• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.285-299
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• 2020

In many cases, we are interested in identifying independence between variables. For continuous random variables, correlation coefficients are often used to describe the relationship between variables; however, correlation does not imply independence. For finite discrete random variables, we can use the Pearson chi-square test to find independency. For the mixed type of continuous and discrete random variables, we do not have a general type of independent test. In this study, we develop a independence test of a continuous random variable and a discrete random variable without assuming a specific distribution using kernel density estimation. We provide some statistical criteria to test independence under some special settings and apply the proposed independence test to Pima Indian diabetes data. Through simulations, we calculate false positive rates and true positive rates to compare the proposed test and Kolmogorov-Smirnov test.

• 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.

• International Journal of CAD/CAM
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• v.9 no.1
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• pp.31-36
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• 2010

3D point data acquired from laser scan or stereo vision can be quite noisy. A preprocessing step is often needed before a surface reconstruction algorithm can be applied. In this paper, we propose a nonparametric approach for noisy point data preprocessing. In particular, we proposed an anisotropic kernel based nonparametric density estimation method for outlier removal, and a hill-climbing line search approach for projecting data points onto the real surface boundary. Our approach is simple, robust and efficient. We demonstrate our method on both real and synthetic point datasets.

• Communications of Mathematical Education
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• v.16
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• pp.139-147
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• 2003

While the statistical concept 'order statistics' has a great number of applications in our society ranging from industry to military analysis, it is not necessarily an easy concept to understand for many people. Adding some interesting simulation activities of this concept to the probability or statistics curriculum, however, can enhance the learning curve greatly. A hands-on and a graphic calculator based activities of a missile simulation were introduced by Kim(2003) in the context of order statistics. This article revisits the two activities in his paper and point out a dilemma that occurs from the violation of an assumption on two deviation parameters associated with the missile simulation. A third activity is introduced to resolve the dilemma in the terms of a kernel density estimation which is a nonparametric approach.

• Computers and Concrete
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• v.26 no.6
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• pp.577-585
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• 2020

By the increasing amount of waste materials, it eventually dumped into the environment and covering a larger area of the landfill which cause several environmental pollution problems. The utilization of Palm Kernal Shell Ash (PKSA) in concrete might bring a great benefit in addressing both environmental and economic issues. This article investigates the effect of PKSA as a partial cement replacement of High Strength Concrete (HSC). Several concrete mixtures were prepared with different PKSA of 0%, 10%, 20%, and 30% replaced by the cement mass. This procedure was replicated twice for the two different target mean strengths of 40 MPa and 50 MPa. The mixtures were prepared to test different fresh and hardened properties of HSC including slump test, the compressive strength of 3, 7, 14, 28, and 90 days, flexural strength of 28-days, drying shrinkage, density measurement, and sorptivity. It was observed 10% PKSA replacement as optimum percentage which reduced the drying shrinkage, sorptivity, and density and improved the late-age compressive strength of concrete.

• Journal of Biosystems Engineering
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• v.6 no.1
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• pp.73-82
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• 1981

The physical properties of grain are very important for the design of handling, sorting, processing, and storage system. On the physical properties of grain, volume, bulk density, true density, specific gravity, and porosity arc the major factors affecting the thermal properties of grain. This study was conducted to determine experimentally the above physical properties of rough rice (3 Japonica-type, 3 Indica-type) and barley (covered, naked) as a function of moisture content ranged from about 10% to 25% (w.b). The results of this study are summarized as follows; 1. The volume of grain kernel increased with moisture content for both rice and barley. The volume of those grain kernel was in the range of $2.2068{\times}10^{-8}{\sim}3.3960{\times}10^{-8}m^3$ at the moisture content of 14%. 2. The bulk density of rice increased linearly with moisture content for Japonica-type rough rice and quadratically for Indica-type rough rice, but the bulk density of barley decreased linearly with moisture content. The bulk density of the grain was in the range of 501.14~689.13kg/$m^3$ at the moisture content of 14%. 3. The true density of whole grain decreased linearly with moisture content, and was in the range of 1019.49~1139.75kg/$m^3$ at the moisture content of 14%. 4. The porosity of rice decreased linearly with moisture content for Japonica-type rough rice and quadratically for Indica-type rough rice, but the porosity of barley increased linearly with moisture content. The porosity of the grain was in the range of 39.51~50.83% at the moisture content of 14%. 5. The regression equations of the physical properties such as volume, bulk density, true density, and porosity of the grain were determined as a function of moisture content.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.714-720
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• 2021

