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포천 반월산성 출토 슬래그로부터 추정되는 한국 고대의 제련기술
朴長植 弘益大學校 産業技術硏究所 2004 産業技術 Vol.14 No.-
Three pieces of strongly magnetic slag have been excavated from the ancient military fortress, Banwolsanseong, located in the City of Pocheon. Their metallurgical microstructure has been found to consist primarily of wustite (FeO) in the form of dendrite. This kind of slag, high in Fe content, is typically generated in the low temperature reduction process that is for the smelting of bloomery iron. By reviewing the experimental results in terms of the context of excavation, this paper is to propose that the slag artifacts may serve as the evidence of the production of bloomery iron in ancient Korea.
박장식,Park, Jang-Sik 한국전자통신학회 2014 한국전자통신학회 논문지 Vol.9 No.2
In this paper, an algorithm using RGB-depth camera is proposed to detect smoke in interrior. RGB-depth camera, the Kinect provides RGB color image and depth information. The Kinect sensor consists of an infra-red laser emitter, infra-red camera and an RGB camera. A specific pattern of speckles radiated from the laser source is projected onto the scene. This pattern is captured by the infra-red camera and is analyzed to get depth information. The distance of each speckle of the specific pattern is measured and the depth of object is estimated. As the depth of object is highly changed, the depth of object plain can not be determined by the Kinect. The depth of smoke can not be determined too because the density of smoke is changed with constant frequency and intensity of infra-red image is varied between each pixels. In this paper, a smoke detection algorithm using characteristics of the Kinect is proposed. The region that the depth information is not determined sets the candidate region of smoke. If the intensity of the candidate region of color image is larger than a threshold, the region is confirmed as smoke region. As results of simulations, it is shown that the proposed method is effective to detect smoke in interior. 본 논문에서 RGB-Depth 카메라를 이용하여 실내에서의 연기를 검출하는 알고리즘을 제안한다. RGB-Depth 카메라는 RGB 색영상과 깊이 정보를 제공한다. 키넥트(Kinect)는 특정한 패턴의 적외선을 출력하고 이를 적외선 카메라로 수집하고 분석하여 깊이 정보를 획득한다. 특정한 패턴을 구성하는 점들 각각에 대하여 거리를 측정하고 객체면의 깊이를 추정한다. 따라서, 이웃하는 점들의 깊이 변화가 많은 객체인 경우에는 객체면의 깊이를 결정하지 못한다. 연기의 농도가 일정 주파수로 변화하고, 적외선 영상의 이웃하는 화소간의 변화가 많기 때문에 키넥트가 깊이를 결정하지 못한다. 본 논문에서는 연기에 대한 키넥트의 특성을 이용하여 연기를 검출한다. 키넥트가 깊이를 결정하지 못한 영역을 후보영역으로 설정하고, 색영상의 밝기가 임계값보다 큰 경우 연기영역으로 결정한다. 본 논문에서는 시뮬레이션을 통하여 실내에서의 연기 검출에 RGB-Depth 카메라가 효과적임을 확인할 수 있다.
Bronze Artifacts from Pocheon Banwol Fortress and the Korean High-tin Bronze Technology
박장식 한국문화사학회 2006 文化史學 Vol.0 No.26
The metallurgical examination of three bronze artifacts from an ancient military fortress found clues revealing the developmental process of a high-tin bronze technology in Korea. Each of them was given a distinctly different treatment, and could chronologically be arranged on the basis of the technical sophistication involved. The most primitive was made exclusively by casting, and the next was given a quench treatment after casting, and the most advanced was hot forged after casting and then quenched. The last was special in that it was excavated near the surface, had thin walls of 0.4mm to 1.1mm, and was made from an unleaded Cu-22 mass % Sn alloy. These traits all demonstrate that the last was made most recently when the high-tin bronze tradition, characterized by the specific alloy composition and the advanced thermo-mechanical treatments, was fully established.
Gaussian 혼합모델을 이용한 영상기반 화재검출 알고리즘
박장식,김현태,유윤식,Park, Jang-Sik,Kim, Hyun-Tae,Yu, Yun-Sik 한국전자통신학회 2011 한국전자통신학회 논문지 Vol.6 No.2
본 논문에서는 Gaussian 혼합모델을 이용한 영상기반 화재검출 알고리즘을 제안한다. CCTV로부터 입력되는 영상으로부터 배경영상을 추출한 후 입력영상과 배경영상간의 차신호로부터 전경영상을 분리한다. 분리된 전경영상은 배경영상에 대하여 변화가 생긴 후보영역으로 간주하고 후보영역에 대하여 Gaussian 혼합모델 기법을 적용하여 연기 또는 화염의 특성을 갖는 영역을 화재로 인식한다. 실험 결과를 통하여 제안하는 화재검출 알고리즘이 실내에서의 화염 및 연기를 검출할 수 있음을 보인다. In this paper, a fire detection algorithm based on video processing is proposed. At the first stage, background image extracted from CCTV video input signal, and then foreground image were separated by differencing CCTV input signal from background image. At the second stage, candidated area were extracted by using color information from foreground image. At the final stage, smoke or flame characteristic area were separated by using Gaussian mixture modeling applied to candidated area, and then fire can be detected. Through real experiments at the inner room, it is shown that the proposed system works well.