• Asia pacific journal of information systems
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• v.12 no.2
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• pp.197-216
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• 2002

Cooperativeness within an organization can be conceptualized as the degree of members' willingness to work with others. The simulation study investigates the relationships of cooperativeness with team performance at different levels of information redundancy by using a multi-agents model called Team-Soar. The model consists of a group of four individual Al agents situated in a network, which models a naval command and control team consisting of four members. The study used a $9{\times}3$ design in which agent cooperativeness was manipulated at nine levels by gradually replacing selfish team members with increasing numbers of neutral and cooperative members, while information redundancy was controlled at three different levels(i.e., low, medium, and high). Results of the Team-Soar simulation show that cooperation has positive impacts on team performance. Further, the results reveal that the impact of agent cooperativeness on team performance depends on the amount of information needed to be processed during the decision making process.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.3
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• pp.209-233
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• 1998

In this study, we demonstrate how the Analytic Network Process (ANP) model which is combined with Michael Porter's diamond framework can be used for assessing conditions of selected countries : Brazil. India and China for Korean steel exports. The problem of assessing country conditions requires a model that evaluates several factors on different dimensions. Those dimensions are needed for ranking them according to their likeliness of being a target for Korean steel exports. The ANP consists of four kinds of dimensions called control hierarchy : benefits, opportunities, costs, risks, each of which represents the relationship of its own clusters and elements. To develop the clusters and elements of each dimension, Porter's diamond framework will be used. The final results show that China is the most attractive country to export steel, followed by Brazil and India. This is consistent with the information that we found with respect to the elements that were taken into consideration.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.839-842
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• 2000

A new and modified neural network model Is proposed for CT image reconstruction from four projection directions only. The model uses the Resilient Back-Propagation (Rprop) algorithm, which is derived from the original Back-Propagation, for adaptation of its weights. In addition to the error in projection directions of the image being reconstructed, the proposed network makes use of errors in pixels between an image which passed the median filter and the reconstructed one. Improved reconstruction was obtained, and the proposed method was found to be very effective in CT image reconstruction when the given number of projection directions is very limited.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.96-100
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• 2023

In this paper, each inconsistency management process activities was addressed. In addition, a guideline to deal with inconsistency by viewpoints method are introduced. At the end of the paper you should have clear idea to support inconsistency management in future research and having good knowledge of inconsistency management process activities and research issues. Moreover, it gives the researcher ability to design new framework by using powerful concept in inconsistency management and viewpoint techniques. The paper is organized as follows: an introduction is presented in section one, section two contains process viewpoint, while section three includes the proposed model and conclusions are in section four.

• Journal of Sasang Constitutional Medicine
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• v.33 no.4
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• pp.1-9
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• 2021

Objectives The purpose of this study was to check whether the classification of the individual's Body Mass Index (BMI) could be predicted by analyzing the voice data constructed at the Korean medicine data center (KDC) using machine learning. Methods In this study, we proposed a convolutional neural network (CNN)-based BMI classification model. The subjects of this study were Korean adults who had completed voice recording and BMI measurement in 2006-2015 among the data established at the Korean Medicine Data Center. Among them, 2,825 data were used for training to build the model, and 566 data were used to assess the performance of the model. As an input feature of CNN, Mel-frequency cepstral coefficient (MFCC) extracted from vowel utterances was used. A model was constructed to predict a total of four groups according to gender and BMI criteria: overweight male, normal male, overweight female, and normal female. Results & Conclusions Performance evaluation was conducted using F1-score and Accuracy. As a result of the prediction for four groups, The average accuracy was 0.6016, and the average F1-score was 0.5922. Although it showed good performance in gender discrimination, it is judged that performance improvement through follow-up studies is necessary for distinguishing BMI within gender. As research on deep learning is active, performance improvement is expected through future research.

• Journal of Distribution Research
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• v.16 no.2
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• pp.95-118
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• 2011

The purpose of this study is as follows. First, we investigate empirically the effects of social network properties such as social network density and centrality of a franchisee on its information sharing with various subjects such as the franchisor and other franchisees in the franchise system. Second, we examine exploratively if tie strength between a franchisee and its franchisor plays a moderating role on the relationship between social network properties and information sharing. The study model was established as shown in

• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.193-204
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• 2012

The characteristics of citation and centrality measures in citation networks can be identified using multiple linear regression analyses. In this study, we examine the relationships between bibliometric indices and centrality measures in an article-level co-citation network to determine whether the linear model is the best fitting model and to suggest the necessity of data transformation in the analysis. 703 highly cited articles in Physics published in 2004 were sampled, and four indicators were developed as variables in this study: citation counts, degree centrality, closeness centrality, and betweenness centrality in the co-citation network. As a result, the relationship pattern between citation counts and degree centrality in a co-citation network fits a non-linear rather than linear model. Also, the relationship between degree and closeness centrality measures, or that between degree and betweenness centrality measures, can be better explained by non-linear models than by a linear model. It may be controversial, however, to choose non-linear models as the best-fitting for the relationship between closeness and betweenness centrality measures, as this result implies that data transformation may be a necessary step for inferential statistics.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.167-177
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• 2004

The Hub-and-spoke strategy is widely used in the field of transportation. According to containerization and the development of transshipment technology, it is also introduced into European rail freight transportation. The objective of this article is to develop a service network design model for rail freight transportation based on the Hub-and-spoke strategy and efficient algorithms that can be applied to large-scale network. Although this model is for strategic decision, it includes not only general operational cost but also time-delay cost. The non-linearity of objective function due to time-delay factor is transformed into linearity by establishing train service variables by frequency. To solve large scale problem, this model used a heuristic method based on decomposition and three newly-developed algorithms. The new algorithms were examined with respect to four test problems base on the actual network of European rail freight and discussed the accuracy of solutions and the efficiency of proposed algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1835-1845
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• 2021

To accurately identify black ice and warn the drivers of information in advance so they can control speed and take preventive measures. In this paper, we propose a lightweight black ice detection network based on infrared road images. A black ice recognition network model based on CNN transfer learning has been developed. Additionally, to further improve the accuracy of black ice recognition, an enhanced lightweight network based on MobileNetV2 has been developed. To reduce the amount of calculation, linear bottlenecks and inverse residuals was used, and four bottleneck groups were used. At the same time, to improve the recognition rate of the model, each bottleneck group was connected to a 3×3 convolutional layer to enhance regional feature extraction and increase the number of feature maps. Finally, a black ice recognition experiment was performed on the constructed infrared road black ice dataset. The network model proposed in this paper had an accurate recognition rate of 99.07% for black ice.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.1-6
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• 2018

In smart grid an accurate load forecasting is crucial in planning resources, which aids in improving its operation efficiency and reducing the dynamic uncertainties of energy systems. Research in this area has included the use of shallow neural networks and other machine learning techniques to solve this problem. Recent researches in the field of computer vision and speech recognition, have shown great promise for Deep Neural Networks (DNN). To improve the performance of daily electric peak load forecasting the paper presents a new deep neural network model which has the architecture of two multi-layer neural networks being serially connected. The proposed network model is progressively pre-learned layer by layer ahead of learning the whole network. For both one day and two day ahead peak load forecasting the proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange (KPX).