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3rd IEEE International Conference on Smart Technologies for Power, Energy and control, STPEC 2023

作者： Kumar, Anil Altaf, Aatif Kumar, Mahendra Alhussainy, Abdullah A. Alghamdi, Sultan Alobaidi, Abdulraheem H. Dr. B R Ambedkar National Institute of Technology Jalandhar Instrumentation and Control Engineering Department Punjab Jalandhar144008 India King Abdulaziz University Faculty of Engineering Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia

ISBN: (纸本)9798350304732

The work addresses PID control design based on the velocity-pausing particle swarm optimization (VPPSO) technique. The suggested control design is utilized to develop a load frequency control (LFC) approach for an isolated microgrid (MG) with heat-pumps (HPs) and electric-vehicles (EVs). The efficiency and effectiveness of suggested control design are assessed when there are randomized solar power and load-demand disturbances. Furthermore, the robustness and effectiveness of suggested control schemes are evaluated under uncertainty in system parameters and different EVs & HPs controller's operating time-scheduling situations. The simulation results demonstrated the efficiency and usefulness of the recommended VPPSO-PID control strategy under several practical situations. © 2023 IEEE.

关键词： Electric Vehicles Load frequency control (LFC) Microgrid PID controller Velocity Pausing PSO (VPPSO)

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An Energy Efficient Dynamic Scheduling Algorithm for Reliability Constraints in Distributed Heterogeneous Systems 11

An Energy Efficient Dynamic Scheduling Algorithm for Reliabi...

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11th Joint International Information technology and Artificial Intelligence Conference, ITAIC 2023

作者： Liu, Yifan Du, Chenglie Chen, Jinchao Du, Xiaoyan Yu, Wenquan School of Software Northwestern Polytechnical University Xi'an China School of Computer Science Northwestern Poly Technical University Xi'an China North Automatic Control Technology Research Institute Department of Land Unmanned System Research and Development Taiyuan China

ISBN: (纸本)9798350333664

Scheduling algorithms in distributed heterogeneous systems have been the subject of extensive research. However, with the advancement of technology, addressing real-time, reliability, and energy consumption issues in dynamic environments has become increasingly critical. Existing studies mostly focus on static approaches, which are challenging to achieve scheduling objectives rapidly and adaptively. Therefore, we propose a novel dynamic energy-aware scheduling algorithm for deadlines and reliability constrained applications. Firstly, we introduce a prioritization method for multitasking applications to ensure more applications meet their deadlines. Subsequently, we present specific strategies for task allocation to achieve a better trade-off between energy consumption and reliability. Experimental results on randomly generated applications validate the superiority of our proposed method in terms of performance compared to other algorithms. © 2023 IEEE.

关键词： Deadline constrained Dynamic systems Energy consumption Online scheduling Reliability

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HKB-YOLO: transmission line fire detection method based on hierarchical feature fusion

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International Journal of Security and Networks 2024年第4期19卷 188-198页

作者： Ying, Zhilei Meng, Yanan Chen, Ruoxi Lou, Jianlou School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin 132022 China Department of Automation Jilin Institute of Chemical Technology Jilin 132022 China School of Computer Engineering Northeast Electric Power University Jilin 132012 China

Timely transmission line fire inspections are vital for power system safety. Although deep learning models are widely used for flame detection, struggle with small target recognition due to background interference and are vulnerable to input data perturbations, posing security risks. This study introduces a hierarchical feature fusion method based on the you only look once Version 8 (YOLOV8) framework. It employs high-performance GPU network version 2 (HGNetV2) to enhance small-target feature extraction while reducing computational complexity. A spatial pyramid pooling-fast module with large separable kernel attention is designed to highlight key features and suppress background noise. The proposed bidirectional slim neck structure (BiSlimneck) reduces feature loss. Adversarial training enhances the model’s robustness. Experimental results show a 1.4% accuracy improvement, with a reduction in parameters and complexity by 18.3% and 21.6%, respectively. After adversarial training, the accuracy of the network in the face of attacks increased by 10% compared to the pre-training. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Premixed flames

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scIAC: clustering scATAC-seq data based on Student's t-distribution similarity imputation and denoising autoencoder

scIAC: clustering scATAC-seq data based on Student's t-distr...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Lan, Wei Ye, Jin Sun, Xun Zhu, Xiaoshu Chen, Qingfeng Pan, Yi Guangxi University School of Computer Electronic and Information Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning China School of Computer Electronic and Information Guangxi University Nanning China Yulin Normal University School of Computer Science and Engineering Yulin China School of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (纸本)9781665468190

