• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7C
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• pp.554-564
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• 2012

The vehicle recognition consists of two steps; the vehicle region detection step and the vehicle identification step based on the feature extracted from the detected region. Features using linear transformations have the effect of dimension reduction as well as represent statistical characteristics, and show the robustness in translation and rotation of objects. Among the linear transformations, the NMF(Non-negative Matrix Factorization) is one of part-based representation. Therefore, we can extract NMF features with sparsity and improve the vehicle recognition rate by the representation of local features of a car as a basis vector. In this paper, we propose a feature extraction using NMF suitable for the vehicle recognition, and verify the recognition rate with it. Also, we compared the vehicle recognition rate for the occluded area using the SNMF(sparse NMF) which has basis vectors with constraint and LVQ2 neural network. We showed that the feature through the proposed NMF is robust in the urban scene where occlusions are frequently occur.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.393-396
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• 2011

In this paper, we propose a constrained alternating least squares nonnegative matrix factorization algorithm (cALSNMF) to detect active brain regions from single subject's task-related fMRI data. In cALSNMF, we define a new cost function which considers the uncorrelation and noisy problems of fMRI data by adding decorrelation and smoothing constraints in original Euclidean distance cost function. We also generate a novel training procedure by modifying the update rules and combining with optimal brain surgeon (OBS) algorithm. The experimental results on visuomotor task fMRI data show that our cALSNMF fits fMRI data better than original ALSNMF in detecting task-related brain activation from single subject's fMRI data.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.383-385
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• 2011

본 논문에서는 동영상을 대상으로 하는 기존의 시각주의 시스템의 성능을 향상시킨 새로운 시스템에 대하여 설명한다. 제안하는 시스템은 기존의 시스템이 가지고 있던 한계점인 서로 반대되는 특징을 가지는 색상에서 하나의 특징만을 고정적으로 선택하던 것을 극복하여, 서로 반대되는 특징 중 현저함이 더 높은 색상 특징을 선택하여 입력 들어오는 영상에 적응적인 현저함 추출을 하였다. 도한 시간 현저함 정보를 추가적으로 고려할 수 있도록 하여 동영상에 대한 처리도 가능하도록 하였고, 성능 평가 시 인간을 대상으로 한 설문 조사 실험을 추가하여 보다 인간의 시각 인식과 유사한 시스템임을 증명하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.16-17
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• 2015

본 논문에서는 실내 환경 중 TV 시청환경에서 마이크로폰 어레이를 이용하여 다양한 다중 음원 방향을 추정하는 기법을 제안한다. 제안된 기법은 기존의 하나의 음원에 특화되어 있는 GCC-PHAT 기반의 방법을 GCC-PHAT 버퍼와 NMF를 도입하여 다중음원의 방향 추정을 가능하게 만들었다. 제안된 기법의 성능을 평가하기 위해서 실 거주 환경에서 발생하는 소음원과 TV 소리 방향 추정 결과에 대한 실측치와 추정치 간의 오차인 절대 평균오차를 측정하였으며, 실험 결과 제안한 기법이 기존의 방법인 GCC-PHAT보다 우수한 추정 성능을 보임을 확인하였다.

• 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.

• 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.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.123-129
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• 2017

This paper proposes a new nonnegative matrix factorization (NMF) based direction-of-arrival (DOA) estimation method for multiple sound sources using a dual microphone array. First of all, sound signals coming from the dual microphone array are segmented into consecutive analysis frames, and a steered-response power phase transform (SRP-PHAT) beamformer is applied to each frame so that stereo signals of each frame are represented in a time-direction domain. The time-direction outputs of SRP-PHAT are stored for a pre-defined number of frames, which is referred to as a time-direction block. Next, In order to estimate DOAs robust to noise, each time-direction block is normalized along the time by using a block subtraction technique. After that, an unsupervised NMF method is applied to the normalized time-direction block in order to cluster the directions of each sound source in a multiple sound source environments. In particular, the activation and basis matrices are used to estimate the number of sound sources and their DOAs, respectively. The DOA estimation performance of the proposed method is evaluated by measuring a mean absolute error (MAE) and the standard deviation of errors between the oracle and estimated DOAs under a three source condition, where the sources are located in [$-35{\circ}$, 5m], [$12{\circ}$, 4m], and [$38{\circ}$, 4.m] from the dual microphone array. It is shown from the experiment that the proposed method could relatively reduce MAE by 56.83%, compared to a conventional SRP-PHAT based DOA estimation method.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.35-44
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• 2023

In recent, there are many research cases that analyze trends or research trends with text analysis. When collecting documents by searching for keywords in abbreviations for data analysis, it is necessary to disambiguate abbreviations. In many studies, documents are classified by hand-work reading the data one by one to find the data necessary for the study. Most of the studies to disambiguate abbreviations are studies that clarify the meaning of words and use supervised learning. The previous method to disambiguate abbreviation is not suitable for classification studies of documents looking for research data from abbreviation search documents, and related studies are also insufficient. This paper proposes a method of semi-automatically classifying documents collected by abbreviations by going topic modeling with Non-Negative Matrix Factorization, an unsupervised learning method, in the data pre-processing step. To verify the proposed method, papers were collected from academic DB with the abbreviation 'MSA'. The proposed method found 316 papers related to Micro Services Architecture in 1,401 papers. The document classification accuracy of the proposed method was measured at 92.36%. It is expected that the proposed method can reduce the researcher's time and cost due to hand work.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.293-295
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• 2010

본 논문은 문서의 의미특징과 퍼지를 이용한 새로운 문서군집 방법을 제안한다. 제안된 방법은 비음수 행렬 분해된 의미특징을 이용하여 군집 레이블과 군집의 대표 용어들을 선택함으로서 문서군집의 내부구조를 더 잘 표현할 수 있으며, 퍼지를 이용한 군집은 문서군집에 유사하지 않은 문서를 더 잘 구분함으로써 문서군집의 성능을 높일 수 있다. 실험결과 제안방법을 적용한 문서군집방법이 다른 문서군집 방법에 비하여 좋은 성능을 보인다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.704-706
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• 2012

In this paper, we proposes a document summarization method using the term weighting. The proposed method can minimize the user intervention to use the pseudo relevance feedback. It also can improve the quality of document summaries because the inherent semantic of the sentence set are well reflected by term weighting derived from semantic feature.