{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T13:59:17Z","timestamp":1784642357722,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Key technologies and applications of smart energy for securing important loads in mega cities","award":["18DZ1203400"],"award-info":[{"award-number":["18DZ1203400"]}]},{"name":"Shanghai Natural Science Foundation","award":["20ZR1455900"],"award-info":[{"award-number":["20ZR1455900"]}]},{"name":"National Engineering Laboratory for Collaborative Security of Big Data","award":["201803"],"award-info":[{"award-number":["201803"]}]},{"name":"Feio Shanghai Industrial Internet Project","award":["2018-GYHLW-02030"],"award-info":[{"award-number":["2018-GYHLW-02030"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,11,17]]},"DOI":"10.1145\/3581807.3581881","type":"proceedings-article","created":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T00:02:28Z","timestamp":1684800148000},"page":"503-508","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Gas flow meter anomaly data detection based on fused LOF-DBSCAN algorithm"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9588-7227","authenticated-orcid":false,"given":"Xichao","family":"Yue","sequence":"first","affiliation":[{"name":"Shanghai University of Electric Power, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1564-9075","authenticated-orcid":false,"given":"Chaoqun","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Aerospace Energy Co., Ltd, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1448-8938","authenticated-orcid":false,"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai University of Electric Power, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7389-2327","authenticated-orcid":false,"given":"Le","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Aerospace Energy Co., Ltd, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3310-8427","authenticated-orcid":false,"given":"Weifei","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai University of Electric Power, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5517-2964","authenticated-orcid":false,"given":"Yuhang","family":"Lei","sequence":"additional","affiliation":[{"name":"Shanghai University of Electric Power, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Experimental research on online monitoring and fault diagnosis of gas turbine flowmeter[D]","author":"Ran Zhao","year":"2018","unstructured":"Zhao Ran . Experimental research on online monitoring and fault diagnosis of gas turbine flowmeter[D] . Beijing University of Architecture , 2018 . Zhao Ran. Experimental research on online monitoring and fault diagnosis of gas turbine flowmeter[D]. Beijing University of Architecture,2018."},{"key":"e_1_3_2_1_2_1","volume-title":"Analysis of influencing factors of gas meter measurement [J]. Shanghai gas","author":"Chaohui Han","year":"2020","unstructured":"Han Chaohui , Liang Chuanyun , Zhang Jing . Analysis of influencing factors of gas meter measurement [J]. Shanghai gas , 2020 (05): 22-24 Han Chaohui, Liang Chuanyun, Zhang Jing. Analysis of influencing factors of gas meter measurement [J]. Shanghai gas, 2020 (05): 22-24"},{"key":"e_1_3_2_1_3_1","volume-title":"South China University of technology","author":"Cao Yong","year":"2018","unstructured":"Cao Yong Research on online calibration method of natural gas flowmeter [D]. South China University of technology , 2018 Cao Yong Research on online calibration method of natural gas flowmeter [D]. South China University of technology, 2018"},{"issue":"10","key":"e_1_3_2_1_4_1","first-page":"68","article-title":"Analysis on Stable Metering and Troubleshooting of Gas Meters [J]","volume":"26","author":"Jinyong Wang","year":"2016","unstructured":"Wang Jinyong . Analysis on Stable Metering and Troubleshooting of Gas Meters [J] . Science and Technology Outlook , 2016 , 26 ( 10 ): 68 Wang Jinyong. Analysis on Stable Metering and Troubleshooting of Gas Meters [J]. Science and Technology Outlook, 2016, 26 (10): 68","journal-title":"Science and Technology Outlook"},{"issue":"21","key":"e_1_3_2_1_5_1","first-page":"19","article-title":"An identification method for power theft combining clustering and outlier detection algorithm [J]","volume":"55","author":"Ning Li","year":"2018","unstructured":"Li Ning , Yin Xiaoming , Ding Xuefeng , Cai Hui , Wang Wei . An identification method for power theft combining clustering and outlier detection algorithm [J] . Electric Measurement and Instrumentation , 2018 , 55 ( 21 ): 19 - 24 Li Ning, Yin Xiaoming, Ding Xuefeng, Cai Hui, Wang Wei. An identification method for power theft combining clustering and outlier detection algorithm [J]. Electric Measurement and Instrumentation, 2018, 55 (21): 19-24","journal-title":"Electric Measurement and Instrumentation"},{"issue":"18","key":"e_1_3_2_1_6_1","first-page":"6259","article-title":"Comprehensive clustering method of user load characteristics and adjustable potential for power big data [J]","volume":"41","author":"Yi Sun","year":"2021","unstructured":"Sun Yi , Mao Yehua , Li Zekun , Comprehensive clustering method of user load characteristics and adjustable potential for power big data [J] . Chinese Journal of Electrical Engineering , 2021 , 41 ( 18 ): 6259 - 6271 Sun Yi, Mao Yehua, Li Zekun, Comprehensive clustering method of user load characteristics and adjustable potential for power big data [J]. Chinese Journal of Electrical Engineering, 2021,41 (18): 6259-6271","journal-title":"Chinese Journal of Electrical Engineering"},{"key":"e_1_3_2_1_7_1","first-page":"1","volume-title":"Analysis of power consumer behavior based on the complementation of K-means and DBSCAN,\" 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2)","author":"Zhang L.","year":"2017","unstructured":"L. Zhang , S. Deng and S. Li , \" Analysis of power consumer behavior based on the complementation of K-means and DBSCAN,\" 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2) , 2017 , pp. 1 - 5 , doi: 10.1109\/EI2.2017.8245490. 10.1109\/EI2.2017.8245490 L. Zhang, S. Deng and S. Li, \"Analysis of power consumer behavior based on the complementation of K-means and DBSCAN,\" 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), 2017, pp. 1-5, doi: 10.1109\/EI2.2017.8245490."},{"issue":"18","key":"e_1_3_2_1_8_1","first-page":"161","article-title":"Power big data anomaly detection method based on improved PSO-PFCM clustering algorithm [J]","volume":"49","author":"Qing Li","year":"2021","unstructured":"Li Qing . Power big data anomaly detection method based on improved PSO-PFCM clustering algorithm [J] . Power System Protection and Control , 2021 , 49 ( 18 ): 161 - 166 Li Qing. Power big data anomaly detection method based on improved PSO-PFCM clustering algorithm [J]. Power System Protection and Control, 2021,49 (18): 161-166","journal-title":"Power System Protection and Control"},{"issue":"04","key":"e_1_3_2_1_9_1","first-page":"6","article-title":"Edge power data anomaly detection based on CFSFDP algorithm [J]","volume":"44","author":"Zhemin Zhang","year":"2021","unstructured":"Zhang Zhemin , Li Qilin , Yan Ping , Cheng Guixue . Edge power data anomaly detection based on CFSFDP algorithm [J] . Sichuan Power Technology , 2021 , 44 ( 04 ): 6 - 10 Zhang Zhemin, Li Qilin, Yan Ping, Cheng Guixue. Edge power data anomaly detection based on CFSFDP algorithm [J]. Sichuan Power Technology, 2021,44 (04): 6-10","journal-title":"Sichuan Power Technology"},{"issue":"03","key":"e_1_3_2_1_10_1","first-page":"53","article-title":"Research on anomaly detection of power industrial control system based on Gaussian mixture clustering [J]","volume":"21","author":"Jiawei Li","year":"2021","unstructured":"Li Jiawei , Wu Kehe , Zhang Bo . Research on anomaly detection of power industrial control system based on Gaussian mixture clustering [J] . Information Network Security , 2021 , 21 ( 03 ): 53 - 63 [11] N. Ohadi, A. Kamandi, M. Shabankhah, S. M. Fatemi, S. M. Hosseini and A. Mahmoudi, \"SW-DBSCAN: A Grid-based DBSCAN Algorithm for Large Datasets,\" 2020 6th International Conference on Web Research (ICWR), 2020,pp.139-145,doi:10.1109\/ICWR49608.2020.9122313. 