• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.441-445
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• 2004

In this paper, we deal with an inventory model of a multi-stage, serial supply chain system where a single product type and nonstationary customer demand pattern are considered. The retailer and suppliers place their orders according to an echelon-stock based replenishment control policy. We assume that the suppliers can access online information on the demand history and use this information when making their replenishment decisions. Using a reinforcement learning technique, the inventory control parameters are designed to adaptively change as the customer demand pattern is altered, in order to maintain a given target service level. Through a simulation based experiment, we verified that our approach is good for maintaining the target service level.

• Korean Journal of Child Studies
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• v.37 no.4
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• pp.17-29
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• 2016

Objective: The purpose of this study was to examine the bidirectional relation between mobile phone dependency (MPD) and self-regulated learning (SRL) of early Korean adolescents in $4^{th}$, $6^{th}$ and $8^{th}$ grade, while taking into account gender differences. Methods: The study made use of panel data from the Korean Children and Youth Panel Study (KCYPS), and three waves of data collected from 2,264 adolescents were analyzed by means of autoregressive cross-lagged modeling. Results: The results can be summarized as follows. Firstly, MPD and SRL were consistently stable for adolescents in $4^{th}$, $6^{th}$ to $8^{th}$ grades. Secondly, a bidirectional relations between MPD and SRL were confirmed. In other words, there was a significant influence of a high level of MPD on a subsequent low level of SRL, and the high level of SRL also had a significant effect on the lower level of MPD across time. According to multi-group analysis, no gender differences were found in the relations between two constructs during the studied period. Conclusion: Findings highlighted not only the necessary media usage education but also parenting intervention strategies may help early adolescents to be prevented from negative effects of media usage and to enhance self-regulated learning ability. Based on the results, more implications were also discussed.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• Proceedings of the Korean Institute of Interior Design Conference
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• 2007.05a
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• pp.227-230
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• 2007

The expansion of open education and the 7th revision of educational curriculum have brought big changes in the school facilities. In response to the integrated curriculum for the first and second grades of elementary school, various plans, such as open classroom, expanded classroom size, and the installation of multi-purpose space, have been attempted. However, such plans have appeared in the form of an open classroom--a uniform spatial composition. As a result, a plan for unit learning space to support the educational curriculum and activities for low grade levels is still insufficient. In the case of advanced countries, a lot of studies on space are being actively conducted to develop the creativity of children and to facilitate free-style learning, and such space is actually applied to a real educational environment. Therefore, this study will analyze the spatial composition of unit learning space for low grade level elementary schools in Korea. From the cases of advanced countries, a more concrete proposal will be suggested to diversify unit learning space for low grade levels.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.171-174
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• 2022

Weakly-supervised learning is a widely adopted approach in video anomaly detection whereby only video labels are utilized instead of expensive frame-level annotations. Since the success of multi-instance learning (MIL), almost all recent approaches are based on maximizing the margin between the set of abnormal video snippets and those of normal video snippets. In this work, we present a simple contrastive approach for weakly supervised video anomaly detection (WS-VAD) with aims to enhance the performance of existing models. The method is generic in nature and introduces a loss function to encourage attraction of output features from the same video class and repel those from different video classes. Experimental results demonstrate our method can be applied to existing algorithms to improve detection accuracy in public video anomaly dataset.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.463-471
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• 2023

This paper proposes a method to estimate the level of interest shown by visitors towards a specific target, a guide robot, in spaces where a large number of visitors, such as exhibition halls and museums, can show interest in a specific subject. To accomplish this, we apply deep learning-based behavior recognition and object tracking techniques for multiple visitors, and based on this, we derive the behavior analysis and interest level of visitors. To implement this research, a personalized dataset tailored to the characteristics of exhibition hall and museum environments was created, and a deep learning model was constructed based on this. Four scenarios that visitors can exhibit were classified, and through this, prediction and experimental values were obtained, thus completing the validation for the interest estimation method proposed in this paper.

• The Mathematical Education
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• v.60 no.4
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• pp.493-508
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• 2021

This study aims to examine the effects of learner-centered mathematical instruction perceived by middle school students such as discussion learning, self-directed learning, and cooperative learning on their math self-efficacy and engagement in mathematics class. Moreover, it attempts to verify if there are differences in the mean of latent variables and effect among groups divided based on achievement level. Research results are as follows. First, discussion learning did not have a direct effect on students' engagement in mathematics class, but still created an indirect effect on it through math self-efficacy. Self-directed learning and cooperative learning created a direct effect on engagement in mathematics class as well as an indirect effect through self-efficacy on mathematics. Second, high-achievement group had a higher perception of discussion learning, self-directed learning, and cooperative learning than a low-achievement group, and showed a higher level of math self-efficacy and engagement in mathematics class. Third, there were significant differences among groups, in the effect of discussion learning on self-efficacy in mathematics, effect of self-directed learning on self-efficacy in mathematics, and effect of math self-efficacy on engagement in mathematics class. Thus, this study offers meaningful implications for the role of math teachers as assistants in learning for learner-centered math classes.

• Journal of the Korea Computer Graphics Society
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• v.23 no.3
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• pp.95-103
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• 2017

Horizon correction is a crucial stage for image composition enhancement. In this paper, we propose a deep learning based method for estimating the slanted angle of a photograph and correcting it. To estimate and correct the horizon direction, existing methods use hand-crafted low-level features such as lines, planes, and gradient distributions. However, these methods may not work well on the images that contain no lines or planes. To tackle this limitation and robustly estimate the slanted angle, we propose a convolutional neural network (CNN) based method to estimate the slanted angle by learning more generic features using a huge dataset. In addition, we utilize multiple adaptive spatial pooling layers to extract multi-scale image features for better performance. In the experimental results, we show our CNN-based approach robustly and accurately estimates the slanted angle of an image regardless of the image content, even if the image contains no lines or planes at all.