• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.445-457
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• 2022

The purposes of this research are to identify the subjective perception type of science and engineering graduates about good university and to analyze the differences of their undergraduates' experiences among types. Using Q methodology, 29 statements about a good university, reflecting on the previous research as well as quality assurance criteria in higher education, were administered to 16 science and engineering graduates for ranking using a Q-sort procedure. As a result 16 graduates were classified into three types according to their preference for 29 statements. Type 1, oriented student experience, recognized that a good university encourages students to participate in various activities and experiences. Type 2, oriented institutional outcomes, recognized that a good university is ranked high in criteria such as employment rate, research outcome, and entrance exam scores etc. Type 3, oriented educational activity, recognized that a good university is regarded as a community focusing on teaching and learning. Finally, considering the finding of the research, some pedagogical and administrational implications were suggested for quality improvement in higher education.

• Journal of The Korean Association of Information Education
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• v.6 no.1
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• pp.13-29
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• 2002

Mathematical curriculum has been developed based on learners' level and difficulties of contents. Succeed in solving problem in mathematics depends on the completion of the precedent learning. Thus, it is important to diagnose students beforehand. It is also important to develop problem-solving skills for students. In this thesis, Q&A system is proposed to help students learn various problem solving skills in mathematics. Although the system is currently applicable to mathematics, it can be applied to any other subjects.

• Clinical and Experimental Pediatrics
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• v.54 no.2
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• pp.55-63
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• 2011

Prader-Willi syndrome (PWS) is a complex multisystem genetic disorder that is caused by the lack of expression of paternally inherited imprinted genes on chromosome 15q11-q13. This syndrome has a characteristic phenotype including severe neonatal hypotonia, early-onset hyperphagia, development of morbid obesity, short stature, hypogonadism, learning disabilities, behavioral problems, and psychiatric problems. PWS is an example of a genetic condition caused by genomic imprinting. It can occur via 3 main mechanisms that lead to the absence of expression of paternally inherited genes in the 15q11.2-q13 region: paternal microdeletion, maternal uniparental disomy, and an imprinting defect. Over 99% of PWS cases can be diagnosed using DNA methylation analysis. Early diagnosis of PWS is important for effective long-term management. Growth hormone (GH) treatment improves the growth, physical phenotype, and body composition of patients with PWS. In recent years, GH treatment in infants has been shown to have beneficial effects on the growth and neurological development of patients diagnosed during infancy. There is a clear need for an integrated multidisciplinary approach to facilitate early diagnosis and optimize management to improve quality of life, prevent complications, and prolong life expectancy in patients with PWS.

• 전자공학회논문지 IE
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• v.48 no.4
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• pp.30-39
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• 2011

This paper proposed a new concept of information space called Smart Floor. Smart Floor is an intelligent space where a mobile robot can read and write specific information through Radio Frequency IDentification (RFID) tags which are mounted on Smart Floor to drive its goal position. RFID tag packaging technology is described for building Smart Floor. Also a mobile robot equipped passive RFID System with ultra high frequency (UHF) bandwidth has developed. The information that consists of an absolute position in the Smart Floor and desired direction saved on RFID tags is a simulated Q-value based on Q-learning algorithm. Proposed Smart Floor will be a proper method to communicate between space and robot.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.297-300
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• 2001

Artificial neural networks have become important tools in many fields. This paper describes a new algorithm fur training an RBF network. This algorithm has two main advantages: higher accuracy and a too stable learning process. In addition, it can be used as a good classifier in pattern recognition.

• Fashion & Textile Research Journal
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• v.18 no.6
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• pp.766-775
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• 2016

In the era of globalization and unlimited competition, Korean universities need a breakthrough in their education system according to the changing education landscape, such as lower graduation requirements to cultivate more multi-talented convergence leaders. While each student has different learning capabilities, which results in different performance and achievements in the same class, the uniform education that most universities are currently offering fails to accommodate such differences. Blended learning, synergically combining offline and online classes, enlarges learning space and enriches learning experiences through diversified tools and materials, including multimedia. Recently, universities are increasingly adopting video contents and on-offline convergence learning strategy. Thus, this study suggests a teaching method based on blended learning to more effectively teach existing pattern CAD and virtual CAD in the Apparel Pattern CAD class. To this end, this researcher developed a teaching-learning method and curriculum according to the blended learning phase and video-based contents. The curriculum consisted of 2D CAD (SuperAlpha: Plus) and 3D CAD (CLO) software learning for 15 weeks. Then, it was loaded to the Learning Management System (LMS) and operated for 15 weeks both online and offline. The performance analysis of LMS usage found that class materials, among online postings, were viewed the most. The discussion menu most accurately depicted students' participation, and students who did not participate in discussions were estimated to check postings less than participating students. A survey on the blended learning found that students prefer digital or more digitized classes, while preferring face to face for Q&As.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.187-193
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• 2013

In multi-agent systems, How to coordinate the behaviors of the agents through learning is a very important problem. The most important problems in the multi-agent system are to accomplish a goal through the efficient coordination of several agents and to prevent collision with other agents. In this paper, we propose a novel approach by using hybrid learning strategy. It is used hybrid learning strategy to control the multi-agent system efficiently by using the spatial relationship among the agents. Through experiments, we can see approximate faster the goal then other strategies and avoids collision among the agents.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.264-272
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• 2022

Human-robot cooperative tasks are increasingly required in our daily life with the development of robotics and artificial intelligence technology. Interactive reinforcement learning strategies suggest that robots learn task by receiving feedback from an experienced human trainer during a training process. However, most of the previous studies on Interactive reinforcement learning have required an extra feedback input device such as a mouse or keyboard in addition to robot itself, and the scenario where a robot can interactively learn a task with human have been also limited to virtual environment. To solve these limitations, this paper studies training strategies of robot that learn table balancing tasks interactively using deep reinforcement learning with human's facial expression feedback. In the proposed system, the robot learns a cooperative table balancing task using Deep Q-Network (DQN), which is a deep reinforcement learning technique, with human facial emotion expression feedback. As a result of the experiment, the proposed system achieved a high optimal policy convergence rate of up to 83.3% in training and successful assumption rate of up to 91.6% in testing, showing improved performance compared to the model without human facial expression feedback.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.87-106
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• 2024

Vehicle-to-Home (V2H) and Home Centralized Photovoltaic (HCPV) systems can address various energy storage issues and enhance demand response programs. Renewable energy, such as solar energy and wind turbines, address the energy gap. However, no energy management system is currently available to regulate the uncertainty of renewable energy sources, electric vehicles, and appliance consumption within a smart microgrid. Therefore, this study investigated the impact of solar photovoltaic (PV) panels, electric vehicles, and Micro-Grid (MG) storage on maximum solar radiation hours. Several Deep Learning (DL) algorithms were applied to account for the uncertainty. Moreover, a Reinforcement Learning HCPV (RL-HCPV) algorithm was created for efficient real-time energy scheduling decisions. The proposed algorithm managed the energy demand between PV solar energy generation and vehicle energy storage. RL-HCPV was modeled according to several constraints to meet household electricity demands in sunny and cloudy weather. Simulations demonstrated how the proposed RL-HCPV system could efficiently handle the demand response and how V2H can help to smooth the appliance load profile and reduce power consumption costs with sustainable power generation. The results demonstrated the advantages of utilizing RL and V2H as potential storage technology for smart buildings.