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2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Ouyang, Wenjie Jiao, Yiwen Liu, Yang Li, Yang Hu, Manjiang Qin, Hongmao College of Mechanical and Vehicle Engineering Hunan University Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electrical Mechanical and Computer Science Delft University of Technology Delft Netherlands College of Mechanical and Vehicle Engineering Hunan University State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Changsha China Institute of Hunan University Wuxi Intelligent Control Research Wuxi China

ISBN: (纸本)9798350316308

Reinforcement learning (RL) has gained wide attention, but its implementation in autonomous vehicles is still limited by insufficient sample efficiency and heavy training costs. The training efficiency of RL agents is influenced by the dimension of the state space, which can be partitioned to reduce the complexity of sampling and computation. This study proposes a hierarchical clustering-based state grouping reinforcement learning (HCSG-RL) method for the switching decision of autonomous vehicles. First, we partition the base state space into groups and generate a hierarchical tree of state space groups. Then, we train multiple sub-agents for each node in the hierarchical tree. Finally, we add these trained-well sub-model into master policy. This method allows us to fully explore all state spaces and improve the training efficiency of individual agents, which handles the 'long-tail' issue and the curse of dimensionality issue. We conduct experiments in a simulation environment and results show that the proposed method has 16-72% reward improvement compared to the tree model in different road length. © 2023 IEEE.

关键词： Reinforcement learning

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Prediction of Radiated Electromagnetic Interference Based on Four-module Motor Drive System

Prediction of Radiated Electromagnetic Interference Based on...

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Energy, Power and Grid (ICEPG), International Conference on

作者： Yixin Shi Wenjie Chen Fengjiao Li Jinpeng Kan Yongqing Meng Guang Yang School of Electrical Engineering Xi’an Jiaotong University Xi’an China Department of Computer and Electronic Systems Engineering AVIC Xi’an Flight Automatic Control Research Institute Xi’an China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

In recent years, wide bandgap semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) have been increasingly applied in electric drive systems, effectively enhancing system power density. However, the high-frequency and high-speed characteristics of SiC devices exacerbate electromagnetic interference (EMI) issues within the system. Additionally, the cables in electric drive systems exhibit significant antenna effects, radiating conducted EMI into the surrounding space, thereby affecting the normal operation of other sensitive equipment. Therefore, it is essential to research motor drive system topologies that can actively suppress EMI and predict radiated interference in motor drive systems. The topology of a four-module motor paired with parallel inverters can achieve active suppression of EMI at the source. This paper conducts a simulation-based prediction of radiated interference for a parallel inverter-driven four-module motor system. It analyzes the coupling mechanisms of common mode and differential mode radiated interference and validates the radiated interference from both DC and AC cables through simulations, predicting the impact on surrounding sensitive equipment in practical systems.

关键词： Motor drives Accuracy Silicon carbide Electromagnetic interference Layout Predictive models Motors Topology Gallium nitride Cables

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A Very-Short-Term Online PV Power Prediction Model Based on RAN With Secondary Dynamic Adjustment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1214-1224页

作者： Zhang, Tengfei Ma, Fumin Peng, Chen Yu, Yang Yue, Dong Dou, Chunxia O'Hare, Gregory M. P. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence The Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing210023 China Nanjing University of Finance and Economics College of Information Engineering Nanjing210023 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200072 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210023 China Trinity College Dublin School of Computer Science and Statistics Dublin 2 Ireland

Photovoltaic (PV) power is progressively being subsumed into power grids. As a consequence, reliable PV power forecasting has become essential in order to ensure the optimal functioning of the power grid. Neural networks remain the dominant prediction model utilized. Conventional neural network forecasting models are wholly dependent upon offline data. Subsequent to offline training, no further structural adjustments can be made during the forecasting process, which therefore they fail to cater for PV power supply fluctuations that are fundamentally dynamic. To address this failing, this article proposes a very-short-term online prediction model based on a resource-allocating network (RAN) incorporating a secondary dynamic adjustment. The RAN is initially trained offline to obtain a basic forecasting model. Thereafter, in an online prediction process, those samples with large prediction errors that exceed a preset value are recorded in a specific buffer. When the set conditions are triggered, the secondary dynamic adjustment strategy is employed, which enables the online prediction model to effectively relearn previously unmodeled samples while shielding external interference. Experimental results obtained from actual testing demonstrate the validity of the secondary dynamic adjustment strategy for online learning while also providing higher accuracy levels from the prediction model. © 2020 IEEE.

