• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.27-33
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• 2008

The clustering of gene expression data is used to analyze the results of microarray studies. This clustering is one of the frequently used methods in understanding degrees of biological change and gene expression. In biological research, MCL algorithm is an algorithm that clusters nodes within a graph, and is quick and efficient. We have modified the existing MCL algorithm and applied it to microarray data. In applying the MCL algorithm we put forth a simulation that adjusts two factors, namely inflation and diagonal tent and converted them by making use of Markov matrix. Furthermore, in order to distinguish class more clearly in the modified MCL algorithm we took the average of each row and used it as a threshold. Therefore, the improved algorithm can increase accuracy better than the existing ones. In other words, in the actual experiment, it showed an average of 70% accuracy when compared with an existing class. We also compared the MCL algorithm with the self-organizing map(SOM) clustering, K-means clustering and hierarchical clustering (HC) algorithms. And the result showed that it showed better results than ones derived from hierarchical clustering and K-means method.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.4
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• pp.765-771
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• 2009

The medical informatics for clustering Sasang types with collected clinical data is important for the personalized medicine, but it has not been thoroughly studied yet. The purpose of this study was to examine the usefulness of neural network data mining algorithm for traditional Korean medicine. We used Kohonen neural network, the Self-Organizing Map (SOM), for the analysis of biomedical information following data pre-processing and calculated the validity index as percentage correctly predicted and type-specific sensitivity. We can extract 12 data fields from 30 after data pre-processing with correlation analysis and latent functional relationship analysis. The profile of Myers-Briggs Type Inidcator and Bio-Impedance Analysis data which are clustered with SOM was similar to that of original measurements. The percentage correctly predicted was 56%, and sensitivity for So-Yang, Tae-Eum and So-Eum type were 56%, 48%, and 61%, respectively. This study showed that the neural network algorithm for clustering Sasang types based on clinical data is useful for the sasang type diagnostic test itself. We discussed the importance of data pre-processing and clustering algorithm for the validity of medical devices in traditional Korean medicine.

• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.45-52
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• 2012

In this paper, an crowd density in elevator estimation method based on multi-class Adaboost classifier is proposed. The SOM (Self-Organizing Map) based conventional methods have shown insufficient performance in practical scenarios and have weakness for low reproducibility. The proposed method estimates the crowd density using multi-class Adaboost classifier with texture features, namely, GLDM(Grey-Level Dependency Matrix) or GGDM(Grey-Gradient Dependency Matrix). In order to classify into multi-label, weak classifier which have better performance is generated by modifying a weight update equation of general Adaboost algorithm. The crowd density is classified into four categories depending on the number of persons in the crowd, which can be 0 person, 1-2 people, 3-4 people, and 5 or more people. The experimental results under indoor environment show the proposed method improves detection rate by about 20% compared to that of the conventional method.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.189-193
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• 2011

유역관리 및 수질 향상을 위해 다양한 환경정책이 시행되고 있으며, 최근 수질오염총량관리제의 시행으로 인해 보다 집중적인 유역관리와 수질 향상을 위한 노력이 배가되고 있다. 이러한 노력의 일환으로 현재 환경부 국립환경과학원에서는 수질오염총량관리를 위하여 단위유역의 말단지점에서 수질 및 유량자료에 대한 정기적인 측정을 8일 간격으로 시행하고 있으며, 데이터 베이스 및 웹시스템을 통하여 자료를 공개하고 있다(이호열, 2009). 이와 같은 자료의 측정과 축적은 그 분석을 통해 수질 개선을 위한 우선 관리 대상지점의 파악 등과 같이 수질오염총량관리제의 시행과 평가를 위해 사용될 수 있을 뿐만 아니라 새로운 환경정책의 수립에도 활용될 수 있을 것으로 기대된다. 그러나 현재 소수의 연구들에서만 상기의 자료를 단순히 활용한 결과를 찾을 수 있으며, 특히 측정된 수질 및 유량자료를 분석하여 발표한 연구결과 역시 소수에 지나지 않는다(김철겸 등, 2009). 측정 자료에 대한 분석 및 이에 따른 자료의 활용성 제고를 위해서 다양한 자료 분석 기법의 개발과 적용이 절실하다. 이러한 자료 분석 기법의 개발 및 적용에 관한 연구의 일환으로 최근 패턴분류를 위해 다양한 분야에서 활용되고 있는 자기조직화 특성 지도(Self Organizing Feature Map: SOFM)를 상기의 측정 자료에 적용한 연구 결과가 보고된 바 있다(진영훈 등, 2009; 2010). 본 연구에서는 수질오염총량관리제를 위해 측정되고 있는 수질 및 유량자료를 수집하여 자료에 내재되어 있는 시 공간적 특성을 분석하고자 하였다. 영산강 유역을 대상으로 하여, 본 유역 내의 단위유역들 중 황룡_A, 지석_A, 영본_A, 영본_B, 영본_C, 영본_D의 말단지점에서 측정되고 있는 BOD (Biochemical Oxygen Demand), TOC (Total Organic Carbon), T-N (Total Nitrogen), T-P (Total Phosphorus), SS (Suspended Solids) 수질농도 및 유량자료를 대상으로 연구를 진행하였다.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.535-538
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• 1990

