• 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.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.413-418
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• 2020

It is necessary to manage the prediction accuracy of the machine learning model to prevent the decrease in the performance of the grid network condition prediction model due to overfitting of the initial training data and to continuously utilize the prediction model in the field by maintaining the prediction accuracy. In this paper, we propose an automation technique for maintaining the performance of the model, which increases the accuracy and reliability of the prediction model by considering the characteristics of the power grid state data that constantly changes due to various factors, and enables quality maintenance at a level applicable to the field. The proposed technique modeled a series of tasks for maintaining the performance of the power grid condition prediction model through the application of the workflow management technology in the form of a workflow, and then automated it to make the work more efficient. In addition, the reliability of the performance result is secured by evaluating the performance of the prediction model taking into account both the degree of change in the statistical characteristics of the data and the level of generalization of the prediction, which has not been attempted in the existing technology. Through this, the accuracy of the prediction model is maintained at a certain level, and further new development of predictive models with excellent performance is possible. As a result, the proposed technique not only solves the problem of performance degradation of the predictive model, but also improves the field utilization of the condition prediction model in a complex power grid system.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1255-1263
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• 2021

While it could become an alternative water resource, fog could undermine traffic safety and operational performance of infrastructures. To reduce such adverse impacts, it is necessary to have spatially continuous fog risk information. In this work, tree-based machine-learning models were developed in order to quantify fog risks with routine meteorological observations alone. The Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), and Random Forests (RF) were chosen for the regional fog models using operational weather and visibility observations within the Jeollabuk-do province. Results showed that RF seemed to show the most robust performance to categorize between fog and non-fog situations during the training and evaluation period of 2017-2019. While the LGB performed better than in predicting fog occurrences than the others, its false alarm ratio was the highest (0.695) among the three models. The predictability of the three models considerably declined when applying them for an independent period of 2020, potentially due to the distinctively enhanced air quality in the year under the global lockdown. Nonetheless, even in 2020, the three models were all able to produce fog risk information consistent with the spatial variation of observed fog occurrences. This work suggests that the tree-based machine learning models could be used as tools to find locations with relatively high fog risks.

• The Journal of Korean society of community based occupational therapy
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• v.8 no.3
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• pp.77-89
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• 2018

Objective : The purpose of this study was to analyze occupational therapy intervention on the community mobility for stroke patients, and to provide evidence of intervention in the clinical fields. Methods : A systematic review was executed according to the PRISMA checklist. The accessed database was PubMed, EMBASE, Cochrane Library (CENTRAL), ProQuest Dissertations & thesis (PQDT), RISS, and KoreaMed. We included the articles published from 2005 to September 2018. RoBANS checklist was used to evaluate the quality of the articles. Included articles, totally eight, were categorized according to the type of intervention. Results : The study design of the literature was varied from two-group randomized trial, quasi-experimental study, case-control trial, one group pre-post comparison study, and cross-sectional study. In the evidence level, 6 articles were included in level II (75%). The percentage of low risk of bias in each article ranged from 52.5%~87.5%. Four studies (50%) provided intervention based on virtual reality or virtual environment. The three (37.5%) provided intervention based on the protocol, and the other (12.5%) did wheelchair training. All studies reported significant effects of the intervention. Conclusion : This systematic review provided evidences to use proper intervention in the clinical fields. Various type of studies should be conducted to prove the effect of occupational therapy intervention for community mobility.