• Journal of the Health Care and Life Science
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• v.11 no.2
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• pp.271-277
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• 2023

This study targeted 125 prospective early childhood teachers at G University in G Metropolitan City to investigate their perceptions of safety care practices. The collected data were analyzed using One-way ANOVA and Scheffe's post hoc test. As a result of the study, there was a statistically significant difference in the overall awareness of safe care practices of prospective early childhood teachers according to grade. Statistically significant differences were found in the sub-factors 'Life Safety', 'Traffic Safety', 'Violence and Personal Safety', 'Drugs and Cyber Addiction', and 'Disaster Safety'. On the other hand, there was no statistically significant difference in 'occupational safety' and 'first aid'. Through these results, Through these results, we aim to increase safety competency by measuring safety care practices, expand practical training opportunities for prospective early childhood teachers, develop various media, and above all, improve class operation and content related to safety in early childhood teacher training courses. Based on this, we aim to provide basic data to develop a pre-service early childhood teacher education program.

• Journal of the Health Care and Life Science
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• v.9 no.2
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• pp.237-244
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• 2021

The purpose of this study is to examine the differences in emotional intelligence and resilience of pre-service early childhood teachers according to the level of teaching efficacy. To this end, 112 prospective early childhood teachers attending the Early Childhood Education Department located in the G were surveyed. The collected data was analyzed by one-way ANOVA and Scheffe post-test analysis. As a result of the study, first, according to the level of teaching efficacy, there were statistically significant differences in the emotional intelligence and the sub-factors. Second, according to the level of teaching efficacy, there were statistically significant differences in resilience and sub-factor s. These results will serve as basic data to explore various ways to improve the level of teaching efficacy of pre-service early childhood teachers and to enhance the personal qualities required for pre-service early childhood teachers.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.59-69
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• 2011

Recently, as people's interest in health care has been rising, the health care service awareness and utilization has been increasing. However, the existing healthcare services have problems such as inconvenience of mobility, the low reliability of input for information and low accuracy of information provided as well. in this study, we developed the m-Health application by utilizing smart phone with improvement of these problems. This application provided the total of 5 services such as notification for risk of cardiovascular disease, personalized dietary recommendations targeted to 20s and 30s who do not properly manage their health care by bad habits. In addition, the benefits and problems of these services were found out through the analysis for the general importance and satisfaction of these services by Importance-Performance Analysis (IPA) technique. In result of IPA analysis, The six items such as 'input accuracy and reliability of information', 'content reliability', 'proper health service recommendations', etc. among 12 of the items needed to receive the effective services on m-Health were belonged to importance and satisfaction area with high level. And, in the 'information security', the importance is high but the satisfaction was low. In conclusion, the further study for strengthening security of information, service update provided with PHR to consistently keep the advantage of these services will be conducted.

• Korean Journal of Environmental Biology
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• v.21 no.2
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• pp.87-100
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• 2003

It is important to understand the potential human health implications of exposure to environmental chemicals that may act as hormonally active agents. It is necessary to have an understanding of how pharmaceutical and personal care products and other chemicals affect the ecosystem of our planet as well as human health. Endocrine disruption is defined as the ability of a chemical contaminating the workplace or the environment to interfere with homeostasis, development, reproduction, and/or behavior in a living organism or it's offspring. Certain classes of environmentally persistent chemicals such as polychlorinated biphenyls (PCBs), dioxins, furans, and some pesticides can adversely effect the endocrine systems of aquatic life and terrestrial wildlife. Research continues to support the theory of endocrine disruption. However, endocrine disruption researches have been applied to proteomics poorly. Proteomics can be defined as the systematic analysis of proteins for their identity, quantity and function. It could increase the predictability of early drug development and identify non-invasive biomarkers of tonicity or efficacy. Proteome analysis is most commonly accomplished by the combination of two-dimensional gel electrophoresis (2D/E) and MALDI-TOF mass spectrometry (MS) sr protein chip array and SELDI-TOF MS. Proteomics have an opportunity to play an important role in resolving the question of what role endocrine disruptors play in initiating human disease. Proteomics can also play an imfortant role in the evaluation of the risk assessment and use of risk management and risk communication tools required to address public health concerns related to notions of endocrine disruptors. Understanding the need for the proteomics and possessing knowledge of the developing biomakers used to abbess endocrine activity potential will he essential components relevant to the topic of endocrine disruptors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1599-1607
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• 2022

The data mainly used for the model are as follows: the personal information, the information of insurance product, etc. With the data, we suggest three types of models: content-based filtering model, collaborative filtering model and classification models-based model. The content-based filtering model finds the cosine of the angle between the users and items, and recommends items based on the cosine similarity; however, before finding the cosine similarity, we divide into several groups by their features. Segmentation is executed by K-means clustering algorithm and manually operated algorithm. The collaborative filtering model uses interactions that users have with items. The classification models-based model uses decision tree and random forest classifier to recommend items. According to the results of the research, the contents-based filtering model provides the best result. Since the model recommends the item based on the demographic and user features, it indicates that demographic and user features are keys to offer more appropriate items.

• (The)Study of the Eastern Classic
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• no.69
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• pp.115-146
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• 2017

This paper is about the research on ontology design for a digital archive of seonsaengan(先生案) of the Joseon Period. Seonsaengan is the register of staff officials at each government office, along with their personal information and records of their transfer from one office to another, in addition to their DOBs, family clan, etc. A total of 176 types of registers are known to be kept at libraries and museums in the country. This paper intends to engage in the ontology design of 47 cases of such registers preserved at the Jangseogak Archives of the Academy of Korean Studies (AKS) with a focus on their content and structure including the names of the relevant government offices and posts assumed by the officials, etc. The work for the ontology design was done with a focus on the officials, the offices they belong to, and records about their transfers kept in the registers. The ontology design categorized relevant resources into classes according to the attributes common to the individuals. Each individual has defined a semantic postposition word that can explicitly express the relationship with other individuals. As for the classes, they were divided into eight categories, i.e. registers, figures, offices, official posts, state examination, records, and concepts. For design of relationships and attributes, terms and phrases such as Dublin Core, Europeana Data Mode, CIDOC-CRM, data model for database of those who passed the exam in the past, which are already designed and used, were referred to. Where terms and phrases designed in existing data models are used, the work used Namespace of the relevant data model. The writer defined the relationships where necessary. The designed ontology shows an exemplary implementation of the Myeongneung seonsaengan(明陵先生案). The work gave consideration to expected effects of information entered when a single registered is expanded to plural registers, along with ways to use it. The ontology design is not one made based on the review of all of the 176 registers. The model needs to be improved each time relevant information is obtained. The aim of such efforts is the systematic arrangement of information contained in the registers. It should be remembered that information arranged in this manner may be rearranged with the aid of databases or archives existing currently or to be built in the future. It is expected that the pieces of information entered through the ontology design will be used as data showing how government offices were operated and what their personnel system was like, along with politics, economy, society, and culture of the Joseon Period, in linkage with databases already established.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.145-165
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• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.