• 한국철도학회:학술대회논문집
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• 한국철도학회 2005년도 추계학술대회 논문집
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• pp.191-196
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• 2005

In this study, to describe vehicle-track-bridge dynamic interaction phenomena with 1/4 vehicle model, nonlinear Hertzian contact spring and nonlinear contact damper are introduced. In this approach external loads acting on 1/4 vehicle model are self weight of vehicle and geometry information of running surface. The constraint equation on contact surface is implemented by Penalty method. Also, to improve the numerical stability and to maintain accuracy of solution, the artificial damper and the reaction from constraint violation are introduced. A nonlinear time integration method, in this study, Newmark method is adopted for both equations of vehicles and structure. And to reduce the error caused by inadequate time step size, adaptive time-stepping technique is partially introduced. As the nonlinear Hertzian contact spring has no resistance to tensile force, the bouncing phenomena of wheelset can be described. Thus, it is expected that more versatile dynamic interaction phenomena can be described by this approach and it can be applied to various railway dynamic problems.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.58-63
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• 1998

B.Widrow established fundamental relations between the least-mean-square (LMS) algorithm and the digital Fourier transform［1］. By extending these relations, we proposed the short time spectra analysis system using the LMS algorithm［2]. In that paper, we used the normal LMS algorithm on the thought of dealing with only real analytical signal. This algorithm minimizes the real mean-square by recursively altering the complex weight vector at each sampling instant. But, the short time spectra analysis sometimes deals with the complex signal that is outputted from complex analog filter. So, in order to optimize and develop this methods, furthermore it is necessary to derive an algorithm for the complex analytical signal. In this paper, we first discuss the new adaptive system for the spectra analysis using the complex LMS algorithm and then derive convergence condition, time constant of coefficient adjustment and frequency resolution by extending the discussion. Finally, the effectiveness of the proposed method is experimentally demonstrated by applying it to the measurement of transfer performance on complex analog filter.

• 제어로봇시스템학회논문지
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• 제8권2호
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• pp.113-119
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• 2002

An FLNN-based neural network controller is applied to precise positioning of XY table with friction as the extension study of [11]. The neural network identifies the frictional farces of the table. Its weight adaptation rule, named the reinforcement adaptive learning rule, is derived from the Lyapunov stability theory. The experimental results with 2-DOF XY table verify the effectiveness of the proposed control scheme. It is also expected that the proposed control approach is applicable to a wide class of mechanical systems.

• 전자공학회논문지B
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• 제32B권9호
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• pp.62-70
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• 1995

In some practical applications of the LMS Algorithm the coefficient adaptation can be performed only after some fixed delay. The resulting algorithm is known as the Delayed Least Mean Square (DLMS) algorithm in the literature. There exist analyses for this algorithm, but most of them are based on the unrealistic independence assumption between successive input vectors. Inthis paper we consider the DLMS algorithm with decreasing step size .mu.(n)=n/a, a>0 and prove the almost-sure convergence ofthe weight vector W(n) to the Wiener solution W$_{opt}$ as n .rarw. .inf. under the mixing unput condition and the satisfaction of the law of large numbers. Computer simulations for decision-directed adaptive equalizer with decoding delay are performed to demonstrate the functioning of the proposed algorithm.m.

• 전자공학회논문지C
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• 제34C권6호
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• pp.71-78
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• 1997

In this paper, a new neural network training algorithm will be devised for function approximator with good generalization characteristics and tested with the time series prediction problem using santaFe competition data sets. To enhance the generalization ability a constraint term of hidden neuraon activations is added to the conventional output error, which gives the curvature smoothing characteristics to multi-layer neural networks. A hybrid learning algorithm of the error-back propagation and Hebbian learning algorithm with weight decay constraint will be naturally developed by the steepest decent algorithm minimizing the proposed cost function without much increase of computational requriements.