The main problem in speech recognition is the enormous variability in acoustic signals due to complex but predictable contextual effects. Especially in plosive consonants it is very difficult to find invariant cue due to various contextual effects, but humans use these contextual effects as helpful information in plosive consonant recognition. In this paper we experimented on three artificial neural net models for the recognition of plosive consonants. Neural Net Model I used "Multi-layer Perceptron ". Model II used a variation of the "Self-organizing Feature Map Model". And Model III used "Interactive and Competitive Model" to experiment contextual effects. The recognition experiment was performed on 9 Korean plosive consonants. We used VCV speech chains for the experiment on contextual effects. The speech chain consists of Korean plosive consonants /g, d, b, K, T, P, k, t, p/ (/ㄱ, ㄷ, ㅂ, ㄲ, ㄸ, ㅃ, ㅋ, ㅌ, ㅍ/) and eight Korean monothongs. The inputs to Neural Net Models were several temporal cues - duration of the silence, transition and vot -, and the extent of the VC formant transitions to the presence of voicing energy during closure, burst intensity, presence of asperation, amount of low frequency energy present at voicing onset, and CV formant transition extent from the acoustic signals. Model I showed about 55 - 67 %, Model II showed about 60%, and Model III showed about 67% recognition rate.

• Ocean and Polar Research
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• v.25 no.3
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• pp.237-247
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• 2003

The temporal dynamics of the meiofauna community in Marian Cove, King George Island were observed from January 22 to October 29 1996. Generally, 14 taxa of metazoan meiofauna were found. Nematodes were dominant comprising 90.12% of the community, harpacticoid 6.55%, and Kinorhynchs 1.54%. Meiofauna abundance increased monthly from January to May 1996, while varying in abundance after August 1996. Overall mean abundance of metazoan meiofauna was $2634ind./10cm^2$ during the study periods, which is about as high as that found in temperate regions. Nematodes were most abundant representing $2399ind./10cm^2$. Mean abundance of harpacticoids, including copepodite and nauplius was $131ind./10cm^2$ by kinorhynchs $(26ind./10cm^2)$. The overall abundance of other identified organisms was $31ind./10cm^2$ Other organisms consisted of a total of 11 taxa including Ostracoda $(6ind./10cm^2)$, Polycheata $(7ind./10cm^2)$, Oligochaeta $(8ind./10cm^2)$, and Bivalvia $(6ind./10cm^2)$. Additionally, protozoan Foraminifera occurred at the study area with a mean abundance of $263ind./10cm^2$. Foraminiferans were second in dominance to nematodes. The dominant taxa such as nematodes, harpacticoids, kinorhynchs and the other tua were trained and extensively scattered in the map through the Kohonen network. The temporal pattern of the community composition was most affected by the abundance dynamics of kinorhynchs and harpacticoids. The neural network model also allowed for simulation of data that was missing during two months of inclement weather. The lowest meiofauna abundance was found in August 1996 during winter. The seasonal changes were likely caused by temperature and salinity changes as a result of meltwater runoff, and the physical impact by passing icebergs.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.438-441
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• 2007

