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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151-160 订阅

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Aerodynamic Parameter Identification of Aircraft Based on GA-BP Algorithm 13

Aerodynamic Parameter Identification of Aircraft Based on GA...

引用

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

作者： Zhao, Yu Wang, Shihui Sun, You Chen, Si Lan, Tianpeng Zhang, Yingbin Beijing Aerospace Automatic Control Institute China Academy of Launch Vehicle Technology Beijing100039 China

ISBN: (纸本)9798350361674

Aiming at the problem of aerodynamic parameter identification of a certain type aircraft, the GA-BP algorithm is used to identify the coefficients of aerodynamic force. Firstly, the coefficients are solved by using flight trajectory data obtained from wind tunnel simulation experiments. then, the relationships between aerodynamic force coefficients and Mach, angle of attack, rudder pendulum angle, etc. are fitted by BP neural network. the thresholds and weights between neurons are optimized by genetic algorithm during the training of neural network, so that the optimal structure of neural network can be obtained through training of multiple sets of flight trajectory data under different flight conditions. Simulation results show that the aerodynamic force coefficients obtained by the GA-BP algorithm have higher accuracy compared with traditionally used engineering methods. © 2024 ieee.

关键词： Flight paths

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Mobile Robot Navigation with Incremental Self-Organizing Developmental Network Learned Based on Modified RRT 13

Mobile Robot Navigation with Incremental Self-Organizing Dev...

引用

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

作者： Shi, Mengchao Wang, Dongshu School of Electrical & Information Engineering Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9798350361674

When a mobile robot performs tasks, it may encounter changeable unknown environments. When navigating in the changing unknown environment, mobile robot should be able to learn incrementally to gradually improve their navigation capabilities. Considering that the fixed network structure will limit the continuous learning ability of the robot, this paper proposes an incremental self-organized developmental network, which does not need to define the number of neurons in advance, and its memory capacity adaptively increases with the learned knowledge. the network is employed to learn continuously in multiple complex environments without forgetting previously learned knowledge and without neuronal redundancy or insufficiency. In addition, this paper integrates the Rapidly Exploring Random Tree (RRT) algorithm into autonomous continuous learning process of incremental self-organizing developmental network, using the modified RRT algorithm to perform a global search of the environment to obtain a feasible path that the network can learn. this method can target the information in the specific environment, reduce the sample data for training the developmental network, improve the learning efficiency, and the network can show better generalization ability after continuous learning. the experimental results validate the effectiveness and potential of the proposed network as well as its learning method. © 2024 ieee.

关键词： Adversarial machine learning

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data Preprocessing Method for Landslide Displacement Prediction Based on XG Boost 13

Data Preprocessing Method for Landslide Displacement Predict...

引用

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

作者： Luo, Shuang El, Xinhua Li, Xiaoli Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9798350361674

In machine learning-driven landslide prediction models, data quality directly determines the accuracy of landslide predictions. Currently, data preprocessing methods mainly involve fitting the data using moving averages or least squares methods. While these methods are effective and convenient for processing global data, they face limitations when dealing with local data during heavy rainfall events. the sharp increase in cumulative landslide displacement during significant rainfall makes it challenging for overall data preprocessing methods to effectively handle local data, leading to potential data loss and compromising data quality. To address this limitation, this paper proposes a segmented data processing approach. During periods of heavy rainfall, cubic spline interpolation is employed to interpolate missing data, providing a better representation of data variability. For smaller rainfall amounts, linear interpolation is utilized. Finally, the LOESS smoothing method is applied to further enhance data quality. these meticulous data processing steps aim to accurately capture the changing trends in rainfall-induced landslides. By providing more reliable inputs to machine learning prediction models, this approach seeks to improve the accuracy and reliability of landslide predictions. © 2024 ieee.

关键词： data accuracy

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FedAWS: A Federated Tuning Approach with Adaptive Weight Shrinking for Pre-trained Foundation Models 13

FedAWS: A Federated Tuning Approach with Adaptive Weight Shr...

