2021 3rd International Conference on Industrial Artificial Intelligence (IAI)最新文献_第9页

Difference of Gaussian Convolutional Sparse Principal Component Thermography for Defect Signal Enhance in Composite Materials 高斯卷积稀疏主成分热成像差分法增强复合材料缺陷信号

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619245

Wei Liu, Yuan Zhang, Le Zhou, Yuting Lyu

{"title":"Difference of Gaussian Convolutional Sparse Principal Component Thermography for Defect Signal Enhance in Composite Materials","authors":"Wei Liu, Yuan Zhang, Le Zhou, Yuting Lyu","doi":"10.1109/IAI53119.2021.9619245","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619245","url":null,"abstract":"Pulsed thermography (PT) is a well-established non-destructive testing technique for the subsurface defect detection in Carbon Fiber Reinforced Polymer (CFRP). Among the analysis methods for the thermographic data, principal component thermography (PCT) and sparse principal component thermography (SPCT) are recommended for visualization enhancement of defect signals. However, since the methods of PCT and SPCT are performed directly based on the characteristic matrix model of the original thermal images, their results are heavily affected by the noise and uneven background signals inside the images. To solve the problem above, a new method known as difference of Gaussian convolutional sparse principal component thermography (DoG-SPCT) is proposed in this paper. The method first separates defect signals from the interference with a DoG filter, and then extracts features for defective areas by SPCT to enhance visualization of defects. In the experimental part, one CFRP specimen with subsurface defects is detected by PT and the proposed DoG-SPCT is evaluated for the defect visualization enhancing purpose. The result of the experiment shows that the DoG filter can separate the defect components from the noise and uneven background signals, so that the features for defective regions can be effectively extracted in the following SPCT.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133483611","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

Research on Fault Diagnosis Method of Electric Valve Based on Convolutional Gated Recurrent Unit and Support vector machine 基于卷积门控循环单元和支持向量机的电动阀故障诊断方法研究

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619381

Qiang Deng, Hang Wang, Xiaokun Wang

{"title":"Research on Fault Diagnosis Method of Electric Valve Based on Convolutional Gated Recurrent Unit and Support vector machine","authors":"Qiang Deng, Hang Wang, Xiaokun Wang","doi":"10.1109/IAI53119.2021.9619381","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619381","url":null,"abstract":"Ensuring the safe operation of nuclear facilities has always been an important research topic in the development of nuclear energy. Therefore, a variety of methods have been proposed in the world for fault diagnosis of nuclear facilities to assist operators. In order to make full use of the characteristic information of time series data and improve the accuracy of fault diagnosis of electric valves in nuclear facilities, this paper proposes a new convolutional gated recurrent unit and support vector machine (CGRU_SVM) fault diagnosis network model. This model uses the convolution kernel to extract the features of the data, then uses the gated recurrent unit (GRU) to extract the timing features, and finally inputs the processed feature information into the support vector machine (SVM) for classification. Experiments have shown that the accuracy of this method for fault diagnosis of electric valves can reach more than 99.9%, for the failure to detect nuclear facilities electric valves, electric valves guarantee safe and reliable operation of guiding significance.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123848945","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

Research on an autonomous and controllable portable universal interface test platform 自主可控便携式通用接口测试平台的研究

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619437

Yiming Xu, Baoqiang Liu, Xiaoqiang Wang, Haitao Zhang, Zhongcai Zhang

{"title":"Research on an autonomous and controllable portable universal interface test platform","authors":"Yiming Xu, Baoqiang Liu, Xiaoqiang Wang, Haitao Zhang, Zhongcai Zhang","doi":"10.1109/IAI53119.2021.9619437","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619437","url":null,"abstract":"Industrial software testing including software development and debugging depends on the external input interface. The development, debugging and adaptation of interface software simulation consumes a lot of time. The process of software evaluation and self-test lack a portable general software testing equipment suitable for the industrial field, in order to greatly improve the testing efficiency, test integrity and adequacy. Therefore, it is urgent for the general interface generation platform to be transformed into high performance such as hardware, distributed, hardware interface adaptation, test task load and high real-time. In this paper, the overall design framework of portable general software test equipment is carried out, which includes the design and software development of the execution host, the software transformation of general control host and other research contents. At the same time, a portable general software testing equipment for complex industrial system software and multiple interfaces is developed. This platform can satisfy the diversity of complex industrial software system interfaces and the real-time requirements of special systems. It is expected to further promote the development of interface testing automation.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124725269","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

Research on Multi-target Recognition Algorithm of Pipeline Magnetic Flux Leakage Signal Based on Improved Cascade RCNN 基于改进级联RCNN的管道漏磁信号多目标识别算法研究

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619400

Xian-geng Shen, Jinhai Liu, He Zhao, Xiaoyuan Liu, Baojin Zhang

引用次数: 3

Research on Big Data Evaluation of China’s Public Cultural Service Level in the Internet Era 互联网时代中国公共文化服务水平大数据评估研究

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619288

Jiyang Yuan, Mengwen Zhang, Yumei Wang

引用次数: 0

Pre-specified-time Distributed Nash Equilibrium Seeking for Games 预先指定时间的博弈分布式纳什均衡寻求

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619392

Qianle Tao, Chengxin Xian, Yu Zhao

引用次数: 0

Study on the Integrated Optimization of Heating Furnace Production Process 加热炉生产过程集成优化研究

2021 3rd International Conference on Industrial Artificial Intelligence (IAI) Pub Date : 2021-11-08 DOI: 10.1109/IAI53119.2021.9619305

Tianyi Lu, Qiong Xia, Liangliang Sun, Yupeng Li, Wanying Zhu, Juan Wang, Baolong Yuan, Yi Pan

{"title":"Study on the Integrated Optimization of Heating Furnace Production Process","authors":"Tianyi Lu, Qiong Xia, Liangliang Sun, Yupeng Li, Wanying Zhu, Juan Wang, Baolong Yuan, Yi Pan","doi":"10.1109/IAI53119.2021.9619305","DOIUrl":"https://doi.org/10.1109/IAI53119.2021.9619305","url":null,"abstract":"The objective of this paper is to optimise the slab heating process in a dynamic environment. Considering the nonlinear and hysteresis characteristics of slab heating in the furnace production process, an operational optimisation model based on a mixture of mechanism and data and a predictive control model for the furnace are developed. The operation optimisation model determines the current optimal furnace temperature distribution based on the desired slab temperature and the current slab temperature, which is then fed into the predictive control model. The predictive control model uses a rolling optimisation method to predict the furnace temperature and adjusts the fuel flow to change the furnace temperature with the desired temperature as the target, thus enabling the slab to reach the desired temperature through an integrated optimisation method. Finally, a large number of simulation data experiments prove that the furnace temperature change process meets the set requirements, and the goal of improving the production process of the heating furnace is achieved.","PeriodicalId":106675,"journal":{"name":"2021 3rd International Conference on Industrial Artificial Intelligence (IAI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126187694","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