{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:37:52Z","timestamp":1705106272842},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684802","type":"print"},{"value":"9781643684819","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,12]]},"abstract":"<jats:p>With the rapid development of the science and technology era, digital technology has been widely used in power grid infrastructure projects, and intelligent equipment, high-precision sensing technology, and wireless ad hoc network equipment have been used in various fields of power grid infrastructure. Nowadays, the pay-off construction of overhead transmission lines has also realized intelligent visualization of centralized control, which has improved the safety of stringing construction. Overhead tension pay-off is a key infrastructure construction project, and for the safety of overhead tension pay-off, there are major breakthroughs in reducing labor costs, improving the automation degree of tension pay-off, and improving the working environment, which effectively improves safety construction efficiency. In this paper, the long-term and short-term memory network model using deep learning is used to model and quantitatively analyze the overhead tension pay-off, predict its future security risks, take timely measures, and adjust the operation strategy according to the prediction results of the model.<\/jats:p>","DOI":"10.3233\/faia231212","type":"book-chapter","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:56:07Z","timestamp":1705064167000},"source":"Crossref","is-referenced-by-count":0,"title":["Risk Prediction of Tension Pay-Off Method for Overhead Lines Based on Deep Learning and Short-Term Memory"],"prefix":"10.3233","author":[{"given":"Zhongbo","family":"Xu","sequence":"first","affiliation":[{"name":"Ningbo Power Transmission and Transformation Engineering Technology Department, No. 168 Ningci Middle Road, Jiangbei District, Ningbo City, Zhejiang Province, China"}]},{"given":"Qihao","family":"Shen","sequence":"additional","affiliation":[{"name":"Transmission line operation and inspection, No. 168 Ningci Middle Road, Jiangbei District, Ningbo City, Zhejiang Province, China"}]},{"given":"Weihong","family":"Lv","sequence":"additional","affiliation":[{"name":"Ningbo Tianhong electric appliance co., ltd, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Electronics, Communications and Networks"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231212","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:56:08Z","timestamp":1705064168000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231212"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"ISBN":["9781643684802","9781643684819"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231212","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]}}}