2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)最新文献_第9页

Obstacle Avoidance Path Planning and Motion Control for a Multi-Joint Soft Manipulator 多关节柔性机械臂避障路径规划与运动控制

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858538

Zhiyan Cao, Tianyu Huang, Zhiwen Bao, Yangmin Xie, Hang Shi

引用次数: 2

Design of encrypted secure wireless video real-time transmission and storage system based on 5G network 基于5G网络的加密安全无线视频实时传输存储系统设计

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858430

Hong Xiao, Jingkang Xia, Guangming Tang, Deqing Huang

{"title":"Design of encrypted secure wireless video real-time transmission and storage system based on 5G network","authors":"Hong Xiao, Jingkang Xia, Guangming Tang, Deqing Huang","doi":"10.1109/DDCLS55054.2022.9858430","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858430","url":null,"abstract":"In order to ensure the real-time transmission and storage of video data collected by UAV camera and the security of system control commands, an encrypted and secure video transmission and storage system based the on 5G network is studied and designed in this paper. The 5G video transmission device of the system uploads the camera video data to the Linux server in H.264 compression mode and non-compression mode through the 5G network, respectively. The server saves the compressed video data in MP4 format and forwards the non-compression video data to the back-end playback device for real-time playback. The back-end video playback device can also send encrypted control commands to the 5G video transmission device. Finally, from experiments in the 5G SA environment, it can be found that the average transmission rate of uncompressed video data is 109Mb/s, the playback frame rate is 22fps, and the end-to-end playback delay is 51ms. In addition, the server's storage space for storing compressed video data consumes an average of 90Kb/s, and the average transmission delay of the system encryption command is 19ms. These performances meet the application requirements of military UAVs and civilian UAVs.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122083009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved Path Planning Algorithm Based on RRT Algorithm and Quintic B-spline Curve 基于 RRT 算法和五次 B-样条曲线的改进路径规划算法

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858552

Duo Zhao, Wen-Tsai Huang

引用次数: 0

Attention Based CNN-LSTM Network for Anomaly Pattern Classification of Multivariate Time Series 基于注意力的CNN-LSTM网络多变量时间序列异常模式分类

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858500

Xianbo Zhang, Chao Wang, Jing Zhang, Feng Lin, Zezhou Li

{"title":"Attention Based CNN-LSTM Network for Anomaly Pattern Classification of Multivariate Time Series","authors":"Xianbo Zhang, Chao Wang, Jing Zhang, Feng Lin, Zezhou Li","doi":"10.1109/DDCLS55054.2022.9858500","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858500","url":null,"abstract":"The multivariate time series (MTS) is observed in various fields nowadays so as to monitor the operating status of equipment like server machines, satellites, engines, etc. Tasks like anomaly detection and anomaly pattern classification of MTS, accordingly, are of great importance to fault warning and system stability. Data-driven methods are increasingly becoming prevailing regarding these problems with a huge amount of input information. In this paper, we propose a new architecture where MTS is processed as images and fed into an attention-based CNN-LSTM network (AC-LSTM). CNN-LSTM network is responsible for extracting both spatial and temporal features among and within each dimension of MTS, while the modified attention mechanism helps promote reasonable weight allocations without the introduction of additional parameters. The proposed model firstly gives the result of anomaly detection and makes a further decision on anomaly classifications if the input is detected as an abnormal one. With the modified attention mechanism and the cooperation of CNN and LSTM, experiments on both public datasets and the operation data collected from real-world applications prove the effectiveness and promotion of the proposed model.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128396595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diabetic Retinopathy Diagnosis Based on Transfer Learning and Improved Residual Network 基于迁移学习和改进残差网络的糖尿病视网膜病变诊断

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858557

Rongmin Chai, Di Chen, Xin Ma, Shengyan Liu, Yi Wang, Youqing Wang

引用次数: 0

Discrete-time Recurrent Neural Network Algorithm with Different Discretization Formulas for Finding Solution of Discrete-Time Complex Division 不同离散化公式的离散时间递归神经网络算法求解离散时间复除法

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858546

Yang Shi, Jiwen Lu, Wenhan Zhao, W. Sheng, Jian Li, Dimitrios Gerontitis

引用次数: 0

Stability of Linear Hyperbolic Distributed Parameter Systems Based on Event-Triggered Control 基于事件触发控制的线性双曲型分布参数系统的稳定性

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858478

M. Ni, X. Dai, Jiaming Zhang, Qiqi Wu, Huang Zuo

引用次数: 0

Iterative Learning Model Predictive Control for Lateral Control of Autonomous Vehicles 自动驾驶汽车横向控制的迭代学习模型预测控制

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858355

Yanfang Chen, Xuefang Li

引用次数: 0

Fast Configuration Identification and Matching Gains of RFA Based on Deep Learning Technology 基于深度学习技术的RFA快速组态识别与匹配增益

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858488

Yong Peng, Chunxi Yang, Yiming Li, J. Na

{"title":"Fast Configuration Identification and Matching Gains of RFA Based on Deep Learning Technology","authors":"Yong Peng, Chunxi Yang, Yiming Li, J. Na","doi":"10.1109/DDCLS55054.2022.9858488","DOIUrl":"https://doi.org/10.1109/DDCLS55054.2022.9858488","url":null,"abstract":"In order to expand the application scene of rotorcraft UAVs, a reconfigurable flight array(RFA) has been proposed, which can be quickly assembled into different configurations to adapt complex environments. However, the flight stability of RFA depends on the controller, which depends on the model, and the models of different configurations are inconsistent. In this paper, we first give a general dynamic model of reconfigurable flight array, which shows that obtaining the coordinates of each unit module is the key to design stable control schedule. Then, a target detection algorithm is employed to identify the current configuration and the unit modules as to find the coordinates of each unit module. Finally, the tracking controllers of the two configurations are matched according to the configuration information of visual recognition, and the module coordinates obtained by visual recognition and manual recognition are simulated and compared. The results show that the method proposed in this paper can identify the right configuration quickly and match suitable tracking controller for RFA to achieve fast and stable control.","PeriodicalId":412672,"journal":{"name":"2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114843615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust ILC Approach for 2-D Linear Time-varying Continuous-discrete Systems 二维线性时变连续离散系统的鲁棒ILC方法

2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2022-08-03 DOI: 10.1109/DDCLS55054.2022.9858534

Kai Wan, Xiaohui Wei, Dafeng Long, Qing-Yuan Xu

引用次数: 0