• Asia pacific journal of information systems
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• 제27권1호
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• pp.38-53
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• 2017

As sensor technologies and image processing technologies make collecting information on users' behavior easy, many researchers have examined automatic emotion recognition based on facial expressions, body expressions, and tone of voice, among others. Specifically, many studies have used normal cameras in the multimodal case using facial and body expressions. Thus, previous studies used a limited number of information because normal cameras generally produce only two-dimensional images. In the present research, we propose an artificial neural network-based model using a high-definition webcam and Kinect to recognize users' emotions from facial and bodily expressions when watching a movie trailer. We validate the proposed model in a naturally occurring field environment rather than in an artificially controlled laboratory environment. The result of this research will be helpful in the wide use of emotion recognition models in advertisements, exhibitions, and interactive shows.

• Journal of Platform Technology
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• 제10권1호
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• pp.20-28
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• 2022

인공지능기반의 건강 데이터 검증은 임상 연구에 도움을 줄 뿐만 아니라, 새로운 치료법을 개발하는데 필수 요소가 되었다. 미국 식품의약 관리국이 의학진단 분야 중 인공지능을 이용하여 성인 당뇨병 환자의 경증 이상 당뇨병성 망막증을 감지하는 의료기기 마케팅을 승인한 이래, 인공지능을 이용한 테스트가 증가하고 있다. 본 연구에서는 구글에서 지원하는 Teachable Machine 을 이용하여 이미지 분류 기반의 인공지능모델을 생성하고, 학습을 통한 예측 모델을 완성하였다. 이는 현재 만성질환의 환자들 중 발생하는 안구 질환 중 백내장의 조기 발견하는데 용이하게 할 뿐만 아니라, 눈 건강을 위해 헬스케어 프로그램으로 안 질환 예방을 위한 디지털 개인건강 헬스케어 앱을 개발하기 위한 기초 연구로 진행되었다.

• 제어로봇시스템학회논문지
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• 제10권9호
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• pp.771-778
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• 2004

Recently, various applications of robot system become more popular in accordance with rapid development of computer hardware/software, artificial intelligence, and automatic control technology. Formerly robots mainly have been used in industrial field, however, nowadays it is said that the robot will do an important role in the home service application. To make the robot more useful, we require further researches on implementation of natural communication method between the human and the robot system, and autonomous behavior generation. The gesture recognition technique is one of the most convenient methods for natural human-robot interaction, so it is to be solved for implementation of intelligent robot system. In this paper, we describe the state-of-the-art of advanced gesture recognition technologies for intelligent robots according to three methods; sensor based method, feature based method, appearance based method, and 3D model based method. And we also discuss some problems and real applications in the research field.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권4호
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• pp.238-245
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• 2016

Inverters are considered the basic building blocks of industrial electrical drive systems that are widely used for various applications; however, the failure of electronic switches mainly affects the constancy of these inverters. For safe and reliable operation of an electrical drive system, faults in power electronic switches must be detected by an efficient system that is capable of identifying the type of faults. In this paper, an open switch fault identification technique for a three-phase inverter is presented. Single, double, and triple switching faults can be diagnosed using this method. The detection mechanism is based on stator current analysis. Discrete wavelet transform (DWT) using Daubechies is performed on the Clarke transformed (-) stator current and features are extracted from the wavelets. An artificial neural network is then used for the detection and identification of faults. To prove the feasibility of this method, a Simulink model of the DWT-based feature extraction scheme using a neural network for the proposed fault detection system in a three-phase inverter with an induction motor is briefly discussed with simulation results. The simulation results show that the designed system can detect faults quite efficiently, with the ability to differentiate between single and multiple switching faults.

• 한국지능시스템학회논문지
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• 제16권4호
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• pp.449-453
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• 2006

본 논문은 시설물의 효율적인 유지보수 관리를 위해 이미지 도면의 부품정보를 효율적으로 활용할 수 있는 방안을 제시한다. 시설물 설계 도면에는 시설물을 구성하는 부품에 대한 정보가 표현되어 있고, 각 부품을 구분하기 위해 레전드 문자가 표기되어 있다. 본 논문은 이미지 도면의 레전드 문자 인식을 위해 인공지능 기법을 적용한다. 마지막으로, 본 논문에서 제안한 방법의 효율성을 평가하기 위해 인공지능기법을 도면관리시스템에 적용한다.

