• The Transactions of the Korea Information Processing Society
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• v.3 no.2
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• pp.225-230
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• 1996

We propose a statistical test for the nonlinear combiner logics which are usually combined with two maximal Linear Feedback Shift Registers and generate pseudorandom bit sequences. This test uses the mutual information between the output and set of inputs which will be a random variable and its distribution is obeyed to an approximate $\{chi}^2$ -distribution. We adopt this statistic to a $\{chi}^2$ -test of independence by using contingency table. We also apply a proposed test to some non-linear crptosystems and show that this useful to evaluate the strength of the cryptosystems.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.3
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• pp.301-309
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• 2003

The non linear control algorithm with actuator saturation for a randomly excited oscillator has been widely explored and has shown promising results, but the probabilistic analysis of the algorithm has been rarely made due to its non-linear nature and the fact that the analytical solution of probability density function (PDF) for controlled responses does not exist. In this paper, a method for the probabilistic analysis on the non linear control algorithm with actuator saturation is proposed based on the equivalent non linear system method. Numerical examples are given to verify the approximation solution of PDF comparing to a statistically obtained PDF using a Gaussian white noise and a Kanai - Tagimi filtered Gaussian white noise.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.1
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• pp.32-42
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• 2000

This paper presents an on-line learning algorithm for sequential construction of generalized radial basis function networks (GRBFNs) to model nonlinear systems from empirical data. The GRBFN, an extended from of standard radial basis function (RBF) networks with constant weights, is an architecture capable of representing nonlinear systems by smoothly integrating local linear models. The proposed learning algorithm has a two-stage learning scheme that performs both structure learning and parameter learning. The structure learning stage constructs the GRBFN model using two construction criteria, based on both training error criterion and Mahalanobis distance criterion, to assign new hidden units and the linear local models for given empirical training data. In the parameter learning stage the network parameters are updated using the gradient descent rule. To evaluate the modeling performance of the proposed algorithm, simulations and their results applied to two well-known benchmarks are discussed.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.11
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• pp.1111-1117
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• 2011

This paper proposed a fault detection and diagnosis (FDD) algorithm using nonlinear regression models, focusing especially on heat exchanger faults. This research concerned four working modes: those with no fault, evaporator fault, condenser fault, and evaporator and condenser faults. This research used no fault mode data to create an FDD algorithm. Using the no fault mode data, correlation functions for predicting the degree of superheat or subcool of heat exchangers (an evaporator and a condenser) were derived. Each correlation function has five inputs and one output. Based on these correlation functions, it is possible to predict the degree of superheat or subcool of each heat exchanger under various working conditions. The FDD algorithm was developed by comparing the predicted value and the simulation value. The FDD algorithm works well in all four working modes.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2549-2551
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• 2005

본 논문은 제어 공정의 파라미터의 동정과 축소모델을 이용하여 선형 및 비선형 특성을 고려한 PID 제어기 설계를 제안하였다. 제어기 파라미터값은 2차의 지연시간을 갖는 축소 모델의 파라미터값에 의해 결정되며, 외란 및 제어 공정의 파라미터 값이 변할 때에는 실제 모델의 동정을 통해 구하며, 또한 실제 공정과 축소 모델의 관계식을 통해 제어 파라미터 값을 실시간으로 보정하여 제어한다. 시뮬레이션을 통하여 실시간 모델 동정 및 제어 파라미터 값이 보정됨을 확인 할 수 있다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.1
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• pp.43-52
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• 2003

The present study is to investigate the ultimate behavior and limit state design of 2-I) R/C structures, with the changing of crack direction, and the yielding of the reinforcing steel bars, and Is to introduce an algorithm for the limit state design and analysis of 2-D R/C structures, directly from the finite element model. For the design of reinforcement in concrete the limit state design equation is incorporated into finite element algorithm to be based on the pointwise elemental ultimate behavior. It is also introduced a simplified nonlinear analysis algorithm for stress-strain relationship of R/C plane stress problem considering the cracking and its rotation in concrete and the yielding of the reinforcing steel bar. The algorithm is incorporated into the nonlinear finite element analysis. The analysis model is compared with the experimental model of R/C shear wall. In a simple design example for a shear wall, the required reinforcement ratios in each finite element is obtained from the limit state design equations.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.127-129
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• 2007

