• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.91-91
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• 2022

Popcorn grains are consumed around 10,000 tons per year, in South Korea. It is consumed in amusement parks, movie theaters and snacks. The size of the popcorn processed market in Korea is estimated to be 3.6 billon won per year. So, the popcorn grain market has good prospects. On the other hand, domestic grain is at the level of 1 %, which is less domestic production than the size of the market. Maize Research Institute has developed domestic varieties in order to increase the use of domestically produced grains. The Oyrunpopcorn variety which was commonly distributed is a preferred cultivar because it has a good popping rate compared to imported grains. In addition, 'G-Popcom', 'Oyrun #2' and 'Kichan Popcorn' were developed, which diversified the choice of the farmers. Yield per unit area is important to improve farmers' income. At present, domestic grain production is traded at 5,000 won/kg, so if the yield improves, a high farmer^ income is expected. Therefore, this study compared the growth characteristics and yield according to the appropriate sowing time and plant density to improve the yield of domestically grown popcorn. We used 'Oryunpopcorn', for this research. Agronomic characteristics were compared by sowing times April 23, May 22 and June 22. The 100 seeds weight were 15.9g, 17.7g and 15.0g, respectively. Kernel weight planted in May 22 is the highest value. Yield per 10a were 414kg, 434kg and 296kg, respectively. It shows the yield planted in May 22 was higher than other trials. Therefore, the sowing time to increase the kernel weight and yield is appropriate for planting in mid-May. The number of plants in planting density trial was 5,700 plants, 4,700 plants and 4,000 plants in 10a area. Plant height at each trial were 221 cm, 214cm and 218cm, respectively. It was the highest height in 5,700 plants trials. The 100 kernel weight were 14.8g, 15.9g and 16.5g, respectively. Low planting density trial indicated high kernel weight. Yield per 10a was 415kg, 357kg and 314kg, respectively. It was higher at high density trial than other experimental plots. Therefore, appropriate sowing time was in mid-May and planting density was 5,700 plants/10a in order to increase the yield of popcorn in South Korea. This study will be useful for farmer's income to use the domestic cultivars.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.23 no.2
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• pp.133-140
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• 1978

Kernel softness. density. size and 100 kernel weight of Korean local corn lines (Zea mays, 1.) were studied to find any relationship with cultural magnitude in regions. Kernel density. softness and size were greater in the Kangwon area than in other less growing areas. Kernel size was the only character showing great relationship with growing magnitude. Most of the collected lines were flinty type and no differences were found among various growing magnitudes. Kernel density was also the same through the growing magnitudes.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.3
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• pp.221-230
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• 2016

To establish objective criteria for high pedestrian accident zones, we combined Getis-ord Gi* and Kernel Density Estimation to select high pedestrian accident zones for 54,208 pedestrian accidents in Seoul from 2009 to 2013. By applying Getis-ord Gi* and considering spatial patterns where pedestrian accident hot spots were clustered, this study identified high pedestrian accident zones. The research examined the microscopic distribution of accidents in high pedestrian accident zones, identified the critical hot spots through Kernel Density Estimation, and analyzed the inner distribution of hot spots by identifying the areas with high density levels.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.765-775
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• 2012

The cross-validation is a popular method to select bandwidth in all types of kernel estimation. The maximum likelihood cross-validation, the least squares cross-validation and biased cross-validation have been proposed for bandwidth selection in kernel density estimation. In the case that the probability density function has a discontinuity point, Huh (2012) proposed a method of bandwidth selection using the maximum likelihood cross-validation. In this paper, two forms of cross-validation with the one-sided kernel function are proposed for bandwidth selection to estimate the location and jump size of the discontinuity point of density. These methods are motivated by the least squares cross-validation and the biased cross-validation. By simulated examples, the finite sample performances of two proposed methods with the one of Huh (2012) are compared.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.483-497
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• 2001

When one fits a parametric density function to a data set, it is usually advisable to test the goodness of the postulated model. In this paper we study the nonparametric tests for testing the null hypothesis against general alternatives, when the null hypothesis specifies the density function up to unknown parameters. We modify the test statistic which was proposed by the first author and his colleagues. Asymptotic distribution of the modified statistic is derived and its performance is compared with some other tests through simulation.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.349-360
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• 1995