Assay of single cell transposase-accessible chromatin with high-throughput sequencing (scATAC-seq) have enabled massively profiling of the chromatin accessibility landscape at the single-cell level. The essential step in analyzing scATAC-seq data is to cluster the cells into different clusters and utilize the clustering information in the subsequent downstream analysis. However, there are some challenges in the clustering analysis of scATAC-seq data. For example, scATAC-seq data are often high-dimensional and extremely sparse, as well as featuring high loss rate or noise. In this study, we proposed the scIAC to address these challenges of scATACseq data. In particular, scIAC combines the Student's t-distribution similarity imputation and the denoising autoencoder based on the Zero-inflated Negative Binomial (ZINB) distribution. The Student's t-distribution similarity imputation is used to solve the problem of high sparsity and high loss rate. The denoising autoencoder is employ to extract features which are useful for clustering and to reduce data noises. In addition, the self-training soft K-means and pairwise constraints are utilized in the clustering phase to enhance clustering performance. The experimental validation on several datasets shows that the proposed method performed better than other state-of-the-art methods. In conclusion, scIAC is an effective method to accurately cluster and identify cell types in scATAC-seq data. © 2022 IEEE.

关键词： Learning systems

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Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

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computers, Materials & Continua 2023年第8期76卷 1463-1477页

作者： Ily s Abdullaev Natalia Prodanova KAruna Bhaskar ELaxmi Lydia Seifedine Kadry Jungeun Kim Dean of the Faculty of Economics Department of Management and MarketingFaculty of EconomicsUrgench State UniversityUrganch220100Uzbekistan Basic Department Financial Control Analysis and Audit of Moscow Main Control DepartmentPlekhanov Russian University of EconomicsMoscow117997Russia Department of Computer Science and Engineering KL Deemed to UniversityVaddeswaramGunturAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data *** city benefitted from offloading to edge *** a mobile edge computing(MEC)network in multiple *** comprise N MDs and many access points,in which everyMDhasM independent real-time *** study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)*** proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system *** addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted *** TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading ***,the SGO algorithm is used for the parameter tuning of the DBN *** simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

关键词： Mobile edge computing seagull optimization deep belief network resource management parameter tuning

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Method for Evaluating the Performance of Aircraft Collaborative Decision-Making Based on Dynamic Bayesian Network

Method for Evaluating the Performance of Aircraft Collaborat...

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Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Wenbo Zhang Boxiao Yu Jiahui Tong Jing Li National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing Electro-Mechanical Engineering Institute Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

This research addresses the challenge of assessing equipment decision-making effectiveness in environments characterized by uncertainty and multiple influencing factors. We propose a novel approach involving the creation and optimization of a dynamic Bayesian network-based model tailored for equipment decision-making processes. The model facilitates a systematic evaluation of decision-making effectiveness by incorporating various factors, including equipment performance, environmental conditions, and enemy defensive capabilities. A comprehensive assessment system has been developed to simulate the penetration capabilities of different equipment through enemy defenses. Moreover, this study identifies potential errors in the effectiveness evaluation model by analyzing its performance in typical scenarios, pinpointing areas for enhancement.

关键词： Accuracy Uncertainty Systematics Scientific computing Atmospheric modeling Computational modeling Decision making Data models Bayes methods Optimization

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Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review

arXiv

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arXiv 2025年

作者： Gu, Yan Liu, Zhaoze Dai, Shuhong Liu, Cong Wang, Ying Wang, Shen Theodoropoulos, Georgios Cheng, Long The School of Control and Computer Engineering North China Electric Power University Beijing China The School of Computer Science and Technology Shandong University of Technology China The Institute of Computing Technology Chinese Academy of Sciences Beijing China The School of Computer Science University College Dublin Ireland Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job scheduling and resource management, which are critical for optimizing system performance and ensuring timely and cost-effective service delivery. However, the dynamic and heterogeneous nature of cloud environments presents significant challenges for these tasks, as workloads and resource availability can fluctuate unpredictably. Traditional approaches, including heuristic and meta-heuristic algorithms, often struggle to adapt to these real-time changes due to their reliance on static models or predefined rules. Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges by enabling systems to learn and adapt policies based on continuous observations of the environment, facilitating intelligent and responsive decision-making. This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing, analyzing their methodologies, performance metrics, and practical applications. We also highlight emerging trends and future research directions, offering valuable insights into leveraging DRL to advance both job scheduling and resource management in cloud computing. © 2025, CC BY.

