• Journal of Gifted/Talented Education
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• v.24 no.6
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• pp.897-916
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• 2014

In this study, we presented a teaching-learning method that can apply process-focused assessment for mathematical creativity of problem solving process of the gifted student, By necessity of appropriate teaching-learning program development to the level and ability of students who belong to high school gifted classes and courses evaluation for students who participated in education programs for the gifted. In the construction implementation process of students utilizing a kind of teaching-learning software, GeoGebra. We analyzed process of a variety of creative constructing figures using interfaces of GeoGebra and algebraic calculation. Utilizing 'Construction Protocol' and 'Navigation Bar' of GeoGebra, We identified computer languages, construction order, run times used in construction process of individual student and found mathematical creativity of students in the process. Comparing this result with prerequisite learning degree of individual student, We verified that this teaching-learning method can apply at the high school gifted classes as well as institutes for the gifted education in the city office.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2001.04a
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• pp.168-168
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• 2001

제지공정의 지절 현상은 많은 공정 변수들이 복합적으로 작용하여 발생하는 가장 큰 공정 트러블 중의 하나이다. 지절은 생산량 감소 뿐만 아니라 발생 후 공정의 복구 와 정리, 생산재가동 및 공정의 재안정화를 위해 많은 시간과 비용, 그리고 노력이 투 입되어야 하므로 공정의 효율과 생산성을 크게 저하시키는 요인이다. 그러나 지절 현상 의 복잡성 때문에 이에 대해 쉽게 접근하거나 해결하지 못하고 있는 것이 현실이지만 그 필요성은 더욱 더 증대되고 있다. 본 연구에서는 최근 들어 각종 산업분야에서 복잡 한 공정상의 결점 발견 및 진단에 효과적이라고 인정받고 있는 예측 분석기법인 인공 신경망(artificial neural network) 시율레이션과 일반적인 통계기법 중의 하나인 주성분 분석을 이용하여 제지 공정의 지절 현상의 검토 가능성을 타진하였다. 인공신경망이란 인간두뇌에서 일어나는 자극-반응-학습과정을 모사하여 현실세계에 존재하는 다양한 현상들의 업력벡터와 출력상태 간의 비선형 mapping올 컴퓨터 시율 레이션을 통하여 분석하고자 하는 기법으로, 여러 가지 현상들을 학습을 통해서 인식하 는 신경망 내의 신경단위들이 병렬처리에 의해 많은 양의 자료에 대한 추론이나 판단 을 신속하고 정확하게 해주는 특징이 있으며 실시간 패턴인식이나 분류 응용분야에도 매우 매력적으로 이용되고 있는 방법이다. 이러한 인공 신경망 기법 중에서도 본 연구 에서는 퍼셉트론의 한계점을 극복하기 위하여 입력총과 출력층에 한 개 이상의 은닉층 ( (hidden layer)을 사용하여 다층 네트워으로 구성하고, 모든 입력패턴에 대하여 발생하 는 오차함수를 최소화하는 방향으로 연결강도를 조정하는 back propagation 학습 알고 리즘을 사용하였다. 지절의 원인으로 추정 가능한 공정인자들을 변수로 하여 최적의 인 공신경망을 구축하기 위해 학습률과 모멘트 상수의 변화 및 은닉층의 수와 출력층의 뉴런 수를 조절하는 동의 작업을 거쳐 네트워크의 정확도가 높은 인공신경망을 설계하 였다. 또한 이러한 인공신경망과의 비교분석을 위해 동일한 공정 데이터들올 이용하여 보편적으로 사용하는 통계기법 중의 하나인 주성분회귀분석을 실시하였다. 주성분 분석은 여러 개의 반응변수에 대하여 얻어진 다변량 자료의 다차원적인 변 수들을 축소, 요약하는 차원의 단순화와 더불어 서로 상관되어있는 반응변수들 상호간 의 복잡한 구조를 분석하는 기법이다. 본 발표에서는 공정 자료를 활용하여 인공신경망 과 주성분분석을 통해 공정 트러블의 발생에 영향 하는 인자들을 보다 현실적으로 추 정하고, 그 대책을 모색함으로써 이를 최소화할 수 있는 방안을 소개하고자 한다.

• Journal of Engineering Education Research
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• v.6 no.1
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• pp.15-21
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• 2003

This paper reports the results of course restructuring employing design projects in an introductory mechanics of materials course at Pennsylvania State University. Unlike traditional lecture courses, students were encouraged to learn the rudiments of mechanical design and how materials standards, economics, manufacturing, environmental, legal (liability) and societal (safety) concerns relate to design. Through conducting collaborative design projects, the instructors helped students to acquire more advanced skills such as team-based decision making, integration and establishment of criteria, use of modern design theory, consideration of alternative solutions, and application of realistic constraints. In order to examine the impact of new course changes on students' learning, a survey was conducted in 1998 Fall semester. According to the results of survey analyses, students reported high values on this introductory mechanics of materials course. However, they did not give high values on the design projects. Rather, they preferred lecture sessions. Additionally, it was also found that students who earned higher grades from a prerequisite course(statics) showed lower values on the design projects. Implications for engineering educators and suggestions for future research studies were discussed.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1363-1372
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• 2021

Red tide, defined as the major reproduction of harmful birds, has the characteristics of being generated and diffused in a wide area. This has limitations in detection only with the existing investigation method. Therefore, in this study, red tide was detected using a remote sensing technique. In addition, it was intended to increase the accuracy of detection by using optical characteristics, not just the concentration of chlorophyll. Red tide mainly occurs on the southern coast where sea signals are complex, and the main red tide control species on the southern coast is Cochlodinium polykirkoides. Therefore, it was intended to secure objectivity by reflecting features that could not be found depending on the researcher's observation and experience, not limited to visual judgment using machine learning techniques. In this study, support background machines and random forest were used among machine learning models, and as a result of calculating accuracy as performance evaluation indicators of the two models, the accuracy was 85.7% and 80.2%, respectively.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.355-360
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• 2021

