• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.6
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• pp.422-432
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• 2013

A System Identification method to develop parametric models linking free surface elevation and wave pressure is presented and two models are built allowing for either wave pressure or free surface elevation simulation. Linear, time invariant model structures with static nonlinearities are assumed and solutions are sought in a form of autoregressive model with extra input (ARX). An arbitrary chosen free-surface elevation and wave pressure dataset is used for estimation of the models, which are subsequently verified against datasets with similar pressure gauge depth but different free-surface elevation spectra due to different meteorological conditions. It is shown that free-surface simulation using System Identification methods can perform better than traditional linear transfer function derived from linear wave theory (LTF), while wave pressure simulation quality using presented methods is generally similar to that obtained with corrected LTF.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.3
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• pp.268-276
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• 1992

In this paper, we present an discrimination method of unknown digital modulated signals in noisy communication environment. We propose the use of an identification procedure based on time domain signal parameters. First, We extract instantaneous envelope. Frequency and difference phase as the basic feature informations from received signals. In order to identify signals using the extracted feature informations, we design the two dimensional feature space. The extracted feature infomations are mapped into2Dfeature space using 2D feature points. The procedure has been tested by simulations on a computer in noisy communication environment, and the considered signals are ASK-W, ASK-4, BPSK, QPSK, 8PSK, FSK, and QAM.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.493-495
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• 2001

본 논문에서는 문자 인식에서 두 단계의 식별과정을 통하여 인식률을 향상시키는 방법에 대하여 연구하였다. 한글 문자인식에서의 어려움은 인식대상 클래스가 많고 유사문자가 많은 반면, 여러 폰트의 글자를 하나의 글자를 하나의 클래스로 할 경우에는 그 문자의 분산이 더욱 커지게 되는 점이다. 따라서 본 연구에서는 문자의 분포를 고려하여 거리를 계산하는 Bayes에 의한 식별 함수를 1단계 인식과정에서 사용하여 1위 후보문자를 인식하였다. 2단계에서는 미리 준비된 1위 후부문자의 유사문자세트의 최적선형변환 공간에서 상세분류를 행하였다. 결과적으로 1단계의 Bayes거리반에 의한 인식률(91.1%)보다도, 또한 처음부터 모든 클래스에 대하여 최적선형변환에 의한 인식률(87.9%)보다 좋은 결과(92.9%)를 얻게되었다. 이로서 1단계의 대규모 문자세트에 대한 대분류에서는 문자의 분포를 고려하는 Bayes에 의한 인식이 유효하고, 2단계의 최적선형변환에 의한 인식은 소수의 유사문자들에 대한 변별력을 높이는데 유효함을 입증하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.33-40
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• 1998

This paper presents an Modified Orthogonal Neural Network(MONN), new modified model of Orthogonal Neural Network(0NN) based on orthogonal functions, and applies it to nonlinear system approximator. ONN proposed by Yang and Tseng, doesn't have the problems of traditional multilayer feedforward neural networks such as the determination of initial weights and the numbers of layers and processing elements. And tranining of ONN converges rapidly. But ONN cannot adapt its orthogonal functions to a given system. The accuracy of ONN, in terms of the minimal possible deviation between system and approximator, is essentially dependent on the choice of basic orthogonal functions. In order to improve ability and effectiveness of approximate nonlinear systems, MONN has an input transformation layer to adapt its basic orthogonal functions to a given nonlinear system. The results show that MONN has the excellent performance of approximate nonlinear systems and the input transfnrmation makes the ability of MONN better than one of ONN.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2756-2758
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• 2005

본 논문에서는 제어시스템의 신뢰성 향상을 위한 고장허용제어계를 실현하기 위하여, 함수관측자를 도입한 장치고장검출(IFD)기법 및 고장보상알고리즘을 제안하고, 이를 전형적인 불안정 비선형시스템인 역진자제어계에 적용하였다. 제안된 IFDS는 기존의 다중관측자기법과 동일한 구조를 가지지만 최저차 설계가 가능한 미지입력 함수관측자를 채택함으로서 알고리즘의 단순화를 가하였을 뿐 아니라 고장량의 추정 등 다양한 설계목적을 고려하여 설계될 수 있다는 특징을 가진다. 제안된 고장 검출식별 기법의 제안된 역진자제어계를 위한 제어기, 고장검출식별 및 보상알고리즘을 모두 포함한 상태에서 고장허용제어의 가능성을 검증하였다.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.2
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• pp.8-12
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• 1984

