• Journal of the Korean earth science society
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• v.35 no.4
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• pp.225-236
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• 2014

Multivariate statistical analyses have been extensively applied to hydrochemical measurements to analyze and interpret the data. This study examines anthropogenic factors obtained from applications of correspondence analysis (CA) and principal component analysis (PCA) to a hydrogeochemical data set. The goal was to synthesize the hydrogeochemical information using these multivariate statistical techniques by incorporating hydrogeochemical speciation results calculated by the program, commonly used, WATEQ4F included in the NETPATH. The selected case study was LPG underground storage caverns, which is located in the southeastern Korea. The highly alkaline groundwaters at this study area are an analogue for the repository system. High pH, speciation of Al and possible precipitation of calcite characterize these groundwaters. Available groundwater quality monitoring data were used to confirm these statistical models. The present study focused on understanding the hydrogeochemical attributes and establishing the changes of phase when two anthropogenic effects (i.e., disinfection activity and cement pore water) in the study area have been introduced. Comparisons made between two statistical results presented and the findings of previous investigations highlight the descriptive capabilities of PCA using calculated saturation index and CA as exploratory tools in hydrogeochemical research.

• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.504-511
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• 2010

In this paper, we propose the face region recognition system for City-Security CCTV(Closed Circuit Television) using hippocampal neural network which is modelling of human brain's hippocampus. This system is composed of feature extraction, learning and recognition part. The feature extraction part is constructed using PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis). In the learning part, it can label the features of the image-data which are inputted according to the order of hippocampal neuron structure to reaction-pattern according to the adjustment of a good impression in a dentate gyrus and remove the noise through the auto-associative memory in the CA3 region. In the CA1 region receiving the information of the CA3, it can make long-term memory learned by neuron. Experiments confirm the each recognition rate, that are shape change and light change. The experimental results show that we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.1987-1996
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• 2009

Recently, the color of vehicle license plate has been changed from green to white. Thus the vehicle plate recognition system used for parking management systems, speed and signal violation detection systems should be robust to the both colors. This paper presents a vehicle license plate recognition system, which works on both of green and white plate at the same time. In the proposed system, the image of license plate is taken from a captured vehicle image by using morphological information. In the next, each character region in the license plate image is extracted based on the vertical and horizontal projection of plate image and the relative position of individual characters. Finally, for the recognition process of extracted characters, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis) are sequentially utilized. In the experiment, vehicle license plates of both green background and white background captured under irregular illumination conditions have been tested, and the relatively high extraction and recognition rates are observed.

• Food Science of Animal Resources
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• v.30 no.2
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• pp.185-189
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• 2010

An electronic nose was used to identify Salmonella contamination on beef based on odors. To detect pathogen contamination of beef, $100{\mu}L$ of $10^5CFU/g$ Salmonella Enteritidis or Salmonella Typhimurium cell suspensions were spiked onto 5 g beef sirloin samples in individual vials. Odor changes over time were then measured and analyzed using an electronic nose system to identify pathogen contamination. In principle, the electronic nose system based on a surface acoustic wave (SAW) detector produced different frequency responses depending on the time and amount of each chemical. Multivariate analysis of the odor data was conducted to detect Salmonella contamination of beef. Salmonella odors were successfully distinguished from uncontaminated beef odors by principal component analysis (PCA). The PCA results showed that Salmonella contamination of beef could be detected after 4 h of incubation. The numbers of cells enumerated by standard plate count after 4 h of inoculation were $2{\times}10^6CFU/g$ for both Salmonella Enteritidis and Salmonella Typhimurium.

• Journal of IKEEE
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• v.12 no.4
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• pp.217-224
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• 2008

This paper provides a framework for semantic correspondence of heterogeneous databases using self- organizing map. It solves the problem of overlapping between different databases due to their different schemas. Clustering technique using self-organizing maps (SOM) is tested and evaluated to assess its performance when using different kinds of data. Preprocessing of database is performed prior to clustering using edit distance algorithm, principal component analysis (PCA), and normalization function to identify the features necessary for clustering.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.267-270
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• 2001

홍채인식 시스템에서 입력된 영상으로부터 정합(matching)에 사용될 홍채 영역을 추출해 내는 과정은 필수적인 과정으로 빠른 처리 속도와 정확성을 요구한다. 기존의 원형검출기나 허프(Hough) 변환을 이용한 방법 등은 홍채의 바깥쪽과 안쪽 경계를 비교적 정확하게 검출해내는 장점이 있으나 탐색영역이 커서 수행시 간이 매우 많이 걸리는 단점이 있다. 따라서 본 논문에서는 이진화와 형태학적 연산(morphology)을 이용하는 새로운 탐색 영역 단축 방법을 제안한다. 제안한 방법은 기존의 홍채영역 검출 방법에 적용할 경우 수행 시간을 효율적으로 단축시킬 수 있다. 검출된 영역에 대해서 주성분 분석법(principal component analysis, PCA)을 이용해 매칭을 수행한 결과 약 95%의 인식율을 나타내었다.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.443-446
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• 2000

인간은 일상 생활에서 제스처, 표정과 같은 비언어적인 수단을 이용하여 수많은 정보를 전달한다. 따라서 자연스럽고 지적인 인터페이스를 구축하기 위해서는 제스처 인식에 관한 연구가 매우 중요하다. 본 논문에서는 영상 시퀸스의 각 영상들이 가지고 있는 정적인 양이 아닌, 영상과 이웃하는 영상들의 변화량을 수치적으로 측정하고 이를 주성분 분석법(PCA : Principal Component Analysis)과 은닉 마르코프 모델(HMM : Hidden Markov Model)을 이용하여 인식하는 방법을 소개한다.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2002.04a
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• pp.237-242
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• 2002

This study is objected to assess the recharges of phreatic aquifers in the south Korea. The water level data of the national ground-water monitoring network were analysed by PCA(Principal Component Analysis), and classified to 8 types. The recharge were estimated by ‘water-level change method’ on basis of the classified types and compared with the previous methods(hydrograph separation methods) on basis of 4 river basins. The recharge were various type by type and site by site. But the recharge estimated by this study were consistent with that of the other studies.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.585-588
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• 1999

This paper concentrates on an experimental results on visual only recognition tasks using an image transform approach and HMM based recognition system. There are two approaches for extracting features of lipreading, a lip contour based approach and an image transform based one. The latter obtains a compressed representation of the image pixel values that contain the speaker's mouth results in superior lipreading performance. In addition, PCA(Principal component analysis) is used for fast algorithm. Finally, HMM recognition tasks are compared with the another.

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.115-118
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• 2003

This paper describes a system of detecting multi-views faces and estimating their face poses in an omni-directional camera environment for non-intrusive iris recognition. The paper is divided into two parts; First, moving region is identified by using difference-image information. Then this region is analyzed with face-color information to find the face candidate region. Second part is applying PCA (Principal Component Analysis) to detect multi-view faces, to estimate face pose.