• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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• pp.1061-1065
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• 2004

The knowledge-based society of the 21st century requires effective education and learning methods in each professional field because the development of human resource determines its competence more than any other factors. It is highly desirable to develop an intelligent tutoring system, which meets ever increasing demands of education and learning. Such a system should be adaptive to each individual learner's demands as well as the continuously changing state of the learning process, thus enabling the effective education. The development of a learning environment based on learner modeling is necessary in order to be adaptive to individual learning variants. An intelligent learning environment is being developed targeting the heritage education, which is able to provide a customized and refined learning guide by storing the content of interactions between the system and the learner, analyzing the correlations in learning situations, and inferring the learning preference from the learner's learning history. This paper proposes a heritage learning system of Bulguksa temple, integrating the ontology-based learner modeling and the learning preference which considers perception styles, input and processing methods, and understanding process of information.

• Asian Pacific Journal of Cancer Prevention
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• 제13권9호
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• pp.4593-4595
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• 2012

Introduction: Mobile learning enables users to interact with educational resources while in variable locations. Medical students in residency positions need to assimilate considerable knowledge besides their practical training and we therefore aimed to evaluate the impact of using short message service via cell phone as a learning tool in residents of Obstetrics and Gynecology in our hospital. Methods: We sent short messages including data about breast cancer to the cell phones of 25 residents of gynecology and obstetrics and asked them to study a well-designed booklet containing another set of information about the disease in the same period. The rate of learning derived from the two methods was compared by pre- and post-tests and self-satisfaction assessed by a relevant questionnaire at the end of the program. Results: The mobile learning method had a significantly better effect on learning and created more interest in the subject. Conclusion: Learning via receiving SMS can be an effective and appealing method of knowledge acquisition in higher levels of education.

• 한방안이비인후피부과학회지
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• 제23권2호
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• pp.163-173
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• 2010

Objectives : The purpose of this study is to survey the needs of oriental medical students about the existing teaching methods and to investigate satisfaction of student-oriented teaching & learning method on the oriental medical ophthalmology & otolaryngology class. Methods : 1. Oriental medical ophthalmology & otolaryngology were studied by student-oriented teaching method with 36 persons( 4th grade, school of Oriental medicine). 2. The satisfaction of school lesson was surveyed with questioning paper before & after applying student-oriented teaching & learning method. Results : 1. 88.9% students replied that the general teaching method is a lecture at school of oriental medicine. 2. They replied that Problem based learning(36.1%) & Team based learning(22.2%) are more effective teaching methods than lecture(19.4)%). 3. 66.6% students replied about the necessity of improvement of major subject's teaching method. Conclusions : 1. The interest & understanding degree were improved compare with other class by lecture. 2. It was positive about the interaction with professor and students(64.8%). 3. It was positive about the diagnosis and treatment of patients in the future(64.7%). 4. It was negative about the present national examination(67.6%). 5. Meditation was helpful at classwork(64.7%).

• 한국과학교육학회지
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• 제23권2호
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• pp.131-143
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• 2003

과학이 윤리적 특성을 지니고 있다는 말은 과학의 본성을 제대로 이해시키기 위해서는 각급 학교에서 과학의 윤리적 특성도 교수해야 함을 뜻한다. 외국에는 생명공학과 관련된 윤리적 문제를 다룰 것을 강조 한 교사용의 생명공학 지도서와 과학의 윤리적 측면에 관한 교수-학습의 원리와 방법을 제시한 교재가 있다. 현재 우리나라에서도 과학의 윤리적 특성에 관한 교육의 필요성이 제기되고 있으며, 그 교수-학습의 원리 방법 자료 등도 연구 개발되어 있다. 그러나 과학교육 현장에서는 과학의 윤리적인 특성에 관한 교수-학습이 이루어지지 않고 있다. 이는 그 교수-학습 방법과 자료를 현장에 적용할 방안이 미흡하기 때문이기도 하다. 이 연구는 특별히 연구자들이 3년간 수행한 연구의 결과를 바탕으로 과학의 윤리적 특성 교수-학습 방법과 자료를 현장에 투입할 방안을 제시할 목적으로 수행하였다.

• 문화기술의 융합
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• 제8권1호
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• pp.545-550
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• 2022

딥러닝은 많은 데이터를 필요로 한다는 것은 이미 널리 알려져있다. 이를 통해, 딥러닝에 쓰이는 신경망의 수없이 많은 parameter들을 학습시킨다. 학습과정에는 데이터뿐 아니라, 각 데이터별로 전문가가 입력한 label이 필요한 경우가 대부분인데, 이 label을 얻는 과정은 시간과 자원 소비가 심하다. 이 문제를 완화하기 위해, few-shot learning, self-supervised learning, weak-supervised learning등이 연구되어오고 있다. 본 논문에서는, label을 상대적으로 적은 노력으로 수행하기 위한 연구들의 동향을 살펴보고, 앞으로의 개선 방향을 제시하도록 한다.

