Earticle

초록

영어

This paper recognizes life environment risks which variously exist to guarantee safety of users from all kinds of risk factors that do in resident environment and suggests a plan to infer degree of risk. The artificial neural network theory which makes a great contribution to the artificial intelligence and data-mining fields detects risk factors through mechanical learning even in the environment that cannot in advance recognize them and provides clues of good methods to be able to evaluate the degree of risk of real-life situations. The risk factors which exist in each residential environment are not uniform and there are many cases that don't have single factors. It's the plan which can suppose high level of each risk factor and risk environment by handling these various and multiple risk factors. This paper includes the pre-clustering to the risk calculation using the artificial neural network. It was confirmed that the risk calculation using the artificial neural network could be improved through a pre-clustering of the input data.

목차

Abstract
 1. Introduction
 2. Related Research
 3. Assessment of Risk Factor for Residential Safety
  3.1. The Artificial Neural Network Theory
  3.2. The Plan to Recognize Risk Situations
  3.3. Build the System to Detect Risk Factors of the Artificial Neural Network
 4. An Experiment and Evaluation
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Donghyok Suh [ Department of Architecture, Namseoul University, Cheonan, Chungnam, 331-707, Korea ] Correspond author
• Jeonghwa Song [ Department of Architecture, Namseoul University, Cheonan, Chungnam, 331-707, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605