• Proceedings of the KSPS conference
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• 2006.11a
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• pp.14-18
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• 2006

The role of dialogue manager is to select proper actions based on observed environment and inferred user intention. This paper presents stochastic model for dialogue manager based on Markov decision process. To build a mixed initiative dialogue manager, we used accumulated user utterance, previous act of dialogue manager, and domain dependent knowledge as the input to the MDP. We also used dialogue corpus to train the automatically optimized policy of MDP with reinforcement learning algorithm. The states which have unique and intuitive actions were removed from the design of MDP by using the domain knowledge. The design of dialogue manager included the usage of natural language understanding and response generator to build short message based remote control of home networked appliances.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.3
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• pp.169-175
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• 2008

A fine carbon fibers dispersion model is implemented to calculate the scattering range and ground level concentration of carbon fibers emitted at certain altitudes of atmospheric boundary layer. This carbon fibers dispersion model was composed by coupling a commonly used atmospheric dispersion model and an atmospheric boundary layer model. The atmospheric boundary layer model, applying the Monin-Obukov Similarity Rule obtained from measurement input data at ground level, was used to create the atmospheric boundary layer structure. In the atmospheric dispersion model, the Lagrangian Particle Model and the Markov Process were applied to calculate the trajectory of scattered carbon fibers relative to gravity and aerodynamic force, as well as carbon fibers specification.

• Fisheries and Aquatic Sciences
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• v.24 no.4
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• pp.139-152
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• 2021

The main purpose of this study is to fit catch-per-unit-effort (CPUE) data about Korea chub mackerel (Scomber japonicus) stock with a state-space production (SSP) model, and to provide stock assessment results. We chose a surplus production model for the chub mackerel data, namely annual yield and CPUE. Then we employed a state-space layer for a production model to consider two sources of variability arising from unmodelled factors (process error) and noise in the data (observation error). We implemented the model via script software ADMB-RE because it reduces the computational cost of high-dimensional integration and provides Markov Chain Monte Carlo sampling, which is required for Bayesian approaches. To stabilize the numerical optimization, we considered prior distributions for model parameters. Applying the SSP model to data collected from commercial fisheries from 1999 to 2017, we estimated model parameters and management references, as well as uncertainties for the estimates. We also applied various production models and showed parameter estimates and goodness of fit statistics to compare the model performance. This study presents two significant findings. First, we concluded that the stock has been overexploited in terms of harvest rate from 1999 to 2017. Second, we suggest a SSP model for the smallest goodness of fit statistics among several production models, especially for fitting CPUE data with fluctuations.

• Journal of Biomedical Engineering Research
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• v.25 no.3
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• pp.217-225
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• 2004

In this paper, a new automatic classification method for the normal EEC and schizophrenia EEC using hidden Markov model(HMM) is proposed. We used the feature parameters which are the variance for statistical stationary interval of the EEC and power spectrum ratio of the alpha, beta, and theta wave. The results were shown that high classification accuracy of 90.9％ in the case of normal person, and 90.5％ in the case of schizophrenia patient. It seems that proposed classification system is more efficient than the system using complicate signal processing process. Hence, the proposed method can be used at analysis and classification for complicated biosignal such as EEC and is expected to give considerable assistance to clinical diagnosis.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.6
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• pp.37-46
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• 2005

In the Web environment with a huge amount of information, Web page access patterns for the users visiting certain web site can be diverse and change continually in accordance with the change of its environment. Therefore it is almost impossible to develop and design web sites which fit perfectly for every web user's desire. Adaptive web site was proposed as solution to this problem. In this paper, we will present an effective method that uses a probabilistic model of DTMC(Discrete-Time Markov Chain) for learning user's access patterns and applying these patterns to construct an adaptive web site.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.6
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• pp.813-819
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• 2014

The purpose of this study was to extract accurate parameters of facial movement features using 3-D motion capture system in speech recognition technology through lip-reading. Instead of using the features obtained through traditional camera image, the 3-D motion system was used to obtain quantitative data for actual facial movements, and to analyze 11 variables that exhibit particular patterns such as nose, lip, jaw and cheek movements in monosyllable vocalizations. Fourteen subjects, all in 20s of age, were asked to vocalize 11 types of Korean vowel monosyllables for three times with 36 reflective markers on their faces. The obtained facial movement data were then calculated into 11 parameters and presented as patterns for each monosyllable vocalization. The parameter patterns were performed through learning and recognizing process for each monosyllable with speech recognition algorithms with Hidden Markov Model (HMM) and Viterbi algorithm. The accuracy rate of 11 monosyllables recognition was 97.2%, which suggests the possibility of voice recognition of Korean language through quantitative facial movement analysis.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.9
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• pp.66-75
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• 1999

Image segmentation is the process where an image is segmented into regions that are set of homogeneous pixels. The result has a ciritical effect on accuracy of image understanding. In this paper, an Markov random field (MRF) image segmentation is proposed using genetic algorithm(GA). We model an image using MRF which is resistant to noise and blurring. While MRF based methods are robust to degradation, these require accurate parameter estimation. So GA is used as a segmentation algorithm which is effective at dealing with combinatorial problems. The efficiency of the proposed method is shown by experimental results with real images and application to automatic vehicle extraction system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.981-984
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• 2010

In this paper, we used Support Vector Machines(SVMs) as the learning method, one of Artificial Neural Network, to segregated from the continuous speech into phonemes, an initial, medial, and final sound, and then, performed continuous speech recognition from it. A Decision boundary of phoneme is determined by algorithm with maximum frequency in a short interval. Speech recognition process is performed by Continuous Hidden Markov Model(CHMM), and we compared it with another phoneme segregated from the eye-measurement. From the simulation results, we confirmed that the method, SVMs, we proposed is more effective in an initial sound than Gaussian Mixture Models(GMMs).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3447-3469
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• 2019

It is well-known that the cooperative communication and error control technology can improve the network performance, but most existing cooperative MAC protocols have not focused on how to cope with the contention process caused by cooperation and how to reduce the bad influence of channel packet error rate on the system performance. Inspired by this, this paper first modifies and improves the basic rules of the IEEE 802.11 Medium Access Control (MAC) protocol to optimize the contention among the multi-relay in a cooperative ARQ scheme. Secondly, a hybrid ARQ protocol with soft combining is adopted to make full use of the effective information in the error data packet and hence improve the ability of the receiver to decode the data packet correctly. The closed expressions of network performance including throughput and average packet transmission delay in a saturated network are then analyzed and derived by establishing a dedicated two-dimensional Markov model and solving its steady-state distribution. Finally, the performance evaluation and superiority of the proposed protocol are validated in different representative study cases through MATLAB simulations.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.1
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• pp.45-53
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• 2017

A high resolution model is proposed for calculating the temperature field of a large city, based upon a Lagrangian particle model. Utilizing the analogy between the heat and mass transport phenomena in turbulent flows, a Lagrangian particle model, originally developed for air pollutant dispersion problems, is adapted for simulating heat transport. In the model conceptual heat particles are released into the atmosphere from the heat sources and move along with the turbulent winds in accordance with the Markov process. The potential temperature assumed to be conserved along with heat particles serves as a tag, so the temperature fields can be deduced from the distribution of particles. The wind fields are constructed from a diagnostic meteorology model incorporating a morphological model designed for building flows. Test run shows the robustness of the modeling system.