• Journal of the Korean earth science society
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• v.42 no.1
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• pp.88-101
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• 2021

The purpose of this study was to qualitatively examine the meaning of students' drawings in outdoor classes and modeling-based learning cycles. Ten students were observed in a gifted education center in Seoul. Under the theme of the Hantan River, three outdoor classes and three modeling activities were conducted. Data were collected to document all student activities during field trips and classroom modeling activities using simultaneous video and audio recording and observation notes made by the researcher and students. Please note it is unclear what this citation refers to. If it is the previous sentence it should be placed within that sentence's punctuation. Hatisaru (2020) Ddrawing typess were classified by modifying the representations in a learning context in geological field trips. We used deductive content analysis to describe the drawing characteristics, including students writing. The results suggest that students have symbolic images that consist of geologic concepts, visual images that describe topographical features, and affective images that express students' emotion domains. The characteristics were classified into explanation, generality, elaboration, evidence, coherence, and state-of-mind. The characteristics and drawing types are consecutive in the modeling-based learning cycle and reflect the students' positive attitude and cognitive scientific domain. Drawing is a useful tool for reflecting students' thoughts and opinions in both outdoor class and classroom modeling activities. This study provides implications for emphasizing the importance of drawing activities.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.3
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• pp.176-183
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• 2010

The benefits of information technology cannot be obtained unless potential users utilize it for their work. This led to a lot of research works on computer system acceptance. But most of the works address the early stage of system introduction, leaving the learning effect on system acceptance unexplored. In this longitudinal study, two groups of novice and experienced users have been empirically investigated and compared for their acceptance of computer system and for the learning effect of actual usage. A research model based on the technology acceptance theory has been proposed and applied to the two groups. The result shows that the factor job relevance gets more important and the effect of user training on system acceptance diminishes as time passes. This finding may help introducing computer systems which can be easily accepted by users over the whole life cycle period of computer systems.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.124-131
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• 2020

As users of internet-based software applications increase, functional and non-functional problems for software applications are quickly exposed to user reviews. These user reviews are an important source of information for software improvement. User review mining has become an important topic of intelligent software engineering. This study proposes a user review mining method for software improvement. User review data collected by crawling on the app review page is analyzed to check user satisfaction. It analyzes the sentiment of positive and negative that users feel with a machine learning method. And it analyzes user requirement issues through topic analysis based on structural topic modeling. The user review mining process proposed in this study conducted a case study with the a non-face-to-face video conferencing app. Software improvement through user review mining contributes to the user lock-in effect and extending the life cycle of the software. The results of this study will contribute to providing insight on improvement not only for developers, but also for service operators and marketing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2665-2673
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• 2015

The purpose of this study is to develop a web-based database system to manage effectively data collected to evaluate program learning outcomes (PO) in a nursing school. The database management system (DBMS) was developed using a software development life cycle method: Analysis, Design, Implementation, and Evaluation. The demands for the content and system of users were collected. The system structure, database using an entity-relationship modeling, and user interface were designed based on the demands. The designed DBMS was created using GWT, Java and Apache HTTP server. The expert group and users evaluated the implemented DBMS. Problems derived from them were modified. The average of end-user computing satisfaction evaluated by 8 nursing faculty and 5 teaching assistants was 4.14 (SD =.44). The web-based PO DBMS makes it possible for nursing faculty members to access and use much of the information needed for analysis and decision-making.

• Journal of The Korean Association of Information Education
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• v.23 no.5
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• pp.499-511
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• 2019

The preceding study of this study developed puzzle-based computer science education programs to enhance the computational thinking of elementary school students over 1 to 3 times. The preceding study then applied such programs into the field, categorized the effects of education into CT creativity and CT cognitive ability to improve the education programs. Based on the results of these preceding studies, the hierarchical Bayesian inference modeling was performed using age and CT thinking ability as parameters. From the results, this study predicted the effectiveness of puzzle-based computer science education programs in middle and high schools and proposed major improvement areas and directions for puzzle-based computer science education programs that are to be deployed in the future throughout middle and high schools.

• Journal of the Korean Institute of Gas
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• v.24 no.6
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• pp.91-97
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• 2020

Lithium-ion batteries (LIBs) have a longer lifespan, higher energy density, and lower self-discharge rates than other batteries, therefore, they are preferred as an Energy Storage System (ESS). However, during years 2017-2019, 28 ESS fire accidents occurred in Korea, and accurate capacity estimation of LIB is essential to ensure safety and reliability during operations. In this study, data-driven modeling that predicts capacity changes according to the charging cycle of LIB was conducted, and developed models were compared their performance for the selection of the optimal machine learning model, which includes the Decision Tree, Ensemble Learning Method, Support Vector Regression, and Gaussian Process Regression (GPR). For model training, lithium battery test data provided by NASA was used, and GPR showed the best prediction performance. Based on this study, we will develop an enhanced LIB capacity prediction and remaining useful life estimation model through additional data training, and improve the performance of anomaly detection and monitoring during operations, enabling safe and stable ESS operations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1690-1707
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• 2021

Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

• Journal of IKEEE
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• v.26 no.4
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• pp.545-552
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• 2022

The number of patent application filing for a specific technology has a good relation with the technology's life cycle and future industry development on that area. So industry and governments are highly interested in forecasting the number of patent application filing in order to take appropriate preparations in advance. In this paper, a new method based on the bidirectional long short-term memory(LSTM), a kind of recurrent neural network(RNN), is proposed to improve the forecasting accuracy compared to related methods. Compared with the Bass model which is one of conventional diffusion modeling methods, the proposed method shows the 16% higher performance with the Korean patent filing data on the five selected technology areas.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.20 no.3
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• pp.269-278
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• 2022

The buffer is a critical barrier component in an engineered barrier system, and its purpose is to prevent potential radionuclides from leaking out from a damaged canister by filling the void in the repository. No experimental parameters exist that can describe the buffer expansion phenomenon when Kyeongju bentonite, which is a buffer candidate material available in Korea, is exposed to groundwater. As conventional experiments to determine these parameters are time consuming and complicated, simple swelling pressure tests, numerical modeling, and machine learning are used in this study to obtain the parameters required to establish a numerical model that can simulate swelling. Swelling tests conducted using Kyeongju bentonite are emulated using the COMSOL Multiphysics numerical analysis tool. Relationships between the swelling phenomenon and mechanical parameters are determined via an artificial neural network. Subsequently, by inputting the swelling tests results into the network, the values for the mechanical parameters of Kyeongju bentonite are obtained. Sensitivity analysis is performed to identify the influential parameters. Results of the numerical analysis based on the identified mechanical parameters are consistent with the experimental values.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.299-306
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• 2022

In recent, sensors embedded in robots, equipment, and circuits have become common, and research for diagnosing device failures by learning measured sensor data is being actively conducted. This failure diagnosis study is divided into a classification model for predicting failure situations or types and a regression model for numerically predicting failure conditions. In the case of a classification model, it simply checks the presence or absence of a failure or defect (Class), whereas a regression model has a higher learning difficulty because it has to predict one value among countless numbers. So, the reason that regression modeling is more difficult is that there are many irregular situations in which it is difficult to determine one output from a similar input when predicting by matching input and output. Therefore, in this paper, we focus on input and output data with periodicity, analyze the input/output relationship, and secure regularity between input and output data by performing sliding window-based input data patterning. In order to apply the proposed method, in this study, current and temperature data with periodicity were collected from MMC(Modular Multilevel Converter) circuit system and learning was carried out using ANN. As a result of the experiment, it was confirmed that when a window of 2% or more of one cycle was applied, performance of 97% or more of fit could be secured.