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

作者： Cheng, Yubin Song, Zhihuan Jiang, Xiaoyu Yao, Le Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Hangzhou310027 China School of Mathematics Hangzhou Normal University Hangzhou China

ISBN: (纸本)9798350361674

In recent years, pre-trained foundation models have developed rapidly and have been applied to various downstream tasks through fine-tuning methods. However, due to the existence of data silos, the lack of data for fine-tuning has become a vital issue preventing the application of the foundation model to industry. In addition, the high complexity of the network makes the foundation model prone to overfitting when used for data-constrained industrial tasks. therefore, a federated tuning approach with adaptive weight shrinking, named FedAWS, is proposed for collaborative fine-tuning between factories. FedAWS adaptively selects the weight-shrinking factor to reduce overfitting and improve the generalization of the global model without relying on additional data sets or human experience. the case study on an industrial dataset demonstrates the effectiveness of FedAWS. this successful validation provides a new way for inter-factory collaboration to apply the foundation model to industry, thus facilitating the efficient utilization of industrial data. © 2024 ieee.

关键词： Federated learning Fine-tuning Foundation Model Weight Shrinking

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Nonlinear Dynamic System Based on SAE-LDS Model for Fault Diagnosis 13

Nonlinear Dynamic System Based on SAE-LDS Model for Fault Di...

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

作者： Tang, Xiaochu Zhang, Yi Zhang, Xinmin Song, Zhihuan School of Automation Shenyang Aerospace University Liaoning Province Shenyang110136 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Zhejiang Province Hangzhou310027 China

ISBN: (纸本)9798350361674

Dynamic, uncertainty and nonlinearity are three very important characteristics of complex industrial process data. In this paper, a method of fault detection and classification based on stacked autoencoder-linear dynamic system (SAE-LDS) model is proposed. this method aims to simultaneously consider multiple characteristics of industrial process data for fault detection and classification. Firstly, the deep learning algorithm SAE is used to mine the nonlinear characteristic of process data. On this basis, the SAE model is embedded into the LDS model. the LDS model is used to further extract the dynamics and uncertainty, so as to establish a SAE-LDS model that can comprehensively consider multiple characteristics of data. Finally, the SAE-LDS model is applied to the TE process, compared with the traditional method for fault detection and fault classification. the experimental results show that SAE- LDS model has good effectiveness. © 2024 ieee.

关键词： data-driven fault classification fault detection linear dynamic system stacked autoencoder

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Application of ADRC in Stability control of Tank Gun System 7

Application of ADRC in Stability Control of Tank Gun System

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Lei, Yang Xu, Jing Hao, Qiang China North Vehicle Res Inst Beijing 100072 Peoples R China

ISBN: (纸本)9781538626184

Overcoming the disturbance of vehicle body is an important function of the tank gun control system. therefore, the tank artillery can be stabilized to a given angle in space. However, due to the change of load factors such as inertia, friction and clearance, the stability precision of the gun control system cannot be further improved, and hitting probability will drop under the condition of tank high speed maneuverability. In this paper, the tank gun control system and the disturbance model were analyzed, and the active disturbance rejection controller was designed. the control performance of this active disturbance rejection controller was verified by MATLAB simulation and the experiments of analog prototype. the results showed that the application of the ADRC is more effective than the traditional PID controller in improving the stability precision of gun control system.

关键词： active disturbance rejection control (ADRC) tank gun control system stable control stability precision

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Cooperative Awareness Message Generation Interval Prediction Model Based on Bayesian Optimized Long Short-Term Memory Neural Network 13

Cooperative Awareness Message Generation Interval Prediction...

