• Journal of The Korean Association of Information Education
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• v.15 no.4
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• pp.525-531
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• 2011

This study is about the system for knowledge management in the Wiki-based project learning. We implement the Wiki-based project learning system which is focused on a new Web paradigm and technology development to grasp the knowledge flow of a learner effectively under a project learning condition. Implementation of the system has used a Web 2.0 technology to easily understand SECI Knowledge Management types which form the Externalization, Combination and Internalization steps. Moreover, the system structure has been designed instinctively for harmonious knowledge use or reuse. As a result of the experiment, we found out that the collaborative knowledge steps moved along the flow of project learning.

• The Journal of Korean Association of Computer Education
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• v.10 no.1
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• pp.77-88
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• 2007

More flexible learning models are needed, and learning environments that operate through mobile technologies such as portable multimedia players(PMP) provide useful tools in implementing these learning models. The main attractant of PMP is often their versatility: being able to load and play different formats of video, audio, digital images, and interactive media. In this paper, we investigate the factors influencing the usage and acceptance of the PMP for study, based on the extended version of the Technology Acceptance Model (TAM). Based on data collected from online survey, we show that perceived usefulness, perceived ease of use, flow and perceived enjoyment are the major determinants for users to play PMP for study purpose. Factors, including ease of use, contents-credibility are shown to determine the level of perceived usefulness; additionally, perceived usefulness, ease of use and perceived enjoyment are shown to directly affect the level of flow. Based upon the statistical results, some useful guidelines for developing learning contents are also provided.

• Journal of architectural history
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• v.13 no.4 s.40
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• pp.7-18
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• 2004

From the results of an examination of the transition process of the site plan divided into 5 stages based on literature and materials relating to the Sangju Confucian School as well as the construction history, we can see the general transition flow as follows. The arrangement form of Sangju Confucian School shows the structures with both the sacrificial rites function and the learning function in the early period. This shows a large general flow where the form with the learning function structure at the front and sacrificial rites function structure at the back changed to a form where the learning function structure was positioned behind the boarding facilities, after which there was a transformation which left only the learning function (the form where the learning function structure was positioned in front of the boarding facilities). The type where the learning function structure is positioned in front of the boarding facilities is hard to find in the Yeongnam area, also, there are not many examples of the 2 story Myeonglyundang (hall of confucianism teachings) throughout the country Sangju Confucian School which possess the value of rarity is appraised as being a precious material showing another area characteristic in Sangju of the Yeongnam area. Also, during the late Chosun period the scale of the Dongseojae (boarding facility) was reduced and the appearance of Yangsajae can be said to be a typical example of confucian school constructions of late Chosun era.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5135-5142
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• 2011

A flexible mechanism is proposed in this paper to improve the dynamic response performance of a traffic flow control system in an urban area. The roads, vehicles, and traffic control systems are all modeled as intelligent systems, wherein a wireless communication network is used as the medium of communication between the vehicles and the roads. The necessary sensor networks are installed in the roads and on the roadside upon which reinforcement learning is adopted as the core algorithm for this mechanism. A traffic policy can be planned online according to the updated situations on the roads, based on all the information from the vehicles and the roads. This improves the flexibility of traffic flow and offers a much more efficient use of the roads over a traditional traffic control system. The optimum intersection signals can be learned automatically online. An intersection control system is studied as an example of the mechanism using Q-learning based algorithm, and simulation results showed that the proposed mechanism can improve the traffic efficiency and the waiting time at the signal light by more than 30% in various conditions compare to the traditional signaling system.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1031-1041
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• 2020

Cloud computing technology plays an important role in the deep learning industry as deep learning services are deployed frequently on top of cloud infrastructures. In such cloud environment, virtualization technology provides logically independent and isolated computing space for each tenant. However, recent studies demonstrate that by leveraging vulnerabilities of virtualization techniques and shared processor architectures in the cloud system, various side-channels can be established between cloud tenants. In this paper, we propose a novel attack scenario that can steal internal information of deep learning models by exploiting the Meltdown vulnerability in a multi-tenant system environment. On the basis of our experiment, the proposed attack method could extract internal information of a TensorFlow deep-learning service with 92.875% accuracy and 1.325kB/s extraction speed.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.209-216
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• 2009

The employees in social welfare institutions have an importance of learning due to jop specialty more than any one else. That is because there are a variety of increasing needs in social welfare services and customer-oriented welfare services. On the basis of that, this study shows how learning orientation theories as a new organization management strategy affects the employees in social welfare institutions. It shows cause and effect relation by establishing hypothesis model based on the preceding researches. It performed empirical analysis on the employees in social welfare institutions in G metropolitan city. As a result, the hypothesis was tested that learning flow, vision sharing, openness have significant effects on member's jop satisfaction. It implies that job satisfaction can be improved, if the level of learning flow, vision sharing, openness of the employees in social welfare institutions are promoted.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.36-48
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• 2022

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.29-34
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• 2023

The flow from developing a machine learning model to deploying it in a production environment suffers challenges. Efficient and reliable deployment is critical for realizing the true value of machine learning models. Bridging this gap between development and publication has become a pivotal concern in the machine learning community. FastAPI, a modern and fast web framework for building APIs with Python, has gained substantial popularity for its speed, ease of use, and asynchronous capabilities. This paper focused on leveraging FastAPI for deploying machine learning models, addressing the potentials associated with integration, scalability, and performance in a production setting. In this work, we explored the seamless integration of machine learning models into FastAPI applications, enabling real-time predictions and showing a possibility of scaling up for a more diverse range of use cases. We discussed the intricacies of integrating popular machine learning frameworks with FastAPI, ensuring smooth interactions between data processing, model inference, and API responses. This study focused on elucidating the integration of machine learning models into production environments using FastAPI, exploring its capabilities, features, and best practices. We delved into the potential of FastAPI in providing a robust and efficient solution for deploying machine learning systems, handling real-time predictions, managing input/output data, and ensuring optimal performance and reliability.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.143-147
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• 2024

Image noise classification is a classical problem in the field of image processing, machine learning, deep learning and computer vision. In this paper, image noise classification is performed using deep learning. Keras deep learning library of TensorFlow is used for this purpose. 6900 images images are selected from the Kaggle database for the classification purpose. Dataset for labeled noisy images of multiple type was generated with the help of Matlab from a dataset of non-noisy images. Labeled dataset comprised of Salt & Pepper, Gaussian and Sinusoidal noise. Different training and tests sets were partitioned to train and test the model for image classification. In deep neural networks CNN (Convolutional Neural Network) is used due to its in-depth and hidden patterns and features learning in the images to be classified. This deep learning of features and patterns in images make CNN outperform the other classical methods in many classification problems.