• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1501-1512
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• 2024

Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.

• The Journal of Korean Academic Society of Nursing Education
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• v.5 no.2
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• pp.177-190
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• 1999

This study has been conducted on the basis of the literature review of Nursing Education Research in China from 1990 through August 1998. Its purpose was to support the basic data of nursing education which is risen as major revolutionary of nursing in China and those for exchange of information between Korea-China nursing education. It is retrospective and descriptive research analyzing one hundred eighty articles published in The Journal of China Nursing. The results of the study were as follows. 1. Only 33.3% of the professors of Technical Nursing School who have played of major role of nursing education in China have carried out the study related to nursing education. Baccalaureate program professors have marked 22.2% of all studies, and diploma program professors have done 12.2% of all. Therefore, the professors of above the diploma program have done total 44.4%. It explains that the professors of baccalaureate and diploma programs have done more studies related to nursing education than those of Technical Nursing School. 2. In terms of the study design, most of the studies(38.8%) were case studies introducing the curriculum contents that were done at education institutions. And then, 28.5% were reviewing the articles, and 15.6% were descriptive studies. 3. In terms of the content of the study, 38.3% were relevant to education of Technical Nursing School, 15.0% were about baccalaureate education, and 10.4% is about diploma. 4. To analyze the specific contents of the studies ; a. In baccalaureate program, human resources (professor or teaching), course extension, lab, classes, teaching method, education philosophy, goal of education, evaluation method, and human resource development were included. b. In diploma program, teaching contents evaluation method, teaching method, and educational system were included c. In the technical school, there were qualification of professors , teaching method, evaluation method, opening the courses, teaching contents, goal of education and so on. d. Beyond these, there were practice guidance and appraisement, teaching method, and opening new courses which were not specially indicated as educational curriculum and score management as continuing education. What is above tell us that the study regarding development of university system has been progressed actively and widely. It has been for the effort of revolution which based on the China government force to reform of nursing education process during last 10 years. On the base of the result, we suggest the following questions and the alternatives. 1) Since most articles are case studies related to teaching methods and the others doesn't propose the research method. the study which is applied more exact research method is needed. 2) No study is regarding social change and health policy. Because University program, founded in 1983 is on the beginning point, the research about curriculum have to be taken as a top priority as well as to reflect social needs which are based on social changes and national health policy 3) Only one review article study tells nursing Human resource. To appear in large numbers in nursing manpower, avoid the present hospital nurses training system. Then, the study for manpower development which is able to accomplish in many fields has to be advanced. 4) Most studies did not have literature review processes, so it was impossible for researcher to know the past study tendency and there is no relation among studies as to same subject, the education about research method is needed.

• The Journal of Korean Society for School & Community Health Education
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• v.2 no.1
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• pp.67-84
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• 2001

This study was conducted to examine beauty shop workers' views of job. Data were collected from the workers in Seoul, Daegu, Pohang, Junjoo, and Kimhae from June 1, 2000 to August 31, 2000. The results of this study are summarized as follows. 1. According to general characteristics of the subjects, 28,7% of them was female; 94.2% 'specialized in hair'; 46.4% 'below twenty nine years old'; 47.1% 'married'; 59.7% 'highschool graduate'; 33.9% 'worked for below three years'; 28.5% 'monthly income of five hundred thousand to nine hundred ninety thousand won'; 62.3% 'working for above twelve hours a day' ; 41.0% 'above five workers' ; 40.6% 'working in city'. 2. 54.8% of the respondents thought that they were in good health. 76.3% of them smoked and 54.8% drank. 62.8% of them did not exercise and 78.7% was under stress. 61.5% responded that they chose the job because of its possibility of professional vocation. 91.0% of them obtained the beauty skill from beauty schools. 3. Among the factors which influenced job satisfaction, 'stable job and life security' was highest(43.9%), while 'interest in the job and amount of pay' was lowest(3.2%). 'Personal ability and use of originality' was 19.4% and 'harmonious relationship with fellow workers' was 18.1%. 'Job environment' was 7.1% and 'harmonious relationship with higher workers' was 4.5%. 4. The level workers' view of job was $113.8{\pm}17.3$ points on the basis of 150 points. On the basis of 75 points, each item showed it points in order of self-development($22.3{\pm}3.8$), service for customers($20.1{\pm}3.1$), vocational mission($15.6{\pm}3.1$), harmony with the others($18.9{\pm}3.5$), working environment($18.6{\pm}3.6$), and working condition($14.3{\pm}5.1$). 5. Among the reasons why they considered leaving the job, 24.0% of them considered it because they could not free time, while 15.4% considered it because undesirable living environment or long distance from home. 15.0% thought it because they could not receive proper treatment as much as they worked and 12.8% thought they overworked. 6. When they move into new working places, they consider such factors as good working environment(24.1%), good place to open their own beauty shops(16.7%), good beauty shop to learn beauty skill(15.6%), chance to have job training(9.5%), and close place from home(9.0%). 7. 40.6% of the respondents wanted to leave the job, while 32.3% of them did not want to leave the job. The intention of leaving the displayed significant difference in the variables of age, working period, monthly income, marital status, the number of workers, location of the shop, rank, and reason of selecting the job. 8. According to the results of a regression analysis of factors which influenced job satisfaction, it was affected significantly by intention of leaving job, the number of workers, health condition, level of stress, and monthly income. The beauty shop workers showed low satisfaction level with working environment, working condition, and working mission, They considered leaving the job because of lack of free time, overwork, poor working environment, improper treatment, etc. Therefore, related professionals and organizations must device adequate measures in order to make them work with pride as creators of beauty.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.4
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• pp.225-232
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• 2024

