• Journal of Korea Multimedia Society
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• v.19 no.1
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• pp.22-29
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• 2016

Hidden Markov Model is one of the most successful and popular tools for modeling real world sequential data. Real world signals come in a variety of shapes and variabilities, among which temporal and spectral ones are the prime targets that the HMM aims at. A new problem that is gaining increasing attention is characterizing missing observations in incomplete data sequences. They are incomplete in that there are holes or omitted measurements. The standard HMM algorithms have been developed for complete data with a measurements at each regular point in time. This paper presents a modified algorithm for a discrete HMM that allows substantial amount of omissions in the input sequence. Basically it is a variant of Baum-Welch which explicitly considers the case of isolated or a number of omissions in succession. The algorithm has been tested on online handwriting samples expressed in direction codes. An extensive set of experiments show that the HMM so modeled are highly flexible showing a consistent and robust performance regardless of the amount of omissions.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.853-855
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• 2005

은닉 마르코프(HMM)의 HMM의 구조 최적을 위한 모델 선택 방법에 많은 방법들이 연구되어지고 있다. HMM의 구조를 어떻게 최적으로 정해야 하는 가에 대해 HMM의 구조를 체계적인 방법으로 정함과 동시에 변별력의 단점을 개선 할 수 있는 방법으로 Anti-likelihood(ALC1)를 제안하였고 이를 모델 선택 기준인 BIC와의 결합(ALC2)하여 필기 데이터에 대해 실험한 결과 기존의 방법보다 파라미터의 수는 감소되고 인식률이 향상됨을 알 수 있었다. 이를 Discrete HMM에도 적용하여 제안된 ALC2가 HMM 구조를 최적화하는 모델 선택 기준임을 Continuous-Density HMM과 비교하여 실험 검증 한다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.1
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• pp.23-32
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• 2015

Modeling of fighter pilots, which is a fundamental technology for war games using defense M&S (Modeling & Simulation) becomes one of the prominent research issues as the importance of defense M&S increases. Especially, the recent accumulation of combat logs makes it possible to adopt statistical learning methods to pilot modeling, and an HMM (Hidden Markov Model) which is able to utilize the sequential characteristic of combat logs is suitable for the modeling. However, since an HMM works only by using one type of features, discrete or continuous, to apply an HMM to heterogeneous features, type integration is required. Therefore, we propose a dPCA-HMM method, where dPCA (Discrete Principal Component Analysis) is combined with an HMM for the type integration. From experiments conducted on combat logs acquired from a simulator furnished by agency for defense development, the performance of the proposed model is evaluated and was satisfactory.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1
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• pp.81-89
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• 1995

In this paper, we report the continuous speech recognition system using the continuous HMM with discrete duration control and the regression coefficients. Also, we do recognition experiment using One Pass DP method(for 25 sentences of robot control commands) with finite state automata context control. In the experiment for 4 connected spoken digits, the recognition rates are $93.8\%$ when the discrete duration control and the regression coefficients are included, and $80.7\%$ when they are not included. In the experiment for 25 sentences of the robot control commands, the recognition rate are $90.9\%$ when FSN is not included and $98.4\%$ when FSN is included.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06c
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• pp.157-160
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• 1994

본 논문에서는 한국어 숫자음 인식에 대한 이산분포 HMM과 연속분포 HMM의 인식 성능을 비교하였다. 일반적으로 연속분포 HMM은 많은 계산량이 필요하고, 학습시 초기값이 매우 민감하다는 단점이 있지만, 이산분포 HMM의 VQ로 인한 왜곡을 제거함으로써 인식률을 향상시킬 수 있다. 여기서는 성능비교를 위해서 mel-cepstrum의 분석차수, 이산분포 HMM의 codebook 크기, 연속분포 HMM의 miture 개수등에 따른 인식성능을 비교하였다. 실험 결과 이산분포 HMM에서는 mel-cepstrum 벡터가 14차이고, codebook 크기가 64일 때 가장 좋은 성능을 나타냈으며, 연속부포 HMM에서는 mel-cepstrum 벡터가 16차이고 miture가 3개일 때 가장 좋은 결과를 얻을 수 있었다. 특히 학습 데이터의 양이 적은 경우에는 연속분포 HMM이 이산분포 HMM보다 더 좋은 인식률을 나타내었다.

