• 한국게임학회 논문지
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• 제9권4호
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• pp.97-106
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• 2009

본 논문은 적응적 메쉬 구조를 이용하여 가상 종이 객체를 실시간에 시뮬레이션 하는 방법을 제안한다. 제안된 기법은 임의의 삼각 메쉬에 적용될 수 있으며, 안정적인 수치 적분과 변형 기반 메쉬 세분화를 이용하여 효율적으로 종이 표면의 주름과 구김을 생성한다. 종이 객체의 구겨짐을 사실적으로 표현하기 위하여 부러지는 스프링을 가진 적응적 메쉬 구조를 사용한다. 적응적 구조가 지속적으로 질점을 삽입 혹은 삭제하기 때문에, 질량과 운동량의 보존이 고려되어야만 사실적인 종이 시뮬레이션이 가능하다. 제안된 기법은 실시간 환경에서 종이와 같이 얇은 쉘 구조의 사실적인 애니메이션을 생성한다.

• 한국전자파학회논문지
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• 제29권8호
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• pp.608-614
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• 2018

본 논문은 1차원 배열안테나를 이용한 레이다 시스템에서 간섭 환경에 강인한 적응적 SLB 채널을 합성하는 연구에 대해 기술한 내용이다. 기존에 연구된 spatial DLC를 이용한 SLB 채널 합성은 매우 간단히 구현 가능하면서도 효과적으로 각도 방향의 부엽 신호를 blanking시킨다. 이 방식을 기반으로 correlation 행렬을 이용하여 간섭 신호를 제거하는 적응적 빔형성 기법을 추가로 적용하였다. 적응적 SLB 채널은 강한 간섭 신호를 잡음 레벨 이하로 억제시키므로, 간섭 환경에서도 좋은 SLB 성능을 보장할 수 있다. 해당 연구는 추후 평면배열안테나의 레이다 시스템으로 확장할 계획이다.

• Wind and Structures
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• 제5권2_3_4호
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• pp.301-316
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• 2002

An important goal of computational wind engineering is to impact the design process with simulations of flow around buildings and bridges. One challenging aspect of this goal is to solve the Navier-Stokes (NS) equations accurately. For the unsteady computations, an adaptive finite element technique may reduce the computer time and storage. The preliminary application of a p-version as well as an h-version adaptive technique to computational wind engineering has been reported in previous paper. The details on the implementation of p-adaptive technique will be discussed in this paper. In this technique, two posteriori error estimations, which are based on the velocity and vorticity, are first presented. Then, the polynomial order of the interpolation function is increased continuously element by element until the estimated error is less than the accepted. The second through sixth orders of hierarchical functions are used as the interpolation polynomials. Unequal order interpolations are used for velocity and pressure. Using the flow around a circular cylinder with Reynolds number of 1000 the two error estimators are compared. The result show that the estimated error based on the velocity is lower than that based on the vorticity.

• Communications for Statistical Applications and Methods
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• 제16권3호
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• pp.479-485
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• 2009

When a part of data is unobserved the marginal likelihood of parameters given the observed data often involves analytically intractable high dimensional integral and hence it is hard to find the maximum likelihood estimate of the parameters. Simulated maximum likelihood(SML) method which estimates the marginal likelihood via Monte Carlo importance sampling and optimize the estimated marginal likelihood has been used in many applications. A key issue in SML is to find a good proposal density from which Monte Carlo samples are generated. The optimal proposal density is the conditional density of the unobserved data given the parameters and the observed data, and attempts have been given to find a good approximation to the optimal proposal density. Algorithms which adaptively improve the proposal density have been widely used due to its simplicity and efficiency. In this paper, we describe a fully adaptive algorithm which has been used by some practitioners but has not been well recognized in statistical literature, and evaluate its estimation performance and robustness via a simulation study. The simulation study shows a great improvement in the order of magnitudes in the mean squared error, compared to non-adaptive or partially adaptive SML methods. Also, it is shown that the fully adaptive SML is robust in a sense that it is insensitive to the starting points in the optimization routine.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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• pp.148-153
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• 1999

In this paper, it is proposed the adaptive-sliding mode control technique which is new approach to implement the robust control of industrial robot manipulator with external disturbances and parameter uncertainties. Over the past decade, the design of advanced control systems for industrial robotic manipulators has been a very active area of research and two major design categories have emerged. Sliding mode control is a well-known technique for robust control of uncertain nonlinear systems. The robustness of sliding model controllers can be shown in continuous time, but digital implementation may not preserve robustness properties because the sampling process limits the existence of a true sliding mode. Adaptive control algorithm is designed by using the principle of the model reference adaptive control method based upon the hyperstability theory. The proposed control scheme has a simple structure is computationally fast and does not require knowledge of the complex dynamic model or the parameter values of the manipulator or the payload. Simulation results how that the proposed method not only improves the performance of the system but also reduces the chattering problem of sliding mode control. Consequently, it is expected that the new adaptive sliding mode control algorithm will be suited for various practical applications of industrial robot control system.

