2022 China Automation Congress (CAC)最新文献_第7页

ESO-based backstepping control for air-breathing hypersonic vehicle with acceleration feedback 吸气式高超声速飞行器加速度反馈的eso反演控制

2022 China Automation Congress (CAC) Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10054978

Ge Yang, Shen Zhang, Xin Liu, Shan Li, Kaina Zhang, Richeng Guo

引用次数: 0

Distributed Q-learning Algorithm for Economic Dispatch of Smart Grid with Unknown Cost Functions 成本函数未知的智能电网经济调度分布式q学习算法

2022 China Automation Congress (CAC) Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10055962

Qian Xu, Chutian Yu, Xiang Yuan, Zao Fu, Hongzhe Liu

引用次数: 0

Prescribed-Time Backstepping Algorithms for Leader-Follower Multi-Agent Systems 领导-随从多智能体系统的规定时间反演算法

2022 China Automation Congress (CAC) Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10054783

Yingjiang Zhou, Yong Ding, Zicheng Yu, Guoping Jiang

引用次数: 0

Fuzzy adaptive event-triggered control for a class of uncertain nonlinear systems subject to actuator dead-zone 一类具有致动器死区不确定非线性系统的模糊自适应事件触发控制

2022 China Automation Congress (CAC) Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10055453

Yifan Hu, Wenhui Liu

引用次数: 1

Application of Motion Planning in UAVs: A Review 运动规划在无人机中的应用综述

2022 China Automation Congress (CAC) Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10055974

Qingpei Fan, Jun Meng

引用次数: 0

A Novel Robust Broad Nonlinear Representation CVA Method for Monitoring Blast Furnace Iron-making Process 一种新的高炉炼铁过程鲁棒广义非线性表示CVA方法

2022 China Automation Congress (CAC) Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10055768

Yuelin Yang, Chunjie Yang, Bo Yang, Yu Chen, Siwei Lou, Xiong Zhu

{"title":"A Novel Robust Broad Nonlinear Representation CVA Method for Monitoring Blast Furnace Iron-making Process","authors":"Yuelin Yang, Chunjie Yang, Bo Yang, Yu Chen, Siwei Lou, Xiong Zhu","doi":"10.1109/CAC57257.2022.10055768","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055768","url":null,"abstract":"Blast furnace iron-making process is one of the most crucial parts in iron and steel industry. Due to many interference factors and a series of complex physical and chemical reactions, abnormal furnace conditions often occur. The nonlinear characteristics hidden in the blast furnace data and the existence of large noise and outliers make it difficult to establish an effective monitoring model. In this paper, a novel robust broad nonlinear representation canonical variate analysis (RBNCVA) method is proposed to overcome the above problems. First, a feature extraction strategy is developed to extract robust broad nonlinear features based on stacked denoising autoencoder (SDAE). The robust broad nonlinear features can assist the model to cope with the complex nonlinearity and resist the interference of noise and outliers. Then canonical variate analysis (CVA) method is used to analyze the relationship between past and future feature vectors. Subsequently, control limits are computed through probability density functions defined by kernel density estimation. Finally, the practical blast furnace data is adopted to validate the effectiveness and robustness of the proposed method.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126320577","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

Attitude Feedforward Compensation Control for Multistage Satellite with Scanning Mechanism 带扫描机构的多级卫星姿态前馈补偿控制

2022 China Automation Congress (CAC) Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10055179

Mao Fan, Liang Tang, Xin Guan, Zixi Guo, Renjian Hao, Kebei Zhang

引用次数: 0

An Improved DANN-based Mixed Gas Booster Station Fault Diagnosis Method 改进的基于dann的混合气体增压站故障诊断方法

2022 China Automation Congress (CAC) Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10055032

Shuaiyi Liu, Fan Zhou, Ying Liu, Jun Zhao

{"title":"An Improved DANN-based Mixed Gas Booster Station Fault Diagnosis Method","authors":"Shuaiyi Liu, Fan Zhou, Ying Liu, Jun Zhao","doi":"10.1109/CAC57257.2022.10055032","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055032","url":null,"abstract":"A by-product gas booster station plays an important role in the steel production process, however, due to environmental and other factors can occur jump machine failure, affecting the normal steel production. This study proposes a fault diagnosis method based on one improved domain adversarial neural network (IDANN) for the by-product gas booster station. The method considers the influence of hidden variable factors such as discharge gas pressure and resonance on fault identification and designs an improved domain-adaptive network structure. Assessing the impact of the implicit variable injection ratio on the recognition accuracy of the model, the inter-class distance is adopted to optimize the parameters of the injected implicit variable factor ratio to maximize the inter-class distance. To verify the effectiveness in this study, the operating data of a steel plant LDG booster station is selected for experiments and compared with deep neural networks (DNN) and other fault diagnosis methods. The experimental results show that the recognition rate of this paper can reach 95% for the jump machine faults and the method of this paper has good robustness and network generalization ability.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128075624","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

Process Input Optimization for Continuous Crystallization of β form L-glutamic acid β型l -谷氨酸连续结晶工艺输入优化

2022 China Automation Congress (CAC) Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10055746

Siwei Yang, Haichen Yu, Mingyan Zhao, Tao Liu, X. Ni

引用次数: 0

MSDR-Net: Multi-Scale Detail-Recovery Network for Single Image Deraining MSDR-Net:用于单幅图像训练的多尺度细节恢复网络

2022 China Automation Congress (CAC) Pub Date : 2022-11-25 DOI: 10.1109/CAC57257.2022.10055299

Shuyu Han, Jun Wang, Zaiyu Pan, Zhengwen Shen

{"title":"MSDR-Net: Multi-Scale Detail-Recovery Network for Single Image Deraining","authors":"Shuyu Han, Jun Wang, Zaiyu Pan, Zhengwen Shen","doi":"10.1109/CAC57257.2022.10055299","DOIUrl":"https://doi.org/10.1109/CAC57257.2022.10055299","url":null,"abstract":"Rain streaks vary in size, direction, and density, resulting in serious blurring and image quality degradation, which often directly affect the downstream visual tasks. At present, many end-to-end image removal networks have achieved good results, but image details are often lost during processing. Therefore, we propose a novel detail-recovery network to solve this problem. Unlike the existing works, we regard image rain removal and detail restoration as two different tasks simultaneously. Specifically, we use two encoder-decoder networks to extract rain streaks and detailed features and design different feature extraction blocks for two encoder-decoder networks. Due to the different receptive fields of feature layers at different scales, the information extracted at each scale is also different. The tasks of image rain removal and detail restoration are considered from the multi-scale feature level. To better respond to the image details and take full advantage of semantic information of multi-scale features, rain removal and image detail restoration are carried out at different scales. The proposed method has been validated on datasets to verify its effectiveness.","PeriodicalId":287137,"journal":{"name":"2022 China Automation Congress (CAC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125677096","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