• The Journal of the Acoustical Society of Korea
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• v.13 no.3
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• pp.63-70
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• 1994

In this Paper, we propose a new codeword tying algorithm based on a tree structured classfier. The proposed algorithm which can be viewed as a kind of soft decision using statistical properties between codewords and states has an advantage of fast construction, and guarantees a unique optimal solution. Also, it can easily be applied to any speech recognition system based on discrete hidden Markov model (HMM). Experimental results on speaker-independent isolated word recognition show error reduction of $6\%$ for the codebook of size 256 and $9\%$ for 512 size and also HMM parameter reduction of about $20\%$.

• The KIPS Transactions:PartB
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• v.12B no.1 s.97
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• pp.9-16
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• 2005

This paper focuses on the study of the face authentication system and the robustness of fact authentication methods under illumination changes. Four different face authentication methods are tried. These methods are as fellows; PCA(Principal Component Analysis), GMM(Gaussian Mixture Modeis), 1D HMM(1 Dimensional Hidden Markov Models), Pseudo 2D HMM(Pseudo 2 Dimensional Hidden Markov Models). Experiment results involving an artificial illumination change to fate images are compared with each other. Face feature vector extraction based on the 2D DCT(2 Dimensional Discrete Cosine Transform) if used. Experiments to evaluate the above four different fate authentication methods are carried out on the ORL(Olivetti Research Laboratory) face database. Experiment results show the EER(Equal Error Rate) performance degrade in ail occasions for the varying ${\delta}$. For the non illumination changes, Pseudo 2D HMM is $2.54{\%}$,1D HMM is $3.18{\%}$, PCA is $11.7{\%}$, GMM is $13.38{\%}$. The 1D HMM have the bettor performance than PCA where there is no illumination changes. But the 1D HMM have worse performance than PCA where there is large illumination changes(${\delta}{\geq}40$). For the Pseudo 2D HMM, The best EER performance is observed regardless of the illumination changes.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.11
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• pp.1864-1872
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• 2003

Hidden Markov Model(HMM) has been widely used in speech recognition, however, its use in machine condition monitoring has been very limited despite its good potential. In this paper, HMM is used to recognize rotor fault pattern. First, we set up rotor kit under unbalance and oil whirl conditions. Time signals of two failure conditions were sampled and translated to auto power spectrums. Using filter bank, feature vectors were calculated from these auto power spectrums. Next, continuous HMM and discrete HMM were trained with scaled forward/backward variables and diagonal covariance matrix. Finally, each HMM was applied to all sampled data to prove fault recognition ability. It was found that HMM has good recognition ability despite of small number of training data set in rotor fault pattern recognition.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3
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• pp.35-42
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• 1999

In this paper, a modified clustering algorithm is proposed to improve the discrimination of discrete HMM(Hidden Markov Model), so that it has increased recognition rate of 2.16% in comparison with the original HMM using the K-means or LBG algorithm. And, for preventing the decrease of recognition rate because of insufficient training data at the training scheme of HMM, a modified probabilistic smoothing method is proposed, which has increased recognition rate of 3.07% for the speaker-independent case. In the experiment applied the two proposed algorithms, the average rate of recognition has increased 4.66% for the speaker-independent case in comparison with that of original VQ/HMM.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.499-507
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• 2006

Recently human gait has been considered as a useful biometric supporting high performance human identification systems. This paper proposes a view-based pedestrian identification method using the dynamic silhouettes of a human body modeled with the Hidden Markov Model(HMM). Two types of gait models have been developed both with an endless cycle architecture: one is a discrete HMM method using a self-organizing map-based VQ codebook and the other is a continuous HMM method using feature vectors transformed into a PCA space. Experimental results showed a consistent performance trend over a range of model parameters and the recognition rate up to 88.1%. Compared with other methods, the proposed models and techniques are believed to have a sufficient potential for a successful application to gait recognition.