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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

共 716 条记录，以下是471-480 订阅

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Finding an Optimal Geometric Configuration for TDoA Location systems with ADWP-GWO Algorithm

Finding an Optimal Geometric Configuration for TDoA Location...

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data driven control and learning systems (ddcls)

作者： Jingyuan Li Huiwei Wang Yawei Shi Menggang Chen Huaqing Li Chuandong Li College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Anchor deployment is a necessary prerequisite for achieving high-accuracy indoor localization. In order to enhance the comprehensive positioning performance, a criterion for minimizing Dilution of Precision (DoP) is proposed to measure the positioning performance. Based on this, we model the anchor deployment as an optimization problem and propose an Adaptive Dynamic Weight Position Update Gray Wolf Optimization(ADWP-GWO) algorithm to solve the multivariate dimensionality problem. Two basic anchor deployment strategies are designed and validated by real-world experiments with the TDoA localization system. the experimental results indicate that the RMSE achieved on the basis of the ADWP-GWO algorithm deployment scheme is 0.3766m is better than the triangular deployment of 0.5447m and the rectangular deployment scheme of 0.5963m. this is considered as the best deployment which realizes high-precision positioning.

关键词： Location awareness learning systems Adaptation models Heuristic algorithms Diamonds Position measurement Minimization

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Fault Diagnosis Model of Suspension System Based on Robust Observer

Fault Diagnosis Model of Suspension System Based on Robust O...

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data driven control and learning systems (ddcls)

作者： Shangrongxi Sun Chunhui Dai Junhao Liu Hao Wang Xiaolong Li College of Intelligent Science and Technology National University of Defense Technology Changsha China Hunan Province Key Laboratory of Electromagnetic Levitation and Propulsion Technology Changsha China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In the discipline of control engineering, the field of fault diagnosis technology stands as a critical component for ensuring system stability and safety. this research concentrates on fault identification and treatment within the suspension control system of a maglev train, providing a theoretical foundation for fault diagnosis through the establishment of a suspension module model encompassing both disturbance and potential faults. To bolster the robustness of fault diagnosis, a fault diagnosis strategy employing a robust observer is proposed, capable of not only detecting faults, but also isolating faults within actuators and sensors. Furthermore, this paper compares the application of sliding mode observer and adaptive observer in fault diagnosis, with results demonstrating that the adaptive observer exhibits superior performance in fault diagnosis. Building upon this, the paper delves into a discussion of a fault detection method that combines model and data-driven approaches, enhancing fault detection accuracy.

关键词： Fault diagnosis learning systems Adaptation models Magnetic levitation vehicles Fault detection Observers Stability analysis

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Bifurcation Analysis and Synchronization control of e-HR Neuron Model with Time Delay

Bifurcation Analysis and Synchronization Control of e-HR Neu...

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data driven control and learning systems (ddcls)

作者： Jingna Jiang Dongsheng Sun Zunshui Cheng Yun Shang Youming Xin School of Mathematics and Physics Qingdao University of Science and Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Derived from the Hindmarsh-Rose (HR) neuron model, the extended Hindmarsh-Rose (e-HR) neuron model with time delay is constructed considering the effect of intracellular ion transfer and the inherent slow current delay on the change of neuron membrane potential. Firstly, the stability of the model is analyzed by using the Routh-Hurwitz criterion, and the conditions for the existence of Hopf bifurcation are derived by using the Hopf bifurcation theorem. Secondly, through numerical simulation, it is found that synchronization occurs in the limited range of delay value without control. therefore, this paper introduces a nonlinear feedback control scheme, designed based on a Lyapunov-Krasovskii functional with a double integral term. the derived sufficient conditions for the synchronization of the master-slave neuron model with self-feedback delay are presented. Finally, the results of theoretical analysis are verified by numerical simulation and the effectiveness and viability of the controller are proved.

关键词： Analytical models Neurons Stability criteria Bifurcation Numerical simulation Main-secondary Numerical models

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Destination Floors-Oriented Group Elevators Dispatching Algorithm Based on Q-learning and Simulated Annealing Method

Destination Floors-Oriented Group Elevators Dispatching Algo...

