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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161-170 订阅

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Class-Imbalance and Client-Imbalance Federated learning for Fault Diagnosis 13

Class-Imbalance and Client-Imbalance Federated Learning for ...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Sun, Shu Qian, Jinchuan Zhang, Xinmin Song, Zhihuan College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350361674

With the development of big data, data-driven fault diagnosis models have experienced rapid growth. However, as the complexity of models increases, there is a corresponding rise in the demand for data. To obtain a substantial amount of data, traditional approaches involve centralizing datasets from multiple factories for training. However, due to heightened emphasis on data privacy from enterprises and regulatory bodies, this method becomes more challenging to implement and poses risks of data leakage. In response to this challenge, this paper designs a novel federated learning-based fault diagnosis modeling approach. Each local device trains its model on its local dataset and subsequently uploads updated model parameters to a central server for global model aggregation and updating. However, it is worth noting that in industrial processes, there may be concurrent class imbalance and data client imbalance, which brings challenges to fault diagnosis modeling based on federated learning. To address this challenge, we further propose a dynamic client aggregation fault diagnosis method based on category loss in federated learning. Experimental results indicate that the proposed approach not only protects data privacy in imbalanced class and client scenarios but also achieves outstanding fault recognition accuracy and robustness. © 2024 ieee.

关键词： Federated learning

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SFS-PSO: An Improved data Preprocessing Approach in Fault Diagnosis under Variable Working Conditions 13

SFS-PSO: An Improved Data Preprocessing Approach in Fault Di...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhang, Fanglue Yang, Rui School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China School of Electrical Engineering Electronics and Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9798350361674

this paper presents an approach for bearing fault diagnosis that leverages sensitive feature selection to tackle the challenge of high intra-class distances and low inter-class distances in data pre-processing of conventional deep transfer learning methods under variable working conditions. Initially, bearing fault signals are transformed from time to frequency domain using the Fast Fourier Transform. then, these frequency-domain features are refined through a selection process optimized by particle swarm optimization, focusing on those with higher sensitivity weights to reduce intra-class distance and enhance inter-class distance. these selected features are input into domain adversarial neural networks for accurate bearing fault diagnosis under different conditions. Experimental results demonstrate the method's effectiveness and increased diagnostic accuracy. © 2024 ieee.

关键词： Frequency domain analysis

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Intersection Access Decision Making of Robot Guide Dog via a Novel Deformable Attention Transformer-Based Detection Algorithm 13

Intersection Access Decision Making of Robot Guide Dog via a...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Liu, Shida Liu, Qingsheng Wang, Li Honghai, J. School of Electronics & Control Engineering North China University of Technology Beijing100043 China

ISBN: (纸本)9798350361674

Aiming at the problem of quadruped robotic dogs passing at intersections with low postures and much occlusion, which are not easy to recognize traffic signals, a novel Deformable Attention Transfrmer-based YOLO (DAT-YOLO) detection algorithm is proposed. the proposed DAT-YOLO method enhances the YOLO V5 framework by incorporating a dattention attention mechanism to the backbone section, which empowers the DAT-YOLO algorithm to detect traffic signals with enhanced precision. Moreover, the proposed algorithm was deployed and experimented on the practical Unitree Go1-type quadruped robot. By using the real dataset collected from the practical sidewalk scenario, the effectiveness and applicability of the proposed algorithm is verified via a series of experiments. © 2024 ieee.

关键词： Traffic signals

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Enhancing Autonomous Driving: A Scalable Multi-Layered DRL Approach for Decision and control 13

Enhancing Autonomous Driving: A Scalable Multi-Layered DRL A...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Wu, Liangguang Zhou, Kailong Fei, Ting Yao, Maojia Han, Hao Li, Honglin Dongfeng Motor Corporation Research and Development Institute Wuhan432000 China School of Automotive Engineering Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9798350361674

Autonomous driving technology is rapidly progressing, promising significant societal benefits. this paper presents an advanced DRL-based framework for end-to-end autonomous vehicle decision-making and control. Our technical proposal is articulated through a scalable, multi-layered architecture that integrates sensory data aggregation, trajectory prediction, and dynamic control layers. the framework is distinctive for its Actor-Critic approach, where the Critic network evaluates potential trajectories and the Actor network implements the control commands, ensuring safety and compliance with traffic norms. the adaptability and robustness of our system are validated in extensive simulations and real-world trials, demonstrating its efficacy across various sensory system configurations. this research contributes to the autonomous driving domain by providing a versatile and reliable framework for intelligent vehicle navigation. the methodologies and systems presented in this paper have undergone rigorous validation through simulation experiments and practical real-vehicle tests, confirming the viability and effectiveness of our advanced DRL-based framework for autonomous vehicle decision-making and control. © 2024 ieee.

关键词： Deep reinforcement learning

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data-driven Anomaly Monitoring for Nonstationary Industrial Process 13

Data-Driven Anomaly Monitoring for Nonstationary Industrial ...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhang, Xue Zhang, Lu College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9798350361674

With the changes of set points, external environment, and other factors, industrial processes are always operating under multiple conditions. the variation of the operating conditions easily causes the nonstationary characteristics, which is not conductive to the anomaly monitoring. To solve this problem, data-driven anomaly monitoring method, with the aid of kernel slow feature analysis and Gaussian mixture model, is proposed in this paper. Firstly, a data reorganization strategy is used to identify the changing operating conditions according to the statistical characteristics. then the static and dynamic data slices and segments can be divided. Secondly, kernel slow feature analysis is introduced to extract the features of these nonlinear data segments. then both slow and fast features can be obtained. thirdly, Gaussian mixture model is presented to approximate the non-Gaussian distribution of the extracted slow and fast features. then the evaluating index of these features, based on the Mahalanobis distance, is utilized to realize the anomaly monitoring. Finally, Tennessee-Eastman benchmark data are applied to evaluate the effectiveness of the proposed anomaly monitoring method. the results demonstrate that the proposed method can accurately monitor the anomalies under nonstationary operating process. © 2024 ieee.

