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Chinese automation Congress (CAC)

作者： Yanxing Bai 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 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 AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene.

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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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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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Aeroservoelastic induced drag modelling and minimization for the T-FLEX demonstrator

Aeroservoelastic induced drag modelling and minimization for...

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AIAA SciTech Forum and Exposition, 2023

作者： Meddaikar, Yasser M. Krüger, Wolf R. Kier, Thiemo Bartasevicius, Julius Yu, Fanglin Vanek, Bálint Olgyay, Ábel Takarics, Béla Loads Analysis and Aeroelastic Design DLR - Institute of Aeroelasticity Bunsenstrasse 10 Goettingen37073 Germany Department of System Dynamics and Control DLR - Institute of System Dynamics and Control Weßling82234 Germany Institute of Aircraft Design Technical University of Munich Boltzmannstraße 15 Garching/München85748 Germany Systems and Control Lab SZTAKI Kende u. 13-17 Budapest1111 Hungary Computer and Automation Research Institute SZTAKI Kendeu. 13-17 Budapest1111 Hungary

ISBN: (数字)9781624106996

ISBN: (纸本)9781624106996

This work is part of the on-going EU-project FliPASED, wherein flight tests using a UAV demonstrator (T-FLEX) and a multi-disciplinary optimization (MDO) exercise involving the aeroservoelastic optimization of UAV demonstrator and commercial-scale aircraft wings are performed. In this paper, several potential flow methods have been applied to estimate induced drag of the T-FLEX aircraft, followed by solving an optimization problem to minimize it using the control surfaces on the wings. The results from this study were used in the retrofit design of a pair of manufactured wings - the number and position of the control surfaces and the wing selection. In a next step, the developed tools will form the basis for a controller that will minimize induced drag, which will be tested on the T-FLEX testbed. The developed tools and the validated results will also be used within a broader MDO task involving optimization studies on a commercial short/medium range aircraft. © 2023, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Wings

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DEAL: Decoupled Classifier with Adaptive Linear Modulation for Group Robust Early Diagnosis of MCI to AD Conversion

arXiv

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arXiv 2024年

作者： Lee, Donggyu Park, Juhyeon Moon, Taesup The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of The Department of Electrical and Computer Engineering Automation and Systems Research Institute Institute of New Media and Communications Interdisciplinary Program in Artificial Intelligence Artificial Intelligent Institute Seoul National University Seoul08826 Korea Republic of

While deep learning-based Alzheimer's disease (AD) diagnosis has recently made significant advancements, particularly in predicting the conversion of mild cognitive impairment (MCI) to AD based on MRI images, there remains a critical gap in research regarding the group robustness of the diagnosis. Although numerous studies pointed out that deep learning-based classifiers may exhibit poor performance in certain groups by relying on unimportant attributes, this issue has been largely overlooked in the early diagnosis of MCI to AD conversion. In this paper, we present the first comprehensive investigation of the group robustness in the early diagnosis of MCI to AD conversion using MRI images, focusing on disparities in accuracy between groups, specifically sMCI and pMCI individuals divided by age. Our experiments reveal that standard classifiers consistently underperform for certain groups across different architectures, highlighting the need for more tailored approaches. To address this, we propose a novel method, dubbed DEAL (DEcoupled classifier with Adaptive Linear modulation), comprising two key components: (1) a linear modulation of features from the penultimate layer, incorporating easily obtainable age and cognitive indicative tabular features, and (2) a decoupled classifier that provides more tailored decision boundaries for each group, further improving performance. Through extensive experiments and evaluations across different architectures, we demonstrate the efficacy of DEAL in improving the group robustness of the MCI to AD conversion prediction. © 2024, CC BY-NC-SA.

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case

Optimal Linear Deception Attacks on Remote State Estimation ...

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IEEE Conference on Decision and Control

作者： Jun Shang Hanwen Zhang Jing Zhou Tongwen Chen Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack’s stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback–Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples.

关键词： Wireless communication Measurement Technological innovation System performance Kalman filters State estimation Intelligent sensors

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Using a Shape from Polarization to Determine the 3D Surface of Objects with Thermal Radiation

Using a Shape from Polarization to Determine the 3D Surface ...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Vyacheslav Gulvanskii Maxim Minenko Georgii Gavrilov Lev Bratchenko Dmitrii Kaplun Oleg Zhavoronkov Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" St. Petersburg Russia Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Computer Vision Systems LLC St. Petersburg Russia

ISBN: (数字)9798350387568

ISBN: (纸本)9798350387575

Various algorithms are used to reconstruct the 3D shape of object surfaces using visible light cameras: stereo reconstruction, shape from motion, shape from shadow, and others. Special attention is paid to the shape from polarization algorithm, which is based on the physics of light reflection and changes in the degree of polarization. However, all of these methods are based on light reflection, not radiation. The paper presents a method for reconstructing the 3D shape of hot metal surfaces using a modification of the shape from the polarization algorithm.

