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arXiv 2021年

作者： Kim, Youngjoo Wang, Peng Mihaylova, Lyudmila Sevendof AS Trondheim Norway The Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

This paper presents a scalable deep learning approach for short-term traffic prediction based on historical traffic data in a vehicular road network. Capturing the spatio-temporal relationship of the big data often requires a significant amount of computational burden or an ad-hoc design aiming for a specific type of road network. To tackle the problem, we combine a road network graph with recurrent neural networks (RNNs) to construct a structural RNN (SRNN). The SRNN employs a spatio-temporal graph to infer the interaction between adjacent road segments as well as the temporal dynamics of the time series data. The model is scalable thanks to two key aspects. First, the proposed SRNN architecture is built by using the semantic similarity of the spatio-temporal dynamic interactions of all segments. Second, we design the architecture to deal with fixed-length tensors regardless of the graph topology. With the real traffic speed data measured in the city of Santander, we demonstrate the proposed SRNN outperforms the image-based approaches using the capsule network (CapsNet) by 14.1% and the convolutional neural network (CNN) by 5.87%, respectively, in terms of root mean squared error (RMSE). Moreover, we show that the proposed model is scalable. The SRNN model trained with data of a road network is able to predict traffic speed of different road networks, with the fixed number of parameters to train. © 2021, CC BY-NC-ND.

关键词： Time series analysis

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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IF: Iterative Fractional Optimization 29

IF: Iterative Fractional Optimization

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29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021

作者： Chatterjee, Sarthak Das, Subhro Pequito, Sérgio Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States MIT-IBM Watson AI Lab IBM Research CambridgeMA United States Delft Center for Systems and Control Delft University of Technology Netherlands

ISBN: (纸本)9782875870827

Most optimization problems lack closed-form solutions of the argument that minimizes a given function, and even if these were available it might be prohibitive to compute it. As such, we rely on iterative numerical algorithms to find an approximate solution. In this paper, we propose to leverage fractional calculus in the context of time series analysis methods to devise a new iterative algorithm. Specifically, we propose to leverage autoregressive fractional-order integrative moving average time series, whose coefficients encode a proxy for local spatial information. We provide evidence that our algorithm is efficient and particularly suitable for cases where the Hessian is ill-conditioned. © 2021 ESANN Intelligence and Machine Learning. All rights reserved.

关键词： Time series analysis

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Robust Fault-tolerant control of Periodic Piecewise Time-varying systems Based on Matrix Polynomials

Robust Fault-tolerant Control of Periodic Piecewise Time-var...

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Chinese Automation Congress (CAC)

作者： Jiantai Huang Xiaochen Xie James Lam Department of Automation Harbin Institute of Technology Shenzhen Shenzhen China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg Germany Department of Mechanical Engineering The University of Hong Kong Hong Kong China HKU Shenzhen Institute of Research and Innovation Shenzhen China

This paper investigates the robust fault-tolerant control issue for a type of periodic piecewise time-varying systems (PPTVSs). The PPTVS is modeled considering the coexistence of external disturbances, structural parameter perturbations, and actuator failures in the system. Stability analysis is conducted for the considered PPTVS, and a sufficient condition concerning the exponential stability of the closed-loop system is given. Through a matrix polynomial approach capable of decoupling time-varying parameters, the robust fault-tolerant controller synthesis is transformed into a convex optimization problem. Flexible controller gains are enabled through selectable subinterval partitioning parameters. A sufficient condition is presented for maintaining the exponential stability, achieving $H_{\infty}$ disturbance attenuation, and obtaining a tractable robust fault-tolerant controller. Simulation verification using a multi-input multi-output PPTVS model shows that the designed controller can maintain the exponential stability under actuator failures, satisfy the $H_{\infty}$ performance index, and exhibiting robustness against structural parameter perturbations.

