检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Wang, Qian Gu, Qinghua Zhou, Qing Xiong, Neal Naixue Liu, Di School of Management Xi’an University of Architecture and Technology Shaanxi Xi’an China School of Resources Engineering Xi’an University of Architecture and Technology Shaanxi Xi’an China Xi’an Key Laboratory for Intelligent Industrial Perception Calculation and Decision Xi’an University of Architecture and Technology Xi’an710055 China Department of Mathematics and Computer Science Northeastern State University TahlequahOK74464 United States

Currently, real-world complex engineering problems often have more than three objectives that need to be optimized simultaneously. Existing algorithms for solving them mostly rely on the shape of the Pareto front to maintain diversity, which makes it difficult to effectively balance the convergence and diversity of the solutions. To address the above problems, a many-objective evolutionary algorithm based on indicator selection and adaptive angle estimation (MaOEA-ISAE) is proposed in this paper. Firstly, a unit vector metric selection strategy is presented, which uses the favorable physical properties of the metric to identify dominant individuals, thus striking a balance between convergence and diversity. Then, to preserve the diversity of the population, an adaptive angle estimation strategy based on Pareto front curvature prediction is developed. The angular distances of individuals are adaptively measured according to the predicted curvature, thereby selecting individuals with good diversity in the dominant region. Besides, the algorithm has a simple evolutionary process, and no additional parameters are involved. Finally, the proposed MaOEA-ISAE is compared with five other representative many-objective evolutionary algorithms on test instances of 3-20 objectives with different Pareto front shapes. The experimental results show the strong competitiveness of the proposed algorithm on different Pareto front problems. © 2024, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Latency-aware Partial Task Offloading in Collaborative Edge Computing

Latency-aware Partial Task Offloading in Collaborative Edge ...

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International Conference on computer Supported Cooperative Work in Design

作者： Yingmeng Gao Jie Zhu Haiping Huang Chen Chen Nanjing University of Posts & Telecommunications Nanjing China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing China Jiangsu High Technology Reasearch Key Laboratory of Wireless Sensor Networks Nanjing China Nanjing University of Aeronautics and Astronautics Nanjing China

When it comes to the fifth generation, collaborative edge computing is preferred for offloading computation-intensive tasks of low-latency applications in Internet of Things. In this paper, we consider the partial task offloading problem where tasks can be divided into subtasks and offloaded to nearby devices. The flow scheduling problem is integrated in the offloading process, i.e., multiple and conflicting route paths are considered. We propose the latency-aware partial task offloading framework (LaPTOF) for the considered problem. LaPTOF integrates a weighted priority ranking strategy (WPRS) which generates multiple solutions with different weights on task arrival time and the task processing time. A feasible solution generation method (FSGM) is designed where the best offloaded proportion of tasks are computed, and the appropriate offloaded devices and offload paths are determined. The proposed LaPTOF has an advantage in providing a scheduling plan that minimizes total completion time of task offloading in a shorter duration. The experimental results show that the proposal is suitable for the considered problem compared with JPOFH and its variants on both effectiveness and efficiency.

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Dissipative state Feedback of General Linear Time-Delay systems: Applications of the Kronecker-Seuret Decomposition Concept

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SSRN 2024年

作者： Feng, Qian Zheng, Wei Xing Wang, Xiao Yu Xiao, Feng School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

Stabilizing autonomous linear time delay systems, particularly when addressing an unlimited number of pointwise and distributed delays (DDs) under dissipative constraints, poses a significant challenge. Existing solutions are often hindered by theoretical limitations, numerical obstacles, or an inability to address the complexities of the delay integral kernels. In this paper, we propose a unified framework to tackle the above problem by employing the concept of the Kronecker-Seuret decomposition (KSD) for matrix-valued functions, which we recently have developed for the analysis of complex delay structures in coordination with the Krasovski\u{\i} functional approach. Our strategy can simultaneously address two distinct control problems, where the matrix kernels of DDs can contain an unlimited number of square-integrable functions. We show in detail how the KSD can factorize and approximate different kernel functions simultaneously without introducing conservatism. Furthermore, the use of KSD also enables us to construct complete-type functionals, whose integral kernels can include any number of weakly differentiable and linearly independent functions, underpinned by the utilization of novel integral inequalities derived from the least-squares principle. The solution to each synthesis problem comprises two theorems accompanied by an iterative algorithm, which can be utilized as a single package to compute controller gains, thus eliminating the need for nonlinear solvers. We present the testing results of two challenging examples, which could not be addressed by existing methods, to demonstrate the effectiveness of our methodology. Additionally, the paper reviews recent advancements in the research of time-delay systems, providing a valuable reference for both emerging and established researchers. © 2024, The Authors. All rights reserved.