10.1109\/ICWR49608.2020.9122313 Li Jiawei, Wu Kehe, Zhang Bo. Research on anomaly detection of power industrial control system based on Gaussian mixture clustering [J]. Information Network Security, 2021,21 (03): 53-63 [11] N. Ohadi, A. Kamandi, M. Shabankhah, S. M. Fatemi, S. M. Hosseini and A. Mahmoudi, \"SW-DBSCAN: A Grid-based DBSCAN Algorithm for Large Datasets,\" 2020 6th International Conference on Web Research (ICWR), 2020,pp.139-145,doi:10.1109\/ICWR49608.2020.9122313.","journal-title":"Information Network Security"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/ICWR49608.2020.9122313","volume-title":"SW-DBSCAN: A Grid-based DBSCAN Algorithm for Large Datasets,\" 2020 6th International Conference on Web Research (ICWR)","author":"Ohadi N.","year":"2020","unstructured":"N. Ohadi , A. Kamandi , M. Shabankhah , S. M. Fatemi , S. M. Hosseini and A. Mahmoudi , \" SW-DBSCAN: A Grid-based DBSCAN Algorithm for Large Datasets,\" 2020 6th International Conference on Web Research (ICWR) , 2020 ,pp. 139 - 145 ,doi:10.1109\/ICWR49608.2020.9122313. 10.1109\/ICWR49608.2020.9122313 N. Ohadi, A. Kamandi, M. Shabankhah, S. M. Fatemi, S. M. Hosseini and A. Mahmoudi, \"SW-DBSCAN: A Grid-based DBSCAN Algorithm for Large Datasets,\" 2020 6th International Conference on Web Research (ICWR), 2020,pp.139-145,doi:10.1109\/ICWR49608.2020.9122313."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2011.2162583"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CC.2015.7275260"},{"key":"e_1_3_2_1_14_1","first-page":"000001","volume-title":"AF-DBSCAN: An unsupervised Automatic Fuzzy Clustering method based on DBSCAN approach,\" 2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","author":"Jebari S.","year":"2019","unstructured":"S. Jebari , A. Smiti and A. Louati , \" AF-DBSCAN: An unsupervised Automatic Fuzzy Clustering method based on DBSCAN approach,\" 2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI) , 2019 , pp. 000001 - 000006 ,doi:10.1109\/IWOBI47054.2019.9114411. 10.1109\/IWOBI47054.2019.9114411 S. Jebari, A. Smiti and A. Louati, \"AF-DBSCAN: An unsupervised Automatic Fuzzy Clustering method based on DBSCAN approach,\" 2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), 2019, pp.000001-000006,doi:10.1109\/IWOBI47054.2019.9114411."},{"key":"e_1_3_2_1_15_1","volume-title":"sun Biao. Research on lane line detection based on rc-dbscan [J]","author":"Yuanwang Deng","year":"2021","unstructured":"Deng Yuanwang , Pu Hongtao , Hua Xinbin , sun Biao. Research on lane line detection based on rc-dbscan [J] . Journal of Hunan University (NATURAL SCIENCE EDITION) , 2021 , 48 (10): 85-92 Deng Yuanwang, Pu Hongtao, Hua Xinbin, sun Biao. Research on lane line detection based on rc-dbscan [J]. Journal of Hunan University (NATURAL SCIENCE EDITION), 2021, 48 (10): 85-92"},{"issue":"05","key":"e_1_3_2_1_16_1","first-page":"58","article-title":"A method of radar trace processing based on DBSCAN clustering [J]","volume":"44","author":"Yujian Zhai","year":"2021","unstructured":"Zhai Yujian , Yu Chengzhi , Gao Xing . A method of radar trace processing based on DBSCAN clustering [J] . Ship Electronic Countermeasure , 2021 , 44 ( 05 ): 58 - 61 Zhai Yujian, Yu Chengzhi, Gao Xing. A method of radar trace processing based on DBSCAN clustering [J]. Ship Electronic