• Journal of vocational education research
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• v.35 no.4
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• pp.131-153
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• 2016

The purpose of this study was to derive evaluator's role and capability for institution accreditation evaluation of NCS-based vocational competency development training. This study attempted to explore in various ways evaluator's minute roles using Delphi method, and to derive knowledge, skill, attitude and integrity needed to verify the validity. To the end, this study conducted the Delphi research for over three rounds by selecting education training professionals and review evaluation professions as professional panels. From the results, roles of evaluators were defined as the total eight items including operator, moderator-mediator, cooperator, analyzer, verifier, institution evaluator, institution consultant, and learner, and the derived capabilities with respect to each role were 25 items in total. The area of knowledge included four items of capabilities such as HRD knowledge, NCS knowledge, knowledge of vocational competency development training, and knowledge of training institution accreditation evaluation, and the area of skill comprised fourteen items of capabilities such as conflict management ability, interpersonal relation ability, word processing ability, problem-solving ability, analysis ability, pre-preparation ability, time management ability, decision making ability, information comprehension and utilization ability, comprehensive thinking ability, understanding ability of vocational competency development training institutions, communication ability, feedback ability, and core understanding ability. The area of attitude was summarized with the seven items in total including subjectivity and fairness, service mind, sense of calling, ethics, self-development, responsibility, and teamwork. The knowledge, skill and attitude derived from the results of this study may be utilized to design and provide education programs conducive to qualitative and systematic accreditation and assessment to evaluators equipped with essential prerequisites. It is finally expected that this study will be helpful for designing module education programs by ability and for managing evaluator's quality in order to perform pre-service education and in-service education according to evaluator's experience and role.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.18 no.3
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• pp.99-106
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• 2018

Mucolipidosis type III (pseudo-Hurler polydystrophy) is a mucolipids degrading disorder caused by a mutation in the GNPTAB gene and is inherited by autosomal recessive. It is diagnosed by examining highly concentrated mucolipids in blood and the diagnosis can be confirmed by genetic testing. Mucolipidosis type III is a rare and progressive metabolic disorder. Its initial signs and symptoms usually occur around 3 years of age. Clinical manifestations of the disease include slow growth, joint stiffness, arthralgia, skeletal abnormalities, heart valve abnormalities, recurrent respiratory infection, distinctive facial features, and mild intellectual disability. Here, we are presenting two siblings of mucolipidosis type III, a 4-year-old female and a 2 years and 7 months old male with features of delayed growth and coarse face. The diagnosis was confirmed by [c.2715+1G>A(p.Glu906Leufs*4), c.2544del(p.Glu849Lysfs*22)] mutation in targeted gene panel sequencing. In this case, c.2544del is a heterozygote newly identified mutation in mucolipidosis type III and was not found in the control group including the genome aggregation database. And it is interpreted as a pathogenic variant considering the association with phenotype. Here, we report a Korean mucolipidosis type III patients with novel mutations in GNPTAB gene who have been treated since early childhood. Owing to recent development of molecular genetic techniques, it was possible to make early diagnosis and treatment with pamidronate was initiated appropriately in case 1. In addition to these supportive therapies, efforts must be made to develop fundamental treatment for patients with early diagnosis of mucolipidosis.

• Korean Journal of Heritage: History & Science
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• v.43 no.3
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• pp.144-179
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• 2010

Investigation into iron manufacture relics has been active since 1970s, especially accelerated in 1990s across the country. Consideration of the importance of production site relics has lately attracted attention to iron manufacture relics. Methodological studies of the investigation into iron manufacture relics, however, were less made compared with those of the investigation into tomb, dwelling, or swampy place relics. It is because the process of iron manufacture is too complicated to understand and also requires professional knowledge of metal engineering. With the recognition of these problems this research is to form an opinion about how to excavate, to rearrange and classify, and to examine iron manufacture relics, based upon the understanding of the nature of iron, iron production process, and metal engineering features of related relics like slag, iron lumps and so on. This research classifies iron manufacture relics into seven types according to the production process; mining, smelting, refining, tempering, melting, steelmaking, and the others. Then it arranges methods to survey in each stage of field study, trial digging, and excavation. It also explains how to classify and examine excavated relics, what field of natural science to be used to know the features of relics, and what efforts have been made to reconstruct a furnace and what their problems were, making the best use of examples, drawings, and photos. It comes to the conclusion, in spite of the lack of in-depth discussion on application and development of various investigation methods, that iron manufacture relics can be classified according to the production process, that natural sciences should be applied to get comprehensive understanding of relics as well as archeological knowledge, and that efforts to reconstruct a furnace should be continued from the aspect of experimental archeology.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of Christian Education in Korea
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• v.64
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• pp.51-79
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• 2020

