• 대한수학교육학회지:수학교육학연구
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• 제11권1호
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• pp.51-66
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• 2001

In this paper, we tried to examine the didactical transpositions of the mathematical knowledges in the computer-based environment for mathematics learning and teaching, and also analyse the extreme didactical problems Computer has been regarded as an alterative that could overcome the difficulties in the teaching and learning of mathematics and many broad studies have been made to use computers in mathematics teaching and learning. But Any systematic analysis on the didactical problems of the computer-based environment for mathematics education has not been tried up to this time. In this paper, first of all, we analysed the didactical problems in the computer-based environment, and then, the mode of using computer for mathematics teaching and learning.

• 공학교육연구
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• 제12권4호
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• pp.150-158
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• 2009

본 연구의 목적은 비실시간 온라인 토론에서 학습자의 자기조절학습전략이 토론 만족도와 참여 메시지 유형에 미치는 효과를 살펴보는 것이다. 본 연구의 결과 분석을 통해 도출된 결론은 다음과 같다. 우선, 비실시간 온라인 토론에서 자기조절학습전략이 토론 만족도에 영향을 주는 것으로 나타났다. 다음으로, 자기조절학습전략이 인지적 차원의 참여 유형 형태에 영향을 주는 것으로 나타났다. 이상의 연구결과를 통해, 비실시간 온라인 토론에서 학습자의 인지적 차원의 참여와 토론 만족 수준의 향상을 위해 자기조절학습전략을 활용할 것이 제안되었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.880-883
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• 1996

This paper suggests a speed control algorithm for the ICC(Intelligent Cruise Controller) system. The speed controller is designed using the fuzzy controller which shows the good performance in nonlinear system having the complex mathematical model. The fuzzy controller was equipped with the capability of a self-learning in real time in order to maintain the good performance of the speed controller in a time-varying environment the self-learning properties and the performance of the fuzzy controller are showed via computer simulation. The suggested fuzzy controller will be applied to the PRV-III which is our test vehicle.

• 대한의용생체공학회:의공학회지
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• 제41권1호
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• pp.48-54
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• 2020

In this paper, we deal with a liver fibrosis classification problem using ultrasound B-mode images. Commonly representative methods for classifying the stages of liver fibrosis include liver biopsy and diagnosis based on ultrasound images. The overall liver shape and the smoothness and roughness of speckle pattern represented in ultrasound images are used for determining the fibrosis stages. Although the ultrasound image based classification is used frequently as an alternative or complementary method of the invasive biopsy, it also has the limitations that liver fibrosis stage decision depends on the image quality and the doctor's experience. With the rapid development of deep learning algorithms, several studies using deep learning methods have been carried out for automated liver fibrosis classification and showed superior performance of high accuracy. The performance of those deep learning methods depends closely on the amount of datasets. We propose an enhanced U-net architecture to maximize the classification accuracy with limited small amount of image datasets. U-net is well known as a neural network for fast and precise segmentation of medical images. We design it newly for the purpose of classifying liver fibrosis stages. In order to assess the performance of the proposed architecture, numerical experiments are conducted on a total of 118 ultrasound B-mode images acquired from 78 patients with liver fibrosis symptoms of F0~F4 stages. The experimental results support that the performance of the proposed architecture is much better compared to the transfer learning using the pre-trained model of VGGNet.

• 제어로봇시스템학회논문지
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• 제19권12호
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• pp.1099-1104
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• 2013

This paper presents a learning-based visual inspection method that addresses the need for an improved adaptability of a visual inspection system for parts verification in panorama sunroof assembly lines. It is essential to ensure that the many parts required (bolts and nuts, etc.) are properly installed in the PLC sunroof manufacturing process. Instead of human inspectors, a visual inspection system can automatically perform parts verification tasks to assure that parts are properly installed while rejecting any that are improperly assembled. The proposed visual inspection method is able to adapt to changing inspection tasks and environmental conditions through an efficient learning process. The proposed system consists of two major modules: learning mode and test mode. The SVM (Support Vector Machine) learning algorithm is employed to implement part learning and verification. The proposed method is very robust for changing environmental conditions, and various experimental results show the effectiveness of the proposed method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.254-257
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• 1993

This paper presents a new method to implement Hebbian learning method on artificial neural network. In hebbian learning algorithm, complexity in terms of multiplications is high. To save the chip area, we consider a new learning circuit. By calculating similarity, or correlation between $X_i$ and $O_i$, large portion of circuits commonly used in conventional neural networks is not necessary for this new hebbian learning circuit named COR. The output signals of COR is applied to weight storage capacitors for direct control the voltages of the capacitors. The weighted sum, ${\Sigma}W_{ij}O_j$, is realized by multipliers, whose output currents are summed up in one line which goes to learning circuit or output circuit. The drain current of the multiplier can produce positive or negative synaptic weights. The pass transistor selects eight learning mode or recall mode. The layout of an learnable six-neuron fully connected Hopfield neural network is designed, and is simulated using PSPICE. The network memorizes, and retrieves the patterns correctly under the existence of minor noises.

