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7th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2020

作者： Fan, Zhipeng Sun, Yaping Su, Housheng Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Wuhan China

ISBN: (纸本)9781728162461

The thesis studies the semi-global scaled edge-consensus of linear discrete-time multi-agent systems under both the directed networks and undirected networks, where the states of each edge are subject to input saturation and positive constraint. By virtue of the MARE-based low-gain feedback method, two control protocols about directed networks and undirected networks are devised respectively to promise the positive scaled edge-consensus with input saturation. Thus we find the sufficient conditions to promise the positive edge states and overcome the input saturation. Finally, two representative examples are presented to prove the results. © 2020 IEEE.

关键词： Multi agent systems

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Short-Term Residential Load Forecasting Via Pooling-Ensemble Model with Smoothing Clustering

SSRN

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SSRN 2023年

作者： Fang, Hongliang Xiao, Jiang-Wen Wang, Yan-Wu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

Short-term residential load forecasting is essential to demand side response. However, the frequent spikes in the load and the volatile daily load patterns make it difficult to accurately forecast the load. To deal with these problems, this paper proposes a smoothing clustering method for daily load clustering and a pooling-ensemble model for one day ahead load forecasting. The whole shortterm load forecasting framework in this paper contains three steps. Specifically and firstly, the states of the residents are obtained by clustering the daily load curves with the proposed smoothing clustering method. Secondly, a weighted mixed Markov model is built to predict the probability distribution of the load state in the next day. Thirdly, multiple predictors in the pooling-ensemble model are selected for different states and the load is forecasted by weighing the results of the multiple predictors based on the predicted states. Results of the case studies and comparison studies on two public datasets verify the advantages of the smoothing clustering method and the pooling-ensemble model. © 2023, The Authors. All rights reserved.

关键词： Probability distributions

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Joint Load Prediction of Multiple Buildings Using Multi-Task Learning with Selected-Shared-Private Mechanism

SSRN

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SSRN 2023年

作者： Cao, Minghui Xiao, Jiang-Wen Fang, Hongliang Li, Yuan-Zheng Wang, Yan-Wu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

Short-term load forecasting for residential buildings is of great significance to ensure the safe and economic operation of power grid. However, most of the existing prediction methods focus on the temporal characteristics of individual buildings and ignore the importance of spatial characteristics between different buildings. To tackle this problem and realize joint prediction for multiple buildings, a novel multi-task learning model with the selected-shared-private mechanism is proposed in this paper. Firstly, the temporal and spatial characteristics hidden in electricity consumption patterns are analyzed and represented by auto-correlation and cross-correlation, respectively. Then, a correlation-oriented combination strategy considering the auto-correlation and time dependency is proposed to build input set for the prediction model, and the temporal convolutional network is adopted to extract features. Furthermore, this paper designs a novel selected-shared-private mechanism based on the cross-correlation, which can selectively utilize information from related tasks while focusing on private features to improve prediction accuracy. The proposed model is compared with other methods on the public dataset, and the results demonstrate the superiority of this model in load prediction for multiple buildings. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Generation Maintenance Scheduling for Power Systems Considering the Risk Quantification of Hybrid Uncertainty

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IEEE Transactions on Power Systems 2025年

作者： Yang, Xiao Zhao, Yong Li, Yuanzheng Huang, Cheng Ding, Qiang Key Laboratory of Image Information Processing and Intelligent Control of Ministry of Education of China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Power Science Research Institute of State Grid Jiangsu Electric Power Company China China Electric Power Research Institute Company China

The accurate quantification of risk caused by uncertainty forms a crucial foundation for formulating the generation maintenance scheduling (GMS) of power systems. However, the probability distribution functions (PDFs) of uncertain variables such as wind power and load are challenging to model accurately or are unknown, which makes it difficult to measure their economic risk and formulate appropriate GMS for power systems. To address this issue, we consider hybrid uncertainty from wind power and load, and propose a novel interval-probabilistic worst conditional value-at-risk (IP-WCVaR)-based generation maintenance scheduling method. Firstly, a novel IP-WCVaR method is proposed to measure the risk of the interval and probabilistic hybrid uncertainty, and the analytical mathematical model of the IP-WCVaR is derived through typical scenarios of probability correction. On this basis, the positive and negative spinning reserve models are established using the IP-WCVaR and then integrated into the GMS model, which enhances the resilience of the power system. Finally, the new risk-averse GMS model is formulated as the lower and upper boundary optimal models, which are transformed into tractable mixed integer linear programming problems based on the interval extreme value theory. The effectiveness and superiority of the proposed IP-WCVaR method are verified on the modified IEEE 24-bus and IEEE 118-bus power systems. © 1969-2012 IEEE.

关键词： Mixed-integer linear programming

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Optimizing Parameter Design with Frequency and Clock Drift Constraints in Microgrids⋆

Optimizing Parameter Design with Frequency and Clock Drift C...

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IEEE Conference on Decision and control

作者： Chang Yu Xiaoqing Lu School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan China School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Microgrids subjected to secondary cooperative control encounter significant challenges, including operational constraints and clock drifts, adversely affecting their stability and efficiency. This paper provides conditions that assure optimal microgrid performance in both transient and steady-state scenarios, focusing on the effects of clock drifts and fluctuations in load. Furthermore, we introduce a novel approach for designing secondary control parameters, specifically engineered to minimize steady-state discrepancies attributable to clock drifts while ensuring adherence to standards for transient operations. Comprehensive experimental validations corroborate the effectiveness of our proposed solutions.