• 대한임베디드공학회논문지
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• 제10권5호
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• pp.273-279
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• 2015

The vehicle has lots of embedded systems. Each of systems has its own role. In case of the vehicle, simple failure of system can be critical to driver. Therefore all of embedded system should be managed based on importance factors to be effective. In this paper, we consider the resilience as the importance factor for the driving system with ACC(Adaptive Cruise Control). We propose metrics to calculate the resilience of the embedded system. To get the resilience of system, we calculate the reliability and the resilience of nodes in the system using its failure rate. The resilience of whole system can be presented by the resilience of nodes and its weight. We calculate the resilience and compare the centralized structure and the distributed structure.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.2
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• pp.411-414
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• 2006

본 논문은 다양한 GPS 재밍대응 기술 중에서 안테나 기반의 CRPA(Controlled Reception Pattern Antenna) 시스템에 관한 소개 및 안테나의 널(null) 패턴 합성에 필요한 핵심 모듈인 디지털 I/Q 벡터모듈레이터(Vector Modulator)를 이용한 GPS용 $2{\times}2$ CRPA 패턴 제어방법에 관한 연구 내용이다. 일반적으로 시스템에서 미리 결정된 방향으로 만들어진 안테나 빔을 이용한 위상배열안테나(phased array antenna)와는 달리 CRPA 구조의 GPS 위성수신용 적응배열안테나(adaptive array antenna)는 각 안테나 소자(element)로부터 수신된 신호들을 실시간으로 해석하여, GPS 배열 안테나의 패턴 특성을 제어한다. 본 연구에서는 $2{\times}2$ 배열 정사각(square) 구조의 CRPA를 채택했으며, 재밍신호 방향으로 널(null) 패턴을 합성하기 위한 방법으로 PC에서 제어 가능한 DAC(Digital to Analog Converter)을 이용하여 I/Q 벡터모듈레이터로 인가되는 RF신호의 위상(phase) 및 진폭(amplitude)을 조절한다. 이때 원하는 널(null) 패턴이 합성되도록 네 개의 각 안테나 소자 쪽으로 8채널의 복소가중치(complex weight)를 인가한다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2000년도 가을 학술발표논문집 Vol.27 No.2 (2)
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• pp.305-307
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• 2000

본 논문에서는 다층 퍼셉트론 신경망에서 연결선 수를 최소로 하면서 일반화 성능을 향상시키기 위해 가장 널리 쓰여지고 있는 Optimal Brain Surgeon을 이용한 프루닝(pruning)을 기반으로 하여 오차 함수의 가중치 감소항을 추가시키는 방법을 사용한다. 이때 학습 및 프루닝의 성능에 많은 영향을 미치는 가중치 감소항의 방영정도를 베이시안 테크닉에 기반하여 적응적으로 최적화 하는 방법을 제안한다. 제안하는 방법의 성능을 검증하기 위해 벤치마크 데이터를 이용하여 실험을 수행하였다. 순수한 OBS 방법과 고정된 반영정도를 가진 가중치 감소항을 추가시킨 OBS, 그리고 제안하는 적응적 가중치 감소항을 적용한 OBS 방법을 비교하여 제한하는 방법이 기존의 두 방법에 비해 신경망 구조의 최적화 능력이 뛰어남을 확인할 수 있었다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2003년도 가을 학술발표논문집 Vol.30 No.2 (2)
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• pp.568-570
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• 2003

비월주사(interlaced)에서 순차주사(progressive)로의 변환을 디인터레이싱(de-interlacing)이라 한다. 제안하는 방식은 움직임 검출을 통해 움직임이 없는 영역에서는 앞선 필드정보를 이용하여 별도의 계산량 없이 디인터레이싱을 하게 되며, 움직임이 있는 영역에서는 공간정보(spatial information)를 이용하여 디인터레이싱하는 ELA(Edge based line average) 방식과 양방향 움직임 추정(bi-directional motion estimation)을 통한 시간정보(temporal information)를 이용하여 디인터레이싱하는 움직임 보상방법 간의 가중합산(weighted summation)을 이용하여 디인터레이싱을 수행하는 방법을 제안한다. 이 때 가중치(weight)는 공간 및 시간 정보 모두를 사용하여 결정되어지며, 이렇게 결정되어진 가중치를 통해 각 방식의 단점을 극복하게 된다. 이러한 가중합산을 이용한 방법은 높은 계산복잡도 없이 단순한 구현을 통해 다양한 조건에서 높은 성능의 디인터레이싱이 가능토록 해주며, 그 하드웨어 구현을 용이하게 해준다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 추계학술대회 논문집 학회본부
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• pp.58-61
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• 1996

For a class of single-input single-output continuous-time nonlinear systems, a multilayer neural network-based controller that feedback-linearizes the system is presented. Control action is used to achieve tracking performance for a state-feedback linearizable but unknown nonlinear system. The multilayer neural network(NN) is used to approximate nonlinear continuous function to any desired degree of accuracy. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. It is shown that all the signals in the closed-loop system are uniformly bounded. Initialization of the network weights is straightforward.