• 한국과학교육학회지
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• 제23권1호
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• pp.1-8
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• 2003

The purpose of present study was to develop teaching-learning materials based virtual reality technology(VRT), and to examine the effect of it on the knowledge achievement of biology. Authoring tool of virtual reality(VR) was 3D Webmaster made in Superscape Ltd., United Kingdom. Educational materials was developed for the structure and function of eye of life field in the 10th science. It was learner-directed and interactive educational material using the Web-based and desktop VR. The result showed a meaningful improvement on the achievement. Using 3D VR shows the potential of available education media in the next generation as science teaching-aided materials, which especially was efficient in the understanding and perception of abstract or difficult to direct experience learning contents.

• 한국수학교육학회지시리즈C:초등수학교육
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• 제10권1호
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• pp.15-28
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• 2007

From a social constructivists' perspective, knowledge is not transmitted by language but it is constructed by social interactions with others. That is, it is viewed in social constructivism that learning is a process in which knowledge is constructed by communicative interactions with more capable others. In this vein, a class might be analyzed and characterized in terms of interactional patterns of teacher-student and student-student in class. For this, a primary math class was selected and observed and it was analyzed by the Flanders category system to investigate the effects of the math teaching based on verbal interactions on the learning of math. The class was taught in a teacher-centered and direct way but in the class math knowledge was taught through univocal communications in the form of question-answer. The results of this study appeared to suggest that verbal interactional patterns should take place frequently in math teaching in the sequence of a teacher's questions$\to$students' extensive responses $\to$ positive feedback for the students' responses by the teacher $\to$ the acceptance of the students' responses $\to$ the teacher's explanation or students' questions. In other words, math might be taught more effectively through the verbal discourse patterns proposed in this study.

• Journal of Ginseng Research
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• 제18권3호
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• pp.165-174
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• 1994

To investigate the significant indicators Improving the undisturbed memory in animal behavior, we employed several behavioral methods (learning, relearning in radial maze, and active avoidance) with ginseng components. Results showed that the repeated intranasal administration of $Rb_1$ and total saponins from Korean red ginseng induced direct effects on the brain mechanisms in rats, and improved the spatial memory during the learning, relearning and retention in the 12-arm radial maze test. The intranasal treatment of the total saponins also effectively improved the disturbed memory (amnesia) by pentylentetrazole, and simultaneously protected the brain by decreasing the severity of motor epileptic seizures. The intraperitonial administration of polysaccharide fraction of Korean red ginseng could improve avoidance behavior (amount of the total ecapes) in the active-avoidance test. In addition, local changes of the temperature and resistance of skin observed after Rb, administration were suggested to reflect some action of sympathetic nerve Key words Memory, intranasal administration, pentylenetetrazole, Korea red ginseng.

• 대한가정학회지
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• 제41권3호
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• pp.73-91
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• 2003

This research tested the path model which examined the direct and indirect effects of family violence experience on perpetration of dating violence among college students. Two family violence variables such as witnessing parents' marital violence and being abused by parent were the exogeneous variables in the path model, while the mediated variables were consisted of (1) the social-learning-theory-derived variables such as acceptance of violence, positive outcome expectations of using violence, and aggressive conflict-coping behavior, and (2) control-theory-derived variables such as attachment, belief, and commitment. Data were from self-administered questionnaires completed by 332 male and 469 female students selected by stratified quota sampling method. The path analysis was done for males and females separately, since females reported significantly higher degree of dating violence than males. Results of the path analysis showed that first, for both males and females, being abused by parents directly and indirectly influenced dating violence, while witnessing parents' marital violence did not have effect on dating violence either directly or indirectly. Second, for male students, acceptance of violence and conflict coping behavior found to be the mediated variables in the effect of being abused by parents on dating violence. Third, for females, a control-theory-derived variable of belief as well as all three social learning theory-derived variables mediated the influence of being abused by parents on dating violence.

• Smart Structures and Systems
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• 제28권6호
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• pp.811-825
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• 2021

High-voltage isolating switches play a paramount role in ensuring the safety of power supply systems. However, their exposure to outdoor environmental conditions may cause serious physical defects, which may result in great risk to power supply systems and society. Image processing-based methods have been used for anomaly detection. However, their accuracy is affected by numerous uncertainties due to manually extracted features, which makes the anomaly detection of isolating switches still challenging. In this paper, a vision-based anomaly detection method for isolating switches, which uses the rotational angle of the switch system for more accurate and direct anomaly detection with the help of deep learning (DL) and image processing methods (Single Shot Multibox Detector (SSD), improved frame differencing method, and Hough transform), is proposed. The SSD is a deep learning method for object classification and localization. In addition, an improved frame differencing method is introduced for better feature extraction and a hough transform method is adopted for rotational angle calculation. A number of experiments are conducted for anomaly detection of single and multiple switches using video frames. The results of the experiments demonstrate that the SSD outperforms the You-Only-Look-Once network. The effectiveness and robustness of the proposed method have been proven under various conditions, such as different illumination and camera locations using 96 videos from the experiments.

