2023 6th International Symposium on Autonomous Systems (ISAS)最新文献_第5页

Research on autonomous control technology of group formation imitating the behavior of geese flock 模仿鹅群行为的群体形成自主控制技术研究

2023 6th International Symposium on Autonomous Systems (ISAS) Pub Date : 2023-06-23 DOI: 10.1109/ISAS59543.2023.10164399

Fukang Zhao, Yadong Feng, Xinhua Wang

引用次数: 0

Stability Analysis for Nonlinear Switched Positive Systems With Markov Chain Impulses 马尔可夫链脉冲非线性切换正系统的稳定性分析

2023 6th International Symposium on Autonomous Systems (ISAS) Pub Date : 2023-06-23 DOI: 10.1109/ISAS59543.2023.10164567

Mingzheng Yu, Ticao Jiao, Housheng Zhang, Lei Wang

引用次数: 0

A CNN-BiGRU Based Life Prediction Method for Rolling Pins of Rail Vehicle Door System 基于CNN-BiGRU的轨道车辆车门滚动销寿命预测方法

2023 6th International Symposium on Autonomous Systems (ISAS) Pub Date : 2023-06-23 DOI: 10.1109/ISAS59543.2023.10164474

Yefan Gan, N. Lu, Baoli Zhang, Jianfei Chen, Ling Sun, Yanling Ji

{"title":"A CNN-BiGRU Based Life Prediction Method for Rolling Pins of Rail Vehicle Door System","authors":"Yefan Gan, N. Lu, Baoli Zhang, Jianfei Chen, Ling Sun, Yanling Ji","doi":"10.1109/ISAS59543.2023.10164474","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164474","url":null,"abstract":"As a key mechanical component in the door system of rail vehicles, the rolling pin is closely related to the safe operation of the door system. For the purpose of maintaining the safety of the door system of rail vehicles, it is necessary to accurately predict the Remaining Useful Life (RUL) of the rolling pin. Since the degree of wear is difficult to measure, it is quite hard to predict its life in real time. Synchronously, the amount of data that can characterize the life of the rolling pin is rarely available. To predict the RUL of rolling pin online as well as provide decision support for active maintenance, this paper proposes an RUL prediction method of rolling pin based on the Convolutional Neural Network (CNN) and Bi-directional Gated Recursive Unit (BiGRU), which combines the feature extraction ability of CNN and the information retention ability of BiGRU, enabling this model to be effective in dealing with several small sample issues. The simulation results demonstrate that such a method can accurately predict the life of the rolling pin, which has essential engineering application value.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121867483","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

Accumulating Priority Queue for Charging of Unmanned Aerial Vehicles in Cognitive Radio Networks 认知无线网络中无人机充电的累积优先队列

2023 6th International Symposium on Autonomous Systems (ISAS) Pub Date : 2023-06-23 DOI: 10.1109/ISAS59543.2023.10164390

Yuran Li, X. Zhai, Ran Wang, Jing Zhu, Chenghua Yao

引用次数: 0

Carrier Aircraft Landing Control Technology Based on Deep Reinforcement Learning 基于深度强化学习的舰载机着陆控制技术

2023 6th International Symposium on Autonomous Systems (ISAS) Pub Date : 2023-06-23 DOI: 10.1109/ISAS59543.2023.10164523

Rendi Liu, Ju Jiang, Xiang Liu, Haowei Sun, Tingyu Ma

{"title":"Carrier Aircraft Landing Control Technology Based on Deep Reinforcement Learning","authors":"Rendi Liu, Ju Jiang, Xiang Liu, Haowei Sun, Tingyu Ma","doi":"10.1109/ISAS59543.2023.10164523","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164523","url":null,"abstract":"In this paper, a pitching control method based on Deep Deterministic Policy Gradient (DDPG) algorithm for carrier aircraft landing and descending stage is studied. DDPG controller takes pitch angle rate error, pitch angle error and altitude error as input, and output as elevator deflection, realizing the rapid pitch angle response of carrier-aircraft under different landing states. Compared with traditional PID controller, network training of Actor-Critic for DDPG attitude controller greatly improves the calculation efficiency of control quantity, and reduces the difficulty of parameter optimization. The simulation experiment in this paper was based on the F/A-18 aircraft aerodynamics model constructed in Matlab/Simulink, and the intensive learning and training environment built on PyCharm platform was used to realize the data interaction between the two platforms through UDP communication. The simulation results show that the attitude controller based on reinforcement learning designed in this paper has the characteristics of fast response speed and small dynamic error, and meets the control accuracy requirements in the experiment.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124661906","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

Task Management for Autonomous Flights of Micro Aerial Vehicles: A Behavior Tree Approach 微型飞行器自主飞行任务管理:一种行为树方法

2023 6th International Symposium on Autonomous Systems (ISAS) Pub Date : 2023-06-23 DOI: 10.1109/ISAS59543.2023.10164329

Chaoran Wang, Hai Zhu, Xiaozhou Zhu, C. Wu, Wen Yao, Xiaoqian Chen

引用次数: 0

Camera Calibration Method for Autonomous Navigation based on Space Non-cooperative Target Features 基于空间非合作目标特征的自主导航摄像机标定方法