Countermeasure, 2021,44 (05): 58-61","journal-title":"Ship Electronic Countermeasure"},{"issue":"05","key":"e_1_3_2_1_17_1","first-page":"64","article-title":"Comparison of regional distribution of multi-directional pedestrian flow clusters based on density clustering [J]","volume":"34","author":"Yuepeng Sun","year":"2021","unstructured":"Sun Yuepeng , Guo Renyong , Yu Tao . Comparison of regional distribution of multi-directional pedestrian flow clusters based on density clustering [J] . Shandong Science , 2021 , 34 ( 05 ): 64 - 74 Sun Yuepeng, Guo Renyong, Yu Tao. Comparison of regional distribution of multi-directional pedestrian flow clusters based on density clustering [J]. Shandong Science, 2021,34 (05): 64-74","journal-title":"Shandong Science"},{"key":"e_1_3_2_1_18_1","volume-title":"Yu Shilei, Zhao Shangyu. Research on early warning of abnormal working conditions of wind turbines based on DBSCAN and sdae [J]. Journal of power engineering","author":"Liangyu Ma","year":"2021","unstructured":"Ma Liangyu , sun Jiaming , Yu Shilei, Zhao Shangyu. Research on early warning of abnormal working conditions of wind turbines based on DBSCAN and sdae [J]. Journal of power engineering , 2021 , 41 (09): 786-793 + 808 Ma Liangyu, sun Jiaming, Yu Shilei, Zhao Shangyu. Research on early warning of abnormal working conditions of wind turbines based on DBSCAN and sdae [J]. Journal of power engineering, 2021, 41 (09): 786-793 + 808"},{"key":"e_1_3_2_1_19_1","volume-title":"Multi sample recurrent copy number mutation detection algorithm based on clustering [J]","author":"Nianhua Chen","year":"2021","unstructured":"Chen Nianhua , Yuan Xiguo . Multi sample recurrent copy number mutation detection algorithm based on clustering [J] . Journal of Liaocheng University (Natural Science Edition) , 2021 ,34 (06): 20-28 Chen Nianhua, Yuan Xiguo. Multi sample recurrent copy number mutation detection algorithm based on clustering [J]. Journal of Liaocheng University (Natural Science Edition), 2021,34 (06): 20-28"},{"issue":"12","key":"e_1_3_2_1_20_1","first-page":"179","article-title":"Dimension reduction of power consumption characteristic index and power theft detection of extreme learning machine algorithm [J]","volume":"35","author":"Zixin Li","year":"2018","unstructured":"Li Zixin , Li Chuan , Li Yingna . Dimension reduction of power consumption characteristic index and power theft detection of extreme learning machine algorithm [J] . Computer Application and Software , 2018 , 35 ( 12 ): 179 - 186 Li Zixin, Li Chuan, Li Yingna. Dimension reduction of power consumption characteristic index and power theft detection of extreme learning machine algorithm [J]. Computer Application and Software, 2018, 35 (12): 179-186","journal-title":"Computer Application and Software"},{"key":"e_1_3_2_1_21_1","unstructured":"Research on parameter configuration method of DBSCAN clustering algorithm_ Song Jinyu  Research on parameter configuration method of DBSCAN clustering algorithm_ Song Jinyu"}],"event":{"name":"ICCPR 2022: 2022 11th International Conference on Computing and Pattern Recognition","location":"Beijing China","acronym":"ICCPR 2022"},"container-title":["Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581807.3581881","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581807.3581881","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:30Z","timestamp":1750182570000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581807.3581881"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,17]]},"references-count":21,"alternative-id":["10.1145\/3581807.3581881","10.1145\/3581807"],"URL":"https:\/\/doi.org\/10.1145\/3581807.3581881","relation":{},"subject":[],"published":{"date-parts":[[2022,11,17]]},"assertion":[{"value":"2023-05-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}