• Journal of the Korean Data and Information Science Society
• /
• v.18 no.4
• /
• pp.945-953
• /
• 2007

In this paper, we propose a smoothing and thus noise-reducing method of data sequences for stock price prediction with hidden Markov models, HMMs. The suggested method just uses simple moving average. A proper average size is obtained from forecasting experiments with stock prices of bank sector of Korean Exchange. Forecasting method with HMM and moving average smoothing is compared with a conventional method.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.5
• /
• pp.19-28
• /
• 2009

Prediction problem of the time-series data has been a research issue for a long time among many researchers and a number of methods have been proposed in the literatures. In this paper, a method is proposed that similarities among time-series data are examined by use of Hidden Markov Model and Likelihood and future direction of the data movement is determined. Query sequence is modeled by Hidden Markov Modeling and then the model is examined over the pre-recorded time-series to find the subsequence which has the greatest similarity between the model and the extracted subsequence. The similarity is evaluated by likelihood. When the best subsequence is chosen, the next portion of the subsequence is used to predict the next phase of the data movement. A number of experiments with different parameters have been conducted to confirm the validity of the method. We used KOSPI to verify suggested method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.4 no.1
• /
• pp.3-8
• /
• 2011

The work presented in this paper describes a Hidden Markov Model(HMM)-based framework for face recognition and face detection. The observation vectors used to characterize the statics of the HMM are obtained using the coefficients of the Karhuman-Loves Transform(KLT). The face recognition method presented in this paper reduces significantly the computational complexity of previous HMM-based face recognition systems, while slightly improving the recognition rate. In addition, the suggested method is more effective than the exiting ones in face extraction in terms of accuracy and others even under complex changes to the surroundings such as lighting.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.25 no.9
• /
• pp.637-643
• /
• 2015

In recent years, pattern recognition methods have been widely used by many researchers for fault diagnoses of mechanical systems. The soundness of a mechanical system can be checked by analyzing the variation of the system vibration characteristic along with a pattern recognition method. Recently, the hidden Markov model has been widely used as a pattern recognition method in various fields. In this paper, the hidden Markov model is employed for the fault diagnosis of the mass unbalance of a rotating system. Mass unbalance is one of the critical faults in the rotating system. A procedure to identity the location and size of the mass unbalance is proposed and the accuracy of the procedure is validated through experiment.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.6
• /
• pp.673-678
• /
• 2003

In this paper, we proposes a method for face recognition using HMM(hidden Markov model) and wavelet coefficients First, input images are compressed by using the multi-resolution analysis based on the discrete wavelet transform. And then, the wavelet coefficients obtained from each subband are used as feature vectors to construct the HMMs. In the recognition stage, we obtained higher recognition rate by summing of each recognition rate of wavelet subband. The usefulness of the proposed method was shown by comparing with conventional VQ and DCT-HMM ones. The experimental results show that the proposed method is more satisfactory than previous ones.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.17 no.11
• /
• pp.1199-1205
• /
• 1992

In this paper, a Korean word recognition method which usese Neural Network and Hidden Markov Models(HMM) is proposed to improve a recognition rate with a small amount of learning data. The method reduces the fluctuation due to personal differences which is a problem to a HMM recognition system. In this method, effective recognizer is designed by the complement of each recognition result of the Hidden Markov Models(HMM) and Neural Network. In order to evaluate this model, word recognition experiment is carried out for 28 cities which is DDD area names uttered by two male and a female in twenties. As a result of testing HMM with 8 state, codeword is 64, the recognition rate 91[%], as a result of testing Neural network(NN) with 64 codeword the recognition rate is 89[%]. Finally, as a result of testing NN-HMM with 64 codeword which the best condition in former tests, the recognition rate is 95[%].

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.13 no.3
• /
• pp.164-170
• /
• 2013

Recent years have been higher demands for automatic speech recognition (ASR) systems that are able to operate robustly in an acoustically noisy environment. This paper proposes an improved product hidden markov model (HMM) used for bimodal speech recognition. A two-dimensional training model is built based on dependently trained audio-HMM and visual-HMM, reflecting the asynchronous characteristics of the audio and video streams. A weight coefficient is introduced to adjust the weight of the video and audio streams automatically according to differences in the noise environment. Experimental results show that compared with other bimodal speech recognition approaches, this approach obtains better speech recognition performance.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.27 no.4
• /
• pp.636-644
• /
• 1990

Hidden Markov models(HMM's) have been known to be useful representation for speech signal and are used in a wide variety of speech systems. For speech recognition applications, it is desirable to incorporate durational information of states in model which correspond to phonetic duration of speech segments. In this paper we propose duration-dependent HMM's that include durational information of states appropriately for the left-to-right model. Reestimation formulae for the parameters of the proposed model are derived and their convergence is verified. Finally, the performance of the proposed models is verified by applying to an isolated word, speaker independent speech recognition system.

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.11
• /
• pp.222-227
• /
• 1997

To diagnose cardiac arrhythmia owing to reentry mechanism, cardiac conduction system was modeled by modified Hidden Markov modeled by evaluated. First, simulation of transient conduction states and output waves were made with initially assumed parametric values of cardiac muscle repolariztion time, conduction velocity and its automaticity. The output was a series of onset time and the name of the wave. Parameters determined the rate of beating, lengths of wave intervals, rate of abnormal beats, and the like. Several parameter sets were found to simulate normal sinus rhythm, supraventricular /ventricular tachycardia, atrial /vetricular extrasystole, etc. Then, utilizing the estimation theorems of Hidden Markov Model, the best conduction path was estimated given the previous output. With this modified estimation method, close matching between the simulated conduction path and the estimated one was confirmed.

• Journal of Korea Multimedia Society
• /
• v.20 no.9
• /
• pp.1502-1508
• /
• 2017

A musical key is a fundamental concept in Western music theory. It is a collective characterization of pitches and chords that together create a musical perception of the entire piece. It is based on a group of pitches in a scale with which a music is constructed. Each key specifies the set of seven primary chromatic notes that are used out of the twelve possible notes. This paper presents a method that identifies the key of a song using Hidden Markov Models given a sequence of chroma features. Given an input song, a sequence of chroma features are computed. It is then classified into one of the 24 keys using a discrete Hidden Markov Models. The proposed method can help musicians and disc-jockeys in mixing a segment of tracks to create a medley. When tested on 120 songs, the success rate of the music key identification reached around 87.5%.