• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 하계종합학술대회논문집
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• pp.42-45
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• 1998

Adaptive array antenas have emerged as a useful technique to enhance the cell capacity of mobile communications. In this research, to analyze the noncoherent CDMA systems employing adaptive array antennas, we modeled the transmitting signal of CDMA systems using M-ary orthogonal modulation. And we induced the conditional probability density function about the decision variable, the output of 2D-RAKE receiver and mean symbol error prabability through statistical analysis about MAI(Multiple Access Interference), SI(Self Interference) and Noise. Also, we analyzed the charateristics of adaptive array antenna for noncoherent CDMA systems using M-ary orthogonal modulation according to the distance between the array elements, doppler frequency and AOS(Angle of Spread).

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.65-69
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• 2002

The main drawback of “classical”fuzzy systems is the inability to design and maintain their database. To overcome this disadvantage many types of extensions adding the adaptivity property to those systems were designed. This paper deals with one of them a new hybrid adaptation structure, called gradient-incremental adaptive fuzzy controller connecting gradient-descent methods with the so-called self-organizing fuzzy logic controller designed by Procyk and Mamdani. The aim is to incorporate the advantages of both Principles. This controller was implemented and tested on the system of LEGO robots. The results and comparison to a ‘classical’(non-adaptive) fuzzy controller designed by a human operator are also shown here.

• International journal of advanced smart convergence
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• 제7권4호
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• pp.92-100
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• 2018

This paper presents a link adaptation scheme for adaptive full duplex (AFD) systems. The signal modulation levels and communication link patterns are adaptively selected according to the changing channel conditions. The link pattern selection process consists of two successive steps such as a transmit-receive antenna pair selection based on maximum sum rate or minimum maximum symbol error rate, and an adaptive modulation based on maximum minimum norm. In AFD systems, the antennas of both nodes are jointly determined with modulation levels depending on the channel conditions. An adaptive algorithm with relatively low complexity is also proposed to select the link parameters. Simulation results show that the proposed AFD system offers significant bit error rate (BER) performance improvement compared with conventional full duplex systems with perfect or imperfect self-interference cancellation under the same fixed sum rate.

• 정보과학회 논문지
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• 제42권11호
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• pp.1339-1348
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• 2015

서비스 지향 시스템 환경에서는 하나의 독립된 시스템이 다른 시스템에서 제공하는 서비스를 사용함으로써 시스템의 목표를 달성한다. 이러한 시스템 환경에서는 사용중인 서비스의 품질 저하가 실시간으로 시스템 장애를 유발할 수 있다. 기존 자가 적응 방법들은 서비스의 품질 저하 발생 후 대처하는 fault tolerance 방식을 따르고 있기 때문에 서비스 품질 저하로 인한 피해 발생을 예방하지 못한다. 따라서, 자가 적응 시스템의 신뢰도를 보존하기 위해서는 사용 중인 서비스를 fault tolerance 방식의 품질 기반 적응이 아닌 서비스 안정성 케이스를 기반으로 자가 적응해야 한다. 본 논문에서는 기존의 보증 속성 표현 방법인 Goal Structuring Notation 모델링을 활용하여 서비스 안정성 케이스를 표현 및 분석하기 위한 방법과, 서비스 안정성 케이스 분석 결과에 따른 적응 조치 방법, 서비스 생명주기에 따른 서비스 안정성 케이스 기반 자가 적응 메커니즘 적용 방법, 서비스 안정성 케이스 기반 자가 적응을 위한 프레임워크 아키텍처를 제안한다.

• 제어로봇시스템학회논문지
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• 제6권1호
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• pp.8-14
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• 2000

In this paper, three adaptive sliding mode control algorithms, which self-tune both the sliding mode gain and the boundary layer thickness, are proposed. The first algorithm uses a gain adaptation rule is combined with the boundary layer thickness adaptatioin rule to satisfy the sliding condition. In the third algorithm, the computation burden of the second algorithm is reduced further, and therefore no extra cost is required for real-time implementation. Due to the mixed sliding mode gain and the boundary layer thickness adaptation scheme, the tracking error and the chattering of the control input can be reduced greatly.

