• 디지털산업정보학회논문지
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• 제8권4호
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• pp.25-34
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• 2012

In this study, in the process of testing before the release of the software products designed, software testing manager in advance should be aware of the testing-information. Therefore, the effective learning effects perspective has been studied using the NHPP software. The finite failure nonhomogeneous Poisson process models presented and applied property of learning effect based on truncated time and delayed S-shaped software reliability. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than autonomous errors-detected factor that is generally efficient model can be confirmed. This paper, a failure data analysis was performed, using time between failures, according to the small sample and large sample sizes. The parameter estimation was carried out using maximum likelihood estimation method. Model selection was performed using the mean square error and coefficient of determination, after the data efficiency from the data through trend analysis was performed.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.1841-1851
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• 2018

For the safe and stable operation of the power system, accurate wind power prediction is of great significance. A wind power prediction method based on empirical mode decomposition and improved extreme learning machine is proposed in this paper. Firstly, wind power time series is decomposed into several components with different frequency by empirical mode decomposition, which can reduce the non-stationary of time series. The components after decomposing remove the long correlation and promote the different local characteristics of original wind power time series. Secondly, an improved extreme learning machine prediction model is introduced to overcome the sample data updating disadvantages of standard extreme learning machine. Different improved extreme learning machine prediction model of each component is established. Finally, the prediction value of each component is superimposed to obtain the final result. Compared with other prediction models, the simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

• 로봇학회논문지
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• 제14권4호
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• pp.357-362
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• 2019

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

• 한국공간구조학회논문집
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• 제22권2호
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• pp.39-46
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• 2022

Recently, machine learning is widely used to solve optimization problems in various engineering fields. In this study, machine learning is applied to development of a control algorithm for a smart control device for reduction of seismic responses. For this purpose, Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm. A single degree of freedom (SDOF) structure with a smart tuned mass damper (TMD) was used as an example structure. A smart TMD system was composed of MR (magnetorheological) damper instead of passive damper. Reward design of reinforcement learning mainly affects the control performance of the smart TMD. Various hyper-parameters were investigated to optimize the control performance of DQN-based control algorithm. Usually, decrease of the time step for numerical simulation is desirable to increase the accuracy of simulation results. However, the numerical simulation results presented that decrease of the time step for reward calculation might decrease the control performance of DQN-based control algorithm. Therefore, a proper time step for reward calculation should be selected in a DQN training process.

• Investigative Magnetic Resonance Imaging
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• 제25권4호
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• pp.300-312
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• 2021

Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learning-based methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learning-based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.

• 정보교육학회논문지
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• 제9권3호
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• pp.511-522
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• 2005

정보통신기술이 급변함에 따라 교육에 대한 패러다임도 변화되고 있다. 최근 PDA, 태블릿 PC, 휴대전화 단말기 등의 개별화 정보기기를 통해 언제 어디서나 학습이 가능한 u-러닝이 도입되고 있다. u-러닝에서 개별화 정보기기의 이동성과 개인성 등을 이용한다면 시간과 공간의 제약 없이 학습자 개인의 특성에 적합한 맞춤형 학습을 가능하게 하고, 상황학습과 체험학습에도 효과적이게 될 것이다. 본 연구에서는 PDA의 교수-학습에의 활용 방안과 PDA 활용 교수-학습 기초 모델을 모색하고자 PDA를 직접 수업에 적용하고 그 효과를 알아보았다. 그 결과 PDA를 활용한 수업에 대하여 대부분 만족스럽다는 응답을 얻었으나, 접속의 문제, PDA용 콘텐츠의 부족, 화질의 문제 등이 개선되어야 할 점으로 지적되었다.

• 한국실천공학교육학회논문지
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• 제4권2호
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• pp.60-66
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• 2012

본 논문에서는 대학에서의 학습의 질을 높이기 위해 지금까지 연구된 많은 이론들을 정리하여, 더 나은 수업을 준비하고 실제로 더 높은 수준의 수업을 듣는데 도움이 되는 학습방법을 제안한다. 대학생들은 대학에 들어와서 자기주도적 학습 방법을 각자 확보하여, 학창시절에 전공 및 중요한 교양 과목을 효과적으로 학습하여야 한다. 그런데 대부분의 대학생들은 저학년일 때 이러한 자기주도적 학습방법을 확보하지 못한 상태에서 고학년으로 진급하게 된다. 그리고 그 때에 가서야 비로서 학습의 중요성을 인지하여 나름대로 학습할려 하지만 이미 학창시절의 상당기간이 지나 우왕좌왕하는 경향이 있다. 본 논문에서는 각종 기존의 각종 학습방법에 대해 소개 및 분석을 하였다. 그리고 학습목적찾기, 시간관리, 수업을 효율적이고 효과적으로 수강하기 위한 수업 준비에 대해 논의하였다. 그리고, 효율적인 학습법의 모델을 제안하였다. 제안한 방법은 각 학생들의 수준과 강의실 환경 등을 고려하여 적절히 선택적으로 적용한다면 효과적인 학습방법이 될 것으로 사료된다.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 1996년도 추계 학술 발표회 발표논문집
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• pp.217-227
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• 1996

The new filed of learning control develops controllers that learn to improve their performance at executing a given task , based on experience performing this specific task. In a previous work[6], authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper develops improved indirect learning control algorithms, and studies the use of such controller indecentralized systems. The original motivation of the learning control field was learning in robots doing repetitive tasks such as on an asssembly line. This paper starts with decentralized discrete time systems. and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. The resultof the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample tie in the digital learning controller is sufficiently short.

• 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.

• 한국정보시스템학회지:정보시스템연구
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• 제20권4호
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• pp.181-204
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• 2011

Recently, the development of ICT(information & communications technology) with the advent of new media paradigm shift in learning has brought a dramatic impact on the competitiveness of universities. The previous studies on the academic performance of e-learning mainly targeted on e-learning users, studying additional synchronization and system quality factors to measure academic performance. This study empirically analyzed the learning flow and academic performance considering both DeLone & McLean model system quality and synchronizing factors based on ARCS model. Relating to quality and synchronization factors, the academic performance of e-learning system was tested, and the difference between learning flow and academic performance was also analyzed based on time-series data, by the test difference(in the beginning, during, and final of the semester). The results of the study are as follows. First, the study shows that both system quality and synchronization directly affected the learning performance. Thus, when designing e-learning system, it is necessary to consider these two factors at the same time. Second, the indirectly mediating effect on the system quality and synchronization factors turned out to be significant in learning flow. Third, the result of regression analysis on the contents of utilizing dummy variable presents that the teacher's explanation has greater influence than multimedia has to the academic performance, and furthermore, the test difference showed no significance. Further research should be undertaken to consider the learner's degree of acceptance which reflects various aspects for building m-learning or u-learning.