• 디지털산업정보학회논문지
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• 제18권4호
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• pp.67-75
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• 2022

Recently, e-learning has been attracting significant attention due to COVID-19. However, while e-learning has many advantages, it has disadvantages as well. One of the main disadvantages of e-learning is that it is difficult for teachers to continuously and systematically monitor learners. Although services such as personalized e-learning are provided to compensate for the shortcoming, systematic monitoring of learners' concentration is insufficient. This study suggests a method to evaluate the learner's concentration by applying machine learning techniques. In this study, emotion and gaze data were extracted from 184 videos of 92 participants. First, the learners' concentration was labeled by experts. Then, statistical-based status indicators were preprocessed from the data. Random Forests (RF), Support Vector Machines (SVMs), Multilayer Perceptron (MLP), and an ensemble model have been used in the experiment. Long Short-Term Memory (LSTM) has also been used for comparison. As a result, it was possible to predict e-learners' concentration with an accuracy of 90.54%. This study is expected to improve learners' immersion by providing a customized educational curriculum according to the learner's concentration level.

• 한국위성정보통신학회논문지
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• 제12권3호
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• pp.74-79
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• 2017

본 논문에서는 머신러닝 알고리즘 중 하나인 다층 퍼셉트론을 기반으로 전력선통신 시스템에서의 수신 신호를 이용하여 송신단에서 전송한 원신호를 예측하는 시스템 모델을 제안한다. 전력망을 활용한 통신 방식을 사용하는 전력선통신 시스템은 일반적인 통신설로를 활용하는 통신 방식에 비해 잡음이 많다. 이 때문에 전력선통신 시스템의 성능이 저하가 되는 문제가 발생한다. 이를 해결하기 위해 본 논문에서 제안하는 통신 시스템 모델을 이용하면 원신호 예측을 통해 잡음의 영향이 최소화되어 전력선통신 시스템의 성능저하를 완화시킨다. 본 논문에서는 제안한 통신 시스템 모델을 백색 잡음 환경에 적용하여 시뮬레이션을 해봄으로써 원신호가 예측 되는지를 입증한다.

• 한국산학기술학회논문지
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• 제22권4호
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• pp.580-586
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• 2021

현재 사출성형분야의 Computer Aided Testing(CAT) 방법론으로 CAE(Computer Aided Engineering)를 이용한 수치 해석 기법이 주를 이루고 있다. 그러나 최근 시뮬레이션에 추가로 인공지능 기법을 응용하는 방법론이 연구되고 있다. 우리는 지난 연구에서 다양한 Machine Learning 기법을 활용하여 사출 성형 공정에 따른 변형 결과를 비교하였으며, 최종적으로 MLP(Multi-Layer Perceptron) 예측모델을 생성하였고, HMA(Hybrid Metaheuristic Algorithm)를 이용하여 최적화 결과를 얻어냈다. 그러나 MLP는 예측 성능이 우수한 반면 블랙박스와 같이 결정 과정에 대한 설명이 부족하다. 본 연구에서는 Radiator Tank 부품에 대하여 사출 성형 해석 소프트웨어인 Autodesk Moldflow 2018을 이용하여 수치 해석 기법으로 데이터를 생성하고, Machine Learning 소프트웨어인 RapidMiner Studio version 9.5를 활용하여 여러 Machine Learning Algorithms 모델을 생성하여 평균 제곱근 오차를 비교하였다. Decision-tree는 Root Mean Square Error(RMSE) 값이 다른 Machine Learning 기법에 비해 양호한 예측 성능을 갖추고 있었다. Decision-tree의 크기를 결정하는 Maximal Depth에 따라 분류 기준을 높일 수 있지만 복잡성도 함께 증가시켰다. Decision-tree를 이용하여 구속 조건을 만족하는 중간 값을 선정하여 시뮬레이션을 진행한 결과 기존의 시뮬레이션만 진행한 것보다 7.7%의 개선 효과가 있었다.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1251-1258
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• 2019

Intelligent tutoring system enables users to effectively learn by utilizing various artificial intelligence techniques. For instance, it can recommend a proper curriculum or learning method to individual users based on their learning history. To do this effectively, user's characteristics need to be analyzed and classified based on various aspects such as interest, learning ability, and personality. Even though data labeled by the characteristics are required for more accurate classification, it is not easy to acquire enough amount of labeled data due to the labeling cost. On the other hand, unlabeled data should not need labeling process to make a large number of unlabeled data be collected and utilized. In this paper, we propose a semi-supervised learning method based on feedback variational auto-encoder(FVAE), which uses both labeled data and unlabeled data. FVAE is a variation of variational auto-encoder(VAE), where a multi-layer perceptron is added for giving feedback. Using unlabeled data, we train FVAE and fetch the encoder of FVAE. And then, we extract features from labeled data by using the encoder and train classifiers with the extracted features. In the experiments, we proved that FVAE-based semi-supervised learning was superior to VAE-based method in terms with accuracy and F1 score.

