• Journal of Korean society of Dental Hygiene
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• v.17 no.5
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• pp.791-803
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• 2017

Objectives: Oral health management is important to improve the quality of life among the elderly. This study investigated the performance of elderly oral health management among some care workers in long-term-care hospitals. Methods: The study subjects were 174 care workers in 10 long-term-care hospitals. Data on general characteristics of care workers, attitude, recognition and knowledge of elderly health, performance of elderly oral health management were collected by a self-administered questionnaire. Data were analyzed through descriptive analysis, t-test, ANOVA, correlation and multiple regression analysis by using a SPSS version 23.0 statistical program. Results: The performance score of oral health management was $4.34{\pm}0.64$ on the 5-point Likert scale. The subjects who exercised more than 2 times a month were significantly higher in their performance of elderly oral health management compared to subjects who did not exercise (${\beta}=0.232$, p=0.035). And, the subjects who cared 10-19 persons were significantly higher in performance of elderly oral health management compared to subjects who cared more than 20 elderly (${\beta}=0.246$, p=0.020). The oral health behavior of care worker (${\beta}=0.271$, p<0.001) and the knowledge of oral health care (${\beta}=0.055$, p=0.008) were positively related to the performance of elderly oral health management. Conclusions: The educational program designed to improve knowledge of care workers in accordance with the standard textbook for training care workers should be developed, and the long term education program should be reinforced to improve the performance for elderly oral health care. If care workers can care a proper number of old persons, they will give oral health care to them.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.89-98
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• 2016

In this paper, we proposed an automated areal feature matching method based on geometric similarity without user intervention and is applied into areal features of many-to-many relation, for confusion of spatial data-sets of different scale and updating cycle. Firstly, areal feature(node) that a value of inclusion function is more than 0.4 was connected as an edge in adjacency matrix and candidate corresponding areal features included many-to-many relation was identified by multiplication of adjacency matrix. For geometrical matching, these multiple candidates corresponding areal features were transformed into an aggregated polygon as a convex hull generated by a curve-fitting algorithm. Secondly, we defined matching criteria to measure geometrical quality, and these criteria were changed into normalized values, similarity, by similarity function. Next, shape similarity is defined as a weighted linear combination of these similarities and weights which are calculated by Criteria Importance Through Intercriteria Correlation(CRITIC) method. Finally, in training data, we identified Equal Error Rate(EER) which is trade-off value in a plot of precision versus recall for all threshold values(PR curve) as a threshold and decided if these candidate pairs are corresponding pairs or not. To the result of applying the proposed method in a digital topographic map and a base map of address system(KAIS), we confirmed that some many-to-many areal features were mis-detected in visual evaluation and precision, recall and F-Measure was highly 0.951, 0.906, 0.928, respectively in statistical evaluation. These means that accuracy of the automated matching between different spatial data-sets by the proposed method is highly. However, we should do a research on an inclusion function and a detail matching criterion to exactly quantify many-to-many areal features in future.

• 대한공업교육학회지
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• v.33 no.1
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• pp.44-66
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• 2008

As the way to intensify vocational education, the places to learn and work merge into one educational system and teaching-learning methods that were pointed at lecturer level are adjusted to learners level. However, the current education environment of vocational highschool doesn't keep up with a new teaching and learning paradigm due to the lack of government-level support and the lecturer level education as well. In this paper, we present a novel learner-leading e-Learning teaching & learning contents framework that intensifying self leading education. In this framework, we concern the ability of self leading, space perception and lesson preparation, preferences of multimedia, scholastic aptitude level and achievement level of students in vocational highschool. Moreover, with following the protocol of the suggested model, we generate a new e-learning contents applying to the subject "Introduction to industry". Especially, to perform better quality of contents, we have survey the whole content of the subject and assorted part of it that would work best with learner-leading e-Learning teaching and learning contents. As we tentatively apply e-learning contents to students in vocational highschool, 79% of them replied that contents brought advantages to get school accomplishment, whereas 19% of students answered in negative that contents wasn't helpful than we've expected. Therefore, the statistical results probe that the e-learning contents we proposed show positive effect, and we analyzed the reasons that deteriorate the effects of contents as shortage of budget and copyright limitation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.224-234
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• 2017

This paper compares the realities of the salary Peak Policy's status and management processes in Korea and Japan, with the aim of determining the development direction for Korea's salary Peak Policy's. Unlike Japan, which successfully achieved close cooperation between government, firms and workers in implementing the Retirement Age Extension Type salary Peak Policy, Korea experienced many problems due to lack of preparation. In order to rationally develop the salary Peak Policy, the government, firms, and workers must cooperate to increase the policy's efficiency via the following steps. First, gradually increase the proportion of retirement age extension. Second, career development that takes into account the various employment types, flexible working hours and aged workers. Third, development of training programs for senior citizen workers, as well as increasing support for changing of jobs and startups. Fourth, expansion of re-employment after retirement age and ways to make use of the skilled labor. Fifth, increasing work efficiency through bonuses and work evaluation that is specialized for aged workers. This paper argues that such measures are necessary for the co-existence of firms and workers, as well as for improving employment stability and labor market flexibility.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.293-303
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• 2017

