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arXiv 2022年

作者： Delabays, Robin Jafarpour, Saber Bullo, Francesco Center for Control Dynamical Systems and Computation Uc Santa Barbara Santa BarbaraCA93106-5070 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332-0250 United States

The analysis of dissipatively coupled oscillators is challenging and highly relevant in power grids. Standard mathematical methods are not applicable, due to the lack of network symmetry induced by dissipative couplings. Here we demonstrate a close correspondence between stable synchronous states in dissipatively coupled oscillators, and the winding partition of their state space, a geometric notion induced by the network topology. Leveraging this winding partition, we accompany this article with an algorithms to compute all synchronous solutions of complex networks of dissipatively coupled oscillators. These geometric and computational tools allow us to identify anomalous behaviors of lossy networked systems. Counterintuitively, we show that loop flows and dissipation can increase the system's transfer capacity, and that dissipation can promote multistability. We apply our geometric framework to compute power flows on the IEEE RTS-96 test system, where we identify two high voltage solutions with distinct loop flows. © 2022, CC BY.

关键词： Complex networks

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Bi-YOLOv10: Sample Imbalance-Aware Feature Fusion-Based Object Detection for Power Inspection

Bi-YOLOv10: Sample Imbalance-Aware Feature Fusion-Based Obje...

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Data Intelligence and Innovative Application (DIIA), IEEE International Conference on

作者： Man Wu Gaocai Wang Wei Zhang Lili Wen Hui Qu School of electrical engineering Guangxi University Nanning China School of Computer and Electronic Information Guangxi University Nanning China Guangxi Key Laboratory of Intelligent Control and Operation and Maintenance of Power Equipment Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Nanning China Research Center for Carbon Sink and Low-Carbon Engineering in the Beibu Gulf of Guangxi Guangxi Academy of Sciences Nanning China National Marine Data and Information Service Tianjin China

ISBN: (数字)9798331504434

ISBN: (纸本)9798331504441

The examination of power systems plays a pivotal role in averting power interruptions while safeguarding grid integrity as well as fostering economic resilience. Within imagery from these inspections lie diminutive target entities alongside multiscale complexities; challenges arise in distinguishing foreground from background amidst uneven sample distributions. To fortify our model's capacity for discerning intricate targets amid mitigating background noise disruptions along with environmental fluctuations necessitates amalgamating multilevel multidimensional image features. Henceforth emerges our introduction of BiFPN (Bidirectional Feature Pyramid Network) module coupled with our proposition of YOLOvlO object detection network featuring bidirectional feature fusion. Within this framework's architecture lies an integration of BiFPN replacing Concat for streamlined multiscale feature fusion efficiency; further complemented by a novel Focal-EloU loss function aimed at harmonizing contributions from high- versus low-quality samples towards enhancing model efficacy across imbalanced datasets. Experimental evaluations conducted on power system inspection datasets demonstrate superior detection capabilities exhibited by our proposed network compared against incumbent models as well as mainstream object detection networks-thus advancing secure intelligent scrutiny over power lines.

关键词： YOLO Fluctuations Refining Power system dynamics Inspection Streaming media Network architecture Feature extraction Testing Resilience

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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

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Chinese Automation Congress (CAC)

作者： Liu Hu Luefeng Chen Jie Hu Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan The Tokyo Institute of Technology Tokyo Japan

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method.

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Extended Horizon Predictive control for Software Defined Networked Microgrids

SSRN

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SSRN 2024年

作者： Guzmán, Ricardo Pérez Rivera, Marco Araya, Baldomero Gómez, Juan S. Salgueiro, Yamisleydi Restrepo, Carlos Wheeler, Patrick You, Minglei Sumner, Mark Department of Computer Science Faculty of Engineering Universidad de Talca Curicó Maule 3341717 Chile Power Electronics Machines and Control Research Group Faculty of Engineering University of Nottingham NottinghamNG7 2GT United Kingdom Laboratorio de Conversión de Energías y Electrónica de Potencia LCEEP Dirección de Investigación Universidad de Talca Curicó Maule 3341717 Chile Department of Electrical Engineering Faculty of Engineering Universidad de Talca Curicó Maule 3341717 Chile Department of Engineering Sciences The Energy Transformation Center Universidad Andrés Bello Santiago-Chile Santiago de Chile8370035 Chile Department of Industrial Engineering Faculty of Engineering Universidad de Talca Curicó Maule 3341717 Chile

