• Journal of Korean Elementary Science Education
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• v.20 no.2
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• pp.239-254
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• 2001

The in-depth learning course newly established in the 7th National Curriculum of Science is for students who have mastered regular subject matters on a science topic and want to learn it more deeply or by different ways. Individual learners have their own unique intellectual properties. The study examined the effects of in-depth science learning using multiple intelligence activities on the science inquiry abilities and interests of elementary school children. This study involved two fifth-grade science classes in Busan. Each class was assigned to comparison and experimental group. The science topics covered during the period of the study were Units of Matter and Earth. After studying each regular content formulated by the National Curriculum, the students of comparison group experienced traditional practices of in-depth science, whereas those of experimental one performed the Multiple Intelligence(MI) activities related to the content. Students of both groups were pre- and posttested using the inventories of Science Inquiry Ability and Science Interest. Also, after instruction on the topics, students were interviewed to collect more information related to their loaming. The results are as follows. First, the science inquiry abilities of children were increased by using activities based on MI during the in-depth science teaming. Two inquiry processes, that is, the Prediction which is regarded as one of the basic process skills in science and the Generalization regarded as one of integrated process skills showed statistically significant differences between the groups, although the differences of other skills not significant but more improvements in experimental group than comparison one. Second, the in-depth science loaming through MI contributed to the increasing of interests of the children in science. The scores on Science Interest measured in pretest and posttest with the two groups showed st statistically significant difference. For interest in science instruction, children of experimental group showed high level of interest for the various MI activities, and, although the comparison groups' level of the interest was low, they revealed that they want to experience the MI activities in future instruction of science. Interviews with the children randomly selected from the experimental group when they completed the in-depth programs showed that most of them had much interest in MI activities. Especially, they attributed significant meanings to the experiences of teaming with their friends and doing activities that they want to do. These findings have important implications about usefulness of MI in science instruction. The results also highlight the need for science teachers to provide a variety of experiences and to create environments which encourage the children to use MI to learn a science topic.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.329-335
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• 2014

Collaborative filtering has been popular in commercial recommender systems, as it successfully implements social behavior of customers by suggesting items that might fit to the interests of a user. So far, most common method to find proper items for recommendation is by searching for similar users and consulting their ratings. This paper suggests a new similarity measure for movie recommendation that is based on genre interest, instead of differences between ratings made by two users as in previous similarity measures. From extensive experiments, the proposed measure is proved to perform significantly better than classic similarity measures in terms of both prediction and recommendation qualities.

• Bulletin of the Korean Chemical Society
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• v.33 no.3
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• pp.1009-1014
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• 2012

The influence of magnetic fields on chemical processes has long been the subject of interest to researchers. For this time numerous investigations show that commonly the effect of a magnetic field on chemical reactions is insignificant with impact less than 10 percent. However, there are some papers that point to the observation of external magnetic field effect on chemical and biochemical systems actually having a significant impact on the reactions. Thus, of great interest is an active search for rather simple but realistic models, that are based on physically explicit assumptions and able to account for a strong effect of low magnetic fields. The present work theoretically deals with two models explaining how an applied weak magnetic field might influence the steady state of a non-equilibrium chemical system. It is assumed that external magnetic field can have effect on the rates of radical reactions occurring in a system. This, in turn, leads to bifurcation of the nonequilibrium stationary state and, thus, to a drastic change in the properties of chemical systems (temperature and reagent concentration).

• International Journal of Contents
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• v.13 no.1
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• pp.53-61
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• 2017

Recently, the Social Internet of Things (IoT), the follow-up of the IoT, has been studied to expand the existing IoT services, by integrating devices into the social network of people. In the Social IoT environment, humans, devices and digital contents are connected with social relationships, to guarantee the network navigability and establish levels of trustworthiness. However, this environment handles massive data, including social data of humans (e.g., profile, interest and relationship), profiles of IoT devices, and digital contents. Hence, users and service providers in the Social IoT are exposed to arbitrary data when searching for specific information. A study about the recommender system for the Social IoT environment is therefore needed, to provide the required information only. In this paper, we propose the Social correlation group based Recommender System (SRS). The SRS generates a target group, depending on the social correlation of the service requirement. To generate the target group, we have designed an architecture, and proposed a procedure of the SRS based on features of social interest similarity and principles of the Collaborative Filtering and the Content-based Recommender System. With simulation results of the target scenario, we present the possibility of the SRS to be adapted to various Social IoT services.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.4
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• pp.243-253
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• 2010

Multihop data delivery in vehicular ad hoc networks (VANETs) suffers from the fact that vehicles are highly mobile and inter-vehicle links are frequently disconnected. In such networks, for efficient multihop routing of road safety information (e.g. road accident and emergency message) to the area of interest, reliable communication and fast delivery with minimum delay are mandatory. In this paper, we propose a multihop vehicle-to-infrastructure routing protocol named Vertex-Based Predictive Greedy Routing (VPGR), which predicts a sequence of valid vertices (or junctions) from a source vehicle to fixed infrastructure (or a roadside unit) in the area of interest and, then, forwards data to the fixed infrastructure through the sequence of vertices in urban environments. The well known predictive directional greedy routing mechanism is used for data forwarding phase in VPGR. The proposed VPGR leverages the geographic position, velocity, direction and acceleration of vehicles for both the calculation of a sequence of valid vertices and the predictive directional greedy routing. Simulation results show significant performance improvement compared to conventional routing protocols in terms of packet delivery ratio, end-to-end delay and routing overhead.

