• Korean Journal of Ecology and Environment
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• v.33 no.3 s.91
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• pp.230-243
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• 2000

A large-scale community data were patterned by utilizing an unsupervised learning algorithm in artificial neural networks. Data for benthic macroinvertebrates in streams of South Korea reported in publications for 12 years from 1984 to 1995 were provided as inputs for training with the Kohonen network. Taxa included for the training were 5 phylum, 10 class, 26 order, 108 family and 571 species in 27 streams. Abundant groups were Diptera, Ephemeroptera, Trichoptera, Plecoptera, Coleoptera, Odonata, Oligochaeta, and Physidae. A wide spectrum of community compositions was observed: a few tolerant taxa were collected at polluted sites while a high species richness was observed at relatively clean sites. The trained mapping by the Kohonen network effectively showed patterns of communities from different river systems, followed by patterns of communities from different environmental disturbances. The training by the proposed artificial neural network could be an alternative for organizing community data in a large-scale ecological survey.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.547-553
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• 2008

We propose a two-stage document layout segmentation method. At the first stage, as top-down segmentation, morphological distance map algorithm extracts a collection of rectangular regions from a given input image. This preliminary result from the first stage is employed as input parameters for the process of next stage. At the second stage, a machine-learning algorithm is adopted RBF network, one of neural networks based on statistical model, is selected. In order for constructing the hidden layer of RBF network, a data clustering technique bared on the self-organizing property of Kohonen network is utilized. We present a result showing that the supervised neural network, trained by 300 number of sample data, improves the preliminary results of the first stage.

• The Journal of the Convergence on Culture Technology
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• v.2 no.1
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• pp.93-98
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• 2016

As information technologies are rapidly developed recently, social networking services through a variety of mobile devices and smart screen is becoming popular. SNS is a social networking based services which is online forms from existed offline. SNS can also be used differently which is confused with the online community. A modelling algorithm is a variety of techniques, which are assocoation, clustering, neural networks, and decision trees, etc. By utilizing this technique, it is necessary to study to effectively using the large number of materials. In this paper, we evaluate in particular the performance of the algorithm based on the results of the clustering using Facebook Insights data for the EM algorithm to be evaluated as a good performance in clustering. Through this analysis it was based on the results of the application of the experimental data of the change and the South Australian state library according to the performance of the EM algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.251-254
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• 2001

본 논문에서는 Self Organizing Map을 이용하여 fMRI data를 분석해 보았다. fMRl (functional Magnetic Resonance Imaging)는 인간의 뇌에 대한 비 침투적 연구 방법 중 최근에 각광받고 있는 것이다. Motor task를 수행하고 있는 피험자로부터 image data를 얻어내어 SOM을 적용하여 clustering한 결과 motor cortex 영역이 뚜렷하게 clustering 되었음을 알 수 있었다.

• The Journal of the Acoustical Society of Korea
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• v.35 no.4
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• pp.303-309
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• 2016

This paper proposes a novel approach to the problem of audio signal clustering using a stacked autoencoder. The proposed stacked autoencoder learns an efficient representation for the input signal, enables clustering constituent signals with similar characteristics, and therefore the original sources can be separated based on the clustering results. STFT (Short-Time Fourier Transform) is performed to extract time-frequency spectrum, and rectangular windows at all the possible locations are used as input values to the autoencoder. The outputs at the middle, encoding layer, are used to cluster the rectangular windows and the original sources are separated by the Wiener filters derived from the clustering results. Source separation experiments were carried out in comparison to the conventional NMF (Non-negative Matrix Factorization), and the estimated sources by the proposed method well represent the characteristics of the orignal sources as shown in the time-frequency representation.

• Journal of the Korean earth science society
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• v.22 no.2
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• pp.138-146
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• 2001

The land cover over Korean peninsula was classified using a multi-temporal NOAA/AVHRR (Advanced Very High Resolution Radiometer) data. Four types of phenological data derived from the 10-day composited NDVI (Normalized Differences Vegetation Index), maximum and annual mean land surface temperature, and topographical data were used not only reducing the data volume but also increasing the accuracy of classification. Self organizing feature map (SOFM), a kind of neural network technique, was used for the clustering of satellite data. We used a decision tree for the classification of the clusters. When we compared the classification results with the time series of NDVI and some other available ground truth data, the urban, agricultural area, deciduous tree and evergreen tree were clearly classified.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.2
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• pp.77-88
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• 2018

