• Knowledge Management Research
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• v.17 no.4
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• pp.147-162
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• 2016

Knowledge sharing has many benefits; however, employees are generally reluctant to share their knowledge with co-workers. This reluctance can be attributed to the facts that sharing knowledge involves the threat of losing personal competitiveness and the codification of knowledge for sharing requires additional effort. This study explains why employees engage in knowledge sharing despite the threat and cost of sharing knowledge. Specifically, it examines the effects of the quality of co-worker exchange (CWX) on knowledge sharing and the moderating effect of learning goal orientation on the relationship between CWX and knowledge sharing. Data from 186 individuals indicate that there is a positive relationship between CWX and knowledge sharing, and that this relationship is strengthened when learning goal orientation is low rather than when it is high. The theoretical and practical implications of the findings are also discussed.

• Journal of Environmental Science International
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• v.28 no.1
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• pp.125-135
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• 2019

For the purposes of enhancing usability of Numerical Weather Prediction (NWP), the quantitative precipitation prediction scheme by machine learning has been proposed. In this study, heavy rainfall was corrected for by utilizing rainfall predictors from LENS and Radar from 2017 to 2018, as well as machine learning tools LightGBM and XGBoost. The results were analyzed using Mean Absolute Error (MAE), Normalized Peak Error (NPE), and Peak Timing Error (PTE) for rainfall corrected through machine learning. Machine learning results (i.e. using LightGBM and XGBoost) showed improvements in the overall correction of rainfall and maximum rainfall compared to LENS. For example, the MAE of case 5 was found to be 24.252 using LENS, 11.564 using LightGBM, and 11.693 using XGBoost, showing excellent error improvement in machine learning results. This rainfall correction technique can provide hydrologically meaningful rainfall information such as predictions of flooding. Future research on the interpretation of various hydrologic processes using machine learning is necessary.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.225-230
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• 1992

In this paper, a new learning fuzzy logic controller(LFLC) is presented. The proposed controller is composed of the main control part and the learning part. The main control part is a fuzzy logic controller(FLC) based on linguistic rules and fuzzy inference. For the learning part, artificial neural network(ANN) is added to FLC so that the controller may adapt to unknown plant and environment. According to the output values of the ANN part, which is learned using error back-propagation algorithm, scale factors of the FLC part are determined. These scale factors transfer the range of values of input variables into corresponding universe of discourse in the FLC part in order to achieve good performance. The effectiveness of the proposed control strategy has been demonstrated through simulations involving the control of an unknown robot manipulator with load disturbance.

• Asia pacific journal of information systems
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• v.16 no.4
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• pp.179-202
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• 2006

The objective of this study is to identify the impacts of communication reinforcement on performance of learning in Web-PBL. Communication reinforcement is defined as the combination of information sharing and co-construction. As factors facilitating communication reinforcement, we propose learner's characteristics, task characteristics, and group characteristics. Learner's characteristics are collaboration-orientation, openness, holistic approach, and online community-orientation which reflects e-learning environment. Collaboration-oriented tasks as group projects were developed and given to groups with 5-6 members. The group characteristics are categorized into 'horizontal' and 'vertical', according to the patterns of communication between a group leader and members. To verify empirically the proposed research model, an experimental design was performed to learners who took on-line and off-line courses with group projects. We found important results as follows; First, field dependence has positive impacts on information sharing, and online community-orientation has positive impacts on co-construction. These results correspond with prior studies on relationship between field dependence and collaborative learning. Second, collaboration-oriented task directly impacts on information sharing, and indirectly affects co-construction, This result implicates that information sharing is pre-requisite of co-construction. Third, 'horizontal' was identified as a factor giving positive effects on information sharing and co-construction. This result implies that horizontal communication is very important to facilitate communication reinforcement.