关键词： Gaussian distribution

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Robustness Performance Improvement for Input-Delay systems: An Additive-State- Decomposition- Based control Method 13

Robustness Performance Improvement for Input-Delay Systems: ...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Ren, Jinrui Du, Jiaxi Che, Yu Quan, Quan School of Automation Xi'an University of Posts & Telecommunications Xi'an710121 China Xi'an Microelectronics Technology Institute Xi'an710000 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798350361674

Time delays exist in a large number of practical systems, and it is meaningful to study the robust performance improvement of time-delay systems. this paper proposes an additive-state-decomposition-based robust performance improvement control method for time-delay linear systems. First, additive state decomposition is performed on the studied time-delay system. then, a state feedback controller is designed based on the decomposed primary system. In addition, the small-gain theorem is also used to prove that the additive-state-decomposition-based control method can enhance the robustness of time-delay systems compared with the traditional state feedback control method. Finally, simulations verify that the method based on additive state decomposition outperforms the traditional state feedback control method in terms of robustness performance improvement. © 2024 ieee.

关键词： Delay control systems

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A Transformer Based Network in Monocular Satellite Pose Estimation 13

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

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13th ieee data driven control and learning systems conference, ddcls 2024

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

ISBN: (纸本)9798350361674

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. © 2024 ieee.

关键词： Deep neural networks

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A Brief Survey of Deep Reinforcement learning for Intersection Navigation of Autonomous Vehicles 13

A Brief Survey of Deep Reinforcement Learning for Intersecti...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Liu, Yuqi Zhang, Qichao Zhao, Dongbin Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798350361674

this paper presents a brief survey of deep reinforcement learning (DRL) for intersection navigation in autonomous driving. Intersection navigation poses significant challenges for autonomous driving (AD), considering the dynamic environment, high complexity, and safety requirements. DRL has emerged as a powerful learning framework in the field of AD, enabling agents to learn interactive driving policies in complex intersection scenarios. the paper begins by outlining the architecture of a general AD system. It then elaborates on the concepts of DRL. Additionally, the paper discusses emerging technologies such as social attention and graph attention mechanisms, which enhance the performance of DRL-based AD driving policies. Finally, the challenges and future prospects are also given. Challenges include complex intersection scenarios, generalization across different layouts, and the lack of standardized benchmarks and evaluation metrics. Overcoming these challenges and leveraging future advancements can lead to improved safety and efficiency in road traffic. © 2024 ieee.

关键词： Deep reinforcement learning

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A Novel Time Diffusion Model for Industrial Time Series data Generation 13

A Novel Time Diffusion Model for Industrial Time Series Data...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zheng, Chen Jiang, Xiaoyu Song, Zhihuan Zhang, Xinmin Shen, Bingbing College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China School of Mathematics Hangzhou Normal University Hangzhou311121 China

ISBN: (纸本)9798350361674

Given the intricate temporal correlations inherent in most process industry systems, temporal dynamic modeling methods based on time series data have demonstrated superior performance in the area of industrial data-driven modeling. However, due to constraints in data acquisition and maintenance methods, obtaining a substantial volume of high-quality and intact time series data from process industries is often challenging. To address this challenge, AI-Generated Content technologies (AIGC) can be employed to produce high-quality virtual data and ensure data sufficiency. this paper proposes a novel Time Diffusion Model (TDM) for the generation of industrial time series data. TDM introduces diffusion models to better capture the temporal correlations and data distributions of industrial time series data, enabling the generation of high-quality virtual time series data. this capability extends its applicability to time series data imputation and time series data augmentation tasks. the proposed TDM model is applied to the practical primary reformer process case. the experimental results substantiate that TDM can generate industrial time series data of high quality and effectively enhance the performance of industrial data-driven tasks. © 2024 ieee.

关键词： Spatio-temporal data

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A New Algorithm for Gap Metric Calculation Based on Frequency Response data 13

A New Algorithm for Gap Metric Calculation Based on Frequenc...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Ibrahim, Motaz Musa Ma, Lei Zhao, Yiming Cheng, Shaokun Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9798350361674

the gap metric is a very essential tool for robust stability analysis of control systems and fault tolerant control. It is widely calculated as a model base. However, it is challenging to derive a precise mathematical model that accurately represents the real system. Consequently, the validity of the gap computation based on the model can not be guaranteed. therefore, development of different approaches for gap calculation becomes more serious. Motivated by the aforementioned problem, we introduce a new algorithm for gap computation based on frequency response data. the data will be recorded in the time domain and then converted to the frequency domain using the fast Fourier transform. the frequency response data easily can be represented in the Euclidean plane and hence the gap at each frequency can be calculated since the the gap is the angular measure distance between two systems. Subsequently, the algorithm is constructed using the geometric interpretation of the gap and some properties of normed vector spaces. A comparative study with a model base is provided to demonstrate the effectiveness of the proposed algorithm. © 2024 ieee.

关键词： Frequency domain analysis

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