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Conference Paper
Sensor Data based Work Area Environment Risk Prediction and Monitoring in the Shipbuilding
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- Authors
- JaeMyoung KIM, Young Wook Cho, Byung-Tae Jang
- Issue Date
- 2019-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.906-909
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- HSE(Health, Safety Environment) management process must be maintained efficiently to achieve disease-free, zero-accident, and pollution-free environments in large-scale work areas. HSE sensor data should be periodically monitored in real time to provide an IoT system which helps users to avoid being in danger by predicting the situation and providing it. In particular, environmental changes in those areas must be able to take immediate action as they affect the whole. In this paper, we propose a monitoring system for environmental risk prediction monitoring. It collects and analyzes environmental sensor data, predicts environmental risks using machine learning generated models and informs the environmental risks in real time. We developed a comprehensive work area environment risk index to obtain a well prediction model. The results were 98.8% for LSTM model and 99.1% for GRU. As a result of applying this prediction model to the system, we can monitor the environmental risks, but find future tasks due to the characteristics of the work area.
- KSP Keywords
- Environmental change, Environmental risk, Environmental sensor, Management process, Monitoring system, Pollution-free, Real-Time, Risk index, Safety and Environment(QHSE), large-scale, machine Learning