• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.173-184
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• 2018

In human walking, muscle co-contraction which produces simultaneous activities of multiple muscles is important in motor control mechanism of the central nervous system. This study aims to understand positive and negative covariation mechanism of inter-muscle activities during walking. In this study, we measured electromyography (EMG) in leg muscles. To identify motor modules, we recored EMG from 4 leg muscles bilaterally (the tibialis anterior, medial gastrocnemius, rectus femoris and medial hamstring muscles) and performed non-negative matrix factorization (NMF) and principa component analysis (PCA). Then, we computed covariation values from various combinations between muscles or motor modules and used two-way repeated measures analysis of variance to identify significantly different covariation patterns between muscle combinations. As the results, we found significant differences between covariation values of muscle combinations (p < 0.05). muscle groups within the same motor modules produced the positive covariations. However, there were strong negative covariation between motor modules. There was negative covariation in all muscle combination. Stable inter-module negative covariation suggests that motor modules may be the control unit in the complex motor coordination.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.171-174
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• 2005

본 논문에서는 인가의 인지학적인 두뇌 원리인 대뇌피질과 해마 신경망을 공학적으로 모델링하여 얼굴 영상의 특징 벡터들을 고속 학습하고, 각 영상의 최적의 특징을 구성할 수 있는 해마 학습 알고리즘(Hippocampal Learning Algorithm)을 개발하여 RFID를 이용한 생체인식 시스템을 제안한다. 입력되는 얼굴 영상 데이터들은 NMF(Non-negative Matrix Factorization)를 이용하여 특징이 구성되고, 이러한 특징들은 해마의 치아 이랑 영역에서 호감도 조정에 따라서 반응 패턴으로 이진화 되고, CA3 영역에서 자기 연상 메모리 단계를 거쳐 노이즈를 제거한다. CA3의 정보를 받는 CA1영역에서는 단층 신경망에 의해 단기기억과 장기기억으로 나누어서 저장되고 해당 특징의 누적 개수가 문턱치(threshold)를 만족하면 장기 기억 장소로 저장시키도록 한다. 위와 같은 개념을 바탕으로 구현되는 RFID 생체인식 시스템은 특징의 분별력과 학습속도면에서 우수한 성능을 보일 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.688-690
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• 2004

최근 전자화된 문서 영상을 효율적으로 관리하고 검색하기 위한 문서구조분석 방법과 문서의 자동 분류에 관한 많은 연구가 발표되고 있다. 본 논문에서는 NMF(non-negative matrix factorization) 알고리즘을 사용하여 폰트를 자동으로 분류하는 방법을 제안한다. 제안된 방법은 폰트의 구분 특징들이 공간적으로 국부성을 가지는 부분으로 표현될 수 있다는 가정을 바탕으로, 전체의 폰트 이미지들로부터 각 폰트들의 구분 특징인 부분을 학습하고, 학습된 부분들을 특징으로 사용하여 폰트를 분류하는 방법이다. 학습된 폰트의 특징들은 계층적 군집화 알고리즘을 이용하여 템플릿을 생성하고, 테스트 패턴을 분류하기 위하여 템플릿 패턴과의 EMD(earth mover's distance)를 사용한다. 실험결과에서 폰트 이미지들의 공간적으로 국부적인 특징들이 조사되고, 그 특징들의 폰트 식별을 위한 적절성을 보였다. 제안된 방법이 기존의 문자인식. 문서 검색 시스템들의 전처리기로 사용되면. 그 시스템들의 성능을 향상시킬 것으로 기대된다.

• The Journal of the Acoustical Society of Korea
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• v.29 no.8
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• pp.509-515
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• 2010

This paper presents a new method for upmixing mono signal to stereo signal with guaranteeing high stereophonic image quality (SIQ) and large apparent source width (ASW). The proposed method consists of analysis phase and synthesis phase. In analysis phase, a mono signal is first decomposed into multiple sound sources by the use of high-rank nonnegative matrix factorization. Then the multiple sources are clustered into two groups based on tonality criterion. In synthesis phase, one group is directly fed into left and right channels while the other group is decorrelated before being fed into each channel. Subjective tests reveals that the proposed method gives listener high SIQ and large ASW with minimizing timbral distortions.

