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2011 International Conference on Advanced Mechatronic Systems, ICAMechS 2011

作者： Hladowski, Lukasz Galkowski, Krzysztof Rogers, Eric Kummert, Anton Institute of Control and Computation Engineering University of Zielona Gora Podgorna 50 Zielona Gora Poland University of Wuppertal Germany School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ United Kingdom Faculty of Electrical Information and Media Engineering Communication Theory University of Wuppertal Germany

ISBN: (纸本)9780955529375

Iterative learning control can be applied to systems that repeat the same task over a finite duration with resetting to the starting point once each one is complete. The idea of iterative learning control is to make use of information from previous executions of the task in order to update the control signal applied during the current execution and thereby sequentially improve performance. This form of control can be treated as a class of 2D systems. In this paper, a critical overview of progress on this application area for 2D systems theory is given, including results from experimental verification on a gantry robot, and discussion of some open research questions. © 2011 Intl Journal of Adv Mechatr.

关键词： Learning algorithms

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Fault Diagnosis Research of Laser Gyroscope Based on Optimized-Kernel Extreme Learning Machine

SSRN

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SSRN 2023年

作者： Xiaojun, Bai Ma, Zhenxi Chen, Wei Wang, Shenhang Fu, Yanfang Xi’an Technological University School of Computer Science and Engineering Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Traditional diagnostic models for laser gyroscopes, widely utilized as high-precision angular velocity sensors in aerospace applications, often suffer from limited reliability and accuracy due to the difficulty of feature extraction, a high computational cost, and training time. In light of these challenges, the present study puts forth a new method for diagnosing faults in laser gyroscopes using the Kernel Extreme Learning Machine (KELM). Specifically, the proposed method leverages Wavelet Packet Decomposition (WPD) to effectively extract features from the laser gyroscope signal as input for our diagnostic model. Furthermore, the KELM model is trained for fault diagnosis, after which we employ the Improved Dung Beetle Optimizer (IDBO) algorithm to optimize its parameters for enhanced optimization performance. According to the experimental results, our proposed IDBO-KELM model demonstrates a 3.68% improvement in diagnostic accuracy compared to traditional approaches, while also offering the advantages of a shorter training time and increased precision. © 2023, The Authors. All rights reserved.

关键词： Failure analysis

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Bimodal Fusion Network for Basic Taste Sensation Recognition from Electroencephalography and Electromyography

Bimodal Fusion Network for Basic Taste Sensation Recognition...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Han Gao Shuo Zhao Huiyan Li Li Liu You Wang Ruifen Hu Jin Zhang Guang Li State Key Laboratory of Industrial Control Technology Zhejiang University School of Computer and Communication Engineering Changsha University of Science and Technology

Taste sensation can be objectively measured using electroencephalography (EEG) or electromyography (EMG). How-ever, it is still challenging to effectively utilize the complementary information from EEG and EMG signals in taste sensation recognition. This paper proposes a bimodal fusion network (Bi-FusionNet) for recognizing basic taste sensations (sour, sweet, bitter, salty, umami, and blank). Two convolutional backbones with similar structures are designed to separately extract the single-modal features of EEG and EMG. Then, EEG and EMG features are concatenated for bimodal interaction and complementarity. Finally, three loss functions are adopted: a center loss for aggregating intra-class samples, a mean squared error loss for sequence positions for minimizing the difference between signals during the stimulation, and a softmax loss for minimizing the entropy of prediction and true labels. The results on the taste sensation dataset show that bimodal fusion improves recognition performance, and Bi-FusionNet outperforms single-modal methods and other fusion methods. Bi-FusionNet paves the way for the application of multimodal fusion in taste sensation recognition.

关键词： Convolution Speech recognition Brain modeling Feature extraction Electromyography Electroencephalography Entropy

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Comparative Study of Optimal Nonlinear control Schemes for Hanging Mass Actuated by Uncertain Pneumatic Muscle

Comparative Study of Optimal Nonlinear Control Schemes for H...

