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Forecasting-Aided Graphical Learning for Robust State Estimation of Distribution System 1

8th Purple Mountain Forum on Smart Grid Protection and control, PMF 2023

作者： Di, Cao Zhao, Junbo Hu, Jiaxiang Huang, Yuehui Huang, Qi Chen, Zhe Hu, Weihao University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing China Department of Energy Technology Aalborg University Pontoppidanstraede 111 Aalborg Denmark

ISBN: (数字)9789819992515

ISBN: (纸本)9789819992508

The forecasting of of pseudo-measurements play an important role in distribution system state estimation (DSSE). This paper proposes robust DSSE method based on forecasting-aided graphical learning method. The nodal power consumption models are first built to produce pseudo-measurements based on deep neural network. Then, the pseudo-measurements and real-time measurements are represented as a graph according to the topology of the distribution network, which are further processed by a graph attention network to capture the mapping relationship between the graphical measurements and the state variables based on the error modeling of pseudo-measurements. The robustness against anomalous measurements is achieved through the embedding of structural information of DN. The modeling of pseudo-measurements further enhance its robustness by guiding the formulation of edge weights of the graph neural network. Comparative tests are carried out on a IEEE 119-node system to demonstrate the effectiveness and robustness of the proposed method. © 2024, State Grid Electric Power.

关键词： Electric power distribution

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Hypercomplex Generative Adversarial Networks for Lightweight Semantic Labeling 3rd

Hypercomplex Generative Adversarial Networks for Lightweigh...

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3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Sfikas, Giorgos Retsinas, George Gatos, Basilis Nikou, Christophoros Computational Intelligence Laboratory Institute of Informatics and Telecommunications National Center for Scientific Research "Demokritos" Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Surveying and Geoinformatics Engineering University of West Attica Athens Greece Department of Computer Science and Engineering University of Ioannina Ioannina Greece

ISBN: (纸本)9783031090363

Following recent advances on parameterized hypercomplex multiplication [21], we explore the usefulness of hypercomplex convolutions and deconvolutions in a document labeling task. We show that the proposed Hypercomplex Generative Adversarial Networks achieve excellent results while requiring significantly less independent parameters than real-valued models. © 2022, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Securing Mobile Robotic Networks Against Replacement Attack

引用

IEEE Transactions on Automatic control 2025年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping Cai, Lin Shanghai Jiao Tong University Dept. of Automation China Ministry of Education of China The Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China University of Victoria Dept. of Electrical and Computer Engineering BC Canada

The security of mobile robotic networks (MRNs) has been an active research topic in recent years. This paper aims to secure the ubiquitous formation control of MRNs against the replacement attack, where an external robot can replace a formation robot by compromising the communication and physically interfering with the victim simultaneously. To counter this advanced attack, the novel idea of this work is to leverage the physical proximity of the formation shape and the interaction topology among robots for defense design. First, from the physical proximity perspective, we propose the convex neighbor polygon (CNP) to capture the geometric characteristic of the formation robots, and design a CNP-based security mechanism for the robots during the replacement attack. Then, from the interaction perspective, we introduce the indirect controllability to characterize the possibility that the attacker leverages the interaction between robots to deactivate the CNP mechanism, and establish the conditions regarding the topology structure and the attack input to counter the replacement. Finally, based on the obtained conditions, we demonstrate how to design the initiatory topology among the formation robots to enhance the defense performance. Comparative simulations verify the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Topology

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Minimum Time Escape from a Circular Region of a Dubins Car

Minimum Time Escape from a Circular Region of a Dubins Car

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Isaac E. Weintraub Alexander Von Moll Meir N. Pachter Control Science Center Air Force Research Laboratory Wright-Patterson AFB OH Electrical and Computer Engineering Air Force Institute of Technology Wright-Patterson AFB OH

A turn-constrained evader strives to escape a circular region in minimum time.

ISBN: (数字)9798350367621

ISBN: (纸本)9798350367638

A turn-constrained evader strives to escape a circular region in minimum time.

