• Knowledge Management Research
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• v.13 no.2
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• pp.73-93
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• 2012

Although a wide range of theoretical researches have emphasized on the importance of knowledge management in cooperative R&D network, the empirical researches to synthetically examine the role of organizational learning and open innovation which influence on the performance of technological innovation are not enough to meet academic and practical demands. This study is to investigate the effect of open innovation and organizational learning in venture business on technological competitive advantage and establish the mediating role of organizational learning. For the purpose, the questionnaires, made based on the reviewing previous researches, were collected from 274 Korean venture businesses whose managerial focus is on developing technological innovation. As a result of analysis, the relational dimensions of open innovation - network, intensity and trust shared by a firm with external R&D partners - as well as the internal organizational learning system and competence have positive influence on building technological competitive advantage whose sub-variables are technological excellence, market growth potential and business feasibility. In addition, it is identified that organizational learning has the mediating and moderating effect in the relationship between open innovation and technological competitive advantage. These results imply that open innovation complements and expend the range of limited resources and the scope of innovation in technology-intensive small and medium-sized enterprises. Besides, organizational learning activity reinforces the use of knowledge and resources, obtained from external R&D partners. On the basis of these results, detailed issues and discussion were made in the conclusion.

• The Journal of Korean Academic Society of Nursing Education
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• v.3 no.2
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• pp.207-213
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• 1997

Basic science teaching and clinical education should be integrated whenever appropriate, and the development of skills, values, and attitudes which are emphasized to the same extent as the acquisition of knowledge in nursing. Problem-based learning provides a students-centered learning environment and encourages an inquisitive style of learning. The purpose of this paper is to review and comment the role of tutors and students on problem-based learning. The use of problem-based learning places a high demand on faculty members' time and support. The role of tutors in Problem-based learning focuses primarily on issues of developing and teaching the curriculum and on organizational implementation and institutionalization. Tutors are an integral part of course planning. Tutors serve as a constant source of feedback on student needs and concerns to the course director and constitute an informal steering committee while the course is in progress. Tutors write cases, develop student evaluation methods, recommend resources, suggest modifications in lectures and laboratories. Students have a limited amount of time available to study what is traditionally defined as the core content of nursing. But, the role of students in Problem-based learning would be active, independent learners and problem-solvers rather than passive recipients of information. Students using a deep level approach attempt to integrate what they learn with what they already know, to understand the meaning underlying the material to be learned, and to look for explanations rather than facts. Students are encouraged, with appropriate guidance, to define their own learning goals, to select appropriate experiences to achieve these goals, and to be responsible for assessing their own learning progress. Problem-based learning is more flexible and meaningful, by encouraging student interaction, and by having a better emotional climate than the conventional learning.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.98-107
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• 2018

In this paper, we propose a method to classify the skeletal maturity with a small amount of hand wrist X-ray image using deep learning-based meta-learning. General deep-learning techniques require large amounts of data, but in many cases, these data sets are not available for practical application. Lack of learning data is usually solved through transfer learning using pre-trained models with large data sets. However, transfer learning performance may be degraded due to over fitting for unknown new task with small data, which results in poor generalization capability. In addition, medical images require high cost resources such as a professional manpower and mcuh time to obtain labeled data. Therefore, in this paper, we use meta-learning that can classify using only a small amount of new data by pre-trained models trained with various learning tasks. First, we train the meta-model by using a separate data set composed of various learning tasks. The network learns to classify the bone maturity using the bone maturity data composed of the radiographs of the wrist. Then, we compare the results of the classification using the conventional learning algorithm with the results of the meta learning by the same number of learning data sets.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.11
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• pp.339-344
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• 2020

During COVID-19, education industry is organizing the concept of 'Edutech', which has evolved one step further from the existing e-Learning, by introducing various intelligent information technologues based on the core technology of the 4th industrial revolution and spreading it through diverse contents. Meanwhile, each industries are creating new industries by applying new technology to existing businesses and ask for needs of cultivating human resources who understand the existing traditional ICT technology and industrial business which can solve a newly rising problems. However, it is difficult to build contents for cultivating such human resources with the existing e-learning of transferring knowledge by one-way or some two-way commnication system which has established some interactive conversational system. Accordingly, this study conducted a research on a cooperative e-learning system that enables educators to communicate with learners in real time and allows problem-solving education based on the existing two-way communication system. As a result, frame for contents and prototype was developedp and artially applied to the actual class and conducted an efficiency analysis, which resulted in the validation of being applied to the actual class as a simulation-based cooperative content.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• Journal of Platform Technology
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• v.11 no.4
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• pp.71-79
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• 2023

