• Journal of Korean Association for Spatial Structures
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• v.20 no.2
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• pp.51-58
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• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• Journal of KIISE
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• v.43 no.2
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• pp.269-274
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• 2016

Named entity recognition is a process which extracts named entities in sentences and determines categories of the named entities. Previous studies on named entity recognition have primarily been used for supervised learning. For supervised learning, a large training corpus manually annotated with named entity categories is needed, and it is a time-consuming and labor-intensive job to manually construct a large training corpus. We propose a semi-supervised learning method to minimize the cost needed for training corpus construction and to rapidly enhance the performance of named entity recognition. The proposed method uses distance supervision for the construction of the initial training corpus. It can then effectively remove noise sentences in the initial training corpus through the use of an active bagging method, an ensemble method of bagging and active learning. In the experiments, the proposed method improved the F1-score of named entity recognition from 67.36% to 76.42% after active bagging for 15 times.

• Tuberculosis and Respiratory Diseases
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• v.86 no.3
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• pp.226-233
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• 2023

Background: Inactive or old, healed tuberculosis (TB) on chest radiograph (CR) is often found in high TB incidence countries, and to avoid unnecessary evaluation and medication, differentiation from active TB is important. This study develops a deep learning (DL) model to estimate activity in a single chest radiographic analysis. Methods: A total of 3,824 active TB CRs from 511 individuals and 2,277 inactive TB CRs from 558 individuals were retrospectively collected. A pretrained convolutional neural network was fine-tuned to classify active and inactive TB. The model was pretrained with 8,964 pneumonia and 8,525 normal cases from the National Institute of Health (NIH) dataset. During the pretraining phase, the DL model learns the following tasks: pneumonia vs. normal, pneumonia vs. active TB, and active TB vs. normal. The performance of the DL model was validated using three external datasets. Receiver operating characteristic analyses were performed to evaluate the diagnostic performance to determine active TB by DL model and radiologists. Sensitivities and specificities for determining active TB were evaluated for both the DL model and radiologists. Results: The performance of the DL model showed area under the curve (AUC) values of 0.980 in internal validation, and 0.815 and 0.887 in external validation. The AUC values for the DL model, thoracic radiologist, and general radiologist, evaluated using one of the external validation datasets, were 0.815, 0.871, and 0.811, respectively. Conclusion: This DL-based algorithm showed potential as an effective diagnostic tool to identify TB activity, and could be useful for the follow-up of patients with inactive TB in high TB burden countries.

• Archives of Pharmacal Research
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• v.24 no.3
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• pp.234-239
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• 2001

Three types of learning and memory tests (Morris water maze, active and passive avoidance) were performed in rats following intracerebroventricular infusion of ethylcholine aziridium (AF64A). In Morris water maze, AF64A-treated rats showed the delayed latencies to find the platform iron 6th day after the infusion. In pretrained rats, AF64A caused the significant delay of latency at 7th days but not 8th day. In the active avoidance for the pretrained rats, the escape latency was significantly delayed in AF64A-treatment. The percentages of avoidance in AF64A-treated rats were less increased than those in the control. Especially, the percentage of no response in the AF64A-treated rats was markedly increased in the first half trials. In the passive avoidance, AF64A-treated rats shortened the latency 1.5 h after the electronic shock, but not 24 h. AF64A also caused the pretrained rats to shorten the latency 7th day after the infusion, but not 8th day. These results indicate that AF64A might impair the learning and memory. However, these results indicate that the disturbed memory by AF64A might rapidly recover after the first retrain. Furthermore, these results suggest that AF64A may be a useful agent for the animal model of learning for Spatial cognition .

• Journal of dental hygiene science
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• v.23 no.2
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• pp.93-102
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• 2023

Background: To investigate political participation by dental hygiene students and analyze the differences therein based on the learning efficacy of dental hygiene policy. Methods: A total of 239 dental hygiene students who were expected to graduate responded to the survey. The data were collected online using a structured questionnaire consisting of 6 items on general characteristics, 10 on political participation, and 15 on the learning efficacy of dental hygiene policy. Statistical analysis was performed using SPSS 23.0. Political participation based on the learning efficacy of dental hygiene policy was analyzed using independent t-tests, ANOVA, and multiple regression analysis (p<0.05). Results: Among the dental hygiene students, 60.7% voted in all three recent presidential, general, and local elections, and 14.2% did not. For political parties supported, 65.7% responded that they had "no supporting party," and 34.3% indicated that they had a "supporting party." In terms of the level of political participation of dental hygiene students (0~50 points), the average score was 25.8 points, with the average passive political participation (0~25 points) score at 15.6 points and the average active political participation (0~25 points) score at 10.2 points. With an increase in dental hygiene policy learning efficacy, both passive and active political participation showed higher scores (p<0.05). Conclusion: Dental hygiene students showed low political participation. The presence of a supporting party, higher voting participation, and higher learning efficacy of dental hygiene policy were associated with higher passive and active political participation. Therefore, to increase this population's interest in political participation, various opportunities for related learning need to be promoted and provided in academia, leading to the enhancement of their political capabilities. In this manner, dental hygienists should expand their capabilities in various roles such as advocates, policy makers, and leaders.

