• 한국컴퓨터산업학회논문지
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• 제2권4호
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• pp.537-546
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• 2001

DSSS(Direct-Sequence Spread-Spectrum) CDMA 시스템에서 MAI(Multiple Access Interference)와 원근 문제를 해결할 수 있는 단일-사용자 검파에 적합한 알고리즘으로 Griffiths’알고리즘과 LCCMA(Linearly Constrained Constant Modulus Algorithm)에 제안되었으며 MMSE 검파기에 적합한 다중-사용자 알고리즘인 MOE 알고리즘 또한 제안되었다. 본 논문은 training sequence의 요구 없이 시스템의 성능을 향상시킬 수 있는 이 세 가지 Blind 적합 알고리즘을 가지고 간섭 사용자의 수나 원하는 사용자의 데이터 업데이트율에 따라 각각의 알고리즘별 성능을 비교 분석하였다. 시뮬레이션 결과 간섭 사용자수와 원하는 사용자의 업데이트율의 변화에 따라 모두 LCCMA 알고리즘이 뛰어난 성능을 보았다. Blind 적용은 하나의 training sequence의 필요성을 없앰으로써 더욱 융통성 있는 네트웍디자인을 가능케 했다.

• 한국멀티미디어학회논문지
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• 제16권5호
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• pp.629-636
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• 2013

최근 정보통신 기술과 복지수준이 발전함에 따라 일반인들을 위한 여러 인터페이스 장비나 훈련 시스템들이 개발되고 있으나 발달 장애인을 위한 훈련 시스템은 미흡한 실정이다. 본 논문에서는 발달 장애인이 일상생활에 잘 적응할 수 있도록 인지능력과 상황대처 능력의 향상을 위한 증강현실 기반 실시간 상황 훈련시스템을 제안한다. 본 시스템은 '음식점 서빙'이라는 주제에 기반을 두고 있다. 이 시스템은 증강현실 기술을 기반으로 훈련자에게 다양한 상황에 대한 경험을 안전하게 반복 훈련 가능하도록 한다. 훈련자는 HMD를 착용하고 훈련 공간 주위를 볼 수 있으며, 시나리오에 따른 상황 훈련을 수행한다. 본 시스템을 3개월간 특수학교에서 실시한 결과 발달 장애 학생들은 HMD 착용에 대한 거부감 없이 흥미를 가지고 훈련에 참여했으며 점차 인지속도가 빨라지고 상황대처 능력이 향상됨을 알 수 있었다. 또한 특수학교 교사들로부터 본 시스템이 교육적 효과가 매우 뛰어나다는 피드백을 받았다.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2011년도 추계학술논문집 1부
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• pp.227-230
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• 2011

In this research, new conceptual model is being established to advocate a hypothesis during the hands-on to guarantee an effectiveness of the training with a scientific procedures and techniques under the management of KCTC training system. From this, current requirements of facilitator's qualifications and technical standards can be correspond with educate and their individual characteristics. Establishing education model basis will increase facilitator's training and conservatism in the representative KCTC training operative as the actual fight; contribute to effective training procedures. Like this model, it can be flexibly applied to the units in armies' actual training in circumstantial situations other than KCTC training; also can be applicable in many quarters.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권9호
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• pp.569-575
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for high performance of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control Performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of analysis prove that the proposed control system has strong high performance and robustness to parameter variation. and steady- state accuracy and transient response.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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• pp.451-465
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• 2017

This paper presents a novel discriminative visual tracking algorithm with an adaptive incremental extreme learning machine. The parameters for an adaptive incremental extreme learning machine are initialized at the first frame with a target that is manually assigned. At each frame, the training samples are collected and random Haar-like features are extracted. The proposed tracker updates the overall output weights for each frame, and the updated tracker is used to estimate the new location of the target in the next frame. The adaptive learning rate for the update of the overall output weights is estimated by using the confidence of the predicted target location at the current frame. Our experimental results indicate that the proposed tracker can manage various difficulties and can achieve better performance than other state-of-the-art trackers.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 춘계학술대회 및 창립 30주년 심포지엄(논문집)
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• pp.67-74
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• 2006

In this paper, an adaptive neural network controller and its application to automatic berthing control of ship is presented. The neural network controller is trained online using adaptive interaction technique without any teaching data and off-line training phase. Firstly, the neural networks used to control rudder and propeller during automatic berthing process are presented. Finally, computer simulations of automatic ship berthing are carried out to verify the proposed controller with and without the influence of wind disturbance and measurement noise.

