• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.250-260
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• 2018

In order to control the quadcopter using reinforcement learning, hovering of 1-axial drones prototype is implemented through reinforcement learning. A complementary filter is used to measure the correct angle, and the range of angles is from -180 degrees to +180 degrees using modified complementary filter. The policy gradient method is used together with the REINFORCE algorithm for reinforcement learning. The prototype learned in this way confirmed the difference in performance depending on the length of the episode.

• Asia pacific journal of information systems
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• v.23 no.1
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• pp.107-132
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• 2013

Despite the importance of knowledge complementarities (KC) to firm performance, very little is known about exactly what constitutes KC and how synergistic knowledge is created in KC. This research looks into the dimensionality of KC and how synergistic knowledge as an essential component of KC is generated in a process innovation (PI) project. We propose that KC consists of potential complementary knowledge, collaborative elaboration (CE) process, and synergistic knowledge. The model is investigated quantitatively, using a sample of 26 matched-pairs of client and consultant who participated in a PI project, and then qualitatively using interviews of a sub-sample of 7 matched-pairs of client and consultant. Data were collected in a longitudinal way at four different points during the four month project period. Results show that consultant's learning about the client's business occurs first and then client learning about IT capabilities follows through CE. With this enhanced clients' knowledge about IT capabilities, clients play an initiative role in designing the To-Be business processes, while consultants play a supporting role by introducing best practices or making suggestions based on their experiences. Future research implications as well as practical implications are also discussed.

• Journal of Industrial Convergence
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• v.21 no.3
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• pp.57-64
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• 2023

As the technology advances and convergence progresses, companies are increasingly interested in the appropriability mechanism. Therefore in this study tried to understand the effectiveness of the appropriability mechanism of domestic IT companies. To this end, 7 appropriability mechanisms were finally derived and empirically analyzed from a review of previous studies and experts. As a result of the analysis, the importance of lead time advantage, patent, secrecy, complementary sales and service, design registered, complementary manufacturing, and learning curve effect was shown in the order of SW companies. HW companies, the importance of patent, secrecy, lead time advantage, design registered, complementary sales and service, learning curve effect, and complementary manufacturing were shown in the order of importance. Also patent, secrecy, and lead time advantage was selected as important factors. The results of in this study are expected to be used as useful guidelines on establishing an appropriability mechanism strategy in companies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.305-326
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• 2019

Discriminative correlation filter (DCF) based tracking algorithms have recently shown impressive performance on benchmark datasets. However, amount of recent researches are vulnerable to heavy occlusions, irregular deformations and so on. In this paper, we intend to solve these problems and handle the contradiction between accuracy and real-time in the framework of tracking-by-detection. Firstly, we propose an innovative strategy to combine the template and color-based models instead of a simple linear superposition and rely on the strengths of both to promote the accuracy. Secondly, to enhance the discriminative power of the learned template model, the spatial regularization is introduced in the learning stage to penalize the objective boundary information corresponding to features in the background. Thirdly, we utilize a discriminative multi-scale estimate method to solve the problem of scale variations. Finally, we research strategies to limit the computational complexity of our tracker. Abundant experiments demonstrate that our tracker performs superiorly against several advanced algorithms on both the OTB2013 and OTB2015 datasets while maintaining the high frame rates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.438-455
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• 2024

Recently, many unsupervised learning-based models have emerged for Just Noticeable Difference (JND) estimation, demonstrating remarkable improvements in accuracy. However, these models suffer from a significant drawback is that their heavy reliance on handcrafted priors for guidance. This restricts the information for estimating JND simply extracted from regions that are highly related to handcrafted priors, while information from the rest of the regions is disregarded, thus limiting the accuracy of JND estimation. To address such issue, on the one hand, we extract the information for estimating JND in an Adversarial Complementary Learning (ACoL) way and propose an ACoL-JND network to estimate the JND by comprehensively considering the handcrafted priors-related regions and non-related regions. On the other hand, to make the handcrafted priors richer, we take two additional priors that are highly related to JND modeling into account, i.e., Patterned Masking (PM) and Contrast Masking (CM). Experimental results demonstrate that our proposed model outperforms the existing JND models and achieves state-of-the-art performance in both subjective viewing tests and objective metrics assessments.

