• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.14-24
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• 1996

A fuzzy classifier which needs various analyses of features using genetic algorithms is proposed. The fuzzy classifier has a simple structure, which contains a classification part based on fuzzy logic theory and a rule generation ation padptu sing genetic algorithms. The rule generation part determines optimal fuzzy membership functions and inclusior~ or exclusion of each feature in fuzzy classification rules. We analyzed recognition rate of a specific object, then added finer features repetitively, if necessary, to the object which has large misclassification rate. And we introduce repetitive analyses method for the minimum size of string and population, and for the improvement of recognition rates. This classifier is applied to three examples of the classification of iris data, the discrimination of thyroid gland cancer cells and the recognition of confusing handwritten and printed numerals. In the recognition of confusing handwritten and printed numerals, each sample numeral is classified into one of the groups which are divided according to the sample structure. The fuzzy classifier proposed in this paper has recognition rates of 98. 67% for iris data, 98.25% for thyroid gland cancer cells and 96.3% for confusing handwritten and printed numeral!;.

• Journal of Korea Water Resources Association
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• v.40 no.10
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• pp.769-781
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• 2007

Factor analysis has been usually employed in reducing quantity of data and summarizing information on a system or phenomenon. In this analysis methodology, variables are grouped into several factors by consideration of statistic characteristics, and the results are used for dropping variables which have lower weight than others. In this study, factor analysis was applied for extracting primary factors influencing multi-dam system operation in the Han River basin, where there are two multi-purpose dams such as Soyanggang Dam and Chungju Dam, and water has been supplied by integrating two dams in water use season. In order to fulfill factor analysis, first the variables related to two dams operation were gathered and divided into five groups (Soyanggang Dam: inflow, hydropower product, storage management, storage, and operation results of the past; Chungju Dam: inflow, hydropower product, water demand, storage, and operation results of the past). And then, considering statistic properties, in the gathered variables, some variables were chosen and grouped into five factors; hydrological condition, dam operation of the past, dam operation at normal season, water demand, and downstream dam operation. In order to check the appropriateness and applicability of factors, a multiple regression equation was newly constructed using factors as description variables, and those factors were compared with terms of objective function used in operation water resources optimally in a river basin. Reviewing the results through two check processes, it was revealed that the suggested approach provided satisfactory results. And, it was expected for extracted primary factors to be useful for making dam operation schedule considering the future situation and previous results.

• Progress in Medical Physics
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• v.21 no.1
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• pp.93-98
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• 2010

The aim of this work was to establish the methodology for event positioning by measuring depth of interaction (DOI) information and to evaluate the system sensitivity and spatial resolution of the new detector for I-125 and Tc-99m imaging. For this purpose, a Monte Carlo simulation tool, DETECT2000 and GATE were used to model the energy deposition and light distribution in the detector and to validate this approach. Our proposed detector module consists of a monolithic CsI(Tl) crystal with dimensions of $50.0{\times}50.0{\times}3.0\;mm^3$. The results of simulation demonstrated that the resolution is less than 1.5 mm for both I-125 and Tc-99m. The main advantage of the proposed detector module is that by using 3 mm thick CsI(Tl) with maximum-likelihood position-estimation (MLPE) method, high resolution I-125 imaging and high sensitivity Tc-99m imaging are possible. In this paper, we proved that our new detector to be a reliable design as a detector for a multi-energy SPECT.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.12
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• pp.1339-1349
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• 2008

In this paper, a nonlinear relationship between an input complex envelope and an output complex envelope of m-th harmonic zone is theoretically analyzed, and AM/$AM_m$ and AM/$PM_m$ are defined. A scheme to extract these characteristics from measured in-phase and quadrature-phase data is suggested. The proposed analysis is verified with a fundamental-fundamental and fundamental-third harmonic measurements for a InGaP power amplifier(PA). Based on the harmonic-band nonlinear analysis and extraction scheme, a new technique to send a signal in m-th harmonic band with a harmonic signal Linearization Digital Predistortion(DPD) scheme is presented. A numerical analysis and a Look-Up Table(LUT) based DPD algorithms to linearize output signal on m-th harmonic zone are developed. For a 16- and a 64-QAM input signals, a DPD for third harmonic signal linearization is implemented, and output spectrum and signal constellation are measured. The wholly distorted signals are linearized, and thus the measured Error Vector Magnitudes (EVM) are 6.4 % and 6.5 % respectively. The results show that a proposed scheme linearizes a nonlinearly distorted harmonic band signals. The proposed nonlinear analysis and predistortion scheme can be applied to multiband transmitter in next generation software defined radio(SDR)/cognitive radio(CR) wireless system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.8
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• pp.17-22
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• 2016

