• 한국인공지능학회지
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• 제11권4호
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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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• 제24권7호
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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.

• 환경영향평가
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• 제18권5호
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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.

• 정보교육학회논문지
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• 제3권2호
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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.

• 한국콘텐츠학회논문지
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• 제10권7호
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• pp.63-69
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• 2010

본 연구는 산업현장에서 학습훈련이 이루어질 때 작업현장, 교육현장, 기타 시공간에서 작업자의 현재 상황이나 담당업무 맥락에 따라 개인의 숙련도나 학습진도에 맞추어 3D 몰입형 지식 가시화를 통해 비공식학습과 공식학습 모두 실시간으로 발생할 수 있는 서비스 구현을 목표로 한다. 이를 위해서 복합지식을 기반으로 실시간으로 코칭과 조언을 들을 수 있으며, 다차원적인 관계를 쉽게 식별하고 검색할 수 있는 실감형 3D 기반의 지식 검색 방법을 개발하였다.

• 반도체디스플레이기술학회지
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• 제19권2호
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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.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 한국컴퓨터종합학술대회논문집 Vol.34 No.1 (B)
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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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• 제21권3호
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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.

• 한국응용과학기술학회지
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• 제41권2호
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• pp.305-317
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• 2024

본 연구는 온라인 플랫폼을 활용한 건강사정 학습 프로그램이 간호 대학생의 문제해결과정, 비판적 사고 성향, 수업참여도 및 수업몰입도에 미치는 효과를 알아보기 위한 단일집단 사전사후설계(one-group pretest-posttest design)를 적용한 원시실험연구(pre-experimental research)이다. 연구 대상자는 C북도 G군 소재 J대학교 건강사정 및 실습 교과목을 수강하는 간호학과 2학년 학생 52명을 대상으로 온라인 플랫폼을 활용한 건강사정 학습 프로그램 참여 전·후 자료를 수집하였다. 분석한 결과, 문제해결과정(t=-2.569, p=.013), 비판적 사고 성향(t=-5.363, p<.001), 수업참여도(t=-4.429, p<.001), 수업몰입도(t=-3.747, p<.001)가 프로그램 참여 전·후 통계적으로 유의하게 향상된 것으로 나타났다. 따라서 건강사정 수업 시 간호 대학생들의 문제해결과정, 비판적 사고 성향, 수업참여도, 수업몰입도 향상을 위해서 온라인 플랫폼을 활용한 학습 프로그램을 활용할 수 있을 것으로 사료된다.

• Educational Technology International
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• 제19권1호
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• pp.35-60
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• 2018

The purpose of this study was to analyze the types of lesson and its effectiveness with digital textbook. To address those goals, we had observed five classes of the primary school, which designated as a research pilot school for digital textbook. Based on the result of observation, 3 types of lesson with digital textbook were categorized: Teacher-directed lecture (type 1), Blended learning (type 2), and Flipped learning (type 3). Depending on the type of lesson was analyzed the positive and negative effectiveness by means of matrix analysis method. As a result, in Teacher-directed lecture (type 1), there was found out the participation of the lesson in atmosphere of stable and comfortable as positive experience, also digital textbook operating immature and boring as negative experience. In Blended learning (type 2), there was found out the fun by sharing the product and peer feedback, and flow by learning transfer as positive experience, also digital textbook operating immature and understanding the difference between assignments as negative experience. In Flipped learning (type 3), there was shown the positive attitude and ownership in the lesson as positive experience, also distracting and boring in the lesson when learner was excluded in participation as negative experience. Based on the results, we suggested some strategies for improving positive experience and protecting negative experience in the lesson with using digital textbook.