关键词： Geometry Aerospace electronics Automobiles

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Joint Precoding and Reflection Optimization for IRS-Assisted MU-MIMO-OTFS Downlink Systems

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IEEE Transactions on Vehicular Technology 2025年

作者： Zou, Tingting Wang, Fengyu Xu, Wenjun Zhang, Yimeng Pan, Miao Han, Zhu Beijing University of Posts and Telecommunications State Key Laboratory of Network and Switching Technology Beijing100876 China Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing100876 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Mobile Research Institute Beijing100876 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Orthogonal time frequency space (OTFS) modulation has been proposed as a promising technique to achieve high-reliability communications in high-mobility scenarios. In this paper, we consider a multi-user (MU) multiple-input multiple-output (MIMO)-OTFS downlink communication scenario, where an intelligent reflecting surface (IRS) is deployed to enhance the performance. Specifically, the delay- Doppler (DD) domain input-output relationship of such a system is first derived, based on which, the joint optimization of the precoding matrices and IRS reflection phase is studied to maximize the weighted sum rate (WSR) considering user fairness. Note that the corresponding optimization problem is non-convex. To tackle this challenge, we first reformulate the problem into a more traceable equivalent form by the weighted mean minimum square error (WMMSE) approach. Utilizing the characteristics of the DD domain signal relationship, we then decouple the reformulated problem and further transform it into two subproblems, which are solved by the Lagrangian function method and the project-descent-retracting (PDR) algorithm, respectively. The precoding matrices and reflection phase are jointly optimized by alternative optimization to maximize WSR. Simulation results demonstrate that, compared with benchmark schemes, the proposed WMMSE-ALT can improve the rate more than 85% in IRS-assisted MU-MIMO-OTFS systems. © 1967-2012 IEEE.

关键词： Mean square error

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Steps Toward Industry 5.0:Building“6S”Parallel Industries With Cyber-Physical-Social Intelligence

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1692-1703页

作者： Xingxia Wang Jing Yang Yutong Wang Qinghai Miao Fei-Yue Wang Aijun Zhao Jian-Ling Deng Lingxi Li Xiaoxiang Na Ljubo Vlacic School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE Macao University of Science and Technology Macao 999078China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China North Automatic Control Technology Institute Taiyuan 030006China China Huaneng Group Co. Ltd.Beijing 100031China Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis(IUPUI)IndianapolisIN 46202 USA Department of Engineering University of CambridgeCambridgeCB21PZUK School of Engineering and Built Environment Griffith UniversityGold CoastQLD 4222Australia

Very recently,intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers,entrepreneurs,and policymakers from various sectors around the ***,there is no consensus on why and what is Industry 5.0 *** this paper,we define Industry 5.0from its philosophical and historical origin and evolution,emphasize its new thinking on virtual-real duality and human-machine interaction,and introduce its new theory and technology based on parallel intelligence(PI),artificial societies,computational experiments,and parallel execution(the ACP method),and cyber-physical-social systems(CPSS).Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future *** believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart *** are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces,S ecure in cyberspaces,Sustainable in ecology,Sensitive in individual privacy and rights,Service for all,and Smartness of all.

关键词： ACP artificial intelligence CPS CPSS Industry 4.0 Industry 5.0 parallel industries parallel intelligence

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Dubins Vehicle Intercept of a Brownian Target on a Sphere

Dubins Vehicle Intercept of a Brownian Target on a Sphere

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IEEE Conference on Decision and control

作者： Dejan Milutinović David W. Casbeer Department of Electrical and Computer Engineering University of California Santa Cruz CA USA Control Science Center Air Force Research Laboratory Wright-Patterson AFB OH USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper focuses on a navigation of a Dubins vehicle (DV) to intercept a moving target on a sphere. The uncertainty in the target motion is described by a Brownian motion model. An Itô-type stochastic differential equation for the relative motion between DV and the Brownian moving target is derived. This stochastic differential equation is used to formulate an intercept controller as a minimum time optimal controller. The properties of the controller are incorporated into the numerical computations of the controller, which by its construction can intercept a Brownian target. Our numerical simulation results show that the computed controller can intercept both stationary and moving targets on the sphere.

关键词： Space vehicles Uncertainty Planets Space debris Stochastic processes Optimal control Differential equations Satellite navigation systems Numerical simulation Numerical models

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High-Performance Image Compression and Reconstruction Using Lightweight Deep Neural Networks

High-Performance Image Compression and Reconstruction Using ...