• Smart Media Journal
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• v.10 no.2
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• pp.22-29
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• 2021

In the past decade, deep learning has been applied to various medical image analysis tasks. Skeletal bone age estimation is clinically important as it can help prevent age-related illness and pave the way for new anti-aging therapies. Recent research has applied deep learning techniques to the task of bone age assessment and achieved positive results. In this paper, we propose a bone age prediction method using a deep convolutional neural network. Specifically, we first train a classification model that automatically localizes the most discriminative region of an image and crops it from the original image. The regions of interest are then used as input for a regression model to estimate the age of the patient. The experiments are conducted on a whole-body scintigraphy dataset that was collected by Chonnam National University Hwasun Hospital. The experimental results illustrate the potential of our proposed method, which has a mean absolute error of 3.35 years. Our proposed framework can be used as a robust supporting tool for clinicians to prevent age-related diseases.

• The Journal of Information Systems
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• v.21 no.3
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• pp.137-161
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• 2012

The increasing interest in big data analysis using various data mining techniques indicates that many commercial data mining tools now need to be equipped with fundamental text analysis modules. The most essential prerequisite for accurate analysis of text documents is an understanding of the exact semantics of each term in a document. The main difficulties in understanding the exact semantics of terms are mainly attributable to homonym and synonym problems, which is a traditional problem in the natural language processing field. Some major text mining tools provide a thesaurus to solve these problems, but a thesaurus cannot be used to resolve complex synonym problems. Furthermore, the use of a thesaurus is irrelevant to the issue of homonym problems and hence cannot solve them. In this paper, we propose a semantic text mining methodology that uses ontologies to improve the quality of text mining results by resolving the semantic ambiguity caused by homonym and synonym problems. We evaluate the practical applicability of the proposed methodology by performing a classification analysis to predict customer churn using real transactional data and Q&A articles from the "S" online shopping mall in Korea. The experiments revealed that the prediction model produced by our proposed semantic text mining method outperformed the model produced by traditional text mining in terms of prediction accuracy such as the response, captured response, and lift.

• Journal of Korean Society of Water and Wastewater
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• v.28 no.4
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• pp.377-389
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• 2014

In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and this has led to various studies regarding energy saving and improvement of water supply reliability. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The concepts was demonstrated through application to observed from water plant (A) in the South Korea. Various statistics (e.g. the efficiency coefficient, the correlation coefficient, the root mean square error, and a maximum error rate) were evaluated to investigate model efficiency. The ensemble based model with an cross-validate prediction procedure showed better predictability for water demand forecasting at different temporal resolutions. In particular, the performance of the ensemble model on hourly water demand data showed promising results against other individual prediction schemes.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.3
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• pp.163-173
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• 1997

Although numerous studies demonstrate that one technique outperforms the others for a given data set, it is hard to tell a priori which of these techniques will be the most effective to solve a specific problem. It has been suggested that the better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the predictive performance. This paper proposes the post-model integration method, which tries to find the best combination of the results provided by individual techniques. To get the optimal or near optimal combination of different prediction techniques, Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an object function subject to numerous hard and soft constraints. This study applies three individual classification techniques (Discriminant analysis, Logit model and Neural Networks) as base models for the corporate failure prediction. The results of composite predictions are compared with the individual models. Preliminary results suggests that the use of integrated methods improve the performance of business classification.

• Knowledge Management Research
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• v.15 no.3
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• pp.169-188
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• 2014

Big data seems to change knowledge management system and method of enterprises to large extent. Further, the type of method for utilization of unstructured data including image, v ideo, sensor data a nd text may determine the decision on expansion of knowledge management of the enterprise or government. This paper, in this light, attempts to figure out the prediction model of demands and supply for big data market of Korea trough data mining decision making tree by utilizing text bit data generated for 3 years on web and SNS for expansion of form for knowledge management. The results indicate that the market focused on H/W and storage leading by the government is big data market of Korea. Further, the demanders of big data have been found to put important on attribute factors including interest, quickness and economics. Meanwhile, innovation and growth have been found to be the attribute factors onto which the supplier puts importance. The results of this research show that the factors affect acceptance of big data technology differ for supplier and demander. This article may provide basic method for study on expansion of analysis form of enterprise and connection with its management activities.