• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1899-1909
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• 2015

Glaucoma is one of the most common causes of blindness which is caused by increase of fluid pressure in the eye which damages the optic nerve and eventually causing vision loss. An automated technique to diagnose glaucoma disease can reduce the physicians’ effort in screening of Glaucoma in a person through the fundal retinal images. In this paper, optimal hyper analytic wavelet transform for Glaucoma detection technique from fundal retinal images is proposed. The optimal coefficients for transformation process are found out using the hybrid GSO-Cuckoo search algorithm. This technique consists of pre-processing module, optimal transformation module, feature extraction module and classification module. The implementation is carried out with MATLAB and the evaluation metrics employed are accuracy, sensitivity and specificity. Comparative analysis is carried out by comparing the hybrid GSO with the conventional GSO. The results reported in our paper show that the proposed technique has performed well and has achieved good evaluation metric values. Two 10- fold cross validated test runs are performed, yielding an average fitness of 91.13% and 96.2% accuracy with CGD-BPN (Conjugate Gradient Descent- Back Propagation Network) and Support Vector Machines (SVM) respectively. The techniques also gives high sensitivity and specificity values. The attained high evaluation metric values show the efficiency of detecting Glaucoma by the proposed technique.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.879-886
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• 2009

The purpose of this article is to present the application of Least Squares Support Vector Machine in analyzing the existing structure of brand. We estimate the parameters of the Market Share Attraction Model using a non-parametric technique for function estimation called Least Squares Support Vector Machine, which allows us to perform even nonlinear regression by constructing a linear regression function in a high dimensional feature space. Estimation by Least Squares Support Vector Machine technique makes it a good candidate for solving the Market Share Attraction Model. To illustrate the performance of the proposed method, we use the car sales data in South Korea's car market.

• Journal of the Korean Home Economics Association
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• v.44 no.2 s.216
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• pp.71-81
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• 2006

The purpose of this study was to analyze the preferences for the physical features of senior congregate housing. The survey was conducted among middle-aged people in their fifties, who lived in Seoul, using the systematic random sampling method. The data were collected from November 3, 2003 to November 14, 2003 and the final subjects consisted of 498 respondents. Various statistical methods such as frequency, mean, cross tabulation, t-test, factor analysis, and multiple regression were used in this study. The results of this study were as follows. Firstly, most of the respondents preferred 55 to $70m^2$ sized individual units and they rarely wanted smaller units of less than $35m^2$. Individual units of one or two bedrooms were also preferred by future users. Small towns were preferred to large complex. For housing type, they preferred row houses or single detached houses to high-rise apartments. Secondly, there were no significant statistical differences between income and the preference of the physical features. From the results, we concluded that senior congregate housing should be developed not only in accordance with the users' preferences but also over a certain minimum physical quality level, regardless of the users' income.

• Applied Biological Chemistry
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• v.32 no.1
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• pp.64-72
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• 1989

Near full length cDNA clones encoding the rice seed storage protein, prolamine, were isolated and divided into two homology classes based on cross-hybridization and DNA sequencing analysis. These cDNA clones contain a single open reading frame encoding a putative rice prolamine precursor(M.W.=17,200) possessing atypical 14 amino acid signal peptide. Clones of these two homology classes diverge mainly by insertions/deletions of short nucleotide stretches and point mutations. The deduced primary structures of both types of prolamine polypeptides are devoid of any major tandem repetitive sequences, a feature prevalent in other cereal prolamines. No significant homology teas detected between the rice prolamine and other cereal prolamines, indicating that the rice gene evolved from a different ancestor that gave rise to other cereal prolamine genes. Developing wheat and rice endosperms were examined using ultrathin sections prepared from tissues harvested at various days after flowering. By immunocytochemical localization techniques, wheat prolamines are localized within vesicles from Golgi apparatus and in homogeneous regions of protein bodies. The involvement of the goli apparatus in the packaging of wheat prolamines into protein bodies indicates a pathway which differs from the mode of other cereal prolamines and resembles the mechanism employed for the storage of rice glutelin and legume globulins.