引用

China Instrument and control Society (ACCIS), Academic Conference of

作者： Shuhua Gao Zekun Qin Xingyu Jiang Zhengfang Wang Wenjun Xu Jing Wang School of Control Science and Engineering Shandong University Jinan China Electrical and Computer Engineering National University of Singapore Singapore Research Institute of MA&EI Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350350326

ISBN: (纸本)9798350350333

In many remote areas, tunnels, and underground spaces, network bandwidth is extremely limited. However, with the development of high-resolution cameras, the size of image data has significantly increased, making it challenging to frequently transmit large-scale images for detection and monitoring purposes. To address this issue, this paper proposes an image compression and reconstruction method based on lightweight neural networks, utilizing a dual-module deep learning architecture comprising a Compressed Convolutional Neural Network (CCNN) and a Reconstructed Convolutional Neural Network (RCNN). The lightweight design facilitates easy deployment on edge devices. The CCNN module compresses the original image for fast network transmission, and upon reception, the RCNN module reconstructs the image. This approach significantly reduces the transmission payload and enhances speed under limited bandwidth conditions. Furthermore, the developed method outperforms the classic JPEG algorithm across four representative metrics as evaluated on a widely-used public dataset. To further optimize the CCNN for edge deployment, a model distillation technique is employed, reducing the model size by approximately 70% with negligible performance impact. In summary, the proposed deep neural network model offers high performance and a low memory footprint for image compression and reconstruction, making it particularly suitable for resourcelimited edge devices and bandwidth-constrained applications.

关键词： Performance evaluation Deep learning Image coding Image edge detection Computational modeling Transform coding Bandwidth Cameras Convolutional neural networks Image reconstruction

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Collision Avoidance for Convex Primitives via Differentiable Optimization Based High-Order control Barrier Functions

arXiv

引用

arXiv 2024年

作者： Wei, Shiqing Khorrambakht, Rooholla Krishnamurthy, Prashanth Gonçalves, Vinicius Mariano Khorrami, Farshad Control/Robotics Research Laboratory Electrical and Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States MG Belo Horizonte31270-901 Brazil

Ensuring the safety of dynamical systems is crucial, where collision avoidance is a primary concern. Recently, control barrier functions (CBFs) have emerged as an effective method to integrate safety constraints into control synthesis through optimization techniques. However, challenges persist when dealing with convex primitives and tasks requiring torque control, as well as the occurrence of unintended equilibria. This work addresses these challenges by introducing a high-order CBF (HOCBF) framework for collision avoidance among convex primitives. We transform nonconvex safety constraints into linear constraints by differentiable optimization and prove the high-order continuous differentiability. Then, we employ HOCBFs to accommodate torque control, enabling tasks involving forces or high dynamics. Additionally, we analyze the issue of spurious equilibria in high-order cases and propose a circulation mechanism to prevent the undesired equilibria on the boundary of the safe set. Finally, we validate our framework with three experiments on the Franka research 3 robotic manipulator, demonstrating successful collision avoidance and the efficacy of the circulation mechanism. © 2024, CC BY.

关键词： Quadratic programming

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Acoustic Sensing for Fitness Activities Recognition: A Deep Learning Approach

Acoustic Sensing for Fitness Activities Recognition: A Deep ...

引用

IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Chen Dai Weihao Zhou Linqing Gui Haotong Cao Sahil Garg Fu Xiao School of Computer Science Nanjing University of Posts and Telecommunications China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks China College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications China Electrical Engineering Department École de technologie supérieure Montréal Canada

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Currently, the demand for home fitness has increased as more people choose to work remotely. Acoustic sensing technology has gained popularity due to its practicality on fitness activity monitoring. Due to the fact that sound waves do not propagate uniformly in all directions, identifying fitness activities can be challenging when sound waves do not directly face the device. To address it, we propose a method that models the reflected signal based on the sound field characteristics of a piston sound source and introduces a novel signal processing technique to reduce angle correlation. Then we combine deep learning methods for fitness activity recognition. Experimental results demonstrate improved recognition accuracy for fitness activities performed at different angles around the device.

关键词： Pistons Deep learning Performance evaluation Correlation Accuracy Activity recognition Signal processing

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