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arXiv 2024年

作者： Ren, Zihao Wang, Lei Yi, Xinlei Wang, Xi Yuan, Deming Yang, Tao Wu, Zhengguang Shi, Guodong State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China College of Electronics and Information Engineering Tongji University China School of Electrical Engineering Telecommunications University of New South Wales Sydney Australia School of Automation Nanjing University of Science and Technology China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Australian Centre for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney SydneyNSW2006 Australia

Several data compressors have been proposed in distributed optimization frameworks of network systems to reduce communication overhead in large-scale applications. In this paper, we demonstrate that effective information compression may occur over time or space during sequences of node communications in distributed algorithms, leading to the concept of spatio-temporal compressors. This abstraction classifies existing compressors as spatio-temporal compressors, with their effectiveness described by constructive stability criteria from nonlinear system theory. Subsequently, we apply these spatio-temporal compressors to standard continuous-time consensus flows and distributed prime-dual flows, establishing conditions ensuring convergence. Additionally, we introduce a novel observer-based distributed primal-dual continuous flow integrated with spatio-temporal compressors, which provides broader convergence conditions. These continuous flows achieve exponential convergence to the global optimum when the objective function is strongly convex and can be discretized using Euler approximations. Finally, numerical simulations illustrate the versatility of the proposed spatio-temporal compressors and verify the convergence of algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Stability criteria

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Location and capacity of distributed photovoltaic generation connected to distribution network based on multi-objective genetic algorithm 5

Location and capacity of distributed photovoltaic generation...

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5th IEEE Advanced Information Management, Communicates, Electronic and Automation control Conference, IMCEC 2022

作者： Qing, Song Xie, Tuo Bai, Qingjun Li, Pai Institute of Water Resources and Hydro-Electric Engineering Xi'An University of Technology Xi'an710048 China China Electric Power Research Institute State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems Beijing102401 China

ISBN: (纸本)9781665479677

In order to further improve the ecological environment in desert areas and actively respond to the development of the 'dual carbon' policy, numerous distributed photovoltaics have been connected to the distribution network in recent years. Network loss caused a greater impact. For the location and capacity planning of distributed photovoltaic access to the distribution network, this paper proposes a distributed photovoltaic location and capacity model that considers investment costs, distribution network losses, voltage stability, and environmental protection costs. The improved multi-objective genetic algorithm is used to solve the optimization model, which is verified by example simulation. After the optimized configuration, the power supply quality and network loss of the distribution network are significantly improved, which verifies the feasibility of the model. This research provides a certain reference and reference for the location and capacity determination of the distribution network with distributed photovoltaics. © 2022 IEEE.

关键词： Genetic algorithms

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Detection methods of surface defects of sintering material based on machine vision

Detection methods of surface defects of sintering material b...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Wensi Liu Xiao-Yu Tang Yi Yang Liang Zhao Chunjie Yang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Reliability and Systems Engineering Beihang University Beijing China School of Software Technology Dalian University of Technology Dalian China

The iron and steel industry is one of the pillar industries in the national economy. Blast furnace (BF) ironmaking is the core step in long-process ironmaking. Before BF production, to improve the ore quality, sintering is adopted to provide high-quality man-made ore from raw materials. In the sintering process, the material surface state is the main indicator for monitoring the ignition effect. During the actual production process, due to the influence of the ingredients and the ignition effect, there are defects on the sintering material surface, including yellow mud, mouse holes, and cracks. Detection of defect information on the material surface is conducive to ensuring the stable quality of the sinter and the stable operation of sintering production. This paper designs the corresponding detection methods for the three defects based on machine vision algorithms, 1) Yellow mud detection: separate the material surface area of the sintering trolley by semantic segmentation, and judge whether there is yellow mud according to the HSV histogram correlation combined with the shape judgment condition of yellow mud; 2) Mouse hole detection: use the target detection method of YOLOv5 without Mosaic detects mouse holes and improves the detection ability of small targets; 3) Crack detection: transfer learning and improved DeepLabv3+ network semantic segmentation are used for crack detection to improve the accuracy of crack detection. Finally, all methods are applied to the actual production line, and the detection accuracy and effectiveness of the above methods are verified through image testing and calculation of evaluation indicators, which can meet the needs of on-site use.

