• Structural Engineering and Mechanics
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• v.27 no.1
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• pp.77-97
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• 2007

When assessing buildings that may collapse during a large earthquake, conventional rapid visual screening procedures generally provide good results when identifying buildings for further investigation. Unfortunately, their accuracy at identify buildings at risk is not so good. In addition, there appears to be little room for improvement. This paper investigates an alternative screening procedure based on fuzzy logic and artificial neural networks. Two databases of buildings damaged during the Athens earthquake of 1999 are used for training purposes. Extremely good results are obtained from one database and not so good results are obtained from the second database. This finding illustrates the importance of specifically collecting data tailored to the requirements of the fuzzy logic based rapid visual screening procedure. In general, results demonstrate that the trained fuzzy logic based rapid visual screening procedure represents a marked improvement when identifying buildings at risk. In particular, when smaller percentages of the buildings with high damage scores are extracted for further investigation, the proposed fuzzy screening procedure becomes more efficient. This paper shows that the proposed procedure has a significant optimisation potential, is worth pursuing and, to this end, a strategy that outlines the future development of the fuzzy logic based rapid visual screening procedure is proposed.

• Korean Journal of Health Education and Promotion
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• v.15 no.2
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• pp.81-93
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• 1998

The purpose of this study was to analyze the results of teachers' self-evaluation on their health teaching behaviors, then to furnish the basic data to be able to improve teachers' health teaching activities and the educational issues on the education of teachers. To put above aims into practice, these were required: 1. Are there any differences in the results of self-evaluation on health teaching behaviors factors by teachers? 2. Are there any differences in the results of health teaching self-evaluation whether he/she take P.E as major of study in-serviece training period? 3. Does it have any influence on the results of health teaching self-evaluation whether he or she completed on the job training for the school health? 4. Are there any differences in the results of health teaching self-evaluation by sex and career? To carry out a research for this purpose, the factors of health teaching self-evaluation were divided into the clearness of the procedure, the active interaction, the variety of the ways showing the contents, and the individualization of the procedure. Then a questionnaire form, consisting of 28 specific inquires to evaluate health teaching behaviors, was delivered and conducted by 450 teacher of the elementary school in Kyungki-do. The analysis of data was done by SPSS; producing mean and standard deviation and they were inspected statistically to compare the evaluation levels and find out the differences by teachers' personal variables. The conclusion were as follows: 1. In the self-evaluation level of teachers' health teaching behaviors, teachers showed 68.23 point as are percentile distribution. And it was in order of a school-nurse(71.68), an athletic teacher(67.29), and a class-room teacher (65.66). Score obtained by teacher was statistically significant difference (p〈.001) 2. In the factors affecting to teachers' health instruction, “active interaction” showed the highest score(18.55), “variety of ways showing the contents”(17.38), “clearness of the procedure” (16.70), and “individualization of the procedure” (15.59). In the analysis of the differences by teachers, according to factors, there were significant differences in “active interaction”, “variety of the ways showing contents”, “clearness of the procedure”(p〈.001). 3. Self-evaluation score for graduates from Dept. of P. E in Teachers' collage was not significant difference compared with other majors(p〉.05). 4. Teachers receiving health education was significantly higher self-evaluation score than that of teachers not-receiving health education (P〈.01). 5. Self-evaluation score of female teacher was significant difference compared with that of male teacher (p〈.001). 6. Career (working duration) did not influenced to self-evaluation score on health teaching behaviors (P〉 .05). On the basis of the conclusion of this study, the next are suggested: First, the further studies to make use of the results of health teaching behaviors and to examine the effect are needed. Second, the further studies to examine the relations between academic achievement and teachers' major(a school-nurse, an athletic teacher, and a class-room teacher) are needed. Third, the following studies to improve health teaching by both teachers' self-evaluation on health teaching behaviors and students' evaluation of teachers, and to find out more effective health teaching, are needed. Fourth, Health education for pre-service training course and On-the-Job training program are need the effective factors on the teachers' Health teaching obtained from this study.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.141-149
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• 2020

This study was undertaken to analyze the intervening effect of nursing simulation among nursing students. This was a critical review study, and data obtained were reviewed using various data bases, including RISS, KISS, NDSL, DBpia, and KRI. The terminologies entered in the data base were nursing and simulation. Selected studies were assessed for methodological quality; and narrative, descriptive or one group post-test studies were excluded from the analysis. Ed. Notes: Please review for accuracy. I have suggested the edit to the best of my understanding. Finally, 234 studies were included for investigation. Results included studies of nursing simulation intervention in Korea, commencing from 2008. One group pre-post test and two group post test were more designed in journals comparing to master thesis or doctoral dissertation. Clinical practice was the most frequently studied aspect by both the assessor and student in the two groups' pre-post test design. Nursing competences associated with dependent variables during simulation were integrated skills, critical thinking, communication, cooperation, professional recognition and leadership. The two groups pre-post design explored more varied competences as compared to other designs. Considering the results obtained, we conclude that simulation intervention is an effective teaching method for nursing students to help improve their clinical practice. However, further studies are required to assess the impact of critical thinking and problem solving.

