• KIPS Transactions on Software and Data Engineering
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• v.1 no.2
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• pp.109-114
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• 2012

Honeycombs are dense structures that small cysts, which generally have about 2~10 mm in diameter, are surrounded by the wall of fibrosis. When honeycomb is found in the patients, the incidence of acute exacerbation is generally very high. Thus, the observation and quantitative measurement of honeycomb are considered as a significant marker for clinical diagnosis. In this point of view, we propose an automatic segmentation method using morphological image processing and assessment of the degree of clustering techniques. Firstly, image noises were removed by the Gaussian filtering and then a morphological dilation method was applied to segment lung regions. Secondly, honeycomb cyst candidates were detected through the 8-neighborhood pixel exploration, and then non-cyst regions were removed using the region growing method and wall pattern testing. Lastly, final honeycomb regions were segmented through the extraction of dense regions which are consisted of two or more cysts using cluster analysis. The proposed method applied to 80 High resolution computed tomography (HRCT) images and achieved a sensitivity of 89.4% and PPV (Positive Predictive Value) of 72.2%.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.359-366
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• 2021

A number of construction companies in Korea invest considerable human and financial resources to construct a system for managing apartment defect data and for categorizing repair tasks. Thus, this study proposes machine learning models to automatically classify defect complaint text-data into one of the sub categories of 'finishing work' (i.e., one of the defect repair tasks). In the proposed models, we employed two word representation methods (Bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF)) and two machine learning classifiers (Support Vector Machine, Random Forest). In particular, we conducted both binary- and multi- classification tasks to classify 9 sub categories of finishing work: home appliance installation work, paperwork, painting work, plastering work, interior masonry work, plaster finishing work, indoor furniture installation work, kitchen facility installation work, and tiling work. The machine learning classifiers using the TF-IDF representation method and Random Forest classification achieved more than 90% accuracy, precision, recall, and F1 score. We shed light on the possibility of constructing automated defect classification systems based on the proposed machine learning models.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.5-20
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• 2021

With the available national spatial information that started from the sinkholes that occurred nationwide in 2014 and integrated 15 areas of underground information, the Underground Spatial Integrated Map has been continuously maintained since 2015. However, until recently, as disasters and accidents in underground spaces such as hot water pipes rupture, cable tunnel fires, and ground subsidence continue to occur, there is an increasing demand for quality improvement of underground information. Thus, this paper attempted to prepare a plan to improve the quality of the Underground Spatial Integrated Map data. In particular, among the 15 types of underground information managed through the Underground Spatial Integrated Map, quality validation improvement measures were proposed for underground facility (structure-type) data, which has the highest proportion of new constructions. To improve the current inspection methods that primarily rely on visual inspection, we elaborate on and subdivide the current quality inspection standards. Specifically, we present an approach for software-based automated inspection of databases, including graphics and attribute information, by adding three quality inspection items, namely, quality inspection methods, rules, and flow diagram, solvable error types, to the current four quality inspection items consisting of quality elements, sub-elements, detailed sub-elements, and quality inspection standards.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.569-578
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• 2021

Thanks to web development today, we can easily access online news via various media. As much as it is easy to access online news, we often face fake news pretending to be true. As fake news items have become a global problem, fact-checking services are provided domestically, too. However, these are based on expert-based manual detection, and research to provide technologies that automate the detection of fake news is being actively conducted. As for the existing research, detection is made available based on contextual characteristics of an article and the comparison of a title and the main article. However, there is a limit to such an attempt making detection difficult when manipulation precision has become high. Therefore, this study suggests using a verifying article to decide whether a news item is genuine or not to be affected by article manipulation. Also, to improve the precision of fake news detection, the study added a process to summarize a subject article and a verifying article through the summarization model. In order to verify the suggested algorithm, this study conducted verification for summarization method of documents, verification for search method of verification articles, and verification for the precision of fake news detection in the finally suggested algorithm. The algorithm suggested in this study can be helpful to identify the truth of an article before it is applied to media sources and made available online via various media sources.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.4
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• pp.457-467
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• 2022

Electron bombardment to silicate glass during electron probe microanalysis (EPMA) causes outward migration of Na from the excitation volume and subsequent decrease in the measured X-ray count rates of Na. To acquire precise Na₂O content of silicate glass, one should use proper analytical technique to avoid or minimize Na migration effect or should correct for decreases in the measured Na X-ray counts. In this study, we analyzed 8 silicate glass standard samples using automated Time Dependent Intensity (TDI) correction method of Probe for EPMA software that can calculate zero-time intercept by extrapolating X-ray count changes over analysis time. We evaluated an accuracy of TDI correction for Na measurements of silicate glasses with EPMA at 15 kV acceleration voltage and 20 nA probe current electron beam, which is commonly utilized analytical condition for geological samples. Results show that Na loss can be avoided with 20 ㎛-sized large beam (<0.1 nA/㎛²), thus silicate glasses can be analyzed without TDI correction. When the beam size is smaller than 10 ㎛, Na loss results in large relative errors up to -55% of Na₂O values without correction. By applying TDI corrections, we can acquire Na₂O values close to the reference values with relative errors of ~ ±10%. Use of weighted linear-fit can reduce relative errors down to ±6%. Thus, quantitative analysis of silicate glasses with EPMA is required for TDI correction for alkali elements such as Na and K.