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data driven control and learning systems (ddcls)

作者： Rongxi Liang Yunjian Peng Kun Zhang School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Optimal dispatching strategies wield a pivotal influence on improving the operational performance of modern hybrid group elevators, which directly contributes to the heightened operational efficiency of the elevators and an improved traveling experience for passengers. Existing elevator dispatching algorithms are struggling to cope with the intricacies and diversities present in real-world scenarios, especially in instances of peak hours in superstructures. Designing an elevator destination floor group dispatching algorithm has posed intricate challenges due to the complexity and non-linearity of elevator systems and unpredictability of the passenger flow. this paper proposes a novel algorithm for elevator destination floor group dispatching that skillfully combines the Q-Iearning algorithm and the simulated annealing algorithm in alignment with current state-of-the-art investigations within the domain. Numerical simulations are conducted to verify the effectiveness of the proposed composite algorithm in reducing the average waiting time of passengers comparing to the traditional proximity principle and conventional dispatching algorithm.

关键词： Q-learning Machine learning algorithms Simulated annealing Machine learning Predictive models Numerical simulation Elevators

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Optimization Scheme of Fuzzy control System based on Reinforcement learning Compensation

Optimization Scheme of Fuzzy Control System based on Reinfor...

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data driven control and learning systems (ddcls)

作者： Saichao He Jia'nan Liu Wenjing Hong Jia Shi Institute of Artificial Intelligence Xiamen University Xiamen Fujian P. R. China College of Chemistry and Chemical Engineering Xiamen University Xiamen Fujian P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Fuzzy control technology has been extensively studied and widely used in reality due to its ability to transform human control experience, described in vague terms, into automatic control law. However, when dealing with complex systems that require valuable control experience, fuzzy control technology may not necessarily guarantee optimal control performance. therefore, developing an advanced optimization scheme of fuzzy control systems is essential. In this paper, a model-free optimization scheme is developed for the fuzzy control system based on reinforcement learning algorithm, which is named Reinforcement learning Assisted Fuzzy control (RLAFC). the main feature of this scheme is that it can automatically obtain an effective compensation policy for the fuzzy controller by employing a data-driven reinforcement learning algorithm. Compared to the standalone reinforcement learning algorithm, the proposed method can achieve a synergistic combination of experiential and data-driven learning approaches, which brings about better control performance as well as improved safety assurance during the learning process. Finally, the simulation results of a car-following control task demonstrate the effectiveness of the proposed method. We believe that RLAFC is an effective solution for the control problems of complex systems by the combination of human control knowledge and artificial intelligence technology.

关键词： Fuzzy control Simulation Process control Optimal control Reinforcement learning Transforms Safety

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Exploring New Frontiers in Facial Expression Recognition: Dual DenseNet-201 and Landmark Distance Analytics in the Wild

Exploring New Frontiers in Facial Expression Recognition: Du...

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data driven control and learning systems (ddcls)

作者： Abdullahi Mohamed Hassan Xiaojun Zhou Laeeq Aslam School of Automation Central South University Changsha P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper presents a novel approach to Facial Expression Recognition (FER), a critical component in human-computer interaction. Unlike traditional methods focusing solely on facial image analysis, our methodology intricately processes the distances between facial landmarks, employing dual DenseNet-201 models. this technique allows for comprehensive feature extraction from both the facial images and landmark distance data. the integration of these features using a multi-head attention mechanism within a transformer model marks a significant advancement in FER technology. Our method demonstrates improvements in performance, particularly in recognizing “Sad” expressions, as evidenced by extensive testing on the AffectNet dataset. this research not only sets a new benchmark in FER but also opens avenues for its application in areas such as psychology, surveillance, and interactive technologies.

关键词： Human computer interaction learning systems Analytical models Image analysis Face recognition Surveillance Neural networks

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Synergistic Fusion of Multi-Contrast Imaging and Computational Image Synthesis for Quadruple Contrast-Enhanced Images and Multi-Map Generation 13

Synergistic Fusion of Multi-Contrast Imaging and Computation...

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13th ieee International conference on Communication systems and Network Technologies, CSNT 2024

作者： Dwivedi, Shashi Prakash Dasari, Kavitha Asha, V.A. Kansal, Lavish Alzubaidi, Laith H. Tiwari, Anurag Kumar Lloyd Institute of Engineering & Technology Uttar Pradesh Greater Noida201322 India Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Computer Applications Bangalore India Lovely Professional University Phagwara India The Islamic University Najaf Iraq Ies College of Technology Department of Electronics & Communication Engineering M. P. Bhopal India

