• The Korean Journal of Rheology
• /
• v.8 no.2
• /
• pp.103-118
• /
• 1996

변형된 Maxwell 모델을 모의 데이터와 폴리스티렌의 동적 실험 데이터인 G＇과 G ＂에 적용하여 연속 완화시간 스펙트럼을 결정하였고, Maxwell 모델을 사용했을 때 얻어지 는 불연속 완화시간 스펙트럼과 비교하였다. MGMM과 GMM 모두 선형회귀 방법과 비선 형회귀 방법을 사용하여 완화시간 스펙트럼을 결정하였는데 비선형 방법을 사용했을 때 선 형방법에 비해 좀 더 만족스러운 결과를 얻을수 있었다. 모의 데이터의 경우 사용한 완화시 간의 수가 많은 경우에는 MGMM과 GMM 모두 원래의 스펙트럼을 잘 재현했으나 완화시 간의 수가 작은 경우에는 MGMM이 GMM에 비해 원래의 완화시간 스펙트럼을 보다 잘 나 타내었다. 또 단분산성폴리스티렌의 경우 MGMM과 GMM의 완화시간 스펙트럼이 모두 작 은 완화시간 영역에서는 분자량에 무관했고 큰 완화시간 영역에서는 분자량이 커질수록 스 펙트럼이 완화시간이 커지는 쪽으로 이동하였다. 또 두드러진 terminal 완화시간을 볼수 있 었다. 그러나 다분산성 폴리스티렌의 경우에는 단분산성의 경우와는 달리 두드러진 terminal 완화시간을 볼수 없었다. 그리고 MGMM의 파라미터 m은 분자량 분포에 크게 의존함을 알 수 있었으며 연속 완화시간 스펙트럼에서 계산된 불연속 완화시간 스펙트럼이 GMM에서 얻어진 불연속 완화시간 스펙트럼과 잘 일치함을 볼수 있었다.

• Journal of Digital Convergence
• /
• v.13 no.2
• /
• pp.135-140
• /
• 2015

General CHMM vocabulary recognition system is model observation probability for vocabulary recognition of recognition rate's low. Used as the limiting unit is applied only to some problem in the phoneme model. Also, they have a problem that does not conform to the needs of the search range to meaning of the words in the vocabulary. Performs a phoneme recognition using GMM to improve these problems. We solve the problem according to the limited search words characterized by an improved k-means algorithm. Measure the effectiveness represented by the accuracy and reproducibility as compared to conventional system performance experiments. Performance test results accuracy is 83%p, and recall is 67%p.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.2
• /
• pp.131-136
• /
• 2020

Wake Up Word (WUW) is a short utterance used to convert speech recognizer to recognition mode. The WUW defined by the user who actually use the speech recognizer is called user-defined WUW. In this paper, to recognize user-defined WUW, we construct traditional Gaussian Mixture Model-Hidden Markov Model (GMM-HMM), Linear Discriminant Analysis (LDA)-GMM-HMM and LDA-Deep Neural Network (DNN)-HMM based system and compare their performances. Also, to improve recognition accuracy of the WUW system, a threshold method is applied to each model, which significantly reduces the error rate of the WUW recognition and the rejection failure rate of non-WUW simultaneously. For LDA-DNN-HMM system, when the WUW error rate is 9.84 %, the rejection failure rate of non-WUW is 0.0058 %, which is about 4.82 times lower than the LDA-GMM-HMM system. These results demonstrate that LDA-DNN-HMM model developed in this paper proves to be highly effective for constructing user-defined WUW recognition system.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.9
• /
• pp.1870-1877
• /
• 2012

Transient signals generally have short duration and variable length with time-varying and non-stationary characteristics. Thus frame-based pattern matching method is useful for classification of transient signals. In this paper, we propose a new method for classification of underwater transient signals using a Gaussian mixture model(GMM). We carried out classification experiments for various underwater transient signals depending upon the types of noise, signal-to-noise ratio, and number of mixtures in the GMM. Experimental results have verified that the proposed method works quite well for classification of underwater transient signals.

