{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T08:33:03Z","timestamp":1747211583558,"version":"3.37.3"},"reference-count":26,"publisher":"Korea Multimedia Society - English Version Journal","issue":"2","license":[{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010880","name":"State Grid Corporation of China","doi-asserted-by":"publisher","award":["B311JP220003"],"award-info":[{"award-number":["B311JP220003"]}],"id":[{"id":"10.13039\/501100010880","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.jmis.org"],"crossmark-restriction":true},"short-container-title":["J Multimed Inf Syst"],"DOI":"10.33851\/jmis.2023.10.2.199","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T05:09:46Z","timestamp":1689052186000},"page":"199-206","update-policy":"https:\/\/doi.org\/10.33851\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Heterogeneous Large-Scale Data Fusion Mechanism of Energy Storage Power Station Based on Neural Network"],"prefix":"10.33851","volume":"10","author":[{"given":"Yimin","family":"Deng","sequence":"first","affiliation":[]},{"given":"Zhoubo","family":"Weng","sequence":"additional","affiliation":[]},{"given":"Tianlong","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"19702","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"key2.0230711072135e+13_B1","doi-asserted-by":"crossref","unstructured":"D. Muirhead, M. A. Imran, and K. Arshad, \"A survey of the challenges, opportunities and use of multiple antennas in current and future 5G small cell base stations,\" IEEE Access, vol. 4, pp. 2952-2964, 2016. 10.1109\/ACCESS.2016.2569483","DOI":"10.1109\/ACCESS.2016.2569483"},{"key":"key2.0230711072135e+13_B2","doi-asserted-by":"crossref","unstructured":"Y. Yang, W. Gao, S. Guo, Y. Mao, and Y. Yang, \"Introduction to BeiDou\u20103 navigation satellite system,\" Navigation, vol. 66, no. 1, pp. 7-18, 2019. 10.1002\/navi.291","DOI":"10.1002\/navi.291"},{"key":"key2.0230711072135e+13_B3","doi-asserted-by":"crossref","unstructured":"A. Mirzabaev, A. J. Isakov, S. Mirzabekov, T. Makhkamov, and D. Kodirov, \"Problems of integration of the photovoltaic power stations with the grid systems,\" in IOP Conference Series: Earth and Environmental Science, Dec. 2020, vol. 614, no. 1, p. 012016. 10.1088\/1755-1315\/614\/1\/012016","DOI":"10.1088\/1755-1315\/614\/1\/012016"},{"key":"key2.0230711072135e+13_B4","doi-asserted-by":"crossref","unstructured":"J. Chongwatpol, \"Managing big data in coal-fired power plants: A business intelligence framework,\" Industrial Management & Data Systems, 2016. 10.1108\/IMDS-11-2015-0473","DOI":"10.1108\/IMDS-11-2015-0473"},{"key":"key2.0230711072135e+13_B5","doi-asserted-by":"crossref","unstructured":"A. Majumder, L. Behera, and V. K. Subramanian, \"Automatic facial expression recognition system using deep network-based data fusion,\" IEEE Transactions on Cybernetics, vol. 48, no. 1, pp. 103-114, 2016. 10.1109\/TCYB.2016.2625419 27875237","DOI":"10.1109\/TCYB.2016.2625419"},{"key":"key2.0230711072135e+13_B6","doi-asserted-by":"crossref","unstructured":"Y. Gu, H. Yan, X. Zhang, Z. Liu, and F. Ren, \"3-D facial expression recognition via attention-based multichannel data fusion network,\" IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-10, 2021. 10.1109\/TIM.2021.3125972","DOI":"10.1109\/TIM.2021.3125972"},{"key":"key2.0230711072135e+13_B7","doi-asserted-by":"crossref","unstructured":"C. Zhao, C. R. Tang, and S. Wan, \"Multisensor information fusion based on DS evidence theory and BP neural network,\" in Key Engineering Materials, 2013, vol. 567, pp. 113-117. 