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Detection and Classification of Transmission Line Transient Faults Based on Graph Convolutional Neural Network

CSEE Journal of Power and Energy Systems 2021年第3期7卷 456-471页

作者： Houjie Tong Robert C.Qiu Dongxia Zhang Haosen Yang Qi Ding Xin Shi Department of Electrical Engineering Center for Big Data and Artificial IntelligenceShanghai Jiao Tong UniversityShanghai 200240China School of Electronic Information and Communication Huazhong University of Science and TechnologyWuhan 430000China Electric Power Research Institute Haidian DistrictBeijing 100192China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural *** with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction *** this basis,a framework for transient fault detection and classification is *** structure is generated to provide topology information to the *** approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results ***,the proposed approach is tested in various situations and its generalization ability is verified by experimental *** results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.

关键词： Graph convolutional network(GCN) power transmission line fault detection and classification spatio-temporal data topology information

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Intrusion Detection System Using a Distributed Ensemble Design Based Convolutional Neural Network in Fog Computing

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Journal of Information Hiding and Privacy Protection 2022年第1期4卷 25-39页

作者： Aiming Wu Shanshan Tu Muhammad Wagas Yongjie Yang Yihe Zhang Xuetao Bai Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi 23460Pakistan School of Engineering Edith Cowan UniversityJoondalup PerthWA 6027Australia

With the rapid development of the Internet of Things(IoT),all kinds of data are increasing *** storage and computing on cloud servers are increasingly restricted by *** has prompted the development of fog *** computing is to place the calculation and storage of data at the edge of the network,so that the entire Internet of Things system can run more *** main function of fog computing is to reduce the burden of cloud *** placing fog nodes in the IoT network,the data in the IoT devices can be transferred to the fog nodes for storage and *** of the information collected by IoT devices are malicious traffic,which contains a large number of malicious *** IoT devices do not have strong computing power and the ability to detect malicious traffic,we need to deploy a system to detect malicious attacks on the fog *** response to this situation,we propose an intrusion detection system based on distributed ensemble *** system mainly uses Convolutional Neural Network(CNN)as the first-level *** the second level,the random forest will finally classify the prediction results obtained in the first *** paper uses the UNSW-NB15 dataset to evaluate the performance of the *** results show that the model has good detection performance for most attacks.

关键词： Intrusion detection system fog computing convolutional neural network feature selection

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Robust Voltage Regulation for DC Microgrids via Passivity-Based Sliding Mode control

Robust Voltage Regulation for DC Microgrids via Passivity-Ba...

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International Workshop on Variable Structure Systems, VSS

作者： Edoardo Vacchini Michele Cucuzzella Pablo Borja Antonella Ferrara Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Jan C. Willems Center for Systems and Control Engineering and Technology Institute Groningen (ENTEG) Faculty of Science and Engineering University of Groningen Groningen The Netherlands School of Engineering Computing and Mathematics University of Plymouth Plymouth U.K.

ISBN: (数字)9798350353686

ISBN: (纸本)9798350353693

This paper proposes a decentralized sliding mode control approach to the voltage regulation problem in a DC microgrid consisting of distributed generation units interconnected with each other through resistive-inductive power lines and supplying unknown nonlinear loads. In particular, the port-Hamiltonian structure of the system suggests the design of a suitable sliding manifold such that the system on this manifold exhibits desired passivity properties. This approach simplifies the control design and relaxes some restrictive assumptions required by other controllers proposed in the literature, while offering satisfactory performance.

关键词： Manifolds Variable structure systems Power transmission lines Microgrids Power system stability Stability analysis Robustness Reduced order systems Voltage control Sliding mode control

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On the link between explicit MPC and the face lattice of the lifted feasible domain

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IFAC-PapersOnLine 2022年第16期55卷 308-313页

作者： Ştefan S. Mihai Florin Stoican Bogdan D. Ciubotaru Department of Automatic Control and Systems Engineering University "Politehnica" of Bucharest 313 Splaiul Independenţei Sector 6 060042 Bucharest Romania Research Center for Logic Optimization and Security (LOS) Department of Computer Science Faculty of Mathematics and Computer Science University of Bucharest.

The paper provides a geometric interpretation for the explicit solution of the quadratic cost, linear-constrained MPC (model predictive control) problem. We link the face lattice of the lifted feasible domain (defined in the input and parameter space) with the critical regions which partition the parameter space and serve as polyhedral support for the piecewise affine explicit MPC solution. We provide geometric (face visibility) and algebraic (polyhedron emptiness) tests for the pruning of the candidate sets of active constraints.

