• Journal of Korea Multimedia Society
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• v.11 no.5
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• pp.597-609
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• 2008

Due to the recursive updating nature of background model, previous background modeling methods are often perturbed by crowd scenes where foreground pixels occurs more frequently than background pixels. To resolve this problem, an adaptive background modeling method, which is based on the well-known Gaussian mixture background model, is proposed. In the proposed method, the learning rate of background model is adaptively adjusted with respect to the crowdedness of the scene. Consequently, the learning process is suppressed in crowded scene to maintain proper background model. Experiments on real dataset revealed that the proposed method could perform background subtraction effectively even in crowd situation while the performance is almost the same to the previous method in normal scenes. Also, the F-measure was increased by 5-10% compared to the previous background modeling methods in the video of crowded situations.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.89-90
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• 2008

AWGN(Addictive white gaussian noise)에 의해 영상은 자주 훼손되곤 한다. 최근 이를 복원하기위해 웨이블릿(Wavelet) 영역에서의 베이시안(Bayesian) 추정법이 연구되고 있다. 웨이블릿 변환된 영상 신호의 밀도 함수(pdf)는 표족한 첨두와 긴 꼬리(long-tail)를 갖는 경망이 있다. 이러한 사전 밀도 함수(a priori probability density function)를 상황에 적합하게 추정한다면 좋은 성능의 복원 결과를 얻을 수 있다. 빈번이 제안되는 릴도 함수로 가우시안(Gaussian) 분포 참수와 라플라스(Laplace) 분포 함수가 있다. 이들 각각의 모델은 훌륭히 변환 계수들을 모델링하며 나름대로의 장점을 나타낸다. 본 연구에서는 가우시안 분포와 라플라스(Laplace) 분포의 혼합 분포 모델을 밀도 함수로 제안하여, 이 들의 장점을 종합하였다. 이를 MAP(Maximum a Posteriori) 추정 방법에 적용하여 잡음을 제거 하였다. 그 결과 기존의 알고리즘에 비해 시각적인 면(Visual aspect), 수치적인 면(PSNR), 그리고 연산량(Complexity) 측면에서 망상된 결과를 얻었다.

• International Journal of Highway Engineering
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• v.14 no.3
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• pp.77-85
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• 2012

Most of Automatic Accident Detection Algorithm has a problem of detecting an accident as traffic congestion. Actually, center's managers deal with accidents depend on watching CCTV or accident report by drivers even though they run the Automatic Accident Detection system. It is because of the system's detecting errors such as detecting non-accidents as accidents, and it makes decreasing in the system's overall reliability. It means that Automatic Accident Detection Algorithm should not only have high detection probability but also have low false alarm probability, and it has to detect accurate accident spot. The study tries to verify and evaluate the effectiveness of using Gaussian Mixture Model and individual vehicle tracking to adapt Accident Detection Algorithm to Center Management System by measuring accident detection probability and false alarm probability's frequency in the real accident.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.268-272
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• 2009

In this paper, we propose a Gaussian Mixture Model (GMM) based incremental robust adaptation with a forgetting factor for the speaker verification. Speaker recognition system uses a speaker model adaptation method with small amounts of data in order to obtain a good performance. However, a conventional adaptation method has vulnerable to the outlier from the irregular utterance variations and the presence noise, which results in inaccurate speaker model. As time goes by, a rate in which new data are adapted to a model is reduced. The proposed algorithm uses an incremental robust adaptation in order to reduce effect of outlier and use forgetting factor in order to maintain adaptive rate of new data on GMM based speaker model. The incremental robust adaptation uses a method which registers small amount of data in a speaker recognition model and adapts a model to new data to be tested. Experimental results from the data set gathered over seven months show that the proposed algorithm is robust against outliers and maintains adaptive rate of new data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1047-1054
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• 2015