• Korean Journal of Clinical Pharmacy
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• v.23 no.4
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• pp.291-299
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• 2013

Purpose: Problem-based learning (PBL) has been adopted to foster active and self-directed learning and enhance critical thinking and problem-solving skills in many health-care academic disciplines in Korea. Interest in PBL has rapidly grown with a 6 year pharmacy degree program in Korea. The objective of this study was to evaluate feasibility of PBL, student satisfaction and academic performance with a self-assessment survey questionnaire. Method: Sixty students participated in the PBL for pharmacotherapy course. Average scores from student self-assessment on participation, satisfaction, and academic performance were $3.85{\pm}0.55$, $2.94{\pm}1.04$, $3.09{\pm}0.91$ out of 5 point lickert scale (1-do not agree at all, 5-agree completely), respectively. Results & Conclusion: The level of participation was positively correlated with improvement of communication skill in academic performance (correlation coefficient 0.27, p=0.037). In the quality analysis of the cases provided for PBL, students who participated more in the PBL greatly agreed the cases given were appropriate to learn fundamental knowledge for each disease state. The students disagreed that PBL was fun. The students stated that PBL was good to experience self-directed learning and clinical context beforehand but too time-consuming to devote and too demanding to commit. Lack of facilitator and insight on active learning should be rectified for successful launch of PBL in Korean pharmacy education.

• International Journal of Contents
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• v.12 no.4
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• pp.62-68
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• 2016

This paper describes an attempt to integrate flip teaching into a language classroom by adopting wikispaces as an online learning platform. The purpose of this study is to examine student perceptions of the effectiveness of using video lectures and wikispaces to foster active participation and collaborative learning. Flipped learning was implemented in an English writing class over one semester. Participants were 27 low intermediate level Korean university students. Data collection methods included background questionnaires at the beginning of the semester, learning experience questionnaires at the end of the semester, and semi-structured interviews with 6 focal participants. Because of the significance of video lectures in flip teaching, oCam was used for making weekly online lectures as a way of pre-class activities. Every week, online lectures were posted on the school LMS system (moodle). Every week, participants met in a computer room to perform in-class activities. Both in-class activities and post-class activities were managed by wikispaces. The results indicate that the flipped classroom facilitated student learning in the writing class. More than 53% of the respondents felt that it was useful to develop writing skills in a flipped classroom. Particularly, students felt that the video lectures prior to the class helped them improve their grammar skills. However, with respect to their satisfaction with collaborative works, about 44% of the participants responded positively. Similarly, 44% of the participants felt that in-class group work helped them interact with the other group members. Considering these results, this paper concludes with pedagogical suggestions and implications for further research.

• Journal of Multimedia Information System
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• v.7 no.2
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• pp.115-124
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• 2020

This paper is a study of deep learning among artificial intelligence technology which has been developing many technologies recently. Especially, I am talking about emotional computing that has been mentioned a lot recently during deep learning. Emotional computing, in other words, is a passive concept that is dominated by people who scientifically analyze human sensibilities and reflect them in product development or system design, and a more active concept that studies how devices and systems understand humans and communicate with people in different modes. This emotional signal extraction, sensitivity, and psychology recognition technology is defined as a technology to process, analyze, and recognize psycho-sensitivity based on micro-small, hyper-sensor technology, and sensitive signals and information that can be sensed by the active movement of the autonomic nervous system caused by human emotional changes in everyday life. Chapter 1 talks about overview and Chapter 2 shows related research. Chapter 3 shows the problems and models of real emotional computing and Chapter 4 shows this paper as a conclusion.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.122-129
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• 2021

For the Simultaneous Localization and Mapping (SLAM) problem, a different path results in different SLAM results. Usually, SLAM follows a trail of input data. Active SLAM, which determines where to sense for the next step, can suggest a better path for a better SLAM result during the data acquisition step. In this paper, we will use reinforcement learning to find where to perceive. By assigning entire target area coverage to a goal and uncertainty as a negative reward, the reinforcement learning network finds an optimal path to minimize trajectory uncertainty and maximize map coverage. However, most active SLAM researches are performed in indoor or aerial environments where robots can move in every direction. In the urban environment, vehicles only can move following road structure and traffic rules. Graph structure can efficiently express road environment, considering crossroads and streets as nodes and edges, respectively. In this paper, we propose a novel method to find optimal SLAM path using graph structure and reinforcement learning technique.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.863-875
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• 2003

The existing e-learning systems have short functions for learners to lead their self-directed learning activities because those systems have not been integrated with functions supporting activities of learners, instructors and operators. Therefore, we designed and implemented an efficient e-learning system having fully integrated functions to let learners induce their active learning, instructors teach learners effectively and evaluate their learning activities, and operators handle curriculum affairs and system environments.