• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.925-931
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• 2009

In this paper we propose a variance function estimation method for replicated data based on averages of squared residuals obtained from estimated mean function by the least squares support vector machine. Newton-Raphson method is used to obtain associated parameter vector for the variance function estimation. Furthermore, the cross validation functions are introduced to select the hyper-parameters which affect the performance of the proposed estimation method. Experimental results are then presented which illustrate the performance of the proposed procedure.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.383-388
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• 2009

In this paper we propose a variance function estimation method based on kernel trick for replicated data or data consisted of sample variances. Newton-Raphson method is used to obtain associated parameter vector. Furthermore, the generalized approximate cross validation function is introduced to select the hyper-parameters which affect the performance of the proposed variance function estimation method. Experimental results are then presented which illustrate the performance of the proposed procedure.

• ETRI Journal
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• v.40 no.5
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• pp.582-591
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• 2018

In this paper, we present a performance analysis of large-scale multi-input multi-output (MIMO) systems for wireless backhaul networks. We focus on fully connected N nodes in a wireless meshed and multi-hop network topology. We also consider a large number of antennas at both the receiver and transmitter. We investigate the transmission schemes to support fully connected N nodes for half-duplex and full-duplex transmission, analyze the achievable ergodic sum rate among N nodes, and propose a closed-form expression of the achievable ergodic sum rate for each scheme. Furthermore, we present numerical evaluation results and compare the resuts with closed-form expressions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.56-64
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• 2018

In recent years, imbalanced data is one of the most important and frequent issue for quality control in industrial field. As an example, defect rate has been drastically reduced thanks to highly developed technology and quality management, so that only few defective data can be obtained from production process. Therefore, quality classification should be performed under the condition that one class (defective dataset) is even smaller than the other class (good dataset). However, traditional multi-class classification methods are not appropriate to deal with such an imbalanced dataset, since they classify data from the difference between one class and the others that can hardly be found in imbalanced datasets. Thus, one-class classification that thoroughly learns patterns of target class is more suitable for imbalanced dataset since it only focuses on data in a target class. So far, several one-class classification methods such as one-class support vector machine, neural network and decision tree there have been suggested. One-class support vector machine and neural network can guarantee good classification rate, and decision tree can provide a set of rules that can be clearly interpreted. However, the classifiers obtained from the former two methods consist of complex mathematical functions and cannot be easily understood by users. In case of decision tree, the criterion for rule generation is ambiguous. Therefore, as an alternative, a new one-class classifier using hyper-rectangles was proposed, which performs precise classification compared to other methods and generates rules clearly understood by users as well. In this paper, we suggest an approach for improving the limitations of those previous one-class classification algorithms. Specifically, the suggested approach produces more improved one-class classifier using hyper-rectangles generated by using Gaussian function. The performance of the suggested algorithm is verified by a numerical experiment, which uses several datasets in UCI machine learning repository.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.6
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• pp.75-80
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• 2013

In this study, a forced ignition method with a plasma jet torch is studied in Mach 2 laboratory scaled wind-tunnel. The hyper-mixer is used as a mixer. For two normal injection cases, the one is collided against a wedge plate of the hyper-mixer and the other is directly injected into the cold main flow. For the first case, the hyper-mixer disperses the injected fuel, leading to the mixing enhancement. Furthermore, the fuel-air mixture is provided into the plasma hot gas, which enhances the combustion performance. However, the direct injection into the main flow method spends amount of fuel without ignition in the cold supersonic flow. In the end, for the forced combustion, it is important to supply the fuel-air mixture into the heat source.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.12
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• pp.1863-1870
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• 2018

Objective: The Bayesian first-order antedependence models, which specified single nucleotide polymorphisms (SNP) effects as being spatially correlated in the conventional BayesA/B, had more accurate genomic prediction than their corresponding classical counterparts. Given advantages of $BayesC{\pi}$ over BayesA/B, we have developed hyper-$BayesC{\pi}$, ante-$BayesC{\pi}$, and ante-hyper-$BayesC{\pi}$ to evaluate influences of the antedependence model and hyperparameters for $v_g$ and $s_g^2$ on $BayesC{\pi}$.Methods: Three public data (two simulated data and one mouse data) were used to validate our proposed methods. Genomic prediction performance of proposed methods was compared to traditional $BayesC{\pi}$, ante-BayesA and ante-BayesB. Results: Through both simulation and real data analyses, we found that hyper-$BayesC{\pi}$, ante-$BayesC{\pi}$ and ante-hyper-$BayesC{\pi}$ were comparable with $BayesC{\pi}$, ante-BayesB, and ante-BayesA regarding the prediction accuracy and bias, except the situation in which ante-BayesB performed significantly worse when using a few SNPs and ${\pi}=0.95$. Conclusion: Hyper-$BayesC{\pi}$ is recommended because it avoids pre-estimated total genetic variance of a trait compared with $BayesC{\pi}$ and shortens computing time compared with ante-BayesB. Although the antedependence model in $BayesC{\pi}$ did not show the advantages in our study, larger real data with high density chip may be used to validate it again in the future.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.103-111
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• 2020