• Journal of Korean Clinical Nursing Research
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• v.17 no.3
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• pp.329-339
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• 2011

Purpose: The purpose of this research was to develop and train caregivers in tracheostomy tube care using a self-management program to assist patients with an 'at home' tracheostomy procedure. Caregivers' self-efficacy and knowledge of tracheostomy management before and after the training was also identified. Methods: Research participants were the main caregivers for patients with tracheostomies who were affiliated with a 'Home Healthcare Center'. Training and observation were done at 'A Hospital' and 'G Hospital' both affiliated with 'K University' in Seoul. Data were collected from May 3, 2010 to January 25, 2011 and analyzed using Wilcoxon signed rank test with SPSS program version 12.0. Results: Significant differences were found for the pre and post evaluation of the 'self-management program' for the implementation of tracheostomy care. The development and implementation of the 'self-management program' improved the main caregivers' knowledge of tracheostomy tube management (Z=-3.599, p<.001). Conclusion: Results show that this program has identified an effective nursing intervention for promoting the caregivers' knowledge of tracheostomy care and self-efficacy. We recommend that further research should be done to test primary caregivers' maintenance of knowledge and self-efficacy in tracheostomy tube management and identify factors affecting knowledge and self-efficacy in the care of these patients.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.337-346
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• 2021

Detection of abnormal signal generally can be done by using features of normal signals as main information because of data imbalance. This paper propose an efficient method for abnormal signal detection using parallel AutoEncoder (AE) which can use features of abnormal signals as well. The proposed Parallel AE (PAE) is composed of a normal and an abnormal reconstructors having identical AE structure and train features of normal and abnormal signals, respectively. The PAE can effectively solve the imbalanced data problem by sequentially training normal and abnormal data. For further detection performance improvement, additional binary classifier can be added to the PAE. Through experiments using public acoustic data, we obtain that the proposed PAE shows Area Under Curve (AUC) improvement of minimum 22 % at the expenses of training time increased by 1.31 ~ 1.61 times to the single AE. Furthermore, the PAE shows 93 % AUC improvement in detecting abnormal underwater acoustic signal when pre-trained PAE is transferred to train open underwater acoustic data.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.751-759
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• 2021

This study developed an artificial intelligence education program using localized public data as an educational method for improving computing thinking skills of elementary school students. According to the ADDIE model, the program design was carried out based on the results of pre-requisite analysis for elementary school students, and textbooks and education programs were developed. Based on localized public data, the training program was constructed to learn the principles of artificial intelligence using machine learning for kids and scratches and to solve problems and improve computational thinking through abstracting public data for purpose. It is necessary to put this training program into the field through further research and verify the change in students' computational thinking as a result.

• Smart Structures and Systems
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• v.29 no.1
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• pp.29-39
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• 2022

Crack detection plays an important role in the maintenance and protection of steel box girder of bridges. However, since the cracks only occupy an extremely small region of the high-resolution images captured from actual conditions, the existing methods cannot deal with this kind of image effectively. To solve this problem, this paper proposed a novel three-stage method based on deep learning technology and morphology operations. The training set and test set used in this paper are composed of 360 images (4928 × 3264 pixels) in steel girder box. The first stage of the proposed model converted high-resolution images into sub-images by using patch-based method and located the region of cracks by CBAM ResNet-50 model. The Recall reaches 0.95 on the test set. The second stage of our method uses the Attention U-Net model to get the accurate geometric edges of cracks based on results in the first stage. The IoU of the segmentation model implemented in this stage attains 0.48. In the third stage of the model, we remove the wrong-predicted isolated points in the predicted results through dilate operation and outlier elimination algorithm. The IoU of test set ascends to 0.70 after this stage. Ablation experiments are conducted to optimize the parameters and further promote the accuracy of the proposed method. The result shows that: (1) the best patch size of sub-images is 1024 × 1024. (2) the CBAM ResNet-50 and the Attention U-Net achieved the best results in the first and the second stage, respectively. (3) Pre-training the model of the first two stages can improve the IoU by 2.9%. In general, our method is of great significance for crack detection.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.4
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• pp.193-202
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• 2023

In recent years, the outstanding performance of large language models (LLMs) trained on extensive datasets has become a hot topic. Since studies on LLMs are available on open-source approaches, the ecosystem is expanding rapidly. Models that are task-specific, lightweight, and high-performing are being actively disseminated using additional training techniques using pre-trained LLMs as foundation models. On the other hand, the performance of LLMs for Korean is subpar because English comprises a significant proportion of the training dataset of existing LLMs. Therefore, research is being carried out on Korean-specific LLMs that allow for further learning with Korean language data. This paper identifies trends of open source based LLMs and introduces research on Korean specific large language models; moreover, the applications and limitations of large language models are described.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.5
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• pp.2845-2850
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• 2013

