• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.697-705
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• 2009

An unsupervised (blind) method is proposed aiming at extracting rhythmic sources from commercial polyphonic music whose number of channels is limited to one. Commercial music signals are not usually provided with more than two channels while they often contain multiple instruments including singing voice. Therefore, instead of using conventional modeling of mixing environments or statistical characteristics, we should introduce other source-specific characteristics for separating or extracting sources in the under determined environments. In this paper, we concentrate on extracting rhythmic sources from the mixture with the other harmonic sources. An extension of nonnegative matrix factorization (NMF), which is called nonnegative matrix partial co-factorization (NMPCF), is used to analyze multiple relationships between spectral and temporal properties in the given input matrices. Moreover, temporal repeatability of the rhythmic sound sources is implicated as a common rhythmic property among segments of an input mixture signal. The proposed method shows acceptable, but not superior separation quality to referred prior knowledge-based drum source separation systems, but it has better applicability due to its blind manner in separation, for example, when there is no prior information or the target rhythmic source is irregular.

• 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 Advanced Navigation Technology
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• v.12 no.5
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• pp.502-508
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• 2008

With the increased use of the internet and the tremendous amount of data it transfers, it is more necessary to summarize documents. We propose a new method using the Non-negative Semantic Variable Matrix (NSVM) and the generic important of semantic features obtained by Non-negative Matrix Factorization (NMF) to extract the sentences for automatic generic summarization. The proposed method use non-negative constraints which is more similar to the human's cognition process. As a result, the proposed method selects more meaningful sentences for summarization than the unsupervised method used the Latent Semantic Analysis (LSA) or clustering methods. The experimental results show that the proposed method archives better performance than other methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.224-227
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• 2016

본 논문에서는 스테레오 채널 신호 간 강도비를 이용하여 음원을 분리하는 EADRess 알고리즘과 부분기반 표현을 특징으로 한 비음수 행렬 인수분해를 통해 음원을 분리하는 NMF 가 결합된 새로운 음원분리 알고리즘을 제안한다. 입력 오디오 신호로부터 frequency-azimuth 평면 구성을 통해 식별된 방위각에 상응하는 신호 강도비로 표현되는 확률밀도함수를 이용하여 1 단계 음원분리를 수행하고, 얻어진 개별 분리음원을 대상으로 supervised NMF 및 Wiener 필터 기반 마스킹 함수를 적용함으로써 잔류 혼합성분을 제거하는 2 단계 음원분리를 수행한다. 제안된 EADRess/NMF 결합 음원분리 알고리즘의 성능을 검증하기 위하여 SASSEC 에서 제공하는 테스트 음원을 이용하여 측정한 결과, 개별 음원분리 알고리즘에 비해 SIR 이 각각 1.41dB, 10.43dB 향상된 결과를 얻었다.

• Communications of Mathematical Education
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• v.8
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• pp.287-301
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• 1999

방과후 수학수업이나 현행 수학능력시험 후 고3학생의 수학지도는 그 방법과 목적이 기존의 수학교과의 내용과 운영방식과는 차별화 되야 한다. 특히 교사는 이에 대한 인식과 필요한 지식이 증대 되야 하며, 교내 방과후 영재반 또는 수학관련 동아리에서 사용할 주제의 선정과 교수법이 개발되어야한다. 주제선정은 대수, 해석영역에서 연계성이 강하게 나타나는 것이 바람직하며, 수학교육의 목표에 실질적으로 부합되어야한다. 본 논문에서 우리는 일${\cdot}$이차 다항식을 예로 제시하고자 한다. 다항식은 중학교 수학교과에서 인수분해와 전개의 대상이고 고교과정에선 접선이나 정적분의 대상이다. 우리는 일${\cdot}$이차다항식을 미분, 적분, 행렬, 그리고 벡터의 입장에서 근사(approximation)의 주체로 다루었다.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 1992.11a
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• pp.131-138
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• 1992

