• Journal of KIISE
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• v.43 no.1
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• pp.80-86
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• 2016

A speech-act is a behavior intended by users in an utterance. Speech-act classification is important in a dialogue system. The machine learning and rule-based methods have mainly been used for speech-act classification. In this paper, we propose a speech-act classification method based on the combination of support vector machine (SVM) and transformation-based learning (TBL). The user's utterance is first classified by SVM that is preferentially applied to categories with a low utterance rate in training data. Next, when an utterance has negative scores throughout the whole of the categories, the utterance is applied to the correction phase by rules. The results from our method were higher performance over the baseline system long with error-reduction.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.1083-1091
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• 2004

This paper presents a chunking system employed as a preprocessing module to the parser in a Chinese to Korean machine translation system. The parser can benefit from the dependency information provided by the chunking module. The chunking system was implemented using transformation-based learning technique and an effective interface that conveys the dependency information to the parser was also devised. The module was integrated into the machine translation system and experiments were performed with corpuses collected from Chinese websites. The experimental results show the introduction of chunking module provides noticeable improvements in the parser's performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3121-3144
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• 2023

As interest in the metaverse has grown, there has been a demand for avatars that can represent individual users. Consequently, research has been conducted to reduce the time and cost required for the current 3D human modeling process. However, the recent automatic generation of 3D humans has been focused on creating avatars with a realistic human form. Furthermore, the existing methods have limitations in generating avatars with imbalanced or unrealistic body shapes, and their utilization is limited due to the absence of datasets. Therefore, this paper proposes a new framework for automatically transforming and creating stylized 3D avatars. Our research presents a definitional approach and methodology for creating non-realistic character avatars, in contrast to previous studies that focused on creating realistic humans. We define a new shape representation parameter and use a deep learning-based method to extract character body information and perform automatic template mesh transformation, thereby obtaining non-realistic or unbalanced human meshes. We present the resulting outputs visually, conducting user evaluations to demonstrate the effectiveness of our proposed method. Our approach provides an automatic mesh transformation method tailored to the growing demand for avatars of various body types and extends the existing method to the 3D cartoon stylized avatar domain.

• Journal of the Korean Chemical Society
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• v.59 no.2
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• pp.156-163
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• 2015

In Malaysia, the students' poor performance in mathematics and sciences needs immediate attention and remedies. In order to tackle this problem, an active learning environment that encourages students' question-asking capability must be molded. Transformation from traditional teacher-based approach to active-learning classroom is the key to develop question-asking capability. The classroom activity that the authors used in this study is based on the uncritical inference test to promote students' deeper thinking which encouraged students to verify facts that was previously learnt in classroom through group discussion activity. Three sets of uncritical inference test were developed and applied to Malaysian college course of basic organic chemistry. Students' answers to the impact of using uncritical inference test with a group discussion on learning and communication skills were positive.

• Journal of the Korea Safety Management & Science
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• v.12 no.3
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• pp.271-281
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• 2010

In an industrial Era, OJT(On-the-Job Training) has been accepted as the field learning. But in a breaking up era, traditional field training needs to change and make an evolutionary model. Also, we need to make evolutionary model for various changing ways and means and need means to maximize the transformation of learning by operating learning organization. In knowledge based society, as people work and learn new knowledge in order to pass the experience knowledge and capabilities, they are not the traditional relationship between trainer and trainee but maximize work and learning, development and performance through several different ways. So, the study about new learning model is needed because the learning is creating the value and makes low cost and high efficiency about the elements of cost and time. We study the evolutionary model, OJF(On-the-Job Facilitating) - new learning methodology - through operating learning organization in S Electronics and its application practices.