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

作者： Zhang, Xiaoping Feng, Hui Wang, Li School of Electrical and Control Engineering North China University of Technology Beijing100044 China University of Cambridge Department of Engineering CambridgeCB2 1PZ United Kingdom

ISBN: (纸本)9798350361674

the prediction accuracy of the generation interval of Cooperative Awareness Message (CAM) is known to directly affect the accuracy of reserved communication resources for CAM in cellular vehicle-to-everything (C-V2X) systems. In order to improve the prediction accuracy of CAM generation interval, a prediction model based on the Bayesian optimized long short-term memory neural network (BAO-LSTM) is proposed. the model utilizes LSTM to predict the generation interval of CAM while employing the Bayesian optimization algorithm iteratively to optimize the hyperparameters of LSTM until they reach their optimal values. the pre-processed Next Generation Simulation (NGSIM) dataset is used to verify the performance of the BAO-LSTM model. Experimental results demonstrate that the BAO-LSTM model outperforms both of the BP model and the LSTM model without the Bayesian optimization process in terms of capturing, and constructing patterns and rules for the generation time interval of CAM. © 2024 ieee.

关键词： Prediction models

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Distributed control Problems of Second-Order Directed Signed Networks Based on Sampled data 13

Distributed Control Problems of Second-Order Directed Signed...

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

作者： Lil, Jinchao Zhang, Yi Du, Mingjun Pang, Shaopeng Ji, Peng Shandong Province Jinan250353 China

ISBN: (纸本)9798350361674

In this study, the bipartite consensus problem of directed signed network based on sampled data is studied. A control protocol for second-order signed network is proposed, which is based on sampling data. By leveraging principles of the Laplacian matrix and graph theory, the protocol is derived, and the necessary and sufficient conditions for achieving bipartite consensus and state stability are identified. the research findings suggest that bipartite consensus can be reached in structurally balanced network topologies with the correct choice of sampling period and parameters, whereas state stability is achievable in structurally unbalanced configurations. In addition, simulation examples are used to verify the accuracy of the derived results. © 2024 ieee.

关键词： bipartite consensus Sampled data signed network structural balance

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data-driven Adaptive Predictive Cooperative control for Multi-Robot Aircraft Skin Measurement 13

Data-Driven Adaptive Predictive Cooperative Control for Mult...

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

作者： Zhang, Xueming Tan, Haoran Jiang, Xing Wang, Yaonan Wang, Gang College of Electrical and Information Engineering Hunan University Yuelu District Hunan Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Changsha410082 China School of Automation Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9798350361674

this paper presents a novel approach to addressing the relative position problem in aircraft skin measurement based on data-driven predictive cooperative control (DDPCC) for multi-robot systems. Specifically, the study focuses on the interaction between two compound mobile robots, namely the tracker and scanner, and transforms the relative position problem into a formation control problem involving two robot chassis and a virtual leader. Leveraging the principles of predictive control, we introduce a control input criterion function, by incorporating the cross-tracking error between each robot and its neighbors, as well as its anticipated control input increment in the future. It turns out that the controller design and stability analysis of this DDPCC approach do not require explicit mathematical models. through extensive simulations, we demonstrate the effectiveness and practicality of the proposed strategy. © 2024 ieee.

关键词： Multipurpose robots

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A Roadside Vehicle Line Pressing Detection Algorithm Based on YOLOv7 13

A Roadside Vehicle Line Pressing Detection Algorithm Based o...

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

作者： Wang, Yuquan Feng, Shuhui Wang, Li School of Electronics & Control Engineering North China University of Technology Beijing100144 China

ISBN: (纸本)9798350361674

In order to solve the problem of inaccurate vehicle line pressing detection and recognition, a roadside vehicle line pressing detection algorithm based on YOLOv7 is proposed. Firstly, we utilize the YOLOv7 detection method to select the vehicle object in the video, and employ the keypoint estimation method to obtain the vehicle's reference point information. then, the parking line detection method was used to obtain the parking location information. Finally, according to the information about vehicles and parking spaces, we propose an algorithm to judge the vehicle line pressing by comparing the intersection of the vehicle and the parking space. Experiment results show that this algorithm has good detection performance on 294 test images. Compared with the existing algorithm, it does not require collecting the motion trajectory of the vehicle, and can be effectively applied to roadside parking scene. © 2024 ieee.

关键词： Line pressing detection Roadside parking Vehicle detection YOLOv7

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