• Korean Management Science Review
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• v.31 no.3
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• pp.77-94
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• 2014

This study empirically investigated the relationships among inter-organizational cost management (IOCM), cooperation with suppliers, information exchange between partners, inter-organizational learning, control integration, and the supply-chain performance of a firm. The results showed that the adoption of IOCM positively affects the collaboration between buyers and suppliers, which also leads to the increased information flow between them. According to the results of this study, it was found that inter-organizational information flow causes inter-organizational learning, and this learning contributes to the improved supply-chain performance. In this study, the positive effects of the cooperation with suppliers through IOCM on the control integration in supply-chains were not empirically confirmed. However, the impact of IOCM on control integration was significant and positive. Finally, the fact that the enhanced control integration can improve the supply-chain performance of a firm was empirically demonstrated.

• Computers and Concrete
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• v.16 no.5
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• pp.741-757
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• 2015

In the last decade, several modeling approaches have been proposed and applied to estimate the high-performance concrete (HPC) slump flow. While HPC is a highly complex material, modeling its behavior is a very difficult issue. Thus, the selection and application of proper modeling methods remain therefore a crucial task. Like many other applications, HPC slump flow prediction suffers from noise which negatively affects the prediction accuracy and increases the variance. In the recent years, ensemble learning methods have introduced to optimize the prediction accuracy and reduce the prediction error. This study investigates the potential usage of bagging (Bag), which is among the most popular ensemble learning methods, in building ensemble models. Four well-known artificial intelligence models (i.e., classification and regression trees CART, support vector machines SVM, multilayer perceptron MLP and radial basis function neural networks RBF) are deployed as base learner. As a result of this study, bagging ensemble models (i.e., Bag-SVM, Bag-RT, Bag-MLP and Bag-RBF) are found superior to their base learners (i.e., SVM, CART, MLP and RBF) and bagging could noticeable optimize prediction accuracy and reduce the prediction error of proposed predictive models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.441-444
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• 2016

Peace on the Korean Peninsula is threatened by physical aggressions and cyber terrors such as nuclear tests, missile launchings, senior government officials' smart phone hackings and DDos attacks to banking systems. Cyber attacks such as vulnerability for the hackings, malware distributions are generally defended by passive defense through the detecting signs of first invasion and attack, data analysis, adding library and updating vaccine programs. In this paper the concept of security program based on Google TensorFlow machine learning ability to perform adding libraries and solving security vulnerabilities by itself is researched and proposed.

• Journal of Korean Dental Science
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• v.16 no.1
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• pp.47-62
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• 2023

Purpose: We aimed to investigate the objective cutoff values of unstimulated flow rates (UFR) and stimulated salivary flow rates (SFR) in patients with xerostomia and to present an optimal machine learning model with a classification and regression tree (CART) for all ages. Materials and Methods: A total of 829 patients with oral diseases were enrolled (591 females; mean age, 59.29±16.40 years; 8~95 years old), 199 patients with xerostomia and 630 patients without xerostomia. Salivary and clinical characteristics were collected and analyzed. Result: Patients with xerostomia had significantly lower levels of UFR (0.29±0.22 vs. 0.41±0.24 ml/min) and SFR (1.12±0.55 vs. 1.39±0.94 ml/min) (P<0.001), respectively, compared to those with non-xerostomia. The presence of xerostomia had a significantly negative correlation with UFR (r=-0.603, P=0.002) and SFR (r=-0.301, P=0.017). In the diagnosis of xerostomia based on the CART algorithm, the presence of stomatitis, candidiasis, halitosis, psychiatric disorder, and hyperlipidemia were significant predictors for xerostomia, and the cutoff ranges for xerostomia for UFR and SFR were 0.03~0.18 ml/min and 0.85~1.6 ml/min, respectively. Conclusion: Xerostomia was correlated with decreases in UFR and SFR, and their cutoff values varied depending on the patient's underlying oral and systemic conditions.

• Journal of Technologic Dentistry
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• v.42 no.3
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• pp.248-253
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• 2020

Purpose: This study aimed to verify whether blended learning is worth alternating with traditional face-to-face learning for some dental technology students in practice teaching. Methods: A total of 68 students were included in this study. They were divided into two groups to compare blended learning and traditional face-to-face learning. The experiment had been carried out over 15 weeks. The following tests were performed: test of instructional quality, test of learning satisfaction, test of perceived usefulness, and test of learning flow. The IBM SPSS software was used to analyze the data. Results: The learning satisfaction and the perceived useful of blended learning by students appeared to be higher than that of traditional face-to-face learning. However, there was no significant difference in the variables of traditional face-to-face learning and those of blended learning (p<0.05). Conclusion: Blended learning is an alternative to traditional face-to-face learning for some dental technology students in practice teaching.