10.4028\/www.scientific.net\/KEM.567.113","DOI":"10.4028\/www.scientific.net\/KEM.567.113"},{"key":"key2.0230711072135e+13_B8","doi-asserted-by":"crossref","unstructured":"K. Ma, H. Zhang, R. Wang, and Z. Zhang, \"Target tracking system for multi-sensor data fusion,\" in 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Dec. 2017, pp. 1768-1772.","DOI":"10.1109\/ITNEC.2017.8285099"},{"key":"key2.0230711072135e+13_B9","doi-asserted-by":"crossref","unstructured":"T. C. Fu, \"A review on time series data mining,\" Engineering Applications of Artificial Intelligence, vol. 24, no. 1, pp. 164-181, 2011. 10.1016\/j.engappai.2010.09.007","DOI":"10.1016\/j.engappai.2010.09.007"},{"key":"key2.0230711072135e+13_B10","doi-asserted-by":"crossref","unstructured":"P. Esling and C. Agon, \"Time-series data mining,\" ACM Computing Surveys (CSUR), vol. 45, no. 1, pp. 1-34, 2012. 10.1145\/2379776.2379788","DOI":"10.1145\/2379776.2379788"},{"key":"key2.0230711072135e+13_B11","doi-asserted-by":"crossref","unstructured":"X. Tao, D. Kong, Y. Wei, and Y. Wang, \"A big network traffic data fusion approach based on fisher and deep auto-encoder,\" Information, vol. 7, no. 2, p. 20, 2016. 10.3390\/info7020020","DOI":"10.3390\/info7020020"},{"key":"key2.0230711072135e+13_B12","doi-asserted-by":"crossref","unstructured":"A. Noureldin, R. Sharaf, A. Osman, and N. El-Sheimy, \"INS\/GPS data fusion technique utilizing radial basis functions neural networks,\" in PLANS 2004. Position Location and Navigation Symposium, Apr. 2004, pp. 280-284.","DOI":"10.1109\/PLANS.2004.1309006"},{"key":"key2.0230711072135e+13_B13","doi-asserted-by":"crossref","unstructured":"Y. W. Li and K. Cao, \"Establishment and application of intelligent city building information model based on BP neural network model,\" Computer Communications, vol. 153, pp. 382-389, 2020. 10.1016\/j.comcom.2020.02.013","DOI":"10.1016\/j.comcom.2020.02.013"},{"key":"key2.0230711072135e+13_B14","doi-asserted-by":"crossref","unstructured":"L. Wu, L. Chen, and X. Hao, \"Multi-sensor data fusion algorithm for indoor fire early warning based on BP neural network,\" Information, vol. 12, no. 2, p. 59, 2021. 10.3390\/info12020059","DOI":"10.3390\/info12020059"},{"key":"key2.0230711072135e+13_B15","doi-asserted-by":"crossref","unstructured":"S. Dabetwar, S. Ekwaro-Osire, and J. P. Dias, \"Damage detection of composite materials using data fusion with deep neural networks,\" in Turbo Expo: Power for Land, Sea, and Air, Sep. 2020, vol. 84225, p. V10-BT27A019. 10.1115\/GT2020-15097","DOI":"10.1115\/GT2020-15097"},{"key":"key2.0230711072135e+13_B16","doi-asserted-by":"crossref","unstructured":"S. Li, H. Wang, L. Song, P. Wang, L. Cui, and T. Lin, \"An adaptive data fusion strategy for fault diagnosis based on the convolutional neural network,\" Measurement, vol. 165, p. 108122, 2020. 10.1016\/j.measurement.2020.108122","DOI":"10.1016\/j.measurement.2020.108122"},{"key":"key2.0230711072135e+13_B17","unstructured":"M. A. Nielsen, Neural Networks and Deep Learning. San Francisco, CA: Determination press, 2015, vol. 25."},{"key":"key2.0230711072135e+13_B18","doi-asserted-by":"crossref","unstructured":"P. Fergus, C. Chalmers, C. C. Montanez, D. Reilly, P. Lisboa, and B. Pineles, \"Modelling segmented cardiotocography time-series signals using one-dimensional convolutional neural networks for the early detection of abnormal birth outcomes,\" IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 5, no. 6, pp. 882-892, 2020. 