{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:08:26Z","timestamp":1760238506089,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,19]],"date-time":"2020-08-19T00:00:00Z","timestamp":1597795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB1307504"],"award-info":[{"award-number":["2018YFB1307504"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41776199"],"award-info":[{"award-number":["41776199"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the inland areas of Antarctica, the establishment of an unmanned automatic observation support system is an urgent problem and challenge. This article introduces the development and application of an unmanned control system suitable for inland Antarctica. The system is called RIOD (Remote Control, Image Acquisition, Operation Maintenance, and Document Management System) for short. At the beginning of this research project, a mathematical model of heat conduction in the surface observation chamber was established, and the control strategy was determined through mathematical relationships and field experiments. Based on the analysis of local meteorological data, various neural network models are compared, and the training model with the smallest error is used to predict the future ambient temperature. Moreover, the future temperature is substituted into the mathematical model of thermal conductivity to obtain the input value of the next input power, to formulate the operation strategy for the system. This method maintains the regular operation of the sensor while reducing energy consumption. The RIOD system has been deployed in the Tai-Shan camp in China\u2019s Antarctic inland inspection route. The application results 4.5 months after deployment show that the RIOD system can maintain stable operation at lower temperatures. This technology solves the demand for unmanned high-altitude physical observation or astronomical observation stations in inland areas.<\/jats:p>","DOI":"10.3390\/s20174662","type":"journal-article","created":{"date-parts":[[2020,8,19]],"date-time":"2020-08-19T09:22:31Z","timestamp":1597828951000},"page":"4662","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Space Physical Sensor Protection and Control System Based on Neural Network Prediction: Application in Princess Elizabeth Area of Antarctica"],"prefix":"10.3390","volume":"20","author":[{"given":"Yuchen","family":"Wang","sequence":"first","affiliation":[{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"},{"name":"SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinke","family":"Dou","sequence":"additional","affiliation":[{"name":"College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingxue","family":"Guo","sequence":"additional","affiliation":[{"name":"SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dehong","family":"Huang","sequence":"additional","affiliation":[{"name":"SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"44501","DOI":"10.1063\/1.3108527","article-title":"Autonomous low-power magnetic data collection platform to enable remote high latitude array deployment","volume":"80","author":"Musko","year":"2009","journal-title":"Rev. Sci. Instruments"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s11207-018-1348-8","article-title":"High-Speed Solar Wind Streams and Geomagnetic Storms During Solar Cycle 24","volume":"293","author":"Gerontidou","year":"2018","journal-title":"Sol. Phys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"081101","DOI":"10.1063\/1.3480478","article-title":"Invited review article: IceCube: An instrument for neutrino astronomy","volume":"81","author":"Halzen","year":"2010","journal-title":"Rev. Sci. Instrum."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6","DOI":"10.3847\/1538-3881\/aa73dc","article-title":"Optical Sky Brightness and Transparency during the Winter Season at Dome A Antarctica from the Gattini-All-Sky Camera","volume":"154","author":"Yang","year":"2017","journal-title":"Astron. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"672","DOI":"10.3390\/s140100672","article-title":"Embedded ARM System for Volcano Monitoring in Remote Areas: Application to the Active Volcano on Deception Island (Antarctica)","volume":"14","author":"Peci","year":"2014","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dou, Y., Zuo, G., Chang, X., and Chen, Y. (2019). A Study of a Standalone Renewable Energy System of the Chinese Zhongshan Station in Antarctica. Appl. Sci., 9.","DOI":"10.3390\/app9101968"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Carminati, M., Turolla, A., Mezzera, L., Di Mauro, M., Tizzoni, M., Pani, G., Zanetto, F., Foschi, J., and Antonelli, M. (2020). A Self-Powered Wireless Water Quality Sensing Network Enabling Smart Monitoring of Biological and Chemical Stability in Supply Systems. Sensors, 20.","DOI":"10.3390\/s20041125"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.energy.2018.05.052","article-title":"Effective long short-term memory with differential evolution algorithm for electricity price prediction","volume":"162","author":"Peng","year":"2018","journal-title":"Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.csl.2018.06.005","article-title":"A Bi-LSTM memory network for end-to-end goal-oriented dialogue learning","volume":"53","author":"Kim","year":"2019","journal-title":"Comput. Speech Lang."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1109\/TIM.2017.2669947","article-title":"Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network","volume":"66","author":"Chen","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103323","DOI":"10.1016\/j.engappai.2019.103323","article-title":"Short-term natural gas consumption prediction based on Volterra adaptive filter and improved whale optimization algorithm","volume":"87","author":"Qiao","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"70","DOI":"10.2307\/1909384","article-title":"Forecast evaluation based on a multiplicative decomposition of mean square errors","volume":"35","author":"Theil","year":"1967","journal-title":"Econometrica"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lee, J., and Yoo, S.K. (2020). Recognition of Negative Emotion Using Long Short-Term Memory with Bio-Signal Feature Compression. Sensors, 20.","DOI":"10.3390\/s20020573"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107248","DOI":"10.1016\/j.apacoust.2020.107248","article-title":"Recognition of ships based on vector sensor and bidirectional long short-term memory networks","volume":"164","author":"Li","year":"2020","journal-title":"Appl. Acoust."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1002\/ese3.543","article-title":"Analysis of particle deposition in a new-type rectifying plate system during shale gas extraction","volume":"8","author":"Peng","year":"2020","journal-title":"Energy Sci. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sun, W., Li, P.Y., Liu, Z., Xue, X., Li, Q.Y., Zhang, H.Y., and Wang, J.B. (2020). LSTM based link quality confidence interval boundary prediction for wireless communication in smart grid. Computing, 1\u201319.","DOI":"10.1007\/s00607-020-00816-7"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s40264-018-0765-9","article-title":"Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding","volume":"42","author":"Wunnava","year":"2019","journal-title":"Drug Saf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhao, R., Yan, R.Q., Wang, J.J., and Mao, K.Z. (2017). Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks. Sensors, 17.","DOI":"10.3390\/s17020273"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zuo, G., Dou, Y., and Lei, R. (2018). Discrimination Algorithm and Procedure of Snow Depth and Sea Ice Thickness Determination Using Measurements of the Vertical Ice Temperature Profile by the Ice-tethered Buoys. Sensors, 18.","DOI":"10.3390\/s18124162"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4662\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:02:53Z","timestamp":1760176973000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4662"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,19]]},"references-count":21,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20174662"],"URL":"https:\/\/doi.org\/10.3390\/s20174662","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,8,19]]}}}