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arXiv 2023年

作者： Ghavami, Mahsa Bakhshayesh, Babak Ghaffarzadeh Haeri, Mohammad Como, Giacomo Kebriaei, Hamed The Advanced Control Systems Lab Department of Electrical Engineering Sharif University of Technology Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran The Department of Mathematical Sciences Politecnico di Torino Torino Italy

This paper introduces a consensus-based generalized multi-population aggregative game coordination approach with application to electric vehicles charging under transmission line constraints. The algorithm enables agents to seek an equilibrium solution while considering the limited infrastructure capacities that impose coupling constraints among the users. The Nash-seeking algorithm consists of two interrelated iterations. In the upper layer, population coordinators collaborate for a distributed estimation of the coupling aggregate term in the agents’ cost function and the associated Lagrange multiplier of the coupling constraint, transmitting the latest updated values to their population’s agents. In the lower layer, each agent updates its best response based on the most recent information received and communicates it back to its population coordinator. For the case when the agents’ best response mappings are non-expansive, we prove the algorithm’s convergence to the generalized Nash equilibrium point of the game. Simulation results demonstrate the algorithm’s effectiveness in achieving equilibrium in the presence of a coupling constraint. © 2023, CC BY-NC-ND.

关键词： Electric vehicles

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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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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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2023 China Automation Congress, CAC 2023

作者： Hu, Liu Chen, Luefeng Hu, Jie Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University The Department of Computer Engineering Istanbul Sariyer34396 Turkey Tokyo Institute of Technology Tokyo Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

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. © 2023 IEEE.

关键词： Decision trees

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Propagation Characteristics of Partial Discharges in an Oil-Filled Power Transformer

Propagation Characteristics of Partial Discharges in an Oil-...

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Compatibility in Power Electronics (CPE)

作者： Sayed Mohammad Kameli Shady S. Refaat Ali Ghrayeb Haitham Abu-Rub Jarosław Guziński Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Department of Engineering School of Physics Engineering and Computer Science University of Hertfordshire Electrical and Control Engineering Faculty Gdansk University of Technology ul. Gabriela Narutowicza 11/12 Gdańsk Poland

Power transformers are among the most important assets in the power transmission and distribution grid. However, they suffer from degradation and possible faults causing major electrical and financial losses. Partial discharges (PDs) are used to identify the insulation health status and their degradation level. PDs are incipient, low-magnitude faults caused by localized dielectric breakdown. Those activities emit signals in many forms, including electrical, chemical, acoustic, electromagnetic, and optical, facilitating various detection methods. This paper provides a theoretical basis for the condition evaluation of an oil-filled power transformer and clarifies the relationship between the operating voltage, void location, and electric-field intensity within the void. This was achieved by investigating the propagation characteristics of partial discharge signals in an oil-filled power transformer using a 3D finite element method (FEM) based simulation. Moreover, the characterization of simulated PD sources at different positions is investigated in this paper. The simulation results are curried out to show that air voids near the windings are subject to greatest peak electric field intensity.

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A New Deep Learning Model for Semi-supervised Soft-sensing of an Industrial Production Process

A New Deep Learning Model for Semi-supervised Soft-sensing o...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： XuDong Shi ChenYu Tian Qi Kang MengChu Zhou HanQiu Bao Jing An Department of Control Science and Engineering Tongji University Shanghai China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China School of Internet of Things Engineering Jiangnan University Wuxi China School of Software Dalian University of Technology Liaoning China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Soft sensing offers a promising solution for predicting key quality variables in various production industries. One of the major challenges in developing an effective data-driven soft sensor is the scarcity of labeled data and the obstacle of extracting useful information from unlabeled samples. To address this issue, this work proposes a new deep learning-based soft sensor model called a spatiotemporal deep learning network. It leverages an encoder-decoder structure to explicitly exploit the spatial and temporal information in both labeled and unlabeled data, enabling efficient utilization of the latter to facilitate prediction performance. The encoder realizes more detailed spatiotemporal dependencies extraction by the proposed gated recurrent unit-based attention mechanism and the channel-calibration attention-based one. Finally, the extracted spatiotemporal features are fed to a multi-layer perceptron-based prediction head for soft sensor modeling. A mixed form loss is employed in the decoder to train our proposed model which incorporates both labeled and unlabeled data. Experiments are conducted on a real-life industrial process, demonstrating the feasibility and effectiveness of the proposed method.

关键词： Deep learning Attention mechanisms Accuracy Soft sensors Production Predictive models Feature extraction Data models Spatiotemporal phenomena Data mining

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Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Classification of skin lesions using morpho-granulometry features derived from color clusters

Classification of skin lesions using morpho-granulometry fea...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics "Dunarea de Jos" University of Galati Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics The Modelling & Simulation Laboratory "Dunarea de Jos" University of Galati Galati Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

The skin lesion can be thought of as a biological system, so the morpho-granulometry of significant color clusters found in skin lesions is one of the elements that reproduce in a natural way the structure of the lesion, this novelty is highlighted in this study. Important features of skin lesions can be modulated by fusing neural networks (NN) and machine learning (ML). By choosing the nevus and melanoma classes, the primary goal was accomplished, and three databases were used to test the methodology. The characteristics based on morpho-granulometry allowed for the identification of microstructure within the images, which can be very helpful in characterizing the biological system. Based on random forest (RF) and extreme gradient boosting (XGboost) classifiers, this work aimed to improve the classification performance of important feature selection. The selected features from three free image databases with three NNs were classified. In a binary classification of nevus vs. melanoma, the results showed that the pattern recognition neural network (PRNN), according to the PH2 database, provided an accuracy of 0.923 and an F1-score of 0.876. The classification is interpretable if it is not validated. In our study, the best results were verified with a logistic regression (LR) classifier.