For the self-adaptive equalization of higher-order QAM systems, this paper proposes a new soft decision-directed (SDD) algorithm that opens the eye patterns quickly as well as significantly reducing the error level in the steady-state when it is applied to the initial equalization stage with completely closed eye patterns. The proposed method for M-QAM application minimized the computational complexity of the existing SDD by the symbol estimated based on the two symbols closest to the observation, and greatly simplified the soft decision independently of the QAM order. Furthermore, in the symbol estimating it increased the reliability of the estimates by applying the superior properties of the sigmoid function and avoiding the erroneous estimation of the threshold function. In addition, the initialization performance was improved when an error is generated to update the equalizer, weighting the symbol decision by the threshold function to the error, resulting in an extension of the range of error fluctuations. As a result, the proposed method improves remarkably the computational complexity and the properties of initialization and convergence of the traditional SDD. Through simulations for 64-QAM and 256-QAM under multipath channel conditions with additive noise, the usefulness of the proposed methods was confirmed by comparing the performance of the proposed 2-SDD and two forms of weighted 2-SDD with CMA.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.32-41
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• 1999

In this paper, an inversion method using chirp signals and two near field receivers is proposed. Inversion problems can be formulated into the probabilistic models composed of signals, a forward model and noise. Forward model to simulate chirp signals is chosen to be the source-wavelet-convolution planewave modeling method. The solution of the inversion problem is defined by a posteriori pdf. The wavelet matching technique, using weighted least-squares fitting, estimates the sediment sound-speed and thickness on which determination of the ranges for a priori uniform distribution is based. The genetic algorithm can be applied to a global optimization problem to find a maximum a posteriori solution for determined a priori search space. Here the object function is defined by an L₂norm of the difference between measured and modeled signals. The observed signals can be separated into a set of two signals reflected from the upper and lower boundaries of a sediment. The separation of signals and successive applications of the genetic algorithm optimization process reduce the search space, therefore improving the inversion results. Not only the marginal pdf but also the statistics are calculated by numerical evaluation of integrals using the samples selected during importance sampling process of the genetic algorithm. The examples applied here show that, for synthetic data with noise, it is possible to carry out an inversion for sedimentary layers using the proposed inversion method.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.84-90
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• 2010

In this paper, we propose a quadratic discriminant analysis based approach for improving the discriminating strength of weak classifiers based on simple Haar-like features that were used in the Viola-Jones object detection framework. Viola and Jones built a strong classifier using a boosted ensemble of weak classifiers. However, their single threshold (or decision boundary) based weak classifier is sub-optimal and too weak for efficient discrimination between object class and background. A quadratic discriminant analysis based approach is presented which leads to hyper-quadric boundary between the object class and background class, thus realizing multiple thresholds based weak classifiers. Experiments carried out for car detection using 1000 positive and 3000 negative images for training, and 500 positive and 500 negative images for testing show that our method yields higher classification performance with fewer classifiers than single threshold based weak classifiers.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.191-196
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• 2009

In this paper, we proposed a feature selection method using Binary Particle Swarm Optimization(BPSO) and Mutual information. This proposed method consists of the feature selection part for selecting candidate feature subset by mutual information and the optimal feature selection part for choosing optimal feature subset by BPSO in the candidate feature subsets. In the candidate feature selection part, we computed the mutual information of all features, respectively and selected a candidate feature subset by the ranking of mutual information. In the optimal feature selection part, optimal feature subset can be found by BPSO in the candidate feature subset. In the BPSO process, we used multi-object function to optimize both accuracy of classifier and selected feature subset size. DNA expression dataset are used for estimating the performance of the proposed method. Experimental results show that this method can achieve better performance for pattern recognition problems than conventional ones.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.18 no.3
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• pp.291-302
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• 2005

Due to the importance of the parameter in structural response, the uncertain elastic modulus was located at the center of stochastic analysis, where the response variability caused by the uncertain system parameters is pursued. However when we analyze the so-called stochastic systems, as many parameters as possible must be included in the analysis if we want to obtain the response variability that can reach a true one, even in an approximate sense. In this paper, a formulation to determine the statistical behavior of in-plane structures due to multiple uncertain material parameters, i.e., elastic modulus and Poisson's ratio, is suggested. To this end, the polynomial expansion on the coefficients of constitutive matrix is employed. In constructing the modified auto-and cross-correlation functions, use is made of the general equation for n-th moment. For the computational purpose, the infinite series of stochastic sub-stiffness matrices is truncated preserving required accuracy. To demons4rate the validity of the proposed formulation, an exemplary example is analyzed and the results are compared with those obtained by means of classical Monte Carlo simulation, which is based on the local averaging scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.781-788
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• 2017

This paper proposes a new algorithm for comparing amplitude between multiple digital input signals and its digital logic architecture. After simultaneously comparing multiple inputs, the proposed algorithm can provide the information of the largest (or smallest) value among them by using a simple digital logic function. The drawback of the method is to increase hardware resource. To overcome this we propose a reuse method of the overlapped logic operation. The proposed method focuses on enhancing the operational clock frequency, in other words decreasing combinational delay time. After implementing the comparing method with HDL (hardware description language), we experiment on it with environment of Cyclone III EP3C40F324A7 FPGA of Altera Inc. In case of 4 input signals, it can increase the operational speed as mush as 1.66 times with 1.20 times the hardware resource. In case of 8, it can also have 2.29 times the clock frequency and 2.15 times the hardware resource.