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Security Issues and Defensive Approaches in Deep Learning Frameworks

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Tsinghua Science and technology 2021年第6期26卷 894-905页

作者： Hongsong Chen Yongpeng Zhang Yongrui Cao Jing Xie Department of Computer Science University of Science and Technology Beijing(USTB)Beijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Department of Computer Science and Technology University of Science and Technology Beijing(USTB)Beijing 100083China Defense Electronics Institute China Industrial Control System Cyber Emergency Response TeamBeijing 100040China

Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous ***,the security issues of deep learning frameworks are among the main risks preventing the wide application of *** on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and *** start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in *** propose a highly comprehensive classification approach for security issues and defensive approaches in deep learning frameworks and connect different attacks to corresponding defensive ***,we analyze a case of the physical-world use of deep learning security *** addition,we discuss future directions and open issues in deep learning *** hope that our research will inspire future developments and draw attention from academic and industrial domains to the security of deep learning frameworks.

关键词： adversarial examples deep learning frameworks defensive approaches security issues

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DIDO: Deep Inertial Quadrotor Dynamical Odometry

arXiv

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arXiv 2022年

作者： Zhang, Kunyi Jiang, Chenxing Li, Jinghang Yang, Sheng Ma, Teng Xu, Chao Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China Alibaba DAMO Academy Autonomous Driving Lab Hangzhou311121 China

—In this work, we propose an interoceptive-only state estimation system for a quadrotor with deep neural network processing, where the quadrotor dynamics is considered as a perceptive supplement of the inertial kinematics. To improve the precision of multi-sensor fusion, we train cascaded networks on real-world quadrotor flight data to learn IMU kinematic properties, quadrotor dynamic characteristics, and motion states of the quadrotor along with their uncertainty information, respectively. This encoded information empowers us to address the issues of IMU bias stability, quadrotor dynamics, and multi-sensor calibration during sensor fusion. The above multisource information is fused into a two-stage Extended Kalman Filter (EKF) framework for better estimation. Experiments have demonstrated the advantages of our proposed work over several conventional and learning-based methods. © 2022, CC BY.

关键词： Extended Kalman filters

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Optimal Model and Dispatch Strategy of a Multi-Energy Microgrid Based on Hybrid Pumped Hydro-Battery Storage and Ground Source Heat Pump

SSRN

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SSRN 2023年

作者： Li, Xinyue Wang, Honglei Li, Bin Tan, Zhukui Electrical Engineering College Guizhou University Guiyang550025 China Key Laboratory of "Internet+" Collaborative Intelligent Manufacturing in Guizhou Provence Guiyang Guizhou550025 China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Electric Power Research Institute Guizhou Power Grid Co. Ltd Guiyang550002 China

The carbon neutrality target and the carbon emission problem caused by the ever-increasing energy demand necessitate the extensive use of renewable energy (RES) generation technologies. Multi-energy microgrid (MEMG) become effective vehicles for managing RES generation and storage. However, the uncertainty of RES and the randomness of load demand poses a challenge to the design and operation of MEMG. This study proposes a MEMG including combined cooling, heating, power system, pumped hydro storage, and ground source heat pump to solve the problem. Combined with demand-side management strategies, a system is designed considering complementary configuration and energy conversion management. A corresponding optimization model is established to minimize the total cost and maximize the island operation while satisfying the demand. The experimental results show that the established model and scheduling method improves the system’s ability to cope with the RES’ uncertainty and reduces the capacity of the energy storage system. In summer, the daily investment cost is saved by 1439RMB, and the utilization rate of clean energy reaches 96%. Compared with the uncombined system, the MEMG reduces 1620.02kg of carbon dioxide per day, realizing the full utilization of clean energy and having the role of demonstration and promotion. © 2023, The Authors. All rights reserved.

关键词： Battery storage

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Immunohistochemical Image Quality control Algorithm for Colorectal Cancer Based on Deep Learning

Immunohistochemical Image Quality Control Algorithm for Colo...

