• Korean Journal of Health Education and Promotion
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• v.1 no.1
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• pp.21-36
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• 1983

It is assumed that menarche is affected not only by the biological factors such as nutrition and genetic heritage, but also it is affected by other socio-cultural environmental factors including weather, geographic location, education and level of modernization. Also recent trend of menarche in Korea indicates that a lot of discussion are being generated to the need of sex education as a part of formal school education. The purpose of this study is to develop the school health education program by determine the age of menarche, the factors relavant to time of menarche and psycho-mental state of students at the time in menarche and investigate the present state of school health education relate to menarche of adolescents. The total number of 732 girls was drown from first, second and third grades of 4 middle schools in Seoul. For the data collection the survey was conducted during the period from May 1 to May 20, 1982 by using prepared questionair. The major results are summarized as follow; 1. Mean age at menarche and the percent distribution of menarche experienced. It was observed that about 68.7% of sampled students have been experienced menarche at the time interviewed. For the each group, age at menarche is revealed that among the students about 37.8% are experienced menarche for under 12 years old group, 62.1% for 13 year-old group, 80.6％ for 14 year-old group and 95.5% for over 15 years old. In sum it was found that the mean age at menarche was 12.3 years old, ranged from age at 10 as earlist the age at 15 as latest. 2. Variables associated with age at menarche. 1) There was tendency those student who belong to upper class economic status have had menarche earlier than those student who belong to lower class. Therefore, economic status is closely related to age at menarche. 2) In time of mother's education level, it is also found that those students whose mother's education levels from high school and college are experienced menarche earlier than those students whose mother's education levels from primary school and no-education. 3) However, in connection with home discipline, there was no significant relationship between age at menarche and home disciplines which are being treated "Rigid", "Moderated ", "Indifferent". 4) Degree of communication between parents and daughter about sex matters was found to be associated each others in determination of age at menarche. 5) It was found that high association between mother's menarche age and their daughter's menarche age was observed. Mother's age at menarche earlier trend to be shown also as earlier of their daughters. 6) Those students belong to "D & E" of physical substantiality index are trend to be earlier in menarche than those students in the index "A & B". 3. Psycho-mental state at the time of menarche. Out of the total students 68.2% had at least one or more than one of subjective symptoms. Shyness was shown as most higher prevalent symptom and others are fear, emotional instability, unpleasant feeling, depression, radical behavior, inferior complex and satisfaction appeared. Very few cases are appeared be guilty and stealing feeling. 4. The present status of school health education program related to menarche. As to the source of information about menarche, teacher was a main source with average index 5.88 and the other informants were mother & family member, friends, books and magagines, movies, television, and radio. For the problem solving at menarche, mother & family members were subject to discussion with an average index 6.02 as high. The others for discuss and knowledge about menarche were books, magagine, friends, teachers, and self-learning based on own experienced. The time of learning about menarche, it was learned as highest percentage with 43.2% at a 6 grades of primary school, middle school with 34.4%, 5 grade of primary school with 18.2%, and 4 grade of primary school with 4.0% respectively.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• (The) Research of the performance art and culture
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• no.36
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• pp.441-471
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• 2018

