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A Hybrid Data-Driven Granular Model for Battery Remaining Useful Life Prediction

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Jing, TaiLong Du, Sheng Wang, Cong Wu, Chao Pedrycz, Witold Sharaf, Mohamed Li, ZhiWu Luoyang Institute of Science and Technology School of Electrical Engineering and Automation Luoyang471023 China China University of Geosciences School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Xi’an710072 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland King Saud University College of Engineering Department of Industrial Engineering Riyadh11421 Saudi Arabia Institute of Systems Engineering Macau University of Science and Technology Macau China

Accurate and meaningful prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) provides a significant guide for the maintenance and management of these LIBs. A singular numerical predictive result by numerous conventional models is insufficient to furnish decision-makers with comprehensive or nuanced insights. To address this issue, predictive RUL results in the form of information granules are essential. In this study, a hybrid data-driven granular model is established for battery RUL prediction with granular (interval-based) quantification for LIBs. This granular model is designed and formulated in a way that it produces comprehensive prediction outcomes realized as information granules, which are expressed in a more descriptive and comprehensive manner. Thus, the resulting information granules can effectively reflect and communicate the confidence associated with the predictions. The granular model is realized in two steps: 1) probabilistic prediction results are produced by a hybrid numeric predictive model and 2) a comprehensive granulation of the probabilistic results is achieved under the principle of information granularity. The granular predictive results are holistically evaluated and optimized by the two associated criteria of granularity (coverage and specificity). This transformation of information granules provides an intuitive and interpretable display for battery maintenance and safe work. The proposed granular model is verified by using an open-access LIBs dataset, and the experimental results demonstrate that the proposed granular model outperforms other comparative methods. © 2025 IEEE. All rights reserved.

关键词： Prediction models

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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Evaluation of interfacial strength by an instrumented indentation method and its application to an actual TBC vane

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Acta Metallurgica Sinica(English Letters) 2011年第2期24卷 109-117页

作者： Yasuhiro YAMAZAKI Shin-ichiro KUGA Toshihiko YOSHIDA Department of Mechanical and Control Engineering Department of Advanced Production Systems Engineering Niigata Institute of Technology Graduate School Ryodensya Co.

The thermal fatigue behaviour of an air plasma sprayed thermal barrier coating was investigated. And also the interfacial strengths of thermal barrier coated specimens subjected to thermal fatigue, as well as a retired TBC vane were also evaluated by means of an instrumented indentation machine. The results indicated that, （1） the TGO grew at the interface during thermal fatigue cycle as a function of the exposure time at elevated temperature; （2） the microcracks were initiated in the top coating and at the interface after thermal cycle tests; （3） the interfacial strength of TBC, which was evaluated by the indentation method, increased with the thermal cycles; （4） the interfacial strength of the retired TBC vane was almost equal with that of the as-sprayed TBC specimen.

关键词： Thermal barrier coating Interfacial fracture toughness Indentation method

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Energy management in smart homes with adversary detection and noise mitigation using a moving prediction window scheme

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Sustainable Energy, Grids and Networks 2025年 43卷

作者： Ghazvini, Hamid Reza Babaei Adelipour, Saeed Haeri, Mohammad Advanced Control Systems Lab. Electrical Engineering Department Sharif University of Technology Tehran 11155-4363 Iran

This paper presents an energy management algorithm for scheduling a large number of residential households, utilizing the moving prediction window to make it resilient against false data injection attacks and communication noise. We model multiple communities of smart homes, each managed by a local controller, where self-interested residential households engage in a global non-cooperative game. The cost functions of the households are influenced by a constrained aggregated power consumption term across all households from all communities. The interactions among households are modeled through a multi-community aggregative game. To reach a Nash equilibrium, we propose an iterative algorithm wherein local controllers estimate the coupling aggregate term and corresponding Lagrange multiplier for their respective households and collaborate with other controllers via an unreliable communication network to refine the aggregate estimations. Given the vulnerability of the communication network to external intrusions and the potential for internal controllers to behave maliciously, we explore a moving horizon window technique to detect false data injection attacks and mitigate communication noise. In this regard, first, a moving horizon estimator predicts the community's current behavior based on historical data;second, a residual-based detection mechanism flags an attack when predicted residuals exceed a dynamic threshold;and third, corrupted measurements are discarded, and the average of the predictions is used in the Krasnoselskii-Mann update to reduce the noise impact. Numerical simulations show the effectiveness of the proposed algorithm in increasing the speed of reaching consensus by about 30 percent while managing the energy consumption of households. © 2025 Elsevier Ltd

关键词： Energy management False data injection attack detection Moving prediction window Multi-community aggregative game Nash equilibrium seeking

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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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Cooperative neural-adaptive fault-tolerant output regulation for heterogeneous nonlinear uncertain multiagent systems with disturbance

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Science China(Information Sciences) 2021年第7期64卷 181-192页

作者： Shanling DONG Guanrong CHEN Meiqin LIU Zheng-Guang WU College of Electrical Engineering Zhejiang University Department of Electrical Engineering City University of Hong Kong National Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University Institute for Advanced Study Chengdu University

This paper investigates the cooperative output regulation problem for heterogeneous nonlinear uncertain multiagent networked systems subject to actuator failure, bounded matched or mismatched disturbances or disturbances produced by a given linear exosystem. Accurate information about nonlinearity,actuator failure and disturbance may be completely unknown. First, a distributed finite-time observer is designed to estimate the dynamics of the exosystem on a finite-time interval over a communication ***, a neural-adaptive control protocol is proposed. It is shown that(i) closed-loop multiagent systems are asymptotically stable, with output synchronization errors that tend to zero in the absence of mismatched disturbance, and(ii) the states of the closed-loop multiagent systems and the output synchronization errors are bounded in the presence of mismatched disturbance. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control strategy.

