• Geophysics and Geophysical Exploration
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• v.11 no.1
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• pp.68-77
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• 2008

We have developed an inversion algorithm for loop-loop electromagnetic (EM) data, based on the localised non-linear or extended Born approximation to the solution of the 2.5D integral equation describing an EM scattering problem. Source and receiver configuration may be horizontal co-planar (HCP) or vertical co-planar (VCP). Both multi-frequency and multi-separation data can be incorporated. Our inversion code runs on a PC platform without heavy computational load. For the sake of stable and high-resolution performance of the inversion, we implemented an algorithm determining an optimum spatially varying Lagrangian multiplier as a function of sensitivity distribution, through parameter resolution matrix and Backus-Gilbert spread function analysis. Considering that the different source-receiver orientation characteristics cause inconsistent sensitivities to the resistivity structure in simultaneous inversion of HCP and VCP data, which affects the stability and resolution of the inversion result, we adapted a weighting scheme based on the variances of misfits between the measured and calculated datasets. The accuracy of the modelling code that we have developed has been proven over the frequency, conductivity, and geometric ranges typically used in a loop-loop EM system through comparison with 2.5D finite-element modelling results. We first applied the inversion to synthetic data, from a model with resistive as well as conductive inhomogeneities embedded in a homogeneous half-space, to validate its performance. Applying the inversion to field data and comparing the result with that of dc resistivity data, we conclude that the newly developed algorithm provides a reasonable image of the subsurface.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.629-635
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• 2019

In Deep Learning models derivative is implemented by error back-propagation which enables the model to learn the error and update parameters. It can find the global (or local) optimal points of parameters even in the complex models taking advantage of a huge improvement in computing power. However, deliberately generated data points can 'fool' models and degrade the performance such as prediction accuracy. Not only these adversarial examples reduce the performance but also these examples are not easily detectable with human's eyes. In this work, we propose the method to detect adversarial datasets with random noise addition. We exploit the fact that when random noise is added, prediction accuracy of non-adversarial dataset remains almost unchanged, but that of adversarial dataset changes. We set attack methods (FGSM, Saliency Map) and noise level (0-19 with max pixel value 255) as independent variables and difference of prediction accuracy when noise was added as dependent variable in a simulation experiment. We have succeeded in extracting the threshold that separates non-adversarial and adversarial dataset. We detected the adversarial dataset using this threshold.

• Journal of Korean Society of Forest Science
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• v.104 no.3
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• pp.476-487
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• 2015

This study was conducted to analyze the potential of Baekdu-daegan Mountain System (BDMS) in the Republic of Korea for World Heritage inscription and undertook preliminary global comparative analysis. UNEP WCMC global datasets, World Heritage global gap analyses and thematic studies conducted by IUCN were reviewed to see if the BDMS could have been identified within these as a priority area for World Heritage. With respect to potential Outstanding Universal Value this study found that the case for BDMS was weak. The BDMS lies within biogeographic regions which are already represented on the World Heritage List and at a global scale its natural values do not stand out. It was emphasized that a more fine scale analysis of the values should be undertaken. The BDMS stands out at a global scale in terms of the degree of contiguity between protected areas along its length and the legal and institutional frameworks established in the Republic of Korea. The BDMS has potential for a trans-national and serial properties along the full length of the BDMS, if two Koreas agree to work together.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.10
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• pp.5761-5767
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• 2013

The potential correlation of X-ray repair cross-complementing group 1 (XRCC1) Arg399Gln polymorphism with hepatocellular carcinoma (HCC) susceptibility is ambiguous. Taking account of inconsistent results of previous meta-analyses and new emerging literatures, we conducted a meta-analysis covering 15 case-control datasets to evaluate the relationship. Relevant studies from Medline, Embase and CNKI were retrieved. A fixed-effect model or a random-effect model, depending on between-study heterogeneity, were applied to estimate the association between XRCC1 polymorphism Arg399Gln and HCC risk with the results presented as odds ratios (ORs) and 95% confidence intervals (95% CIs). In accordance with Hardy-Weinberg equilibrium, 15 studies with data for 6,556 individuals were enrolled in this systematic review. For overall HCC,thr XRCC1 polymorphism Arg399Gln was significantly associated with HCC susceptibility in a homozygote model as well as in a dominant model (G/G vs. A/A, OR=1.253, p=0.028; G/G+A/G vs. A/A, OR= 1.281, p=0.047, respectively), but not in a heterozygote model (A/G vs. A/A, OR=1.271, p=0.066) or a recessive model (G/G vs. A/G + A/A, OR= 1.049, p=0.542). Similar results were also observed on stratification analysis by ethnicity (A/G vs. A/A, OR=1.357, p=0.025; G/G vs. A/A, OR=1.310, p=0.011; G/G+A/G vs. A/A, OR= 1.371, p=0.013). However, no potential contribution of XRCC1 Arg399Gln polymorphism to HCC susceptibility in HBV/HCV subgroups was identified. No publication bias was found in this study. In conclusion, the XRCC1 Arg399Gln polymorphism contributes to HCC susceptibility. Due to the lack of studies in Western countries, further large-sample and rigorous studies are needed to validate the findings.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.2
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• pp.691-694
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• 2014

