2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC)最新文献_第8页

Embedding-Based Asynchronous Entity Classification Algorithm Framework for the Defect Knowledge Graph of Distribution Network Equipment 基于嵌入的配电网设备缺陷知识图异步实体分类算法框架

2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2022-11-19 DOI: 10.1109/yac57282.2022.10023892

Yu Wang, Dapeng Yan, Peiqi Hou, Gang Chen, Hui Cao

引用次数: 0

Research on Hierarchical Consensus Fault-tolerant Control Strategy for Large-scale Facilities 大型设施层次共识容错控制策略研究

2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2022-11-19 DOI: 10.1109/YAC57282.2022.10023631

Baoran An, Huai Wu, Qinghe Zhou

引用次数: 0

Feature consistency anomaly detection method based on adversarial training deep autoencoder 基于对抗性训练深度自编码器的特征一致性异常检测方法

2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2022-11-19 DOI: 10.1109/YAC57282.2022.10023751

Cheng Lai, P. Jia

{"title":"Feature consistency anomaly detection method based on adversarial training deep autoencoder","authors":"Cheng Lai, P. Jia","doi":"10.1109/YAC57282.2022.10023751","DOIUrl":"https://doi.org/10.1109/YAC57282.2022.10023751","url":null,"abstract":"Real-time and accurate fault diagnosis is essential to ensure the safe and stable operation of industrial systems. However, most of the monitoring data collected by sensors in actual industrial sites are obtained when the system is operating in a healthy state, so it is difficult to obtain abnormal data with fault labels. Therefore, it is of great practical significance to carry out research on unsupervised anomaly detection during equipment operation. This paper proposed an anomaly detection method based on the consistency of features of adversarial training deep autoencoder. This method fed two random training sets into two deep autoencoder network, and designed loss functions by the error of input and output consistency and the error defined by the degree of feature inconsistency. Then the network parameters are updated by back-propagation through adversarial training of the two deep autoencoder network, and then the anomaly score is obtained by using the weighted sum of the generated and discriminated losses, and finally unsupervised anomaly detection is performed by the discrepancy of the anomaly score. The efficiency of the proposed method is verified by the data set of gearboxes.","PeriodicalId":272227,"journal":{"name":"2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131378747","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 diagnosis of rolling bearings based on improved multiscale diversity entropy (MDE) 基于改进多尺度多样性熵的滚动轴承故障诊断

2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2022-11-19 DOI: 10.1109/YAC57282.2022.10023780

Qinyu Lei, K. Che, Fan Gao, Guo Xie, Ning Han

{"title":"Fault diagnosis of rolling bearings based on improved multiscale diversity entropy (MDE)","authors":"Qinyu Lei, K. Che, Fan Gao, Guo Xie, Ning Han","doi":"10.1109/YAC57282.2022.10023780","DOIUrl":"https://doi.org/10.1109/YAC57282.2022.10023780","url":null,"abstract":"In multiscale diversity entropy (MDE), after multiscale coarse-grained reorganization, the data distance between the reconstructed subsequences is too close, and the difference decreases after average operation, the feature extracted by this coarse-grained method is not conducive to fault classification. To solve this problem, in this study, an improvement on the multiscale coarse-graining process by adding a sliding factor is proposed, and a reasonable range of sliding factor value is set according to the value of scale factor. Under the same scale factor, the original sequence is coarsely reconstructed by using multiple values within the value range of the sliding factor, then multiple entropy values are calculated, finally, average the MDE entropy value calculated for many times as the result. Such coarsening method avoids the distance between data too close or too far, and makes entropy calculation more accurate. The fault diagnosis framework of coarse-grained improved MDE combined with extreme learning machine (ELM) and support vector machine (SVM) respectively is constructed, and the algorithm is verified with German Paderborn bearing data set. Compared with MDE, the diagnosis accuracy of coarsegrained improved MDE combined with the same machine learning method has increased significantly.","PeriodicalId":272227,"journal":{"name":"2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122019496","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

Exploration of Machine Vision Curriculum Construction Facing on Industry Demand and Talent Training Demand of Higher Education 面向行业需求和高等教育人才培养需求的机器视觉课程建设探索

2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2022-11-19 DOI: 10.1109/YAC57282.2022.10023841