최적조류계산(Optimal Power Flow:OPF)은 전력계통에서 여러 가지 제약 조건을 만족하면서 경제적이고 안전하게 계통을 운영하기 위한 기법이다. 종래의 계산방법에는 비선형 계획법, 선형계획법 같은 수치해석적인 방법을 사용하였다. 그러나, 이러한 방법들은 전역 최저해를 구하기 위해서는 목적함수가 convex해야 한다. 또한, 계통 규모가 클 경우, 최적해 수렴이 안 되거나 수렴이 되더라도 시간이 많이 걸리는 단점이 있다. 최근에는 이러한 문제를 극복하고자 여러 가지 진화연산기법들이 최석조류계산 문제에 적용되고 있다. 본 논문에서 최근에 등장한 PSO알고리즘을 수정한 MPSO알고리즘은 이용한 최적조류계산 기법을 소개하고, 제안한 방법의 유용성을 보이기 위하여 IEEE 30,118 모선 계통의 최적 조류계산 문제에 적용하였다.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.4
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• pp.437-445
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• 2009

In this paper, we present the performance of the LOB(line of bearing) - based emitter localization algorithm. The linear LSE(least-squared error) algorithm, nonlinear LSE algorithm and Stansfield algorithm are considered. In addition, we focus on the performance improvement of the weighted estimation compared with the unweighted estimation. Each estimation algorithm is briefly introduced, and the performance of the algorithm is illustrated using the numerical results.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.405-406
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• 2007

본 논문에서는 FCM 기반 퍼지 뉴럴네트워크 구조를 제안하고 진화 알고리즘을 이용한 FCM 기반 퍼지 뉴럴네트워크의 구조와 파라미터의 최적화 방법을 제시한다. 클러스터링 알고리즘은 퍼지 뉴럴 네트워크에서 멤버쉽함수의 중심점과 반경 등을 결정하는 학습에 일반적으로 사용된다. 제안된 FCM 기반 뉴럴 네트워크에서 멤버쉽함수는 가우시안, 삼각형 타입등의 정해진 형태를 사용하지 않고 데이터들 사이의 거리에 관계된 계산을 수행하는 FCM에 의해 결정된다. 후반부는 상수형, 선형, 2차식 등의 다양한 다항식 구조로 표현될 수 있으며 다항식의 계수는 LSE를 이용하여 결정한다. FCM 기반 퍼지 뉴럴 네트워크는 퍼지규칙의 수, 입력변수의 선택, 후반부 다항식의 차수, FCM의 퍼지화 계수의 결정은 성능에 많은 차이가 있으며 이러한 구조와 파라미터의 최적화가 요구된다. 본 논문에서는 유전자 알고리즘을 이용하여 FCM 기반 퍼지뉴럴네트워크의 구조에 관련된 입력변수의 수, 퍼지규칙의 수 그리고 후반부 다항식의 차수와 파라미터에 관련된 퍼지화 계수를 최적화 한다. 제안된 방법은 비선형 시스템의 모델링에 적용하여 성능을 분석하였다.

• Journal of KIISE:Databases
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• v.36 no.5
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• pp.366-373
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• 2009

In this paper, we propose a similarity search algorithm for image databases. One of the central problems regarding content-based image retrieval (CBIR) is the semantic gap between the low-level features computed automatically from images and the human interpretation of image content. Many search algorithms used in CBIR have used the Minkowski metric (or $L_p$-norm) to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information. Our new search algorithm tackles this problem by employing new similarity measures and ranking strategies that reflect the nonlinearity of human perception and contextual information. Our search algorithm yields superior experimental results on a real handwritten digit image database and demonstrates its effectiveness.