• The Journal of the Korea Contents Association
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• v.15 no.12
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• pp.407-420
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• 2015

This study analyzes the effect of CoP(Community of Practice) influence factors on satisfaction and learning culture activation in R&D groups. Research model and hypothesis is designed the relationship the effect factors for CoP which are consist of personal factor, interacting factor, support factor and environmental factor and satisfaction and the learning culture activation focused on comparing between maturity and immaturity CoP member group. It conducted an analysis based on 371 survey responses significantly. Hence, interacting, supporting and personal factor have a significant positive effect on satisfaction but environmental factor was negative effect to it. CoP Satisfaction has a positive effect on the learning culture activation. However average between two groups has not a statistically significant difference in all of the factors. At the result, interacting between members is the most important factor to the successful CoP development of R&D groups.

• Korean Journal of Computational Design and Engineering
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• v.13 no.5
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• pp.362-371
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• 2008

Generally, a PLM education program in university consists of lectures of theory, software lab and software development raining as an advanced subject. Most industries want more than these, such as practical problem solving capabilities, teamwork skills and engineering communications including human relationship, rhetoric, technical writing, presentation and etc. Problem-Based Learning is a problem-stimulated and student-centered teaming method, and an innovative education strategy for collaborative and self-directed learning by applying real world problems. Education paradigm changes from "teaching" to "learning" accomplished by team working, and students are encouraged to develop, present, explain and defense their ideas, suggestions or solutions of a problem, and the "cooperative teaming" proceeds spontaneously during team operations. Co-operative education program is an into-grated academic model and a structured educational program combining classroom learning with productive work experience in a field related to a student's academic or career goals. Based on the partnership between academic institutions and industries, students are engaged in real and productive "work" in the industry, in contrast with merely observing. In this paper, PBL with Co-op program is suggested as an effective approach for PLM education, and we made and operated a PBL-based education course with industry co-op program. The Co-op education in industry accompanied with the PBL course in university can improve practical problem solving capabilities of students, including modeling and management of P3R(Product, Process, resource and Plant) using commercial PLM software tools. By the result, we found this to be an effective strategy for helping students, professors and industries succeed in engineering education, especially PLM area.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.367-373
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• 2021

In this study, deep learning model was set up to predict the wave heights inside a harbour. Various machine learning techniques were applied to the model in consideration of the transformation characteristics of offshore waves while propagating into the harbour. Pohang New Port was selected for model application, which had a serious problem of unloading due to swell and has lots of available wave data. Wave height, wave period, and wave direction at offshore sites and wave heights inside the harbour were used for the model input and output, respectively, and then the model was trained using deep learning method. By considering the correlation between the time series wave data of offshore and inside the harbour, the data set was separated into prevailing wave directions as a pre-processing method. As a result, It was confirmed that accuracy and stability of the model prediction are considerably increased.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.479-482
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• 2001

Construction industry included speciality compare with others industry. Systemically approach and enterprise cultural approach is required in order to perform Knowledge Management in construction industry. But, most of construction enterprise immersed in system approach to perform Knowledge Management, in this reason caused failure of Knowledge Management. To resolve the structural contradiction, Learning organization based the Community of Practice(CoP) is studied in this paper.

• The Journal of the Korea Contents Association
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• v.6 no.3
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• pp.116-124
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• 2006

Mentoring is defined as a sustained relationship between a mentor and a mentee. Through continued involvement, the mentor offers support, guidance, and assistance as the mentee faces new challenges, or works to correct earlier problems. A mentoring for cooperative learning has many merits including higher order thinking, collaborative competencies, socialization and development. In this paper, a U(Ubiquitous)-CoMM(Community of mentor & mentee) system was supposed to design an instructional learning strategy using cyber community of mentor & mentee in a ubiquitous environment. The proposed system provides participants with campus mentoring program in which they share their experience and expertise. By experimental result showed that the proposed system is effect in education about cooperative learning than existing system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.10
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• pp.883-892
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• 2021

This paper provides a process for developing deep learning-based aerial object detection models that can run in realtime on onboard. To improve object detection performance, we pre-process and augment the training data in the training stage. In addition, we perform transfer learning and apply a weighted cross-entropy method to reduce the variations of detection performance for each class. To improve the inference speed, we have generated inference acceleration engines with quantization. Then, we analyze the real-time performance and detection performance on custom aerial image dataset to verify generalization.