Liu Chaoqun, Cai Zhenhua, Luo Jie

引用次数: 1

Daily natural gas load forecasting based on sequence autocorrelation 基于序列自相关的天然气日负荷预测

2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2022-11-19 DOI: 10.1109/yac57282.2022.10023872

Xin Xiang, Jiayu Shen, Kaixiang Yang, Guoming Zhang, Jiren Qian, Chengyuan Zhu

{"title":"Daily natural gas load forecasting based on sequence autocorrelation","authors":"Xin Xiang, Jiayu Shen, Kaixiang Yang, Guoming Zhang, Jiren Qian, Chengyuan Zhu","doi":"10.1109/yac57282.2022.10023872","DOIUrl":"https://doi.org/10.1109/yac57282.2022.10023872","url":null,"abstract":"As low-carbon clean energy, natural gas plays an essential role in the energy transition, and the demand for natural gas is increasing rapidly. Natural gas load forecasting can not only understand the gas consumption characteristics of users, but also guide the formulation of natural gas pipeline network construction and dispatching plans. It is of great significance to improve the economic benefits of energy enterprises. The construction time of the natural gas pipeline network in Zhejiang Province is short, the amount of data for new users is small, and some external factors are difficult to obtain, which significantly affects the accuracy of load forecasting. To address the above issues, we conduct research on natural gas load forecasting, and design a time series forecasting model based on serial autocorrelation. Moreover, we have investigated the “leverage effect” in the financial field, and discussed the heteroscedasticity phenomenon. The principle and applicability of the SARIMA model and the EGARCH model are analyzed. The design ideas and modeling process of the fusion model are introduced. Finally, extensive experimental results show that the designed prediction model can achieve better performance.","PeriodicalId":272227,"journal":{"name":"2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121227162","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: 2

A Discrete Design Method for single-phase CVCF PWM inverters 单相CVCF PWM逆变器的离散设计方法

2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2022-11-19 DOI: 10.1109/YAC57282.2022.10023716

Panfeng Cao, Cuilan Tan, Sheng Zhou, Zhihong Yang

引用次数: 0

On-line fault diagnosis of rotating machinery based on deep residual network 基于深度残差网络的旋转机械在线故障诊断

2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2022-11-19 DOI: 10.1109/YAC57282.2022.10023904

Dongyu Guo, Xiangshun Li, Fan Luo

{"title":"On-line fault diagnosis of rotating machinery based on deep residual network","authors":"Dongyu Guo, Xiangshun Li, Fan Luo","doi":"10.1109/YAC57282.2022.10023904","DOIUrl":"https://doi.org/10.1109/YAC57282.2022.10023904","url":null,"abstract":"Rotating machinery is an indispensable basic component in industrial applications. Because the working environment of rotating machinery often has severe conditions such as heavy loads, failures will inevitably occur. In the fault diagnosis of rotating machinery, the original time series signals often needs to be extracted by the time-frequency analysis method, which is very dependent on prior knowledge and manual experience. The deep neural network is an end-to-end fitting algorithm with self-learning capability, which can automatically find highdimensional features from the incoming signals or images, thereby avoiding the complex feature extraction process in traditional algorithms that requires a lot of experience. This research constructed a deep residual network model and proposed an online fault diagnosis method for rotating machinery. Using the data processing method of converting the multi-axis vibration time-series signal into a multi-channel sample matrix, 100% accuracy is obtained in the fault diagnosis tasks of the Case Western Reserve University bearing fault data and the pump fault data experimental platform. Compared with traditional CNN, the convergence speed and accuracy are improved. At the same time, compared with traditional fault diagnosis methods, this method reduces the feature extraction process that requires experience and has good generalization and applicability.","PeriodicalId":272227,"journal":{"name":"2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126248084","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: 1

Beta Oscillations Suppression in a Population Model of Parkinson’s Disease by Linear Delayed Feedback Control 线性延迟反馈控制抑制帕金森病群体模型中的β振荡

2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2022-11-19 DOI: 10.1109/YAC57282.2022.10023779

Jingyu Yang, Y. Che

引用次数: 0

Synchronous Distributed Receding Horizon Control for Formation of Nonholonomic Multi-Vehicle Systems 非完整多飞行器编队的同步分布式后退地平线控制

2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC) Pub Date : 2022-11-19 DOI: 10.1109/YAC57282.2022.10023630

Haoda Shi, Y. Li, Li Li

引用次数: 0