• Knowledge Management Research
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• v.10 no.4
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• pp.31-42
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• 2009

With the rapid development of information technology, e-learning is growing in corporate. However, there are still problems in learning, such as low learning persistence rate. Learning outcomes are complex phenomenon influenced by a multitude of factors, it is need to considering the direct and indirect causal relationship among various factors. Therefore, the purpose of this study was to develop the causal model that explains the learning outcomes (satisfaction learning persistence) in corporate e-learning. This study was also intended to examine the causal relationship between the interaction and learning persistence through satisfaction mediators. For this, online survey was conducted with a sample of 270 learners who enrolled in cyber training course at A company. The major findings of this study are as follows: First, internality (internal locus of control, ${\beta}=.154$), interaction (${\beta}=.489$), satisfaction (${\beta}=.304$) have direct effect on learning persistence. Second, the interaction has direct effect on the satisfaction (${\beta}=.320$). Third, the satisfaction has direct effect on the learning persistence, and mediating the interaction and learning persistence. This result will contribute to build a learning strategy to improve learning outcomes.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.77-84
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• 1992

This paper constructs Croup Technology process-based learning curve model adjusted to a Group Technology environment which accounts for shared learning that occurs when multiple products utilize some of the same process steps. Through this constructed model, the estimated times and productivity of labor calculated by the Group Technology process-based learning curve model are compared with those generated by employing product-based 1 earning curve model. For sensitivity analysis of the model, the impact of learning rate and the ordered production quantity on the ratio differences between Group Technology process-based learning curve model and product-based learning curve model are examined. These results indicate the critical importance of employing Group Technology process-based learning curve model when a process spans multiple products.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.174-174
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• 2000

Q-learning, based on discrete state and action space, is a most widely used reinforcement Learning. However, this requires a lot of memory and much time for learning all actions of each state when it is applied to a real mobile robot navigation using continuous state and action space Region-based Q-learning is a reinforcement learning method that estimates action values of real state by using triangular-type action distribution model and relationship with its neighboring state which was defined and learned before. This paper proposes a new Region-based Q-learning which uses a reward assigned only when the agent reached the target, and get out of the Local optimal path with adjustment of random action rate. If this is applied to mobile robot navigation, less memory can be used and robot can move smoothly, and optimal solution can be learned fast. To show the validity of our method, computer simulations are illusrated.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.1-9
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• 2021

Deep learning in computer vision has made accelerated improvement over a short period but large-scale learning data and computing power are still essential that required time-consuming trial and error tasks are involved to derive an optimal network model. In this study, we propose a similar image classification performance improvement method based on CR (Confusion Rate) that considers only the characteristics of the data itself regardless of network optimization or data reinforcement. The proposed method is a technique that improves the performance of the deep learning model by calculating the CRs for images in a dataset with similar characteristics and reflecting it in the weight of the Loss Function. Also, the CR-based recognition method is advantageous for image identification with high similarity because it enables image recognition in consideration of similarity between classes. As a result of applying the proposed method to the Resnet18 model, it showed a performance improvement of 0.22% in HanDB and 3.38% in Animal-10N. The proposed method is expected to be the basis for artificial intelligence research using noisy labeled data accompanying large-scale learning data.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.237-247
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• 2022

This study predicts the economic activity level of the elderly in Korea using various machine learning methods. While the previous studies mainly focused on testing the relationship between the economic activity level and the life satisfaction or the social security system, this study aims at the accurate prediction on the economic activity level of the elderly using various machine learning methods and the forecast combination. Dependent variables such as the activity rate, employment rate, etc and independent variables such as the income, average wage, etc compose the dataset in this study. Five different machine learning methods and two forecast combinations are applied to the given dataset. The prediction performances of the machine learning method and the forecast combination varied across the dependent variables and prediction intervals, but it was found that the forecast combination was relatively superior to other methods in terms of the stability of prediction. This study has significance in that it accurately predicted the economic activity level of the elderly and achieved the stability of the prediction, raising practicality from a policy perspective.

• Computers and Concrete
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• v.32 no.3
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• pp.327-337
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• 2023

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.180-181
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• 2019

This paper proposes a rate control algorithm for intra coding frame in HEVC encoder using a deep learning approach. The proposed algorithm is designed for CTU level bit allocation in intra frame by considering visual features spatially and temporally. Our features are generated using visual geometry group (VGG-16) with deep convolutional layers, then it is used for bit allocation per each CTU within an intra frame. According to our experiments, the proposed algorithm can achieve -2.04% Luma component BD-rate gain with minimal bit accuracy loss against the HM-16.20 rate control model.

• East Asian mathematical journal
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• v.33 no.4
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• pp.431-451
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• 2017

This is a descriptive study with the intent of providing a rich characterization of teachers' perceptions of rate of change. The nature of teachers' perceptions and differences among teachers were examined by collecting data through a survey on teachers' conceptions of rate of change in terms of learning goals, prerequisites, and beliefs about teaching and learning of rate of change, and an interview individually assessing teachers' concept images and definitions. The participating 13 teachers were selected to provide a range of similar and contrasting levels of experiences based on the teachers' educational background and the number of years they had been teaching. Findings and implications of this study are discussed.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.4
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• pp.73-93
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• 2012

In recent years, service delivery systems employing a self-service approach have been rapidly spreading. Since a self-service system provides a lower product price, it attracts more customers. However, some system managers are still hesitant to accept a self-service system, because there is no systematic model to predict its performance. Therefore, this research attempts to provide a systematic and quantitative model to predict the performance of a self-service system, focused specifically on a self-service gas station. Under this model, the traditional queuing theory was adopted to describe the general self-service process, but it is also assumed that some changes occur in both the customer arrival rate and the service performance rate. In particular, the price elasticity was introduced to capture the change in the customer arrival rate, and the existence of learning effect and helpers were assumed to design the changed service performance rate. Under these assumptions, a simulation model for a self-service gas station is established, and three performance measurements, such as average number of customers, average waiting time, and Utilization are observed, depending on the changes in price difference and helper-operating time. In this research, the optimal operation strategy for price differentiation and helper-operating time is proposed in accordance with the level of the customer learning rate. Although this research confines the scope of the study to the self-service gas station model, the results of this research can be applied to any type of self-service system.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.465-469
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• 1990

This thesis proposes a new scheme for the recognition of presegmented Hangul characters. The proposed approach is rather insensitive to noise and variation by applying 2 dimensional convolution to learning patterns. In this thesis, the hangul recognition neural network is implemented in the basis of this scheme and recognition rate is analyzed in boo cases of learning which are learning by binary patterns and learning by binary patterns and convoluted patterns together.