• Korean Journal of Remote Sensing
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• v.5 no.2
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• pp.91-107
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• 1989

The purpose of this study is to investigate ways to use Landsat TM data for geological mapping and minieralized area detection. The study was carried out in Kyongju-Pohang area where toseki and bentonite mines are distributed. Rock samples of 18 granites, andesites, toseki, betonites, sedimentary rocks and altered rocks in the study area were collected for the study. The radiometric measurtment of the rock samples were carried out with a radiometer in the laboratory and in the field. The Landsat TM bands 2,3,4,5,7 were used for the measurement. The radiometric characteristics of the sample were mainly processed by the principal component analysis. It was found that the pricipal component analysis of the radiometric characteristics of geologic materials is very useful for the detection of the alteration of rocks and grade of mineral contents. It is expected that the technique can be used in the future for the efficient exploration of minerals in this country and abroad.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.341-344
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• 2023

건식 반도체 공정에서 저온플라즈마를 일정한 상태로 유지하는 것은 반도체 공정의 효율을 높이기 위해서 매우 중요한 문제이다. 그러나 저온플라즈마 반응로를 진공상태로 유지해야하기 때문에 플라즈마의 상태를 예측하는 작업은 매우 어렵다. 본 연구에서는 OES 센서에서 수집된 데이터를 이용하여 플라즈마의 상태를 예측하는 모형을 개발하였다. 질소가스를 이용한 플라즈마 반응로에서 15개의 서로 다른 플라즈마를 생성하여 OES 데이터를 수집하였고 15개 플라즈마의 상태를 분류할 수 있는 Gaussian Mixture Model(GMM)을 개발하였다. 총 7,296개 파장에서 측정된 분광강도(intensity)를 주성분분석(Pricipal Component Analysis)를 통해 2개의 주성분으로 차원 축소하여 GMM 모형을 개발하엿다. 모형의 정확도는 약 81.72%으로 플라즈마의 OES데이터에 대한 해석력은 뛰어났다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.6
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• pp.12-18
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• 2010

Automatic Target Detection (ATD) systems that use forward-looking infrared (FLIR) consists of three stages. preprocessing, detection, and clutter rejection. All potential targets are extracted in preprocessing and detection stages. But, this results in a high false alarm rates. To reduce false alarm rates of ATD system, true targets are extracted in the clutter rejection stage. This paper focuses on clutter rejection stage. This paper presents a new clutter rejection technique using PCA features and stochastic features of clutters and targets. PCA features are obtained from Euclidian distances using which potential targets are projected to reduced eigenspace selected from target eigenvectors. CV is used for calculating stochastic features of edges in targets and clutters images. To distinguish between target and clutter, LDA (Linear Discriminant Analysis) is applied. The experimental results show that the proposed algorithm accurately classify clutters with a low false rate compared to PCA method or CV method