• Journal of Digital Convergence
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• v.9 no.4
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• pp.281-289
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• 2011

This paper provides the emerging flow of new communication technology using ubiquitous learning (u-Learning). In the intelligent Ubiquitous environment, humans and devices with computing abilities become interoperable. u-Learning will lead students to open their minds to the world and motivate self-learning, which may lead them to learn and communicate more efficiently, and save time, cost and energy. Through case research, regarding education, learning attitude, custom and, personal relations, one must solve the fundamental issues of misuse and outflow problems regarding personal information that will be widely collected in detail than the present condition, and in order for this not to happen, further support of the law and system, plus ethical perspectives must be considered in order to progress.

• Computers and Concrete
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• v.25 no.6
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• pp.565-574
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• 2020

Over the years, several machine learning approaches have been proposed and utilized to create a prediction model for the high-performance concrete (HPC) slump flow. Despite HPC is a highly complex material, predicting its pattern is a rather ambitious process. Hence, choosing and applying the correct method remain a crucial task. Like some other problems, prediction of HPC slump flow suffers from abnormal attributes which might both have an influence on prediction accuracy and increases variance. In recent years, different studies are proposed to optimize the prediction accuracy for HPC slump flow. However, more state-of-the-art regression algorithms can be implemented to create a better model. This study focuses on several methods with different mathematical backgrounds to get the best possible results. Four well-known algorithms Support Vector Regression, M5P Trees, Random Forest, and MLPReg are implemented with optimum parameters as base learners. Also, redundant features are examined to better understand both how ingredients influence on prediction models and whether possible to achieve acceptable results with a few components. Based on the findings, the MLPReg algorithm with optimum parameters gives better results than others in terms of commonly used statistical error evaluation metrics. Besides, chosen algorithms can give rather accurate results using just a few attributes of a slump flow dataset.

• Journal of The Korean Association of Information Education
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• v.24 no.4
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• pp.379-390
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• 2020

Recently, interest in online teaching·learning and evaluation tools has increased in the context of Covid-19. In order to use tools effectively, it is necessary to identify the structural influence and causal relationship between the learner's affective and cognitive variables. In this study, to identify a causal relationship between motivation and thinking while using online judge, research and competing model were established and model fit/path analysis were performed. It was found that there was a linear causal relationship from tool usage, self-efficacy, flow, logical thinking, to computational thinking. It was confirmed that 'self-efficacy → flow', or 'flow' had mediating effect on the path from tool usage to thinking, and tool usage was not exerted to thinking through 'flow → self-efficacy'. The causality of 'logical thinking → computational thinking' was identified on the path where tool usage affects thinking ability through learning motivation, but the causality of 'computational thinking → logical thinking' was not identified.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.58-66
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• 2007

This paper introduces the new generic dynamic neuro-fuzzy local modeling system (DNFLMS) that is based on a dynamic Takagi-Sugeno (TS) type fuzzy inference system for complex dynamic hydrological modeling tasks. The proposed DNFLMS applies a local generalization principle and an one-pass training procedure by using the evolving clustering method to create and update fuzzy local models dynamically and the extended Kalman filtering learning algorithm to optimize the parameters of the consequence part of fuzzy local models. The proposed DNFLMS is applied to develop the inference model to forecast the flow of Waikoropupu Springs, located in the Takaka Valley, South Island, New Zealand, and the influence of the operation of the 32 Megawatts Cobb hydropower station on springs flow. It is demonstrated that the proposed DNFLMS is superior in terms of model accuracy, model complexity, and computational efficiency when compared with a multi-layer perceptron trained with the back propagation learning algorithm and well-known adaptive neural-fuzzy inference system, both of which adopt global generalization.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.1-5
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• 2019

As the number of IOT devices is growing rapidly, various 'see-thru connection' techniques have been reported for efficient communication with them. In this paper, we propose a deep learning based IOT device recognition system for interaction with these devices. The overall system consists of a TensorFlow based deep learning server and two Android apps for data collection and recognition purposes. As the basic neural network model, we adopted Google's inception-v3, and modified the output stage to classify 20 types of IOT devices. After creating a data set consisting of 1000 images of 20 categories, we trained our deep learning network using a transfer learning technology. As a result of the experiment, we achieve 94.5% top-1 accuracy and 98.1% top-2 accuracy.