• Educational Technology International
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• 제19권2호
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• pp.175-197
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• 2018

Flipped classroom (FC) has gained attention as an active learning approach. Designing effective out-of-class activities to help prepare students for in-class activities is fundamental for successful implementation of FC. This study investigated the effectiveness of Goal-Based Scenarios (GBS) for out-of-class learning in FC. Four out of twelve units in a medical humanities course for Year 2 medical students was redesigned into a FC format, where e-learning modules were designed using a GBS approach for out-of-class activities and classroom debates were implemented for in-class activities. The other eight units were delivered in a conventional classroom debate format, which included reading text materials as pre-class assignments. A formative evaluation study was conducted using questionnaires and interview methods and students' academic achievements were evaluated by comparing their pre- and post-test scores between FC and conventional units. Students had positive perceptions of the e-learning modules in GBS approach and preferred the structure of learning in the FC format. Students' pre-test scores were slightly higher in the FC units, yet their post-test scores were comparable with conventional units. This study illustrates students' perceptions that the learning was bettered structured in FC and that the out-of-class learning using the GBS approach helped them better prepared for in-class activities.

• 산업경영시스템학회지
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• 제47권2호
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• pp.116-123
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• 2024

In the manufacturing industry, dispatching systems play a crucial role in enhancing production efficiency and optimizing production volume. However, in dynamic production environments, conventional static dispatching methods struggle to adapt to various environmental conditions and constraints, leading to problems such as reduced production volume, delays, and resource wastage. Therefore, there is a need for dynamic dispatching methods that can quickly adapt to changes in the environment. In this study, we aim to develop an agent-based model that considers dynamic situations through interaction between agents. Additionally, we intend to utilize the Q-learning algorithm, which possesses the characteristics of temporal difference (TD) learning, to automatically update and adapt to dynamic situations. This means that Q-learning can effectively consider dynamic environments by sensitively responding to changes in the state space and selecting optimal dispatching rules accordingly. The state space includes information such as inventory and work-in-process levels, order fulfilment status, and machine status, which are used to select the optimal dispatching rules. Furthermore, we aim to minimize total tardiness and the number of setup changes using reinforcement learning. Finally, we will develop a dynamic dispatching system using Q-learning and compare its performance with conventional static dispatching methods.

• 정보보호학회논문지
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• 제18권6A호
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• pp.115-127
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• 2008

Native API(Application Programming Interfaces)는 관리자 권한에서 수행되는 system call의 일종으로 관리자 권한을 획득하여 공격하는 다양한 종류의 악성코드를 탐지하는데 사용된다. 이에 따라 Native API의 특징을 기반으로한 탐지방법들이 제안되고 있으며 다수의 탐지방법이 교사학습(supervised learning) 방법의 기계학습(machine learning)을 사용하고 있다. 하지만 Anti-Virus 업체의 분류기준은 Native API의 특징점을 반영하지 않았기 때문에 교사학습을 이용한 탐지에 적합한 학습 집합을 제공하지 못한다. 따라서 Native API를 이용한 탐지에 적합한 분류기준에 대한 연구가 필요하다. 본 논문에서는 정량적으로 악성코드를 분류하기 위해 Native API를 기준으로 악성코드를 퍼지 군집화하여 재그룹화하는 방법을 제시한다. 제시하는 재그룹화 방법의 적합성은 기계학습을 이용한 탐지성능의 차이를 기존 분류방법을 결과와 비교하여 검증한다.

• International Journal of Computer Science & Network Security
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• 제21권5호
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• pp.243-255
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• 2021

This study aimed to identify the effectiveness of the use of distance-evaluation tools and methods among students with learning difficulties from the teachers' point of view, to achieve this goal. A scale was built, and the psychometric characteristics were validated. It consisted, in its final form, of 17 items distributed on four axes, in addition to three open questions. It was applied to a random sample of (149) teachers of students with learning difficulties in Makkah Region. The results showed that teachers' keenness to encourage students with learning difficulties, so that they would not feel frustrated with the distance learning process. It was also evident that teachers did not use achievement portfolios in the evaluation process. In connection with the appropriate evaluation methods, the majority indicated the use of work sheets and visual evaluation methods that rely on audio and visual skills, such as presenting videos, pictures, audio and games, and applying short objective tests. Among the proposals to improve evaluation methods and tools: Individual evaluation, attention to individual treatment, obligating personal attendance of students to school, splitting the required tasks, and not increasing the skills required to be mastered. As for the obstacles that teachers face: Lack of time, difficulty in communicating with students with distance learning difficulties and problems related to the Internet such as interruption, weakness, or lack of availability.

• Wind and Structures
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• 제34권6호
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• pp.511-523
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• 2022

Detecting the icing on wind turbine blades built-in cold regions with conventional methods is always a very laborious, expensive and very difficult task. Regarding this issue, the use of smart systems has recently come to the agenda. It is quite possible to eliminate this issue by using the deep learning method, which is one of these methods. In this study, an application has been implemented that can detect icing on wind turbine blades images with visualization techniques based on deep learning using images. Pre-trained models of Resnet-50, VGG-16, VGG-19 and Inception-V3, which are well-known deep learning approaches, are used to classify objects automatically. Grad-CAM, Grad-CAM++, and Score-CAM visualization techniques were considered depending on the deep learning methods used to predict the location of icing regions on the wind turbine blades accurately. It was clearly shown that the best visualization technique for localization is Score-CAM. Finally, visualization performance analyses in various cases which are close-up and remote photos of a wind turbine, density of icing and light were carried out using Score-CAM for Resnet-50. As a result, it is understood that these methods can detect icing occurring on the wind turbine with acceptable high accuracy.