• Journal of Multimedia Information System
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• 제7권1호
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• pp.1-10
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• 2020

The image analysis is an important and predominant task for classifying the different parts of the image. The analysis of complex image analysis like histopathological define a crucial factor in oncology due to its ability to help pathologists for interpretation of images and therefore various feature extraction techniques have been evolved from time to time for such analysis. Although deep reinforcement learning is a new and emerging technique but very less effort has been made to compare the deep learning and deep reinforcement learning for image analysis. The paper highlights how both techniques differ in feature extraction from complex images and discusses the potential pros and cons. The use of Convolution Neural Network (CNN) in image segmentation, detection and diagnosis of tumour, feature extraction is important but there are several challenges that need to be overcome before Deep Learning can be applied to digital pathology. The one being is the availability of sufficient training examples for medical image datasets, feature extraction from whole area of the image, ground truth localized annotations, adversarial effects of input representations and extremely large size of the digital pathological slides (in gigabytes).Even though formulating Histopathological Image Analysis (HIA) as Multi Instance Learning (MIL) problem is a remarkable step where histopathological image is divided into high resolution patches to make predictions for the patch and then combining them for overall slide predictions but it suffers from loss of contextual and spatial information. In such cases the deep reinforcement learning techniques can be used to learn feature from the limited data without losing contextual and spatial information.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.361-367
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• 1998

The main two applications of the Genetic Algorithms(GA) are the optimization and the machine learning. Machine Learning has two objectives that make the complex system learn its environment and produce the proper output of a system. The machine learning using the Genetic Algorithms is called GA machine learning or genetic-based machine learning (GBML). The machine learning is different from the optimization problems in finding the rule set. In optimization problems, the population of GA should converge into the best individual because optimization problems, the population of GA should converge into the best individual because their objective is the production of the individual near the optimal solution. On the contrary, the machine learning systems need to find the set of cooperative rules. There are two methods in GBML, Michigan method and Pittsburgh method. The former is that each rule is expressed with a string, the latter is that the set of rules is coded into a string. Th classifier system of Holland is the representative model of the Michigan method. The classifier systems arrange the strength of classifiers of classifier list using the message list. In this method, the real time process and on-line learning is possible because a set of rule is adjusted on-line. A classifier system has three major components: Performance system, apportionment of credit system, rule discovery system. In this paper, we solve the food search problem with the learning and evolution of an artificial ant using the learning classifier system.

• 한국정보통신학회논문지
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• 제16권4호
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• pp.843-852
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• 2012

본 논문에서는 복잡적응계 이론에 근거하여 공학교육인증제도와 글쓰기와의 관계를 분석하고 효과적인 공학적 글쓰기 교육 방법을 연구하였다. 인증제도 도입으로 인하여 공학교육은 창발현상을 지속적으로 유지하며 새로운 질서체계를 갖추어 나가고 있는 가운데 의사소통의 중요성을 부각시키고, 다양한 공학적 글쓰기 교육 모형을 요구하고 있다. 이에 대하여 본 연구는 상황학습방법과 글쓰기 포트폴리오 도입 방법을 제안하고, 이를 통해 학생들의 글쓰기 능력향상에 변화가 있었음을 확인하였다. 이러한 논의는 공학교육에서 새로운 공학적 글쓰기 교육 방안을 모색하는 계기가 될 것이다.

• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.358-364
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• 2007

Intelligent walking modeling of humanoid robot using learning based neuro-fuzzy system is presented in this paper. Walking pattern, trajectory of the zero moment point (ZMP) in a humanoid robot is used as an important criterion for the balance of the walking robots but its complex dynamics makes robot control difficult. In addition, it is difficult to generate stable and natural walking motion for a robot. To handle these difficulties and explain empirical laws of the humanoid robot, we are modeling practical humanoid robot using neuro-fuzzy system based on the two types of natural motions which are walking trajectories on a t1at floor and on an ascent. Learning based neuro-fuzzy system employed has good learning capability and computational performance. The results from neuro-fuzzy system are compared with previous approach.

