Fei Harbin Inst Technol Weihai Dept Mech Engn Weihai Peoples R China Tsinghua Univ State Key Lab Tribol Adv Equipment Beijing Peoples R China Guangzhou Mech Engn Res Inst Co Ltd Guangzhou Peoples R China

Under varying operational conditions, the contact and relative movement of a polymer and metal result in surface wear, accompanied by the emission of noise. The relationship between friction noise and wear is inherently complex and nonlinear. In light of these tribological characteristics, this paper introduces the implementation of a random forest algorithm and generalized regression neural network algorithm to establish a mathematical model for predicting the wear rate based on friction noise. To enhance the accuracy of wear rate regression, this study incorporates L2 norm feature selection and the sparrow search algorithm, which are tailored toward the friction characteristics. These techniques optimize the machine learning-based friction model, ultimately improving the regression accuracy of the wear rate.

关键词： Machine learning prediction friction sound wear rate optimization algorithm

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Optimal Load Shedding for Maximizing Satisfaction in an Islanded Microgrid

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ENERGIES 2017年第1期10卷 45页

作者： Choi, Yeongho Lim, Yujin Kim, Hak-Man Sookmyung Womens Univ Dept Informat Technol Engn Seoul 04310 South Korea Incheon Natl Univ Dept Elect Engn Inchon 22012 South Korea

A microgrid (MG) is a discrete energy system that can operate either in parallel with or independently from a main power grid. It is designed to enhance reliability, carbon emission reduction, diversification of energy sources, and cost reduction. When a power fault occurs in a grid, an MG operates in an islanded manner from the grid and protects its power generations and loads from disturbance by means of intelligent load shedding. A load shedding is a control procedure that results in autonomous decrease of the power demands of loads in an MG. In this study, we propose a load shedding algorithm for the optimization problem to maximize the satisfaction of system components. The proposed algorithm preferentially assigns the power to the subdemand with a high preference to maximize the satisfaction of power consumers. In addition, the algorithm assigns the power to maximize the power sale and minimize the power surplus for satisfaction of power suppliers. To verify the performance of our algorithm, we implement a multi-agent system (MAS) on top of a conventional development framework and assess the algorithm's adaptability, satisfaction metric, and running time.

关键词： load shedding microgrid (MG) multi-agent system (MAS) optimization algorithm

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OPTIONS attack detection in WSN using Optimized Multitask Multi-Attention Residual Shrinkage Convolutional Neural Network

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KNOWLEDGE-BASED SYSTEMS 2024年 300卷

作者： Selvi, S. Tamil Visalakshi, P. Kumar, M. Senthil SRM Inst Sci & Technol Dept Networking & Commun Kattankulathur 603203 Tamilnadu India SRM Valliammai Engn Coll Dept Cyber Secur Kattankulathur 603203 Tamilnadu India

The rapid increase in Internet users has made web applications essential for daily services, rendering them targets for various cyber-attacks like path traversal, zero-day attacks, and injection attacks. While traditional security measures can prevent many familiar attacks, they are often ineffective against OPTIONS attacks, which involve injecting malicious code via hyperlinks to obstruct user access to legitimate webpage content. To address this novel challenge, we propose the OAD-WSN-MMRCNN technique, leveraging an Optimized Multitask MultiAttention Residual Shrinkage Convolutional Neural Network for OPTIONS attack detection in Wireless Sensor Networks (WSNs). This approach begins by selecting a CPU parameters dataset for attack detection, followed by pre-processing with a Variational Bayesian-Based Maximum Correntropy Cubature Kalman Filter to remove redundant data. Key features such as handles, threads, processor, context switch, deferred procedure call (DPC), interrupt delta, CPU socket, and core are extracted using a variable velocity strategy particle swarm optimization algorithm. The MMRCNN, optimized with the Tyrannosaurus optimization algorithm, is then employed to detect normal and OPTIONS attacks. Implemented in Python, the efficacy of OAD-WSN-MMRCNN is evaluated using metrics such as energy consumption, target window, accuracy, precision, F-measure, recall, and CPU utilization. Experimental results demonstrate that OAD-WSN-MMRCNN outperforms existing techniques, achieving a 20 % improvement in detection accuracy and a 25 % reduction in energy consumption, highlighting its effectiveness and potential for enhancing web application cyber security.