Algorithms for solving a set of nonlinear simultaneous equations, which is frequently required in problems of controlling nonlinear processes, are proposed. Here the equation variables are first parameterized and a recursive identification technique is utilized. The forms and characteristics of the resultant algorithms are vary similar to the Broyden's quasi-Newton method, but their derivations and final recursion equations are different. Our methods possess almost all the merits of the Broyden's and numerical comparisons show our methods to be more efficient and reliable for some difficult problems.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.11a
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• pp.271-271
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• 2019

Estimating ship motion is difficult because it take place in complex environments.. Estimating ship motion is an important factor in ensuring the safety of ship, so accurate estimates are needed. Existing motion-related studies compare the apparent motion of the model acquired and the reference model by experimenting with the ship motion on a particular alignment, making it difficult to intuitively estimate the hull motion. This study introduces the concept of estimating the characteristics of ship motion as a transfer function through pole-zero interpretation and frequency response analysis by applying the method of transfer function of Linear-Time Invariant system. Ship motion analysis model using Linear-Time Invariant system is consist with 1) wave as input signal 2) ship motion as output signal 3) hull defined as black box. This model can be defined by numericalizing the ship motion as a transfer function and is expected to facilitate the characterization of the ship motion through pole-zero analysis and frequency response analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.125-130
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• 2018

Linear discriminant analysis is a statistical analysis method that is generally used for dimensionality reduction of the feature vectors or for class classification. However, in the case of a data set that cannot be linearly separated, it is possible to make a linear separation by mapping a feature vector into a higher dimensional space using a nonlinear function. This method is called generalized discriminant analysis or kernel discriminant analysis. In this paper, we carried out target classification experiments with active sonar target signals available on the Internet using both liner discriminant and generalized discriminant analysis methods. Experimental results are analyzed and compared with discussions. For 104 test data, LDA method has shown correct recognition rate of 73.08%, however, GDA method achieved 95.19% that is also better than the conventional MLP or kernel-based SVM.

• The Journal of the Acoustical Society of Korea
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• v.3 no.2
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• pp.46-58
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• 1984

본 논문은 한국어 음성 인식을 위한 유성음, 무성음, 묵음 식별에 관한 연구이다. 주어진 음성 구간을 3가지 음성 신호 부류로 식별하기 위하여 패턴 인식 방법을 사용하였다. 여기에 사용한 분석 파 라메타는 음성 신호의 영교차율, 대수 에너지, 정규화 된 첫 번째 자동 상관 계수, 선형 예측 분석에서 얻은 첫 번째 예측 계수, 그리고 예측 오차의 에너지이다. 한편 측정된 파라메타들이 다차원 가우스 확 률 밀도 함수에 따라 분산되었다는 가정하에서 어어진 최소 거리 법칙에 기본을 두고 음성 구간을 결정 하였다. 측정된 파라메타들을 여러 가지 방법으로 조합하여 식별한 결과 영교차율, 첫 번째 예측계수, 예측 오차의 에너지를 측정 파라메타로 사용했을 때 1%보다 적은 식별 오차율을 얻었다.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.371-384
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• 2022

The fused LASSO signal approximator (FLSA) can be applied to find change points from the data having piecewise constant mean structure. It is well-known that the FLSA is inconsistent in change points detection. This inconsistency is due to a total-variation denoising penalty of the FLSA. ℓ₁ trend filter, one of the popular tools for finding an underlying trend from data, can be used to identify change points of piecewise linear trends. Since the ℓ₁ trend filter applies the sum of absolute values of slope differences, it can be inconsistent for change points recovery as the FLSA. However, there are few studies on the inconsistency of the ℓ₁ trend filtering. In this paper, we demonstrate the inconsistency of the ℓ₁ trend filtering with a numerical study.