• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.399-406
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• 2009

In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.59 no.1
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• pp.73-82
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• 2014

This study was conducted to investigate the ear and kernel characteristics of waxy corn during ripening by double cropping, and to understand the pattern of starch accumulation in endosperm of waxy corn kernels. Chalok4 and Ilmichal were sown at April 20 (first cropping) and July 20 (second cropping) in 2011~2012. The accumulated temperature from silking to harvesting was about $590{\sim}630^{\circ}C$. It takes 23~24 days when Chalok4 and Ilmichal were sown in April 20, but July 20 sowing takes 32~35 days. Ear length and kernel set length were significantly shorter in second cropping (p<0.05). Kernel length, kernel width, 100-kernel weight, and starch content of waxy corn were increased as ears matured (p<0.05). Growth temperature was getting decreased during the ripening stage of second cropping, the rate of ear and kernel development had slowed. Starch granules started to accumulate in the cells around the pericarp, then developed in the cells around the embryo. In the second cropping, starch granules in the kernel of waxy corn were less compact than the first cropping, harvesting time of waxy corns can be extended. These results will be helpful to farmers for double cropping of waxy corn cultivation and management.

• Korean Journal of Agricultural Science
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• v.4 no.2
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• pp.215-228
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• 1977

In order to provide good germplasm for developing good corn hybrids, a total of 948 Korean indigenous corn lines were collected from various parts of country and major morphological characterstics of ears were investigated. The results obtained were as follows; 1) Ear Type; From the east-north mountaionus region where more than 80% of total corn production is practiced, cylinder (type I) or similar types to cylinder corn were collected, and from the southern plain region, where rather small scales of corn is grown, cone type (type IV) or similar types to cone were prevalent. 2) Kernel color; In the ear colors of all the indigenous corn lines collected from ten regions, ears with mono color were 54.4%, ears with two color mixed were 39.0% and ears with three or more color mixed were 6.6%. In northern mountainous region, region A and region I, ear color was mostly white or white plus other colors, while in other regions ear color was yellow or yellow plus other colors. 3) Denting; Dent type was only 4.3% of Korean indigenous corn lines collected, and others were flint type. Dent type was collected from northern regions, where foreign corn varieties were introduced and grown. 4) Ear row number; Ear row numbers of indigenous corn lines collected were 12 to 16. There was no significant differences among the ear row numbers in a ear ciassified by regions. However, it was observed that ear row number was closely related to kernel size. For instance, the ears with 24 ear-rows were the smallest in kernel size. 5) Quality of starch; 70.9% of the indigenous corn lines collected were kernels with hard starch. Corn with soft starch was 26.0% and medium type was 3.1%. In region A and region I, where lot of corn is grown, corn with hard starch was more frequently collected. 6) Pop corn and waxy corn; In all the indigenous corn lines collected, popcorn was distributed uniformly through the regions except region I, and waxy corn was found more in the northern mountainous region. 7) Ear length; The mean ear length of indigenous corn lines collected was 13cm. In region A and region I ear length was larger than that in other regions. 8) Ear diameter; The mean ear diameter of indigenous corn lines collected was 3.3cm. In region A and region I ear diameter was larger than that in other regions. 9) Kernel length, kernel width and kernel thickness; The mean kernel length, kernel width and kernel thickness of indigenous corn lines collected were 0.82cm, 0.42cm, and 0.78cm, respectively. The kernel size in the region A and region I was larger than that in other regions. 10) Ear weight; The mean ear weight of indigenous corn lines collected was 58.04gr. Ear weight was remarkably heavier in region A and region I. The heaviest ear weighed 330gr, and the lightest ear weighed 5 gr. 11) Kernel weight of a ear and 100 kernel weight; Kernel weight of a ear and 100 kernel of indigenous corn lines collected were 47.07gr and 15.07gr, respectively. Kernel weights and 100 kernel weights were much heavier in region A and region I than other regions.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.179-188
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• 1998

Local polynomial regression estimation is the most popular one among kernel type regression estimator. In local polynomial regression function esimation bandwidth selection is crucial problem like the kernel estimation. When the regression curve has complicated structure variable bandwidth selection will be appropriate. In this paper, we propose a variable bandwidth selection method fully data driven. We will choose the bandwdith by selecting minimising estiamted MSE which is estimated by the pilot bandwidth study via croos-validation method. Monte carlo simulation was conducted in order to show the superiority of proposed bandwidth selection method.