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第34届中国过程控制会议

作者： Xiaolei Guo Jinbo Zhang Pingjie Huang Weiting Ge Yuqi Cao Guangxin Zhang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University Cancer Institute (Key Laboratory of Cancer Prevention and Intervention China National Ministry of Education)the Second Affiliated HospitalZhejiang University School of Medicine

High-quality pathological images are essential for reliable computer-aided ***,tissue-detachment images and cancer tissue deficiency images,which cannot be effectively used,are inevitable in *** address this issue,we propose to use the deep learning method of EfficientNet-b4 to identify unqualified P53 immunohistochemical(IHC) images of colorectal *** view of the limited number of unqualified images,the data augmentation technique is used to expand the dataset and the Focal Loss is used to force the model to pay more attention to the unqualified *** obtain a recall rate of 96.59% for the cancer tissue deficiency images and 100% for tissue-detachment images in our results,which is superior to the existing *** work proves that the improved model can effectively recognize cancer tissue deficiency and tissuedetachment images.

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A Parkinson's Bradykinesia Recognition System Based on Deep Learning Method 12

A Parkinson's Bradykinesia Recognition System Based on Deep ...

引用

12th International Conference on cyber technology in Automation, control, and Intelligent systems, cyber 2022

作者： Tong, Lina Liu, Daisong Zhang, Mingjia Peng, Liang Department of State Key Laboratory of Management and Control for Complex Systems Institute of Automation China Academy of Science Beijing China School of Mechanical Electronic and Information Engineering Chinese University of Mining and Technology-Beijing Beijing China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Parkinson's disease is a neurodegenerative disease common in middle-aged and elderly people, and bradykinesia is one of the most obvious symptoms. In this study, A deep learning-based Parkinson's bradykinesia recognition system is designed which has an inertial sensor-based data acquisition system. It can wirelessly collect bradykinesia kinematic information by wearing it on the wrist and ankle of Parkinson's patients. In addition, a method for identifying the symptom of Parkinson's bradykinesia based on Long Short Term Memory (LSTM) Network is proposed, with an accuracy of 98.6%. It provides a feasible method to identify Parkinson bradykinesia to assist the diagnosis and treatment. © 2022 IEEE.

关键词： Neurodegenerative diseases

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Improved Backward Scenario Reduction Method for Generation Planning Considering Extreme Scenarios 7

Improved Backward Scenario Reduction Method for Generation P...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Xie, Yigong Huo, Ziyu Wu, Chen Yao, Wenfeng Wang, Jiayang Qiao, Ying Power Dispatch Control Center Yunnan Power Grid Guangzhou China Tsinghua University State Key Lab of Power Systems Dept. of Electrical Engineering Beijing China Planning and Construction Research Center Yunnan Power Grid Kunming China China Southern Power Grid Research Institute State Key Laboratory of Current Transmission Technology Guangzhou China

ISBN: (纸本)9798350345094

Renewable energy sources exhibit significant variability and uncertainty, showing numerous distinct scenarios. In the context of generation planning, it's imperative to employ scenario reduction techniques to select representative scenarios that capture the characteristics of renewable energy outputs. However, traditional scenario reduction methods often fail to adequately preserve the extreme output scenarios of these renewables. This paper improves the simultaneous backward reduction method (SBR), enhancing its capability to effectively capture the extreme output scenarios of renewable sources. As a result, the generation planning becomes more economically viable and more reliable. The efficacy of the improved SBR method is demonstrated using data from the Qinhuangdao region in China. © 2023 IEEE.

关键词： Renewable energy

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Heuristic Learning for Co-Design Scheme of Optimal Sequential Attack

arXiv

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arXiv 2023年

作者： Luo, Xiaoyu Pan, Haoxuan Fang, Chongrong Zhao, Chengcheng Cheng, Peng He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China

This paper considers a novel co-design problem of the optimal sequential attack, whose attack strategy changes with the time series, and in which the sequential attack selection strategy and sequential attack signal are simultaneously designed. Different from the existing attack design works that separately focus on attack subsets or attack signals, the joint design of the attack strategy poses a huge challenge due to the deep coupling relation between the sequential attack selection strategy and sequential attack signal. In this manuscript, we decompose the sequential co-design problem into two equivalent sub-problems. Specifically, we first derive an analytical closed-form expression between the optimal attack signal and the sequential attack selection strategy. Furthermore, we prove the finite-time inverse convergence of the critical parameters in the injected optimal attack signal by discrete-time Lyapunov analysis, which enables the efficient off-line design of the attack signal and saves computing resources. Finally, we exploit its relationship to design a heuristic two-stage learning-based joint attack algorithm (HTL-JA), which can accelerate realization of the attack target compared to the one-stage proximal-policy-optimization-based (PPO) algorithm. Extensive simulations are conducted to show the effectiveness of the injected optimal sequential attack. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

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