• Journal of radiological science and technology
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• v.33 no.3
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• pp.193-201
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• 2010

This study aimed to develop an open clinical training program to improve radiology graduates' clinical competency in the hospital setting to raise quality of patient-centered medical service. Development of the training program is similar to that of an instructional system design model. The program was developed according to the ADDIE model. Elements of each phase were collected. Draft of the program was constructed from literature review and clinical demands. Preliminary training program was implemented to sample students with the draft. After consultation with related professionals, the program was modified and completed. Professional groups assessed content validity of the program. Five continuous phases of the program - analysis, design, development, implementation, and evaluation - accommodate changes in clinical environment and demands. They also provide adequate learning needs. Effectiveness of the program and appropriateness of contents in each phase achieved a satisfactory level of significance. This program reflected demands from medical fields and effective learning programs. The purpose of the open clinical training program is to train radiological technologists in clinical setting to actively engage in patient-centered medical service and to help utilize their professional knowledge.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.63-63
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• 2020

최근 머신러닝 기술의 발전에 따라 비선형 시계열자료에 대한 예측이 가능해졌으며, 기존의 과정기반모형을 대체하여 지하수, 하천수 예측 등 다양한 수문분야에 활용되고 있다. 본 연구에서는 기존의 연구들과 달리 과정기반모형을 이용한 하천수 모의결과를 개선하기 위해 과정기반모형과 결합하는 방식으로 머신러닝 기술을 활용하였다. 머신러닝 기술을 통해 관측값과 모의값 간의 차이를 예측하고 과정기반모형의 모의결과에 반영함으로써 관측값을 정확히 재현할 수 있도록 하는 시스템을 구축하고 평가하였다. 과정기반모형으로는 Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro)을 소양강 유역을 대상으로 구축하였다. 머신러닝 모형으로는 순환 신경망 중 하나인 Long Short-Term Memory (LSTM) 신경망을 이용하여 장기시계열예측이 가능하게 하였다(WRF-Hydro-LSTM). 머신러닝 모형은 2013년부터 2017년까지의 기상자료 및 유입량 잔차를 이용하여 학습시키고, 2018년 기상자료를 이용하여 예상되는 유입량 잔차를 모의하였다. 모의된 잔차를 WRF-Hydro 모의결과에 반영시켜 최종 유입량 모의값을 보정하였다. 또한, 연구에서 제안된 새로운 방법론의 성능을 비교평가하기 위해 머신러닝 단독 모형으로 유입량을 학습 후 모의하였다(LSTM-only). 상관계수와 Nash-Sutcliffe 효율계수(NSE)를 사용해 평가한 결과, LSTM을 이용한 두 방법(WRF-Hydro-LSTM과 LSTM-only) 모두 기존의 과정기반모형(WRF-Hydro-only)에 비해 높은 정확도의 하천수 모의가 가능했으며, PBIAS 지수를 사용하여 평가한 결과, LSTM을 단독으로 사용하였을 때보다 WRF-Hydro와 결합했을 때 더 관측값과 가까운 모의가 가능함을 확인할 수 있었다.

• Journal of The Korean Association For Science Education
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• v.39 no.1
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• pp.89-99
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• 2019

The purpose of this study is to investigate the effect of students' formation of evolutionary concept and learning on the development of digital teaching materials. The explanation of biological evolution, which explains the changes that living organisms undergo over a long period of time, can provide various contents for use in a book. The production and editing of images in digital textbooks would provide explanation of difficult concepts in a fun way. For this study, we designed instructional materials consisting of four class hours using iBooks Author, an electronic book authoring tool based on the 5E learning cycle model. In order to verify the effectiveness of the developed digital textbooks, we compared instructions by the general textbooks to those using digital textbooks. Both teaching through general textbook form and teaching using digital textbook materials had a significant effect on the formation of the concept of evolution, but interest in biological science and evolution increased significantly only in the group taught using digital textbooks. As a result of testing the instruction effect by the digital textbooks by classifying the students by type, the group that is familiar with smart devices was more active and interesting in class depending on digital literacy. The satisfaction of the developed digital textbooks also showed a positive score in the group with high digital literacy. The results of this study suggest that the development of digital textbooks in the unit of evolution can be an instructional material for easy and interesting approach to difficult concepts in the teaching of evolution.

• Journal of The Korean Association For Science Education
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• v.40 no.3
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• pp.237-251
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• 2020

Issues on climate change we are facing, such as global warming, are very important as it affects our lives directly. To overcome this, efforts to reduce greenhouse gases emissions (e.g., carbon dioxide) are necessary and these efforts should be based on our integrated understanding of carbon cycle. The purpose of this study is to examine the research trend on carbon cycle education and to suggest the value and direction of carbon cycle education for students who will be citizens of the future. We analyzed 52 carbon cycle education related studies collected from academic research databases (RISS, KCI, ERIC, Google Scholar, and others). As a result, we conclude that resources are still limited and more researches on verification and utilization of developed program, development of accurate and comprehensive tools for students' recognition and level assessment, developing educational model or teacher professional development, providing more appropriate curriculum resources, and the use of various topics or materials for carbon cycle education are necessary. Students' comprehensive understanding of the carbon cycle is important to actively react to the changes in the global environment. Therefore, to support such learning opportunities, resources that can be connected to students' daily experiences to improve students' understanding of carbon cycle and replace misconceptions based on the verification of existing programs should be provided in the classroom as well as the curriculum. In addition, sufficient exemplary cases in carbon cycle education including various materials and topics should be provided through professional development to support teachers teaching strategies with carbon cycle.

