• Research in Community and Public Health Nursing
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• v.30 no.1
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• pp.57-68
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• 2019

Purpose: This study was performed to determine effects of a structure-centered cooperative learning safety education program based on blended learning for elementary school students. Methods: The study is developed in nonequivalent control group non-synchronized design. The subjects included 24 sixth grade students in the experimental group and 23 sixth grade students in the control group, respectively. To prevent diffusion of the experiment, it was carried out from May 20th to June 24th in 2015 with the control group and the other from August 26th to September 30th in 2015 with the experimental group. It was performed on experimental group after the structure-centered cooperative learning safety education program based on blended learning once a week for 6weeks. Data were analyzed by using descriptive statistics, paired t-test and independent t-test. Results: After the intervention, the experimental group showed significant increases in the self-directed learning attitudes and safety behavior compared to the control group except for the academic self-efficacy. Conclusion: The results indicate that the structure-centered cooperative learning safety education program based on blended learning program is effective in safety education for 6th graders.

• The Journal of Korean Association of Computer Education
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• v.11 no.6
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• pp.85-94
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• 2008

This study is to find strategies to promote distance learning effectiveness in the online learning environment. The research showed that the level of learner's perception of the structure of transactional distance was not significantly different according to sex (p>.05). There was significant correlation between the level of learner's perception of the structure and academic satisfaction(p<.01). And, the level of learner's perception of the structure and learning durability appeared to have statistically significant correlation. However there was no significant difference(p>.05) between academic achievement. Among the three subordinate factors, the course interaction organization had the most influence on the learner's academic satisfaction and the learner's learning durability was influenced the most by the content organization.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.12
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• pp.804-810
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• 2004

There are two important things for the general purpose neural network processor. The first is a capability to build various structures of neural network, and the second is to be able to support suitable learning method for that neural network. Some way to process various learning algorithms is required for on-chip learning, because the more neural network types are to be handled, the more learning methods need to be built into. In this paper, an improved hardware structure is proposed to compute various kinds of learning algorithms flexibly. The hardware structure is based on the existing modular neural network structure. It doesn't need to add a new circuit or a new program for the learning process. It is shown that rearrangements of the existing processing elements can produce several neural network learning modules. The performance and utilization of this module are analyzed by comparing with other neural network chips.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.51-58
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• 2021

In this paper, we propose a deep learning classifier for estimating the number of layers in the Earth's structure. When installing a grounding system, knowledge of the subsurface in the area is absolutely necessary. The subsurface structure can be modeled by the earth parameters. Knowing the exact number of layers can significantly reduce the amount of computation to estimate these parameters. The classifier consists of a feedforward neural network. Apparent resistivity curves were used to train the deep learning classifier. The apparent resistivity at 20 equally spaced log points in each curve are used as the features for the input of the deep learning classifier. Apparent resistivity curve data sets are collected either by theoretical calculations or by Wenner's measurement method. Deep learning classifiers are coded by Keras, an open source neural network library written in Python. This model has been shown to converge with close to 100% accuracy.

• The Journal of Korean Association of Computer Education
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• v.10 no.6
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• pp.79-89
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• 2007

The advent of e-Learning paradigm requires a various type of e-Learning models and systems which are appropriate to support effective teaching-learning process. Accordingly, the teaching-learning system using the Internet and the intelligent tutoring system(ITS) in e-Learning environment has attracted a fair amount of critical attention. However there is a wide gap between infrastructure of a present educational site and the u-learning environment. Therefore, in this paper, an ITS teaching-learning model is proposed and system is developed for a school environment, which is based on a learner's cognitive structure and applies a concept of u-Learning, and then is verified for validity. X-Neuronet, the developed system, offers a method of representing a learner's cognitive structure so as to apply the method for the efficient individualized learning.

• Journal of Internet Computing and Services
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• v.4 no.5
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• pp.69-76
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• 2003

In this paper we show a new learning algorithm for pattern classification. This algorithm considered a scheme to find a solution to a problem of incremental learning algorithm when the structure becomes too complex by noise patterns included in learning data set. Our approach for this problem uses a pruning method which terminates the learning process with a predefined criterion. In this process, an iterative model with 3 layer feedforward structure is derived from the incremental model by an appropriate manipulations. Notice that this network structure is not full-connected between upper and lower layers. To verify the effectiveness of pruning method, this network is retrained by EBP. From this results, we can find out that the proposed algorithm is effective, as an aspect of a system performence and the node number included in network structure.

• The Journal of Korean Association of Computer Education
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• v.7 no.3
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• pp.57-66
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• 2004

The purpose of this study is to determine the relationship among the hypermedia structure(hierarchical and network), learner's cognitive style(field-independent and field-dependent), and learning performance in the elementary school. 128 students(4th graders) having field-independent and field-dependent cognitive style were randomly allocated into hierarchical and network structures of hypermedia learning program. There was not significant interaction between hypermedia structure and cognitive style in learning performance. The students in the hierarchical hypermedia structure showed higher learning performance than ones in the network hypermedia structure. Field-independent students significantly got higher results than field-dependent ones. It is concluded that instructional designers should consider hypermedia structure, learner's cognitive style, and learning outcomes when they plan and design hypermedia learning program.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06d
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• pp.105-108
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• 2007

Bayesian learning network is employed for diverse applications. This paper discusses the Bayesian learning network algorithm structure which can be applied in the wireless sensor network environment for various online applications. First, this paper discusses Bayesian parameter learning, Bayesian DAG structure learning, characteristics of wireless sensor network, and data gathering in the wireless sensor network. Second, this paper discusses the important considerations about the online Bayesian learning network and the conceptual structure of the learning network algorithm.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.97-97
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• 2021

Hydrometeorological variables contain highly complex system for temporal revolution and it is quite challenging to illustrate the system with a temporal linear and nonlinear models. In recent years, deep learning algorithms have been developed and a number of studies has focused to model the complex hydrometeorological system with deep learning models. In the current study, we investigated the temporal structure inside deep learning models for the hydrometeorological variables such as streamflow and climate indices. The results present a quite striking such that each hidden unit of the deep learning model presents different dependence structure and when the number of hidden units meet a proper boundary, it reaches the best model performance. This indicates that the deep dependence structure of deep learning models can be used to model selection or investigating whether the constructed model setup present efficient or not.