关键词： Model Predictive control (MPC) Explicit MPC Face Lattice Constrained-Optimization Problem

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Trustzone-Based Dual-Architecture Scheme for Active Defense

SSRN

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SSRN 2022年

作者： Liu, Xiaoqing Lai, Yingxu Liu, Jing Luo, Shiyao College of Computer Science Faculty of Information Technology Beijing University of Technology Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China

With the rapid development of computer and communication technology, embedded systems are being widely adopted in smart devices. The increasing connectivity of these systems and the difficulty in providing overall security has resulted in such devices being vulnerable to malicious attacks. Passive defense firewall technology and traffic-based intrusion detection technology are not entirely effective against such attacks. TrustZone technology can ensure the security of the system against unknown attacks to a certain extent. However, it increases the computational burden on the central processing unit. Thus, to actively defend against attacks, we designed a trusted computing dual architecture scheme based on the TrustZone architecture. To reduce the burden on the trusted computing base, prevent the trusted execution environment from occupying the central processing unit for too long, and reduce the performance overhead, we moved certain sensitive applications and function modules out of the trusted execution environment. To overcome the severe problem that modules in the trusted execution environment require passive invocation to deliver services, we created an active measurement closed-loop immune mechanism to safeguard the internal security of function modules. Furthermore, we designed a trust chain and dynamic measurement method based on this architecture to ensure that the security of sensitive applications is ensured by trust transfer. We changed the traditional attack response method, which involves restarting the entire system once attacked, by designing a fallback mechanism that is more suitable for the system. Finally, we verified the effectiveness of the architecture by designing an attack model. Performance testing and analysis verified that the architecture reduced the impact of the security mechanisms on the system. © 2022, The Authors. All rights reserved.

关键词： Program processors

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Automated AI Research On Cyber Attack Prediction And Security Design

Automated AI Research On Cyber Attack Prediction And Securit...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Ravikiran Madala N. Vijayakumar Nandini N Shanti Verma Samidha Devendra Chandvekar Devesh Pratap Singh Network and Data Center Engineer Ph.D Information Technology University of Cumberlands Durham North Carolina United States Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology Bengaluru Karnataka India Department of Computer Application L J University Ahmedabad Gujarat India Department of Information Technology Changu Kana Thakur Arts Commerce and Science College New Panvel (Autonomous) New Panvel Maharashtra India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India

A fast expanding topic of study on automated AI is focused on the prediction and prevention of cyber-attacks using machine learning algorithms. In this study, we examined the research on applying machine learning algorithms to the problems of strategic cyber defense and attack forecasting. We also provided a technique for assessing and choosing the best machine learning models for anticipating cyber-attacks. Our findings show that machine learning methods, especially random forest and neural network models, are very accurate in predicting cyber-attacks. Additionally, we discovered a number of crucial characteristics, such as source IP, packet size, and malicious traffic that are strongly associated with the likelihood of cyber-attacks. Our results imply that automated AI research on cyber-attack prediction and security planning has tremendous promise for enhancing cyber-security and averting cyber-attacks.

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Neural-Network-Based Recursive State Estimation for Nonlinear Networked Systems With Binary-Encoding Mechanisms

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10072-10083页

作者： Yuhan Zhang Zidong Wang Lei Zou Wei Qian Shuxin Du College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Department of Computer Science Brunel University of London Middlesex Uxbridge U.K. College of Information Science and Technology Donghua University Shanghai China Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo China Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou China

This work addresses the problem of recursive state estimation for networked control systems with unknown nonlinearities and binary-encoding mechanisms (BEMs). To enhance transmission reliability and reduce network resource consumption, BEMs are used to convert measurement signals into binary bit strings (BBSs) of limited length, which are then transmitted to the estimator through noisy communication channels. During transmission, random bit errors may occur in the BBSs due to channel noise. For the considered nonlinear networked control systems affected by random bit errors, a neural-network (NN)-based recursive estimation strategy is proposed, where an NN with a time-varying tuning scalar is employed to approximate the unknown nonlinearity of the networked control systems. By using the proposed strategy, the upper bounds of the estimation error of the system state and the trace of the estimation error of the NN weight (NNW) are first derived. These bounds are then minimized by recursively designing both the estimator gain matrix and the tuning scalar of the NNW. Finally, the effectiveness of the proposed estimation strategy is demonstrated through a numerical example.

关键词： Artificial neural networks State estimation Encoding Noise Networked control systems Electronic mail Symmetric matrices Estimation error Tuning Recursive estimation

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On the Existence and Uniqueness of Steady State Solutions of a Class of Dynamic Hydraulic Networks via Actuator Placement

On the Existence and Uniqueness of Steady State Solutions of...