• Journal of Convergence for Information Technology
• /
• v.10 no.8
• /
• pp.35-39
• /
• 2020

DNN error is small compared to the conventional speech recognition system, DNN is difficult to parallel training, often the amount of calculations, and requires a large amount of data obtained. In this paper, we generate a phoneme unit to estimate the GMM parameters with each phoneme model parameters from the GMM to solve the problem efficiently. And it suggests ways to improve performance through clustering for a specific vocabulary to effectively apply them. To this end, using three types of word speech database was to have a DB build vocabulary model, the noise processing to extract feature with Warner filters were used in the speech recognition experiments. Results using the proposed method showed a 97.9% recognition rate in speech recognition. In this paper, additional studies are needed to improve the problems of improved over fitting.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.12C
• /
• pp.1200-1208
• /
• 2003

Affective computing plays an important role in intelligent Human Computer Interactions(HCI). To detect emotional events, it is desirable to construct a computing model for extracting emotion related features from video. In this paper, we propose a statistical model based on the probabilistic distribution of low level features in video shots. The proposed method extracts low level features from video shots and then from a GMM(Gaussian Mixture Model) for them to detect emotional shots. As low level features, we use color, camera motion and sequence of shot lengths. The features can be modeled as a GMM by using EM(Expectation Maximization) algorithm and the relations between time and emotions are estimated by MLE(Maximum Likelihood Estimation). Finally, the two statistical models are combined together using Bayesian framework to detect emotional events in video.

• Journal of the Korea Society of Computer and Information
• /
• v.10 no.6 s.38
• /
• pp.187-194
• /
• 2005

The background subtraction method is mainly used for the real-time extraction and tracking of moving objects from image sequences. In the outdoor environment, there are many changeable environment factors such as gradually changing illumination, swaying trees and suddenly moving objects , which are to be considered for an adaptive processing. Normally, GMM(Gaussian Mixture Model) is used to subtract the background by considering adaptively the various changes in the scenes, and the adaptive GMMs improving the real-time Performance were Proposed and worked. This paper, for on-line background subtraction, employed the improved adaptive GMM, which uses the small constant for learning rate a and is not able to speedily adapt the suddenly movement of objects, So, this paper Proposed and evaluated the dynamic control method of a using the adaptive selection of the number of component distributions and the global variances of pixel values.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.2
• /
• pp.366-369
• /
• 2005

Background subtraction is mainly used for the real-time extraction and tracking of moving objects from image sequences. In the outdoor environment, there are many changeable factor such as gradually changing illumination, swaying trees and suddenly moving objects, which are to be considered for the adaptive processing. Normally, GMM(Gaussian Mixture Model) is used to subtract the background adaptively considering the various changes in the scenes, and the adaptive GMMs improving the real-time performance were worked. This paper, for on-line background subtraction, applied the improved adaptive GMM, which uses the small constant for learning rate ${\alpha}$ and is not able to speedily adapt the suddenly movement of objects, So, this paper proposed and evaluated the dynamic control method of ${\alpha}$ using the adaptive selection of the number of component distributions and the global variances of pixel values.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.7 no.5
• /
• pp.967-974
• /
• 2012

In this paper, a vehicle detection algorithm with HD CCTV camera images using GMM(Gaussian Mixture Model) algorithm and mathematical morphological processing is proposed. At the first stage, background could be estimated using GMM from CCTV input image signal and then object could be separated from difference image of the input image and background image. At the second stage, candidated object were reformed by using mathematical morphological processing. Finally, vehicle object could be detected using vehicle size informations depend on distance and vehicle type in tunnel. Through real experiments in tunnel, it is shown that the proposed system works well.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.5
• /
• pp.111-117
• /
• 2008

In this paper, we propose an approach to improve the performance of voiced/unvoiced (V/UV) decision under background noise environments for the selectable mode vocoder (SMV) of 3GPP2. We first present an effective analysis of the features and the classification method adopted in the SMV. And then feature vectors which are applied to the GMM are selected from relevant parameters of the SMV for the efficient voiced/unvoiced classification. For the purpose of evaluating the performance of the proposed algorithm, different experiments were carried out under various noise environments and yields better results compared with the conventional scheme of the SMV.