This paper deals with the robustness properties of the minimum disparity estimation in linear regression models. The estimators defined as statistical quantities whcih minimize the blended weight Hellinger distance between a weighted kernel density estimator of the residuals and a smoothed model density of the residuals. It is shown that if the weights of the density estimator are appropriately chosen, the estimates of the regression parameters are robust.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3055-3073
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• 2019

In this paper, a new user identification method is presented using real environmental human-computer-interaction (HCI) behavior data to improve method usability. User behavior data in this paper are collected continuously without setting experimental scenes such as text length, action number, etc. To illustrate the characteristics of real environmental HCI data, probability density distribution and performance of keyboard and mouse data are analyzed through the random sampling method and Support Vector Machine(SVM) algorithm. Based on the analysis of HCI behavior data in a real environment, the Multiple Kernel Learning (MKL) method is first used for user HCI behavior identification due to the heterogeneity of keyboard and mouse data. All possible kernel methods are compared to determine the MKL algorithm's parameters to ensure the robustness of the algorithm. Data analysis results show that keyboard data have a narrower range of probability density distribution than mouse data. Keyboard data have better performance with a 1-min time window, while that of mouse data is achieved with a 10-min time window. Finally, experiments using the MKL algorithm with three global polynomial kernels and ten local Gaussian kernels achieve a user identification accuracy of 83.03% in a real environmental HCI dataset, which demonstrates that the proposed method achieves an encouraging performance.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.301-301
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• 2017

Subspecific difference of the percentage of white immature kernels (WIK) between japonica and indica rice cultivars was analyzed in relation to ripening temperature and yield characters. Thirty-three Chinese and 10 Japanese rice cultivars, including 32 japonica and 11 indica, were cultivated with three different cropping seasons for three years. The results were as follows: (1) Indica had less number of panicles, larger number of spikelets per panicle with higher yield, and longer and narrower kernels than japonica. In japonica, Chinese cultivars had less number of panicles and larger number of spikelets per panicle than Japanese cultivars. In addition, WIK was significantly higher in Chinese cultivars than in Japanese cultivars, because of the higher percentage of milky white kernels, even at similar temperature conditions during ripening. On the other hand, WIK in indica was not significantly different between the production areas and between the cropping seasons. (2) Regardless of subspecies, WIK in a large number of Chinese cultivars increased with increasing temperature during ripening within 20 days after heading, while this relation was uncommon in Japanese cultivars, showing the low temperature response. However, some Chinese cultivars had the low WIK with the low temperature response. (3) WIK in japonicawas positively correlated with 1000-kernel weight, spikelet density, kernel width and thickness, but negatively correlated with panicle length and grain filling percentage, while in indica it was positively correlated with panicle number per area, grain filling percentage, brown rice yield and kernel width, but negatively correlated with kernel length. These results indicated that WIK in both subspecies had a close relation to kernel size, and that WIK was high in japonica cultivars with wide and thick kernels and in indica cultivars with short and wide kernels.

• Journal of applied mathematics & informatics
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• v.3 no.2
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• pp.183-192
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• 1996

The least squares cross validated bandwidth is the mini-mizer of the corss validation function for choosing the smooth parame-ter of a kernel density estimator. It is a completely automatic method but it requires inordinate amounts of computational time. We present a convenient formula for calculation of the cross validation function when the kernel function is a symmetric polynomial with finite sup-port. Also we suggest an algorithm for finding global minima of the crass validation function.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.973-974
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• 2008

As radar signal environments become denser and more complex, the capability of high-speed and accurate signal analysis is required for ES(Electronic warfare Support) system to identify individual radar signals at real-time. In this paper, we propose the new novel clustering algorithm of radar pulses to alleviate the load of signal analysis process and support reliable analysis. The proposed algorithm uses KDE(Kernel Density Estimation) and its CDF(Cumulative Distribution Function) to compose clusters considering the distribution characteristics of pulses. Simulation results show the good performance of the proposed clustering algorithm in clustering and classifying the emitters.