• 제어로봇시스템학회논문지
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• 제14권3호
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• pp.254-262
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• 2008

In this paper, we propose an adaptive neural dynamic surface control (DSC) approach with $H_{\infty}$ tracking performance for full dynamics of nonlinear flight systems. It is assumed that the model uncertainties such as structured and unstrutured uncertainties, and external disturbances influence the nonlinear aircraft model. In our control system, self recurrent wavelet neural networks (SRWNNs) are used to compensate the model uncertainties of nonlinear flight systems, and an adaptive DSC technique is extended for the disturbance attenuation of nonlinear flight systems. All weights of SRWNNs are trained on-line by the smooth projection algorithm. From Lyapunov stability theorem, it is shown that $H_{\infty}$ performance nom external disturbances can be obtained. Finally, we present the simulation results for a nonlinear six-degree-of-freedom F-16 aircraft model to confirm the effectiveness of the proposed control system.

• 경영과정보연구
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• 제32권3호
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• pp.221-245
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• 2013

본 연구는 대학생을 대상으로 회복탄력성을 주요변수로 하여, 이에 영향을 미치는 선행요인을 찾아내고, 조직적응에 미치는 영향을 확인하고자 하였다. 연구결과 첫째, 회복탄력성의 선행요인으로 예측했던 인지적유연성, 목표지향성(학습/증명/회피 목표지향성), 자기개념명확성 중 인지적유연성과 자기개념명확성, 목표지향성의 하위변수 중 학습목표지향성이 정(+)의 영향을 미치는 것으로 나타났다. 둘째, 인지적유연성은 조직적응의 모든 하위변수에 정(+)의 영향을 미쳤으며, 목표지향성의 하위변수인 증명목표지향성은 조직적응의 하위변수인 문제해결, 불확실성 대처에 정(+)의 영향을 미쳤다. 자기개념명확성은 조직적응의 하위변수 중 문제해결, 위기대처, 문화적적응에 정(+)의 영향을 미쳤다. 셋째, 회복탄력성의 매개효과를 검증한 결과, 인지적유연성과 조직적응(문제해결, 위기대처, 문화적적응)을 부분 매개하였으며, 학습목표지향성 및 자기개념명확성과 조직적응 간의 회복탄력성 매개효과는 가설이 기각되었다. 본 연구 결과를 통해 인지적유연성과 자기개념명확성, 학습목표지향성을 통해 회복탄력성을 향상시킬 수 있으며, 이들 변수들이 조직에 대한 성공적인 적응에 부분적으로 영향을 미침을 알 수 있다. 끝으로 조직적응 과정에서 회복탄력성의 중요성을 부분매개효과를 통해 검증하였다. 향후 연구에서는 회복탄력성의 선행요인의 추가적인 탐색과 결과요인에 대한 도출을 기대하는 바이다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.32-35
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• 1987

An adaptive control scheme has been recognized as an effective approach for a robot manipulator to track a desired trajectory in spite of the presence of nonlinearties and parameter uncertainties in robot dynamic models. In this paper, an adaptive control scheme for a robot manipulator is proposed to design the self-tuning controller which combines the pole placement with the extended linearized perturbation model. And this control scheme has two components: a feadforward control and a feedback compensation control. Based on this, the controller is demonstrated by the simulation about position control of a three-link manipulator with payload and parameter uncertainty.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.349-349
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• 2000

A Clustering Adaptive Fuzzy Logic Controller(CAFLC) is applied to the torque control of a brushless do motor drive. Objective of this system includes elimination of torque ripple due to cogging at low speeds under loads. The CAFLC implemented has advantages of computational simplicity, and self-tuning characteristics. Simulation results showed that the torque ripple and dynamic response of the system using a CAFLC were superior to the model reference adaptive controlled system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1315-1318
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• 2003

In this paper, a neural network based approach using a self-organizing feature map is proposed for the segmentation of X ray images. A number of algorithms based on such approaches as histogram analysis, region growing, edge detection and pixel classification have been proposed for segmentation of general images. However, few approaches have been applied to X ray image segmentation because of blur of the X ray image and vagueness of its edge, which are inherent properties of X ray images. To this end, we develop a new model based on the neural network to detect objects in a given X ray image. The new model utilizes Mumford-Shah functional incorporating with a modified adaptive SOFM. Although Mumford-Shah model is an active contour model not based on the gradient of the image for finding edges in image, it has some limitation to accurately represent object images. To avoid this criticism, we utilize an adaptive self organizing feature map developed earlier by the authors.[1] It's learning rule is derived from Mumford-Shah energy function and the boundary of blurred and vague X ray image. The evolution of the neural network is shown to well segment and represent. To demonstrate the performance of the proposed method, segmentation of an industrial part is solved and the experimental results are discussed in detail.