• Journal of the Korea Safety Management & Science
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• v.7 no.5
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• pp.155-174
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• 2005

Parametric life-cycle cost(LCC) models have been integrated with traditional design tools, and used in prior work to demonstrate the rapid solution of holistic, analytical tradeoffs between detailed design variations. During early designs stages there may be competing concepts with dramatic differences. Additionally, detailed information is scarce, and decisions must be models. for a diverse range of concepts, and the lack of detailed information make the integration make the integration of traditional LCC models impractical. This paper explores an approximate method for providing preliminary life-cycle cost. Learning algorithms trained using the known characteristics of existing products be approximated quickly during conceptual design without the overhead of defining new models. Artificial neural networks are trained to generalize on product attributes and life cycle cost date from pre-existing LCC studies. The Product attribute data to quickly obtain and LCC for a new and then an application is provided. In additions, the statistical method, called regression analysis, is suggested to predict the LCC. Tests have shown it is possible to predict the life cycle cost, and the comparison results between a learning LCC model and a regression analysis is also shown

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.499-509
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• 2020

This study explores how to build object classification learning data based on artificial intelligence. The data has been investigated recently in image classification fields and, in turn, has a great potential to use. In order to recognize and extract relatively accurate objects using artificial intelligence, a large amount of learning data is required to be used in artificial intelligence algorithms. However, currently, there are not enough datasets for object recognition learning to share and utilize. In addition, generating data requires long hours of work, high expenses and labor. Therefore, in the present study, a small amount of initial aerial image learning data was used in the GAN (Generative Adversarial Network)-based generator network in order to establish image learning data. Moreover, the experiment also evaluated its quality in order to utilize additional learning datasets. The method of oversampling learning data using GAN can complement the amount of learning data, which have a crucial influence on deep learning data. As a result, this method is expected to be effective particularly with insufficient initial datasets.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.73-82
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• 2022

Chronic diseases such as hypertension require a differentiated approach according to age and life cycle. Chronic diseases such as hypertension require differentiated management according to the life cycle. It is also known that the cause of hypertension is a combination of various factors. This study uses machine learning prediction techniques to analyze various factors affecting hypertension by life cycle. To this end, a total of 35 variables were used through preprocessing and variable selection processes for the National Health and Nutrition Survey data of the Korea Centers for Disease Control and Prevention. As a result of the study, among the tree-based machine learning models, XGBoost was found to have high predictive performance in both middle and old age. Looking at the risk factors for hypertension by life cycle, individual characteristic factors, genetic factors, and nutritional intake factors were found to be risk factors for hypertension in the middle age, and nutritional intake factors, dietary factors, and lifestyle factors were derived as risk factors for hypertension. The results of this study are expected to be used as basic data useful for hypertension management by life cycle.

• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1415-1425
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• 2018

Kompsat-5 is the first Earth Observation Satellite which is equipped with an SAR in Korea. SAR images are generated by receiving signals reflected from an object by microwaves emitted from a SAR antenna. Because the wavelengths of microwaves are longer than the size of particles in the atmosphere, it can penetrate clouds and fog, and high-resolution images can be obtained without distinction between day and night. However, there is no color information in SAR images. To overcome these limitations of SAR images, colorization of SAR images using Cycle GAN, a deep learning model developed for domain translation, was conducted. Training of Cycle GAN is unstable due to the unsupervised learning based on unpaired dataset. Therefore, we proposed MS Cycle GAN applying multi-scale discriminator to solve the training instability of Cycle GAN and to improve the performance of colorization in this paper. To compare colorization performance of MS Cycle GAN and Cycle GAN, generated images by both models were compared qualitatively and quantitatively. Training Cycle GAN with multi-scale discriminator shows the losses of generators and discriminators are significantly reduced compared to the conventional Cycle GAN, and we identified that generated images by MS Cycle GAN are well-matched with the characteristics of regions such as leaves, rivers, and land.

• Journal of Korean Elementary Science Education
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• v.20 no.2
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• pp.217-228
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• 2001

The purpose of this study was to investigate the effect of learning cycle model on the changes of electricity conceptions of elementary students. Four classes in forth grade of an elementary school in Busan were selected and two of them were served as experimental group and the others as control group. The experimental group were taught the unit of "Light an electric bulb" in elementary science textbook with teaching model based on teaming cycle and the control group with traditional teaching style. The instruction effects were analyzed through pre and post-test results using questionnaire on the electricity. The results of pre-test showed that there was not a significant difference between experimental group and control group at .05 level, so two groups could be regarded as homogeneous. The mean score of experimental group was significantly higher than that of control group on the post-test at .05 level. And within-group comparison revealed that both groups made improvement on the mean score and that the improvement of each group had significant difference at .05 level. Above results said that the teaching model based on learning cycle, which focuses on hands-on activity and considers each student as an active subject, was more effective than traditional teaching style in improving the formation of scientific conceptions on electricity.ectricity.

