• Journal of the Korea Safety Management & Science
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• v.14 no.2
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• pp.193-204
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• 2012

This paper deals with an empirical analysis of the structural relationship among the factors such as quality management activities, organizational learning and firm performance of manufacturing corporations. The findings of the analysis are expected to make lots of contribution to manufacturing corporations establishing strategies for quality management activities and organizational learning. From the analysis, following conclusions and suggestions could be drawn: First, an analysis of the relationship between quality management activities and organizational learning showed that most activities of quality management turned out to exercise great influence upon the factors of organizational learning. This means that the activities of quality management will prompt the members of an organization to actively engage in learning activities individually, by team and organizationally, motivating them to spread such activities across the whole organization, leading ultimately to fundamental renovation of the very organization. Second, from an analysis of the relationship between organizational learning and firm performance, that is, financial and non-financial performances of a company, it was found that most factors of organizational learning have tremendous impact upon financial and non-financial performances of the company. Such result implies that decision and management of the things to be performed in the process of organizational performances are essential to determining firm performance because firm performance depend largely on the outcomes of organizational learning.

• The Journal of the Korea Contents Association
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• v.15 no.11
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• pp.616-625
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• 2015

The purpose of the study is to structure the friendship network by the social network analysis and investigate the effects of social network centrality and learners' performances in college students. Both the in-degree centrality of 1st grade class study-network(t=2.722, P<.005) and the in-degree centrality of and $2^{nd}$ grade class study-network(t=2.708, P<.005)are predicted the individual student's learning performances. But there is no correlation between the in-degree centrality of $1^{st}$ and $2^{nd}$ grade class entertainment-network and the individual student's learning performances. Results of the study suggested the significant effect of social network analysis measures on learners' performance in the friendship networks. Based on the results, implication to the teaching strategy and future research direction were discussed.

• Journal of Korean Society for Quality Management
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• v.46 no.1
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• pp.153-168
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• 2018

Purpose: The balanced scorecard (BSC) has been adopted to evaluate factors affecting performances in organizational dealer marketing in Chinese cosmetics market. Four performance measures in BSC: learning & growth, internal business processes, customer performance, and financial performance are employed in our empirical study. Methods: We conducted surveys of dealers in a Chinese cosmetics company and used total 463 samples for analysis. Confirmatory factor analysis and structural equation model analysis were employed using AMOS 20.0. Results: This study found that internal business process had a positive relation with customer performance and learning and growth. Also, customer performance and learning & growth positively affected financial performances. Conclusion: This study has some academic and practical contributions in that the revised BSC model reflects the special aspects of Chinese cosmetics market and it can be used as a guide for companies in the Chinese cosmetics market to understand which factors are affecting performances.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.196-201
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• 2008

Statistical learning theory has three analytical tools which are support vector machine, support vector regression, and support vector clustering for classification, regression, and clustering respectively. In general, their performances are good because they are constructed by convex optimization. But, there are some problems in the methods. One of the problems is the subjective determination of the parameters for kernel function and regularization by the arts of researchers. Also, the results of the learning machines are depended on the selected parameters. In this paper, we propose an efficient method for objective determination of the parameters of support vector clustering which is the clustering method of statistical learning theory. Using evolutionary algorithm and bootstrap method, we select the parameters of kernel function and regularization constant objectively. To verify improved performances of proposed research, we compare our method with established learning algorithms using the data sets form ucr machine learning repository and synthetic data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.911-931
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• 2021

Recently, most cloud services use Docker container environment to provide their services. However, there are no researches to evaluate the performance of communication libraries for multi-GPU based distributed deep learning in a Docker container environment. In this paper, we propose an efficient communication architecture for multi-GPU based deep learning in a Docker container environment by evaluating the performances of various communication libraries. We compare the performances of the parameter server architecture and the All-reduce architecture, which are typical distributed deep learning architectures. Further, we analyze the performances of two separate multi-GPU resource allocation policies - allocating a single GPU to each Docker container and allocating multiple GPUs to each Docker container. We also experiment with the scalability of collective communication by increasing the number of GPUs from one to four. Through experiments, we compare OpenMPI and MPICH, which are representative open source MPI libraries, and NCCL, which is NVIDIA's collective communication library for the multi-GPU setting. In the parameter server architecture, we show that using CUDA-aware OpenMPI with multi-GPU per Docker container environment reduces communication latency by up to 75%. Also, we show that using NCCL in All-reduce architecture reduces communication latency by up to 93% compared to other libraries.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.173-176
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• 1997