• Journal of IKEEE
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• v.24 no.1
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• pp.147-153
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• 2020

Conventional lane detection algorithms have problems in that the detection rate is lowered in road environments having a large change in curvature and illumination. The probabilistic Hough transform method has low lane detection rate since it exploits edges and restrictive angles. On the other hand, the method using a sliding window can detect a curved lane as the lane is detected by dividing the image into windows. However, the detection rate of this method is affected by road slopes because it uses affine transformation. In order to detect lanes robustly and avoid obstacles, we propose driving assist system using semantic segmentation based on deep learning. The architecture for segmentation is SegNet based on VGG-16. The semantic image segmentation feature can be used to calculate safety space and predict collisions so that we control a vehicle using adaptive-MPC to avoid objects and keep lanes. Simulation results with CARLA show that the proposed algorithm detects lanes robustly and avoids unknown obstacles in front of vehicle.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.39-46
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• 2022

The overall digital transformation in society is rapidly progressing with the corona virus epidemic. In particular, in the field of cultural heritage and museums, digital transformation is taking place throughout the preservation, management, and utilization of cultural heritage. To respond to this, the importance of cultivating the digital literacy of museum professionals to select and utilize digital cultural heritage information is increasing. However, the current digital capacity education of museum professionals has not reached the cultivation of digital literacy due to one-way theory and one-way practical education. To overcome this, we propose a digital capacity building program using virtual museums. We propose a curriculum based on participatory museums, cooperative learning, and project-based learning theories. Learners experience the entire process of acquiring, selecting, and utilizing digital cultural heritage information through individual, cooperative, constant, exhibitions, and project-based learning programs. We were evaluated by experts in terms of education, museum education, and ICT technology education to prove its usability and derive improvements. This study will contribute to building the digital capacity of museum professionals.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.171-179
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• 2008

This paper addresses the synthesis of an iterative learning controller for a class of linear systems with norm-bounded parameter uncertainties. We take into account an iterative learning algorithm with current cycle feedback in order to achieve both robust convergence and robust stability. The synthesis problem of the developed iterative learning control (ILC) system is reformulated as the ${\gamma}$-suboptimal $H_{\infty}$ control problem via the linear fractional transformation (LFT). A sufficient convergence condition of the ILC system is presented in terms of linear matrix inequalities (LMIs). Furthermore, the ILC system with fast convergence rate is constructed using a convex optimization technique with LMI constraints. The simulation results demonstrate the effectiveness of the proposed method.

• Journal of the Korean earth science society
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• v.39 no.1
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• pp.103-116
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• 2018

The purpose of this study was to explore $6^{th}$ graders learning progression for lunar phase change focusing astronomical systems thinking. By analyzing the results of previous studies, we developed the constructed-response items, set up the hypothetical learning progressions, and developed the item analysis framework based on the hypothetical learning progressions. Before and after the instruction on the lunar phase change, we collected test data using the constructed-response items. The results of the assessment were used to validate the hypothetical learning progression. Through this, we were able to explore the learning progression of the earth-moon system in a bottom-up. As a result of the study, elementary students seemed to have difficulty in the transformation between the earth-based perspective and the space-based perspective. In addition, based on the elementary school students' learning progression on lunar phase change, we concluded that the concept of the lunar phase change was a bit difficult for elementary students to learn in elementary science curriculum.

• Korean Medical Education Review
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• v.23 no.3
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• pp.147-153
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• 2021

The coronavirus disease 2019 (COVID-19) pandemic made it necessary for medical schools to restructure their curriculum by switching from face-to-face instruction to various forms of flexible learning. Flexible learning is a student-centered approach to learning that has received interest in many educational sectors. It is a critical strategy for expanding access to higher education during the pandemic. As flexible learning includes online, blended, hybrid, and hyflex learning options, learners have the opportunity to select an instruction modality based on their needs and interests. The shift to flexible learning in medical education took place rapidly in response to the COVID-19 pandemic, and learners, instructors, and schools were not prepared for this instructional change. Through the lens of the technology acceptance model, human agency, and a social constructivist perspective, I examine students, instructors, and educational institutions' roles in successfully navigating the digital transformation era. The pandemic has also accelerated the use of advanced information and communication technologies, such as artificial intelligence and virtual reality, in learning. Through a review of the literature, this paper aimed to reflect on current flexible learning practices from the instructional design and educational technology perspective and explore emerging technologies that may be implemented in future medical education.