COVID-19 has brought major changes to school education. Although it was attempted to guarantee students' right to learn through romote learning, the limitations of remote learning compared to face-to-face classes were clear. Nevertheless, the method of remote learning is undoubtedly a learning method that needs to be continuously developed in terms of being able to consider separated time and space and enabling learners to learn individually and autonomously. Therefore, in this study, real-time romote learning cases were analyzed at the elementary school stage, and problems in real-time remote classes were discovered and improved through teacher interviews. The problems with real-time remote classes in elementary school unit classes examined through examples are: First, that the proportion of teacher activity is high due to the anxiety of the unfamiliar environment of remote classes, and second, even though it is a real-time interactive class, it It was impossible to provide feedback. As a solution to this, it is necessary to consider the basic class steps (introduction-deployment-organization) and the use of class tools to provide appropriate communication and feedback was suggested.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.619-624
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• 2023

As the importance of digital competency education is highlighted, this study is a case study on block coding classes for elementary school students during vacation for the purpose of bridging the information education gap among students. The purpose of this study is to design and operate a play-centered block coding class program and find out if it is effective in improving students' interest. As a result of completing the teaching plan through the second consultation and revision, running the class, and analyzing the change in learning interest of the students through the t-test, the play-oriented block coding class designed in this study was effective in improving students' interest. In addition, it was possible to discover interesting elements such as student-led learning process and immersion through realistic play activities, friendship, collaboration, and communication through group activities. This study is significant in suggesting a plan to increase learning interest for students who are new to coding.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.21-29
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• 2024

With the development of ICT and network, security management of IT infrastructure that has grown in size is becoming very difficult. Many companies and public institutions are having difficulty managing system and network security. In addition, as the complexity of hardware and software grows, it is becoming almost impossible for a person to manage all security. Therefore, AI is essential for network security management. However, since it is very dangerous to operate an attack model in a real network environment, cybersecurity emulation research was conducted through reinforcement learning by implementing a real-life network environment. To this end, this study applied reinforcement learning to the network environment, and as the learning progressed, the agent accurately identified the vulnerability of the network. When a network vulnerability is detected through AI, automated customized response becomes possible.

• Journal of Information Technology and Architecture
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• v.11 no.4
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• pp.485-494
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• 2014

This study has been conducted with $1^{st}$ grade elementary students where the purpose was to develop students' interest in themselves and to cultivate their self-awareness as the first step toward establishing a suitable course and career for their start-up features. Students have conducted the cyber study through "Incheon e-School" and cyber reading "Booktoby" and recorded three types of outcomes - affective self-awareness, social self-awareness and intelligence self-awareness - on each side of the Pyramid, thus constructing their own self-awareness pyramids of the system. According to the results, after such activities were undertaken during the $1^{st}$ semester, their opinion toward their self-control and study ability has improved as much as about 67% compared to that of the beginning of the last semester. Regarding the effects of cyber reading, their interest, spontaneity and understanding toward reading books have also improved as much as 54% and 50%, 33% respectively along with positive answers of as much as 75% with regard to self-awareness. When it comes to the results of the SCI-II test (used to evaluate self-awareness), the total average has improved by as much as 3 points and the three components of self-awareness - affective, social, and intelligence - have improved by as much as 4points, 1point, and 5points, respectively.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.483-488
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• 2018

Far field input utterance is one of the major causes of performance degradation of speaker verification systems. In this study, we used teacher student learning framework to compensate for the performance degradation caused by far field utterances. Teacher student learning refers to training the student deep neural network in possible performance degradation condition using the teacher deep neural network trained without such condition. In this study, we use the teacher network trained with near distance utterances to train the student network with far distance utterances. However, through experiments, it was found that performance of near distance utterances were deteriorated. To avoid such phenomenon, we proposed techniques that use trained teacher network as initialization of student network and training the student network using both near and far field utterances. Experiments were conducted using deep neural networks that input raw waveforms of 4-channel utterances recorded in both near and far distance. Results show the equal error rate of near and far-field utterances respectively, 2.55 % / 2.8 % without teacher student learning, 9.75 % / 1.8 % for conventional teacher student learning, and 2.5 % / 2.7 % with proposed techniques.

• Journal of The Korean Association For Science Education
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• v.44 no.2
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• pp.155-166
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• 2024

This study investigated and compared orientations toward scientific inquiry learning among general and science-gifted elementary students. It also investigated and compared the relationship between their orientations toward scientific inquiry learning and their coping strategies for anomalous situations. To realize this, 61 general elementary students and 53 science-gifted elementary students in Seoul were selected, and questionnaires were administered to investigate their orientations toward scientific inquiry learning and coping strategies for anomalous situations. In addition, semi-structured in-depth interviews were conducted individually with some of the general and science-gifted students. The results showed that among orientations toward scientific inquiry learning, regardless of grade level, the general students were most likely to possess 'concept understanding' and second most likely to exhibit 'scientific practice'. On the other hand, the science-gifted students demonstrated the highest frequency of 'scientific practice', with 'concept understanding' and 'complexity' also being relatively common. 'Activity driven' was found only among some of the general students and 'engineering practice' was found only among some of the science-gifted students. 'Process skills' were not found. No clear relationships between orientations toward scientific inquiry learning and coping strategies for anomalous situations were found. However, some differences in the choice of coping strategies for anomalous situations between the general and science-gifted students were discovered, even when they had the same orientations toward scientific inquiry learning. The educational implications of these findings were discussed.