• Journal of the Korean School Mathematics Society
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• v.13 no.1
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• pp.125-141
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• 2010

The aim of this paper is to develop a web-based Virtual Learning System for discrete mathematics learning using CAS (Computer Algebra System), The system contains a series of contents that are common between secondary und university curriculum in discrete mathematics such as sets, relations, matrices, graphs etc. We designed and developed web-based virtual learning contents contained in the proposed system based on Mathematia, webMathematica and phpMath taking advantages of rapid computation and visualization. The virtual learning system for discrete math provides movie lectures and 'practice mode' authored with phpMath in order to enhance conceptual understanding of each movie lesson. In particular, matrix learning is facilitated with conceptual diagram that provides interactive quizzes. Once the quiz results are submitted, Bayesian inference network diagnoses strong and weak parts of learning nodes for generating diagnostic reports to facilitate personalized learning. As part of formative evaluation, the overall responses were collected for future revision of the system with 10 university students.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.136-147
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• 2003

To achieve an optimal performance for a given problem, we need an integrative process of the parameter optimization via learning and the structure optimization via model selection. In this paper, we propose an efficient optimization method for improving generalization performance by considering the property of each sub-method and by combining them with common theoretical properties. First, weight parameters are optimized by natural gradient teaming with adaptive regularization, which uses a diverse error function. Second, the network structure is optimized by eliminating unnecessary parameters with natural pruning. Through iterating these processes, candidate models are constructed and evaluated based on the Bayesian Information Criterion so that an optimal one is finally selected. Through computational experiments on benchmark problems, we confirm the weight parameter and structure optimization performance of the proposed method.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.57-67
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• 2008

Loading zone is necessary to central business district(CBD) due to freight trucks coming into inner city. Currently truck drivers in metropolitan area are forced to illegally park their freight trucks on the roads close to the business facilities because there is no or few loading zones available in the CBD. This situation would bring into traffic congestion and even might block local roads in the CBD. This study reviews the existing improvement plans in city logistics, and examines delivery characteristics related to freight trucks and drivers in Seomyeon, the central downtown of Busan. Also it conducts a location planning for on-street loading/unloading bay using a technical method for arranging loading space. This research estimated the number of parking and stop bays for truck's operation using queueing theory based on truck-service rate and arrival rate, and found locations with the minimum resistance function values for freight movement in arranging on-street loading/unloading bays.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.659-668
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• 2010

Collaborative filtering systems grasp behaviors for a new user and need new information for the user in order to recommend interesting items to the user. For the purpose of acquiring the information the collaborative filtering systems learn behaviors for users based on the previous data and can obtain new information from the results. In this paper, we propose an inductive inference method to obtain new information for users and rank items by using the new information in the proposed method. The proposed method clusters users into groups by learning users through NMF among inductive machine learning methods and selects the group features from the groups by using chi-square. Then, the method classifies a new user into a group by using the bayesian probability model as one of inductive inference methods based on the rating values for the new user and the features of groups. Finally, the method decides the ranks of items by applying the Rocchio algorithm to items with the missing values.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5D
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• pp.663-669
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• 2011

In this study, the optimal length of left-turn lane in permissive left-turn signal system at the signalized intersection which has a left-turn bay is estimated. It is a simulation analysis using the queueing theory that estimate the length of left-turn lane. Traffic density conform to the standards of operating a permissive left-turn system of the Practical Manual Traffic Safety Facilities. And each of a left-turn arrival rate, a left-turn service rate, left-turn average queueing time, for green time average queueing vehicle, for red time average queueing vehicle and average queueing vehicle cycle is calculated. As a result of this study, we would learn how much the space should be secured at the signalized intersection which has a left-turn bay. The methodology using the queueing theory to work out the optimal length of waiting lane in the permissive left-turn signal system was presented.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.477-481
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• 2008

Place recognition is necessary for a mobile user to be provided with place-dependent information. This paper proposes real-time video based place recognition system that identifies users' current place while moving in the building. As for the feature extraction of a scene, there have been existing methods based on global feature analysis that has drawback of sensitive-ness for the case of partial occlusion and noises. There have also been local feature based methods that usually attempted object recognition which seemed hard to be applied in real-time system because of high computational cost. On the other hand, researches using statistical methods such as HMM(hidden Markov models) or bayesian networks have been used to derive place recognition result from the feature data. The former is, however, not practical because it requires huge amounts of efforts to gather the training data while the latter usually depends on object recognition only. This paper proposes a combined approach of global and local feature analysis for feature extraction to complement both approaches' drawbacks. The proposed method is applied to a mobile information system and shows real-time performance with competitive recognition result.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2
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• pp.5-11
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• 1996

The purpose of this paper is to propose a method improving the performance of a semi-continuous hidden Markov model(SCHMM) speaker adaptation system which uses Bayesian Parameter reestimation approach. The performance of Bayesian speaker adaptation could be degraded in case that the features of a new speaker are severely different from those of a reference codebook. The excessive codewords of the reference codebook still remain after adaptation proess. which cause confusion in recognition process. To solve such problems, the proposed method uses formant information which is extracted from the cepstral coefficients of the reference codebook and adaptation data. The reference codebook is adapted to represent the formant distribution of a new speaker and it is used for Bayesian speaker adaptation as an initial codebook. The proposed method provides accurate correspondence between reference codebook and adaptation data. It was observed that the excessive codewords were not selected during recognition process. The experimental results showed that the proposed method improved the recognition performance.

• Journal of the Korean Society of Propulsion Engineers
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• v.18 no.4
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• pp.81-89
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• 2014

Tandem-ejector has been devised for engine-bay cooling. In this study, 1-D model has been developed to analyze Tandem-ejector. In the model, the primary, the secondary and the tertiary flow conditions have been analyzed with isentropic process. The mixing process has been analyzed with conservation laws based on the control volume analysis. The total pressure loss of the primary flow has been analyzed under the matching condition between the static pressure of Tandem-ejector discharge flow and atmospheric pressure. Consequently, 1-D model can predict Tandem-ejector performance accurately and provide the performance map.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.930-938
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• 2007

Captions which appear in images include information that relates to the images. In order to obtain the information carried by captions, the methods for text extraction from images have been developed. However, most existing methods can be applied to captions with fixed height of stroke's width. We propose a method which can be applied to various caption size. Our method is based on connected components. And then the edge pixels are detected and grouped into connected components. We analyze the properties of connected components and build a neural network which discriminates connected components which include captions from ones which do not. Experimental data is collected from broadcast programs such as news, documentaries, and show programs which include various height caption. Experimental result is evaluated by two criteria : recall and precision. Recall is the ratio of the identified captions in all the captions in images and the precision is the ratio of the captions in the objects identified as captions. The experiment shows that the proposed method can efficiently extract captions various in size.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1674-1680
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• 2017

In this paper, an embedded system for face authentication, which exploits high-dimensional local binary pattern (LBP) descriptor and joint Bayesian algorithm, is proposed. We also present a feasible embedded system for the proposed algorithm implemented with a Raspberry Pi 3 model B. Computer simulation for performance evaluation of the presented face authentication algorithm is carried out using a face database of 500 persons. The face data of a person consist of 2 images, one for training and the other for test. As performance measures, we exploit score distribution and face authentication time with respect to the dimensions of principal component analysis (PCA). As a result, it is confirmed that an embedded system having a good face authentication performance can be implemented with a relatively low cost under an optimized embedded environment.