关键词： Surface reconstruction Three-dimensional displays Accuracy Shape control Shape Stimulated emission Refractive index

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation System (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Generating Realistic Arm Movements in Reinforcement Learning: A Quantitative Comparison of Reward Terms and Task Requirements

Generating Realistic Arm Movements in Reinforcement Learning...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Jhon P.F. Charaja Isabell Wochner Pierre Schumacher Winfried Ilg Martin Giese Christophe Maufroy Andreas Bulling Syn Schmitt Georg Martius Daniel F.B. Haeufle Hertie Institute for Clinical Brain Research and Centre for Integrative Neuroscience University of Tübingen Germany Institute of Computer Engineering (ZITI) Heidelberg University Germany Max Planck Institute for Intelligent Systems Tübingen Germany Institut for Industrial Manufacturing and Management (IFF) University of Stuttgart Germany Fraunhofer Institute for Manufacturing Engineering and Automation (IPA) Germany Institute for Visualization and Interactive Systems University of Stuttgart Germany Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Germany Department of Computer Science University of Tübingen Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Mimicking of human-like arm movement characteristics involves considering three factors during control policy synthesis: (a) task requirements, (b) noise during movement execution, and (c) optimality principles. Previous studies showed that when these factors (a-c) are considered individually, it is possible to synthesize arm movements that either kinematically match experimental data or reproduce the stereotypical triphasic muscle activation pattern. However, no quantitative comparison has assessed the realism of arm movements generated by each factor, nor has it been determined whether combining these factors results in movements with human-like kinematic characteristics and the triphasic muscle pattern. To investigate this, we used reinforcement learning to learn a control policy for a musculoskeletal arm model, aiming to discern which combination of factors (a-c) results in realistic arm movements according to four frequently reported stereotypical characteristics. Our findings indicate that incorporating velocity and acceleration requirements into the reaching task, employing reward terms that minimize mechanical work, hand jerk, and control effort, along with the inclusion of noise during movement, leads to realistic human arm movements by reinforcement learning. We expect that the gained insights will help in the future to better predict desired arm movements and corrective forces in wearable assistive devices.

关键词： Noise Reinforcement learning Kinematics Biomechatronics Muscles Manipulators Assistive devices Pattern matching

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Motion Planning Integrated with Vehicle-Terrain Interactions for Off-Road Autonomous Ground Vehicles 27

Motion Planning Integrated with Vehicle-Terrain Interactions...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Yin, Jianhua Zhan, Xianyuan Ji, Tianying Xu, Bingrong He, Yi Zhang, Daqing Li, Lingxi Wuhan University of Technology Intelligent Transportation Systems Research Center Wuhan430063 China Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan430063 China Chongqing Research Institute Wuhan University of Technology Chongqing China Institute for Ai Industry Research Tsinghua University Beijing100084 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Tsinghua University Department of Computer Science and Technology Beijing100084 China School of Automation Wuhan University of Technology Wuhan430070 China College of Mechanical and Electrical Engineering Hunan Agricultural University Changsha410128 China Sunward Intelligent Equipment Co. Ltd. Changsha410100 China Elmore Family School of Electrical and Computer Engineering College of Engineering Purdue University IN46202 United States

ISBN: (纸本)9798331505929

Off-road autonomous ground vehicles (AGVs) work in unstructured environments with varied elevation and deformable ground, where vehicle-terrain interactions are more complex resulting in unpredictable dynamics behavior. Current sensor-based hazards/obstacle identification methods mainly focus on geometric hazards/obstacles with explicit geometry, which might be adequate for vehicles in on-road conditions. However, in the off-road environment, vehicle-terrain interactions could introduce non-geometric hazards that cannot be detected by current geometric hazards-oriented sensing techniques, e.g. vehicle stuck in mud. To enable AGVs to traverse the target terrain and meet the goal while avoiding non-geometric hazards, this work proposes a motion planning method integrated with coupled vehicle-terrain dynamics response. Specifically, terramechanics-based modeling and simulation (M&S) is utilized to characterize vehicle-terrain interactions. Based on the dynamics response, a reinforcement learning (RL) algorithm with a specialized reward function is used to navigate the AGV to find the goal with high mobility and safety. We demonstrate the proposed method with two case studies, 1) high mobility is the foremost concern with less safety requirement, and 2) both high mobility and safety are required. The results show that the proposed motion planning method is effective for navigating the AGV in off-road terrains. © 2024 IEEE.

关键词： Off road vehicles

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