关键词： Forecasting

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Feature selection and prediction of small-for-gestational-age infants

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 1881-1895页

作者： Li, Jianqiang Liu, Lu Zhou, MengChu Yang, Ji-Jiang Chen, Shi Liu, HuiTing Wang, Qing Pan, Hui Sun, ZhiHua Tan, Feng School of Software Engineering Beijing University of Technology Beijing China Department of Electronical and Computer Engineering New Jersey Institute of Technology NewarkNJ United States Tsinghua University Beijing China Department of Endocrinology Peking Union Medical College Hospital Beijing China Department of Internal Medicine Peking Union Medical College Hospital Beijing China Beijing Chaoyang District Maternal and Child Healthcare Hospital Beijing China Chinese Center For Disease Control And Prevention Beijing China

The small-for-gestational-age (SGA) condition often causes serious problems. Therefore, identifying the risk factors for SGA is important. Traditional statistical methods such as stepwise logistic regression (LR) have been widely utilized to discover possible risk factors. However, other feature selection methods from machine learning field have rarely been employed for the task. In this paper, a comparison of five feature selection methods from both fields for SGA risk factors analysis is conducted for the first time. To evaluate their performance, four classification algorithms are used to construct SGA prediction models. The evaluation criteria are precision and the area under the receiver operator characteristic curve. Stepwise LR achieves the best performance among the five feature selection methods, because it conducts both a univariate significance test and a model significance test, which make it more suitable for handling the complex relations among features. The top 20 features selected by each feature selection method and the 27 features selected by four or five of them could assist physicians to revise traditional SGA evaluation models. Ensemble method is also exploited to build effective SGA prediction models based on the feature subsets, which is indeed superior compared with the individual ones shown in the results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Feature Selection

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Unpredictable Switching for Cyber-Physical Security Against Worst-case Attackers

Unpredictable Switching for Cyber-Physical Security Against ...

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IEEE Conference on Decision and control

作者： Aris Kanellopoulos Filippos Fotiadis Kyriakos G. Vamvoudakis Henrik Sandberg Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Daniel Guggenheim School of Aerospace Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we investigate the problem of securing a system against actuator attacks. Specifically, we employ an unpredictability-based defense algorithm according to the principles of Moving Target Defense, while explicitly considering the worst-case attack that can be launched into the system. Consequently, we allow the system to alternate between different actuating modes, but also between optimal and robust control schemes. Via game-theoretic methods, we derive mixed strategies dictating the probabilities of utilizing different controllers for a given mode of operation, whereas an entropy-augmented optimization problem gives the probabilities of using these different modes. Finally, the overall stability of the system is analyzed, and simulation results showcase the efficacy of our proposed method.

关键词： Robust control Actuators Simulation Switches control systems Stability analysis Security Optimization

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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A Linear Parameter-Varying Approach to Data Predictive control

arXiv

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arXiv 2023年

作者： Verhoek, Chris Berberich, Julian Haesaert, Sofie Tóth, Roland Abbas, Hossam S. The Control Systems Group Eindhoven University of Technology Netherlands The University of Stuttgart Institute for Systems Theory and Automatic Control Germany The Institute for Electrical Engineering in Medicine Universität zu Lübeck Germany The Systems and Control Lab Institute for Computer Science and Control Hungary

By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems. In particular, we present output-feedback and state-feedback-based LPV-DPC methods with terminal ingredients, which guarantee exponential stability and recursive feasibility. We provide methods for the data-based computation of these terminal ingredients. Furthermore, an in-depth analysis of the application and implementation aspects of the LPV-DPC schemes is given, including application for nonlinear systems and handling noisy data. We compare and demonstrate the performance of the proposed methods in a detailed simulation example involving a nonlinear unbalanced disc system. © 2023, CC BY-NC-SA.

