• Journal of the Korean GEO-environmental Society
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• v.21 no.5
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• pp.5-11
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• 2020

Outliers detection and elimination included in field monitoring datum are essential for effective foundation of unusual movement, long and short range forecast of stability and future behavior to various structures. Integrated outlier analysis system for assessing long term time series data was developed in this study. Outlier analysis could be conducted in two step of primary analysis targeted at single dataset and second multi datasets analysis using synthesis value. Integrated outlier analysis system presents basic information for evaluating stability and predicting movement of structure combined with real-time safety management platform. Field application results showed increased correlation between synthesis value including similar sort of sensor showing constant trend and each single dataset. Various monitoring data in case of showing different trend can be used to analyse outlier through correlation-weighted value.

• Investigative Magnetic Resonance Imaging
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• v.24 no.1
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• pp.38-45
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• 2020

Background: Differentiation of cerebellopontine angle (CPA) schwannoma from meningioma is often a difficult process to identify. Purpose: To identify imaging features for distinguishing CPA schwannoma from meningioma and to investigate the usefulness of susceptibility-weighted imaging (SWI) in differentiating them. Materials and Methods: Between March 2010 and January 2015, this study pathologically confirmed 11 meningiomas and 20 schwannomas involving CPA with preoperative SWI were retrospectively reviewed. Generally, the following MRI features were evaluated: 1) maximal diameter on axial image, 2) angle between tumor border and adjacent petrous bone, 3) presence of intratumoral dark signal intensity on SWI, 4) tumor consistency, 5) blood-fluid level, 6) involvement of internal auditory canal (IAC), 7) dural tail, and 8) involvement of adjacent intracranial space. On CT, 1) presence of dilatation of IAC, 2) intratumoral calcification, and 3) adjacent hyperostosis were evaluated. All features were compared using Chi-squared tests and Fisher's exact tests. The univariate and multivariate logistic regression analysis were performed to identify imaging features that differentiate both tumors. Results: The results noted that schwannomas more frequently demonstrated dark spots on SWI (P = 0.025), cystic consistency (P = 0.034), and globular angle (P = 0.008); schwannomas showed more dilatation of internal auditory meatus and lack of calcification (P = 0.008 and P = 0.02, respectively). However, it was shown that dural tail was more common in meningiomas (P < 0.007). In general, dark spots on SWI and dural tail remained significant in multivariate analysis (P = 0.037 and P = 0.012, respectively). In this case, the combination of two features showed a sensitivity and specificity of 80% and 100% respectively, with an area under the receiver operating characteristic curve of 0.9. Conclusion: In conclusion, dark spots on SWI were found to be helpful in differentiating CPA schwannoma from meningioma. It is noted that combining dural tail with dark spots on SWI yielded strong diagnostic value in differentiating both tumors.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.847-852
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• 2005

Over the last decade, numerous complexity measurement techniques for Object-Oriented (OO) software system have been proposed for managing the effects of OO codes. These techniques may be based on source code analysis such as WMC (Weighted Methods per Class) and LCOM (Lack of Cohesion in Methods). The techniques are limited to count the number of functions (C++). However. we suggested a new weighted method that checks the number of parameters, the return value and its data type. Then we addressed an effective complexity measurement technique based on the weight of class interfaces to provide guidelines for measuring the class complexity of OO codes in reverse engineering. The results of this research show that the proposed complexity measurement technique ECC(Enhanced Class Complexity) is consistent and accurate in C++ environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.730-736
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• 2009

It is more important to analysis of huge gemonics data in Bioinformatics. Here we present a novel datamining approach to predict structure and function using protein's primnary structure only. We propose not also to develope n-Block substring search algorithm in reducing enormous search space effectively in relation to feature selection, but to formulate weighted linear algorithm in a prediction of structure and function of a protein using primary structure. And we show efficient in protein domain characterization and classification by calculation weight value in determining domain association in each selected substring, and also reveal that more efficient results are acquired through claculated model score result in an inference about degree of association with each CDS(coding sequence) in domain.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.347-358
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• 2021

The objective of this study is to optimize the shape of doubly curved surface where a conformal array is equipped. That surface is modeled with a double-ellipsoid solid controlled by four parameters. By analyzing the performance of the conformal array beams with the beam pattern synthesis, two design parameters are determined. Then, we define the weighted object function which is formulated as the sum of sharp indexes for directivity index, the elevation resolution, and the bearing resolution. The direct calculation on all grids is used to evaluate the weighted object function and seek the optimal value of two design parameters when the weightings are given. In the simulation, four kinds of weighting cases are respectively applied to evaluate the weighted object function. The optimal shapes of double-ellipsoid solid are shown in each case. Especially, when the uniform weightings are used, the double-ellipsoid solid with more smooth surface is obtained.