关键词： Job shop scheduling

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MFE-Net: A remote sensing image dehazing model for the visual system of a flight simulator 6

MFE-Net: A remote sensing image dehazing model for the visua...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Ge, Wenyi Wang, Bo Liu, Qi Qin, Xiaolin Tan, Shihan Wang, Shengjie Yuan, Xia College of Computer Science Chengdu University of Information Technology Chengdu610255 China Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu610213 China Jiuzhou Air Trafic Control Sichuan Jiuzhou Investment Holding GroupCo. Ltd Mianyang621000 China The Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan618307 China

ISBN: (纸本)9781510688018

Obtaining high-quality remote sensing images is crucial in generating a three-dimensional terrain for flight simulators. However, due to the presence of haze and other impact factors, collected remote sensing images usually suffer from blurred details, which limits its applicability real-world applications. To address this issue a deep learning-based dehazing model is proposed to advance hazy remote sensing images to build flight simulator visual systems. The encoder-decoder architecture is proposed to perform the image dehazing, in which an improved feature extraction module is designed to extract both global and local semantic features across multi-resolutions. In addition, the pixel-pixel and region-region mechanism are innovatively designed to achieve deep mapping relationships and further enhance image dehazing. Experimental results demonstrate that the proposed model outperforms other comparative baselines, achieving high-confidence dehazing in multi-scene remote sensing images, and with good generality and applicability in multi-resolution flight simulators and multi-terrain remote sensing. © 2025 SPIE.

关键词： Flight simulators

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Dual-Mode Disturbance Observer-based Predictive control with Harmonic Current Suppression for Dual Three-Phase Permanent Magnet Synchronous Motor Drives

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Liu, Huanli Lin, Weiyang Liu, Zhitai Yu, Xinghu Buccella, Concettina Cecati, Carlo Harbin Institute of Technology Research Institute of Intelligent Control and Systems Harbin150001 China Ningbo Institute of Intelligent Equipment Technology Company Ltd Ningbo315200 China University of L'Aquila DigiPower srl Department of Information Engineering Computer Science and Mathematics L'Aquila67100 Italy

Dual three-phase permanent magnet synchronous motor drives have gained considerable interest in those applications requiring high levels of reliability. However, they often suffer from high-order harmonic currents and mismatch of motor parameters, resulting in degraded performance and reduced efficiency. Therefore, this paper proposes a dual-mode disturbance observer based model predictive current controller. Not only direct current disturbances caused by motor parameters, but also alternating current disturbances caused by high-order harmonic currents are explicitly modeled and estimated to achieve improved model accuracy. In addition, the observer parameter design is given in detail. Furthermore, a generalized predictive current controller with the integration of the proposed observer is developed to provide satisfactory harmonic current suppression capability and parameter robustness simultaneously. Proof of the stability with the proposed controller is given and several experiments and comparisons demonstrate the effectiveness of the proposed method. © 2013 IEEE.

关键词： Permanent magnets

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Ramsey’s Conjecture for the Model with Non-liquid Capital 15th

Ramsey’s Conjecture for the Model with Non-liquid Capita...

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15th International Conference on Optimization and Applications, OPTIMA 2024

作者： Parastaev, Grigorii S. Shananin, Alexander A. Lomonosov Moscow State University Moscow Russia Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia Moscow Institute of Physics and Technology Moscow Oblast Dolgoprudny Russia Peoples’ Friendship University of Russia Moscow Russia Moscow Center of Fundamental and Applied Mathematics Moscow Russia

ISBN: (纸本)9783031791185

Ramsey’s conjecture on social stratification states that economic agents divide into two classes: the class of thrifty agents, who jointly accumulate and share all the economy wealth, and the class of impatient ones forced to live on the wage level only. The present paper outlines the validity of Ramsey’s conjecture on social stratification for the household population in case when capital is non-liquid. The households’ behavior is described by the Ramsey type model of the rational representative consumer. We derive the solution in the synthesis form for the corresponding optimal control problem and use it in the social dynamics model for the household population, where the discount rates, according to Duesenberry’s relative income hypothesis, are set as functions dependent on ratio between capital holdings of specific agent and total capital available of economy. The agents behave in myopic manner, suggesting that the interest rate and the wage rate do not change over time. The connection between the Gini index and the Lyapunov function is also established. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wages

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