关键词： Predictive control systems

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MoFiM: A morphable fish modeling method for underwater binocular vision system

MoFiM: A morphable fish modeling method for underwater binoc...

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作者： Yin, Jingfang Zhu, Dengming Shi, Min Li, Zhaoxin Duan, Ming Mi, Xiangyuan Wang, Zhaoqi Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China Institute of Hydrobiology Chinese Academy of Sciences Hubei Wuhan China

Fish morphology is an essential basis for fishery management, as it can reflect the growth status of fishes. Noncontact 3D reconstruction of underwater fish is a new way to obtain fish morphology. While it is difficult to reconstruct fish on account of the inadequate information caused by fish swimming and poor underwater imaging. This article introduces a morphable fish modeling method for the underwater binocular vision system. First, we define a fish representation based on selected landmarks. Then, we propose a chirality-supervision incorporated hourglass network to estimate fish orientation and fish 2D landmarks simultaneously, and calculate fish 3D landmarks by triangulation. Next, we propose a fish modeling method which is based on 3D landmarks and introduce the optimization procedure of fish modeling. Finally, we obtain the complete 3D fish model corresponding to the input images. To train our network and build a parametric model, we constructed an underwater vision dataset and fish instance dataset respectively. We conducted experiments with grass carp as an example, and the experimental results show that our method can achieve effective fish modeling and is useful for noncontact measurement of underwater fish. © 2022 John Wiley & Sons Ltd.

关键词： Morphology

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Maximizing social power in multiple independent Friedkin-Johnsen models

Maximizing social power in multiple independent Friedkin-Joh...

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European control Conference (ECC)

作者： Lingfei Wang Yu Xing Claudio Altafini Karl H. Johansson Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering Division of Automatic Control Linköping University Linköping Sweden

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper investigates the problem of maximizing social power for a group of agents, who participate in multiple meetings described by independent Friedkin-Johnsen models. A strategic game is obtained, in which the action of each agent (or player) is her stubbornness over all the meetings, and the payoff is her social power on average. It is proved that, for all but some strategy profiles on the boundary of the feasible action set, each agent's best response is the solution of a convex optimization problem. Furthermore, even with the non-convexity on boundary profiles, if the underlying networks are given by a fixed complete graph, the game has a unique Nash equilibrium. For this case, the best response of each agent is analytically characterized, and is achieved in finite time by a proposed algorithm.

关键词： Network topology Europe Games Nash equilibrium Cost function Convex functions

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Distributed State Estimation Under Multi-step Random Transmission Delay and Packet Loss

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Dai, Hongyun Fu, Junjie Duan, Peihu Wen, GuanGhui Huang, Tingwen Southeast University School of Mathematics Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper investigates the problem of underwater distributed state estimation with multi-step random communication delay and packet loss. Since underwater communication adopts acoustic communication, the information transmission speed is relatively slow, and multi-step delay and packet loss situations are frequently encountered. Based on the hybrid consensus filtering algorithm and a proper buffer setting, a distributed Kalman filtering algorithm is proposed, which can deal with possible multistep random time delay and packet loss. A sufficient condition for the filtering stability is rigorously established. In addition, the state estimation problem for an uncertain target model in underwater environments is addressed by combining the Interacting Multiple Model method. Finally, numerical simulations validate the effectiveness of the proposed algorithm and the accuracy of the conclusions, while also demonstrating that this method achieves a 13.9% reduction in the cross-secenario composite RMSE compared to similar buffer-based approaches, further emphasizing its superiority. © 1965-2011 IEEE.

关键词： Convergence of numerical methods

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