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Research Trends for the Deep Learning-based Metabolic Rate Calculation

재실자 활동량 산출을 위한 딥러닝 기반 선행연구 동향

  • Park, Bo-Rang (School of Architecture and Building Science, Chung-Ang University) ;
  • Choi, Eun-Ji (School of Architecture and Building Science, Chung-Ang University) ;
  • Lee, Hyo Eun (School of Architecture and Building Science, Chung-Ang University) ;
  • Kim, Tae-Won (School of Architecture and Building Engineering, Chung-Ang University) ;
  • Moon, Jin Woo (School of Architecture and Building Science, Chung-Ang University)
  • Received : 2017.10.08
  • Accepted : 2017.10.28
  • Published : 2017.10.31

Abstract

Purpose: The purpose of this study is to investigate the prior art based on deep learning to objectively calculate the metabolic rate which is the subjective factor for the PMV optimum control and to make a plan for future research based on this study. Methods: For this purpose, the theoretical and technical review and applicability analysis were conducted through various documents and data both in domestic and foreign. Results: As a result of the prior art research, the machine learning model of artificial neural network and deep learning has been used in various fields such as speech recognition, scene recognition, and image restoration. As a representative case, OpenCV Background Subtraction is a technique to separate backgrounds from objects or people. PASCAL VOC and ILSVRC are surveyed as representative technologies that can recognize people, objects, and backgrounds. Based on the results of previous researches on deep learning based on metabolic rate for occupational metabolic rate, it was found out that basic technology applicable to occupational metabolic rate calculation technology to be developed in future researches. It is considered that the study on the development of the activity quantity calculation model with high accuracy will be done.

Acknowledgement

Supported by : Ministry of Land, Infrastructure and Transport of Korean government

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