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11th IEEE International Conference on System engineering and Technology, ICSET 2021

作者： Sadiq, Musaab Esam Humaidi, Amjad Jaleel Kadhim, Saleem Khalefa Mahdi, Abdulkareem Sh. Alkhayyat, Ahmed Ibraheem, Ibraheem Kasim General Company for Grain Processing Ministry of Trade Baghdad Iraq University of Technology Control and Systems Eng. Dept. Baghdad Iraq School of Computer Science and Eng. Taylor's University Malaysia College of Technical Engineering Islamic University Najaf Iraq College of Engineering University of Baghdad Baghdad Iraq

ISBN: (纸本)9781665437660

The systems actuated by Pneumatic Artificial Muscles (PAMs) are characterized by high nonlinearity and time-varying of their coefficients. Therefore, nonlinear and robust controllers are required to cope with these challenging control problems. This work presents the development of new control design for trajectory tracking of PAM-Actuated manipulator based on Sliding Mode control (SMC), Backstepping Sliding Mode control (BSMC). The control laws of controlled PAM hanging mass based on SMC and BSMC have been developed according to Lyapunov stability analysis. Additionally, the PSO technique is used to tune the design parameters of proposed controllers for further enhancement of controlled PAM system performance. A comparison study has been conducted among the proposed controllers via computer simulation using MATLAB/Simulink programming software. © 2021 IEEE.

关键词： Trajectory tracking System performance Muscles Programming Systems engineering and theory Software Nonlinear dynamical systems

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Improved SqueezeNet-Based Method for Automotive Paint Film Defect Detection

Improved SqueezeNet-Based Method for Automotive Paint Film D...

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Artificial Intelligence and Digital Technology (ICAIDT), International Conference on

作者： Shengnan Yan Yihui Shen School of Computer and Control Engineering Minjiang University Fujian China College of Arts and Sciences Fujian Medical University Fujian China

ISBN: (数字)9798350386905

ISBN: (纸本)9798350386912

To address the issues of lacking datasets and low recognition accuracy for paint film defects, this paper proposes a denoising diffusion implicit model (DDIM) for data augmentation of paint film defects and innovatively suggests a classification method combining Selective Kernel Networks (SKNet) with SqueezeNet model. Initially, the DDIM is used for dataset expansion, followed by an evaluation of the similarity between generated and captured paint film defect images using multiscale structural similarity (MS-SSIM). The training and generation effects of DDIM are then compared with those of DCGAN. In the SqueezeNet model, a Selective Kernel module is added following the fire7 module to enhance the model’s attention mechanism. The results show that all types of paint film defect images generated by DDIM have MS-SSIM indices above 0.64, with most exceeding 0.7. The combined approach of Selective Kernel Networks(SKNet) and SqueezeNet outperforms other attention mechanisms, achieving an accuracy above 96.3%. The method demonstrates promising prospects for paint film defect detection, enhancing identification efficiency and accuracy while reducing detection costs, and is applicable to mobile or embedded devices.

关键词： Training Accuracy Attention mechanisms Noise reduction Data augmentation Data models Object recognition

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Obstacle classification by a line-crawling robot: A rough neurocomputing approach 3rd

Obstacle classification by a line-crawling robot: A rough ne...

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3rd International Conference on Rough Sets and Current Trends in Computing, RSCTC 2002

作者： Peters, James F. Ahn, T.C. Borkowski, Maciej Department of Electrical and Computer Engineering University of Manitoba WinnipegMBR3T5V6 Canada Intelligent Information Control ann System Lab School of Electrical and Electronic Engineering Won-Kwang University 344-2 Shinyong-Dong Iksan Chon-Buk570-749 Korea Republic of

ISBN: (纸本)9783540442745

This article considers a rough neurocomputing approach to the design of the classify layer of a Brooks architecture for a robot control system. In the case of the line-crawling robot (LCR) described in this article, rough neurocomputing is used to classify sometimes noisy signals from sensors. The LCR is a robot designed to crawl along high-voltage transmission lines where noisy sensor signals are common because of the electromagnetic field surrounding conductors. In rough neurocomputing, training a network of neurons is defined by algorithms for adjusting parameters in the approximation space of each neuron. Learning in a rough neural network is defined relative to local parameter adjustments. Input to a sensor signal classifier is in the form of clusters of similar sensor signal values. This article gives a very brief description of a LCR that has been developed over the past three years as part of a Manitoba Hydro research project. This robot is useful in solving maintenance problems in power systems. A description of the basic features of the LCR control system and basic architecture of a rough neurocomputing system for robot navigation are given. A sample LCR sensor signal classification experiment is also given. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Electromagnetic fields

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Green shipping oriented berth allocation based on decentralized decision-making

Green shipping oriented berth allocation based on decentrali...

