{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:50:24Z","timestamp":1750308624482,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":6,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T00:00:00Z","timestamp":1675987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,2,10]]},"DOI":"10.1145\/3592686.3592753","type":"proceedings-article","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T23:21:27Z","timestamp":1685575287000},"page":"372-375","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of Sulfur Hexafluoride (SF6) Gas Pressure Time Series Data Based on Adaptive Particle Swarm Optimization- Long Short Term Memory (APSO-LSTM)"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8321-6265","authenticated-orcid":false,"given":"Baochang","family":"Zhu","sequence":"first","affiliation":[{"name":"State Grid Tianjin Electric Power Corporation, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1122-1898","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Grid Corporation of China, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8963-8568","authenticated-orcid":false,"given":"Jun","family":"Yin","sequence":"additional","affiliation":[{"name":"State Grid Tianjin Electric Power Corporation, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8312-6546","authenticated-orcid":false,"given":"Junjie","family":"Shi","sequence":"additional","affiliation":[{"name":"State Grid Tianjin Electric Power Corporation, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7675-5707","authenticated-orcid":false,"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"State Grid Tianjin Electric Power Corporation, China"}]}],"member":"320","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"A study of carbon peaking and carbon neutral pathways in China's power sector under a 1.5\u00b0C temperature control target. Environ Sci Pollut Res 29, 56(July","author":"Wu Gengqi","year":"2022","unstructured":"Gengqi Wu and Dongxiao Niu . 2022. A study of carbon peaking and carbon neutral pathways in China's power sector under a 1.5\u00b0C temperature control target. Environ Sci Pollut Res 29, 56(July 2022 ), 85062-85080. https:\/\/doi.org\/10.1007\/s11356-022-21594-z. 10.1007\/s11356-022-21594-z Gengqi Wu and Dongxiao Niu. 2022. A study of carbon peaking and carbon neutral pathways in China's power sector under a 1.5\u00b0C temperature control target. Environ Sci Pollut Res 29, 56(July 2022), 85062-85080. https:\/\/doi.org\/10.1007\/s11356-022-21594-z."},{"key":"e_1_3_2_1_2_1","volume-title":"Giovanni Manassero.","author":"El\u00e2nio Francisco","year":"2023","unstructured":"Bezerra, Francisco El\u00e2nio , Di Santo , Silvio Giuseppe and Junior , Giovanni Manassero. 2023 . An approach based on wavelets and machine learning to build a prediction model for SF6 filling pressure of high-voltage circuit breakers. Electr Power Syst Res 216(March 2023). https:\/\/doi.org\/10.1016\/j.epsr.2022.108974. 10.1016\/j.epsr.2022.108974 Bezerra, Francisco El\u00e2nio, Di Santo, Silvio Giuseppe and Junior, Giovanni Manassero. 2023. An approach based on wavelets and machine learning to build a prediction model for SF6 filling pressure of high-voltage circuit breakers. Electr Power Syst Res 216(March 2023). https:\/\/doi.org\/10.1016\/j.epsr.2022.108974."},{"key":"e_1_3_2_1_3_1","volume-title":"Lu Pu and Qiaogen Zhang","author":"Zhang Lu","year":"2018","unstructured":"Lu Zhang , Sen Wang , Lei Sun , Chen Mao , Lu Pu and Qiaogen Zhang . 2018 . Prediction model of voltage-time characteristics for SF6 long gap under VFTO and lightning impulse voltage. IET Gener. Transm. Distrib 12, 4(February, 2018), 880-885. https:\/\/doi.org\/ 10.1049\/iet-gtd.2017.0957. 10.1049\/iet-gtd.2017.0957 Lu Zhang, Sen Wang, Lei Sun, Chen Mao, Lu Pu and Qiaogen Zhang. 2018. Prediction model of voltage-time characteristics for SF6 long gap under VFTO and lightning impulse voltage. IET Gener. Transm. Distrib 12, 4(February, 2018), 880-885. https:\/\/doi.org\/ 10.1049\/iet-gtd.2017.0957."},{"key":"e_1_3_2_1_4_1","first-page":"139","article-title":"A novel fractional grey forecasting model with variable weighted buffer operator and its application in forecasting China's crude oil consumption","volume":"8","author":"Wang Yong","year":"2022","unstructured":"Yong Wang , Yuyang Zhang , Rui Nie , Pei Chi , Xinbo He and Zhang, Lei. 2022 . A novel fractional grey forecasting model with variable weighted buffer operator and its application in forecasting China's crude oil consumption . Pet 8 , 2( June 2022): 139 - 157 . https:\/\/doi.org\/ 10.1016\/j.petlm.2022.03.002. 10.1016\/j.petlm.2022.03.002 Yong Wang, Yuyang Zhang, Rui Nie, Pei Chi, Xinbo He and Zhang, Lei. 2022. A novel fractional grey forecasting model with variable weighted buffer operator and its application in forecasting China's crude oil consumption. Pet 8, 2(June 2022): 139-157. https:\/\/doi.org\/ 10.1016\/j.petlm.2022.03.002.","journal-title":"Pet"},{"key":"e_1_3_2_1_5_1","first-page":"151","article-title":"Hazard prediction of coal and gas outburst based on the Hamming distance artificial intelligence algorithm (HDAIA)","volume":"4","author":"Ji Peng","year":"2023","unstructured":"Peng Ji and Shiliang Shi . 2023 . Hazard prediction of coal and gas outburst based on the Hamming distance artificial intelligence algorithm (HDAIA) . Saf Sci Resil 4 , 2( June 2023): 151 - 158 . https:\/\/doi.org\/ 10.1016\/j.jnlssr.2022.12.001. 10.1016\/j.jnlssr.2022.12.001 Peng Ji and Shiliang Shi. 2023. Hazard prediction of coal and gas outburst based on the Hamming distance artificial intelligence algorithm (HDAIA). Saf Sci Resil 4, 2(June 2023): 151-158. https:\/\/doi.org\/ 10.1016\/j.jnlssr.2022.12.001.","journal-title":"Saf Sci Resil"},{"key":"e_1_3_2_1_6_1","volume-title":"APSO-LSTM: An Improved LSTM Neural Network Model Based on APSO Algorithm. J Phys Conf Ser, 1651 (August","author":"Chen Keqiao","year":"2020","unstructured":"Keqiao Chen . 2020 . APSO-LSTM: An Improved LSTM Neural Network Model Based on APSO Algorithm. J Phys Conf Ser, 1651 (August 2020), 012151. https:\/\/doi.org\/10.1088\/1742-6596\/1651\/1\/012151. 10.1088\/1742-6596 Keqiao Chen. 2020. APSO-LSTM: An Improved LSTM Neural Network Model Based on APSO Algorithm. J Phys Conf Ser, 1651 (August 2020), 012151. https:\/\/doi.org\/10.1088\/1742-6596\/1651\/1\/012151."}],"event":{"name":"BIC 2023: 2023 3rd International Conference on Bioinformatics and Intelligent Computing","acronym":"BIC 2023","location":"Sanya China"},"container-title":["Proceedings of the 2023 3rd International Conference on Bioinformatics and Intelligent Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3592686.3592753","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3592686.3592753","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T19:07:46Z","timestamp":1750273666000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3592686.3592753"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,10]]},"references-count":6,"alternative-id":["10.1145\/3592686.3592753","10.1145\/3592686"],"URL":"https:\/\/doi.org\/10.1145\/3592686.3592753","relation":{},"subject":[],"published":{"date-parts":[[2023,2,10]]},"assertion":[{"value":"2023-05-31","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}