关键词： cooperative output regulation actuator failure matched/mismatched disturbance distributed finite-time observer neural-adaptive control

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ESTIMATION OF STATE VECTOR OF A LINEAR STOCHASTIC SYSTEM WITH A CONSTRAINED ESTIMATOR

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IEEE TRANSACTIONS ON AUTOMATIC control 1967年第4期AC12卷 432-&页

作者： AOKI, M HUDDLE, JR Department of Engineering University of California Los Angeles CA USA Advanced Engineering Laboratory Guidance and Control Systems Division Litton Systems Inc. Woodland Hills CA USA

The paper presents a constructive design procedure for the problem of estimating the state vector of a discrete-time linear stochastic system with time-invariant dynamics when certain constraints are imposed on the number of memory elements of the estimator. The estimator reconstructs the state vector exactly for deterministic systems while the steady-state performance in the stochastic case may be comparable to that obtained by the optimal (unconstrained) Wiener-Kalman filter.

关键词： State estimation Vectors Stochastic systems Variable speed drives Steady-state Wiener filter Tin control systems Gaussian noise Q measurement

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Adaptive event-triggered state estimation for large-scale systems subject to deception attacks

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Science China(Information Sciences) 2022年第2期65卷 139-154页

作者： Hanchen XIAO Derui DING Hongli DONG Guoliang WEI College of Science University of Shanghai for Science and Technology Department of Control Science and Engineering University of Shanghai for Science and Technology School of Software and Electrical Engineering Swinburne University of Technology Institute of Complex Systems and Advanced Control Northeast Petroleum University

This paper addresses state estimation issues of large-scale systems with measurements subject to deception attacks, where the communication topology among sub-estimators is the same as the physical coupling structure of the subsystems. In consideration of the limited channel bandwidth, a novel adaptive event-triggered scheme is proposed for governing the data transmission among sub-estimators. With the help of Lyapunov analysis approaches, sufficient conditions are derived to ensure the input-to-state stability of the dynamics of estimation errors. Meanwhile, the bound of the estimation errors is obtained in the mean-square sense. The desired estimator parameters are presented in an analytical form dependent on the solution of a set of matrix inequalities. The developed scheme is related to the local information of the subsystems and thus satisfies the requirement of scalability. Finally, a simulation example of power systems is given to reveal the usefulness and effectiveness of the developed design scheme.

关键词： large-scale discrete-time systems adaptive event-triggering communication Adaptive event-triggering communication deception attacks input-to-state stability power systems

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Generalized Norm Optimal Iterative Learning control with Intermediate Point and Sub-interval Tracking

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International Journal of Automation and computing 2015年第3期12卷 243-253页

作者： David H.Owens Chris T.Freeman Bing Chu Department of Automatic Control and Systems Engineering University of Sheffield School of Electronics and Computer Science University of Southampton Advanced Robotics Division Italian Institute of Technology

Norm optimal iterative learning control(NOILC) has recently been applied to iterative learning control(ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix "point to point" movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC s well-defined convergence properties. Design guidelines and supporting experimental results are included.

关键词： Iterative learning control learning control optimization linear systems robotics.

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Development of an exoskeleton robot for human wrist and forearm motion assist

Development of an exoskeleton robot for human wrist and fore...

引用

ICIIS 2007 - 2nd International Conference on Industrial and Information systems 2007

作者： Gopura, R.A.R.C. Kiguchi, K. Department of Advanced Systems Control Engineering Faculty of Science and Engineering Saga University Japan

ISBN: (纸本)1424411521

Exoskeleton is an external structural mechanism with joints and links corresponding to those of the human body. It transmits torques from actuators through rigid exoskeletal links to the human joints when it is worn. We have been developing exoskeleton robots for assisting the motion of physically weak individuals such as elderly or slightly disabled in daily life. In this paper we propose a three degree of freedom (3DOF) exoskeleton robot for the forearm pronation/supination motion, wrist flexion/ extension motion and ulnar/radial deviation. The paper describes wrist anatomy toward the development of the robot, the hardware design of the exoskeleton (mechanical structure and mechanism, sensors and actuators, power transmission and safety aspects) and potential control methods. Force control using force/torque sensors located at wrist and forearm and electromyographic (EMG) signals based control using skin surface EMG signals of forearm and wrist muscles are the potential control methods for the robot. Experiments have been performed to evaluate the proposed exoskeleton. ©2007 IEEE.

关键词： Machine design

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