• Journal of the Korean Magnetics Society
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• v.24 no.2
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• pp.66-69
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• 2014

The spreading tube of X-ray cathode tube displayed with an electromagnetic finite element method was designed. To analyze a feature design and the concrete coordinate performance of soft X-ray tube modeling, the orbit of electron beam was simulated by OPERA-3D SW program. The fixed conditions were the applied voltage, the temperature, the work function of thermal electron between cathode and anode of tungsten. Through the analysis of distribution of electron beam and the variation of dividing region, the design of soft X-ray spreading tube equipped with two cross filaments was optimized.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.33-38
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• 2020

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

• Tunnel and Underground Space
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• v.15 no.2 s.55
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• pp.129-136
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• 2005

This paper describes the implementation of rock mass rating evaluation based on genetic algorithm(GA) and conditional simulation technique to estimate RMR in the area without sufficient borehole data RMR were estimated by GA and conditional simulation technique with reflecting distribution feature and spatial correlation. And RMR determined by GA were compared with the results from kriging. Through the analysis of the results from 30 simulations, the uncertainty of estimation could be quantified.

• Computers and Concrete
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• v.21 no.6
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• pp.727-739
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• 2018

Crack is the most common typical feature of concrete deterioration, so routine monitoring and health assessment become essential for identifying failures and to set up an appropriate rehabilitation strategy in order to extend the service life of concrete structures. At present, image segmentation algorithms have been applied to crack analysis based on inspection images of concrete structures. The results of crack segmentation offering crack information, including length, width, and area is helpful to assist inspectors in surface inspection of concrete structures. This study proposed an algorithm of image segmentation enhancement, named morphological segmentation based on edge detection-II (MSED-II), to concrete crack segmentation. Several concrete pavement and building surfaces were imaged as the study materials. In addition, morphological operations followed by cross-curvature evaluation (CCE), an image segmentation technique of linear patterns, were also tested to evaluate their performance in concrete crack segmentation. The result indicates that MSED-II compared to CCE can lead to better quality of concrete crack segmentation. The least area, length, and width measurement errors of the concrete cracks are 5.68%, 0.23%, and 0.00%, respectively, that proves MSED-II effective for automatic measurement of concrete cracks.

• Steel and Composite Structures
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• v.39 no.2
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• pp.149-158
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• 2021

In this research paper, the free vibrational response of laminated composite plates is investigated using a non-polynomial refined shear deformation theory (NP-RSDT). The most interesting feature of this theory is the parabolic distribution of transverse shear deformations while ensuring the conditions of nullity of shear stresses at the free surfaces of the plate without requiring the Shear correction factor "Ks". A fourth-nodded isoparametric element with four degrees of freedom per node is employed for laminated composite plates. The numerical analysis of simply supported square anti-symmetric cross-ply and angle-ply laminated plate is carried out using a special discretization based on four-node finite element method which four degrees of freedom per node. Several numerical results are presented to show the effect of the coupling parameters of the plate such as the modulus ratios, the thickness ratio and the plate layers number on adimensional eigen frequencies. All numerical results presented using the current finite element method (FEM) is presented in 3D curve form.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.236-242
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• 2024

Software Defect Prediction (SDP) enhances the efficiency of software development by proactively identifying modules likely to contain errors. A major challenge in SDP is improving prediction performance. Recent research has applied deep learning techniques to the field of SDP, with the SAINT model particularly gaining attention for its outstanding performance in analyzing structured data. This study compares the SAINT model with other leading models (XGBoost, Random Forest, CatBoost) and investigates the latest deep learning techniques applicable to SDP. SAINT consistently demonstrated superior performance, proving effective in improving defect prediction accuracy. These findings highlight the potential of the SAINT model to advance defect prediction methodologies in practical software development scenarios, and were achieved through a rigorous methodology including cross-validation, feature scaling, and comparative analysis.