• Proceedings of the Korean Society of Crop Science Conference
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• 2006.04a
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• pp.384-385
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• 2006

• Journal of the Korean Statistical Society
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• v.34 no.1
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• pp.49-60
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• 2005

In this paper we introduce a nonparametric method for estimating the probability density function of detection distances in line transect sampling. The estimator is obtained using a frequency histogram density estimation method. The asymptotic properties of the proposed estimator are derived and compared with those of the kernel estimator under the assumption that the data collected satisfy the shoulder condition. We found that the asymptotic mean square error (AMSE) of the two estimators have about the same convergence rate. The formula for the optimal histogram bin width is derived which minimizes AMSE. Moreover, the performances of the corresponding k-nearest-neighbor estimators are studied through simulation techniques. In the absence of our knowledge whether the shoulder condition is valid or not a new semi-parametric model is suggested to fit the line transect data. The performances of the proposed two estimators are studied and compared with some existing nonparametric and semiparametric estimators using simulation techniques. The results demonstrate the superiority of the new estimators in most cases considered.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.7
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• pp.306-312
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• 2006

Support Vector Machine(SVM), of which the foundations have been developed by Vapnik (1995), is gaining popularity thanks to many attractive features and promising empirical performance. In this paper, we propose a new short-term load forecasting technique based on SVM. We discuss the input vector selection of SVM for load forecasting and analyze the prediction performance for various SVM parameters such as kernel function, cost coefficient C, and $\varepsilon$ (the width of 8 $\varepsilon-tube$). The computer simulation shows that the prediction performance of the proposed method is superior to that of the conventional neural networks.

• Korean Journal of Breeding Science
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• v.42 no.5
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• pp.555-564
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• 2010

Aphid and mite are the major insects that harm corn. These insects are difficult to control because they are very sensitive to environmental conditions. The purpose of this experiment was to information the loss in quantity of corn and to select inbred lines with resistance after exposing corn to aphid and mite. When exposed to aphid, the average loss in grain yield, ear length, ear diameter, 100 kernel weight, kernel length, kernel width, and kernel thickness were 18.4, 10.9, 1.7, 5.4, 2.3, 1.5 and -0.2%, respectively. When exposed to mite, the average loss in grain yield, ear length, ear diameter, 100 kernel weight, kernel length, kernel width, and kernel thickness were 49.7, 16.5, 20.7, 18.9, 9.5, and -3.4, respectively. Mite had more significant impact on the loss than aphid. Among these factors, grain yield and ear dimater were most significantly correlated on the loss to aphid ($r=0.83^{**}$), while kernel length and thickness were most significantly correlated on the loss to mite ($r=0.83^{**}$). However, ear length was not related to loss to the two insects. The inbred lines that were often used for $F_1$ hybrid which was lower than the average loss by aphid, were KS7, B68, 61B3, 70A1, and KS5, while the inbred lines that were often used for $F_1$ hybrid which was lower than the average decrease by mite, were KS7, B68, 61B3, 66B2-4, 70A1, 72B2, and KS5. These inbred lines could be used to breed inbred lines, hybrid, and open pollination variety that confer resistant to aphid and mite.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.39 no.1
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• pp.55-62
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• 1994

The germplasms of naked barley evaluated for starch content were 137 crossing blacks and progeny lines obtained from the breeding program of the Wheat and Barley Research Institute, 540 Tabdong Kwonsungu Suwon, in the 1988/1989 season. Starch content was distributed between 49.15 and 62.50% with a mean of 57.20%. It was high in the bred lines and the Uz type lines than the imported, uz type or lax type ones. The range observed for seed coat weight was 1.12-3.02mg and the mean seed coat weight was 1.88mg. The seed coat weight ratio to kernel weight was measured between 4.0 and 8.7% with a mean of 6.5%. The varieties differ greatly in the characteristics depending on the plant type and origin of germplasms. The variation observed with protein content ranged from 9.60 to. 15.90% while the average was 12.86%. Starch content was negative relationships with kernel weight, seed coat weight, seed coat weight ratio., kernel width, kernel thickness and protein content, and starch content was higher in smaller kernels within a variety. The weight and weight ratio. of seed coat were positively correlated with the width, thick-ness of kernels, whilst they were negatively correlated with starch content. The heritability of starch content was 30.0%.