关键词： OPTIONS attack Tyrannosaurus optimization algorithm Variable velocity strategy particle swarm optimization algorithm VBMCCKF WSN

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Mitigating Cyber Anomalies in Virtual Power Plants Using Artificial-Neural-Network-Based Secondary Control with a Federated Learning-Trust Adaptation

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ENERGIES 2024年第3期17卷 619页

作者： Taheri, Seyed Iman Davoodi, Mohammadreza Ali, Mohd Hasan Univ Memphis Dept Elect & Comp Engn Memphis TN 38152 USA

Virtual power plants (VPPs) are susceptible to cyber anomalies due to their extensive communication layer. FL-trust, an improved federated learning (FL) approach, has been recently introduced as a mitigation system for cyber-attacks. However, current FL-trust enhancements, relying solely on proportional-integral (PI), exhibit drawbacks like sensitivity to controller gain fluctuations and a slow response to sudden disturbances, and conventional FL-trust is not directly applicable to the non-independent and identically distributed (non-IID) datasets common in VPPs. To address these limitations, we introduce an artificial neural network (ANN)-based technique to adapt FL-trust to non-IID datasets. The ANN is designed as an intelligent anomaly mitigation control method, employing a dynamic recurrent neural network with exogenous inputs. We consider the effects of the most common VPP attacks, poisoning attacks, on the distributed cooperative controller at the secondary control level. The ANN is trained offline and tested online in the simulated VPP. Using MATLAB simulations on a HOMER-modeled VPP, the proposed technique demonstrates its superior ability to sustain normal VPP operation amidst cyber anomalies, outperforming a PI-based mitigation system in accuracy and detection speed.

关键词： virtual power plant federated learning optimization algorithm power system distributed generation artificial neural network

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Electromagnetic functionalization of mechanical lattice to metastructure with oblique incident broadband microwave absorption

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COMPOSITES SCIENCE AND TECHNOLOGY 2023年 244卷

作者： Zhang, Yuhui Xu, Yinfang Yu, Chen Lei, Han Zhao, Pengzhen Huang, Yixing Beijing Inst Technol Inst Adv Struct Technol Beijing Key Lab Lightweight Multifunct Composite M Beijing 100081 Peoples R China Natl Key Lab Sci & Technol Test Phys & Numer Math Beijing 100076 Peoples R China

Electromagnetic functionalization of mechanical lattice is an important method to integrate mechanical and electromagnetic properties into one structure with effective load bearing and broadband microwave absorption. Herein, the gradient lattice metastructure (GLM) is fabricated by dielectric-magnetic lossy nano composite strengthened by carbon fiber and glass fiber reinforced polymer. A large mutation genetic algorithm is developed to optimize the GLM with fine convergence property. Several large mutation modules are incorporated into the program to speed up the optimization process and improve global convergence. The measured-10 dB ab-sorption bandwidth covers 2-5 GHz and 6.5-17.3 GHz while-9.5 dB absorption bandwidth covers 2-17.3 GHz. The oblique incident broadband absorption is achieved from normal incidence to the grazing incidence of 85 degrees in traverse electrical (TE) and traverse magnetic (TM) polarization. Gradient lattice design is effective to overcome systemic oblique incident absorption deterioration originating from enhanced tangential electric field intensity in large incident angles. The nominal tensile strength of GLM reaches 108.4 MPa with maximum flexural load of 1.83 kN. The high electromagnetic and mechanical properties are achieved simultaneously by electromagnetic functionalization of mechanical lattice.

关键词： Electromagnetic functionalization Gradient lattice metastructure Broadband microwave absorption Mechanical property optimization algorithm

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An adaptive weight search method based on the Grey wolf optimizer algorithm for skin lesion ensemble classification

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2024年第2期34卷 e23049-e23049页

作者： Liu, Luzhou Zhang, Xiaoxia Xu, Zhinan Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan Peoples R China Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan 114051 Peoples R China

Skin cancer is a common type of malignant tumor that poses a serious threat to patients' lives and health, especially melanoma. It may spread to other body parts, resulting in serious complications and death. In the medical field, accurate identification of skin lesion images is crucial for diagnosing different diseases. However, due to the similarity between different skin lesions, it brings some challenges to medical diagnosis. In this paper, a novel Ensemble Learning Model (EL-DLOA) based on deep learning and optimization algorithms is proposed, which uses four different deep neural network architectures to generate confidence levels for classes, and optimization algorithms are used to integrate these confidence levels to make the final predictions. To ensure the model's accuracy and reliability, it is first trained using three different learning rates to find the best classification performance of the model. Then, a new search method based on the grey wolf optimization algorithm is proposed to enhance the grey wolf search efficiency. The method improves the search mechanism by changing the grey wolf's individual position through random perturbation or adaptive mutation, which solves the problem that the grey wolf algorithm is easy to fall into local optimum. Finally, four different ensemble strategies are used to reduce individual model bias in the classification process. The proposed model is trained and evaluated using the publicly available dataset HAM10000. The experimental results show that the improved grey wolf optimization algorithm effectively avoids the premature convergence problem and improves the search combination efficiency. Furthermore, in the ensemble methods, the adaptive weight average ensemble strategy effectively improves the classification performance, yielding accuracy, precision, recall, and F1 scores of 0.888, 0.837, 0.897, and 0.862, respectively. These metrics show varying degrees of improvement over the best performing single model. I

关键词： deep learning ensemble model optimization algorithm skin cancer skin lesion classification

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Optimizing sustainable energy integration: A novel approach using concentrated solar plant and hybrid power supply