In this paper, we analyze the characteristics of machine learning workloads and, based on them, propose a distributed in-memory caching technique to improve the performance of machine learning workloads. The core of machine learning workload is model training, and model training is a computationally intensive task. Performing machine learning workloads in a Kubernetes-based cloud environment in which the computing framework and storage are separated can effectively allocate resources, but delays can occur because IO must be performed through network communication. In this paper, we propose a distributed in-memory caching technique to improve the performance of machine learning workloads performed in such an environment. In particular, we propose a new method of precaching data required for machine learning workloads into the distributed in-memory cache by considering Kubflow pipelines, a Kubernetes-based machine learning pipeline management tool.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.131-131
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• 2021

Deep learning models, especially those based on long short-term memory (LSTM), have presented their superiority in addressing time series data issues recently. This study aims to comprehensively evaluate the performance of deep learning models that belong to the supervised learning category in streamflow prediction. Therefore, six deep learning models-standard LSTM, standard gated recurrent unit (GRU), stacked LSTM, bidirectional LSTM (BiLSTM), feed-forward neural network (FFNN), and convolutional neural network (CNN) models-were of interest in this study. The Red River system, one of the largest river basins in Vietnam, was adopted as a case study. In addition, deep learning models were designed to forecast flowrate for one- and two-day ahead at Son Tay hydrological station on the Red River using a series of observed flowrate data at seven hydrological stations on three major river branches of the Red River system-Thao River, Da River, and Lo River-as the input data for training, validation, and testing. The comparison results have indicated that the four LSTM-based models exhibit significantly better performance and maintain stability than the FFNN and CNN models. Moreover, LSTM-based models may reach impressive predictions even in the presence of upstream reservoirs and dams. In the case of the stacked LSTM and BiLSTM models, the complexity of these models is not accompanied by performance improvement because their respective performance is not higher than the two standard models (LSTM and GRU). As a result, we realized that in the context of hydrological forecasting problems, simple architectural models such as LSTM and GRU (with one hidden layer) are sufficient to produce highly reliable forecasts while minimizing computation time because of the sequential data nature.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• Educational Technology International
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• v.13 no.1
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• pp.27-48
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• 2012

In this paper we are comparing how the OER (open educational resources) are developed in Higher Education in Finland and South Korea. We also present a comparison model for further studies. Essential findings based on our comparison are that in both countries there are many best practices of use of the OER and open learning. Open educational resources have great potential and their use can ensure quality teaching and learning. The activity has not inspired the great mass of higher education teachers in Finland and Korea. Traditionally, a teacher's job is working alone, and so a new operational culture is required. Our comparison indicates that numerous questions, fears and problems and cultural differences are also related to the thematic. There is an evident need for a new kind of strategic leadership, a new kind of teaching and learning culture and a doing together and production ideology for the method to spread. Based on our study the following interlinked elements of OER seem to be pivotal: changes to pedagogies, technology and operational culture; educational policy intention; and attitude to culture. Lastly, comparison frame by OER practice model is developed.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.4
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• pp.105-112
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• 2022

Recently, many studies using reinforcement learning-based autoscaling have been performed to make autoscaling policies that are adaptive to changes in the environment and meet specific purposes. However, training the reinforcement learning-based Horizontal Pod Autoscaler(HPA) policy in a real environment requires a lot of money and time. And it is not practical to retrain the reinforcement learning-based HPA policy from scratch every time in a real environment. In this paper, we implement a reinforcement learning-based HPA in Kubernetes, and propose a transfer leanring technique using a queuing model-based simulation to accelerate the training of a reinforcement learning-based HPA policy. Pre-training using simulation enabled training the policy through simulation experience without consuming time and resources in the real environment, and by using the transfer learning technique, the cost was reduced by about 42.6% compared to the case without transfer learning technique.