• Journal of The Korean Association For Science Education
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• v.15 no.3
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• pp.284-290
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• 1995

This study examined the effects of the learning cycle model by learner's characteristics such as I.Q., cognitive levels, inquiry skins, cognitive style, activity, reflectiveness. To see the effects of the learning cycle model, nonequivalent control group pretest-posttest multiple treatment designs was used in the study. 99 middle school second-graders(female) were divided into two groups. One group was selected as the experimental group (n=50), the other served at the comparison group(n=49). During the eight-month period, the students in the experimental group were instructed according to the learning cycle model, while the students in the comparison group were instructed according to the traditional instruction methods. Achievement data from science achievement test were analyzed by an ANOVA technique. The results of the study are as follows : 1. Science knowledge achievement. For the lower level students of activity, the learning cycle model is superior to the traditional approaches in science knowledge achievement. 2. Science inquiry skills. For the upper level students of I.Q., cognitive levels, inquiry skills, cognitive style and reflectiveness, the learning cycle model is superior to the traditional approaches in science inquiry skills. 3. Attitudes toward science. For the lower level students of I.Q., cognitive levels, inquiry skills, cognitive style, activity and reflectiveness, the learning cycle model is superior to the traditional approaches in attitudes toward science.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.1-12
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• 2022

In the event of a maritime distress accident, rapid search and rescue operations using rescue assets are very important to ensure the safety and life of drowning person's at sea. In this paper, we analyzed the surface layer current in the northwest sea area of Ulleungdo by applying machine learning such as multiple linear regression, decision tree, support vector machine, vector autoregression, and LSTM to the meteorological information collected from the maritime observation buoy. And we predicted the drowning person's route at sea based on the predicted current direction and speed information by constructing each prediction model. Comparing the various machine learning models applied in this paper through the performance evaluation measures of MAE and RMSE, the LSTM model is the best. In addition, LSTM model showed superior performance compared to the other models in the view of the difference distance between the actual and predicted movement point of drowning person.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.552-560
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• 2021

With the recent development of the art distribution system, interest in art investment is increasing rather than seeing art as an object of aesthetic utility. Unlike stocks and bonds, the price of artworks has a heterogeneous characteristic that is determined by reflecting both objective and subjective factors, so the uncertainty in price prediction is high. In this study, we used LSTM Recurrent Neural Network deep learning model to predict the auction winning price by inputting the artist, physical and sales charateristics of the Korean artist. According to the result, the RMSE value, which explains the difference between the predicted and actual price by model, was 0.064. Painter Lee Dae Won had the highest predictive power, and Lee Joong Seop had the lowest. The results suggest the art market becomes more active as investment goods and demand for auction winning price increases.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.588-595
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• 2020

Prediction and control of nitrogen oxides (NO_x) emission is of great interest in industry due to stricter environmental regulations. Herein, we propose an artificial intelligence (AI)-based framework for prediction of NO_x emission. The framework includes pre-processing of data for training of neural networks and evaluation of the AI-based models. In this work, Long-Short-Term Memory (LSTM), one of the recurrent neural networks, was adopted to reflect the time series characteristics of NO_x emissions. A decision tree was used to determine a time window of LSTM prior to training of the network. The neural network was trained with operational data from a heating furnace. The optimal model was obtained by optimizing hyper-parameters. The LSTM model provided a reliable prediction of NO_x emission for both training and test data, showing an accuracy of 93% or more. The application of the proposed AI-based framework will provide new opportunities for predicting the emission of various air pollutants with time series characteristics.

• Journal of Elementary Mathematics Education in Korea
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• v.19 no.3
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• pp.323-345
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• 2015

This study is intended to establish and apply a program created with RME for mathematising instruction and learning and identify how it influences on the mathematical thinking process in the field. In order to deal with this study inquiries, related theories have been analyzed establishing a program for mathematising instruction and learning method based on a model of them and RME theory principles and re-organizing education courses for instruction on the fields concerned. Study subjects were limited to two classes consisting of fifth graders in S elementary school located in the city of Daegu and divided them in an experiment group and a control group. An experiment group was given a mathematising learning method applied with RME, while a control group had a class with regular methods of learning and instruction during the period of experiment. As a summary of aforementioned results of the study, mathematising learning method applied with RME had an effect on improving mathematical thinking ability for students and also on promoting mathematising outcome through a repetitive experience in each procedure obtained on a regular basis.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.155-159
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• 2004

Recently, there are many trials about Artificial neural networks : ANNs structure and studying method of researches for forecasting traffic volume. ANNs have a powerful capabilities of recognizing pattern with a flexible non-linear model. However, ANNs have some overfitting problems in dealing with a lot of parameters because of its non-linear problems. This research deals with the application of a variety of model selection criterion for cancellation of the overfitting problems. Especially, this aims at analyzing which the selecting model cancels the overfitting problems and guarantees the transferability from time measure. Results in this study are as follow. First, the model which is selecting in sample does not guarantees the best capabilities of out-of-sample. So to speak, the best model in sample is no relationship with the capabilities of out-of-sample like many existing researches. Second, in stability of model selecting criterion, AIC3, AICC, BIC are available but AIC4 has a large variation comparing with the best model. In time-series analysis and forecasting, we need more quantitable data analysis and another time-series analysis because uncertainty of a model can have an effect on correlation between in-sample and out-of-sample.