• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.164-171
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• 2006

Recently Digital multimedia broadcasting (DMB) has been available as a commercial service. The users sometimes have difficulty in finding their preferred multimedia contents and need to spend a lot of searching time finding them. They are even very likely to miss their preferred contents while searching for them. In order to solve the problem, we need a method for recommendation users preferred only minimum information. We propose an algorithm and a system for recommending users' preferred contents using preference transition probability from user's usage history. The system includes four agents: a client manager agent, a monitoring agent, a learning agent, and a recommendation agent. The client manager agent interacts and coordinates with the other modules, the monitoring agent gathers usage data for analyzing the user's preference of the contents, the learning agent cleans the gathered usage data and modeling with state transition matrix over time, and the recommendation agent recommends the user's preferred contents by analyzing the cleaned usage data. In the recommendation agent, we developed the recommendation algorithm using a user's preference transition probability for the contents. The prototype of the proposed system is designed and implemented on the WIPI(Wireless Internet Platform for Interoperability). The experimental results show that the recommendation algorithm using a user's preference transition probability can provide better performances than a conventional method.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.1
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• pp.50-56
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• 2010

Military technology transition is the process of transition from the science and technology environment to systems to supply effective weapon systems and support systems to the fighters. In case of technology transition decision, immatured technologies result in increasing acquisition cost and delaying schedule toward the objective system. In this paper, we proposed a method to identify and select critical technology elements by integration relations between technologies or components, for supporting technology transition and risk management of military R&D projects.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.408-413
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• 2005

A new methodology for discrete non-Gaussian probability density estimation is investigated in this paper based on a dynamic Bayesian network (DBN) and kernel functions. The estimator consists of a DBN in which the transition distribution is represented with kernel functions. The estimator parameters are determined through a recursive learning algorithm according to the maximum likelihood (ML) scheme. A discrete-type Poisson distribution is generated in a simulation experiment to evaluate the proposed method. In addition, an unknown probability density generated by nonlinear transformation of a Poisson random variable is simulated. Computer simulations numerically demonstrate that the method successfully estimates the unknown probability distribution function (PDF).

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.763-770
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• 2004

The important issue of emotion recognition from speech is a feature extracting and pattern classification. Features should involve essential information for classifying the emotions. Feature selection is needed to decompose the components of speech and analyze the relation between features and emotions. Specially, a pitch of speech components includes much information for emotion. Accordingly, this paper searches the relation of emotion to features such as the sound loudness, pitch, etc. and classifies the emotions by using the statistic of the collecting data. This paper deals with the method of recognizing emotion from the sound. The most important emotional component of sound is a tone. Also, the inference ability of a brain takes part in the emotion recognition. This paper finds empirically the emotional components from the speech and experiment on the emotion recognition. This paper also proposes the recognition method using these emotional components and the transition probability.

• Journal of Communications and Networks
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• v.5 no.3
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• pp.202-214
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• 2003

The central challenge in the design of ad-hoc networks is the development of dynamic routing protocol that efficiently finds route between mobile nodes. Several routing protocols such as DSR, AODV and DSDV have been proposed in the literature to facilitate communication in such dynamically changing network topology. In this paper, a Node Transition Probability (NTP) based routing algorithm, which determines stable routes using the received power from all other neighboring nodes is proposed. NTP based routing algorithm is designed and implemented using Global Mobile Simulator (GloMoSim), a scalable network simulator. The performance of this routing algorithm is studied for various mobility models and throughput, control overhead, average end-to-end delay, and percentage of packet dropped are compared with the existing routing protocols. This algorithm shows acceptable performance under all mobility conditions. The results show that this algorithm maximizes the bandwidth utilization during heavy traffic with lesser overhead.

• Transactions of the Korean Society of Mechanical Engineers
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• v.10 no.6
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• pp.814-822
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• 1986

Flow patterns and its transitions in vertical two phase flow of air-water isothermal flow are identified objectively by void output signals and moments computed from the Probability Density Function which is associated with the statistical measurement for time average local void fractions using conductance probe. It has been shown that the probe output signals, PDF distributions and its moments are deterministic criteria of flow pattern and transition classification.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.17-24
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• 1994

This paper is a study on Korean word recognition and suggest the method that stabilizes the state-transition in the HMM by applying the `inertia' to the feature vector sequences. In order to reduce the quantized distortion considering probability distribution of input vectors, we used SOFM, an unsupervised learning method, as a vector quantizer, By applying inertia to the feature vector sequences, the overlapping of probability distributions for the response path of each word on the self organizing feature map can be reduced and the state-transition in the Hmm can be Stabilized. In order to evaluate the performance of the method, we carried out experiments for 50 DDD area names. The results showed that applying inertia to the feature vector sequence improved the recognition rate by 7.4% and can make more HMMs available without reducing the recognition rate for the SOFM having the fixed number of neuron.

• Korean Journal of Remote Sensing
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• v.1 no.1
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• pp.53-61
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• 1985

The purpose of the study is to establish models of land use prediction system for development and management of land resources using remotely sensed data as well as ancillary data in the context of multi-disciplinary approach in the application to CheJoo Island. The model adopts multi-date processing techniques and is a spatial/temporal land-use projection strategy emerged as a synthesis of the probability transition model and the discriminant-annlysis model. A discriminant model is applied to all pixels in CheJoo landscape plane to predict the most likely change in land use. The probability transition model provides the number of these pixels that will convert to different land use in a gives future time increment. The synthetic model predicts the future change in land use and its volume of pixels in the landscape plane.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.10 no.3
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• pp.29-36
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• 2000

It is very import role to increase communication quality that fast detection of random sequence synchronization fail in synchronous stream cipher system using initial synchronization mode. Generally it sends additional information to detect random sequency synchronization fail. But we can't transmit additional informations to decide synchronization fail in a system using RMVD to correct channel error. In this paper we propose a method to detect synchronization fail in the receiver even though a system using RMVD has no margin to send additional information, For detecting random sequency synchronization fail we decipher receiver data analyze probability of transition rate for pre-determined period and decide synchronization fail using calculated transition rate probability. This proposed method is fast very reliable and robust in noisy channel and is easily implemented with hardware.