Liver cancer is the most fatal cancer that occurs worldwide. In order to diagnose liver cancer, the patient's physical condition was checked by using a CT technique using radiation. Segmentation was needed to diagnose the liver on the patient's abdominal CT scan, which the radiologists had to do manually, which caused tremendous time and human mistakes. In order to automate, researchers attempted segmentation using image segmentation algorithms in computer vision field, but it was still time-consuming because of the interactive based and the setting value. To reduce time and to get more accurate segmentation, researchers have begun to attempt to segment the liver in CT images using CNNs, which show significant performance in various computer vision fields. The pixel value, or numerical value, of the CT image is called the Hounsfield Unit (HU) value, which is a relative representation of the transmittance of radiation, and usually ranges from about -2000 to 2000. In general, deep learning researchers reduce or limit this range and use it for training to remove noise and focus on the target organ. Here, we observed that the range of HU values was limited in many studies but different in various liver segmentation studies, and assumed that performance could vary depending on the HU range. In this paper, we propose the possibility of considering HU value range as a hyper parameter. U-Net and ResUNet were used to compare and experiment with different HU range limit preprocessing of CHAOS dataset under limited conditions. As a result, it was confirmed that the results are different depending on the HU range. This proves that the range limiting the HU value itself can be a hyper parameter, which means that there are HU ranges that can provide optimal performance for various models.

• Nuclear Engineering and Technology
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• v.44 no.7
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• pp.781-790
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• 2012

The HyperGam program was developed for the analysis of complex HPGe ${\gamma}$-ray spectra. The previous version of HyperGam was mainly limited to the analysis of ${\gamma}$-ray peaks and the manual logging of the result. In this study, it is specifically developed into a tool for the isotopic analysis of spectra. The newly developed features include nuclide identification and activity determination. An algorithm for nuclide identification was developed to identify the peaks in the spectrum by considering the yield, efficiency, energy and peak area for the ${\gamma}$-ray lines emitted from the radionuclide. The detailed performance of nuclide identification and activity determination was accessed using the IAEA 2002 set of test spectra. By analyzing the test spectra, the numbers of radionuclides identified truly (true hit), falsely (false hit) or missed (misses) were counted and compared with the results from the IAEA 2002 tests. The determined activities of the radionuclides were also compared for four test spectra of several samples. The result of the performance test is promising in comparison with those of the well-known software packages for ${\gamma}$-ray spectrum analysis.

• Journal of the Korean Society of Safety
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• v.37 no.6
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• pp.81-88
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• 2022

It is necessary to better understand the effect of age-related degradation on the performance of reinforced concrete shear walls in nuclear power plants in order to ensure their structural safety in the event of earthquakes. Therefore, this paper studies seismic fragility of the typical shear wall in nuclear power plants under earthquake excitation Reinforced concrete shear wall is composed of wall, horizontal and vertical flanges. Due to characteristics of its geometry, it is difficult to predict the ultimate behavior of shear wall under earthquake excitation. In this study, for more realistic numerical simulation, the Latin Hyper-Cube (LHC) simulation technique was used to generate uncertain variables for the material properties of concrete shear walls. The effects of crack, characteristics of inelastic behavior of concrete, and loss of cross section were considered in the nonlinear finite element analysis. The effects of aging-related deterioration were investigated on the performance of reinforced concrete shear walls through analysis of undegraded concrete shear walls and degraded concrete shear walls. The resulting seismic fragility curves present the change of performance of concrete shear wall due to age-related degradation.

• ETRI Journal
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• v.39 no.2
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• pp.275-283
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• 2017

A green technology for reducing energy consumption has become a critical factor in ICT industries. However, for the telecommunications sector in particular, most network elements are not usually optimized for power efficiency. Here, we propose a novel energy-efficient packet switching method for use in an IP network for reducing unnecessary energy consumption. As a green networking approach, we first classify the network nodes into either header or member nodes. The member nodes then put the routing-related module at layer 3 to sleep under the assumption that the layer in the OSI model can operate independently. The entire set of network nodes is then partitioned into clusters consisting of one header node and multiple member nodes. Then, only the header node in a cluster conducts IP routing and its member nodes conduct packet switching using a specially designed identifier, a tag. To investigate the impact of the proposed scheme, we conducted a number of simulations using well-known real network topologies and achieved a more energy- efficient performance than that achieved in previous studies.