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Chinese control Conference (CCC)

作者： Heng Zhang Zengqiang Chen Ya Xu Xiongwen Quan Xiaochuang Jin College of Computer and Control Engineering Nankai University Tianjin Tianjin CN Business School Nankai University Tianjin Tianjin CN

In contrast to the existing literature on berth allocation problem (BAP) considering fuel consumption and emissions in the terminal-centralized decision environment, the coordinated BAP in a decentralized decision environment is proposed. The port and shipping lines are regarded as rational and autonomous agents, which is more reasonable and practical. Moreover, the study of discrete BAP is extended to the case of two adjacent ports in the same region. In the solution based on combinatorial auction, the port agent solves the BAP, and the vessel agent optimizes its sailing speed and arrival time. An extensive numerical experiment conducted and the results show that the proposed strategy can significantly reduce the total waiting time, the fuel consumption and mitigate the vessel emissions without severely compromising the service level of the ports, which can provide theoretical support for energy saving and emission reduction of ports from the operational perspective.

关键词： Fuels Resource management Ports (computers) Legged locomotion Schedules Cats Autonomous agents

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An Object Frame Knowledge Representation Approach for Fault Diagnosis Expert System

An Object Frame Knowledge Representation Approach for Fault ...

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International Conference on Future computer Sciences and Application (ICFCSA)

作者： Bailin Liu Mingye Duan Gang Zhao School of Computer Science and Engineering Xi'an Technological University Xi'an China North Automatic Control Technique Institute Taiyuan China

Knowledge quality and usability is core to a fault diagnosis system. Frame knowledge technique has been used to represent knowledge in many information and expert systems. To simplify the complexity of knowledge representation in fault diagnosis expert system, a novel object-frame knowledge representation approach based on hierarchical model was proposed. In this approach, domain specific knowledge is expressed by combinations of object-frames. An Object-frame is composed by relevant state-object, test-object and rule-object or repair-object. Production rules are used to connect relevant objects' states. General features of object-frame and inference algorithm are introduced. Object-frame based knowledge items are stored in SQL database, inference engine performs the inference operation of knowledge using forward chaining strategy, implements reasoning, finds the cause of faults and gives repair suggestion driven by test data. Inference interpretation completes the task of explanation, which improved the clarity of reasoning. The advantage of this method is that we do not need knowledge representation language support. Experimental results show that the method proposed is effective, which improved the fault diagnosis and maintenance for a meteorological vehicle system.

关键词： Fault diagnosis Knowledge representation Expert systems Maintenance engineering Cognition Engines

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A novel design of parallel and high-speed signal processor architecture for PD radar

A novel design of parallel and high-speed signal processor a...

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IET International Radar Conference 2013

作者： Yuan, Changshun Wang, Jun Bi, Yanxian Zhang, Yuxian School of Electronic and Information Engineering Beihang University Beijing 100191 China Automatic Control and Computer Simulation Liaoning 125001 China

ISBN: (纸本)9781849196031

In this paper, a kind of high speed and parallel hardware architecture is designed with one TMS320C6678 and one XC6VSX315T. This system is used for the pulsed Doppler radar. To map the algorithm effectively, pipeline optimization on system and instruction levels are adopted, and various factors are taken into consideration, such as system complexity, communication between the processors, bandwidth of output signal.

关键词： Doppler radar

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On transfer function representation and frequency response of linear Active Disturbance Rejection control

On transfer function representation and frequency response o...

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Chinese control Conference (CCC)

作者： Congzhi Huang Zhiqiang Gao School of Control and Computer Engineering North China Electric Power University Beijing China Center for Advanced Control Technologies Cleveland State University Cleveland OH USA

The algebraic relationship between the transfer function and the state space representations of Linear Active Disturbance Rejection control (LADRC) is derived, upon which the unique frequency response characteristics of LADRC can be seen and understood. The transfer function form of LADRC for arbitrary order plant is derived from its state space representation and this allows that the open loop frequency characteristics of the LADRC based control system to be conveniently obtained and analyzed. The frequency response of LADRC not only shows excellent loop gain shape but also validates the original ADRC design concept: that the internal and external disturbances can be estimated in real time and cancelled. This time, the evidence comes not from simulation, or test, or time-domain analysis, but in the language of practicing engineers: frequency response.

关键词： Transfer functions Process control Frequency response Frequency estimation control systems Uncertainty Frequency control

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