• 한국정보시스템학회지:정보시스템연구
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• 제18권2호
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• pp.111-126
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• 2009

A high-speed Internet has brought a rapid spread of Web Based Distance Learning(WBDL). Even though the WBDL was considered a new methodology to overcome the limitation of a traditional education, it evolves not as alternatives but as strategic augmenting tools for a traditional face-to-face education. The WBDL systems accommodate diverse services such as e-Learning, e-Mentoring, and Blended Learning in order to give satisfactions to learners and increase the learning effectiveness. This study suggested the WBDL system's and learner's characteristics as two major affecting factors, in which two independent variables were respectively selected. A mediating effect of learning motivation between the independent variables and learning achievement was empirically tested. The interactions between the WBDL sysrem and learners were also tested on the view points of the moderating effects between the learning motivation and the learning achievement. The results showed that the mediating effects of learning motivation and the moderating effects of the system interactions were statistically significant.

• 대한수학교육학회지:학교수학
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• 제18권4호
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• pp.881-901
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• 2016

본 연구는 '시간, 거리, 속력'의 관계에 대한 학생들의 인식과 표현을 교수실험을 통하여 세밀하게 관찰한 연구이다. 이 과정에서 학생들의 '시간, 거리, 속력'의 관계에 대한 인식의 변화가 드러났으며, 학생들은 평균속력에 대하여 거리함수에서 구간의 양 끝점을 잇는 선분의 기울기라는 관점으로 인식하는 것 외에도 속력함수에서 사각형의 높이로 인식하여 '시간, 거리, 속력'을 이해하고 있음을 보여주었다. 이 과정에서 '거리=시간${\times}$속력'의 관계를 '거리=시간${\times}$평균속력'으로 확장하는 장면을 드러내었다. 본 연구는 제한된 소수 학생을 대상으로 교수실험을 진행하였지만, 학생들의 '시간, 거리, 속력'의 관계에 대한 인식과 표현의 변화 과정에 대한 관찰을 통하여 여러 시사점을 제시하였다. 이러한 연구 결과가 추후 미적분 학습 모델 구성을 위한 다양한 연구의 시발점이 되기를 기대해본다.

• 한국기계가공학회지
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• 제20권9호
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• pp.35-41
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• 2021

A rotor is a crucial component in various mechanical assemblies. Additionally, high-speed and high-efficiency components are required in the automotive industry, manufacturing industry, and turbine systems. In particular, the failure of high-speed rotating bearings has catastrophic effects on auxiliary systems. Therefore, bearing reliability and fault diagnosis are essential for bearing maintenance. In this work, we performed failure mode and effect analysis on bearing rotors and determined that corrosion is the most critical failure type. Furthermore, we conducted experiments to extract vibration characteristic data and preprocess the vibration data through principle component analysis. Finally, we applied a machine learning algorithm called support vector machine to diagnose the failure and observed a classification performance of 98%.

• 한국산업융합학회 논문집
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• 제23권2_2호
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• pp.263-270
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• 2020

In this paper, In this paper, we describe the UAV system using image processing for autonomous quadcopters, where they can apply logistics, rescue work etc. we propose high-speed hovering height and posture control method based on state feedback control with CNN from camera because we can get image of the information only every 30ms. Finally, we show the advantages of proposed method by simulations and experiments.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.482-482
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• 2000

The essential requirements of neural network for human skill transfer are fast convergence, high storage capacity, and strong noise immunity. Bidirectional associative memory(BAM) suffering from low storage capacity and abundance of spurious memories is rarely used for skill transfer application though it has fast and wide association characteristics for visual data. This paper suggests generalization of classical BAM structure and new learning algorithm which uses supervised learning to guarantee perfect recall starting with correlation matrix. The generalization is validated to accelerate convergence speed, to increase storage capacity, to lessen spurious memories, to enhance noise immunity, and to enable multiple association using simulation work.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 봄 학술발표논문집 Vol.31 No.1 (B)
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• pp.571-573
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• 2004

Speaker verification systems based on multilayer perceptrons (MLPs) have good prospects in reliability and flexibility required as a successful authentication system. However, the poor learning speed of the error backpropagation (EBP) which is representative learning method of MLPs is the major defect to be complemented to achieve real-time user enrollments. In this paper, we implement an MLP-based speaker verification system and apply the existing two methods of the omitting patterns in instant learning (OIL) and the discriminative cohort speakers (DCS) to approach real-time enrollment. An evaluation of the system on a Korean speech database demonstrates the feasibility of the system as a speaker verification system of high performance.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.173-180
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• 2018

This paper proposes a method to improve the performance of face recognition via super-resolution method using sparse representation and deep learning from low-resolution facial images. Recently, there have been many researches on ultra-high-resolution images using deep learning techniques, but studies are still under way in real-time face recognition. In this paper, we combine the sparse representation and deep learning to generate super-resolution images to improve the performance of face recognition. We have also improved the processing speed by designing in parallel structure when applying sparse representation. Finally, experimental results show that the proposed method is superior to conventional methods on various images.

• 한국멀티미디어학회논문지
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• 제11권6호
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• pp.883-890
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• 2008

It becomes enabled to provide variety of multimedia contents through mobile service with the development of high-speed 3rd generation mobile communication and handsets. MMS (Multimedia Messaging Service) can be displayed in the presentation format which is unified the various multimedia contents such as text, audio, image, video, etc. It is applicable as a new type of ubiquitous learning. In this study we propose to design a mobile learning system by providing profiles which meets the standard of CC/PP and by generating multimedia messages based on SMIL language through the adaptation steps according to the learning environment, the content type, and the device property of learners.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.1033-1036
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• 1999

This paper proposes an efficient fault diagnosis for digital circuits using multilayer neural networks. The efficient learning algorithm is also proposed for the multilayer neural network, which is combined the steepest descent for high-speed optimization and the dynamic tunneling for global optimization. The fault-diagnosis system using the multilayer neural network of the proposed algorithm has been applied to the parity generator circuit. The simulation results shows that the proposed system is higher convergence speed and rate, in comparision with system using the backpropagation algorithm based on the gradient descent.

• 한국전산구조공학회논문집
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• 제35권6호
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• pp.375-380
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• 2022

고속철도 교량은 열차 하중에 의한 공진으로 인한 동적응답 증폭의 위험이 존재하므로 설계기준에 따른 동적해석을 통한 주행안전성 및 승차감 검토를 반드시 수행하여야 한다. 그러나 주행안전성 및 승차감 산정 절차는 열차의 종류별로 임계속도를 포함하여 설계속도의 110km/h까지 10km/h 간격으로 동적해석을 일일이 수행해야 하므로 많은 시간과 경비가 소요된다. 이 연구에서는 딥러닝 알고리즘을 활용하여 별도의 동적해석 없이 주행안전성 및 승차감을 사전에 예측할 수 있는 딥러닝 기반 예측 시스템 개발하였다. 제안된 시스템은 철도교량의 열차별, 속도별 동적해석 결과를 학습한 후 학습 완료된 신경망을 기반으로 한 예측 시스템이며, 열차속도, 교량 특성 등의 입력파라미터에 따른 주행안전성 및 승차감 산정 결과를 사전에 예측할 수 있다. 제안된 시스템의 성능을 확인하기 위하여 단경간 직선 단순보 교량을 대상으로 한 주행안전성 및 승차감 예측을 수행하였고, 주행안전성 및 승차감 산정을 위한 상판 연직변위 및 상판 연직가속도를 높은 정확도로 예측할 수 있음을 확인하였다.