Background: Tumor length in patients with esophageal cancer (EC) has recently received great attention. However, its prognostic role for EC is controversial. The purpose of our study was to characterize the prognostic value of tumor length in EC patients and offer the optimum cut-off point of tumor length by reliable statistical methods. Materials and Methods: A retrospective analysis was conducted on 71 consecutive patients with EC who underwent surgery. ROC curve analysis was used to determine the optimal cut-off point for tumor length, measured with a handheld ruler after formalin fixation. Correlations between tumor length and other factors were surveyed, and overall survival (OS) rates were compared between the two groups. Potential prognostic factors were evaluated by univariate Kaplan-Meier survival analysis. A P value less than 0.05 was considered significant. Results: There were a total of 71 patients, with a male/female divide of 43/28 and a median age of 59. Characteristics were as follows: squamous/adenocarcinoma, 65/6; median tumor length, 4 (0.9-10); cut-off point for tumor length, 4cm. Univariate analysis prognostic factors were tumor length and modality of therapy. One, three and five year OS rates were 84, 43 and 43% for tumors with ${\leq}4cm$ length, whereas the rates were 75, 9 and 0% for tumors >4 cm. There was a significant association between tumor length and age, sex, weight loss, tumor site, histology, T and N scores, differentiation, stage, modality of therapy and longitudinal margin involvement. Conclusions: Future studies for modification of the EC staging system might consider tumor length too as it is an important prognostic factor. Further assessment with larger prospective datasets and practical methods (such as endoscopy) is needed to establish an optimal cut-off point for tumor length.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.2
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• pp.159-173
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• 2017

The Weather Research and Forecast (WRF) - Community Multiscale Air Quality (CMAQ) system was applied to investigate the influence of major point sources located in Chungcheongnam-do (CN) on surface $PM_{2.5}$ (Particulate Matter of which diameter is $2.5{\mu}m$ or less) concentrations in its surrounding areas. Uncertainties associated with contribution estimations were examined through cross-comparison of modeling results using various combinations of model inputs and setups; two meteorological datasets developed with WRF for 2010 and 2014, and two domestic emission inventories for 2010 and 2013 were used to estimate contributions of major point sources in CN. The results show that contributions of major point sources in CN to annual $PM_{2.5}$ concentrations over Seoul, Incheon, Gyeonggi, and CN ranged $0.51{\sim}1.63{\mu}g/m^3$, $0.71{\sim}1.62{\mu}g/m^3$, $0.63{\sim}1.66{\mu}g/m^3$, and $1.04{\sim}1.86{\mu}g/m^3$, respectively, depending on meteorology and emission inventory choice. It indicates that the contributions over the surrounding areas can be affected by model inputs significantly. Nitrate was the most dominant $PM_{2.5}$ component that was increased by major point sources in CN followed by sulfate, ammonium, and others. Based on the model simulations, it was estimated that primary $PM_{2.5}$ $(PPM)-to-PM_{2.5}$ conversion rates were 41.3~50.7 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 12.4~18.3 ($10^{-6}{\mu}g/m^3/TPY$) for Seoul, Incheon, and Gyeonggi, respectively. In addition, spatial gradients of PPM contributions show very steep trends. $NO_X$-to-nitrate conversion rates were 7.61~12.3 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 3.94~11.3 ($10^{-6}{\mu}g/m^3/TPY$) for the sub-regions in the SMA. $SO_2$-to-sulfate conversion rates were 4.04~5.28 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 3.73~4.43 ($10^{-6}{\mu}g/m^3/TPY$) for the SMA, respectively.