This paper presents a model-based predictive control implementation (FCS-MPC) with extended horizon prediction to address the resiliency problem and voltage/frequency deviations for hierarchical microgrids. The proposed framework integrates Software-Defined Networking (SDN) as a set of microservices, where prediction models enable effective coordination and enhance system reliability by including communication constraints. The control strategy considers the variability of communication latency and packet loss to adjust the shared references based on the secondary control’s spatial and temporal correlation. The system is subjected to four test case scenarios to analyze the impact of communications in distributed generation, plugand-play capabilities, and load fluctuations on the microgrid. The proposed SDN-based framework enables the resolution of slow dynamic responses to parameter variations, by exploiting the advantages of FCS-MPC. The prediction block in the proposed secondary control outperforms traditional communication methods in terms of latency, recovery time, and packet loss among distributed energy resources, improving hierarchical microgrid performance. © 2024, The Authors. All rights reserved.

关键词： Software defined networking

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Skeleton-Based Multi-Stream Adaptive Graph Convolutional Network for Indoor Scene Action Recognition

Skeleton-Based Multi-Stream Adaptive Graph Convolutional Net...

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Chinese Automation Congress (CAC)

作者： Jiazhuo Li Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Polish Academy of Sciences Systems Research Institute Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan Tokyo Institute of Technology Tokyo Japan

With the rapid advances in computer vision, human action recognition has gradually received attention, but the current methods still exhibit some problems in indoor environments. The human skeleton, as the framework of human motion, contains high-quality actional feature information, and the skeleton-based action recognition method effectively avoid the interference of interior background noise and has advantages in indoor action recognition. The outstanding effect of graph convolutional networks on graph structure data processing has led to its rapid development and wide application in skeleton-based action recognition. Second-order skeletal information also contains a large number of actional features but is not effectively utilized. The artificial predefined topology of the human skeleton map has limitations, and cannot reflect the interaction between limbs. To solve the above problems, this article designs an adaptive weighted multi-stream graph convolutional network (AM-GCN) based on skeletal information, using an attention mechanism to enhance the network's ability to extract actional features, and an adaptive layer to make the construction graph more flexible, incorporating second-order skeletal features through a dual-stream architecture. In this article, the NTU-RGB+D dataset has been used for the experiments, the results show that the method in this article has good results.

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Non-Euclidean Monotone Operator Theory and Applications

arXiv

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arXiv 2023年

作者： Davydov, Alexander Jafarpour, Saber Proskurnikov, Anton V. Bullo, Francesco Center for Control Dynamical Systems and Computation University of California Santa Barbara Santa BarbaraCA93106-5070 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309-0020 United States Department of Electronics and Telecommunications Politecnico di Torino Turin10129 Italy

While monotone operator theory is often studied on Hilbert spaces, many interesting problems in machine learning and optimization arise naturally in finite-dimensional vector spaces endowed with non-Euclidean norms, such as diagonally-weighted 1 or ∞ norms. This paper provides a natural generalization of monotone operator theory to finite-dimensional non-Euclidean spaces. The key tools are weak pairings and logarithmic norms. We show that the resolvent and reflected resolvent operators of non-Euclidean monotone mappings exhibit similar properties to their counterparts in Hilbert spaces. Furthermore, classical iterative methods and splitting methods for finding zeros of monotone operators are shown to converge in the non-Euclidean case. We apply our theory to equilibrium computation and Lipschitz constant estimation of recurrent neural networks, obtaining novel iterations and tighter upper bounds via forward-backward splitting. © 2023, CC BY.