• Research in Mathematical Education
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• v.19 no.3
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• pp.155-174
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• 2015

The goal of this study was to examine the three components of a laboratory class cycle that empowered teachers to change their teaching practices. Six teachers and their administrator in an elementary school in the southeastern United States participated in the study. All the teachers were interviewed, and their mathematics lessons were observed at the end of each cycle of laboratory classes. The study revealed how planning, observing, and critiquing mathematics lessons as a team assisted the teachers' learning and teaching. We identified opportunities for the teachers to experiment with different teaching approaches, and we found that support from the team and from the school were key factors for the laboratory class cycle to function effectively.

• Journal of Korean Elementary Science Education
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• v.23 no.4
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• pp.273-278
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• 2004

According to Piaget, children aged 11 are in the middle of concrete operation period and formal operation period. So, it is necessary to adopt the Learning Cycle Model (LCM) which helps students improve their cognitive development. After determining the test for the Science Concept of Matter (SCOM), the experimental group showed higher average than the comparative group in the post-test. In the sound understanding, the experimental group showed higher ratio than the comparative group. And in the ratio of imperfect, wrong understanding and no response, the experimental group was lower than the comparative group. On the questions that were needed the complicated inquiry, many students of both groups still couldn't find the fundamental cause. In forming the scientific conceptualization, there was a meaningful difference (p < .001) after post-test Analysis of Covariance (ANCOVA) with pre-test result. After determining the test for the Test Inquiry Science Process (TISP), the experimental group showed higher average than the comparative group in the post-test. In the category of basic inquiry process which is needed in concrete operation, there was a meaningful difference (p < .05). In the category of unified inquiry process which is needed in formal operation, they showed no meaningful difference (p > .05). Therefore, applying the LCM to the chapter of 'Solution and Dissolving' is more effective on improving the scientific conceptualization and on helping the concrete operation abilities than the teacher centered learning.

• Journal of The Korean Association of Information Education
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• v.17 no.3
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• pp.357-365
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• 2013

The purpose of this paper is to implement the educational application related to the Plant's Life Cycle Unit in 4th Grade Science Textbook, based on the National Curriculum. This should lead to teachers incorporating them into classes and for students to get practical help when they are studying about plants. Through this application, teachers will be able to experience the so-called Blended Learning while using both off-line and mobile teaching methods. It will also be possible for students to review what they learn during the classes and do their projects regardless of time and place. This type of learning is expected to motivate learners and enhance the learning experience by stimulating the students' interest.

• Journal of Korean Elementary Science Education
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• v.39 no.3
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• pp.412-432
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• 2020

The purpose of this study is to explore the characteristics and the level of fourth grade elementary students' system thinking when they learn the unit of "Journey of Water" in terms of four key elements of system thinking such as understanding of the structure of a system, non-linearity and cyclic features, inter-relations and feedback between system properties, and temporal and invisible aspects of a system. Data included students' worksheets and their responses to a set of Likert-scaled and written assessment items on water cycle. The results showed that the level of students' system thinking did not have any hierarchy in relation to the key elements of water cycle system. In addition, the aspects of individual student's system thinking on its sub-elements were different from each other. Also, there were core ideas of system thinking which were intensively considered according to a given context to understand a complex systemic subject. When students learn water cycle, understanding of non-linearity and inter-relations were weaker compared with other key elements of system thinking. Therefore, if these two factors are taught in advance, it can promote understanding of whole system of water cycle.

• Transactions of the Korean hydrogen and new energy society
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• v.32 no.6
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• pp.592-611
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• 2021

In this study a rarely available high-throughput cycling data set of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles including in-cycle temperature and per-cycle IR measurements. We worked out own Python codes which reproduced the various data plots and machine learning approaches for cycle life prediction using early cycles and more details not presented in the article and the supplementary information. Particularly, we applied regularized ridge, lasso and elastic net linear regression models using features extracted from capacity fade curves, discharge voltage curves, and other data such as internal resistance and cell can temperature. We found that due to the limitation in the quantity and quality of the data from costly and lengthy battery testing a careful hyperparameter tuning may be required and that model features need to be extracted based on the domain knowledge.