• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.200-208
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• 2003

This paper proposes a separation and feature extraction of image signals using the independent component analysis(ICA) based on neural networks of efficient learning rule. The proposed learning rule is a hybrid fixed-point(FP) algorithm based on secant method and momentum. Secant method is applied to improve the performance by simplifying the 1st-order derivative computation for optimizing the objective function, which is to minimize the mutual informations of the independent components. The momentum is applied for high-speed convergence by restraining the oscillation in the process of converging to the optimal solution. The proposed algorithm has been applied to the composite images generated by random mixing matrix from the 10 images of $512\times512$-pixel. The simulation results show that the proposed algorithm has better performances of the separation speed and rate than those using the FP algorithm based on Newton and secant method. The proposed algorithm has been also applied to extract the features using a 3 set of 10,000 image patches from the 10 fingerprints of $256\times256$-pixel and the front and the rear paper money of $480\times225$-pixel, respectively, The simulation results show that the proposed algorithm has also better extraction speed than those using the another methods. Especially, the 160 basis vectors(features) of $16\times16$-pixel show the local features which have the characteristics of spatial frequency and oriented edges in the images.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.397-402
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• 2003

This paper proposes an efficient fixed-point (FP) algorithm for improving performances of the independent component analysis (ICA) based on neural networks. The proposed algorithm is the FP algorithm based on Newton method for ICA using the adaptive learning parameters. The purpose of this algorithm is to improve the separation speed and performance by using the learning parameters in Newton method, which is based on the first order differential computation of entropy optimization function. The learning rate and the moment are adaptively adjusted according to an updating state of inverse mixing matrix. The proposed algorithm has been applied to the fingerprints and the images generated by random mixing matrix in the 8 fingerprints of 256${\times}$256-pixel and the 10 images of 512$\times$512-pixel, respectively. The simulation results show that the proposed algorithm has the separation speed and performance better than those using the conventional FP algorithm based on Newton method. Especially, the proposed algorithm gives relatively larger improvement degree as the problem size increases.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.213-214
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• 2009

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.583-586
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• 2004

본 논문에서는 자율학습의 속성을 가지는 주요성분분석을 이용한 얼굴인식 기법을 제안하였다. 이는 대용량의 입력 데이터를 통계적으로 독립인 특징들의 집합으로 변환시켜 중복신호를 제거하는 특성을 가지는 주요성분분석의 우수한 속성을 이용한 것이다. 제안된 기법을 Yale 얼굴영상 데이터베이스로부터 선택된 20개의 $320{\ast}243$ 픽셀의 영상을 대상으로 시뮬레이션한 결과, 주요성분의 개수에 따른 압축성능과 city-block, Euclidian, 그리고 negative angle(cosine)의 거리척도에 따른 인식에서의 분류성능에서 우수한 성능이 있음을 확인할 수 있었다.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.629-633
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• 2001

In this paper, we improve noise tolerance in image classification by combining ICA(Independent Component Analysis) with Normalization. When we add noise to the raw image data the degree of noise tolerance becomes N(0, 0.4) for PCA and N(0, 0.53) for ICA. However, when we use the preprocessing approach the degree of noise tolerance after Normalization becomes N(0, 0.75), which shows the improvement of noise tolerance in classification.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.381-384
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• 2006

In this paper, we have proposed an independent component analysis (ICA) technique and a median method based on GBD algorithm which classifies images. The proposed method can measure the distance between images, while it does not lose a portion of the image that changes rapidly. Our simulation results show that it not possible for ICA to recognize the relative discernment between images when K is 7. However, with the median method, it can be possible to such a recognition.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.179-182
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• 2004

본 논문에서는 고정점 알고리즘 독립성분분석을 이용하여 영상의 기하학적 변형에 강건한 워터마킹을 제안하였다. 여기서 고정점 알고리즘은 뉴우턴법에 기초한 것으로 워터마킹의 추출과정에서 빠른 추출과 기하학적 변형(크기, 회전)에 강건한 개선된 추출성능을 얻기 위함이고, 독립성분분석의 이용은 추출과정에서 워터마크의 위치나 크기, 원본과 키 영상 둥에 대한 사전 지식의 요구를 없애기 위함이다. 제안된 기법을 256$\times$256 픽셀의 레나 원 영상, 키 영상, 그리고 문자 워터마크에 적용한 결과, 크기와 회전의 기하학적 공격에 강하면서도 워터마크의 검출 및 추출과정에 원본 영상들에 대한 사전지식이 요구되지 않았다.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.1031-1034
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• 2004

본 논문에서는 고정점 알고리즘 독립성분분석을 이용하여 잡음에 강건한 디지털영상의 워터마킹을 제안하였다. 여기서 고정점 알고리즘은 워터마킹의 추출과정에서 빠른 추출과 잡음에 강건한 개선된 추출성능을 얻기 위함이고, 독립성분분석의 이용은 추출과정에서 워터마크의 위치나 크기, 원본과 키 영상 등에 대한 사전 지식의 요구를 없애기 위함이다. 제안된 기법을 $256{\times}256$ 픽셀의 레나 원 영상, 키 영상, 그리고 문자 워터마크에 적용한 결과, 잡음과 같은 공격에 강하면서도 워터마크의 검출 및 추출과정에 원본 영상들에 대한 사전지식이 요구되지 않았다.

• Proceedings of the KIPE Conference
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• 2009.11a
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• pp.200-202
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• 2009

본 논문은 계통전압 불평형시 이중여자 유도형 풍력발전기 (Doubly Fed Induction Generator-DFIG)의 토크 리플 저감과 dc-link 전압의 맥동을 제거하는 기법을 제안한다. 계통전압 불평형 시 DFIG의 동적 모델링을 통해 토크 맥동 성분과 dc-link 전압 리플 성분을 수식화 한다. 유도된 수식을 기반으로 회전자 측 컨버터는 정상분과 역상분을 독립적으로 제어하는 듀얼 전류 제어기를 통해 토크 리플을 저감하며, 계통 측컨버터는 전력이론을 통해 계산된 보상 전류 지령치를 통해 cd-link 전압 맥동을 제거한다. 3kW급 풍력 발전 시스템에 제안하는 기법을 적용한 시뮬레이션 결과를 통해 타당성을 입증한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.555-562
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• 2012

This paper presents a hybrid method for improving the recognition performance, which is based on the nonlinear histogram equalization, features extraction, and statistical correlation of images. The nonlinear histogram equalization based on a logistic function is applied to adaptively improve the quality by adjusting the brightness of the image according to its intensity level frequency. The statistical correlation that is measured by the normalized cross-correlation(NCC) coefficient, is applied to rapidly and accurately express the similarity between the images. The local features based on independent component analysis(ICA) that is used to calculate the NCC, is also applied to statistically measure the correct similarity in each images. The proposed method has been applied to the problem for recognizing the 30-face images of 40*50 pixels. The experimental results show that the proposed method has a superior recognition performances to the method without performing the preprocessing, or the methods of conventional and adaptively modified histogram equalization, respectively.