• 로봇학회논문지
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• 제17권2호
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• pp.221-229
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• 2022

Sliding mode control (SMC) is a robust control method to control a robot arm with nonlinear properties. A high switching gain of SMC causes chattering problems, although the SMC allows the adequate control performance by giving high switching gain, without the exact robot model containing nonlinear and uncertainty terms. In order to solve this problem, SMC with sliding perturbation observer (SMCSPO) has been researched, where the method can reduce the chattering by compensating the perturbation, which is estimated by the observer, and then choosing a lower switching control gain of SMC. However, optimal gain tuning is necessary to get a better tracking performance and reducing a chattering. This paper proposes a method that the Q-learning automatically tunes the control gains of SMCSPO with an iterative operation. In this tuning method, the rewards of reinforcement learning (RL) are set minus tracking errors of states, and the action of RL is a change of control gain to maximize rewards whenever the iteration number of movements increases. The simple motion test for a 7-DOF robot arm was simulated in MATLAB program to prove this RL tuning algorithm. The simulation showed that this method can automatically tune the control gains for SMCSPO.

• 대한전기학회논문지:전력기술부문A
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• 제53권12호
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• pp.637-644
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• 2004

This paper proposes an expert system with the knowledge learning capability which can enhance the safety and effectiveness of substation operation in the automated substation as well as existing substation by inferring multiple events such as main transformer fault, busbar fault and main transformer work schedule under multiple inference mode and multiple objective mode and by considering totally the switch status and the main transformer operating constraints. Especially inference mode includes the local minimum tree search method and pattern recognition method to enhance the performance of real-time bus reconfiguration strategy. The inference engine of the expert system consists of intuitive inferencing part and logical inferencing part. The intuitive inferencing part offers the control strategy corresponding to the event which is most similar to the real event by searching based on a minimum distance classification method of pattern recognition methods. On the other hand, logical inferencing part makes real-time control strategy using real-time mode(best-first search method) when the intuitive inferencing is failed. Also, it builds up a knowledge base or appends a new knowledge to the knowledge base using pattern learning function. The expert system has main transformer fault, main transformer maintenance work and bus fault processing function. It is implemented as computer language, Visual C++ which has a dynamic programming function for implementing of inference engine and a MFC function for implementing of MMI. Finally, it's accuracy and effectiveness is proved by several event simulation works for a typical substation.

• 컴퓨터교육학회논문지
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• 제16권3호
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• pp.49-60
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• 2013

본 연구는 블렌디드 이러닝이 대학의 강의실 위주의 수업에서 효과성을 향상시킬 수 있는지를 검증하고자 하였다. 먼저, 수업 사례를 분석하여 블렌디드 이러닝 운영방식으로 CbE(Class based E-learning)와 EbC(E-learning based Class) 방식을 도출하고, 수업구조로 Z형(Zigzag model)과 H형(Ladder model)을 정의하였다. 연구의 목적을 달성하기 위하여 A대학의 "엑셀실무" 과목에 CbE 방식의 H형 블렌디드 이러닝을 운영하였다. 집단은 사이버강의 학습참여비율의 50%이상인 집단(그룹 1)과 그렇지 않은 집단(그룹 2)으로 나누고, 자료의 분석은 $x^2$-검정, t -검정으로 학업 성취도를 비교하였다. 사이버강의 학습참여비율과 합격여부의 관계를 규명하기 위해 로지스틱 회귀분석을 실시하였다. 검정 결과, 그룹 1이 학업 성취도에서 통계적으로 유의하게 높게 나타났다. 로지스틱 회귀분석 결과에서 사이버강의 학습참여비율은 합격여부를 예측하는 유의미한 변인으로 규명됨에 따라, 블렌디드 이러닝의 효과성이 확증되었다. 연구 결과, H형 블렌디드 이러닝은 강의실 수업의 단점을 보완하여 학업 성취도와 학습 만족도를 향상시키고, 학습자가 블렌디드 이러닝 수업 방식에 대해 긍정적인 인식을 갖도록 한 것으로 나타났다. 끝으로 성공적으로 블렌디드 이러닝을 운영하기 위한 전략과 대학에서의 활성화 방안에 대해 제언하였다.

• 한국기계가공학회지
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• 제19권9호
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• pp.47-52
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• 2020

With the 4th industrial revolution, condition monitoring using machine learning techniques has become popular among researchers. An overload due to complex operations causes several irregularities in MOSFETs. This study investigated the acquired voltage to analyze the overcurrent effects on MOSFETs using a failure mode effect analysis (FMEA). The results indicated that the voltage pattern changes greatly when the current is beyond the threshold value. Several features were extracted from the collected voltage signals that indicate the health state of a switched-mode power supply (SMPS). Then, the data were reduced to a smaller sample space by using a principal component analysis (PCA). A robust machine learning algorithm, the support vector machine (SVM), was used to classify different health states of an SMPS, and the classification results are presented for different parameters. An SVM approach assisted by a PCA algorithm provides a strong fault diagnosis framework for an SMPS.