ISBN: (纸本)9798350305463

Medical imaging has changed a lot since 'Synergistic Fusion of Multi-Contrast Imaging and Computational Image Synthesis for Quadruple ContrastEnhanced Images and Multi-Map Generation' came out. this innovative technology might revolutionize diagnostic imaging by providing a complete view of tissue features and enhancing accuracy. this study describes a system that utilizes ultrasound, PET, MRI, and CT data. Better detection is the goal of this technology. this first segment begins a new medical imaging age in which clinicians may view the full patient by combining data from diverse sources. the strategy might increase diagnosis accuracy, treatment focus, and healthcare quality. this research uses cutting-edge deep learning, feature extraction, and fusion techniques. DLESA synthesizes images using convolutional neural networks. Since no one does it by hand, errors are less likely. the Feature Extraction and Fusion Algorithm (FEFA) integrates significant features from several picture approaches to maximize their benefits. CERA and SAEA are essential for picture clarity and line accuracy. Reality reveals that the proposed way performs better than usual. PSNR (13.29%), SSIM (6.98%), CNR (11.61%), and Dice Coefficient (8.54%) have all increased. the technology may be utilized outside of healthcare since it employs science to mix data, learn deep, and extract characteristics. this strategy that integrates multiple disciplines of research might revolutionize how we obtain, analyze, and use information, leading to groundbreaking scientific, technological, and health advances. this novel technology advances diagnostic pictures and data-driven diagnostics, bringing accuracy and comprehension to medical and other disciplines. © 2024 ieee.

关键词： Deep learning

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Causal Graph Attention Networks for Quality Prediction of Mass Customization Production Process

Causal Graph Attention Networks for Quality Prediction of Ma...

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data driven control and learning systems (ddcls)

作者： Yun Wang Wenhao Hu Hairong Zhang Yuchen He Zhejiang Tongji Vocational College of Science and Technology Hangzhou College of Mechanical and Electrical Engineering China Jiliang University Hangzhou

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the quality prediction in the mass customization production process is a challenge because this manufacturing process is characterized by modular manufacturing and customized assembly, which make traditional quality prediction methods inapplicable. In this paper, a causal graph attention network (CGAT) is proposed to alleviate this problem. A quality prediction framework is proposed for the characteristics of mass customization production process. then, the mechanism and data are jointly used to construct the causal graph structure. Finally, the graph attention network is used for quality prediction. To verify the effectiveness of the proposed method, the method is tested on the automobile manufacturing simulation data set. the experimental results show that the model has great predictive performance on mass customization datasets.

关键词： learning systems Mass customization Manufacturing processes Process control Production Predictive models control systems

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Distributed control of Multiple Electric Springs based on Droop and Consensus control in Microgrid

Distributed Control of Multiple Electric Springs based on Dr...

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data driven control and learning systems (ddcls)

作者： Hao Wang Dezhi Xu Weilin Yang Wentao Huang Tinglong Pan School of Internet of Things Engineering Jiangnan University Wuxi P. R. China School of Electrical Engineering Southeast University Nanjing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Aiming to address frequency fluctuation and output power imbalances in renewable energy grid connections, this paper proposes a strategy for distributed multilevel control of Electric Springs (ESs) to stabilize bus node voltage and equalize output power. Primary control utilizes the backstepping method to stabilize critical load voltages. Secondary control employs a collaborative approach of droop and consensus adaptive virtual impedance to facilitate ES control with or without communication. Under normal circumstances, consensus control enhances output power control accuracy. In the absence of communication, droop control independently obtains node voltage for backstepping reference voltage correction. Simulation verification demon-strates the proposed method's fast dynamic response, high control accuracy, and robustness.

关键词： Backstepping Accuracy Collaboration Consensus control Stability analysis Robustness Communication networks

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A Transformer Based Network in Monocular Satellite Pose Estimation

A Transformer Based Network in Monocular Satellite Pose Esti...

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data driven control and learning systems (ddcls)

作者： YuanPeng Deng Wei Liu Yang Zhang JianHua Su ChuanKai Liu Beijing Information Science and Technology University Institute of Automation Chinese Academy of Sciences Beijing Aerospace Control Center

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Deep learning methods for optical camera-based pose position estimation are becoming more and more widely used with the improvement of computer hardware, such as target classification, target detection and other fields. One of the roles of deep neural network in pose estimation is to estimate the coordinates of keypoints. However, in some previous methods and applications, there are some drawbacks. For example, it can only be applied to scenes with small changes in distance and rotation angle or road 3D target detection that contains only one axis of rotation, and the accuracy of target estimation for multi-axis rotation is not high. In this paper, we present a monocular pose estimation network that integrates YOLO-v5 with a Transformer module. this approach transforms the pose estimation task into a keypoint Gaussian kernel regression problem, enhancing accuracy and robustness to scale variations.

关键词： Satellites Accuracy three-dimensional displays Roads Pose estimation Object detection Transforms

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