关键词： Fluctuations Stability criteria Focusing Microgrids Steady-state Transient analysis Standards Frequency control Clocks Load modeling

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Large Language Models are Few-shot Generators: Proposing Hybrid Prompt Algorithm To Generate Webshell Escape Samples

arXiv

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

The frequent occurrence of cyber-attacks has made webshell attacks and defense gradually become a research hotspot in the field of network security. However, the lack of publicly available benchmark datasets and the over-reliance on manually defined rules for webshell escape sample generation have slowed down the progress of research related to webshell escape sample generation and artificial intelligence (AI)-based webshell detection. To address the drawbacks of weak webshell sample escape capabilities, the lack of webshell datasets with complex malicious features, and to promote the development of webshell detection, we propose the Hybrid Prompt algorithm for webshell escape sample generation with the help of large language models. As a prompt algorithm specifically developed for webshell sample generation, the Hybrid Prompt algorithm not only combines various prompt ideas including Chain of Thought, Tree of Thought, but also incorporates various components such as webshell hierarchical module and few-shot example to facilitate the LLM in learning and reasoning webshell escape strategies. Experimental results show that the Hybrid Prompt algorithm can work with multiple LLMs with excellent code reasoning ability to generate high-quality webshell samples with high Escape Rate (88.61% with GPT-4 model on VirusTotal detection engine) and Survival Rate (54.98% with GPT-4 model). Copyright © 2024, The Authors. All rights reserved.

关键词： Network security

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Tight Formulations of Unit Commitment with General Start-Up Ramp Rate Constraints

Tight Formulations of Unit Commitment with General Start-Up ...

引用

2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Gao, Yijie Zhang, Xuan Yu, Yaowen Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan China Hitachi Abb Power Grid San JoseCA United States

ISBN: (纸本)9781665414395

Unit commitment (UC) is a central market clearing process in wholesale electricity markets. With the increasing size and complexity of market-clearing models, UC problems become more and more complicated. To improve the computation efficiency, this paper tightens UC formulations with the general start-up ramp rate. Through analyzing their parameters over three periods, units are categorized into several categories for constructing corresponding tight formulations, and three of the categories are beyond existing works. By applying a recent systematic formulation tightening approach on top of these categories, new tight constraints of the general start-up ramp rate have been obtained. Numeral testing results illustrate the tightening process and demonstrate the improvement of computational efficiency when using our tightened formulations. © 2021 IEEE.

关键词： Computational efficiency

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BokehMe: When Neural Rendering Meets Classical Rendering

arXiv

引用

arXiv 2022年

作者： Peng, Juewen Cao, Zhiguo Luo, Xianrui Lu, Hao Xian, Ke Zhang, Jianming Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Adobe Research

We propose BokehMe, a hybrid bokeh rendering framework that marries a neural renderer with a classical physically motivated renderer. Given a single image and a potentially imperfect disparity map, BokehMe generates high-resolution photo-realistic bokeh effects with adjustable blur size, focal plane, and aperture shape. To this end, we analyze the errors from the classical scattering-based method and derive a formulation to calculate an error map. Based on this formulation, we implement the classical renderer by a scattering-based method and propose a two-stage neural renderer to fix the erroneous areas from the classical renderer. The neural renderer employs a dynamic multi-scale scheme to efficiently handle arbitrary blur sizes, and it is trained to handle imperfect disparity input. Experiments show that our method compares favorably against previous methods on both synthetic image data and real image data with predicted disparity. A user study is further conducted to validate the advantage of our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

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Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces

arXiv

引用

arXiv 2024年

作者： Meng, Lubin Jiang, Xue Chen, Xiaoqing Liu, Wenzhong Luo, Hanbin Wu, Dongrui Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is a common input signal for BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals, while ignoring their security. Recent studies have shown that machine learning models in BCIs are vulnerable to adversarial attacks. This paper proposes adversarial filtering based evasion and backdoor attacks to EEG-based BCIs, which are very easy to implement. Experiments on three datasets from different BCI paradigms demonstrated the effectiveness of our proposed attack approaches. To our knowledge, this is the first study on adversarial filtering for EEG-based BCIs, raising a new security concern and calling for more attention on the security of BCIs. © 2024, CC BY.

关键词： Adversarial machine learning

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Two-stage Train Components Defect Detection Based on Prior Knowledge

Two-stage Train Components Defect Detection Based on Prior K...

引用

IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Gang Peng Zhiyong Li Shaowei Wan Zhang Deng Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Wuhan Lisai Technology Co. Ltd. Wuhan China

The existing method of detecting defects in train components, which relies on visual identification, requires extensive involvement from inspectors and presents certain limitations. In this study, a two-stage defect detection based on prior knowledge was developed, which first detects the types and positions of components, and then conducts targeted detection of possible existing defect types. The algorithm introduces the prior knowledge of the relative spatial position relationship of components and optimizes the detection of sub-components by cascaded convolutional neural networks and local scale-up. In this study, three methods were used, including deep learning, template matching, and quantitative evaluation based on prior knowledge, to perform targeted detection of defect types that may occur in components. Experiments have verified the adaptability and accuracy of the method, demonstrating its high value for engineering applications.

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