East Coast Gut, Korean shamanism ritual on its east coastal area, is a Gut held in fishing villages alongside Korean east coastal area from Goseong area in Gangwon-Do to Busan area. East Coast Gut is performed in a series mainly by a successor shaman, Korean shaman, who hasn't received any spiritual power from a God, and the implications of this thesis lie in that we look over the learning aspects of Seokchool Kim shaman group among other East Coast Gut successor shaman groups after dividing it into 2 categories, successor shaman and learner shaman and based upon this, we reveal the meaning of the learning aspects of East Coast Gut. For successor shamans, home means the field of education. Since they are little, they chased Gut events performing dance in a series to accumulate onsite experiences. However, in the families of successor shamans that have passed their shaman work down from generation to generation, their descendents didn't inherit shaman work any longer, which changed the way of succession and learning of shaman work. Since 1980's, Gut has been officially acknowledged as a kind of general art embracing songs, dance and music and designated as a cultural asset of the state and each city and province, and at art universities, it was adopted as a required course for its related major, which caused new learner shamans who majored in shamanism to emerge. These learner shamans are taking systematical succession lessons on the performance skills of East Coast Byeolshin Gut at universities, East Coast Byeolshin Gut preservation community, any places where Guts are held and etc.. As changes along time, the successor shamans accepted the learner shamans to pass shaman work down and changes appeared in the notion of towners who accept the performer groups of Gut and Gut itself. Unlike the past, as Gut has been acknowledged as the origin of Korean traditional arts and as the product of compresensive learning on songs, dance and music and it was designated as a national intangible cultural asset, shaman's social status and personal pride and dignity has become very high. As shaman has become positioned as the traditional artist getting both national and international recognition unlike its past image of getting despised, at the site of Gut event or even in the relation with towners, their status and the treatment they get became far different. Even towners, along with shift in shaman groups' generation, take position to acknowledge and accept the addition of new learning elements unlike the past. Even in every town, rather than just insisting on the type or the event purpose of traditional Gut, they think over on the type of festival and the main direction of a variety of Guts with which all of towners can mingle with each other. They are trying to find new meanings in the trend of changing Gut and the adaptation of new generation to this. In our reality of Gut events getting minimalized along with rapid change of times, East Coast Gut is still very actively performed in a series until now compared to Guts in other regions. This is because following the successor shamans who have struggled to preserve the East Coast Gut, the learner shamans are actively inflowing and the series performance groups preserve the origin of Gut and try hard to use Gut as art contents. Besides, the learner shamans systematically organize what they learned on shamanism from the successor shamans and get prepared and try to hand it down to descendents in the closest possible way to preserve its origin. In the future, East Coast Gut will be succeeded by the learner shamans from the last successor shamans to inherit its tradition and develop it to adapt to the times.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Archives of design research
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• v.16 no.4
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• pp.385-394
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• 2003

An investigation by KRNIC (Korea Network Information Center) on the real state of usage of internet has shown that 96.9％ of children investigated had experiences of using internet. Especially the firstly ranked item that had been answered by children as a necessity of internet was 'Studying to solve tasks' rated by 83.9％. As seen from the research result, the need as a homework sonics is actually so dominant that it cannot be ignored when considering the profitability at the area of education contents, but any profound research has not been accomplished yet. Internet has been positioned as a more effective and fruitful learning tool, and also all activities done by users for exploring informations and choosing learning items under the on-line circumstances are based on the successive mutual reactions between users and computers. Up to now much of the web based learning circumstances has been introducing the User Interface using metaphor, and the same is found dominantly from the sites for children. But in spite of the availability of metaphor mentioned above the current status is much lack of profound researches about metaphor interface; and what is more, in the case of the site for elementary school students the gap of the ability recognizing metaphor is very large between lower classes and higher classes according to the degree of mental growth but that is used to be simply ignored, then a common concept is adapted to interface for all grades of classes and moreover for infant and kindergarten without any objections. Based on foregoing problems this research has put the main focus on the groping and presenting desirable directions on the prospect design of interface for children-oriented sites by analyzing the status of practical usage of metaphor interface in the field of the sites for children-oriented learning sites with concentration upon homework supporting domains.

