{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T14:06:41Z","timestamp":1783433201120,"version":"3.54.6"},"reference-count":41,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T00:00:00Z","timestamp":1657929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Project of State Grid Jiangsu Electric Power Co., Ltd.","award":["J2021120"],"award-info":[{"award-number":["J2021120"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Classical lossless compression algorithm highly relies on artificially designed encoding and quantification strategies for general purposes. With the rapid development of deep learning, data-driven methods based on the neural network can learn features and show better performance on specific data domains. We propose an efficient deep lossless compression algorithm, which uses arithmetic coding to quantify the network output. This scheme compares the training effects of Bi-directional Long Short-Term Memory (Bi-LSTM) and Transformers on minute-level power data that are not sparse in the time-frequency domain. The model can automatically extract features and adapt to the quantification of the probability distribution. The results of minute-level power data show that the average compression ratio (CR) is 4.06, which has a higher compression ratio than the classical entropy coding method.<\/jats:p>","DOI":"10.3390\/s22145331","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T01:53:22Z","timestamp":1658109202000},"page":"5331","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Deep Lossless Compression Algorithm Based on Arithmetic Coding for Power Data"],"prefix":"10.3390","volume":"22","author":[{"given":"Zhoujun","family":"Ma","sequence":"first","affiliation":[{"name":"State Grid Jiangsu Electric Power Co., Ltd., Nanjing Power Supply Branch, Nanjing 210019, China"},{"name":"College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Grid Jiangsu Electric Power Co., Ltd., Nanjing Power Supply Branch, Nanjing 210019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuohao","family":"He","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Lu","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0784-7007","authenticated-orcid":false,"given":"Fuyuan","family":"Song","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez, I., Calder\u00f3n, A.J., and Portalo, J.M. 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