• 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.

• Journal of Environmental Impact Assessment
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• v.18 no.5
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• pp.271-280
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• 2009

The objective of this study is to develop a reliable flow forecasting model based on neural network algorithm in order to provide flow rate at stream sections without flow measurement in Nakdong river. Stream flow rate measured at 8-days interval by Nakdong river environment research center, daily upper dam discharge and precipitation data connecting upstream stage gauge were used in this development. Back propagation neural network and multi-layer with hidden layer that exists between input and output layer are used in model learning and constructing, respectively. Model calibration and verification is conducted based on observed data from 3 station in Nakdong river.

• Journal of The Korean Association of Information Education
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• v.3 no.2
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• pp.113-119
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• 2000

I studied a distance education model for English learning on the Internet. Basic WWW files, that contain courseware, are constructed with HTML, and functions, which are required in learning, are implemented with Java. Students and educators can access the preferred unit composed of the appropriate text, voice and image data by using a WWW browser at any time. The education system supports the automatic generation facility of English problems to practice reading and writing by making good use of the courseware data or various English text resources located on the Internet. Our system has functions to manage and control the flow of distance learning and to offer interaction between students and the system in a distributed environment. Educators can manage students' learning and can immediately be aware of who is attending and who is quitting the lesson in virtual space. Also, students and educators in different places can communicate and discuss a topic through the server. I implemented these functions, which are required in a client/server environment of distance education, with the use of Java. The URL for this system is "http://park.taegu-e.ac.kr" in the name of GAIA.

• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.63-69
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• 2010

This research does focus on supporting all of formal, or informal learning at real time. From industry or education field, learning training is according to worker's current situation or business context. And according to worker's current situation, or business flow, 3D immersion knowledge visualization is effective in the individual ability and the learning progress For this, workers listen to compound knowledge coaching advices at real time. Therefore we developed the realistic 3D based knowledge retrieval method to identify and retrieve multidimensional relation easily.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.100-104
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• 2020

Over the past several years, deep learning has been widely used for feature extraction in image and video for various applications such as object classification and facial recognition. This paper introduces an implantation of embedded Linux system for embossed digits recognition using CNN based deep learning methods. For this purpose, we implemented a coin recognition system based on deep learning with the Keras open source library on Raspberry PI. The performance evaluation has been made with the success rate of coin classification using the images captured with ultra-wide angle camera on Raspberry PI. The simulation result shows 98% of the success rate on average.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06b
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• pp.156-160
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• 2007

정보 통신 기술의 발전으로 교육 환경은 이러닝(e-Learning)과 모바일 러닝(Mobile Learning)이 지원되는 융합(convergence)된 네트워크 환경이 구축되고 있으며, 새로운 교수법을 필요로 한다. 학습자의 학습 환경(learning environment)에 따라 이러닝과 모바일 러닝이 상호작용(interaction)하여 학습 활동이 끊김없이(seamless) 수행되도록 한다. 본 논문에서는 이러닝과 모바일 러닝 환경에서 학습 활동을 수행하는 과정에서 상호작용하는 레이어를 설계한다. 각 레이에의 흐름(flow)에서 필요한 요소로 메타데이터(metadata)를 도출하고, 메타데이터를 다른 요소에서 필요로 할 때 재사용(reusable)할 수 있도록 하였다.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.225-232
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• 2023

Owing to advancements in intelligent transportation systems (ITS) and artificial-intelligence technologies, various machine-learning models can be employed to simulate and predict the number of traffic accidents under different weather conditions. Furthermore, we can analyze the relationship between weather and traffic accidents, allowing us to assess whether the current weather conditions are suitable for travel, which can significantly reduce the risk of traffic accidents. In this study, we analyzed 30000 traffic flow data points collected by traffic cameras at nearby intersections in Washington, D.C., USA from October 2012 to May 2017, using Pearson's heat map. We then predicted, analyzed, and compared the performance of the correlation between continuous features by applying several machine-learning algorithms commonly used in ITS, including random forest, decision tree, gradient-boosting regression, and support vector regression. The experimental results indicated that the gradient-boosting regression machine-learning model had the best performance.

• Journal of the Korean Applied Science and Technology
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• v.41 no.2
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• pp.305-317
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• 2024

This study is a pre-experimental research that applied one-group pretest-posttest design to find out effects of the health assessment learning program using an online platform on the problem-solving process, critical thinking disposition, class participation, and class flow of nursing college students. Data before and after participation in the learning program using the online platform were collected from second-year nursing students who were 52 test subjects taking the health assessment and practice course at J University located in G-gun, C-buk-do. As a result of analyzing, it was found that the problem-solving process (t=-2.569, p=.013), critical thinking disposition (t=-5.363, p<.001), class participation (t=-4.429, p<.001), and class flow (t=-3.747, p<.001) were statistically significantly improved according to before and after participation in the program. Therefore, it is believed that a learning program using an online platform can be used to improve the problem-solving process, critical thinking disposition, class participation, and class flow of nursing college students during health assessment classes.