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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Yujian Zhou Jinhua She Feng Wang Makoto Iwasaki School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Department of Electrical and Mechanical Engineering Nagoya Institute of Technology Nagoya Japan

This study integrated an improved equivalent-input-disturbance (EID) and a repetitive control methods to ensure reference tracking and enhance disturbance-rejection performance for a pedaling rehabilitation robot. A repetitive controller ensures steady-state tracking of a periodic reference input. An EID estimator with a state observer estimates and compensates for the effect of disturbances generated by patients with lower-limb impairment. The stability condition of the closed-loop system is analyzed based on the Lyapunov stability theory. Simulation results show the effectiveness of this method.

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Chengao Bao Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation and the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method.

关键词： Emotion recognition Visualization Accuracy Service robots Semantics Human-robot interaction Speech recognition Feature extraction Data mining Facial features

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High Real-time Locomotion Recognition Algorithm Using Inertial Measurement Units

High Real-time Locomotion Recognition Algorithm Using Inerti...

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

作者： Wang, Jiaqi Wang, Xingjian Miao, Yinan Wang, Shaoping An, Mailing Miao, Yiqi 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

Human-machine interaction devices (HMI), such as assisted exoskeletons and assisted prostheses, that improve human motion behaviours are playing an increasingly important role in various fields. In order for these HMI devices to provide accurate and appropriate assistance to wearers, the real-time recognition of locomotion with accuracy is among the key issues to be solved. This paper proposes a high real-time motion pattern recognition algorithm using inertial measurement units (IMU) that can accurately and quickly identify the current motion state by collecting various motion parameters of the lower limb during human walking. The algorithm uses a trajectory solving and motion direction discrimination method based on the shank zero velocity point and a terrain classification method based on an F-score improved support vector machine (F-score-SVM). In this paper, experimental data was used to verify the performance of the algorithm. Results show that the algorithm performs better than traditional recognition algorithms in complex environments. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Support vector machines

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Stability and Bifurcation control of Fractional-Order Coupled Hindmarsh-Rose Neural Networks

Stability and Bifurcation Control of Fractional-Order Couple...

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Chinese control Conference (CCC)

作者： Feng Liu Feifan Zhou Wei Wang Gang Cai Hua O Wang Xiuqin Yang School of Automation China University of Geosciences Wuhan China Engineering Research Center of Ministry of education for intelligent technology of earth exploration Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Department of Mechanical Engineering Boston University Boston USA School of Education Minzu University of China Beijing China

This article investigates a fractional-order coupled Hindmarsh-Rose neural networks model. Firstly, the existence and stability of an equilibrium point in the system are verified. Then, the periodic bifurcation behavior of the system on a two-parameter plane is studied, and numerical simulations show the existence of both non -chaotic and chaotic plus periodic bifurcation behavior on the two-parameter plane. Finally, a feedback controller was designed to stabilize the bifurcation point of the delayed system and increase the stable range of the system.

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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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Frequent Event Pattern Extraction of Drilling Time Series Using Change Point Detection and Event Sequence Generation

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IFAC-PapersOnLine 2023年第2期56卷 6168-6173页

作者： Yupeng Li Wenkai Hu Weihua Cao R. Bhushan Gopaluni Liang Cao Chao Gan Min Wu School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China Department of Chemical and Biological Engineering University of British Columbia Vancouver BC V6T 1Z3 Canada

In drilling processes, non-stationary phases corresponding to shifts between operating conditions and changes in downhole formations typically lead to false alarms. Extracting these frequent event patterns is critical to build drilling process monitoring and fault diagnosis models. This study aims to extract the frequent event patterns associated with non-stationary phases in drilling time series. In this way, diversified information related to signal changes under normal conditions can be obtained, which is beneficial for suppressing false alarms and improving fault detection performance. The main contributions of this study are twofold: 1) a non-stationary phase detection method is proposed to extract drilling frequent event patterns based on t -distributed stochastic neighbor embedding and relative unconstrained least-squares importance fitting; 2) an event sequence generation method is proposed to express drilling frequent event patterns with a group of symbols. The effectiveness of the proposed method is demonstrated by data from a real drilling project.

关键词： Drilling processes pattern extraction change point detection event sequence generation frequent event pattern

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Forward Kinematics Development of a 3-RRS Parallel Manipulator for Real-Time control

Forward Kinematics Development of a 3-RRS Parallel Manipulat...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： S. Ali A. Moosavian Omid Mahdizadeh Sina Hallajian Sara Ashrafi Mohammad Jamali Vahid Akbari Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) Lab. K. N. Toosi University of Technology Tehran Iran

ISBN: (纸本)9781665454537

In control of parallel robots, forward kinematics calculation is mainly required. In some studies, obtained equations are solved numerically with massive calculations that is not acceptable for control implementations. This paper introduces a new method to extract forward kinematics for a 3- RRS robot. This method calculates the kinematics of passive joints with two separated equations, which can be solved to decrease the total computation demands. Also, a new weight compensation mechanism is proposed to improve the dynamic response by a linear pneumatic actuator placed between the ground and the end-effector. On the other hand, to use various control algorithms, the position of the rolling ball on the end- effector (moving platform) must be measured. This is performed using Convolutional Neural Networks (CNNs) as image processing algorithms. Since the system must work quickly for a high-frequency task, the proposed image processing algorithm can provide the desired fast feedback. Experimental results reveal the merits of the above-described proposed modules.