10.1109\/TETCI.2020.3020061","DOI":"10.1109\/TETCI.2020.3020061"},{"key":"key2.0230711072135e+13_B19","doi-asserted-by":"crossref","unstructured":"L. Jing, T. Wang, M. Zhao, and P. Wang, \"An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox,\" Sensors, vol. 17, vol. 2, pp. 414, 2017. 10.3390\/s17020414 28230767 PMC5335931","DOI":"10.3390\/s17020414"},{"key":"key2.0230711072135e+13_B20","doi-asserted-by":"crossref","unstructured":"H. T. Chiang, Y. Y. Hsieh, S. W. Fu, K. H. Hung, Y. Tsao, and S. Y. Chien, \"Noise reduction in ECG signals using fully convolutional denoising autoencoders,\" IEEE Access, vol. 7, p. 60806-60813, 2019. 10.1109\/ACCESS.2019.2912036","DOI":"10.1109\/ACCESS.2019.2912036"},{"key":"key2.0230711072135e+13_B21","doi-asserted-by":"crossref","unstructured":"A. Roy, M. Saffar, A. Vaswani, and D. Grangier, \"Efficient content-based sparse attention with routing transformers,\" Transactions of the Association for Computational Linguistics, vol. 9, pp. 53-68, 2021. 10.1162\/tacl_a_00353","DOI":"10.1162\/tacl_a_00353"},{"key":"key2.0230711072135e+13_B22","doi-asserted-by":"crossref","unstructured":"K. Cho, A. Courville, and Y. Bengio, \"Describing multimedia content using attention-based encoder-decoder networks,\" IEEE Transactions on Multimedia, vol. 17, no. 11, pp. 1875-1886, 2015. 10.1109\/TMM.2015.2477044","DOI":"10.1109\/TMM.2015.2477044"},{"key":"key2.0230711072135e+13_B23","doi-asserted-by":"crossref","unstructured":"X. Wu, G. Jiang, X. Wang, P. Xie, and X. Li, \"A multi-level-denoising autoencoder approach for wind turbine fault detection,\" IEEE Access, vol. 7, pp. 59376-59387, 2019. 10.1109\/ACCESS.2019.2914731","DOI":"10.1109\/ACCESS.2019.2914731"},{"key":"key2.0230711072135e+13_B24","doi-asserted-by":"crossref","unstructured":"S. Harbola and V. Coors, \"One dimensional convolutional neural network architectures for wind prediction,\" Energy Conversion and Management, vol. 195, pp. 70-75, 2019. 10.1016\/j.enconman.2019.05.007","DOI":"10.1016\/j.enconman.2019.05.007"},{"key":"key2.0230711072135e+13_B25","unstructured":"J. Bouvrie, \"Notes on convolutional neural networks,\" 2006."},{"key":"key2.0230711072135e+13_B26","doi-asserted-by":"crossref","unstructured":"J. Zhou, A. H. Gandomi, F. Chen, and A. Holzinger, \"Evaluating the quality of machine learning explanations: A survey on methods and metrics,\" Electronics, vol. 10, no. 5, p. 593, 2021. 10.3390\/electronics10050593","DOI":"10.3390\/electronics10050593"}],"container-title":["Journal of Multimedia Information System"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.jmis.org\/download\/download_pdf?doi=10.33851\/JMIS.2023.10.2.199","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/www.jmis.org\/download\/download_pdf?doi=10.33851\/JMIS.2023.10.2.199","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T22:56:24Z","timestamp":1729724184000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.jmis.org\/archive\/view_article?doi=10.33851\/JMIS.2023.10.2.199"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,30]]},"references-count":26,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,6,30]]}},"alternative-id":["10.33851\/JMIS.2023.10.2.199"],"URL":"https:\/\/doi.org\/10.33851\/jmis.2023.10.2.199","relation":{},"ISSN":["2383-7632"],"issn-type":[{"type":"electronic","value":"2383-7632"}],"subject":[],"published":{"date-parts":[[2023,6,30]]},"assertion":[{"value":"2023-05-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-06-11","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}