{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T21:38:23Z","timestamp":1777066703728,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Power system facility calibration is a compulsory task that requires in-site operations. In this work, we propose a remote calibration device that incorporates edge intelligence so that the required calibration can be accomplished with little human intervention. Our device entails a wireless serial port module, a Bluetooth module, a video acquisition module, a text recognition module, and a message transmission module. First, the wireless serial port is used to communicate with edge node, the Bluetooth is used to search for nearby Bluetooth devices to obtain their state information and the video is used to monitor the calibration process in the calibration lab. Second, to improve the intelligence, we propose a smart meter reading method in our device that is based on artificial intelligence to obtain information about calibration meters. We use a mini camera to capture images of calibration meters, then we adopt the Efficient and Accurate Scene Text Detector (EAST) to complete text detection, finally we built the Convolutional Recurrent Neural Network (CRNN) to complete the recognition of the meter data. Finally, the message transmission module is used to transmit the recognized data to the database through Extensible Messaging and Presence Protocol (XMPP). Our device solves the problem that some calibration meters cannot return information, thereby improving the remote calibration intelligence.<\/jats:p>","DOI":"10.3390\/s22010322","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"322","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Remote Calibration Device Using Edge Intelligence"],"prefix":"10.3390","volume":"22","author":[{"given":"Quan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"China Electric Power Research Institute Co., Ltd., Wuhan 430074, China"},{"name":"State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"National Institute of Metrology, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Electric Power Research Institute Co., Ltd., Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiliang","family":"Fu","sequence":"additional","affiliation":[{"name":"China Electric Power Research Institute Co., Ltd., Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"91","DOI":"10.4028\/www.scientific.net\/AMM.535.91","article-title":"Research and Development on Remote Calibration System for Electric Vehicles","volume":"535","author":"Li","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"123944","DOI":"10.1016\/j.jhydrol.2019.123944","article-title":"Spatially distributed model calibration of a highly managed hydrological system using remote sensing-derived ET data","volume":"577","author":"Becker","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.4028\/www.scientific.net\/AMM.602-605.2535","article-title":"Remote Calibration System in Metering Device","volume":"602\u2013605","author":"Ma","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mellit, A., Benghanem, M., Herrak, O., and Messalaoui, A. (2021). Design of a Novel Remote Monitoring System for Smart Greenhouses Using the Internet of Things and Deep Convolutional Neural Networks. Energies, 14.","DOI":"10.3390\/en14165045"},{"key":"ref_5","first-page":"700","article-title":"Design of power factor meter using internet of things for power factor improvement remote monitoring and data logging","volume":"17","author":"Gunawan","year":"2020","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"012226","DOI":"10.1088\/1742-6596\/2005\/1\/012226","article-title":"A Remote Automatic Detection And Calibration Device for AC Watt-hour Meters","volume":"2005","author":"Tian","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_7","first-page":"9","article-title":"Research on the Application of Time and Frequency Remote Calibration Based on NIMDO","volume":"65","author":"Han","year":"2021","journal-title":"Metrol. Sci. Technol."},{"key":"ref_8","unstructured":"Jeang, Y.L., Chen, L.B., Huang, C.P., Hsu, Y.H., Yeh, M.Y., and Yang, K.M. (2003, January 17). Design of FPGA-based adaptive remote calibration control system. Proceedings of the 2003 IEEE International Conference on Field-Programmable Technology (FPT) (IEEE Cat. No.03EX798), Tokyo, Japan."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Doermann, D., and Tombre, K. (2014). Text Localization and Recognition in Images and Video. Handbook of Document Image Processing and Recognition, Springer.","DOI":"10.1007\/978-0-85729-859-1"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.1109\/TPAMI.2014.2366765","article-title":"Text Detection and Recognition in Imagery: A Survey","volume":"37","author":"Ye","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s11704-015-4488-0","article-title":"Scene text detection and recognition: Recent advances and future trends","volume":"10","author":"Zhu","year":"2016","journal-title":"Front. Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1109\/TPAMI.2016.2646371","article-title":"An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition","volume":"39","author":"Shi","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s11235-018-0521-6","article-title":"Statistical validation of an LTE emulation tool using live video streaming over reliable transport protocols","volume":"71","author":"Bermudez","year":"2019","journal-title":"Telecommun. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"012126","DOI":"10.1088\/1742-6596\/1913\/1\/012126","article-title":"Instant messaging using xmpp","volume":"1913","author":"Gupta","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_15","first-page":"258","article-title":"Instant messaging application for Smartphone","volume":"11","author":"Serik","year":"2014","journal-title":"Life Sci. J."},{"key":"ref_16","first-page":"379","article-title":"Smart Home System with Bluetooth and Wi-Fi as Communication Mode","volume":"7","author":"Liu","year":"2021","journal-title":"Int. Core J. Eng."