The ITL (information theoretic learning) based on the kernel density estimation method that has successfully been applied to machine learning and signal processing applications has a drawback of severe sensitiveness in choosing proper kernel sizes. For the maximization of correntropy criterion (MCC) as one of the ITL-type criteria, several methods of adapting the remaining kernel size ( ) after removing the term have been studied. In this paper, it is shown that the main cause of sensitivity in choosing the kernel size derives from the term and that the adaptive adjustment of in the remaining terms leads to approach the absolute value of error, which prevents the weight adjustment from continuing. Thus, it is proposed that choosing an appropriate constant as the kernel size for the remaining terms is more effective. In addition, the experiment results when compared to the conventional algorithm show that the proposed method enhances learning performance by about 2dB of steady state MSE with the same convergence rate. In an experiment for channel models, the proposed method enhances performance by 4 dB so that the proposed method is more suitable for more complex or inferior conditions.

• Ecology and Resilient Infrastructure
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• v.6 no.4
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• pp.200-207
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• 2019

The movement distance and home range size of Pelophylax chosenicus were identified in the rice paddy and ecological park as alternative habitats from July to November 2017. A total of 39 frogs were tracked by radio tracking method. As a result, the average move distance in the population of rice paddy was 11.7 ± 1.9 m (n = 64) and the population of ecological park was 24.7 ± 4.3 m (n = 39). The move distance between the two populations was significantly different. The mean MCP of the population of rice paddy was 181.2 ± 110.8 m² (n = 11) and the population of ecological park was 416.1 ± 276.2 m² (n = 10), but there was no significant difference. The population area of rice paddy was 4,160 m² (Kernel density 95%) and the core area was 1,080 m² (Kernel density 50%). The population area (Kernel density 95%) of ecological park was 5,391 m² and the core area (Kernel density 50%) was 736 m². This study shows that it is appropriate to construct the area of alternative habitat for P. chosenicus at least 1.33 ha, and it is more advantageous for the ecological park to be constructed than the paddy field with high development pressure and human interference. If the rice paddies were to be abandoned for several years, or to be used traditional farming methods, such as refraining from using agricultural machinery and chemicals, they could be used as alternative habitat for P. chosenicus.

• Imaging Science in Dentistry
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• v.49 no.4
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• pp.273-279
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• 2019

Purpose: This study was performed to investigate the effects of energy level, reconstruction kernel, and tube rotation time on Hounsfield unit (HU) values of hydroxyapatite (HA) in virtual monochromatic images (VMIs) obtained with dual-energy computed tomography (DECT)(Siemens Healthineers, Erlangen, Germany). Materials and Methods: A bone density calibration phantom with 3 HA inserts of different densities(CTWATER^®; 0, 100, and 200 mg of HA/㎤) was scanned using a twin-beam DECT scanner at 120 kVp with tube rotation times of 0.5 and 1.0 seconds. The VMIs were reconstructed by changing the energy level (with options of 40 keV, 70 keV, and 140 keV). In order to investigate the impact of the reconstruction kernel, virtual monochromatic images were reconstructed after changing the kernel from body regular 40 (Br40) to head regular 40 (Hr40) in the reconstruction phase. The mean HU value was measured by placing a circular region of interests (ROIs) in the middle of each insert obtained from the VMIs. The HU values were compared with regard to energy level, reconstruction kernel, and tube rotation time. Results: Hydroxyapatite density was strongly correlated with HU values(correlation coefficient=0.678, P<0.05). For the HA 100 and 200 inserts, HU decreased significantly at increased energy levels(correlation coefficient= -0.538, P<0.05) but increased by 70 HU when using Hr40 rather than Br40 (correlation coefficient=0.158, P<0.05). The tube rotation time did not significantly affect the HU(P>0.05). Conclusion: The HU values of hydroxyapatite were strongly correlated with hydroxyapatite density and energy level in VMIs obtained with DECT.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.19-27
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• 2022

In general, the performance of ML(Machine Learning) application is determined by various factors such as the type of ML model, the size of model (number of parameters), hyperparameters setting during the training, and training data. In particular, the recognition accuracy of ML may be deteriorated or experienced overfitting problem if the amount of dada used for training is insufficient. Existing studies focusing on image recognition have widely used open datasets for training and evaluating the proposed ML models. However, for specific applications where the sensor used, the target of recognition, and the recognition situation are different, it is necessary to build the dataset manually. In this case, the performance of ML largely depends on the quantity and quality of the data. In this paper, training data used for motion recognition application is augmented using the kernel density estimation algorithm which is a type of non-parametric estimation method. We then compare and analyze the recognition accuracy of a ML application by varying the number of original data, kernel types and augmentation rate used for data augmentation. Finally experimental results show that the recognition accuracy is improved by up to 14.31% when using the narrow bandwidth Tophat kernel.