• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2220-2223
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• 2003

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

• Journal of Korean Institute of Industrial Engineers
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• v.11 no.2
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• pp.101-111
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• 1985

This study deals with problems of estimating the availability of the multi-module software systems. The result presented in this paper is an extension of our previous paper (2) entitled "A modified Markov model for the estimation of computer software performance". The extension is made by assuming that (1) the software system consists of R statistically independent software modules; (2) no failure occurrence while the process is in transition between software modules.

• Management Science and Financial Engineering
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• v.19 no.1
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• pp.49-52
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• 2013

In this paper we provide a valuation formula for a variance swap with regime switching. A variance swap is a forward contract on variance, the square of realized volatility of the underlying asset. We assume that the volatility of underlying asset is governed by Markov regime-switching process with finite states. We find that the proposed model can provide ease of calculation and be superior to the models currently available.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.85-94
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• 2017

In this paper, I proposed a sound model generation and a most frequent model search algorithm for recognizing animal vocalization. The sound model generation algorithm generates a optimal set of models through repeating processes such as the training process, the Viterbi Search process, and the most frequent model search process while adjusting HMM(Hidden Markov Model) structure to improve global recognition rate. The most frequent model search algorithm searches the list of models produced by Viterbi Search Algorithm for the most frequent model and makes it be the final decision of recognition process. It is implemented using MFCC(Mel Frequency Cepstral Coefficient) for the sound feature, HMM for the model, and C# programming language. To evaluate the algorithm, a set of animal sounds for 27 species were prepared and the experiment showed that the sound model generation algorithm generates 27 HMM models with 97.29 percent of recognition rate.

• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1820-1831
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• 2010

When interacting with unknown environments, an autonomous agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. The traditional multiple sequential learning model requires predefined probability of the states' transition. This paper proposes a multiple sequential learning and prediction system with definition of autonomous states to enhance the automatic performance of existing AI algorithms. In sequence learning process, the sensed states are classified into several group by a set of proposed motivation filters to reduce the learning computation. In prediction process, the learning agent makes a decision based on the estimation of each state's cost to get a high payoff from the given environment. The proposed learning and prediction algorithms heightens the automatic planning of the autonomous agent for interacting with the dynamic unknown environment. This model was tested in a virtual library.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.470-475
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• 2002

An alternative to traditional production planning and control systems such as MRP and JIT is the drum-buffer-rope (DBR). Using the DBR system, companies can achieve a large reduction of work-in-process (WIP) and finished-goods inventories (FGI). significant improvement in scheduling performance, and substantial earnings increase. The purpose of this paper is to analyze the effect of the DBR system in a serial production line. Using Markov process, we modeled a DBR system with three stages. For the model developed we analyze the system characteristics and then present an optimization model for system design. The system performance is also analyzed through sensitivity analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.10A
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• pp.1756-1763
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• 2001

In this paper, we propose an ATM-LAN IWU(interworking unit) with threshold based dynamic bandwidth allocation scheme. We analyze a discrete-time based finite queueing model with deterministic service times in order to investigate the performance of the proposed scheme. It is known that the arrival process of IP packets is bursty. So we use an MMPP(Markov Modulated Poisson Process) to model the bursty input traffic. As performance measures, we obtain the packet loss probability and the mean packet delay. We present some numerical results to show the effects of the thresholds on the performance of the DBAS(dynamic bandwidth allocation scheme).

• Journal of applied mathematics & informatics
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• v.8 no.1
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• pp.243-252
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• 2001

The points of failure of a decomposition process are defined to be the union of the points of failure from two component point processes for software reliability systems. Because sampling from the likelihood function of the decomposition model is difficulty, Gibbs Sampler can be applied in a straightforward manner. A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For model determination, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. A numerical example with a simulated data set is given.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.57-64
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• 2000

In this paper we consider a Markovian perfect debugging model for which the software failure is caused by two types of faults, one which is easily detected and the other which is difficult to detect. When a failure occurs, a perfect debugging is immediately performed and consequently one fault is reduced from fault contents. We also treat the debugging time as a variable to develop a new debugging model. Several measures, including the distribution of first passage time to the specified number of removed faults, are also obtained using the proposed debugging model, Numerical examples are provided for illustrative purposes.

• Korean Management Science Review
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• v.19 no.1
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• pp.143-162
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• 2002

The concept of revenue management have been used widely In the hotel and all transportation industries, and considered as a good system for managing a perishable asset. Recently, its' application area is being increasingly expanded to service industries such as the travel, the railway, the Internet and the sport industries. Internet business can be classified into several groups according to the characteristics of the individual business. One of groups is Internet Access Servoce business which connects each users to the internet. In this paper, since internet Access Services (IAS) business has a similar property to the service Industry, we will apply a revenue management concept to It. With some modification of existing model developed by Subramanian et.al. for airlines, we suggest the revenue management model being applied to IAS business. Computational experiment shows that the Increase of the revenue Is up to 7% by appluing our model. It means our model has a potential to manage IAS business effectively.