• Asian-Australasian Journal of Animal Sciences
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• v.22 no.7
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• pp.923-930
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• 2009

This study was conducted to compare three models: two random regression models with and without considering heterogeneity in the residual variances and a lactation model (LM) for evaluating the genetic ability of Holstein cows in Korea. Two datasets were prepared for this study. To apply the test-day random regression model, 94,390 test-day records were prepared from 15,263 cows. The second data set consisted of 14,704 lactation records covering milk production over 305 days. Raw milk yield and composition data were collected from 1998 to 2002 by the National Agricultural Cooperative Federation' dairy cattle improvement center by way of its milk testing program, which is nationally based. The pedigree information for this analysis was collected by the Korean Animal Improvement Association. The random regression models (RRMs) are single-trait animal models that consider each lactation record as an independent trait. Estimates of covariance were assumed to be different ones. In order to consider heterogeneity of residual variance in the analysis, test-days were classified into 29 classes. By considering heterogeneity of residual variance, variation for lactation performance in the early lactation classes was higher than during the middle classes and variance was lower in the late lactation classes than in the other two classes. This may be due to feeding management system and physiological properties of Holstein cows in Korea. Over classes e6 to e26 (covering 61 to 270 DIM), there was little change in residual variance, suggesting that a model with homogeneity of variance be used restricting the data to these days only. Estimates of heritability for milk yield ranged from 0.154 to 0.455, for which the estimates were variable depending on different lactation periods. Most of the heritabilities for milk yield using the RRM were higher than in the lactation model, and the estimate of genetic variance of milk yield was lower in the late lactation period than in the early or middle periods.

• The KIPS Transactions:PartA
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• v.13A no.5 s.102
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• pp.465-472
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• 2006

Recently, many suggestions have been made in image segmentation methods for extracting human organs or disease affected area from huge amounts of medical image datasets. However, images from some areas, such as brain, which have multiple structures with ambiruous structural borders, have limitations in their structural segmentation. To address this problem, clustering technique which classifies voxels into finite number of clusters is often employed. This, however, has its drawback, the influence from noise, which is caused from voxel by voxel operations. Therefore, applying image enhancing method to minimize the influence from noise and to make clearer image borders would allow more robust structural segmentation. This research proposes an efficient structural segmentation method by filtering based clustering to extract detail structures such as white matter, gray matter and cerebrospinal fluid from brain MR. First, coherence enhancing diffusion filtering is adopted to make clearer borders between structures and to reduce the noises in them. To the enhanced images from this process, fuzzy c-means clustering method was applied, conducting structural segmentation by assigning corresponding cluster index to the structure containing each voxel. The suggested structural segmentation method, in comparison with existing ones with clustering using Gaussian or general anisotropic diffusion filtering, showed enhanced accuracy which was determined by how much it agreed with the manual segmentation results. Moreover, by suggesting fine segmentation method on the border area with reproducible results and minimized manual task, it provides efficient diagnostic support for morphological abnormalities in brain.

• Journal of KIISE
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• v.42 no.4
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• pp.504-511
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• 2015

Recently, the amount of data is rapidly increasing with the popularity of the SNS and the development of mobile technology. So, it has been actively studied for the effective data analysis schemes of the large amounts of data. One of the typical schemes is a Voronoi diagram based on kNN join algorithm (VkNN-join) using MapReduce. For two datasets R and S, VkNN-join can reduce the time of the join query processing involving big data because it selects the corresponding subset Sj for each Ri and processes the query with them. However, VkNN-join requires a high computational cost for constructing the Voronoi diagram. Moreover, the computational overhead of the VkNN-join is high because the number of the candidate cells increases as the value of the k increases. In order to solve these problems, we propose a MapReduce-based kNN-join query processing algorithm for analyzing the large amounts of data. Using the seed-based dynamic partitioning, our algorithm can reduce the overhead for constructing the index structure. Also, it can reduce the computational overhead to find the candidate partitions by selecting corresponding partitions with the average distance between two seeds. We show that our algorithm has better performance than the existing scheme in terms of the query processing time.

• Journal of KIISE
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• v.43 no.10
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• pp.1115-1123
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• 2016

Assumption-based truth maintenance system (ATMS) is a tool that maintains the reasoning process of inference engine. It also supports non-monotonic reasoning based on dependency-directed backtracking. Bookkeeping all the reasoning processes allows it to quickly check and retract beliefs and efficiently provide solutions for problems with large search space. However, the amount of data has been exponentially grown recently, making it impossible to use a single machine for solving large-scale problems. The maintaining process for solving such problems can lead to high computation cost due to large memory overhead. To overcome this drawback, this paper presents an approach towards incrementally maintaining the reasoning process of inference engine on cluster using Spark. It maintains data dependencies such as assumption, label, environment and justification on a cluster of machines in parallel and efficiently updates changes in a large amount of inferred datasets. We deployed the proposed ATMS on a cluster with 5 machines, conducted OWL/RDFS reasoning over University benchmark data (LUBM) and evaluated our system in terms of its performance and functionalities such as assertion, explanation and retraction. In our experiments, the proposed system performed the operations in a reasonably short period of time for over 80GB inferred LUBM2000 dataset.