This paper presents an acoustic model transform method using untranscribed speech database for improved speech recognition. In the presented model transform method, an adapted GMM is obtained by employing the conventional adaptation method, and the most similar Gaussian component is selected from the adapted GMM. The bias vector between the mean vectors of the clean GMM and the adapted GMM is used for updating the mean vector of HMM. The presented GAMT combined with MAP or MLLR brings improved speech recognition performance in car noise and speech babble conditions, compared to singly-used MAP or MLLR respectively. The experimental results show that the presented model transform method effectively utilizes untranscribed speech database for acoustic model adaptation in order to increase speech recognition accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.817-821
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• 2012

In this paper we applied the optimal mixture number estimation technique in GMM(Gaussian mixture model) using BIC(Bayesian information criterion) and MDL(minimum description length) as a model selection criterion for environmental sounds recognition. In the experiment, we extracted 12 MFCC(mel-frequency cepstral coefficients) features from 9 kinds of environmental sounds which amounts to 27747 data and classified them with GMM. As mentioned above, BIC and MDL is applied to estimate the optimal number of mixtures in each environmental sounds class. According to the experimental results, while the recognition performances are maintained, the computational complexity decreases by 17.8% with BIC and 31.7% with MDL. It shows that the computational complexity reduction by BIC and MDL is effective for environmental sounds recognition using GMM.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.395-406
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• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.177-183
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• 2010

In continuous vocabulary recognition system by statistical method vocabulary recognition to be performed using probability distribution it also modeling using phoneme clustering for based sample probability parameter presume. When vocabulary search that low recognition rate problem happened in express vocabulary result from presumed probability parameter by not defined phoneme and insert phoneme and it has it's bad points of gaussian model the accuracy unsecure for one clustering modeling. To improve suggested probability distribution mixed gaussian model to optimized for based resemble Euclidean and Bhattacharyya distance measurement method mixed clustering modeling that system modeling for be searching phoneme probability model in clustered model. System performance as a result of represent vocabulary dependence recognition rate of 98.63%, vocabulary independence recognition rate of 97.91%.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.130-132
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• 2005

초고속 네트워크의 폭발적인 확산과 함께 네트워크 침입 사례 또한 증가하고 있다. 이를 검출하기 위한 방안으로 침입 탐지 시스템에 대한 관심과 연구 또한 증가하고 있다. 네트워크 침입을 탐지위한 방법으로 기존의 알려진 공격을 찾는 오용 탐지와 비정상적인 행위를 탐지하는 방법이 존재한다. 본 논문에서는 이를 혼합한 하이브리드 형태의 새로운 침입 탐지 시스템을 제안한다. 기존의 혼합된 방식과는 다르게 네트워크 데이터의 모델링과 탐지를 위해 가우시안 혼합 모델을 사용한다. 가우시안 혼합 모델에 기반한 침입 탐지 시스템의 성능을 평가하기 위해 DARPA'99 데이터에 적용하여 실험하였다. 실험 결과 정상과 공격은 확연히 구분되는 결과를 나타내었으며, 공격 간의 분류도 상당 수 가능하였다.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.550-551
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• 2018

바이오마커는 체내의 상태 및 변화를 파악할 수 있는 지표이다. 이는 암을 비롯한 다양한 질병에 대하여 진단하는데 활용도가 높은 것으로 알려져 있으나, 새로운 바이오마커를 찾아내기 위한 임상 실험은 많은 시간과 비용을 소비되며, 모든 바이오마커가 실제 질병을 진단하는데 유용하게 사용되는 것은 아니다. 따라서 본 연구에서는 자연어처리 기술을 활용해 바이오마커를 발굴할 때 요구되는 많은 시간과 비용을 줄이고자 한다. 이 때 다양한 의미를 가진 어휘들이 해당 질병과 연관성이 높은 것으로 나타나며, 이들을 분류하는 것은 매우 어렵다. 따라서 우리는 Word2Vec과 가우시안 혼합 모델을 사용하여 바이오마커를 분류하고자 한다. 실험 결과, 대다수의 바이오마커 어휘들이 하나의 군집에 나타나는 것을 확인할 수 있었다.