• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.239-241
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• 2022

Currently, in Korea, a multi-seater lane (HOV) and a designated lane system are being implemented to solve traffic congestion. However, in both systems, it is difficult to crack down on cases of violations without permission, so people are required to be assigned to areas that want to crack down. In this process, manpower and budget are inefficiently consumed. To compensate for these shortcomings, we propose the development of an integrated enforcement system through YOLO, a deep learning object recognition model. If the two systems are implemented and integrated using YOLO, they will have advantages in terms of manpower and budget over existing systems because only data learning and system maintenance are considered. In addition, in the case of violations in which it is difficult for the existing unmanned system to crack down, the effect of increasing the crackdown rate through continuous learning can be expected.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.354-359
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• 2006

The analysis of large-scale water resources systems is often complicated by the presence of multiple reservoirs and diversions, the uncertainty of unregulated inflows and demands, and conflicting objectives. Reinforcement learning is presented herein as a new approach to solving the challenging problem of stochastic optimization of multi-reservoir systems. The Q-Learning method, one of the reinforcement learning algorithms, is used for generating integrated monthly operation rules for the Keum River basin in Korea. The Q-Learning model is evaluated by comparing with implicit stochastic dynamic programming and sampling stochastic dynamic programming approaches. Evaluation of the stochastic basin-wide operational models considered several options relating to the choice of hydrologic state and discount factors as well as various stochastic dynamic programming models. The performance of Q-Learning model outperforms the other models in handling of uncertainty of inflows.

• Asia pacific journal of information systems
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• v.17 no.1
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• pp.33-58
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• 2007

Quiet often, an organization tries to grapple with inconsistent and partial information to generate relevant information to support decision making and action. As such, an organization scans the environment interprets scanned data, executes actions, and learns from feedback of actions, which boils down to computational interpretations and learning in terms of machine learning, statistics, and database. The ExOM proposed in this paper is geared to facilitate such knowledge discovery found in large databases in a most flexible manner. It supports a broad range of learning and classification styles and integrates them with traditional database functions. The learning and classification components of the ExOM are tightly integrated so that learning and classification of objects is less burdensome to ordinary users. A brief sketch of a strategy as to the expressiveness of terminological language is followed by a description of prototype implementation of the learning and classification components of the ExOM.

• International Journal of Costume and Fashion
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• v.18 no.2
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• pp.29-43
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• 2018

Service-learning is a form of experiential education in which students participate in organized activities and develop a sense of civic responsibility while acquiring content knowledge of the discipline. The purpose of this study was first, to examine the underlying theories and principles of service-learning, and second, to present a case of systemic implementation of problem-based service-learning into a Product Innovation and Management class in higher education. The New Product Development for an E-Commerce Small Business project was developed for a community partner, BevShots, reflecting the needs of the firm, and was tightly woven into the course content. Students' participation in the project had a significant effect on increasing their awareness of the needs in the community and identifying their roles as citizens as well as enhancing their content knowledge learning. The community partner also received benefits for his business by participating in the project. Through this study, we aim to inspire fashion design and merchandising educators to implement service-learning projects/classes in the curriculum.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.441-452
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• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

• Educational Technology International
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• v.16 no.1
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• pp.1-30
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• 2015

The purpose of this research is to propose a novel social LMS developed for group collaborative learning with a think-aloud tool integrated for sharing cognitive processes in order to improve group collaborative learning performance. In this developmental research, the system was designed with three critical elements: the think-aloud element supports learners through shared cognition, the social network element improves the quality of collaborative learning by forming a structured social environment, and the learning management element provides a understructure for collaborative learning for student groups. Moreover, the three critical elements were combined in an educational context and applied in three directions.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.87-88
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• 2014

• Journal of Information Technology Services
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• v.20 no.5
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• pp.147-158
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• 2021

The digital transformation and COVID-19 are also causing major changes in teaching-learning methods. The biggest change is the spread of remote training and the emergence of various innovative learning platforms. Distance education has been criticized for not meeting technology trends and field demands..However, the problem of distance education is being solved through a system that supports various interactions and collaborations and supports customized learning paths. The researcher conducted a case study on domestic and foreign learning platforms that provide non-face-to-face ICT education. Based on the case study results, the researcher presented the functional characteristics of a learning platform that effectively supports non-face-to-face learning. In common, these sites faithfully supported the basic functions of the information system. In addition to learning progress check and learning guidance, some innovative learning platforms were providing differentiated functions in practice support, performance management, mentoring, learning data analysis, curation provision, and CDP support. Most learning platforms supported one-way, superficial interaction. If the platform effectively supports a variety of learning experiences and provides an integrated learning experience thanks to the development of IT technology, user satisfaction with the learning platform, intention to continue learning, and achievement will increase.

• Journal of Korean Home Economics Education Association
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• v.26 no.1
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• pp.1-16
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• 2014

The purpose of this research is to develop a theme-based integrated teaching/learning plan in clothing, food, and housing in home economics and to apply the developed process in classes for evaluation in order to identify the suitability in schools. The theme-based integrated teaching/learning plan developed on the basis of textbooks consist four sub-themes; choosing($1^{st}$ and $2^{nd)$ lessons), using($3^{rd}$ lesson), processing ($4^{th}$ lesson), and alternatives($5^{th}$ and $6^{th}$ lessons) under the main theme of 'green family life'. The results from 20 individual and group activities showed that the students actively solved the problems when the presented cases were related to their own lives or experiences. The opportunity to implement green life through activities motivated students' willingness to proceed in real life. However, it is vital to assist integrated thinking through various examples before beginning due to students with difficulties connecting the issue from one area to the other during the problem-focused activity. The students' ability to solve the activity workbook had been improved as the lessons continued. From the survey questions on the theme-based integrated lessons, all items associated with integration of clothing, foods, and housing were positively responded. Also, questions regarding general understanding, suitability and satisfaction on the teaching/learning process were marked positive. The conclusion could be that the integrated theme related to clothing, food, and housing in our life would be appropriate for green family life. The theme-based integrated teaching/learning plan is effective in understanding the occurrence of green family life in relation with clothing, food, and housing, identifying the practical ideas implementing green life in those areas, and improving the integrated ability to solve the green life related problems. However, this research has its weakness in generalizing the results due to its limited survey respondents and post-evaluation being the only assessment conducted.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.109-112
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• 2006

본 논문은 클래스들의 대표값들과 입력 벡터와의 거리를 사용한 새로운 퍼지 학습법칙을 제안한다. 이 새로운 퍼지 학습을 supervised IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망에 적용하였다. 이 새로운 신경회로망은 안정성을 유지하면서도 유연성을 가지고 있다. iris 데이터를 사용하여 테스트한 결과 supervised IAFC 신경회로망 4는 오류 역전파 신경회로망과 LVQ 알고리즘보다 성능이 우수하였다.