• Journal of Advanced Navigation Technology
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• v.12 no.4
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• pp.372-377
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• 2008

This paper proposes a new query-based document summarization method using the semantic feature matrix and the semantic variable matrix. The proposed method doesn't need the training phase using training data comprising queries and query specific documents. And it exactly summarizes documents for the given query by using semantic features and semantic variables that is better at identifying sub-topics of document. Because the NMF have a great power to naturally extract semantic features representing the inherent structure of a document. The experimental results show that the proposed method achieves better performance than other methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.79-86
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• 2005

In this paper, we propose a new LINF(Linear Independent Non-negative Factorization) algorithm for real-time face recognition systea This system greatly consists of the two parts: 1) face extraction part; 2) face recognition part. In the face extraction Part we applied subtraction image, the detection of eye and mouth region , and normalization method, and then in the face recognition Part we used LINF in extracted face candidate region images. The existing recognition system using only PCA(Principal Component Analysis) showed low recognition rates, and it was hard in the recognition system using only LDA(Linear Discriminants Analysis) to apply LDA directly when the training set is small. To overcome these shortcomings, we reduced dimension as the matrix that had non-negative value to be different from former eigenfaces and then applied LDA to the matrix in the proposed system We have experimented using self-organized DAIJFace database and ORL database offered by AT(')T laboratory in Cambridge, U.K. to evaluate the performance of the proposed system. The experimental results showed that the proposed method outperformed PCA, LDA, ICA(Independent Component Analysis) and PLMA(PCA-based LDA mixture algorithm) method within the framework of recognition accuracy.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.39-48
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• 2023

This study was conducted to provide a solution to the problem of sizing errors occurring in online purchases due to discrepancies and non-standardization in clothing sizes. This paper discusses an implementation approach for a machine learning-based recommender system capable of providing personalized sizes to online consumers. We trained multiple validated collaborative filtering algorithms including Non-Negative Matrix Factorization (NMF), Singular Value Decomposition (SVD), k-Nearest Neighbors (KNN), and Co-Clustering using purchasing data derived from online commerce and compared their performance. As a result of the study, we were able to confirm that the NMF algorithm showed superior performance compared to other algorithms. Despite the characteristic of purchase data that includes multiple buyers using the same account, the proposed model demonstrated sufficient accuracy. The findings of this study are expected to contribute to reducing the return rate due to sizing errors and improving the customer experience on e-commerce platforms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.2
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• pp.331-336
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• 2012

Red tide is a temporary natural phenomenon involving harmful algal blooms (HABs) in company with a changing sea color from normal to red or reddish brown, and which has a bad influence on coast environments and sea ecosystems. The HABs have inflicted massive mortality on fin fish and shellfish, damaging the economies of fisheries for almost every year from 1990 in South Korea. There have been many studies on red tide due to increasing damage from red tide on fishing and aquaculture industry. However, internal study of automatic red tide image classification is not enough. Especially, extraction of matching center features for recognizing algae image object is difficult because over 200 species of algae in the world have a different size and features. Previously studies used a few type of red tide algae for image classification. In this paper, we proposed the red tide image recognition method using NMF and revison of rotation angle for enhancing of recognition of red tide algae image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.23-29
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• 2011

There have been many studies on red tide due to increasing damage from red tide on fishing and aquaculture industry. However, internal study of automatic red tide image classification is not enough. Recognition of red tide algae is difficult because they do not have matching center features for recognizing algae image object. Previously studies used a few type of red tide algae for image classification. In this paper, we proposed the red tide image recognition method using semantic features of NMF and roundness of image objects.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.392-393
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• 2012

This paper proposes a new document clustering method using clustering and Wikipedia. The proposed method can well represent the concept of cluster topics by means of NMF. It can solve the problem of "bags of words" to be not considered the meaningful relationships between documents and clusters, which expands the important terms of cluster by using of the synonyms of Wikipedia. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.