Space Physical Sensor Protection and Control System Based on Neural Network Prediction: Application in Princess Elizabeth Area of Antarctica.

Yuchen Wang,Jingxue Guo,Dehong Huang,Yinke Dou

doi:10.3390/s20174662

Abstract

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’s 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.

Highlights

The purpose of this research project is to combine the heat transfer modelling analysis and the neural network prediction model algorithm to provide environmental prediction and support strategies to support the work of automated observation equipment related to space physics in the region
Since Tai-Shan Camp is located inland in Antarctica, 520 kilometres from the coastline, the energy demand and fuel transportation costs are higher than other coastal checkpoints
In the research process of the Antarctic continent, including glaciology, geophysics, astronomy, and other disciplines, an essential reason for its progress is the improvement of data quality

Summary

Introduction

The purpose of this research project is to combine the heat transfer modelling analysis and the neural network prediction model algorithm to provide environmental prediction and support strategies to support the work of automated observation equipment related to space physics in the region. China has established five research camps in Antarctica, of which the Tai-Shan field in the Princess. Elizabeth area is the only inland summer camp. Since Tai-Shan Camp is located inland in Antarctica, 520 kilometres from the coastline, the energy demand and fuel transportation costs are higher than other coastal checkpoints. In the research process of the Antarctic continent, including glaciology, geophysics, astronomy, and other disciplines, an essential reason for its progress is the improvement of data quality. The need for more and better measurements along with advances in technical capabilities is driving the ambition to deploy arrays of autonomous geophysical instrument platforms in remote regions. Researchers have an even more urgent need for observations in the Antarctic region. Lack of infrastructure, and harsh environments, the current measurement is sparse [1]

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