• 한국군사과학기술학회지
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• 제25권3호
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• pp.219-230
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• 2022

In implementing a robust automatic target recognition(ATR) system with synthetic aperture radar(SAR) imagery, one of the most important issues is accurate classification of target variants, which are the same targets with different serial numbers, configurations and versions, etc. In this paper, a deep learning network with channel attention modules is proposed to cope with the recognition problem for target variants based on the previous research findings that the channel attention mechanism selectively emphasizes the useful features for target recognition. Different from other existing attention methods, this paper employs the channel attention modules without dimensionality reduction along the channel direction from which direct correspondence between feature map channels can be preserved and the features valuable for recognizing SAR target variants can be effectively derived. Experiments with the public benchmark dataset demonstrate that the proposed scheme is superior to the network with other existing channel attention modules.

• Geomechanics and Engineering
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• 제34권1호
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• pp.43-49
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• 2023

Knowledge of minimum horizontal stress (S_hmin) is a significant step in determining full stress tensor. It provides crucial information for the production of sand, hydraulic fracturing, determination of safe mud weight window, reservoir production behavior, and wellbore stability. Calculating the S_hmin using indirect methods has been proved to be awkward because a lot of data are required in all of these models. Also, direct techniques such as hydraulic fracturing are costly and time-consuming. To figure these problems out, this work aims to apply the long-short-term memory (LSTM) algorithm to S_hmin time-series prediction. 13956 datasets obtained from an oil well logging operation were applied in the models. 80% of the data were used for training, and 20% of the data were used for testing. In order to achieve the maximum accuracy of the LSTM model, its hyper-parameters were optimized significantly. Through different statistical indices, the LSTM model's performance was compared with with other machine learning methods. Finally, the optimized LSTM model was recommended for S_hmin prediction in the well logging operation.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.141-143
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• 2021

The Activity patterns of animal species are difficult to access and the behavior of freely moving individuals can not be assessed by direct observation. As it has become large challenge to understand the activity pattern of animals such as dogs, and cats etc. One approach for monitoring these behaviors is the continuous collection of data by human observers. Therefore, in this study we assess the activity patterns of dog using the wearable sensors data such as accelerometer and gyroscope. A wearable, sensor -based system is suitable for such ends, and it will be able to monitor the dogs in real-time. The basic purpose of this study was to develop a system that can detect the activities based on the accelerometer and gyroscope signals. Therefore, we purpose a method which is based on the data collected from 10 dogs, including different nine breeds of different sizes and ages, and both genders. We applied six different state-of-the-art classifiers such as Random forests (RF), Support vector machine (SVM), Gradient boosting machine (GBM), XGBoost, k-nearest neighbors (KNN), and Decision tree classifier, respectively. The Random Forest showed a good classification result. We achieved an accuracy 86.73% while the detecting the activity.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1146-1153
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• 2022

As the population is aging, accidents involving elderly people are also increasing (2014:11,667 persons; 2018: 11,797 persons). In the case of the elderly population, falling accidents are the primary direct or indirect causes of death; in particular, they face an elevated risk of staircase falls. This study proposes a method of evaluating the safety of staircases using Building Information Modeling (BIM)-based virtual simulation. By making a virtual user with the behavioral characteristics of the elderly respond to a staircase in a BIM model, its safety performance can be evaluated. The evaluation criteria were derived from regulations, elements, and characteristics relevant to the safety of staircases. To validate the proposed method, safety evaluation tests were simulated on actual staircases. The evaluation result of the test simulation shows the safety scores of 1.97 points for the elderly user and 2.95 points for the average male adult user against a required safety score of a minimum of 2 points. That is, safety is relative to users as the safety of the same staircase can be different depending upon the different behavioral characteristics of users. The study suggests that the risk of staircase-related fall accidents to the elderly can be reduced by improving staircase designs through the proposed method.

• 한국항행학회논문지
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• 제28권3호
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• pp.369-374
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• 2024

본 연구는 청각장애인과 비장애인 모두를 위한 수어 교육의 접근성과 효율성을 개선하는 것을 목적으로 한다. 이를 위해 Hand Tracking 기술과 대화형 AI를 통합한 VR 실감형 수어 교육 콘텐츠를 개발하였다. 사용자는 이 콘텐츠를 통해 실시간으로 수어를 학습하며, 가상 환경에서의 직접적인 의사소통을 경험할 수 있다. 연구 결과, 이러한 통합 접근 방식이 수어 학습에 있어 몰입감을 크게 향상시키며, 학습자에게 더 깊은 이해를 제공함으로써 수어 학습의 장벽을 낮추는 데 기여한다는 것을 확인하였다. 이는 수어 교육의 새로운 패러다임을 제시하며, 기술이 교육의 접근성과 효과를 어떻게 변화시킬 수 있는지를 보여준다.