공용키 암호법의 대표적인 것으로 El Gamal 암호법과 RSA 암호법이 있는데, RAS 암호법은 정수의 인수분해가 어렵다는 것에 안전성을 둔 반면에 El Gamal 암호법은 discrete logarithm을 푸는 것이 어렵다는데 안전성을 두고 있다. (6) 그런데 유한체상의 멱승과 곱셈이 효율적으로 수행이 된다면 El Gamal 암호법이 유용하다는 사살을 알게 되었다. 그런데 Copperxmith의 이산로그 알고리즘을 이용하면 n〉1000이 되어야 El Gamal 암호법이 안전성을 보장 받을 수 있으나 이 경우 복잡도의 증가로 인한 gate수의 급속한 증가로 고속연산전용 VLSI 설계시 어려움이 있다. (3) 그래서 본 논문은 복잡도를 줄일 수 있는 정규기저들의 탐색에 연구의 중점을 두었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.715-716
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• 2009

인터넷의 발전은 대량의 정보를 양산하였고, 이러한 대량의 정보 집합 내에서는 비슷한 정보가 재활용 되거나 반복되는 정보중복문제를 가지고 있다. 중복되는 정보들로부터 사용자에게 원하는 정보를 신속히 검색할 수 있도록 하는 정보 요약에 대한 필요성은 점차 증가하고 있다. 본 논문은 비음수 행렬 인수분해(NMF, non-negative matrix factorization)에 의한 문서의 의미특징을 이용하여 주제기반의 다중문서를 요약하는 새로운 방법을 제안한다. 본 논문에서는 다중문서가 포함하고 있는 문서들 간의 고유구조를 문서요약에 이용하여서 요약의 질을 높일 수 있고, 주제와 문장 간의 유사성과 다양성 고려하여서 쉽게 과잉정보를 제거하여 문장을 요약할 수 있는 장점을 갖는다.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.1015-1025
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• 2017

Bayesian personalized ranking (BPR) is a state-of-the-art recommendation system techniques for implicit feedback data. Unfortunately, there might be a loss of information because the BPR model considers only the binary transformation of implicit feedback that is non-binary data in most cases. We propose a modified BPR method using a level of confidence based on the size or strength of implicit feedback to overcome this limitation. The proposed method is useful because it still has a structure of interpretable models for underlying personalized ranking i.e., personal pairwise preferences as in the BPR and that it is capable to reflect a numerical size or the strength of implicit feedback. We propose a computation algorithm based on stochastic gradient descent for the numerical implementation of our proposal. Furthermore, we also show the usefulness of our proposed method compared to ordinary BPR via an analysis of steam video games data.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.64-75
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• 2018

With the development of location-aware technologies and the activation of smart phones, location based social networks(LBSN) have been activated to allow people to easily share their location. In particular, studies on recommending the location of user interests by using the user check-in function in LBSN have been actively conducted. In this paper, we propose a location recommendation scheme considering time and user activities in LBSN. The proposed scheme considers user preference changes over time, local experts, and user interest in rare places. In other words, it uses the check-in history over time and distinguishes the user activity area to identify local experts. It also considers a rare place to give a weight to the user preferred place. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2844-2851
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• 2015

How to recover from the multiple 2D images into 3D object has been widely studied in the field of computer vision. In order to improve the accuracy of the recovered 3D shape, it is more important that noise must be minimized and the number of image frames must be guaranteed. However, potential noise is implied when tracking feature points. And the number of image frames which is consisted of an observation matrix usually decrease because of tracking failure, occlusions, or low image resolution, and so on. Therefore, it is obviously essential that the number of image frames must be secured by recovering the missing feature points under noise. Thus, we propose the analytic approach which can control directly the error distance and orientation of missing feature point by the geometrical properties under noise distribution. The superiority of proposed method is demonstrated through experimental results for synthetic and real object.