• The Journal of Korean Philosophical History
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• no.29
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• pp.153-178
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• 2010

The aim of this research was to examine the traditional Confucian view of self-realization in East Asia and the meaning of life implied therein. The researcher closely reviewed the phase of self-realization of both Confucius and Mencius who are central in Confucianism, especially in the primordial Confucianism, and after investigating maturity of personality as well as educational characteristics thereof, the researcher tried to elicit its modern significance. In Analects, Confucius who is the founder of Confucianism mentioned about 'the pleasure of studying and practicing what he has learned'(學而時習 "Hagisiseup" in Korean), since after, his past was then just the process of self-realization that lasted throughout life. That is, the six phases of self-realization, to wit, 'bending on learning(志學, "Jihak")-'standing firm'(而立, "Irip")-'having no doubts'(不惑, "Bulhok")-'knowing the decrees of Heaven'(知天命, "Jicheonmyeong")-'ear being obedient organ for the reception of truth' (耳順, "Isun")-'able to follow what my heart desires without transgressing what is right'(從心, "Jongsim"), are lying hidden and undeveloped during lifetime, and, at the same time, these phases illustrate the state of enlightenment of life in an in-depth manner. By showing the process of living which is being sublimated in respect of quality, and by going through important process of self-innovation up to six times during lifetime, Confucius edifies us the activity of complete self-realization as well as the importance of education and learning. Meanwhile, these are connected to Mencius in a similar pattern, and strong influence of the characteristics of the learning of the mind and heart( 心學, "Simhak") based on his philosophy permeates the self-actualization phase of Mencius. Mencius' self-actualization phase is expressed in terms of six stages, viz., Person of Goodness(善人, "Seonin")-Trustworthy Person(信人, "Sinin")-Person of Beauty(美人, "Miin")-Great Person(大人, "Daein")-Sage(聖人, "Seongin")-Divine Person(神人, "Sinin"), and these six phases of self-actualization process are educational and learning model for people who dream actualization of perfect personality during their lifetime. Confucian and Mencian view of self-realization congruent with self-discipline internally, and it also reveals a stereotype of human externally. These are a process of performing organic ideals in order for cultivating oneself and regulating others(修己治人, pronounced 'sugichiin' in Korean) which has been pursued by Confucianism. Briefly, these self-realization phases are the arts of living that will lay foundation for "Being Born Human, pronounced Saramim' in Korean" and for becoming "Fully Human, 'Sarmadoem'" and finally for "Human Feelingness, 'Saramdaum'

• Journal of The Korean Association For Science Education
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• v.34 no.2
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• pp.123-134
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• 2014

This study aims to develop and validate an instrument to measure students' attitude toward convergence. To do so, we have defined five constructs (i.e. knowledge about convergence, personal relevance, social relevance, interest and self-efficacy) of 'attitude toward convergence' based on literature review, developed items, and collected data from 233 11th grade science track students. The validity of these items have been evaluated by Messick's framework (1995) (i.e. content, substantive, structural aspects of validity), experts' review, Rasch analysis, and confirmatory factor analysis using structural equation modeling. Our results have confirmed the five constructs and 23 selected items have met the benchmark of item validity. Moreover, the theoretical model illustrating that the high level of attitude toward convergence increases the level of science motivation has also been supported by the data. The items developed in this study will be used to measure students' attitude toward convergence and to estimate the effect of learning program for convergence science.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.

• Journal of The Korean Association For Science Education
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• v.18 no.3
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• pp.443-450
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• 1998

This study was performed to find the environmental factors of inquiry instruction perceived by secondary school science teacher. The instrument consisted of three domains such as teaching conditions, viewpoints of secondary school science teachers of environmental factors for inquiry instruction, and barrier and improve! rent factors of inquiry instruction. Teaching conditions between middle school and high school science teachers were not different significantly. Environmental factors of inquiry instruction of secondary school science teacher included five factors such as 'facilities and encouragement', 'amount of works and materials', 'teacher education and textbook', 'practice and knowledge' and 'perception of necessity and satisfaction'. And all factors except 'perception of necessity and satisfaction' were very low state for inquiry instruction. In the disturbant and improving factors, the critical factors were 'over students per class', 'textbook' and 'learning materials' for middle school science teachers, and 'over students per class', and 'entrance examination' for high school science teachers. Thus the development and diffusion of adequate inquiry learning materials may be helpful to practicing inquiry instruction as decrease of works and psychological charges, and it is needed to reorganize systematically and intensify pre- and in-service teacher education to practice inquiry instruction.