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Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainty Predictions

arXiv

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arXiv 2022年

作者： Zhou, Jian Olofsson, Björn Frisk, Erik The Division of Vehicular Systems Department of Electrical Engineering Linköping University LinköpingSE-581 83 Sweden The Department of Automatic Control Lund University LundSE-221 00 Sweden

This paper proposes an interaction and safety-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates the ability of the interaction-aware interacting multiple model Kalman filter (IAIMM-KF) to predict interactive multimodal maneuvers of surrounding vehicles, and the advantage of model predictive control (MPC) in planning an optimal trajectory in uncertain dynamic environments. The multi-modal prediction uncertainties, containing both the maneuver and trajectory uncertainties of surrounding vehicles, are considered in computing the reference targets and designing the collision-avoidance constraints of MPC for resilient motion planning of the ego vehicle. The MPC achieves safety awareness by incorporating a tunable parameter to adjust the predicted obstacle occupancy in the design of the safety constraints, allowing the approach to achieve a trade-off between performance and robustness. Based on the prediction of the surrounding vehicles, an optimal reference trajectory of the ego vehicle is computed by MPC to follow the time-varying reference targets and avoid collisions with obstacles. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Model predictive control

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A Special Issue:"Key Technologies of Virtual Power Plant and Distributed Energy Resources"for Global Energy Interconnection

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全球能源互联网（英文版） 2024年第2期7卷前插1-前插2页

作者： Wei Pei Xiaojun Shen Xin Zhang North China Electric Power University BeijingChina Electrical Engineering Department Shanghai Jiaotong UniversityShanghaiChina University of Sheffield United Kingdom Electrical Machines and Drives Research Group and the Department of Automatic Control and Systems Engineering UK Research and Innovation (UKRI)

The increasing penetration of renewable and distributed energy resources (DERs) is transforming the power grid into a new type of clean and low-carbon power system. However, the uncertainty and volatility of DERs have also brought severe challenges to the secure and reliable operation of the power systems. In order to successfully integrate renewable DERs, virtual power plant (VPP) has emerged as a new technique for coordinating demand-side DERs, which has drawn significant attention from industry and academia.

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Real-time Hybrid Gait Phase Recognition Algorithm Under Continuous Multimodal Locomotion for Lower Limb Exoskeleton

Real-time Hybrid Gait Phase Recognition Algorithm Under Cont...

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2022 CSAA/IET International Conference on Aircraft Utility systems, AUS 2022

作者： Miao, Yinan Wang, Xingjian Wang, Jiaqi Wang, Shaoping Gavrilov, Alexander I. School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Qingdao Research Institute Beihang University Qingdao266104 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China Bauman Moscow State Technical University Department of Automatic Control Systems Moscow105005 Russia

ISBN: (数字)9781839537769

ISBN: (纸本)9781839537769

Nowadays, exoskeletons' ability to operate in complex environments is increasingly important. It is challenging to obtain an accurate gait phase under continuous multimodal locomotion. A hybrid gait phase recognition algorithm is proposed combining the adaptive-oscillator-based algorithm and the model-based algorithm. The modification law is adopted to make two algorithms amend each other reciprocally. The design of the modification law combines the advantages of oscillator-based algorithms and model-based algorithms. The gait phase estimation results are more accurate via the hybrid gait phase prediction algorithm (GPRA). Therefore, appropriate assistance actuation and higher efficiency are obtained under the complicated multimodal locomotion. An experiment was designed to verify the performance of the hybrid GPRA. As a result, the hybrid GPRA can still produce a high-precision gait phase with low latency even when the locomotion mode switches. The hybrid GPRA can provide accurate gait phase and assistance timing under continuous multimodal locomotion. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Exoskeleton (Robotics)

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Safe Exploration and Escape Local Minima with Model Predictive control under Partially Unknown Constraints

arXiv

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arXiv 2022年

作者： Soloperto, Raffaele Mesbah, Ali Allgöwer, Frank Institute for Systems Theory and Automatic Control University of Stuttgart Pfaffenwaldring 9 Stuttgart70569 Germany Department of Chemical and Biomolecular Engineering University of California BerkeleyCA94720 United States

In this paper, we propose a novel model predictive control (MPC) framework for output tracking that deals with partially unknown constraints. The MPC scheme optimizes over a learning and a backup trajectory. The learning trajectory aims to explore unknown and potentially unsafe areas, if and only if this might lead to a potential performance improvement. On the contrary, the backup trajectory lies in the known space, and is intended to ensure safety and convergence. The cost function for the learning trajectory is divided into a tracking and an offset cost, while the cost function for the backup trajectory is only marginally considered and only penalizes the offset cost. We show that the proposed MPC scheme is not only able to safely explore the unknown constraints, but also escape from local minima that may arise from the presence of obstacles. Moreover, we provide formal guarantees for convergence and recursive feasibility of the MPC scheme, as well as closed-loop constraint satisfaction. Finally, the proposed MPC scheme is demonstrated in simulations using an example of autonomous vehicle driving in a partially unknown environment where unknown obstacles are present. Copyright © 2022, The Authors. All rights reserved.

