• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.407-416
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• 2019

This study is about a Customized Nursery School where working women can support work - life balance and a reduction in retirement or turnover. The research method is to identify the current status of Customized Nursery services and to recognize the recognition and need of the operation of Customized Nursery School. The importance of securing skilled nurses and preventing them from changing their jobs for the health and safety of people cannot be emphasized enough. A Customized Nursery School must be opened to reduce the retirement or change of jobs of working women nurses and to provide care for continuous work in three shifts from 365days to support the balance between the working mother and family. It is considered that nurses will put their children in relief when using retired nurses who have the ability to work 24hour rotation in a Customized Nursery School and when a Customized Nursery School be ran suited for the condition and demand of working women nurses, it is expected to reduce retirement and the change of jobs, also to give positive effect on marrige and family planning which would make improvement in low birthrate. To activate the Customized Nursery School, Creating a secure learning environment and qualification of educators great effort should be put. A program curriculum based on 'basic life and habits' should be the center of education. Continuous management and effort will need to be placed in continuous development of educators.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.45-50
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• 2017

One of the main reasons of information divide of multi-cultural family is caused by language barrier that is associated with low education level. In addition the social problem can be triggered by the information divide that may increase the gap of economic inequality. With respect to the overall capability of accessibility of digital devices and the level of data utilization, the parent of muiti-cultural family's level is inferior to that of the parents of an ordinary family. However the traditional learning contents management system for those parents is not appropriate to decease the gap of the information divide. To handle this problem, it is necessary to construct a customized learning contents management system that is used to support the education of the parents of multi-cultural family depending on the level of understanding the learning contents written in korean. In this paper we design the korean to korean translation based learning contents management system and show the result of its prototype.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.3
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• pp.677-688
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• 2017

The purpose of this study is to analyze the action learning lesson about the improvement process of the job support program of P university students. As a research method, we applied the related classes during the semester to the students who took courses in the course of 'Human Resource Development', which is a subject of P university, and analyzed the learner's reflection journal, interview data. As a result of the research, we went through the problem selection stage, the team construction and the team building stage. And then we searched for the root cause of the problem, clarified the problem, derived the possible solution, determined the priority and created the action plan. There are 10 solutions to the practical problems of poor job camps. Through two interviews with field experts it offered final solutions focused on promoting employment and Camp students participate in the management of post-employment into six camps. According to the first rank, job board integration, vendor selection upon student feedback, reflecting improved late questionnaire, public relations utilizing KakaoTalk, recruiting additional selection criteria, the camp provides recorded images in order. The results of this study suggest that the university's employment support program will strengthen the competitiveness of students' employment and become the basic data for the customized employment support program.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.67-75
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• 2022

Recently, e-learning has been attracting significant attention due to COVID-19. However, while e-learning has many advantages, it has disadvantages as well. One of the main disadvantages of e-learning is that it is difficult for teachers to continuously and systematically monitor learners. Although services such as personalized e-learning are provided to compensate for the shortcoming, systematic monitoring of learners' concentration is insufficient. This study suggests a method to evaluate the learner's concentration by applying machine learning techniques. In this study, emotion and gaze data were extracted from 184 videos of 92 participants. First, the learners' concentration was labeled by experts. Then, statistical-based status indicators were preprocessed from the data. Random Forests (RF), Support Vector Machines (SVMs), Multilayer Perceptron (MLP), and an ensemble model have been used in the experiment. Long Short-Term Memory (LSTM) has also been used for comparison. As a result, it was possible to predict e-learners' concentration with an accuracy of 90.54%. This study is expected to improve learners' immersion by providing a customized educational curriculum according to the learner's concentration level.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.59-66
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• 2022

This research conducted a study classifying gender of speakers by analyzing feature vectors extracted from the voice data. The study provides convenience in automatically recognizing gender of customers without manual classification process when they request any service via voice such as phone call. Furthermore, it is significant that this study can analyze frequently requested services for each gender after gender classification using a learning model and offer customized recommendation services according to the analysis. Based on the voice data of males and females excluding blank spaces, the study extracts feature vectors from each data using MFCC(Mel Frequency Cepstral Coefficient) and utilizes SVM(Support Vector Machine) models to conduct machine learning. As a result of gender classification of voice data using a learning model, the gender recognition rate was 94%.

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.91-98
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• 2000

Despite their wide application of some traditional project management techniques like the Program Evaluation and Review Technique, they lack of learning, one of important factors in many disciplines today, due to a static view for project progression. This study proposes a framework for estimation by loaming based on a Linear Bayesian approach. As a project Progresses, we sequentially observe the durations of completed activities. By reflecting this newly available information to update the distribution of remaining activity durations and thus project duration, we can implement a decision support system that updates e.g., the expected project completion time as well as the probabilities of completing the project within the due bate and by a certain date. By implementing such customized system, project manager can be aware of changing project status more effectively and better revise resource allocation plans.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.460-467
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• 2024

Targeted career development is critical for student success but is often lacking for underrepresented students at many public higher-education institutions due to insufficient career counseling resources. We propose an innovative career development tool leveraging Large Language Models (LLMs) to enhance student career prospects through three steps: (1) identifying relevant jobs by analyzing resumes, (2) pinpointing skill gaps using external resources such as classroom assignments, in addition to resumes, and (3) suggesting customized learning paths. Our tool accurately matches jobs in real-world settings, identifies true skill gaps while reducing false positives, and provides learning paths that receive high satisfaction scores from faculty. Future research will enhance the solution's capabilities by incorporating diverse external resources and leveraging advancements in LLM technology to better support early-stage career seekers.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.191-198
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• 2017

It is needed to support intelligent customized health information service for user convenience in PHR based Personal Health Care Service Platform. In this paper, we specify an ontology-based health data model for Personal Health Care Service Platform. We also design a knowledge search algorithm that can be used to figure out similar health record by applying machine learning and data mining techniques. Axis-based mining algorithm, which we proposed, can be performed based on axis-attributes in order to improve relevance of knowledge exploration and to provide efficient search time by reducing the size of candidate item set. And K-Nearest Neighbor algorithm is used to perform to do grouping users byaccording to the similarity of the user profile. These algorithms improves the efficiency of customized information exploration according to the user 's disease and health condition. It can be useful to apply the proposed algorithm to a process of inference in the Personal Health Care Service Platform and makes it possible to recommend customized health information to the user. It is useful for people to manage smart health care in aging society.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.249-254
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• 2019

The concept of 'Edu-Tech', which is rapidly reorganized around e-Learning, has been spreading along with the development of intelligent information technology according to the fourth industrial revolution such as Artificial Intelligence (AI), Internet of Things (IoT), BigData. Currently, leading companies are conducting online education services, but real-time two-way communication is difficult. In addition, in the case of off-line class, there are many students, and not only the time is limited, but also they often miss the opportunities to ask questions. In order to solve these problems, this paper develops a real - time interactive question and answer management system that can freely questions both on - line and off - line by combining the benefits of offline instant answers and the advantages of online openness. The developed system is a real-time personalized education system that enables the respondent to check the situation of the questioner in real time and provide a customized answer according to the inquirer's request. In addition, by measuring and managing the system usage time in seconds, the questioner and the respondent can efficiently utilize the system.