• 한국화재소방학회:학술대회논문집
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• 한국화재소방학회 2008년도 추계학술논문발표회 논문집
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• pp.461-464
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• 2008

Plan of Human Resource Development is essential to strategically promote poor domestic fire protection industry and as a consequent to grow it to a high value added service industry. To foster fire protection industry, any reform will be needed to for a smooth supplies of work force by training customized high class work force. Consequently, In this study, We will investigate and analyze the status and problems of current domestic fire protection industry so that We can suggest the direction for a way to improve these.

• 한국화재소방학회:학술대회논문집
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• 한국화재소방학회 2008년도 추계학술논문발표회 논문집
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• pp.148-156
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• 2008

• 한국화재소방학회:학술대회논문집
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• 한국화재소방학회 2007년도 추계학술논문발표회 논문집
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• pp.199-204
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• 2007

• 한국화재소방학회:학술대회논문집
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• 한국화재소방학회 2007년도 춘계학술논문발표회 논문집
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• pp.332-337
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• 2007

• 한국화재소방학회:학술대회논문집
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• 한국화재소방학회 2008년도 춘계학술논문발표회 논문집
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• pp.345-352
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• 2008

• Steel and Composite Structures
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• 제16권2호
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• pp.117-133
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• 2014

Natural vibration of truss cable structures is analyzed based upon the general structural analysis software ANSYS, energy variational method and Rayleigh method, the calculated results of three methods are compared, from which the characteristics of free-vibration are obtained. Moreover, vertical seismic response analysis of truss cable structures is carried out via time-history method. Introducing three natural earthquake waves calculated the results including time-history curve of vertical maximal displacement, time-history curve of maximal internal force. Variation curve of maximal displacement of node along span, and variation curve of maximal internal force of member along span are presented. The results show the formulas of frequencies for truss cable structures obtained by energy variational method are of high accuracy. Furthermore, the maximal displacement and the maximal internal force occur near the 1/5 span point. These provide convenient and simple design method for practical engineering.

• 한국화재소방학회논문지
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• 제29권5호
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• pp.67-72
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• 2015

우리나라는 급속한 경재성장 및 도시화로 인해 도시지역의 건축물이 밀집되어 있다. 이러한 지역에서 화재가 발생할 경우, 강풍에 의한 불티의 비산으로 화재가 확대될 위험성이 존재한다. 하지한 현재 불티의 성상에 대한 모델 구축 및 연구가 부족한 실정이다. 본 연구에서는 일본 건축연구소(BRI)의 풍동 시설을 이용한 불티의 성상 실험을 통하여 비산모델을 구축하고 기존의 도시화재 시뮬레이션을 개선하였다.

• 한국재난정보학회 논문집
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• 제5권2호
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• pp.10-21
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• 2009

This study contains the fire protection safety of the public-utilization shops. The toll of fires in the public-utilization shops is so heavy in spite the less occurency. The shops are mostly compartmented into small rooms by partition wall which hinders the evacuation of the people on fire. This study provides additional requirements on the fire safety of the public-utilization shops needed for human life and property.

• 한국화재소방학회논문지
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• 제26권5호
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• pp.92-98
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• 2012

최근 개정된 초고층건물 특별법에서는 피난안전구역을 최소 30개층 마다 설정함과 동시에 계단실을 수직구획 하도록 규정하고 있다. 이에 본 연구에서는 중앙코아 방식의 80층 초고층건물 계단실 수직구획이 연돌효과 및 계단실 급기가압 성능에 미치는 영향을 시뮬레이션 해석을 통해 분석하였다. 해석결과, 계단실 수직구획이 없는 경우 연돌효과로 인해 최대 차압 250 Pa 이상이 발생하였으며, 계단실만 수직구획한 경우에는 10 Pa 정도만 감소하였으나 계단실과 승강기의 승강로를 동시에 수직구획한 경우 최대차압이 약 125 Pa로 낮아져 연돌효과가 약 50 % 감소하였다. 또한, 계단실을 급기가압 할 경우 계단실온도가 실내온도와 외기온도 차이의 약 1/2일 때가 소요급기풍량 측면에서 가장 효율적인 것으로 예측되었다.

• Smart Structures and Systems
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• 제31권5호
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• pp.469-483
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• 2023

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.