Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Kindergarten space design based on BP (back propagation) neural network

BP 신경 망 기반 유치원 공간 설계

  • Liao, PengCheng (Department of Smart Experience Design, TED, Kookmin University) ;
  • Pan, Younghwan (Department of Smart Experience Design, TED, Kookmin University)
  • 랴오펑청 (국민대학교 테크노디자인전문대학원,스마트경험디자인학과) ;
  • 반영환 (국민대학교 테크노디자인전문대학원,스마트경험디자인학과)
  • Received : 2021.06.04
  • Accepted : 2021.09.20
  • Published : 2021.09.28
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Abstract

In the past, designers relied primarily on past experience and reference to industry standard thresholds to design spaces. Such design often results in spaces that do not meet the needs of users. The purpose of this paper is to investigate the process and way of generating design parameters by constructing a BP neural network algorithm for spatial design. From the perspective. This paper adopts an experimental research method to take a kindergarten with a large number of complex needs in space as the object of study, and through the BP neural network algorithm in machine learning, the correlation between environmental behavior parameters and spatial design parameters is imprinted. The way of generating spatial design parameters is studied. In the future, the corresponding spatial design parameters can be derived by replacing specific environmental behavior influence factors, which can be applied to a wider range of scenarios and improve the efficiency of designers.

과거에 설계자는 주로 과거의 경험과 설계 공간에 대한 산업 표준 임계값에 대한 참조에 의존했습니다. 이러한 설계는 종종 사용자의 요구를 충족하지 않는 공간을 초래합니다. 공간설계를 위한 BP신경망 알고리즘을 구축해 설계 매개변수를 생성하는 과정과 방법을 조사하는 것이 목적이다. 그런 관점에서. 본 논문은 공간 내 복잡한 욕구가 많은 유치원을 연구 대상으로 삼고 있으며, 기계학습의 BP신경망 알고리즘을 통해 환경행동변수와 공간설계변수의 상관관계를 각인하고 있다. 공간 설계 매개변수를 생성하는 방법을 연구합니다. 미래에는 특정 환경행동영향요소를 대체하여 해당 공간설계 매개변수를 도출할 수 있어 보다 광범위한 시나리오에 적용할 수 있고 설계자의 효율성을 높일 수 있다.

Keywords

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