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 237卷

作者： Li, Mengyu Liu, Yunlong Li, Ji Li, Fei An, Yagang Gao, Xuezhe State Grid Hebei Mkt Serv Ctr Shijiazhuang 050000 Hebei Peoples R China State Grid Hebei Elect Power Supply Co Ltd Shijiazhuang 050000 Hebei Peoples R China

This study pioneers sustainable energy solutions amidst escalating demand and fossil fuel depletion. Its primary novelty lies in proposing an integrated energy system, encompassing a concentrated solar plant, thermal energy system, and hybrid power supply within the solar energy domain. This integration substantially enhances overall system performance. The architectural design integrates various energy conversion components, including wind turbines, energy storage devices, and electric heaters, fostering synergistic collaboration. The optimization of energy usage is a key focus, utilizing heating and electricity costs as pivotal signals for participation in an Integrated Demand Response program (IRD). The demand response model, encompassing both thermal and electric loads, incorporates a sophisticated price elasticity matrix. Addressing challenges associated with renewable energy intermittency, phased carbon emissions, and the advantages of demand response, this study introduces an advanced system operation optimization model to effectively curtail operational costs. In the realm of methodology, this research breaks new ground by proposing an enhanced optimization algorithm that ingeniously combines the Whale optimization algorithm (WOA) with the Wavelet Transform (WT). This novel amalgamation significantly enhances the algorithm's search capabilities, providing superior performance in tackling the complexities inherent in the optimization problem. The proposed model undergoes rigorous evaluation in a comprehensive study featuring diverse energy sources and multiple scenarios. The outcomes of this evaluation distinctly showcase substantial reductions in overall operational costs compared to conventional approaches. This multifaceted contribution positions the study at the forefront of endeavors to revolutionize energy systems for enhanced sustainability and efficiency.

关键词： Clean energy Demand response Hybrid power supply optimization algorithm Renewable energy Sustainable energy solutions

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On the non-uniqueness of friction forces and the systematic computation of dynamic response boundaries for turbine bladed disks with contacts

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2021年 160卷 107917-107917页

作者： Ferhatoglu, Erhan Zucca, Stefano Politecn Torino Dept Mech & Aerosp Engn Corso Duca Abruzzi 24 I-10129 Turin Italy

Turbine bladed disks with friction contacts may have a large scattering of dynamic response amplitudes in laboratory conditions even for two consecutive tests. The non repeatability of experimental studies might directly be related to a physical phenomenon associated with an uncertainty in contact forces. This observation has also been computationally shown in many studies with non-unique contact forces and multiple responses obtained for the same set of inputs. This study presents a numerical aspect and a deeper insight for understanding the variability observed in the periodic vibration analysis of turbine bladed disks with friction damping. A novel method based on an optimization algorithm is proposed to systematically detect the nonlinear dynamic response boundaries. The main idea of the developed approach is to minimize the system loss factor which ultimately determines the damping ability of the structure. In the meanwhile, algebraic set of dynamic balance equations are simultaneously imposed as the nonlinear constraints to be satisfied. In this way, two cases with the minimum values of the positive and negative loss factor determine the upper and the lower boundaries, respectively. The method is validated and demonstrated on a realistic turbine bladed disk with friction interfaces on the shrouds and on the blade-disk interface. Several case studies are performed on different cases by using the state of the art 2D friction model with varying normal load. The results show that the limits of the variability range can be successfully captured by utilizing the offered optimization algorithm. The great contribution of the study is also discussed with some accompanying numerical drawbacks. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Nonlinear forced response Friction damping Non-unique contact forces Multiple solutions optimization algorithm Turbine bladed disks

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Investigating the optical NOR gate using plasmonic nanorods

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2016年第5期29卷 796-804页

作者： Kaboli, Milad Akhlaghi, Majid Shahmirzaee, Hossein Islamic Azad Univ Omidiyeh Branch Dept Elect & Elect Omidiyeh Iran Islamic Azad Univ Omidiyeh Branch Young Researchers & Elite Club Omidiyeh Iran Univ Malek Ashtar Air Ocean Res Ctr Shahin Shahr Iran

In this paper, a coherent perfect absorption-type NOR gate based on plasmonic nano particles is proposed. It consists of two plasmonic nanorod arrays on top of two serial arms with quartz substrate. The operation principle is based on the absorbable formation of a conductive path in the dielectric layer of a plasmonic nanoparticle waveguide. Because the coherent perfect absorption efficiency depends strongly on the number of plasmonic nanorods and the locations of nanorods, an efficient binary optimization method based the Particle Swarm optimization algorithm is used to design an optimized array of the plasmonic nanorods in order to achieve the maximum absorption coefficient in the off' state and the minimum absorption coefficient in the on' state. In Binary Particle Swarm optimization, a group of birds consists a matrix with binary entries, control the presence (1') or the absence (0') of nanorods in the array. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： optical devices optimization algorithm plasmonic nanoparticles

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