关键词： Pneumatic actuators Image processing Heuristic algorithms Kinematics Position measurement Pneumatic systems Mathematical models

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Do We Run Large-scale Multi-Robot systems on the Edge? More Evidence for Two-Phase Performance in System Size Scaling

Do We Run Large-scale Multi-Robot Systems on the Edge? More ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jonas Kuckling Robin Luckey Viktor Avrutin Andrew Vardy Andreagiovanni Reina Heiko Hamann Department of Computer and Information Science University of Konstanz Konstanz Germany Centre for the Advanced Study of Collective Behaviour University of Konstanz Germany Institute of Computer Engineering University of Lübeck Lübeck Germany Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany Department of Computer Science and the Department of Electrical and Computer Engineering Memorial University of Newfoundland St. John’s Canada IRIDIA Université Libre de Bruxelles Brussels Belgium

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

With increasing numbers of mobile robots arriving in real-world applications, more robots coexist in the same space, interact, and possibly collaborate. Methods to provide such systems with system size scalability are known, for example, from swarm robotics. Example strategies are self-organizing behavior, a strict decentralized approach, and limiting the robot-robot communication. Despite applying such strategies, any multi-robot system breaks above a certain critical system size (i.e., number of robots) as too many robots share a resource (e.g., space, communication channel). We provide additional evidence based on simulations, that at these critical system sizes, the system performance separates into two phases: nearly optimal and minimal performance. We speculate that in real-world applications that are configured for optimal system size, the supposedly high-performing system may actually live on borrowed time as it is on a transient to breakdown. We provide two modeling options (based on queueing theory and a population model) that may help to support this reasoning.

关键词： Limiting System performance Space communications Scalability Sociology Swarm robotics Mobile robots

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Investigation of the Nh3 Consumption and H2o/No Formation During the Oxidation of N-Heptane and Iso-Octane Blended with Ammonia

SSRN

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SSRN 2023年

作者： Zheng, Dao Peng, Zhimin He, Dong Zhang, Meng Li, Jidong Ding, Yanjun Du, Yanjun School of Control and Computer Engineering North China Electric Power University Beijing102206 China State Key Laboratory of Power Systems Department of Energy and Power Engineering Tsinghua University Beijing100084 China Advanced Propulsion Laboratory Department of Modern Mechanics University of Science and Technology of China Hefei230026 China University of Chinese Academy of Sciences School of Engineering Sciences Beijing100049 China Tsinghua University China

Adding highly active fuels with ammonia is a compromising way to increase the mixture's reactivity. However, there are very few studies blending ammonia with large hydrocarbons, such as n-heptane and isooctane. In this study, the NH3, NO, and H2O time-histories were simultaneously measured during the oxidation of ammonia/n-heptane and ammonia/isooctane mixtures (equivalence ratio Φ ~ 1.2) in a shock tube. For the ammonia/n-heptane mixture, a reduced kinetics model, the Wang 2023 model, shows the best predictions in terms of the mixture reactivity and the H2O production but underpredicts the NO plateau. For the ammonia/isooctane mixture, the CRECK_TPRF_Alcohols_NOx (CRECK) model is the only available kinetics model containing ammonia and isooctane mechanisms. The CRECK model captures the H2O plateau but underpredicts the ammonia/isooctane mixture reactivity and the NO plateau. Detailed analyses are conducted based on the Wang 2023 and CRECK models. Rate-of-production (ROP) analyses reveal that: the consumption or formation pathways of the three species in the two models are basically the same but the magnitudes of the ROPs are quite different, especially for the NO formation;the reaction of ethylene or propylene with NH2 radical in the Wang 2023 model also contributes significantly to the NH3 consumption but they are not considered in the CRECK model. Sensitivity analyses show that: H + O2 = O + OH is the most promoting reaction for both mixtures and models;however, several reactions show strong effects in one mixture while having little effect in another mixture, or even playing an opposite effect. After modifying or adding some key reactions, the Wang 2023 and CRECK models get significantly improved in predicting the species' time-history during the oxidation of the ammonia/n-heptane or ammonia/isooctane mixture. © 2023, The Authors. All rights reserved.

关键词： Shock tubes

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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

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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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