• Journal of Power Electronics
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• v.15 no.1
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• pp.78-85
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• 2015

This paper presents a new structure of single-stage flyback power factor correction (PFC) converter with a controllable coupled negative magnetic feedback (NMF) winding. NMF winding is used to reduce the bulk capacitor voltage at high line voltages and light loads. However, it would cause line current distortion at zero crossing condition. In the proposed circuit, a series winding is used with NMF inductor to eliminate the NMF inductor at low line voltages. As a result, the dead angle of the input current, near zero voltage crossing, is eliminated and the power factor is increased. The presented experimental results of the proposed PFC converter confirm the integrity of the new idea and the theoretical analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.6
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• pp.503-511
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• 2013

This paper presents a speech enhancement method using non-negative matrix factorization. In training phase, we can obtain each basis matrix from speech and specific noise database. After training phase, the noisy signal is separated from the speech and noise estimate using basis matrix in enhancement phase. In order to improve the performance, we model the change of encoding matrix from training phase to enhancement phase using independent Gaussian distribution models, and then use the constraint of the objective function almost same as that of the above Gaussian models. Also, we perform a smoothing operation to the encoding matrix by taking into account previous value. Last, we apply the Log-Spectral Amplitude type algorithm as gain function.

• Journal of KIISE:Software and Applications
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• v.36 no.10
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• pp.777-785
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• 2009

Collaborative filtering is a technique used to predict whether a particular user will like a particular item. User-based or item-based collaborative techniques have been used extensively in many commercial recommender systems. In this paper, a hybrid collaborative filtering method that combines user-based and item-based methods using a low-dimensional linear model is proposed. The proposed method solves the problems of sparsity and a large database by using NMF among the low-dimensional linear models. In collaborative filtering systems the methods using the NMF are useful in expressing users as semantic relations. However, they are model-based methods and the process of computation is complex, so they can not recommend items dynamically. In order to complement the shortcomings, the proposed method clusters users into groups by using NMF and selects features of groups by using TF-IDF. Mutual information is then used to compute similarities between items. The proposed method clusters users into groups and extracts features of groups on offline and determines the most suitable group for an active user using the features of groups on online. Finally, the proposed method reduces the time required to classify an active user into a group and outperforms previous methods by combining user-based and item-based collaborative filtering methods.

• Analytical Science and Technology
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• v.26 no.5
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• pp.315-325
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• 2013

The possibility of acute hepatotoxicity caused by dimethylformamide (DMF) requires regular monitoring of the workers who are using DMF to prevent the occupational disease. The authors performed ambient and biological monitoring of workers involved in synthetic leather manufacturing processes using DMF to assess the correlation between the markers of ambient and biological monitoring of DMF. The authors monitored 142 workers occupationally exposed to DMF from 19 workshops in the synthetic leather and ink manufacturing industries located in northern region of Gyeonggi-do. The subjects answered questionnaire on work procedure and use of personal protective equipment to be classified by exposure type. DMF in air samples collected using personal air samplers, diffusive and active sampler, was analysed using gas chromatograph-flame ionization detector (GC-FID) with DB-FFAP column (length 30 m, i.d. 0.25 mm, film thickness 0.25 ${\mu}m$). Urinary N-methylformamide (NMF) was analysed using gas chromatograph-mass selective detector (GC-MSD) at selected ion monitoring (SIM) mode with DB-624 column (length 60 m, i.d. 0.25 mm, film thickness 1.40 ${\mu}m$). Geometric mean (GM) and geometric standard deviation (GSD) of the ambient DMF was $6.85{\pm}3.43$ ppm, and GM and GSD of urinary NMF was $42.3{\pm}2.7$ mg/L. The ratio of subjects with DMF level over 10 ppm was 44%, and those with urinary NMF over 15 mg/L was 87%. NMF in urine adjusted by DMF in air was $4.61{\pm}2.57$ mg/L/ppm and $9.50{\pm}2.41$ mg/L/ppm, respectively, with or without respirator. There was seasonal differences of NMF in urine adjusted by DMF in air, $7.63{\pm}2.74$ mg/L/ppm in summer and $4.53{\pm}2.29$ mg/L/ppm in winter. The urinary NMF concentration which corresponds to 10 ppm of ambient DMF was 52.7 mg/L (r=0.650, n=128). Considering the difference of the route of exposure which resulted from the compliance of wearing personal protective equipment, the estimated contribution of respiratory and dermal exposure route for DMF was 48.5% vs. 51.5%.