2023 6th International Symposium on Autonomous Systems (ISAS) Pub Date : 2023-06-23 DOI: 10.1109/ISAS59543.2023.10164618

Bowen Hou, Han Yuan, Xuanying Zhou, Runran Deng, E. Wei, Ping Liu

引用次数: 0

Person Re-identification Lightweight Network Based on Progressive Attention Mechanism 基于渐进式注意机制的人物再识别轻量级网络

2023 6th International Symposium on Autonomous Systems (ISAS) Pub Date : 2023-06-23 DOI: 10.1109/ISAS59543.2023.10164341

Chunlei Shi, D. Niu, Hao Gong, Mei Zhang, Zhan Cao, Yulong Jin

{"title":"Person Re-identification Lightweight Network Based on Progressive Attention Mechanism","authors":"Chunlei Shi, D. Niu, Hao Gong, Mei Zhang, Zhan Cao, Yulong Jin","doi":"10.1109/ISAS59543.2023.10164341","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164341","url":null,"abstract":"This paper proposes a lightweight person re-identification network that incorporates a progressive attention mechanism The network aims to address the low accuracy issue in person re-identification caused by various factors such as different viewing angles, poses, illumination conditions, occlusions, and low image resolutions. Additionally, the network design takes into consideration the need for lightweight model deployment in practical scenarios. To improve the network’s performance using limited training data, data augmentation techniques are employed to expand the training dataset and enhance the robustness of the network model. The Resnet-50 architecture serves as the backbone network, and a feature shunt structure with depthwise separable convolutions is introduced to reduce computational parameters and accelerate person retrieval inference speed. Furthermore, the feature extraction and embedding processes are separated, and a progressive attention module is introduced. This module gradually segments the features into local blocks of different granularity, allowing for the learning of discriminative features at each granularity level. This progressive approach enhances the network’s ability to perceive foreground information from coarse to fine levels and improves feature matching capability. To supervise the model, a triplet loss function is utilized, specifically designed to address challenging samples. This loss function helps reduce intra-class variations while increasing inter-class separability. The efficacy of the proposed method in person re-identification is substantiated by conducting experimental evaluation on both the Market-1501 and DukeMTMC-ReID datasets. The experimental results demonstrate that the method achieves mAP indices of 88.1% and 79.1% on the respective datasets, providing strong evidence for its effectiveness in addressing the challenges of person re-identification.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126946214","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

Deep Learning Pedestrian Navigation Method Based on Multi-task Loss Function* 基于多任务损失函数的深度学习行人导航方法*

2023 6th International Symposium on Autonomous Systems (ISAS) Pub Date : 2023-06-23 DOI: 10.1109/ISAS59543.2023.10164517

Tao Wang, Jizhou Lai, Cheng Yuan, Jingyi Zhu, Qianqian Zhu, Pin Lyu

{"title":"Deep Learning Pedestrian Navigation Method Based on Multi-task Loss Function*","authors":"Tao Wang, Jizhou Lai, Cheng Yuan, Jingyi Zhu, Qianqian Zhu, Pin Lyu","doi":"10.1109/ISAS59543.2023.10164517","DOIUrl":"https://doi.org/10.1109/ISAS59543.2023.10164517","url":null,"abstract":"In recent years, indoor inertial navigation technology based on pedestrian dead reckoning (PDR) has been widely promoted. Traditional methods often use auxiliary facilities or environmental constraints to suppress PDR heading cumulative errors, but these auxiliary means restrict the application scope of PDR. PDR based on deep learning fills the need for external information dependence, but the heading estimation accuracy is low and the adaptability is poor. To address this problem, an optimized adaptive multitask loss layer based on uncertain weighting is proposed, which constrains the weight of position and attitude estimation in the overall prediction task and dynamically adjusts it adaptively in different stages to enhance attitude estimation capability. A PDR algorithm based on an end-to-end joint residual neural network and bidirectional long short-term memory network is designed to improve the algorithm’s generalization ability. The original inertial navigation data is processed by segmentation and coordinate normalization and is used as input to the deep learning model to detect features and predict trajectories, achieving accurate indoor pedestrian inertial navigation. Finally, the navigation performance of the proposed algorithm is validated in experiments of walking, running, and mixed gait patterns. The results show that the positioning accuracy of the proposed algorithm is better than that of traditional PDR methods and the RONIN algorithm based on deep learning. The positioning errors in walking, running, and mixed gait patterns are reduced by 21.07%, 10.34%, and 32.15%, respectively, compared to the RONIN algorithm.","PeriodicalId":199115,"journal":{"name":"2023 6th International Symposium on Autonomous Systems (ISAS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126431932","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

Fault Detection Filter Design for Polytopic Uncertain Systems in Finite-Frequency Domain 有限频域多面体不确定系统故障检测滤波器设计

2023 6th International Symposium on Autonomous Systems (ISAS) Pub Date : 2023-06-23 DOI: 10.1109/ISAS59543.2023.10164606

Rong Zhao, Lu Liu

引用次数: 0