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IEEE Conference on Decision and control

作者： Mark Jeeninga Juan E. Machado Michele Cucuzzella Giacomo Como Jacquelien Scherpen Department of Automatic Control Lund University Lund Sweden Chair of Control Systems and Network Control Technology Faculty 3 Mechanical Engineering Electrical and Energy Systems Brandenburg University of Technology Cottbus - Senftenberg Cottbus Germany Department of Electrical Computer Science and Biomedical Engineering Univeristy of Pavia Pavia Italy Department of Mathematical Science “G.L. Lagrange” Politecnico di Torino Torino Italy Department of Automatic Control Lund University Faculty of Science and Engineering Jan C. Willems Center for Systems and Control ENTEG University of Groningen Groningen the Netherlands

In this paper, using tools from graph theory we provide verifiable necessary and sufficient conditions for the existence of a unique hydraulic equilibrium in district heating systems of meshed topology and containing multiple heat sources. Even though numerous publications have addressed the design of efficient algorithms for numerically finding hydraulic equilibria in the general context of water distribution networks, this is not the case for the analysis of existence and uniqueness. Moreover, most of the existing work dealing with these aspects exploit the equivalence between the nonlinear algebraic equations describing the hydraulic equilibria and the KKT conditions of a suitably defined nonlinear convex optimization problem. Differently, this paper proposes necessary and sufficient graph-theoretic conditions on the actuator placement for the existence and uniqueness of a hydraulic equilibrium, independent of the actuators' control objective. An example based on a representative district heating network is considered to illustrate the key aspects of our contribution, and an explicit formulation of the steady state solution is given for the case in which pressure drops through pipes are linear with respect to the flow rate.

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DOMAIN RANDOMIZATION VIA ENTROPY MAXIMIZATION

arXiv

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arXiv 2023年

作者： Tiboni, Gabriele Klink, Pascal Peters, Jan Tommasi, Tatiana D’Eramo, Carlo Chalvatzaki, Georgia Department of Control and Computer Engineering Politecnico di Torino Italy Department of Computer Science Technische Universität Darmstadt Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Germany Center for Mind Brain and Behavior Uni. Marburg and JLU Giessen Germany

Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL). Nevertheless, DR heavily hinges on the choice of the sampling distribution of the dynamics parameters, since high variability is crucial to regularize the agent’s behavior but notoriously leads to overly conservative policies when randomizing excessively. In this paper, we propose a novel approach to address sim-to-real transfer, which automatically shapes dynamics distributions during training in simulation without requiring real-world data. We introduce DOmain RAndomization via Entropy MaximizatiON (DORAEMON), a constrained optimization problem that directly maximizes the entropy of the training distribution while retaining generalization capabilities. In achieving this, DORAEMON gradually increases the diversity of sampled dynamics parameters as long as the probability of success of the current policy is sufficiently high. We empirically validate the consistent benefits of DORAEMON in obtaining highly adaptive and generalizable policies, i.e. solving the task at hand across the widest range of dynamics parameters, as opposed to representative baselines from the DR literature. Notably, we also demonstrate the Sim2Real applicability of DORAEMON through its successful zero-shot transfer in a robotic manipulation setup under unknown real-world parameters. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Resolving Class Imbalance Problem for LiDAR-based Object Detector by Balanced Gradients Dynamic Weight Average and Contextual Ground Truth Sampling

arXiv

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arXiv 2022年

作者： Lee, Daeun Park, Jongwon Kim, Jinkyu Department of Computer Science and Engineering Korea University Korea Republic of Autonomous Driving Center Hyundai Motor Company R&D Division Korea Republic of

An autonomous driving system requires a 3D object detector, which must perceive all present road agents reliably to navigate an environment safely. However, real-world driving datasets often suffer from the problem of data imbalance, which causes difficulties in training a model that works well across all classes, resulting in an undesired imbalanced sub-optimal performance. In this work, we propose a method to address this data imbalance problem. Our method consists of two main components: (i) a LiDAR-based 3D object detector with per-class multiple detection heads where losses from each head are modified by dynamic weight average to be balanced. (ii) Contextual ground truth (GT) sampling, where we improve conventional GT sampling techniques by leveraging semantic information to augment point cloud with sampled ground truth GT objects. Our experiment with KITTI and nuScenes datasets confirms our proposed method’s effectiveness in dealing with the data imbalance problem, producing better detection accuracy compared to existing approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： autonomous vehicles

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