This study was conducted to pursue Christian education with the socially underprivileged in the era of the Corona-19 pandemic. Corona-19 is a disaster which is caused, destroyed and exploited by human being. At the time of the indiscriminately spreading global pendemic, we must work together to overcome our selfish self-centeredness and make an attempt for everyone in need. It is a study on how humans can help each other survive in the era of Corona-19 and its post-corona. The problem is that there is too much discrimination between the state, race, and economic capacity, and in the end, the extreme discrimination of capitalism is appeared in society and across the country. There is no significant difference in the confirmation rate when Corona-19 infiltrates, but there is a big difference between those with and less in mortality. As a result, today's reality is that people who have a hard time living because they have less usually are far more vulnerable to blocking and defeating virus attacks. Unfortunately, this is the current situation. From the standpoint of a large discourse, attention is paid to climate change and ecological environment, and as a micro discourse, a number of societies who live with tremendous discrimination according to the gap between the rich and the poor (it is gender, race, disabled, nationality) that exist in almost all countries on the planet. We need attention to the weak. To this end, discourses on vaccine inequality, discourses on the needs of the disabled, discourses on different racial damages, discourses on polarization and dystopia, and discourses on educational inequality were treated as the reality faced by the socially underprivileged in the Corona 19 pandemic. To explore Christian education with the socially underprivileged, to explore ways of sharing, giving, and solidarity for win-win, discourse on inter-dependence and mutual responsibility of mankind, direct counter-measures for the socially underprivileged, and critical literacy education. He proposed a discourse on Korea, a discourse on Homo sapiens, which must return to being a part of creation, and finally a theology of friendship with the weak. Christian education based on Bible words must go forward in the era of the Corona 19 pandemic, hungry, naked, nowhere to go, sick, but dying because of being unable to get a remedy. He emphasized the need to establish a caring theology of friendship and pursue a life in which thought and practice harmonize. Thus, the paper proposed the spirit of Christian education not only doing something for the socially weak, but with the socially weak in the daily life.

• Journal of The Korean Association For Science Education
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• v.40 no.6
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• pp.595-609
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• 2020

The purpose of this study is to analyze the experiences of teachers who participated in the development of online science class videos in the context of covid-19, their perception of online science class, and the characteristics of the online science class content developed by teachers. A survey and interviews were conducted with ten elementary school teachers who made online science class videos themselves. Also the characteristics of the online science class were investigated by analyzing the online science class video produced by the participants. As a result, participants in the study recognized the lack of production time, difficulty in filming and editing, concerns over misconceptions, the problem of solving copyrights for existing materials, and the burden of external disclosure. Although it was a teacher who had experience producing online science class video contents, no research participants actively answered the merits of online science class. On the other hand, the study participants cited that the shortcomings of online science classes were that students had fewer opportunities for inquiry and lack of communication or interaction. In particular, these shortcomings were thought to have a great influence on the quality of online science classes, especially in making inquiry classes difficult. Some teachers took a negative view that online science classes could not completely replace face-to-face classes. However, if multiple teachers are presented with supplementary teaching activities that complement the content-based online teaching method, the method of combining online science classes and face-to-face classes is not. Through the analysis of the contents of the online science class, the introduction and arrangement steps of the online science class were similar to the process of the face-to-face science class, but the inquiry step and the conceptual explanation step showed a big difference from the face-to-face science class.

• (The)Study of the Eastern Classic
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• no.73
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• pp.385-414
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• 2018

In this article, the women as the subject of the family management in the 19th century cataclysm, In particular, I tried to reconstruct the specific life course of a woman who has a status as a jongbu(종부) in the Korean language through the Hangul letter. The Haenam Yoon's the 8th jongbu(종부) Gwangju Lee attempted to find her own unique identity, not the male-centered social order she had learned. Because she had to live a life outside the traditional environment of traditional society because her husband died at the beginning of her marriage. She perceived herself as an independent subject that she had to find and maintain. When Gwangju Lee married and came to the family of Haenam Yun, the economic power of jong-ga(종가) was much inclined. This economic difficulty was caused by the conflict with the slaves and the decrease of tallage(地代) to the change of the slavery system which was the social flow at that time. And uncles of her husband's intervention made the economic situation of the family more difficult. She established her position as a jongbu(종부) and used the right of Adoption option(입후권) of the jongbu(종부) to establish the impoverished family. She chose adoption from distant relatives who were not children of her husband's uncles. Therefore, I was free from her husband uncle's interests. She also believed that it was most important to take control of the economic interests of her family in order to secure her authority as a jongbu(종부). She believed that she had to exercise her economic rights in order to bring slave labor, which is the most important means of sustaining the domestic economy at the time, In the absence of her husband, she established her family in the social upheaval of the nineteenth century, and took her place as a master of a family, not just a family name.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.21-35
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• 2022

The recommended meal composition allows the general people to organize meals using the number of intakes of foods from each of six food groups (grains, meat·fish·eggs·beans, vegetables, fruits, milk·dairy products and oils·sugars) to meet Dietary Reference Intakes for Koreans (KDRIs) without calculating complex nutritional values. Through an integrated analysis of data from the 6th to 7th Korean National Health and Nutrition Examination Surveys (2013-2018), representative foods for each food group were selected, and the amounts of representative foods per person were derived based on energy. Based on the EER by age and gender from the KDRIs, a total of 12 kinds of diets were suggested by differentiating meal compositions by age (aged 1-2, 3-5, 6-11, 12-18, 19-64, 65-74 and ≥ 75 years) and gender. The 2020 Food Balance Wheel included the 6th food group of oils and sugars to raise public awareness and avoid confusion in the practical utilization of the model by industries or individuals in reducing the consistent increasing intakes of oils and sugars. To promote the everyday use of the Food Balance Wheel and recommended meal compositions among the general public, the poster of the Food Balance Wheel was created in five languages (Korean, English, Japanese, Vietnamese and Chinese) along with card news. A survey was conducted to provide a basis for categorizing nutritional problems by life cycles and developing customized web-based messages to the public. Based on survey results two types of card news were produced for the general public and youth. Additionally, the educational program was developed through a series of processes, such as prioritization of educational topics, setting educational goals for each stage, creation of a detailed educational system chart and teaching-learning plans for the development of educational materials and media.