• 한국통신학회논문지
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• 제16권2호
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• pp.128-136
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• 1991

A neural network of multi layer perception type, learned by error back propagation learning rule, is generally used for the verification or clustering of similar type of patterns. When learning is completed, the network has a constant value of output depending on a pattern. This paper shows that the intensity of neuron's out put can be controlled by a function which intensifies the excitatory interconnection coefficients or the inhibitory one between neurons in output layer and those in hidden layer. In this paper the value of factor in the function to control the output is derived from the know values of the neural network after learning is completed And also this paper show that the amount of an increased neuron's output in output layer by arbitary value of the factor is derived. For the applications increased recognition performance of a pattern than has distortion is introduced and the output of partially damaged neurons are first managed and this paper shows that the reduced recognition performance can be recovered.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.60-64
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• 2001

In this paper, a new learning methodology for Kernel Methods is suggested that results in a sparse representation of kernel space from the training patterns for classification problems. Among the traditional algorithms of linear discriminant function(perceptron, relaxation, LMS(least mean squared), pseudoinverse), this paper shows that the relaxation procedure can obtain the maximum margin separating hyperplane of linearly separable pattern classification problem as SVM(Support Vector Machine) classifier does. The original relaxation method gives only the necessary condition of SV patterns. We suggest the sufficient condition to identify the SV patterns in the learning epochs. Experiment results show the new methods have the higher or equivalent performance compared to the conventional approach.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1022-1027
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• 1991

A new method to control a robot manipulator by neural networks is proposed. The controller is composed of both a PD controller and a neural network-based feedforward controller. MLP(multi-layer perceptron) neural network is used for the feedforward controller and trained by BP(back-propagation) learning rule. Error terms for BP learning rule are composed of the outputs of a PD controller and the acceleration errors of manipulator joints. We compare the proposed method with existing ones and contrast performances of them by simulation. Also, We discuss the real application of the proposed method in consideration of the learning time of the neural network and the time required for sensing the joint acceleration.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.85-88
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• 2003

강화학습의 한가지 방법인 Q-learning은 최근에 Linear Quadratic Regulation(이하 LQR) 문제에 성공적으로 적용된 바 있다. 특히, 시스템 모델의 파라미터에 대한 구체적인 정보없이 적절한 입ㆍ출력만으로 학습을 통해 문제의 해결이 가능하므로 상황에 따라 매우 실용적인 방법이 될 수 있다. 뉴럴-큐 기법은 이러한 Q-learning의 Q-value를 MLP(multilayer perceptron) 신경망의 출력으로 대치시켜, 비선형 시스템의 최적제어 문제를 다룰 수 있게 한 방법이다. 그러나, 뉴럴-큐 기법은 신경망의 구조를 먼저 결정한 후 역전파 알고리즘을 이용해 학습하는 절차를 행하므로, 시행착오를 통해 신경망 구조를 결정해야 한다는 점, 역전파 알고리즘의 적용에 따라 신경망의 연결강도 값들이 지역적 최적해로 수렴한다는 점등의 문제점이 있다. 본 논문에서는 뉴럴-큐 학습의 도구로 KFD회귀를 이용하여 Q 함수의 근사 기법을 제안하고 관련 수식을 유도하였다. 그리고, 모의 실험을 통하여, 제안된 뉴럴-큐 방법의 적용 가능성을 알아보았다.

• 전자공학회논문지B
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• 제28B권5호
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• pp.394-403
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• 1991

This paper described the development of input driven multilayer perceptron(IDMLP) neural network and it's application to the Korean spoken digit recognition. The IDMPLP neural network used here and the learning algorithm for this network was proposed newly. In this model, weight value is integer and transfer function in the neuron is hard limit function. According to the result of the network learning for the some kinds of input data, the number of network layers is one or more by the difficulties of classifying the inputs. We tested the recognition of binaried data for the spoken digit 0 to 9 by means of the proposed network. The experimental results are 100% and 96% for the learning data and test data, respectively.

• 전자공학회논문지B
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• 제32B권9호
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• pp.1137-1153
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• 1995

It is not yet possible to solve the optimal number of neurons in hidden layer at neural networks. However, it has been proposed and proved by experiments that there is a limit in increasing the number of neuron in hidden layer, because too much incrememt will cause instability,local minima and large error. This paper proposes a module neural controller with pattern recognition ability to solve the above trade-off problems and to obtain fast learning convergence speed. The proposed neural controller is composed of several module having Multi-layer Perrceptron(MLP). Each module have the less neurons in hidden layer, because it learns only input patterns having a similar learning directions. Experiments with six joint robot manipulator have shown the effectiveness and the feasibility of the proposed the parallel module neural controller with pattern recognition perceptron.