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China International Electrical and Energy Conference (CIEEC)

作者： Yunus Arı Ergün Cücük Seda Karateke Electric and Electronic Engineering Institute of Graduate Studies Istanbul University - Cerrahpaşa Istanbul Turkey Department of Research and Development Ges Teknik Air Conditioning Control and Automation Systems Industry and Trade Inc. Istanbul Turkey Department of Computer Engineering Faculty of Engineering Istanbul Topkapı University Istanbul Turkey

The efficient operation of HVAC&R systems are based on keeping indoor temperature and air quality at an optimum level without disturbing comfort. Starting from this point, in this experimental research, the factors affecting the measurement accuracy during the room temperature detection of a wall mount fan coil thermostat were examined. Among the external effects, the effect of the device components on the measurement was focused and the solution of the measurement deviations occurred in this extent was investigated. Based on the experiments, the algorithm that must be applied to reach the optimum temperature measurement of a room thermostat developed for Fan Coil systems was proposed.

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Robust Frequency Regulation for Dual-Area Interconnected Grids Using Hybrid controller: A Comparative Study

Robust Frequency Regulation for Dual-Area Interconnected Gri...

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Smart Technologies for Power, Energy and control (STPEC), IEEE International Conference on

作者： Awadh Ba Wazir Abdullah A. Alhussainy Abdulraheem H. Alobaidi Aatif Altaf Anil Kumar Mahendra Kumar Sultan Alghamdi Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Instrumentation and Control Engineering Department Dr. B R Ambedkar National Institute of Technology Jalandhar Jalandhar Punjab India

This paper introduces a robust controller designed for load frequency control in a dual-area integrated power grid. Here, the suggested controller is a blend of a fuzzy logic controller with an integral controller. The parameters of the integral controller and frequency bias settings are optimized to enhance the dynamic response of the proposed controller. To attain this, three distinct optimization algorithms are employed, i.e., genetic algorithm, simulated annealing, and pattern search. A new objective function using settling time, settling-max, and settling-min with a suitable weight factor is used to improve the parameters estimation process. A comparative analysis is performed using the proposed approach and classical-I controller under two cases considering several load disturbance scenarios. Furthermore, the suggested hybrid controller is evaluated and compared with similar models in the literature. As expected, the proposed controller exhibits superior performance.

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Error-state Kalman Filtering with Linearized State Constraints

TechRxiv

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TechRxiv 2023年

作者： Do, Hoang Viet Jung, Jae Hyung Park, Chan Gook Song, Jin Woo The Intelligent Navigation and Control Systems Laboratory Department of Convergence Engineering for Intelligent Drone School of Intelligent Mechatronics Engineering Sejong University Seoul05006 Korea Republic of The Department of Aerospace Engineering Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of The Navigation and Electronic System Laboratory Department of Aerospace Engineering Institute of Advanced Aerospace Technology Seoul National University Seoul08826 Korea Republic of

In recent years, the error-state Kalman filter (ErKF) has been extensively employed across various applications, including but not limited to robotics, aerospace, and localization. However, incorporating constraints into the ErKF framework when state constraint is necessary has remained a challenging task due to its intrinsic properties. This paper explores all possible ways to achieve this goal in the context of the estimate projection method. In particular, the constraint can be enforced before or after the ErKF’s correction step. We approach the problem from a mathematical perspective by deriving analytical solutions and discussing their statistical properties. We prove that the two mentioned methods are statistically identical for a linear system with linear constraints. Conversely, the filter’s behavior remains uncertain in the presence of linearized constraints. However, we provide a special case of the nonlinear constraint, wherein the results of the linear case remain valid. To support our theorem and verify the filter’s performance when the assumptions are invalidated, we present two Monte Carlo simulations under the increasing initialization error and the constraint’s incompleteness. The simulation results clearly confirm our insights and lead to the conclusion that constraining the error-state after the correction may offer superior outcomes compared to its competitor. © 2023, CC BY.

关键词： Linearization

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An Adaptive Distributed Observer for A Class of Discrete-Time Uncertain Linear systems over Acyclic Digraphs

An Adaptive Distributed Observer for A Class of Discrete-Tim...