• Journal of Astronomy and Space Sciences
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• v.27 no.1
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• pp.21-30
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• 2010

In processing space geodetic data, mapping functions are used to convert the tropospheric signal delay along the zenith direction to the line of sight direction. In this study, we compared three mapping functions by evaluating their effects on the tropospheric signal delay and position estimates in GPS data processing. The three mapping functions tested are Niell Mapping Function (NMF), Vienna Mapping Function 1 (VMF1), and Global Mapping Function (GMF). The tropospheric delay and height estimates from VMF1 and GMF are compared with the ones obtained with NMF. The differences among mapping functions show annual signals with the maximum occurring in February or August. To quantitatively estimate the discrepancies among mapping functions, we calculated the maximum difference and the amplitude using a curve fitting technique. Both the maximum difference and amplitude have high correlations with the latitude of the site. Also, the smallest difference was found around $30^{\circ}N$ and the amplitudes increase toward higher latitudes. In the height estimates, the choice of mapping function did not significantly affect the vertical velocity estimate, and the precision of height estimates was improved at most of the sites when VMF1 or GMF was used instead of NMF.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.473-476
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• 2006

인터넷의 발달로 인하여 정보의 양은 시간이 지날수록 폭발적으로 증가하고 있다. 이러한 방대한 정보로부터 정보검색시스템은 사용자에게 너무 많은 검색결과를 제시하여 사용자가 원하는 정보를 찾기 위해 너무 많은 시간을 소요하게 하는 정보의 과적재 문제가 있다. 질의 기반의 문서요약은 정보의 사용자가 원하는 정보의 검색시간을 줄임으로써 정보의 과적재 문제를 해결하는 방법으로서 점차 중요성이 증가하고 있다. 본 논문은 비음수 행렬 인수분해 (NMF, Non-negative Matrix Factorization)과 코사인 유사도를 이용하여 질의 기반의 문서를 요약하는 새로운 방법을 제안하였다. 제안된 방법은 질의와 문서 간에 사전학습이 필요 없다. 또한 문서를 그래프로 변형시키는 복잡한 처리 없이 NMF 에 의해 얻어진 의미 특징(semantic feature)과 의미 변수(semantic variable)로 문서의 고유 구조를 반영하여 요약의 정확도를 높일 수 있다. 마지막으로 단순한 방법으로 문장을 쉽게 요약할 수 있다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.65-76
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• 2004

Today, most documents are electronically produced and their paleography is digitalized by imaging, resulting in a tremendous number of electronic documents in the shape of images. Therefore, to process these document images, many methods of document structure analysis and recognition have already been proposed, including font classification. Accordingly, the current paper proposes a font classification method for document images that uses non-negative matrix factorization (NMF), which is able to learn part-based representations of objects. In the proposed method, spatially total features of font images are automatically extracted using NMF, then the appropriateness of the features specifying each font is investigated. The proposed method is expected to improve the performance of optical character recognition (OCR), document indexing, and retrieval systems, when such systems adopt a font classifier as a preprocessor.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.8
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• pp.1129-1135
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• 2012

To employ NMF to transcribe music by extracting feature matrix and weight matrix at the same time, it is necessary to know in advance the dimension of the feature matrix, and to determine the pitch of each extracted feature vector. Another drawback of this approach is that it becomes more difficult to accurately extract the feature matrix as the number of pitches included in the target music increases. In this study, we prepare a feature matrix database, and apply the matrix to transcribe real music. Transcription experiments are conducted by applying the feature matrix to the music played on the same piano on which the feature matrix is extracted, as well as on the music played on another piano. These results are also compared to those of another experiment where the feature matrix and weight matrix are extracted simultaneously, without making use of the database. We could observe that the proposed method outperform the method in which the two matrices are extracted at the same time.

• Journal of KIISE:Software and Applications
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• v.36 no.5
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• pp.347-352
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• 2009

Nonnegative matrix factorization (NMF) is a popular method for multivariate analysis of nonnegative data, the goal of which is decompose a data matrix into a product of two factor matrices with all entries in factor matrices restricted to be nonnegative. NMF was shown to be useful in a task of clustering (especially document clustering). In this paper we present an algorithm for orthogonal nonnegative matrix factorization, where an orthogonality constraint is imposed on the nonnegative decomposition of a term-document matrix. We develop multiplicative updates directly from true gradient on Stiefel manifold, whereas existing algorithms consider additive orthogonality constraints. Experiments on several different document data sets show our orthogonal NMF algorithms perform better in a task of clustering, compared to the standard NMF and an existing orthogonal NMF.

• 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.