This study focuses on the determination of the fracture mode in composite laminates using deep learning. With the increase in the use of laminated composites in numerous engineering applications, the insurance of their integrity and performance is of paramount importance. However, owing to the complex nature of these materials, the identification of fracture modes is often a tedious and time-consuming task that requires critical domain knowledge. Therefore, to alleviate these issues, this study aims to utilize modern artificial intelligence technology to automate the fractographic analysis of laminated composites. To accomplish this goal, scanning electron microscopy (SEM) images of fractured tensile test specimens are obtained from laminated composites to showcase various fracture modes. These SEM images are then categorized based on numerous fracture modes, including fiber breakage, fiber pull-out, mix-mode fracture, matrix brittle fracture, and matrix ductile fracture. Next, the collective data for all classes are divided into train, test, and validation datasets. Two state-of-the-art, deep learning-based pre-trained models, namely, DenseNet and GoogleNet, are trained to learn the discriminative features for each fracture mode. The DenseNet models shows training and testing accuracies of 94.01% and 75.49%, respectively, whereas those of the GoogleNet model are 84.55% and 54.48%, respectively. The trained deep learning models are then validated on unseen validation datasets. This validation demonstrates that the DenseNet model, owing to its deeper architecture, can extract high-quality features, resulting in 84.44% validation accuracy. This value is 36.84% higher than that of the GoogleNet model. Hence, these results affirm that the DenseNet model is effective in performing fractographic analyses of laminated composites by predicting fracture modes with high precision.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.169-196
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• 2017

The fourth industrial revolution represents a revolutionary change in the business environment and its ecosystem, which is a fusion of Information Technology (IT) and other industries. In line with these recent changes, the Ministry of Employment and Labor of South Korea announced 'the Fourth Industrial Revolution Leader Training Program,' which includes five key support areas such as (1) smart manufacturing, (2) Internet of Things (IoT), (3) big data including Artificial Intelligence (AI), (4) information security, and (5) bio innovation. Based on this program, we can get a glimpse of the South Korean government's efforts and willingness to emit leading human resource with advanced IT knowledge in various fusion technology-related and newly emerging industries. On the other hand, in order to nurture excellent IT manpower in preparation for the fourth industrial revolution, the role of educational institutions capable of providing high quality IT education services is most of importance. However, these days, most IT educational institutions have had difficulties in providing customized IT education services that meet the needs of consumers (i.e., learners), without breaking away from the traditional framework of providing supplier-oriented education services. From previous studies, it has been found that the provision of customized education services centered on learners leads to high satisfaction of learners, and that higher satisfaction increases not only task performance and the possibility of business application but also learners' recommendation intention. However, since research has not yet been conducted in a comprehensive way that consider both antecedent and consequent factors of the learner's satisfaction, more empirical research on this is highly desirable. With the advent of the fourth industrial revolution, a rising interest in various convergence technologies utilizing information technology (IT) has brought with the growing realization of the important role played by IT-related education services. However, research on the role of IT education service quality in the context of IT education is relatively scarce in spite of the fact that research on general education service quality and satisfaction has been actively conducted in various contexts. In this study, therefore, the five dimensions of IT education service quality (i.e., tangibles, reliability, responsiveness, assurance, and empathy) are derived from the context of IT education, based on the SERVPERF model and related previous studies. In addition, the effects of these detailed IT education service quality factors on learners' educational satisfaction and their work application/recommendation intentions are examined. Furthermore, the moderating roles of learner position (i.e., practitioner group vs. manager group) and participation motivation (i.e., voluntary participation vs. involuntary participation) in relationships between IT education service quality factors and learners' educational satisfaction, work application intention, and recommendation intention are also investigated. In an analysis using the structural equation model (SEM) technique based on a questionnaire given to 203 participants of IT education programs in an 'M' IT educational institution in Seoul, South Korea, tangibles, reliability, and assurance were found to have a significant effect on educational satisfaction. This educational satisfaction was found to have a significant effect on both work application intention and recommendation intention. Moreover, it was discovered that learner position and participation motivation have a partial moderating impact on the relationship between IT education service quality factors and educational satisfaction. This study holds academic implications in that it is one of the first studies to apply the SERVPERF model (rather than the SERVQUAL model, which has been widely adopted by prior studies) is to demonstrate the influence of IT education service quality on learners' educational satisfaction, work application intention, and recommendation intention in an IT education environment. The results of this study are expected to provide practical guidance for IT education service providers who wish to enhance learners' educational satisfaction and service management efficiency.