• Journal of Forest and Environmental Science
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• v.30 no.1
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• pp.152-160
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• 2014

The purpose of this study is to investigate and analyze the physical characteristics and tranquility of the valleys located in Gangwon region. For this study we analyzed the field survey data 135 valleys using GIS. The elements for measurement of tranquility were divided into visual elements including terrain, objects, forest, water and auditory elements including noise. These elements were divided further into positive and negative factors. The weight of each element and item was calculated by applying the AHP method. The results of this study are as follows. The length of the valley ranged from 126 m to 17 km, and the elevation ranged from 40 m to 1,800 m. Type of mixed forest was common in the valleys. The depth of the water was over 20 cm in 83% of the total area and most of the water was in good condition in visual quality. Regarding the positive factors of tranquility, the weighted scores of the objects, waterfall sounds and visual transparence of the water were of relatively high value. Relatively high values were also shown in closed and curved topography in the landform, forest type and natural forests. In the negative factors, the weights of the objects and forest elements had high values. Within the facility groups, facility of the river produced a considerable negative. After applying the index of tranquility, the natural physical attributes affected the tranquility value, more than the manmade structures to a much greater degree.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.115-126
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• 2009

A software size estimation has to be analyzed in the beginning of the software life-cycle and helpful to the prediction of its size and cost. The software cost has been calculated by estimating software size from the user's point of view since the function point method based on international standards was introduced for the estimation of software size in 2004. However, the current function point method is not easy to be exploited for unfamiliar user, and has a problem that it cannot estimate the proper size for software such as engineering software, scientific calculations and simulation with complicated internal computational logic. This paper presents an improved model which can simplify the existing function point measurement procedure, and perform the estimation of software size in easy and fast way at the initial stage of project. Moreover, it presents a mathematical weighted value calculation model which can solve the problem of the fixed complexity weighted value and reflect the characteristics of organization as its data is pilled up. Our evaluation shows that the presented model has advantage that it can measure the size more rapidly than the existing FPA methods and has more correlation with LOC.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1065-1074
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• 2011

During several decades, many researchers in the field of finance have studied Value at Risk (VaR) to measure the market risk. VaR indicates the worst loss over a target horizon such that there is a low, pre-specified probability that the actual loss will be larger (Jorion, 2006, p.106). In this paper, we compare conditional copula method with two conventional VaR forecasting methods based on simple moving average and exponentially weighted moving average for measuring the risk of the portfolio, consisting of two domestic stock indices. Through real data analysis, we conclude that the conditional copula method can improve the accuracy of portfolio VaR forecasting in the presence of high kurtosis and strong correlation in the data.

• Investigative Magnetic Resonance Imaging
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• v.18 no.2
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• pp.120-132
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• 2014

Purpose : To compare dynamic susceptibility contrast imaging, diffusion-weighted imaging, and susceptibility-weighted imaging (SWI) for the differentiation of tumor recurrence and delayed radiation therapy (RT)-related changes in patients treated with RT for primary brain tumors. Materials and Methods: We enrolled 24 patients treated with RT for various primary brain tumors, who showed newly appearing enhancing lesions more than one year after completion of RT on follow-up MRI. The enhancing-lesions were confirmed as recurrences (n=14) or RT-changes (n=10). We calculated the mean values of normalized cerebral blood volume (nCBV), apparent diffusion coefficient (ADC), and proportion of dark signal intensity on SWI (proSWI) for the enhancing-lesions. All the values between the two groups were compared using t-test. A multivariable logistic regression model was used to determine the best predictor of differential diagnosis. The cutoff value of the best predictor obtained from receiver-operating characteristic curve analysis was applied to calculate the sensitivity, specificity, and accuracy for the diagnosis. Results: The mean nCBV value was significantly higher in the recurrence group than in the RT-change group (P=.004), and the mean proSWI was significantly lower in the recurrence group (P<.001). However, no significant difference was observed in the mean ADC values between the two groups. A multivariable logistic regression analysis showed that proSWI was the only independent variable for the differentiation; the sensitivity, specificity, and accuracy were 78.6% (11 of 14), 100% (10 of 10), and 87.5% (21 of 24), respectively. Conclusion: The proSWI was the most promising parameter for the differentiation of newly developed enhancing-lesions more than one year after RT completion in brain tumor patients.

• Journal of KIISE:Software and Applications
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• v.37 no.1
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• pp.1-8
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• 2010

Recently lots of attention has been paid to gene set analysis for identifying differentially expressed gene-sets between two sample groups. Unlike earlier approaches, the gene set analysis enables us to find significant gene-sets along with their functional characteristics. For this reason, various novel approaches have been suggested lately for gene set analysis. As one of such, PAGE is a parametric approach that employs average difference (AD) as an expression metric to quantify expression differences between two sample groups and assumes that the distribution of gene scores is normal. This approach is preferred to non-parametric approach because of more effective performance. However, the metric AD does not reflect either gene expression intensities or variances over samples in calculating gene scores. Thus, in this paper, we investigate the usefulness of several other expression metrics for parametric gene-set analysis, which consider actual expression intensities of genes or their expression variances over samples. For this purpose, we examined three expression metrics, WAD (weighted average difference), FC (Fisher's criterion), and Abs_SNR (Absolute value of signal-to-noise ratio) for parametric gene set analysis and evaluated their experimental results.