关键词： Recurrent neural networks

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Nonsingular Adaptive Barrier Function-Based Finite Time control for Uncertain Nonlinear Systems

SSRN

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SSRN 2023年

作者： Mobayen, Saleh Zhang, Jianhua Zhu, Quanmin Fekih, Afef Future Technology Research Center Taiwan Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliu Taiwan School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China Department of Engineering Design and Mathematics University of the West of England Frenchy Campus Coldharbour Lane BristolBS16 1QY United Kingdom Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States

This study presents a novel approach for using sliding mode control (SMC) for uncertain nonlinear systems with external unknown disturbances. A nonsingular adaptive barrier function-based finite time control is developed to deal with such systems. In technical implementation, first a novel sliding surface is designed based on the Hurwitz condition, then, using the finite-time stability method, a novel nonsingular terminal sliding manifold is established and the barrier function is obtained, which can guarantee the sliding surface convergence to the origin in finite time. For functional and numerical validation, two examples of uncertain nonlinear systems are selected to illustrate the effectiveness of the proposed method in comparison with the other representative approaches. © 2023, The Authors. All rights reserved.

关键词： Sliding mode control

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on control of Network Systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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Topological entropy of switched nonlinear and interconnected systems

arXiv

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arXiv 2023年

作者： Yang, Guosong Liberzon, Daniel Hespanha, João P. The Department of Electrical and Computer Engineering Rutgers University PiscatawayNJ08854 United States The Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States The Center for Control Dynamical Systems and Computation University of California Santa Barbara Santa BarbaraCA93106 United States

A general upper bound for topological entropy of switched nonlinear systems is constructed, using an asymptotic average of upper limits of the matrix measures of Jacobian matrices of strongly persistent individual modes, weighted by their active rates. A general lower bound is constructed as well, using a similar weighted average of lower limits of the traces of these Jacobian matrices. In a case of interconnected structure, the general upper bound is readily applied to derive upper bounds for entropy that depend only on "network-level" information. In a case of block-diagonal structure, less conservative upper and lower bounds for entropy are constructed. In each case, upper bounds for entropy that require less information about the switching signal are also derived. The upper bounds for entropy and their relations are illustrated by numerical examples of a switched Lotka–Volterra ecosystem *** Codes 37B40, 93C30 (Primary) 93C57 (Secondary) © 2023, CC BY-NC-ND.

关键词： Jacobian matrices

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Decoherence limiting the cost to simulate an anharmonic oscillator

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Physical Review A 2023年第6期108卷 062219-062219页

作者： Tzula B. Propp Sayonee Ray John B. DeBrota Tameem Albash Ivan H. Deutsch Center for Quantum Information & Control University of New Mexico Albuquerque New Mexico 87131 USA Department of Physics and Astronomy University of Waterloo Ontario N2L 3G1 Canada Department of Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico 87131 USA Department of Physics and Astronomy University of New Mexico Albuquerque New Mexico 87131 USA

We study how decoherence increases the efficiency with which we can simulate the quantum dynamics of an anharmonic oscillator governed by the Kerr effect. As decoherence washes out the fine-grained sub-Planck structure associated with phase-space quantum interference in the closed quantum system, open quantum dynamics can be more efficiently simulated using a coarse-grained finite-difference numerical integration. We tie this to the way in which decoherence recovers the semiclassical truncated Wigner approximation, which strongly differs from the exact closed-system dynamics at times when quantum interference leads to cat states and more general superpositions of coherent states. The regression in quadrature measurement statistics to semiclassical dynamics becomes more pronounced as the initial amplitude of the oscillator grows, with implications for the quantum advantage that might be accessible as system size grows in noisy quantum devices. Lastly, we show that this regression does not have the form of a convex noise model, such as for a depolarizing noise channel. Instead, closed quantum system effects interact with the open-system effects, giving rise to distinct open-system behavior.

关键词： Cavity quantum electrodynamics Open quantum systems & decoherence Computational complexity Phase space dynamics Semiclassical methods

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