},{"key":"ref_17","unstructured":"Chaudhari, B.S., and Zennaro, M. (2020). 6-NB-IoT: Concepts, applications, and deployment challenges. LPWAN Technologies for IoT and M2M Applications, Academic Press."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3502987","DOI":"10.1155\/2019\/3502987","article-title":"LoRa (Long-Range) High-Density Sensors for Internet of Things","volume":"2019","author":"Lavric","year":"2019","journal-title":"J. Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ali, A.I., Partal, S.Z., Kepke, S., and Partal, H.P. (2019, January 12\u201315). ZigBee and LoRa based Wireless Sensors for Smart Environment and IoT Applications. Proceedings of the 2019 1st Global Power, Energy and Communication Conference (GPECOM), Nevsehir, Turkey.","DOI":"10.1109\/GPECOM.2019.8778505"},{"key":"ref_20","unstructured":"Berson, A. (2006). Client-Server Architecture. Encyclopedia of Multimedia, Springer."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xiong, W., Sun, C., Fang, C., and Huang, Z. (2009, January 11\u201313). Research and Implementation of Cross-Platform EIM Client Based on Smack. Proceedings of the 2009 International Conference on Computational Intelligence and Software Engineering, Wuhan, China.","DOI":"10.1109\/CISE.2009.5363734"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"142642","DOI":"10.1109\/ACCESS.2020.3012542","article-title":"Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)","volume":"8","author":"Memon","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pratikakis, I., Zagoris, K., Karagiannis, X., Tsochatzidis, L., Mondal, T., and Marthot-Santaniello, I. (2019, January 20\u201325). ICDAR 2019 Competition on Document Image Binarization (DIBCO 2019). Proceedings of the 2019 International Conference on Document Analysis and Recognition (ICDAR), Sydney, Australia.","DOI":"10.1109\/ICDAR.2019.00249"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Hua, Z., Tang, Y., Zhang, Y., Lu, W., and Dai, C. (2021). Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm. Actuators, 10.","DOI":"10.3390\/act10080170"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1534\/g3.115.023788","article-title":"Using Ancient Samples in Projection Analysis","volume":"6","author":"Yang","year":"2015","journal-title":"G3 Genes|Genomes|Genet."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1109\/TSMCB.2007.914695","article-title":"AdaBoost-Based Algorithm for Network Intrusion Detection","volume":"38","author":"Hu","year":"2008","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"key":"ref_27","first-page":"41","article-title":"Applications of Support Vector Machine (SVM) Learning in Cancer Genomics","volume":"15","author":"Huang","year":"2018","journal-title":"Cancer Genom. Proteom."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Islam, S., Khan, S.I.A., Abedin, M.M., Habibullah, K.M., and Das, A.K. (2019, January 27\u201329). Bird Species Classification from an Image Using VGG-16 Network. Proceedings of the 2019 7th International Conference on Computer and Communications Management, Bangkok, Thailand.","DOI":"10.1145\/3348445.3348480"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5349","DOI":"10.1109\/TNNLS.2020.2966319","article-title":"Why ResNet Works? Residuals Generalize","volume":"31","author":"He","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A Search Space Odyssey","volume":"28","author":"Greff","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, H., Sun, F., Zhang, X., and Zheng, L. (2019). License Plate Recognition Model Based on CNN + LSTM + CTC, Springer.","DOI":"10.1007\/978-981-15-0121-0_52"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Karatzas, D., Shafait, F., Uchida, S., Iwamura, M., Bigorda, L., Mestre, S., Mas, J., Mota, D., Almazan, J., and De Las Heras, L. (2013, January 25\u201328). ICDAR 2013 robust reading competition. Proceedings of the International Conference on Document Analysis and Recognition, Washington, DC, USA.","DOI":"10.1109\/ICDAR.2013.221"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., and Liang, J. (2017, January 21\u201326). EAST: An Efficient and Accurate Scene Text Detector. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.283"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Shen, W., Yao, C., and Bai, X. (2015, January 7\u201312). Symmetry-based text line detection in natural scenes. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298871"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shi, B., Bai, X., and Belongie, S. (2017, January 21\u201326). Detecting Oriented Text in Natural Images by Linking Segments. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.371"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, P., Huang, W., He, T., Zhu, Q., Qiao, Y., and Li, X. (2017, January 22\u201329). Single Shot Text Detector with Regional Attention. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.331"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, W., Zhang, X.Y., Yin, F., and Liu, C.L. (2017, January 22\u201329). Deep Direct Regression for Multi-oriented Scene Text Detection. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.87"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bissacco, A., Cummins, M., Netzer, Y., and Neven, H. (2013, January 1\u20138). PhotoOCR: Reading Text in Uncontrolled Conditions. Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","DOI":"10.1109\/ICCV.2013.102"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jaderberg, M., Simonyan, K., Vedaldi, A., and Zisserman, A. (2014). Reading Text in the Wild with Convolutional Neural Networks. arXiv.","DOI":"10.1007\/s11263-015-0823-z"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/322\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:12:28Z","timestamp":1760364748000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/322"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,1]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22010322"],"URL":"https:\/\/doi.org\/10.3390\/s22010322","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,1]]}}}