关键词： Model predictive control

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Generalized Multiscale RBF Networks and the DCT for Breast Cancer Detection

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International Journal of Automation and computing 2020年第1期17卷 55-70页

作者： Carlos Beltran-Perez Hua-Liang Wei Adrian Rubio-Solis Department of Automatic Control and Systems Engineering University of SheffieldSheffield S13JDUK Tecnologico de Monterrey Campus TolucaEduardo Monroy Cardenas 2000San Antonio BuenavistaToluca 50110Mexico

The use of the multiscale generalized radial basis function(MSRBF)neural networks for image feature extraction and medical image analysis and classification is proposed for the first time in this *** MSRBF networks hold a simple and flexible architecture that has been successfully used in forecasting and model structure detection of input-output nonlinear *** this work instead,MSRBF networks are part of an integrated computer-aided diagnosis(CAD)framework for breast cancer detection,which holds three stages:an input-output model is obtained from the image,followed by a high-level image feature extraction from the model and a classification module aimed at predicting breast *** the first stage,the image data is rendered into a multiple-input-single-output(MISO)*** order to improve the characterisation,the nonlinear autoregressive with exogenous inputs(NARX)model is introduced to rearrange the available input-output data in a nonlinear *** forward regression orthogonal least squares(FROLS)algorithm is then used to take advantage of the previous arrangement by solving the system as a model structure detection problem and finding the output layer weights of the NARX-MSRBF *** the second stage,once the network model is available,the feature extraction takes place by stimulating the input to produce output signals to be compressed by the discrete cosine transform(DCT).In the third stage,we leverage the extracted features by using a clustering algorithm for classification to integrate a CAD system for breast cancer *** test the method performance,three different and well-known public image repositories were used:the mini-MIAS and the MMSD for mammography,and the BreaKHis for histopathology images.A comparison exercise was also made between different database partitions to understand the mammogram breast density effect in the performance since there are few remarks in the literature on this *** results show that

关键词： Nonlinear system identification image processing discrete cosine transform radial basis functions computer-aided diagnosis neural networks

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The applied principles of EEG analysis methods in neuroscience and clinical neurology

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Military Medical Research 2024年第6期11卷 907-941页

作者： Hao Zhang Qing-Qi Zhou He Chen Xiao-Qing Hu Wei-Guang Li Yang Bai Jun-Xia Han Yao Wang Zhen-Hu Liang Dan Chen Feng-Yu Cong Jia-Qing Yan Xiao-Li Li School of Systems Science Beijing Normal UniversityBeijing 100875China College of Electrical and Control Engineering North China University of TechnologyBeijing 100041China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Psychology the State Key Laboratory of Brain and Cognitive Sciencesthe University of Hong KongHong Kong SAR 999077China HKU-Shenzhen Institute of Research and Innovation Shenzhen 518057GuangdongChina Department of Health Technology and Informatics the Hong Kong Polytechnic UniversityHong Kong SAR 999077China Department of Rehabilitation Medicine the First Afliated Hospital of Nanchang UniversityNanchang 330006China Rehabilitation Medicine Clinical Research Center of Jiangxi Province Nanchang 330006China Beijing Key Laboratory of Learning and Cognition School of PsychologyCapital Normal UniversityBeijing 100048China School of Communication Science Beijing Language and Culture UniversityBeijing 100083China Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Computer Science Wuhan UniversityWuhan 430072China School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116081LiaoningChina Guangdong Artifcial Intelligence and Digital Economy Laboratory(Guangzhou) Guangzhou 510335China.

Electroencephalography(EEG)is a non-invasive measurement method for brain *** to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical *** article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological ***,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are ***,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same ***,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are *** article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.

关键词： Electroencephalogram analysis methods Applied principles Neuroscience Diagnosis Neurological diseases

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