• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.161-164
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• 1996

The authors have recently proposed a new method for identifying Volterra kernels of nonlinear control systems by use of M-sequence and correlation technique. A specially chosen M-sequence is added to the nonlinear system to be identified, and the crosscorrelation function between the input and output is calculated. Then every crosssection of Volterra kernels up to 3rd order appears at a specified delay time point in the crosscorrelation. This method is applied to a saturation-type nonlinear feedback control system of mechanical-electrical servo system having torque saturation nonlinearity. Simulation experiments show that we can obtain Volterra kernels of saturation-type nonlinear system, and a good agreement is observed between the observed output and the calculated one from the measured Volterra kernels.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.597-600
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• 1997

In this paper, the authors propose a new method for linearizing a nonlinear dynamical system by use of polynomial compensation. In this method, an M-sequence is applied to the nonlinear system and the crosscorrelation function between the input and the output gives us every crosssections of Volterra kernels of the nonlinear system up to 3rd order. We construct a polynomial compensation function from comparison between lst order Volterra kernel and high order kernels. The polynomial compensation function is, in this case, of third order whose coefficients are variable depending on the amplitude of the input signal. Once we can get compensation function of nonlinear system, we can construct a linearization scheme of the nonlinear system. That is. the effect of second and third order Volterra kernels are subtracted from the output, thus we obtain a sort of linearized output. The authors applied this method to a saturation-type nonlinear system by simulation, and the results show good agreement with the theoretical considerations.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2570-2575
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• 2003

Monitoring a power transmission line is significant for power electric companies. In this paper, we propose a new method for detecting an fault point of power transmission line by use of M-sequence correlation technique. In this method, detecting signal is used as one or plural M-sequences ( same characteristic polynomial, including normal and reverse mark, synchronized ). In receiving point, we make same sequence with the input one and take crosscorrelation function between M-sequence and the received signal. We can see transfer fanctions of plural paths between inputs and a output taps separated from different of delay times on the crosscorrelation function, and from these transfer fanctions, so we compare them when fault occurred with in usual.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.569-572
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• 1992

This paper describes a new method of pseudorandom testing of a digital circuit by use of correlation method and a neural network. The authors have recently proposed a new method of fault diagnosis of logical circuit by applying a pseudorandom M-sequence to the circuit under test, calculating the crosscorrelation function between the input and the output, and comparing the crosscorrelation functions with the references. This method, called MSEC method, is further extended by using a neural network in order to not only detect the existence of faults but also find the place or location of the faults. An experiment by using a simple digital circuit shows enough applicability of this method to industrial testing of circuit board.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.137-140
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• 1999

A lot of researcher have proposed a method of kernel identifying nonlinear system by use of Wiener kernels[6-7] or Volterra kernel[5] and so on. In this research, the authors proposed a method of identifying Volterra kernels for nonlinear system by use of pseudorandom M-sequence in which a crosscorrelation function between input and output of a nonlinear system is taken[4]. we can be applied to an MISO nonlinear system or a system which depends on its input amplitude[2]. But, there exist many systems in which it is difficult to determine a Volterra kernel having the same time coordinate on the crosscorrelation function. In those cases, we have to estimate Volterra kernel by using its neighboring points[4]. In this paper, we propose a new method for not estimating but obtaining Volterra kernel having the same time coordinate using calculation between the neighboring points. Some numerical simulations show that this method is effective for obtaining higher order Volterra kernel of nonlinear control systems.

• Review of KIISC
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• v.2 no.3
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• pp.41-49
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• 1992

레이다 시스템, ranging 시스템, 확산 대역 통신 (spread spectrum communication) 시스템, 그리고 요즈음 각광받고 있는 코드분할방식 다중통신 (CDMA com-munication) 시스템에서는 주지하다시피 상관성(correlation) 이 좋은 수열(sequence)들위 사용이 필수 불가결하다. 수열의 상관성은 그 수열 자신의 상관성이냐 다른 수열간의 상관성이냐에 따라 자기상관관계(crosscorrelation)로 나누어 생각할 수 있고 수열의 주기성의 유무에 따라 주기적 상관관계(Periodic correlation)와 비주기적 상관관계(aperiodic correla-tion)로 나누어 볼 수 있다. 여기서 수열의 상관성이 좋다는 말은 정규화된 수열의 자기상관계수(autocorrelation coefficient)와 수열 간의 교차상관 계수(crosscorrelation coefficient)의 최대 크기가 수열의 길이에 비해 상대적으로 작은 값을 갖는다는 것을 의미한다. 본 논문에서는 주기성을 갖는 이진수열군의 하나인 bent수열과 이 bent수열을 구성하는데 기본이 되는 bent함수를 중심으로 주기적 상관성이 우수한 여러 수열군에 대해 알아보고자 한다.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.180-184
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• 2008

Preliminary results are reported on ship detection using coherence images computed from crosscorrelating images of multi-look-processed dual-polarization data (HH and HV) of ENVISAT ASAR. The traditional techniques of ship detection by radars such as CFAR (Constant False Alarm Rate) rely on the amplitude data, and therefore the detection tends to become difficult when the amplitudes of ships images are at similar level as the mean amplitude of surrounding sea clutter. The proposed method utilizes the property that the multi-look images of ships are correlated with each other. Because the inter-look images of sea surface are covered by uncorrelated speckle, crosscorrelation of multi-look images yields the different degrees of coherence between the images and water. The polarimetric information of ships, land and intertidal zone are first compared based on the cross-correlation between HH and HV. In the next step, we examine the technique when the dual-polarization data are split into two multi-look images.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.86.4-86
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• 2002

1. Review of one dimensional M-transform 2. Definition of two dimensional(2D)M-transform 3. Properties of 2D M-transform 4. Mean, Autocorrelation 5. Crosscorrelation of input and output of a system 6. Application to fault detection of mechanical shape

• Bulletin of the Society of Naval Architects of Korea
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• v.5 no.2
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• pp.35-44
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• 1968

The longitudinal cut method of experimental ship wave analysis, originally developed by J.N. Newman, is presented and various plausible sources of error are discussed. A further use of autocorrelation function and crosscorrelation function when two wave systems are superposed is indicated.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.470-470
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• 2000

This paper describes a new method of identifcation of backlash nonlinear systems by use of M-sequence correlation method. In this method, we can obtain not only Volterra kernels of up to 3rd order of the nonlinear system, but also the width of the backlash element from observing the crosscorrelation between the input and the output. Here strictly speaking, a multi-valued nonlinear system such as backlash element can not be expressed by Volterra kernel representation mathematically. But in practice, we encounter many cases where it is difficult to treat them mathematically but they can be controlled from experience. So we here dare to suppose that backlash nonlinear system can be approximated by Volterra kernel representation. Simulations are carried out on a nonlinear system consisting of linear part plus backlash element. And Volterra kernels are measured. The output calculated from the observed Volterra kernels is in good agreement wi th the actual output. And we show that we can obtain the width of backlash element, which is one of the most important parameters, by observing the maximum value of crosscorrelation function between the input M-sequence and the output.