关键词： state feedback

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Resilient Event-Triggered Distributed Resource Allocation for Multi-agent systems Under DoS Attacks 5th

Resilient Event-Triggered Distributed Resource Allocation fo...

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5th China Conference on Intelligent Networked Things, CINT 2022

作者： Cai, Xin Xiao, Feng Wei, Bo State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Electrical Engineering Xinjiang University Urumqi830047 China

ISBN: (纸本)9789811989148

This paper presents a resilient event-triggered distributed algorithm for resource allocation of multi-agent systems under denial-of-service (DoS) attacks. A class of time-sequence-based and aperiodic DoS attacks exists in communication networks. A resilient event-triggered communication scheme is designed to determine when agents communicate with neighbors on the network in the presence of DoS attacks. An explicit condition in terms of the frequency and duration of DoS attacks is obtained to ensure the convergence of the designed resilient algorithm to the optimal allocation. Simulation results are given to validate the proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Denial-of-service attack

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A Multi-Stage Stochastic Dispatching Method for Electricity-Hydrogen Integrated Energy systems Driven by Model and Data

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SSRN 2023年

作者： Yang, Zhixue Ren, Zhouyang Li, Hui Sun, Zhiyuan Feng, Jianbing The State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing400044 China The State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering University of Macau 999078 China The State Key Laboratory of Internet of Things for Smart City University of Macau 999078 China Electric Power Research Institute of Guangxi Power Grid Co. Ltd Guangxi Nanning530000 China

To balance the contradiction between economy, security, and computational burden caused by the uncertainty of the electricity-hydrogen integrated energy systems (EH-IESs), a multi-stage coordinated dispatching framework of "day-ahead deterministic dispatching - online security monitoring - intra-day flexible correction" is proposed. The flexibility of the hydrogen energy system at the source-side and load-side is fully exploited and incorporated into the day-ahead dispatching model. To online monitor the future security of the EH-IESs operation in an uncertain environment, a security monitoring method is proposed by combining deep learning and Monte Carlo simulation. The predetermined dispatching scheme may not ensure the security of system operation due to the renewable energy uncertainty. Thus, an intra-day correction method based on chance-constrained model and multi-agent deep reinforcement learning is established to determine the correction scheme. Finally, the effectiveness and adaptability of the proposed method are demonstrated using the data of two EH-IESs based on the standard IEEE 57-node and IEEE 118-node test systems. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Bargaining Game-Based Peer-to-Peer Trading Optimization Method for Microgrid system on the Island 2

A Bargaining Game-Based Peer-to-Peer Trading Optimization Me...

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2nd IEEE China International Youth Conference on Electrical Engineering, CIYCEE 2021

作者： Li, Hui Trivedi, Anupam Ren, Zhouyang Chongqing University State Key Laboratory of Power Transmission Equipment System Security and New Technology Chongqing400044 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

ISBN: (纸本)9781665400640

This paper proposes a peer-to-peer trading optimization method for a microgrid (MG) system on the island. A bargaining game is applied to describe the trading and operating strategies for two MGs in the off-grid MG system under the regulation of the government. First, the peer-to-peer trading optimization models for both MGs are developed. The electricity price is negotiated by MGs in an interval limited by the government. The trading power between MGs is optimized coordinately to minimize the net cost of each MG. Second, the presented optimization models are linearized and transformed to solve the Pareto front of trading and operating schemes for both MGs. Finally, the effectiveness of the proposed method is verified in the case studies. A sensitivity analysis of pricing intervals is also implemented to reveal the influence of the government's regulation on the net cost of MG. © 2021 IEEE.

关键词： Sensitivity analysis

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Reading Chinese in Natural Scenes with a Bag-of-Radicals Prior

arXiv

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arXiv 2022年

作者： Liu, Ongbin Liu, Ingjie Chen, Iaxin Wang, Unhong School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology System Beihang University Beijing China

Scene text recognition (STR) on Latin datasets has been extensively studied in recent years, and state-of-the-art (SOTA) models often reach high accuracy. However, the performance on non-Latin transcripts, such as Chinese, is not satisfactory. In this paper, we collect six open-source Chinese STR datasets and evaluate a series of classic methods performing well on Latin datasets, finding a significant performance drop. To improve the performance on Chinese datasets, we propose a novel radical-embedding (RE) representation to utilize the ideographic descriptions of Chinese characters. The ideographic descriptions of Chinese characters are firstly converted to bags of radicals and then fused with learnable character embeddings by a character-vector-fusion-module (CVFM). In addition, we utilize a bag of radicals as supervision signals for multi-task training to improve the ideographic structure perception of our model. Experiments show performance of the model with RE + CVFM + multi-task training is superior compared with the baseline on six Chinese STR datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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Multi-Segment Droop Control and Optimal Parameter Setting Strategy of Wind Turbine for Frequency Regulation