Background: Breast cancer is one of the most frequent diseases in women today. Little information exists on modifiable lifestyle factors including effects of ginger supplements (as an anti-oxidant and anti-inflammatory herbal) and water-based exercise on biomarkers related to oxidative stress such as malondialdehyde (MDA), nitric oxide (NO) and glutathione peroxidase (GPx) and adiponectin in obese women with breast cancer. The aim of this study was to determine the single and concomitant effect of 6-wks water-based exercise and oral ginger supplement on the aforesaid markers in obese women with breast cancer. Materials and Methods: Forty women diagnosed with breast cancer ($48{\pm}5.4$ years, $76{\pm}9$ kg, fat mass $41.8{\pm}4%$), volunteered to participate in the study. Subjects were randomly assigned into four groups; placebo, water-based exercise, ginger supplement and water-based exercise+ginger supplement groups. Subjects in the ginger supplement group and the water-based exercise+ginger supplement group orally received 4 capsules (each capsule contained 750 mg), 7 days a week for 6 weeks. The water-based exercise program featured progressive increase in intensity and time, ranging from 50% to 75% of heart rate reserve, in a pool with 15 meters width, 4 times a week for 6 weeks. Fasting blood samples were collected at pre-test and post-test time points. Results: The ginger supplementation and or the water-base exercise resulted in an increase of adiponectin, NO and GPx and reduction MDA, as compared to pre-test values. However, the combined intervention (water-base exercise and ginger supplement) group showed significantly a far better effect on the biomarkers related to oxidative stress and adiponectin levels, as compared to the waterbase exercise or ginger supplement alone groups and the age-matched placebo group. Conclusions: Our results revealed that water-base exercise is a non-drug therapeutic strategy to reduce systemic stress in obese women suffering from breast cancer. Further, ginger supplementation alone or in combination with training, also play an important role in the pathogenesis of oxidative stress in obese women diagnosed with breast cancer.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.391-394
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• 2021

최근 딥러닝(Deep Learning)을 활용하여 텍스트로 표현된 단어나 문장의 의미를 파악하기 위한 다양한 연구가 활발하게 수행되고 있다. 하지만, 딥러닝을 통해 특정 도메인에서 사용되는 언어를 이해하기 위해서는 해당 도메인의 충분한 데이터에 대해 오랜 시간 학습이 수행되어야 한다는 어려움이 있다. 이러한 어려움을 극복하고자, 최근에는 방대한 양의 데이터에 대한 학습 결과인 사전 학습 언어 모델(Pre-trained Language Model)을 다른 도메인의 학습에 적용하는 방법이 딥러닝 연구에서 많이 사용되고 있다. 이들 접근법은 사전 학습을 통해 단어의 일반적인 의미를 학습하고, 이후에 단어가 특정 도메인에서 갖는 의미를 파악하기 위해 추가적인 학습을 진행한다. 추가 학습에는 일반적으로 대표적인 사전 학습 언어 모델인 BERT의 MLM(Masked Language Model)이 다시 사용되며, 마스크(Mask) 되지 않은 단어들의 의미로부터 마스크 된 단어의 의미를 추론하는 형태로 학습이 이루어진다. 따라서 사전 학습을 통해 의미가 파악되어 있는 단어들이 마스크 되지 않고, 신조어와 같이 의미가 알려져 있지 않은 단어들이 마스크 되는 비율이 높을수록 단어 의미의 학습이 정확하게 이루어지게 된다. 하지만 기존의 MLM은 무작위로 마스크 대상 단어를 선정하므로, 사전 학습을 통해 의미가 파악된 단어와 사전 학습에 포함되지 않아 의미 파악이 이루어지지 않은 신조어가 별도의 구분 없이 마스크에 포함된다. 따라서 본 연구에서는 사전 학습에 포함되지 않았던 신조어에 대해서만 집중적으로 마스킹(Masking)을 수행하는 방안을 제시한다. 이를 통해 신조어의 의미 학습이 더욱 정확하게 이루어질 수 있고, 궁극적으로 이러한 학습 결과를 활용한 후속 분석의 품질도 향상시킬 수 있을 것으로 기대한다. 영화 정보 제공 사이트인 N사로부터 영화 댓글 12만 건을 수집하여 실험을 수행한 결과, 제안하는 신조어 표적 마스킹(NTM: Newly Coined Words Target Masking)이 기존의 무작위 마스킹에 비해 감성 분석의 정확도 측면에서 우수한 성능을 보임을 확인하였다.