• Tunnel and Underground Space
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• v.32 no.5
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• pp.298-311
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• 2022

Some problems, such as aging workers, a decreased population due to a low birth rate, and shortage of skilled workers, are rising in construction sites. Therefore research for smart construction technology that can be improved for productivity, safety, and quality has been recently developed with government support by replacing traditional construction technology with advanced digital technology. In particular, the motor grader that mainly performs road surface flattening is a construction machine that requires the application of automation technology for repetitive construction. It is predicted that the construction period will be shortened if the construction automation technology such as trajectory tracking, automation work, and remote control technology is applied. In this study, we introduce the hardware and software architecture of the smart motor grader to apply unmanned and automation technology and then analyze the traditional earthwork method of the motor grader. We suggested the application plans for the path pattern and blade control method of the smart motor grader based on this. In addition, we verified the performance of waypoint-based path-following depending on scenarios and the blade control's performance through tests.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.701-708
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• 2017

The construction cost is estimated based on the drawings at the design stage and constructor will find efficient construction methods for budgeting and budgeting appropriate to the budget. Accurate quantity estimation and budgeting are critical to determining whether the project is profitable or not. However, since this process is mostly performed depending on manpower or 2D drawings, errors are likely to occur and The BIM(Build Information Modeling) program, which can be automated, is very expensive and difficult to apply in the field. In this study, 3D architectural modeling was performed using SketchUp which is a 3D modeling software and suggest a methodology for Quantity Estimation. As a result, 3D modeling was performed effectively using 2D drawings of buildings. Based on the modeling results, it was possible to calculate the difference of the quantity estimation by 2D drawing and 3D modeling. The research suggests that the 3D modeling using the SketchUp and the calculation of the quantity can prevent the error of the conventional 2D calculation method. If the applicability of the research method is verified through continuous research, it will contribute to increase the efficiency of architectural modeling and quantity Estimation work.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1172-1184
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• 2021

Objective: The purposes of this study were to analyze the radiation doses for pediatric abdominopelvic and chest CT examinations from university hospitals in Korea and to establish the local diagnostic reference levels (DRLs) based on the body weight and size. Materials and Methods: At seven university hospitals in Korea, 2494 CT examinations of patients aged 15 years or younger (1625 abdominopelvic and 869 chest CT examinations) between January and December 2017 were analyzed in this study. CT scans were transferred to commercial automated dose management software for the analysis after being de-identified. DRLs were calculated after grouping the patients according to the body weight and effective diameter. DRLs were set at the 75th percentile of the distribution of each institution's typical values. Results: For body weights of 5, 15, 30, 50, and 80 kg, DRLs (volume CT dose index [CTDI_vol]) were 1.4, 2.2, 2.7, 4.0, and 4.7 mGy, respectively, for abdominopelvic CT and 1.2, 1.5, 2.3, 3.7, and 5.8 mGy, respectively, for chest CT. For effective diameters of < 13 cm, 14-16 cm, 17-20 cm, 21-24 cm, and > 24 cm, DRLs (size-specific dose estimates [SSDE]) were 4.1, 5.0, 5.7, 7.1, and 7.2 mGy, respectively, for abdominopelvic CT and 2.8, 4.6, 4.3, 5.3, and 7.5 mGy, respectively, for chest CT. SSDE was greater than CTDI_vol in all age groups. Overall, the local DRL was lower than DRLs in previously conducted dose surveys and other countries. Conclusion: Our study set local DRLs in pediatric abdominopelvic and chest CT examinations for the body weight and size. Further research involving more facilities and CT examinations is required to develop national DRLs and update the current DRLs.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.807-820
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• 2023

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion: CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.3
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• pp.201-208
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• 2007

Purpose: We investigated whether the diagnostic performance of SPECT scintimammography (SMM) can be improved by adding computer-aided diagnosis (CAD) of ultrasonography (US). Materials and methods: We reviewed breast SPECT SMM images and corresponding US images from 40 patients with breast masses (21 malignant and 19 benign tumors). The quantitative data of SPECT SMM were obtained as the uptake ratio of lesion to contralateral normal breast. The morphologic features of the breast lesions on US were extracted and quantitated using the automated CAD software program. The diagnostic performance of SPECT SMM and CAD of US alone was determined using receiver operating characteristic (ROC) curve analysis. The best discriminating parameter (D-value) combining SPECT SMM and the CAD of US was created. The sensitivity, specificity and accuracy of combined two diagnostic modalities were compared to those of a single one. Results: Both SPECT SMM and CAD of US showed a relatively good diagnostic performance (area under curve = 0.846 and 0.831, respectively). Combining the results of SPECT SMM and CAD of US resulted in improved diagnostic performance (area under curve =0.860), but there was no statistical differerence in sensitivity, specificity and accuracy between the combined method and a single modality. Conclusion: It seems that combining the results of SPECT SMM and CAD of breast US do not significantly improve the diagnostic performance for diagnosis of breast cancer, compared with that of SPECT SMM alone. However, SPECT SMM and CAD of US may complement each other in differential diagnosis of breast cancer.