• 한국통신학회논문지
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• 제36권4A호
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• pp.399-406
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• 2011

적응 빔 형성기 (adaptive beamformer)는 원하는 신호의 도래각 정보를 이용하여 간섭신호를 제거하면서 원하는 신호 방향으로 어레이 이득을 최대로 한다. 그러나 도래각 정보가 정확치 않다면, 원하는 신호도 감쇠되어 심각한 성능저하가 발생한다. 이러한 문제에 대처하기위해, 신호차단에 기초하여 적응 빔을 형성하는 Duvall 구조를 이용하여, 효과적으로 원하는 신호의 도래각 추정을 통해 에러에 강인한 적응 빔 형성방법을 제시하였다. 제시된 방법에서는 간섭신호공간을 추정하여 이에 직교하도록 간단한 계산을 통해 가중벡터를 구하며, 특히 센서의 수가 많을수록 기존 방식에 비해 계산량의 절감이 크다.

• 반도체디스플레이기술학회지
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• 제19권3호
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• pp.7-11
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• 2020

Enormous methods have been proposed for the detection and segmentation of blur and non-blur regions of the images. Due to the limited available information about the blur type, scenario and the level of blurriness, detection and segmentation is a challenging task. Hence, the performance of the blur measure operators is an essential factor and needs improvement to attain perfection. In this paper, we propose an effective blur measure based on the local binary pattern (LBP) with the adaptive threshold for blur detection. The sharpness metric developed based on LBP uses a fixed threshold irrespective of the blur type and level which may not be suitable for images with large variations in imaging conditions and blur type and level. Contradictory, the proposed measure uses an adaptive threshold for each image based on the image and the blur properties to generate an improved sharpness metric. The adaptive threshold is computed based on the model learned through the support vector machine (SVM). The performance of the proposed method is evaluated using a well-known dataset and compared with five state-of-the-art methods. The comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all the methods.

• Journal of Electrical Engineering and Technology
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• 제7권3호
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• pp.451-458
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• 2012

The voluntary EMG (vEMG) signal from electrically stimulated muscle is very useful for feedback control in functional electrical stimulation. However, the recorded EMG signal from surface electrodes has unwanted stimulation artifact and M-wave as well as vEMG. Here, we propose an event-synchronous adaptive digital filter for the suppression of stimulation artifact and M-wave in this application. The proposed method requires a simple experimental setup that does not require extra hardware connections to obtain the reference signals of adaptive digital filter. For evaluating the efficiency of this proposed method, the filter was tested and compared with a least square (LS) algorithm using previously measured data. We conclude that the cancellation of both primary and residual stimulation artifacts is enhanced with an event-synchronous adaptive digital filter and shows promise for clinical application to rehabilitate paretic limbs. Moreover because this algorithm is far simpler than the LS algorithm, it is portable and ready for real-time application.

• Structural Engineering and Mechanics
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• 제75권6호
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• pp.771-784
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• 2020

Reliability analysis techniques combining with various surrogate models have attracted increasing attention because of their accuracy and great efficiency. However, they primarily focus on the structures with continuous response, while very rare researches on the reliability analysis for structures with discontinuous response are carried out. Furthermore, existing adaptive reliability analysis methods based on importance sampling (IS) still have some intractable defects when dealing with small failure probability, and there is no related research on reliability analysis for structures involving discontinuous response and small failure probability. Therefore, this paper proposes a novel reliability analysis method called AGPC-IS for such structures, which combines adaptive Gaussian process classification (GPC) and adaptive-kernel-density-estimation-based IS. In AGPC-IS, an efficient adaptive strategy for design of experiments (DoE), taking into consideration the classification uncertainty, the sampling uniformity and the regional classification accuracy improvement, is developed with the purpose of improving the accuracy of Gaussian process classifier. The adaptive kernel density estimation is introduced for constructing the quasi-optimal density function of IS. In addition, a novel and more precise stopping criterion is also developed from the perspective of the stability of failure probability estimation. The efficiency, superiority and practicability of AGPC-IS are verified by three examples.