• 한국BIM학회 논문집
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• 제7권1호
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• pp.28-35
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• 2017

Cities have complex system composed diverse activities. The activities in cities have complex relationship that creates diverse urban phenomena. Big Data is emerging technology in order to understand such complex network. This research aims to understand such relations by analysing the diverse city indexes. 28 indexes were collected in 25 of districts in Seoul city and analysed to find a weighted correlation. By defining the correlation values of certain years, it tries to predict the missed index values, "happiness" of each districts in other years. The result presents that the overall prediction accuracy 70.25%. However, for further discussion, the result is considered that this methods may not enough to use in practice, since the data has inconstant accuracy by different learning years.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.126-129
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• 2020

We introduce a lifetime assessment technique using deep learning algorithm with complex electrical parameters such as resistivity, permittivity, impedance parameters as integrated indicators for predicting the degradation of the organic molecules. The evaluation system consists of fully automated in-situ measurement system and multiple layer perceptron learning system with five hidden layers and 1011 perceptra in each layer. Prediction accuracies are calculated and compared depending on the physical feature, learning hyperparameters. 62.5% of full time-series data are used for training and its prediction accuracy is estimated as r-square value of 0.99. Remaining 37.5% of the data are used for testing with prediction accuracy of 0.95. With k-fold cross-validation, the stability to the instantaneous changes in the measured data is also improved.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2018년도 제58차 하계학술대회논문집 26권2호
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• pp.364-366
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• 2018

본 논문에서는 Digital Twin 기반 건설기계 지능화를 위한 실시간 건설기계 데이터 처리 및 이상 유무 예측 시스템을 제안한다. 이 시스템은 빅 데이터 분산처리 기반으로 실시간 스트리밍 처리가 가능하며, CEP(Complex Event Processing)의 Sliding Window Operator를 활용한 Rule 적용을 통해 건설기계 데이터 처리 및 분석한다. 분석된 결과로 건설기계의 실시간 이상 유무를 판단할 수 있으며, 결과를 기반으로 Deep Learning 기술을 적용하고 학습된 모델을 통해 건설기계의 이상 유무를 예측하여 원활한 부품관리를 할 수 있다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.101-105
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• 1989

The iterative learning controller makes the system output follow the desired output over a finite time interval through iterating trials. In this paper, first we discuss that the design problem of learning controller is originally the design problem of the inverse model. Then we show that the tracking error which is the difference between the desired output and the system output is reduced monotonically by properly modeled inverse system if the magnitude of the learning operator being introduced is bounded within the unit circle in complex domain. Also it would be shown that the conventional learning control method is a kind of extremely simplified inverse model learning control method of the objective controlled system. Hence this control method can be considered as a generalization of the conventional learning control method. The more a designer model the objective controlled system precisely, the better the performance of the approximated inverse model learning controller would be. Finally we compare the performance of the conventional learning control method with that of the approximated inverse model learning control method by computer simulation.

• 대한수학교육학회지:학교수학
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• 제9권2호
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• pp.291-312
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• 2007

복소수의 취급이 처음으로 시작되는 제 10-단계 교과서들을 살펴본 결과 그 도입 방법은 모두 이차방정식 $x^2+1=0$을 만족하는 해를 생각하는 과정에서 새로운 수 i를 도입하여 사용하고 있다. 이 방법은 우선 새로운 수 i의 도입이 인위적이기 때문에 학생들이 도입과정에서 혼란스러워하며, 이차방정식을 잘 이해하지 못하는 학생들이 이해하도록 하게 하는 것이 어렵다. 이에 비하여 복소수 도입을 좌표평면 위의 점인 순서쌍과 화살표를 사용하여 도입하면 이차방정식을 이해하지 못한 학생들까지도 흥미를 갖고 학습에 임하게 할 수 있고, 또 수체계를 체계적인 확장으로 다룰 수 있어 학습 효과도 높일 수 있다. 그러나 고등학교 과정에 적합한 지도 내용의 개발이 없어서인지 고등학교에서 순서쌍을 사용한 복소수 도입은 시도되고 있지 않다. 여기서는 수체계의 확장 과정을 초등학교 과정부터 중학교과정을 거쳐 복소수 도입까지 연계되는 체계적이고 가시적인 표현을 통하여 학습할 수 있도록 지도 내용을 개발하였다. 그리고 이 내용으로 지도를 하여본 결과 개발된 학습내용으로 학습지도가 가능함을 알았고, 이 학습이 바람직한 학습임도 알 수 있었다.

• 한국통신학회논문지
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• 제21권9호
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• pp.2456-2469
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• 1996

A digital satellite communication channel has a nonlinearity with memory due to saturation characeristis of the high poer amplifier in the satellite and transmitter/receiver linear filter used in the overall system. In this paper, we propose a complex radial basis function network(CRBFN) based adaptive equalizer for compensation of nonlinearities in digital satellite communication channels. The proposed CRBFN untilizes a complex-valued hybrid learning algorithm of k-means clustering and LMS(least mean sequare) algorithm that is an extension of Moody Darken's algorithm for real-valued data. We evaluate performance of CRBFN in terms of symbol error rates and mean squared errors nder various noise conditions for 4-PSK(phase shift keying) digital modulation schemes and compare with those of comples pth order inverse adaptive Volterra filter. The computer simulation results show that the proposed CRBFN ehibits good equalization, low computational complexity and fast learning capabilities.