• Journal of Institute of Control, Robotics and Systems
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• v.19 no.12
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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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• v.22 no.1
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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.

• Transactions of the KSME C: Technology and Education
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• v.2 no.2
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• pp.125-130
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• 2014

Although the subject of Mechanical Drawing is very important in mechanical engineering education, it is hard to teach the subject effectively, because it needs to carry out lectures and labs in parallel and needs substantially large portion of CAD lab time. In the department of Mechanical Systems and Design Engineering of SNUST, Project Based Learning is adopted to teach the subject of Mechanical Drawing. In this course, students experience to read and to draw drawings through the PBL project after the lectures on mechanical drawings. In this way, they can learn by heart the drawing skills and the operation of CAD software tools. In this paper, various PBL projects and teaching methods carried out in recent years are presented and the effects of the projects are discussed.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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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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• 1995.04a
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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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• v.15 no.4
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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.

• Journal of Engineering Education Research
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• v.9 no.4
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• pp.28-45
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• 2006

As society has changed to being more knowledge-based, it is indispensable that Internet usage is incorporated into education. Therefore, the e-learning system is being developed in order to provide a proper environment. However, many LCMS that were developed, currently are not based on SCORM, the world e-learning standard, nor KEM, the Korea Educational Metadata of Korea Education & Research Information Service(KERIS), and hence, it is difficult to share learning contents developed from such varied environments. National Science Museum, a non-educational institution, also provides the educational science exhibits in reality or in cyber space, which cannot be produced by elementary schools, and secondary schools. Consequently, new systems are necessary, whose modules should be divided into four, for example, 'teachers', 'learners', 'managers', and 'instructors', and be associated with each other so that they are able to integrate and manage such systems, and be used in school education as well. Therefore, in this study, more advanced LMS and LCMS, which are the web-portal sites used for a cyber science class at the National Science Museum, were designed and established. These sites were surely based on the KEM, and the SCORM.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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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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• v.16 no.10
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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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• v.32 no.3
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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.