Recently, due to molecular biology and engineering technology, DNA microarray makes people watch thousands of genes and the state of variation from the tissue samples of living body. With DNA Microarray, it is possible to construct a genetic group that has similar expression patterns and grasp the progress and variation of gene. This paper practices Cluster Analysis which purposes the discovery of biological subgroup or class by using gene expression information. Hence, the purpose of this paper is to predict a new class which is unknown, open leukaemia data are used for the experiment, and MCL (Markov CLustering) algorithm is applied as an analysis method. The MCL algorithm is based on probability and graph flow theory. MCL simulates random walks on a graph using Markov matrices to determine the transition probabilities among nodes of the graph. If you look at closely to the method, first, MCL algorithm should be applied after getting the distance by using Euclidean distance, then inflation and diagonal factors which are tuning modulus should be tuned, and finally the threshold using the average of each column should be gotten to distinguish one class from another class. Our method has improved the accuracy through using the threshold, namely the average of each column. Our experimental result shows about 70% of accuracy in average compared to the class that is known before. Also, for the comparison evaluation to other algorithm, the proposed method compared to and analyzed SOM (Self-Organizing Map) clustering algorithm which is divided into neural network and hierarchical clustering. The method shows the better result when compared to hierarchical clustering. In further study, it should be studied whether there will be a similar result when the parameter of inflation gotten from our experiment is applied to other gene expression data. We are also trying to make a systematic method to improve the accuracy by regulating the factors mentioned above.

• Smart Media Journal
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• v.1 no.2
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• pp.12-22
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• 2012

Today we are witnessing an increasingly widespread use of cameras in our lives for video surveillance, robot vision, and mobile phones. This has led to a renewed interest in computer vision in general and an on-going boom in human activity recognition in particular. Although not particularly fancy per se, human gait is inarguably the most common and frequent action. Early on this decade there has been a passing interest in human gait recognition, but it soon declined before we came up with a systematic analysis and understanding of walking motion. This paper presents a set of DBN-based models for the analysis of human gait in sequence of increasing complexity and modeling power. The discussion centers around HMM-based statistical methods capable of modeling the variability and incompleteness of input video signals. Finally a novel idea of extending the discrete state Markov chain with a continuous density function is proposed in order to better characterize the gait direction. The proposed modeling framework allows us to recognize pedestrian up to 91.67% and to elegantly decode out two independent gait components of direction and posture through a sequence of experiments.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.36-41
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• 2010

An advanced form of Multistage Feature-based Classification Model(AMFCM), called AMFCM, is proposed in this paper. AMFCM like MFCM does not use the concatenated form of available feature vectors extracted from original data to classify each data, but uses only groups related to each feature vector to classify separately. The prpposed AMFCM improves the contribution rate used in MFCM and proposes a confusion table for each local classifier using a specific feature vector group. The confusion table for each local classifier contains accuracy information of each local classifier on each class of data. The proposed AMFCM is applied to the problem of music genre classification on a set of music data. The results demonstrate that the proposed AMFCM outperforms MFCM by 8% - 15% on average in terms of classification accuracy depending on the grouping algorithms used for local classifiers and the number of clusters.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.4 s.316
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• pp.8-14
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• 2007

Social participation of the elderly and people with disabilities continuously becomes more active due to improvement of social systems and technological development. Various assistive systems, such as intelligence robots and intelligence home systems, have been developed to support the social participation, and obviously contributed to independent lives of the elderly and people with disabilities. These systems, however, usually require expensive special hardware. To relieve a financial burden software-oriented approaches, which utilizes existing hardware such as laptops, can be an alternative. The software should be developed considering users with physical limitation and without much knowledge of computers. This paper suggests software-oriented approaches to solve these problems and describes an actual development procedure of the software with related theories. We also introduce an unified user interface for a special work chair as a real application.