Thus study examined the possible the link between social enterprises and lifelong education for the underprivileged. To this end, this study searched for the definition and position of social enterprises emerging from the welfare system under the influence of neoliberalism and overcoming the problems in terms of creating social jobs and providing welfare services. In addition, the lifelong education for the underprivileged was examined according to the subjects, such as the disabled, migrant women, young and adult low-income group, and senior citizens. The plan was as follows. First, the expansion of the proportion of community-affiliated social enterprises was analyzed. Second, it provides a differentiated support and protection market for social enterprise. Third, the development and dissemination of social entrepreneur training programs was examined. The results showed that the entire society should have a sense of responsibility for the support of the underprivileged. This provides implications for the linkage of lifelong education and social enterprise in the expansion possibility to improve the quality of life and expand lifelong education for the underprivileged.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.109-117
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• 2020

An edutainment system aims to help learners to recognize problems effectively, grasp and classify important information needed to solve the problems and convey the contents of what they have learned. Edutainment contents can be usefully applied to education and training in the both scientific and industrial areas. Our present work proposes an edutainment system that can be applied to a drug discovery process including virtual screening by using intuitive multi-modal interfaces. In this system, a stereoscopic monitor is used to make three-dimensional (3D) macro-molecular images, with supporting multi-modal interfaces to manipulate 3D models of molecular structures effectively. In this paper, our system can easily solve a docking simulation function, which is one of important virtual drug screening methods, by applying gaming factors. The level-up concept is implemented to realize a bio-game approach, in which the gaming factor depends on number of objects and users. The quality of the proposed system is evaluated with performance comparison in terms of a finishing time of a drug docking process to screen new inhibitors against target proteins of human immunodeficiency virus (HIV) in an e-drug discovery process.

• Journal of Music and Human Behavior
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• v.18 no.2
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• pp.67-85
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• 2021

This study reviewed the research in China on music interventions for adult brain injury patients. Eighty-three studies that met the inclusion criteria were included for analysis. Our review revealed that the number of intervention studies using music with adult brain injury patients has been on the rise since 2012, and random control research design methods have been dominant. Studies focused on the physical domain and emotional domain together were most common. Researchers in fields outside of music therapy conducted 43 of the studies, and music therapists carried out 14 of the studies as intervention providers. Most of the studies carried out by experts in fields other than music therapy used listening activities involving preexisting recorded music. However, most of the studies conducted by music therapists adopted reconstructed music and played it live during their intervention. The specificity of the described content of the interventions and level and relevance of stated rationale to the target goal of the intervention suggests that high quality of intervention was conducted with a therapist/investigator who has completed adequate professional education/training, which would emphasize the importance of music therapy professionalism. This study provides the baseline data for how music intervention research has been implemented in China and presents implications for future clinical practice and research.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.75-87
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• 2021

The purpose of this study is to derive the characteristics of interaction design for each stage of content composition so that it can be referred to in the planning and production of virtual reality safety education contents. It was confirmed that each of the following interaction design features was found in the three configuration steps: acquisition of situation response procedure knowledge, accident situation experiential learning, and content confirmation and evaluation. First, it was revealed that the quality of experience was controlled by increasing the fidelity of behaviors and reducing general and repetitive behaviors in order to emphasize the educational content-related experiences in the learner experience stage. Second, in order for learners to easily recognize main interaction objects in order to acquire information on safe behavior procedures in unfamiliar environments, use of spatial UI or signifiers using arrows or symbols, posts that specifically instruct actions, and multisensory signals Therefore, it was found to be important to emphasize essential actions in a way that lowers the degree of freedom of user experience, and the proportion of non-realistic interactions for cognitive interactions was found to increase. Lastly, in the confirmation and evaluation stage of the experience, it is important to use the meta UI to alleviate negative experiences such as physical damage after experiencing a safety accident situation,

• Journal of Korean Society of Disaster and Security
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• v.14 no.3
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• pp.17-27
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• 2021

In this study, classification models were built using machine learning techniques that can classify the soil creep risk into three classes from A to C (A: risk, B: moderate, C: good). A total of six machine learning techniques were used: K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting and then their classification accuracy was analyzed using the nationwide soil creep field survey data in 2019 and 2020. As a result of classification accuracy analysis, all six methods showed excellent accuracy of 0.9 or more. The methods where numerical data were applied for data training showed better performance than the methods based on character data of field survey evaluation table. Moreover, the methods learned with the data group (R1~R4) reflecting the expert opinion had higher accuracy than the field survey evaluation score data group (C1~C4). The machine learning can be used as a tool for prediction of soil creep if high-quality data are continuously secured and updated in the future.