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IEEE Conference on Decision and control

作者： Tao Liu Jie Huang Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China Shenzhen Research Institute and Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong

This paper proposes an adaptive distributed observer for a class of discrete-time uncertain linear leader systems. The leader system is assumed to be neutrally stable with unknown parameters in the system matrix. Such a leader system can produce multi-tone sinusoidal signals with unknown frequencies, magnitudes, and phases. Under the assumption that the digraph of the communication network is a spanning tree with the leader system as the root, the proposed adaptive distributed observer is shown to be capable of estimating over the communication network not only the leader's state, but also the unknown parameters of the leader's system matrix.

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DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatial and Temporal Transformer Network

DSTFormer: 3D Human Pose Estimation with a Dual-scale Spatia...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Shaokun Zhang Xinde Li Chuanfei Hu Jianping Xu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Science and Technology on Information Systems Engineering Laboratory Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Recent transformer-based methods for estimating 3D human pose have gained widespread attention, achieving state-of-the-art results. Previous methods have primarily focused on capturing motion patterns of the human body at a single scale or cascading multiple scales, such as joints, bones, and body-parts. However, they are difficult to simultaneously capture spatial-temporal motion patterns of the human body at different scales due to the complex motion patterns. To address this issue, we propose Dual-scale Spatial and Temporal transFormer (DSTFormer), which can concurrently explore the spatial dependencies and temporal motion patterns of human joints and bones. Additionally, we introduce a Gcn-Spatial Transformer Block (GSTB), which introduces Graph Convolutional Networks (GCN) into transformer to enhance the exploitation of local relationships and global information between adjacent joints or bones. Extensive experiments are conducted on the Human3.6M benchmark dataset, and superior results are reported when comparing to other state-of-the-art methods. More remarkably, our model achieves to-date the best published performance, with P1 errors of 37.9 mm and 15.6 mm, respectively.

关键词： Solid modeling Three-dimensional displays Mechatronics Graph convolutional networks Computational modeling Pose estimation Transformers Bones Joints Robots

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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A Model-Predictive-Enabled Equivalent-Input-Disturbance Approach for Disturbance Rejection

A Model-Predictive-Enabled Equivalent-Input-Disturbance Appr...

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Industrial Cyber-Physical systems (ICPS)

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 Tokyo Japan Department of Electrical and Mechanical Engineering Nagoya Institute of Technology Nagoya Japan

This paper presents a model-predictive-enabled equivalent-input-disturbance (MPEID) method for disturbance rejection. An EID estimator with a state observer estimates the effect of disturbances in a system. The disturbance estimation will not be directly added to the input channel for disturbance rejection. A cost function that considers the disturbance estimation is designed. An MPC controller calculates an optimal control input by minimizing the cost function. The stability condition of the closed-loop system is analyzed based on the Lyapunov stability theory. Simulation results show that our method has better disturbance-rejection performance than an MPC method when the control input is similar.

关键词： Simulation Estimation Optimal control Predictive models Observers Cyber-physical systems Cost function

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention 26

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

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26th International Conference on Industrial Technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Tokyo Institute of Technology Yokohama226-8502 Japan Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798331521950

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. © 2025 IEEE.

关键词： Modal analysis

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Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learning

arXiv

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arXiv 2024年

作者： Zhu, Yuanyang Wang, Zhi Zhu, Yuanheng Chen, Chunlin Zhao, Dongbin The Department of Control Science and Intelligent Engineering School of Management and Engineering Nanjing University Nanjing210093 China The Research Center for Novel Technology of Intelligent Equipment Nanjing University Nanjing210093 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

For on-policy reinforcement learning, discretizing action space for continuous control can easily express multiple modes and is straightforward to optimize. However, without considering the inherent ordering between the discrete atomic actions, the explosion in the number of discrete actions can possess undesired properties and induce a higher variance for the policy gradient estimator. In this paper, we introduce a straightforward architecture that addresses this issue by constraining the discrete policy to be unimodal using Poisson probability distributions. This unimodal architecture can better leverage the continuity in the underlying continuous action space using explicit unimodal probability distributions. We conduct extensive experiments to show that the discrete policy with the unimodal probability distribution provides significantly faster convergence and higher performance for on-policy reinforcement learning algorithms in challenging control tasks, especially in highly complex tasks such as Humanoid. We provide theoretical analysis on the variance of the policy gradient estimator, which suggests that our attentively designed unimodal discrete policy can retain a lower variance and yield a stable learning process. © 2024, CC BY.

关键词： Contrastive Learning

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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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