• 대한공업교육학회지
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• v.30 no.1
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• pp.19-36
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• 2005

The purpose of this study is to develop the Public Practice Center's teaching-learning model by applying blended learning strategies which is complementary to the expected problems such as expansion of the educational object and diversity of the curriculum to maximize the educational effect and to analyze activation types of the Practical Practice Center to expand the Public Practice Center's function and role by studying the document. Blended Learning Strategies are established in consideration of the following eight (8) factors ; learning environment, learning purpose, learning contents, learning time, learning place, learning type, learning media, type of interaction. It is redesigned and amended to the KEDI's individual confirmation instruction model for skill learning (1975) which is considered to be effective in the filed of education by applying features, educational contents of the Public Practice Center's teaching and merit of Blended Learning Strategies simultaneous. This model is composed of six (6) steps as shown below; 1. Understanding on the purpose and orientation 2. Observation for demonstration of fundamental skill 3. Ex on-line learning 4. Acquirement of element skill 5. Confirmation for acquirement of fundamental skill 6. After on-line learning. Further to this, this model is designed so that the above eight factors will be applied to the students effectively and the merit of e-learning and off-line practice will be mixed to the learner's expectation and satisfaction.

• Journal of Engineering Education Research
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• v.23 no.5
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• pp.61-67
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• 2020

In the associate degree curriculum of the department of mechanical engineering, the results of the study are presented on the structure and content of a subject based on the case-based learning method. As an case, equipment called a laminator that is actually used in the manufacturing site was selected. Class deals with specific engineering issues at each stage of laminator manufacturing (design-machining-assembly-measurement-maintenance) in connection with general engineering topics in prerequisites in the curriculum. Topics include tolerance fit, length measurement, assembly practice, measurement design and statistics of machine maintenance, etc. Courses that apply the case-based learning method may be included in the curriculum as complementary roles to those that apply other student-centered learning method.

• Journal of Korean Elementary Science Education
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• v.33 no.4
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• pp.683-699
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• 2014

The purpose of this study was to explore the characteristics of teacher learning and changes in teachers' epistemic beliefs within a learning community of elementary science teachers. Three in-service elementary teachers who majored in elementary science education in a doctoral course of a graduate school of education participated in the study, and learning activities in the teachers' beginning learning community provided a context for the study. Data sources included field notes produced by the researcher who engaged jointly in the teacher learning community as a coach, audio-recordings of the teachers' narratives, and artifacts generated by the teachers during the process of teacher learning. Complementary analyses of these multiple sources of data revealed that epistemic beliefs of the three elementary teachers were different and that each teacher made a different plan of science instruction based on his own epistemic belief even after the learning experiences within the teacher community. It was therefore suggested that science teacher education programs should be organized in consideration of the nature of teachers as constructivist learners and their practical resources.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5179-5196
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• 2019

To explore an effective non-invasion medical imaging diagnostics approach for hepatocellular carcinoma (HCC), we propose a method based on adopting the multiple technologies with the multi-parametric data fusion, transfer learning, and multi-scale deep feature extraction. Firstly, to make full use of complementary and enhancing the contribution of different modalities viz. multi-parametric MRI images in the lesion diagnosis, we propose a data-level fusion strategy. Secondly, based on the fusion data as the input, the multi-scale residual neural network with SPP (Spatial Pyramid Pooling) is utilized for the discriminative feature representation learning. Thirdly, to mitigate the impact of the lack of training samples, we do the pre-training of the proposed multi-scale residual neural network model on the natural image dataset and the fine-tuning with the chosen multi-parametric MRI images as complementary data. The comparative experiment results on the dataset from the clinical cases show that our proposed approach by employing the multiple strategies achieves the highest accuracy of 0.847±0.023 in the classification problem on the HCC differentiation. In the problem of discriminating the HCC lesion from the non-tumor area, we achieve a good performance with accuracy, sensitivity, specificity and AUC (area under the ROC curve) being 0.981±0.002, 0.981±0.002, 0.991±0.007 and 0.999±0.0008, respectively.

• The Mathematical Education
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• v.57 no.4
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• pp.371-391
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• 2018

The purpose of this study is to analyze manifestation examples and effects of group creativity in mathematical modeling and to discuss teaching and learning methods for group creativity. The following two points were examined from the theoretical background. First, we examined the possibility of group activity in mathematical modeling. Second, we examined the meaning and characteristics of group creativity. Six students in the second grade of high school participated in this study in two groups of three each. Mathematical modeling task was "What are your own strategies to prevent or cope with blackouts?". Unit of analysis was the observed types of interaction at each stage of mathematical modeling. Especially, it was confirmed that group creativity can be developed through repetitive occurrences of mutually complementary, conflict-based, metacognitive interactions. The conclusion is as follows. First, examples of mutually complementary interaction, conflict-based interaction, and metacognitive interaction were observed in the real-world inquiry and the factor-finding stage, the simplification stage, and the mathematical model derivation stage, respectively. And the positive effect of group creativity on mathematical modeling were confirmed. Second, example of non interaction was observed, and it was confirmed that there were limitations on students' interaction object and interaction participation, and teacher's failure on appropriate intervention. Third, as teaching learning methods for group creativity, we proposed students' role play and teachers' questioning in the direction of promoting interaction.