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SSRN 2023年

作者： Gao, Haishu Zhang, Feng Ding, Lei Cornélusse, Bertrand State Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan250061 China Departments of Computer Science Electrical Engineering and Geography University of Liège Liège Belgium

Traditionally virtual inertia is the control strategy of wind turbines when participating in frequency regulation. However, it has inherent defects in measurement error amplification, due to the frequency differential, and it may incur large response power deviation or even inverse response. Also, when control delay of wind turbines is taken into consideration, virtual inertia is essentially a kind of fast power response, just the same as droop control of wind turbines. Hence, considering the reliability of droop control, it’s valuable to explorer a novel droop control method without virtual inertia, as the frequency response model of wind turbines. For this reason, a multi-segment droop control strategy was proposed to provide a reliable frequency regulation model for wind turbines, which can be broadly applied in other inverter-based power sources. Firstly, based on the extended system frequency response (SFR) model, a differential equation including piecewise time-varying coefficient was established to analyze the frequency response of the presented strategy. To deal with impulse function emerged during the deduction, the impulse function balancing principle and the integration by parts algorithm were adopted. Then the analytical expression of the frequency response with multi-segment droop control strategy can be obtained. Subsequently, based on analytical expression, the performance of multi-segment droop control strategy was theoretically analyzed via comparison with traditional virtual inertia control, and meanwhile the conclusion that the presented multi-segment droop control has better frequency regulation performance can be got. Furthermore, to achieve optimal frequency regulation performance, a Lagrangian function was established to get the optimal parameters of the proposed control strategy, according to the relationship between the frequency nadir and frequency regulation parameters. During its solution, the exponential homotopy algorithm was adopted. F

关键词： Wind turbines

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Prediction-Based Thunderstorm Path Recovery Method Using CNN-BiLSTM

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Intelligent Automation & Soft Computing 2023年第8期37卷 1637-1654页

作者： Xu Yang Ling Zhuang Yuqiang Sun Wenjie Zhang School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2R3Canada CCCC Highway Consultants Co. Ltd.Beijing100101China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing210044China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing100101China

The loss of three-dimensional atmospheric electric field(3DAEF)data has a negative impact on thunderstorm *** paper proposes a method for thunderstorm point charge path *** on the relation-ship between a point charge and 3DAEF,we derive corresponding localization formulae by establishing a point charge localization ***,point charge movement paths are obtained after fitting time series localization ***,AEF data losses make it difficult to fit and visualize ***,using available AEF data without loss as input,we design a hybrid model combining the convolutional neural network(CNN)and bi-directional long short-term memory(BiLSTM)to predict and recover the lost *** paths are not present during sunny weather,we propose an extreme gradient boosting(XGBoost)model combined with a stacked autoencoder(SAE)to further determine the weather conditions of the recovered ***,historical AEF data of known weathers are input into SAE-XGBoost to obtain the distribution of predicted values(PVs).With threshold adjustments to reduce the negative effects of invalid PVs on SAE-XGBoost,PV intervals corresponding to different weathers are *** recovered AEF is then input into the fixed SAE-XGBoost *** paths need to be fitted is determined by the interval to which the output PV *** results confirm that the proposed method can effectively recover point charge paths,with a maximum path deviation of approximately 0.018 km and a determination coefficient of 94.17%.This method provides a valid reference for visual thunderstorm monitoring.

关键词： Thunderstorm point charge atmospheric electric field(AEF) recovery

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Training Verification-Friendly Neural Networks via Neuron Behavior Consistency

arXiv

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arXiv 2024年

作者： Liu, Zongxin Zhao, Zhe Song, Fu Sun, Jun Yang, Pengfei Huang, Xiaowei Zhang, Lijun Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China RealAI Beijing China Nanjing Institute of Software Technology Nanjing China Singapore Management University Singapore College of Computer and Information Science Software College Southwest University Chongqing China The University of Liverpool Liverpool United Kingdom

Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel method for training verification-friendly neural networks, which are robust, easy to verify, and relatively accurate. Our method integrates neuron behavior consistency into the training process, making neuron activation states consistent across different inputs in a local neighborhood, reducing the number of unstable neurons and tightening the bounds of neurons thereby enhancing neural network verifiability. We evaluated our method using the MNIST, Fashion-MNIST, and CIFAR-10 datasets across various network architectures. The results of the experiment demonstrate that networks trained using our method are verification-friendly across different radii and different model architectures, whereas other tools fail to maintain verifiability as the radius increases. We also show that our method can be combined with existing methods to further improve the verifiability of networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Neurons

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