• Journal of Gifted/Talented Education
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• v.19 no.1
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• pp.119-140
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• 2009

This paper examined its possibility and made its new definition by finding relevant bases in order to make a close inquiry into its Identity and direction at this point when cyber-based gifted education academy is established and operated again by its necessity And 4 models which can be used in special education for the gifted were developed making a link with special education for the gifted by collecting and re-classifying cyber educational methods developed by basic research as priority of the educational method which is considered to be the most urgent issue in practical cyber learning. It is a project-type cooperation education model, an information collection-type research education model, a community-type discussion education model, and a problem focus-type e-PBL education model. To apply developed leaching-learning models to reality, students at gifted education academy in Gyeonggi Cyber Gifted Province were imputed models in different ways respectively for 4 months. As a result of analysis and statistical data of activity level and satisfaction level of students who participated in learning activity, it appeared that high level of satisfaction and active activity level were induced compared to the previous method based on tasks. It is expected that this paper will provide the bases when each cyber-based gifted education academy plans operation plan later on, and it will provide proper methods when cyber guidance teachers plan class activities.

• Journal of the Korean housing association
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• v.24 no.1
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• pp.1-10
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• 2013

Number of children cared by child care centers has getting up almost half of the from zero to five year age group in korea. Home care children' activities are reported more active and natural than those of center care children. So this study seek the design guidelines for the residential core model of child care centers as second home in korea. The residential core model by Anita Lui Olds was selected and ten domestic center cases were surveyed for guidelines. Firstly, daily-residential core model is learning by daily life at home, and is equiped with cooking kitchenet and group activity area in group room. Secondly, play-residental core model is learning by playing by self, and is equiped with acting, eating and reading common area clustering two or three group room. Thirdly, eco-residental core model is learning by eco-friendly activities, and is equiped with companying, cooperating and sharing area. Fourthly, project-residental core model is learning by project by self, and is equiped with drawing, experimenting and presenting common area. Fifthly, the space of residential core model is organized with three or four group room and clustering living or common area. The larger the center is, the more the cluster is vertically. Facility area and outdoor playground per child is about 7 and $3m^2$.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.847-856
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• 2016

Flipped learning is a pedagogic model in which the typical lecture and homework elements of a course are reversed. Short video lectures or e-learning contents or other learning materials are viewed by students at home before the in-class session, while students are mainly carried out diverse active learning activities such as the discussions, exercises, team projects and so on in class time. Recently flipped learning has been emerging as an effective teaching-learning method that can train the 21st century talents who can create creative values based on fusion competencies. Based on the experience in applying the flipped learning to the database class that is an elective course of the school of computer engineering through three semesters, this paper proposes a flipped learning model consists of 7 steps in detail. Also, this paper analyzes the effects and weak points of the flipped learning and proposes several things for the successful flipped learning application.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.418-424
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• 2018

Detection and classification of underwater targets is an important issue for both military and non-military purposes. Recently, many performance improvements are being reported in the field of pattern recognition with the development of deep learning technology. Among the results, DBN showed good performance when used for pre-training of DNN. In this paper, DBN was used for the classification of underwater targets using active sonar, and the results are compared with that of the conventional BPNN. We synthesized active sonar target signals using 3-dimensional highlight model. Then, features were extracted based on FrFT. In the single aspect based experiment, the classification result using DBN was improved about 3.83% compared with the BPNN. In the case of multi-aspect based experiment, a performance of 95% or more is obtained when the number of observation sequence exceeds three.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1737-1741
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• 2022

Active fire monitoring using high-resolution drone images and deep learning technologies is now an initial stage and requires various approaches for research and development. This letter examined the detection of active fire objects using You Look Only Once Version 7 (YOLOv7), a state-of-the-art (SOTA) model that has rarely been used in fire detection with drone images. Our experiments showed a better performance than the previous works in terms of multiple quantitative measures. The proposed method can be applied to continuous monitoring of wide areas, with an integration of additional development of new technologies.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.367-374
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• 2021

Stock price prediction is a subject of research in various fields such as economy, statistics, computer engineering, etc. In recent years, researches on predicting the movement of stock prices by learning artificial intelligence models from various indicators such as basic indicators and technical indicators have become active. This study proposes a deep learning model that predicts the ups and downs of KOSPI from overseas indices such as S&P500, past KOSPI indices, and trading trends by KOSPI investors. The proposed model extracts a latent variable using a stacked auto-encoder to predict stock price fluctuations, and predicts the fluctuation of the closing price compared to the market price of the day by learning an LSTM suitable for learning time series data from the extracted latent variable to decide to buy or sell based on the value. As a result of comparing the returns and prediction accuracy of the proposed model and the comparative models, the proposed model showed better performance than the comparative models.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1239-1249
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• 2018

Optical remote sensing sensors provide visually more familiar images than radar images. However, it is difficult to discriminate sea ice types in optical images using spectral information based machine learning algorithms. This study addresses two topics. First, we propose a semantic segmentation which is a part of the state-of-the-art deep learning algorithms to identify ice types by learning hierarchical and spatial features of sea ice. Second, we propose a new approach by combining of semi-supervised and active learning to obtain accurate and meaningful labels from unlabeled or unseen images to improve the performance of supervised classification for multiple images. Therefore, we successfully added new labels from unlabeled data to automatically update the semantic segmentation model. This should be noted that an operational system to generate ice type products from optical remote sensing data may be possible in the near future.

• Journal of KIISE
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• v.44 no.11
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• pp.1156-1164
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• 2017

Existing studies on automatic bug triage were mostly used the method of designing the prediction system based on the machine learning algorithm. Therefore, it can be said that applying a high-performance machine learning model is the core of the performance of the automatic bug triage system. In the related research, machine learning models that have high performance are mainly used, such as SVM and Naïve Bayes. In this paper, we apply Deep Learning, which has recently shown good performance in the field of machine learning, to automatic bug triage and evaluate its performance. Experimental results show that the Deep Learning based Bug Triage system achieves 48% accuracy in active developer experiments, un improvement of up to 69% over than conventional machine learning techniques.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.414-419
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• 1994

In recent years, there has been an increasing intest in control of active automotive suspension systems with a goal of improving the ride comfort and safety. Many approaches for these purposes have used linearized models of the suspension's dynamics, allowing the use of linear control theory. However, the linearized model does not well descriibe the actual system behavior which is inherently nonlinear. The object of this study is to develop a neuro controlled active suspension for the ride quality improvement. After obtaining active control law using optimal control theory, we use the artificial neural network to train the neuro controller to learn the relation of road input and control force. Form the numerical results, we found that back propagation learning does show good pattern matching and vertical acceleration of the driver's seat and sprung mass.