{"title":"A PROPRIETARILY DEVELOPED BIONIC OLFACTORY SYSTEM USED FOR RAPID DETECTION OF DETERIORATED REFRIGERATED-STORED APPLES","authors":"Hui Tian, Wenshen Jia, Jie Ma, Jihua Wang, J. Hao","doi":"10.2316/J.2021.206-0616","DOIUrl":"https://doi.org/10.2316/J.2021.206-0616","url":null,"abstract":"","PeriodicalId":54943,"journal":{"name":"International Journal of Robotics & Automation","volume":"98 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81619504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"VEGETATION CLASSIFICATION BY MULTI-SCALE HIERARCHICAL SEGMENTATION ON GF-2 REMOTE SENSING IMAGE","authors":"Yang Wugu, T. Weixin, M. Lei","doi":"10.2316/J.2021.206-0617","DOIUrl":"https://doi.org/10.2316/J.2021.206-0617","url":null,"abstract":"","PeriodicalId":54943,"journal":{"name":"International Journal of Robotics & Automation","volume":"8 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73259407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yingxin Zhou, Zhouxiang Chen, Z. Qian, J. Hou, Dejun Hu
{"title":"SAFETY ANALYSIS OF HOLLOW PLATE GIRDER BRIDGE BASED ON ROUTINE DETECTION INDEXES","authors":"Yingxin Zhou, Zhouxiang Chen, Z. Qian, J. Hou, Dejun Hu","doi":"10.2316/J.2021.206-0532","DOIUrl":"https://doi.org/10.2316/J.2021.206-0532","url":null,"abstract":"","PeriodicalId":54943,"journal":{"name":"International Journal of Robotics & Automation","volume":"29 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81778069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
intelligence an important role in the automation field, which can to intelligent decision-making of human beings. AI has wide from researchers and has been applied to almost all aspects of human life. A series of algorithms and models of AI have been used to promote the innovation of product/service in the field of environmental automation systems. On the other hand, in the field of environment and geology, remote sensing data and internet of things (IoT) sensing data are typically used for research and analysis. By combining big data with AI algorithms, issues such as environment change, ecological status as-sessment, geological disaster prediction, and data mining can be done to assist decision-making and other work. The purpose of this special issue is to explore the current research direction of AI applied to environmental automation systems, including environment change monitoring, ecological status assessment, geological disaster prediction, and data mining. In this issue, seven papers regarding AI in environmental automation systems are selected by peer view. These papers present several theoret-ical and practical problems related to AI in environmental automation systems, as well as the analysis, new discov-eries, and innovative ideas and improvements made in the field of AI in environmental automation systems. Subjects of the seven papers include: AI models for ecology or geology (environment and hazardous), machine learning models for ecology or geology, expert systems for ecology or geology, deep learning for ecology or geology, intelligence image processing algorithms for ecology or geology, big data analytics for data processing from ecology or geology, applications of AI in ecology or geology, data fusion for change detection, geological disaster prediction, and forest deforestation monitoring.
{"title":"GUEST EDITORIAL: ARTIFICIAL INTELLIGENCE IN ENVIRONMENTAL AUTOMATION SYSTEMS","authors":"Dong Ren, Bin Li","doi":"10.2316/J.2021.206-0620","DOIUrl":"https://doi.org/10.2316/J.2021.206-0620","url":null,"abstract":"intelligence an important role in the automation field, which can to intelligent decision-making of human beings. AI has wide from researchers and has been applied to almost all aspects of human life. A series of algorithms and models of AI have been used to promote the innovation of product/service in the field of environmental automation systems. On the other hand, in the field of environment and geology, remote sensing data and internet of things (IoT) sensing data are typically used for research and analysis. By combining big data with AI algorithms, issues such as environment change, ecological status as-sessment, geological disaster prediction, and data mining can be done to assist decision-making and other work. The purpose of this special issue is to explore the current research direction of AI applied to environmental automation systems, including environment change monitoring, ecological status assessment, geological disaster prediction, and data mining. In this issue, seven papers regarding AI in environmental automation systems are selected by peer view. These papers present several theoret-ical and practical problems related to AI in environmental automation systems, as well as the analysis, new discov-eries, and innovative ideas and improvements made in the field of AI in environmental automation systems. Subjects of the seven papers include: AI models for ecology or geology (environment and hazardous), machine learning models for ecology or geology, expert systems for ecology or geology, deep learning for ecology or geology, intelligence image processing algorithms for ecology or geology, big data analytics for data processing from ecology or geology, applications of AI in ecology or geology, data fusion for change detection, geological disaster prediction, and forest deforestation monitoring.","PeriodicalId":54943,"journal":{"name":"International Journal of Robotics & Automation","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68649073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Huixiang Ma, Shan-zhong Liu, T. Tang, Runchuan Xia
{"title":"RESEARCH ON STRESS MONITORING OF CRACKED STEEL BOX GIRDER BASED ON SELF-MAGNETIC FLUX LEAKAGE","authors":"Huixiang Ma, Shan-zhong Liu, T. Tang, Runchuan Xia","doi":"10.2316/J.2021.206-0535","DOIUrl":"https://doi.org/10.2316/J.2021.206-0535","url":null,"abstract":"","PeriodicalId":54943,"journal":{"name":"International Journal of Robotics & Automation","volume":"120 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89425743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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