• 한국생산제조학회지
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• 제6권1호
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• pp.7-17
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• 1997

During the past decade, there were many well-established theories for the adaptive control of linear systems, but there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of industrial robot control. Neural network computing methods provide one approach to the development of adaptive and learning behavior in robotic system for manufacturing. Computational neural networks have been demonstrated which exhibit capabilities for supervised learning, matching, and generalization for problems on an experimental scale. Supervised learning could improve the efficiency of training and development of robotic systems. In this paper, a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital signal processors is proposed. Digital signal processors, DSPs, are micro-processors that are developed particularly for fast numerical computations involving sums and products of variables. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be an efficient control scheme for implementation of real-time control for SCARA robot with four-axes by experiment.

• 실천공학교육논문지
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• 제15권2호
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• pp.273-283
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• 2023

Education has recently witnessed a rapid increase in the use of edutech worldwide. This study focuses on Korean workers and explores an edutech-based learning model for vocational education and training. Based on analyses of edutech cases and interviews with edutech experts, a draft edutech model was designed and the validity was evaluated based on two Delphi surveys with a panel of experts in the field. The study's findings suggest that edutech-based employee education and training should prioritize LXP orientation (last CVR=1, last Mean=4.70) , implement adaptive learning through learning analytics (last CVR=1, last Mean=4.90), enhance the human touch effect using edutech (last CVR=1, last Mean=4.90), and emphasize the importance of designing curricula that apply edutech in a step-by-step learning process while incorporating suitable instructional design for the key technologies involved in vocational training programs. In addition, it was revealed that there is a strong need to implement a method that makes each stage of the learning process more effective (before, during, and after). Edutech-based vocational training program should consider the interests of all stakeholders, including learners, instructors, vocational training institutions, and government agencies. Given the promotion of government-sponsored vocational training projects in Korea, the findings of this research are likely to have significant implications for the future of Korea's education and training policies.

• Journal of Information Processing Systems
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• 제9권3호
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• pp.395-404
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• 2013

In order to classify a web page as being benign or malicious, we designed 14 basic and 16 extended features. The basic features that we implemented were selected to represent the essential characteristics of a web page. The system heuristically combines two basic features into one extended feature in order to effectively distinguish benign and malicious pages. The support vector machine can be trained to successfully classify pages by using these features. Because more and more malicious web pages are appearing, and they change so rapidly, classifiers that are trained by old data may misclassify some new pages. To overcome this problem, we selected an adaptive support vector machine (aSVM) as a classifier. The aSVM can learn training data and can quickly learn additional training data based on the support vectors it obtained during its previous learning session. Experimental results verified that the aSVM can classify malicious web pages adaptively.

• Structural Engineering and Mechanics
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• 제78권2호
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• pp.209-218
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• 2021

For engineering, there are two major challenges in reliability analysis. First, to ensure the accuracy of simulation results, mechanical products are usually defined implicitly by complex numerical models that require time-consuming. Second, the mechanical products are fortunately designed with a large safety margin, which leads to a low failure probability. This paper proposes an efficient and high-precision adaptive active learning algorithm based on the Kriging surrogate model to deal with the problems with low failure probability and time-consuming numerical models. In order to solve the problem with multiple failure regions, the adaptive kernel-density estimation is introduced and improved. Meanwhile, a new criterion for selecting points based on the current Kriging model is proposed to improve the computational efficiency. The criterion for choosing the best sampling points considers not only the probability of misjudging the sign of the response value at a point by the Kriging model but also the distribution information at that point. In order to prevent the distance between the selected training points from too close, the correlation between training points is limited to avoid information redundancy and improve the computation efficiency of the algorithm. Finally, the efficiency and accuracy of the proposed method are verified compared with other algorithms through two academic examples and one engineering application.