• KOREAN JOURNAL OF CROP SCIENCE
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• v.33 no.4
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• pp.375-379
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• 1988

To identify the evidence in tolerance to oxyfluorfen between selected rice cultivars (cv. Chokoto as the tolerant and cv. Weld Pally as the susceptible), the differences in ehtylene evolution, chlorophyll contents and electro-conductivity as affected by various concentrations of oxyfluorfen were assessed. -Under 10$^{-5}$ M oxyfluorfen, the susceptible cultivar showed rapid and higher rate of ethylene evolution, and under 10$^{-4}$ M oxyfluorfen, less difference in ethylene evolution between both cultivars. -Upto 10$^{-6}$ M oxyfluorfen, no difference in chlorophyll contents of both cultivars, however, at 10$^{-5}$ ~10$^{-4}$ M, the susceptible showed as less chlorophyll contents as half of the tolerant. At 10$^{-3}$ M, both cultivars showed complete decomposition of chlorophyll. -At 10$^{-4}$ ~10$^{-3}$ M oxyfluorfen. both cultivars indicate the increased rate in electro-conductivity, and the susceptible cultivar released as much electrolytes as 10% of the tolerant.

• Journal of KIISE
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• v.41 no.12
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• pp.1090-1098
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• 2014

In this paper, an improved ACDE (Adaptive Cauchy Differential Evolution) algorithm with faster convergence speed, called ACDE2, is suggested. The baseline ACDE algorithm uses a "DE/rand/1" mutation strategy to provide good population diversity, and it is appropriate for solving multimodal optimization problems. However, the convergence speed of the mutation strategy is slow, and it is therefore not suitable for solving unimodal optimization problems. The ACDE2 algorithm uses a "DE/current-to-best/1" mutation strategy in order to provide a fast convergence speed, where a control parameter initialization operator is used to avoid converging to local optimization. The operator is executed after every predefined number of generations or when every individual fails to evolve, which assigns a value with a high level of exploration property to the control parameter of each individual, providing additional population diversity. Our experimental results show that the ACDE2 algorithm performs better than some state-of-the-art DE algorithms, particularly in unimodal optimization problems.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.225-231
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• 2013

In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.128-135
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• 2015

In this study, we develop the very short-term precipitation forecasting model as well as classifier based on polynomial radial basis function neural networks by using AWS(Automatic Weather Station) and KLAPS(Korea Local Analysis and Prediction System) meteorological data. The polynomial-based radial basis function neural networks is designed to realize precipitation forecasting model as well as classifier. The structure of the proposed RBFNNs consists of three modules such as condition, conclusion, and inference phase. The input space of the condition phase is divided by using Fuzzy C-means(FCM) and the local area of the conclusion phase is represented as four types of polynomial functions. The coefficients of connection weights are estimated by weighted least square estimation(WLSE) for modeling as well as least square estimation(LSE) method for classifier. The final output of the inference phase is obtained through fuzzy inference method. The essential parameters of the proposed model and classifier such ad input variable, polynomial order type, the number of rules, and fuzzification coefficient are optimized by means of Particle Swarm Optimization(PSO) and Differential Evolution(DE). The performance of the proposed precipitation forecasting system is evaluated by using KLAPS meteorological data.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.603-608
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• 2008

In this paper an approach to building fuzzy model to predict daily open, close, high, and low stock prices is presented. One of prior problems in building a stock prediction model is to select most effective indicators for the stock prediction. The problem is overcome by the selection of information used in the analysis of stick-chart as the input variables of our fuzzy model. The fuzzy rules have the premise and the consequent, in which they are composed of trapezoidal membership functions, and nonlinear equations, respectively. DE(Differential Evolution) searches optimal fuzzy rules through an evolutionary process. To evaluate the effectiveness of the proposed approach numerical example is considered. The fuzzy models to predict open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) on a daily basis are built, and their performances are demonstrated and compared with those of neural network.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.689-694
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• 2009

In this paper an approach to building fuzzy models for predicting daily and weekly stock prices is presented. Predicting stock prices with traditional time series analysis has proven to be difficult. Fuzzy logic based models have advantage of expressing the input-output relation linguistically, which facilitates the understanding of the system behavior. In building a stock prediction model we bear a burden of selecting most effective indicators for the stock prediction. In this paper information used in traditional candle stick-chart analysis is considered as input variables of our fuzzy models. The fuzzy rules have the premises and the consequents composed of trapezoidal membership functions and nonlinear equations, respectively. DE(Differential Evolution) identifies optimal fuzzy rules through an evolutionary process. The fuzzy models to predict daily and weekly open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) are built, and their performances are demonstrated.

• Journal of the Korean GEO-environmental Society
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• v.23 no.1
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• pp.25-30
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• 2022

It is very important to investigate the ground properties of a construction site for the stability during the construction of the retaining wall. In the retaining wall construction stage, ground properties are checked through ground investigation, but the actual ground properties may be different from the ground investigation result. In order to analyze the stability of the retaining wall in real time, it is important to reflect the properties of the actual ground. Also, when it is judged that the wall is unstable, an appropriate solution must be provided for the stability of the wall. This study aims to present a technique for predicting the actual ground properties through a differential evolution algorithm and judging the stability of the earth wall in real time through the digital twin of the retaining wall.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.49.2-49.2
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• 2015

IntraCluster Medium (ICM) located at the galaxy cluster is in the state of very hot, tenuous, magnetized, and highly ionized X-ray emitting plasmas. High temperature and low density make ICM very viscous and conductive. In addition to the high conductivity, fluctuating random plasma motions in ICM, occurring at all evolution stages, generate and amplify the magnetic fields in such viscous ionized gas. The amplified magnetic fields in reverse drive and constrain the plasma motions beyond the viscous scale through the magnetic tension. Moreover, without the influence of resistivity viscous damping effect gets balanced only with the magnetic tension in the extended viscous scale leading to peculiar ICM energy spectra. This overall collisionless magnetohydrodynamic (MHD) turbulence in ICM was simulated using a hyper diffusivity method. The results show the plasma motions and frozen magnetic fields have power law of $E_V^k{\sim}k^{-3}$, $E_M^k{\sim}k^{-1}$. To explain these abnormal power spectra we set up two simultaneous differential equations for the kinetic and magnetic energy using an Eddy Damped Quasi Normal Markovianized (EDQNM) approximation. The solutions and dimensions of leading terms in the coupled equations derive the power spectra and tell us how the spectra are formed. We also derived the same results with a more intuitive balance relation and stationary energy transport rate.

• Journal of electromagnetic engineering and science
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• v.14 no.1
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• pp.1-8
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• 2014

A differential-cascode CMOS power amplifier (PA) with a supply modulator for envelope tracking (ET) has been implemented by 0.18 ${\mu}m$ RF CMOS technology. The loss at the output is minimized by implementing the output transformer on a FR-4 printed circuit board (PCB). The CMOS PA utilizes the $2^{nd}$ harmonic short at the input to enhance the linearity. The measurement was done by the 10MHz bandwidth 16QAM 6.88 dB peak-to-average power ratio long-term evolution (LTE) signal at 1.85 GHz. The ET operation of the CMOS PA with the supply modulator enhances the power-added efficiency (PAE) by 2.5, to 10% over the stand-alone CMOS PA for the LTE signal. The ET PA achieves a PAE of 36.5% and an $ACLR_{E-UTRA}$ of -32.7 dBc at an average output power of 27 dBm.