This paper proposes a RBFNN(Radial Basis Function Neural Network) based decentralized adaptive tracking control scheme using PSO(Particle Swarm Optimization) for an uncertain electrically driven robot system with input saturation. Practically, the magnitudes of input voltage and current signals are limited due to the saturation of actuators in robot systems. The proposed controller overcomes this input saturation and does not require any robot link and actuator model parameters. The fitness function used in the presented PSO scheme is expressed as a multi-objective function including the magnitudes of voltages and currents as well as the tracking errors. Using a PSO scheme, the control gains and the number of the RBFs are tuned automatically and thus the performance of the control system is improved. The stability of the total control system is guaranteed by the Lyapunov stability analysis. The validity and robustness of the proposed control scheme are verified through simulation results.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1679-1683
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• 2008

낙동강 하류지점인 물금은 2003년${\sim}$2005년의 대부분이 부영양화의 기준을 넘고 있다. 하구둑 건설이후, 담수화 된 하구둑 상부에서는 부영양화가 가속화되었다. 수질의 악화는 물론 강 생태계의 구조와 기능의 변화까지 초래되었다. 지난 $7{\sim}8$년 간 낙동강 하류 지역은 갈수기 식물성 플랑크톤 군집의 대거 번성으로 인한 부영양화로 연중 심각한 수질 오염문제를 야기하고 있다. 본 연구는 WASP 7.2 모형과 예측된 동물성플랑크톤을 이용하여 낙동강 유역의 하류 지역인 물금의 부영양화를 예측하는 것이다. 2005년의 관측값을 초기조건으로 고정하고 DO, $NO_3$-N, $PO_4$-P, 기상청에서 예보되는 기온을 사용하여 동물성 플랑크톤을 신경망 모형으로 예측한 뒤, 수온 대신 기상청의 기온을 입력하여 $1{\sim}3$일 후의 단기 수질을 예측하였다. 부영양화 예측결과와 2005년의 월별 수질 관측값을 통계량을 이용하여 분석하였다. $1{\sim}3$일 후의 예측결과 수질항목 중 부영양화의 기준이 되는 클로로필-a, 총 질소, 총 인의 경우는 예측기간 모두 관측값에 적합하게 모의되었다. WASP 7.2 모형의 수질항목 관측자료를 초기값으로 입력하고, 예측된 동물성 플랑크톤의 개체수와 기상청에서 예보되는 기온을 사용한 수질모의는 낙동강의 단기 수질예측에 유의한 의미가 있을 것으로 사료된다.

• The Korean Journal of Malacology
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• v.10 no.1
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• pp.1-8
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• 1994

동양달팽이속(Nesiohelix)에 속하는 종으로 한국에서는 유일하게 서식하고 있는 종으로알려진 동양달팽이(Nesiohelix samarangae)성체의 눈에 대하여 해부학적 및 미세구조관찰을 실시한 결과 다음과 같은 결론을 얻을 수 있었다. 동양달팽이의 눈은 각막, 무세포성의 수정체, 망막및 신경망 등으로 구성되어 있는 바 가장 복잡한 구조를 보이는 부분은 망막으로서 이의상피는 2종의 원주세포로 구성되어있다. 그 하나는 광수용세포로서 세포의 상부세포질이 원추형 또는 피라미드형의 돌기를 이루었고 그 유리표면에는 긴 감간(microvilli)이 무수히 많이 존재하고 있으며, 돌기를 이룬 세포질 내에는 용해소체의 활성이 높게 나타난다. 또한 광수용세포들의 상부세포질에는 이웃해 있는 색소세포들의 세포질 돌기들이 여러개 침입해 들어와 있다. 그리고 핵 주변의 세포질에는 수많은 photic vesicle들이 군집을 이루어 존재한다. 다른 한 종류의 세포는 색소세포들로서 광수용세포들과는 달리 세포의 상부에 원추형의 세포질 돌기가 없고 다만 그 유리표면을 따라 광수용세포의 것보다는 굵고 짧은 많은 감간이 존재하는데 두 종류의 세포들에서 뻗어나온 감간들은 서로 만나 때로 파상을 이루고 있다. 망막 상피세포층의 기저막 아래에는 신경망이 컵모양의 망막을 따라 존재한다.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.1016-1023
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• 2009

A license plate recognition system consists of image processing in which characters and features are extracted, and pattern recognition in which extracted characters are classified. Feature extraction plays an important role in not only the level of data reduction but also performance of recognition. Thus, in this paper, we focused on the recognition of numeral characters especially on the feature extraction of numeral characters which has much effect in the result of plate recognition. We suggest a hybrid statistical feature model which assures the best dispersion of input data by reassignment of clustering property of input data. And we verify the effectiveness of suggested model using multi-layer perceptron and learning vector quantization neural networks. The results show that the proposed feature extraction method preserves the information of a license plate well and also is robust and effective for even noisy and external environment.