• 제어로봇시스템학회논문지
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• 제19권12호
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• pp.1094-1098
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• 2013

AVI (Automatic Vision Inspection) systems automatically detect defect features and measure their sizes via camera vision. Defect detection is not an easy process because of noises from various sources and optical distortion. In this paper the acquired images from a TFT panel are enhanced with the adoption of an HVS (Human Visual System). A human visual system is more sensitive on the defect area than the illumination components because it has greater sensitivity to variations of intensity. In this paper we modified an MTF (Modulation Transfer Function) in the Wavelet domain and utilized the characteristics of an HVS. The proposed algorithm flattens the inner illumination components while preserving the defect information intact.

• Asia pacific journal of information systems
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• 제22권1호
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• pp.1-27
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• 2012

Asynchronous Learning Networks (ALN) has become a kind of prevalent information systems to supplement or even substitute a traditional classroom face-to-face teaching method. In this paper, we investigate the impacts of types of courses, lecturers' instruction models and computer self-efficacy on the ALN effectiveness in teaching from lecturers' perspective. We conducted survey to professors in various faculties such as School of Computing, Faculty of Arts, Science, Business, Law and Engineering in the National University of Singapore. According to the responses from ninety-eight professors, instruction modes positively influence the usage of ALN; types of course and lecturers' computer-efficacy influence lectures' satisfaction of ALN in teaching. Both the usage of ALN and satisfaction of ALN positively influence the effectiveness of ALN. The results of this study fill the gap of ALN researches in education by examining it from lecturers' perspective and enable schools to improve their implementation of ALN systems based on our findings.

• 대한기계학회논문집 C: 기술과 교육
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• 제2권2호
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• pp.125-130
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• 2014

기계제도 교과목은 기계공학 교육에서 매우 중요한 위치를 차지하고 있음에도 불구하고, 이론교육과 실습을 병행해야 하고 CAD 소프트웨어 기능 교육도 포함하고 있어서, 내실 있게 운영되기 힘든 교과목이다. 서울과학기술대 기계시스템디자인공학과에서는 기계제도 교육에 PBL 수업 방식을 도입하여 적용하고 있다. 학생들은 이론 수업에서 학습한 기계도면 작성의 규칙, 도면 독해와 작성법 등을 팀별 PBL 과제 수행을 통해 실제로 적용해 봄으로써, 도면 판독과 작성능력을 체득하게 되고 CAD 소프트웨어 활용 기능도 충실히 연습하게 된다. 본 논문에서는 그 동안 시도되었던 다양한 PBL 과제와 교육방법을 소개하고 그 효과를 분석해 보았다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.814-819
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• 2004

Soft engineering, based on symbiotic coexistence of human, machines and environment, is a new engineering field to explore the proper technology and the proper way of engineering. To explore soft engineering intents easily, various robot projects at Pusan National University conducted are presented. Thought experiment, interactive e-leaning, rapid prototyping engineering, biomimciry, tangibility, and ubiquity are concepts to be explored. Thought experiments projects are organized and performed, which include robot assembly game, Turing test, and robotics in science fiction. "Junk robot project" and "ubiquitous Pusan National University (u-PNU) project" have been organized. Also, bug robot project, interactive robot project, and interactive emotional robot projects are introduced. Weekly science fiction films are shown and discussed.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 1995년도 창립기념학술대회
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• pp.38-43
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• 1995

Effect of visualization as a tool for the education of fluid dynamics is mainly discussed. Visualized images are much more understandable compared to the explanation using equations and texts. Several examples are presented to clarify this statement. Then, the software system for teaching fluid dynamics using the results by the numerical simulation is discussed. Two important issues on what is needed in the system are given. First, such systems should be capable of animating images. Second, such systems should be interactively used by students. Changing parameters, coefficients, equations, etc. themselves and watching the difference are important for them to understand the nature of physics underlying the equations. The teaching system with visualization is no doubt a good tool for introducing fluid dynamics.

• Industrial Engineering and Management Systems
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• 제15권4호
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• pp.298-306
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• 2016

The gap between universities, scientific centers and industrial-production units is one of higher education concerns. In academic entrepreneurship, the university work simultaneously in education, research and entrepreneurship. The universities play a key and important role in providing educational opportunities in economic development. This research aims to develop and expand science as well as help managers to explain the process of technology transfer in entrepreneurial university. This research is applied-developmental type and on the other hand, data driven theories have been used in this study. Current model is generally tried to meanwhile compensate previous shortcomings, include some strengths such as considering domestic factors of Iran as well as update effective factors on the process of technology transfer. Finally the suggested model has been compared with existing well-known models that each one of those models have some drawbacks which have been tried to be minimized in suggested model as much as possible.

• 공학교육연구
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• 제9권4호
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• pp.28-45
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• 2006

지식기반 사회로 변화에 따라 교육에 사이버 공간을 도입하는 것은 피할 수 없는 현실이 되었다. 따라서, 이러한 환경을 제공하기 위해 많은 e러닝(e-learning) 시스템이 개발되고 있다. 그러나 현재 개발된 많은 LCMS(Learning Contents Management System)는 세계 e러닝 표준인 SCORM(Sharable Contents Object Reference Model)과 한국교육학술정보원의 전국교육정보공유체계인 KEM을 기반으로 하고 있지 않아, 각기 다른 환경에서 개발된 학습콘텐츠를 공유하기 어렵다. 또한 국립중앙과학관은 비정규교육기관으로 초,중,고에서 개별적으로 해결하기 어려운 과학분야의 교육 전시물을 실제로 혹은 사이버공간에서 제공하고 있다. 이를 통합하여 관리하며 학교 교육에도 활용될 수 있도록 선생님, 학습자, 운영자, 교수자 모듈로 분리하여 기능을 제공하면서 서로 연동되는 시스템이 필요하게 되었다. 이에 이 논문에서는 한국교육학술정보원의 전국교육정보공유체계인 KEM(Korea Educational Metadata)과 세계표준인 SCORM 기반의 선진화 된 LMS(Learnig Management System) 및 LCMS 시스템인 국립중앙과학관 사이버과학교실 웹포털 사이트를 설계 및 구현하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.805-808
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• 2005

We constructed an eddy current tester as education programs of electronics and computers. The tester is designed based on frequency counting of square-waves digitally, where information of phase is lost, but which is very compact. Main controller of the tester is PIC16HC84A. Other parts, detection-coils, oscillators, and stabilizers are made by us. The tester is able to detect change of order(-5) in waves of 1MHz, whose function is kept in several minutes. The measurement period is 10-20 msec in the several minutes. Using the tester, we observed narrow gaps of copper/aluminum wires, and detected resistivity difference of 1.7 or 2.7 [$10^{-8}$ $ohm^{\ast}m$].

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권10호
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• pp.3230-3255
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• 2022

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.

• Smart Structures and Systems
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• 제32권3호
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.