• Journal of Electrical Engineering and Technology
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• 제10권2호
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• pp.518-528
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• 2015

This article presents a closed loop control of DC series motor fed by DC chopper controlled by an PID controller based intelligent control using ANN (Artificial Neural Network). The PID-ANN controller performances are analyzed in both steady state and dynamic operating condition with various set speed and various load torque. Here two different motor parameters are taken for analysis (220V and 110V motor parameters). The static and dynamic performances are taken for comparison with conventional PID controller and existing work. The steady state stability analysis of the system also made using the transfer function model. The equation model is also done to analysis the performances by set speed change and load torque change. The proposed controller have better control over the conventional PID controller and the reported existing work. This system is initially simulated using MATLAB / Simulink and then experimental setup done using P89V51RD2BN microcontroller.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.423-426
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• 2003

In this paper, we use the modular neural network and recurrent neural network structure to implement the artificial brain information processing. We also select related adaptive learning methods to learn the entirely new input in the existed neural network. With this, a part of information process in brain is implemented as and autonomous and adaptive model by neural network and further more, the entire model for information process in brain can be introduced.

• 한국지능시스템학회논문지
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• 제2권3호
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• pp.50-57
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• 1992

Neural networks have become more popular recently and are now being applied to numerous fiedls. One of the major applications of neural networks is image recognition. Various image recognition system have been proposed so far, but there is no definite solution yet. In this paper, we propose a design of Fuzzy Neural Network Image Recognizer(FNNIR). Our model uses a fuzzy neural network model, named SONN[KIM90]. This model returns the information of the number of clusters and cluster and cluster center values for a given image data ste. Unlike the well-kinwn backpropagation technique, we do not need retraining for new data. Our newly designed image recongitionsystem FNNIR that uses fuzzy merger is proposed and experimented for a sample color image.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권3호
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• pp.176-181
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• 2007

This paper presents evolvable neural networks based on a developmental model for navigation control of autonomous mobile robots in dynamic operating environments. Bio-inspired mechanisms have been applied to autonomous design of artificial neural networks for solving practical problems. The proposed neural network architecture is grown from an initial developmental model by a set of production rules of the L-system that are represented by the DNA coding. The L-system is based on parallel rewriting mechanism motivated by the growth models of plants. DNA coding gives an effective method of expressing general production rules. Experiments show that the evolvable neural network designed by the production rules of the L-system develops into a controller for mobile robot navigation to avoid collisions with the obstacles.

• 한국지능시스템학회논문지
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• 제1권1호
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• pp.9-25
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• 1991

This tutorial paper has been written for biologists, physicians or beginners in fuzzy sets theory and applications. This field is introduced in the framework of medical diagnosis problems. The paper describes and illustrates with practical examples, a general methodology of special interest in the processing of borderline cases, that allows a graded assignment of diagnoses to patients. A pattern of medical knowledge consists of a tableau with linguistic entries or of fuzzy propositions. Relationships between symptoms and diagnoses are interpreted as labels of fuzzy sets. It is shown how possibility measures (soft matching) can be used and combined to derive diagnoses after measurements on collected data. The concepts and methods are illustrated in a biomedical application on inflammatory protein variations. In the case of poor diagnostic classifications, it is introduced appropriate ponderations, acting on the characterizations of proteins, in order to decrease their relative influence. As a consequence, when pattern matching is achieved, the final ranking of inflammatory syndromes assigned to a given patient might change to better fit the actual classification. Defuzzification of results (i.e. diagnostic groups assigned to patients) is performed as a non fuzzy sets partition issued from a "separating power", and not as the center of gravity method commonly employed in fuzzy control. It is then introduced a model of fuzzy connectionist expert system, in which an artificial neural network is designed to build the knowledge base of an expert system, from training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the connections: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through MIN-MAX fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feed forward network is described and illustrated in the same biomedical domain as in the first part.