• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.233-243
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• 2008

Recognition for the importance and roles of public transportation is increasing because of traffic problems in many cities. In spite of this paradigm change, previous researches related with public transportation trip assignment have limits in some aspects. Especially, in case of multimodal public transportation networks, many characters should be considered such as transfers. operational time schedules, waiting time and travel cost. After metropolitan integrated transfer discount system was carried out, transfer trips are increasing among traffic modes and this takes the variation of users' route choices. Moreover, the advent of high-technology public transportation card called smart card, public transportation users' travel information can be recorded automatically and this gives many researchers new analytical methodology for multimodal public transportation networks. In this paper, it is suggested that the methodology for establishment of brand new multimodal public transportation networks based on computer programming methods using transportation card data. First, we propose the building method of integrated transportation networks based on bus and urban railroad stations in order to make full use of travel information from transportation card data. Second, it is offered how to connect the broken transfer links by computer-based programming techniques. This is very helpful to solve the transfer problems that existing transportation networks have. Lastly, we give the methodology for users' paths finding and network establishment among multi-modes in multimodal public transportation networks. By using proposed methodology in this research, it becomes easy to build multimodal public transportation networks with existing bus and urban railroad station coordinates. Also, without extra works including transfer links connection, it is possible to make large-scaled multimodal public transportation networks. In the end, this study can contribute to solve users' paths finding problem among multi-modes which is regarded as an unsolved issue in existing transportation networks.

• Korean Journal of School Psychology
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• v.17 no.1
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• pp.1-16
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• 2020

This study aims to investigate peer socialization processes on early adolescents' prosocial behaviors in friendship networks. A longitudinal social network model (SIENA) was used to disentangle socialization process in prosocial behavior. Participants were fourth and fifth graders in South Korea from 39 elementary school classrooms (N=1,040, 48% girls). According to results, students select friends based on similarity in prosocial behavior (peer selection) was found in both prosocial behaviors. Peer influence effects were also observed in prosocial behaviors. Also, moderating effect of social relatedness was significant. In classes with high social relatedness, influence effect of prosocial behavior was significant. As a contrast, prosocial behavior was not socialized in classes with the low social relatedness. Results suggest that peernetwork play a critical role in the co-evolution of friendships, and prosocial behaviors. Findings yielded the importance of the socialization process and promoting healthy peer socialization environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.119-124
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• 2017

In 1997, IBM's DeepBlue won the world chess championship, Garry Kasparov, and recently, Google's AlphaGo won all three games against Ke Jie, who was ranked 1st among all human Baduk players worldwide, interest in deep running has increased rapidly. DeepPurple, proposed in this paper, is a AI chess engine based on deep learning. DeepPurple Chess Engine consists largely of Monte Carlo Tree Search and policy network and value network, which are implemented by convolution neural networks. Through the policy network, the next move is predicted and the given situation is calculated through the value network. To select the most beneficial next move Monte Carlo Tree Search is used. The results show that the accuracy and the loss function cost of the policy network is 43% and 1.9. In the case of the value network, the accuracy is 50% and the loss function cost is 1, respectively.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.2
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• pp.134-142
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• 2022

As industry and technology go through advancement, it is hard to search new materials which satisfy various standards through conventional trial-and-error based research methods. Crystal Graph Convolutional Neural Network(CGCNN) is a neural network which uses material's features as train data, and predicts the material properties(formation energy, bandgap, etc.) much faster than first-principles calculation. This report introduces how to train the CGCNN model which predicts the formation energy using open database. It is anticipated that with a simple programming skill, readers could construct a model using their data and purpose. Developing machine learning model for materials science is going to help researchers who should explore large chemical and structural space to discover materials efficiently.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.330-330
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• 1998

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.10a
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• pp.22-22
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• 1999

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1998.10a
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• pp.171-175
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• 1998

• Journal of the Microelectronics and Packaging Society
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• v.31 no.3
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• pp.50-57
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• 2024

Recently, applying a machine learning to surrogate modeling for rapid optimization of complex designs have been widely researched. Once trained, the machine learning surrogate model can predict similar outputs to Finite Element Analysis (FEA) simulations but require significantly less computing resources. In addition, combined with optimization methodologies, it can identify optimal design variable with less time requirement compared to iterative simulation. This study proposes a Deep Neural Network (DNN) model with Bayesian Optimization (BO) approach for efficiently searching the optimal design variables to minimize the warpage of electronic package. The DNN model was trained by using design variable-warpage dataset from FEA simulation, and the Bayesian optimization was applied to find the optimal design variables which minimizing the warpage. The suggested DNN + BO model shows over 99% consistency compared to actual simulation results, while only require 15 second to identify optimal design variable, which reducing the optimization time by more than 57% compared to FEA simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.6
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• pp.1379-1389
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• 1996

Hangul character always consists of consonants-vowel-consonants in order. Using this point, this paper proposes an approach to design a model for spotting each letter in Hangul, and then recognize characters based on the spotting results. The network model consist of a set of HMMs. The letter search is carried out by Viterbi algorithm, while character recognition is performed by searching the lattice of letter hypotheses. Experimental results show that, in spite of simple architecture of recognition, the performance is quite high reaching 87.47% for discrete regular characters. In particular the approach shows highly plausible segmentation of letters in characters.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.317-323
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• 2008

Mass transit information can contribute many benefits to users. Especially, transportation information technology is developing highly with information technology in Korea recently. Hereafter, it is expected to give customized transportation information to users individually with the advent of ubiquitous age in earnest. This public transportation information service can be realized by path finding algorithm in public transportation networks including travel and transfer attributes. In this research, constraints are constructed with the primary facts influencing users. Moreover, the method reducing user's path finding condition arbitrarily is proposed by making the maximum value as variables. In this study, transfer frequency, total travel time, seat confirmation, transfer time and travel time become constraint condition based on k path finding algorithm considering service time constraint condition. Moreover, case study about user customized transfer information is performed in Seoul and metropolitan subway networks.