关键词： Accuracy Recurrent neural networks Image color analysis Melanoma Biological systems Artificial neural networks Feature extraction Skin Lesions Classification tree analysis

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Knowledge Graph Completion with Low-Dimensional Gated Hierarchical Hyperbolic Embedding

SSRN

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SSRN 2024年

作者： Fang, Yan Liu, Xiaodong Lu, Wei Pedrycz, Witold Lang, Qi Yang, Jianhua School of Control Science and Engineering Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Department of Electrical and Computer Engineering University of Alberta EdmontonT6R 2V4 AB Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia School of Computer Science and Information Technology Northeast Normal University Changchun130117 China

Knowledge Graph Embedding aims to advance the learning performance of knowledge graphs by deliberating the complexity of hierarchical data structures in a finite-dimensional space. Most traditional Euclidean models struggle to accurately describe and navigate the intricate hierarchical relationships between nodes. The hyperbolic embedding methods, particularly in conjunction with graph neural networks, offer promise in effectively modeling complex hierarchies in a limited-dimensional space. These methods can accommodate more information about nodes under the same conditions but are also more susceptible to interference from complex neighborhood features. This paper introduces HyperGatE, which novelty combines a hyperbolic graph hierarchical neighbor structure based on a gated filter and a hyperbolic isometric rotation model for knowledge graph completion. By analyzing the hierarchical structure of the knowledge base and using a gated mechanism to form multi-layer embedding networks of entity-relational paths, the gated filter selects diverse uneven multi-hop relations within the entity neighborhood and measures them through graph attention networks. Furthermore, the simplified hyperbolic isometric model allows for uncovering hierarchical structures in the data. HyperGatE experimentally surpasses the state-of-the-art Euclidean and hyperbolic space models in high and low dimensions across four well-known data, namely WN18RR, FB15k-237, NELL-995, and Kinship. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Distributed Sequential Receding Horizon control of Multi-Agent Systems Under Recurring Signal Temporal Logic

Distributed Sequential Receding Horizon Control of Multi-Age...

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European control Conference (ECC)

作者： Eleftherios E. Vlahakis Lars Lindemann Dimos V. Dimarogonas Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science Viterbi School of Engineering University of Southern California Los Angeles CA USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We consider the synthesis problem of a multi-agent system under signal temporal logic (STL) specifications representing bounded-time tasks that need to be satisfied recurrently over an infinite horizon. Motivated by the limited approaches to handling recurring STL systematically, we tackle the infinite-horizon control problem with a receding horizon scheme equipped with additional STL constraints that introduce minimal complexity and a backward-reachability-based terminal condition that is straightforward to construct and ensures recursive feasibility. Subsequently, we decompose the global receding horizon optimization problem into agent-level programs the objectives of which are to minimize local cost functions subject to local and joint STL constraints. We propose a scheduling policy that allows individual agents to sequentially optimize their control actions while maintaining recursive feasibility. This results in a distributed strategy that can operate online as a model predictive controller. Last, we illustrate the effectiveness of our method via a multi-agent system example assigned a surveillance task.

关键词： Surveillance Decentralized control Europe Predictive models Fasteners Cost function Infinite horizon

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Highly Efficient 3D Human Pose Tracking from Events with Spiking Spatiotemporal Transformer

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zou, Shihao Mu, Yuxuan Ji, Wei Wang, Zi-An Zuo, Xinxin Wang, Sen Si, Weixin Cheng, Li Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Guangdong Shenzhen China University of Alberta Department of Electrical and Computer Engineering ABT6G 1H9 Canada Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Guangdong Shenzhen China

Event camera, as an asynchronous vision sensor capturing scene dynamics, presents new opportunities for highly efficient 3D human pose tracking. Existing approaches typically adopt modern-day Artificial Neural Networks (ANNs), such as CNNs or Transformer, where sparse events are converted into dense images or paired with additional gray-scale images as input. Such practices, however, ignore the inherent sparsity of events, resulting in redundant computations, increased energy consumption, and potentially degraded performance. Motivated by these observations, we introduce the first sparse Spiking Neural Networks (SNNs) framework for 3D human pose tracking based solely on events. Our approach eliminates the need to convert sparse data to dense formats or incorporate additional images, thereby fully exploiting the innate sparsity of input events. Central to our framework is a novel Spiking Spatio-temporal Transformer, which enables bi-directional spatio-temporal fusion of spike pose features and provides a guaranteed similarity measurement between binary spike features in spiking attention. Moreover, we have constructed a largescale synthetic dataset, SynEventHPD, that features a broad and diverse set of 3D human motions, as well as much longer hours of event streams. Empirical experiments demonstrate the superiority of our approach over existing state-of-the-art (SOTA) ANN-based methods, requiring only 19.1% FLOPs and 3.6% energy cost. Furthermore, our approach outperforms existing SNN-based benchmarks in this task, highlighting the effectiveness of our proposed SNN framework. © 1991-2012 IEEE.

关键词： Motion picture cameras

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