In this paper, the classification performances of inductive learning methods are investigated using the credit rating data. The adopted classifiers are Multiple Discriminant Analysis (MDA), C4.5 of Quilan, Multi-Layer Perceptron (MLP) and Cascade Correlation Network (CCN). The data used in this analysis is obtained using the publicly announced rating reports from the three korean rating agencies. The performances of 4 classifiers are analyzed in term of prediction accuracy. The results show that no classifier is dominated by the other classifiers.

• Journal of Information Technology Services
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• v.12 no.2
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• pp.195-214
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• 2013

The purpose of this study is to verify the mediating effect of interaction among learners in a Web Based Collaboration Learning (WBCL) environment. 254 Korean college students served as test subjects and during the 4 weeks of research period, they studied the Test of English for International Communication (TOEIC) in a web-based collaborative learning system. The interaction between learners was looked into by categorizing the concept into task oriented information sharing activities and relationship oriented communication activities and analyzing the causal relationship between the two activities. Learning performances were measured in individual level. The results are as follows. First, task oriented information sharing activities effect positively on relationship oriented information sharing activities. Second, the managerial characteristics of WBCL had a positive effect on interaction between learners but the systematic characteristics had partial influence on interaction between learners. Third, the interaction between learners completely interconnects the managerial characteristics of WBCL and learning performance but partially interconnects the systematic characteristic of WBCL and learning performance. In conclusion, this study implies that managerial and systematic characteristics of WBCL should be considered on the preferential basis for the WBCL to become successful and interactive activities such as information sharing and communication should be encouraged to be active from a small-size WBCL perspective.

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.114-121
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• 2019

This paper presents a new benchmark system for visual odometry (VO) and monocular depth estimation (MDE). As deep learning has become a key technology in computer vision, many researchers are trying to apply deep learning to VO and MDE. Just a couple of years ago, they were independently studied in a supervised way, but now they are coupled and trained together in an unsupervised way. However, before designing fancy models and losses, we have to customize datasets to use them for training and testing. After training, the model has to be compared with the existing models, which is also a huge burden. The benchmark provides input dataset ready-to-use for VO and MDE research in 'tfrecords' format and output dataset that includes model checkpoints and inference results of the existing models. It also provides various tools for data formatting, training, and evaluation. In the experiments, the exsiting models were evaluated to verify their performances presented in the corresponding papers and we found that the evaluation result is inferior to the presented performances.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.300-305
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• 2023

Audience reactions in response to remote virtual performances must be compressed before being transmitted to the server. The server, which aggregates these data for group insights, requires a distribution code for the transfer. Recently, distributed learning algorithms such as federated learning have gained attention as alternatives that satisfy both the information security and efficiency requirements. In distributed learning, no individual user has access to complete information, and the objective is to achieve a learning effect similar to that achieved with the entire information. It is therefore important to distribute interdependent information among users and subsequently aggregate this information following training. In this paper, we present a new extension technique for minimal code that allows a new minimal code with a different length and Hamming weight to be generated through the product of any vector and a given minimal code. Thus, the proposed technique can generate minimal codes with previously unknown parameters. We also present a scenario wherein these combined methods can be applied.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.4
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• pp.89-103
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• 2006

The purpose of this study is to examine influential factors in knowledge-sharing and to analyze how these factors influence the performances of knowledge management (KM) in public enterprises. Influencing factors of KM in this study include evaluation-compensation, knowledge management system, learning culture, and organizational structures. As a result, analysis turned out to be the KM system and organizational structure directly effects knowledge-sharing and KM performances. And knowledge-sharing performed as mediating effect between independent variables such as compensation system and organization structure, and dependent variable like KM performance. Therefore, this study concludes that each factor of evaluation-compensation system and learning culture has directly influenced to knowledge-sharing, yet KM performances have Indirectly influenced.