• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4460-4467
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• 2010

Recent advances in computer technology have brought more dependence on software to railway systems and changed to computer systems. Hence, the reliability and safety assurance of the vital software running on the embedded railway system is going to tend toward very critical task. Accordingly, various software test and validation activities are highly recommended in the international standards related railway software. In this paper, we presented an automated analysis tool and standard for software testing coverage in railway system, and presented its result of implementation. We developed the control flow analysis tool estimating test coverage as an important quantitative item for software safety verification in railway software. Also, we proposed judgement standards due to railway S/W Safety Integrity Level(SWSIL) based on analysis of standards in any other field for utilizing developed tool widely at real railway industrial sites. This tool has more advantage of effective measuring various test coverages than other countries, so we can expect railway S/W development and testing technology of real railway industrial sites in Korea.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.149-155
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• 2021

In this study, we intend to develop a defective road surface object recognition model that automatically detects road surface defects that restrict the movement of the transportation handicapped using electric mobile devices with deep learning. For this purpose, road surface information was collected from the pedestrian and running routes where the electric mobility aid device is expected to move in five areas within the city of Busan. For data, images were collected by dividing the road surface and surroundings into objects constituting the surroundings. A series of recognition items such as the detection of breakage levels of sidewalk blocks were defined by classifying according to the degree of impeding the movement of the transportation handicapped in traffic from the collected data. A road surface object recognition deep learning model was implemented. In the final stage of the study, the performance verification process of a deep learning model that automatically detects defective road surface objects through model learning and validation after processing, refining, and annotation of image data separated and collected in units of objects through actual driving. proceeded.

• 재활복지
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• v.16 no.3
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• pp.165-192
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• 2012

This study aims to develop a valid self-report scale for the community integration of persons with psychiatric disabilities. To this end, conducted were in-depth interviews with individuals with psychiatric disabilities, consultation with experts, and a survey. First, literature review and the in-depth interview with individuals with psychiatric disabilities were collected questionnaires regarding the community integration of persons with psychiatric disabilities. Second, preliminary research 1 focused on the selection and modification of the items collected in the first research. Final 44 items were selected by the verification of the importance and content-validity of items under the advices of professionals. Lastly, preliminaty research 2 applied cross-validation method to the data from 524 cases in order to verify the factor structure and concept-validity of the items. The result of exploratory factor analysis shows that 5 factor structures are the most appropriate, and the confirmatory factor analysis suggests that the Self-reporting Scale of Community Integration for the person with psychiatric disabilities consists of 27 questionnaires which compose 5sub-concepts such as'psychological integration','physical integration', 'social support', 'social integration', 'independence/self-actualization'. Moreover, this scale was significantly related to the 'Life Satisfaction scale for the person with psychiatric disabilities'. This proved concurrent validity of the scale.

• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.773-780
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• 2020

With the development of the 4th industrial, research is being conducted to prevent diseases and reduce damage in various fields of science and technology such as medicine, health, and bio. As a result, artificial intelligence technology has been introduced and researched for image analysis of radiological examinations. In this paper, we will directly apply a deep learning model for classification and detection of pneumonia using chest X-ray images, and evaluate whether the deep learning model of the Inception series is a useful model for detecting pneumonia. As the experimental material, a chest X-ray image data set provided and shared free of charge by Kaggle was used, and out of the total 3,470 chest X-ray image data, it was classified into 1,870 training data sets, 1,100 validation data sets, and 500 test data sets. I did. As a result of the experiment, the result of metric evaluation of the Inception V3 deep learning model was 94.80% for accuracy, 97.24% for precision, 94.00% for recall, and 95.59 for F1 score. In addition, the accuracy of the final epoch for Inception V3 deep learning modeling was 94.91% for learning modeling and 89.68% for verification modeling for pneumonia detection and classification of chest X-ray images. For the evaluation of the loss function value, the learning modeling was 1.127% and the validation modeling was 4.603%. As a result, it was evaluated that the Inception V3 deep learning model is a very excellent deep learning model in extracting and classifying features of chest image data, and its learning state is also very good. As a result of matrix accuracy evaluation for test modeling, the accuracy of 96% for normal chest X-ray image data and 97% for pneumonia chest X-ray image data was proven. The deep learning model of the Inception series is considered to be a useful deep learning model for classification of chest diseases, and it is expected that it can also play an auxiliary role of human resources, so it is considered that it will be a solution to the problem of insufficient medical personnel. In the future, this study is expected to be presented as basic data for similar studies in the case of similar studies on the diagnosis of pneumonia using deep learning.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.

• Journal of the Korean Geotechnical Society
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• v.32 no.4
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• pp.5-14
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• 2016

Numerical simulation of dynamic soil-pile-structure interaction embedded in a dry sand was carried out. 3D model of the dynamic centrifuge model tests was formulated in a time domain to consider nonlinear behavior of soil using the finite difference method program, FLAC3D. As a modeling methodology, Mohr-Coulomb criteria was adopted as soil constitutive model. Soil nonlinearity was considered by adopting the hysteretic damping model, and an interface model which can simulate separation and slip between soil and pile was adopted. Simplified continuum modeling (Kim et al., 2012) was used as boundary condition to reduce analysis time. Calibration process for numerical modeling results and test results was performed through the parametric study. Verification process was then performed by comparing numerical modeling results with another test results. Based on the calibration and validation procedure, it is identified that proposed modeling method can properly simulate dynamic behavior of soil-pile system in dry condition.

• Progress in Medical Physics
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• v.29 no.2
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• pp.59-65
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• 2018

This paper describes the clinical use of the dose verification of multileaf collimator (MLC)-based CyberKnife plans by combining the Octavius 1000SRS detector and water-equivalent RW3 slab phantom. The slab phantom consists of 14 plates, each with a thickness of 10 mm. One plate was modified to support tracking by inserting 14 custom-made fiducials on surface holes positioned at the outer region of $10{\times}10cm^2$. The fiducial-inserted plate was placed on the 1000SRS detector and three plates were additionally stacked up to build the reference depth. Below the detector, 10 plates were placed to avoid longer delivery times caused by proximity detection program alerts. The cross-calibration factor prior to phantom delivery was obtained by performing with 200 monitor units (MU) on the field size of $95{\times}92.5mm^2$. After irradiation, the measured dose distribution of the coronal plane was compared with the dose distribution calculated by the MultiPlan treatment planning system. The results were assessed by comparing the absolute dose at the center point of 1000SRS and the 3-D Gamma (${\gamma}$) index using 220 patient-specific quality assurance (QA). The discrepancy between measured and calculated doses at the center point of 1000SRS detector ranged from -3.9% to 8.2%. In the dosimetric comparison using 3-D ${\gamma}$-function (3%/3 mm criteria), the mean passing rates with ${\gamma}$-parameter ${\leq}1$ were $97.4%{\pm}2.4%$. The combination of the 1000SRS detector and RW3 slab phantom can be utilized for dosimetry validation of patient-specific QA in the CyberKnife MLC system, which made it possible to measure absolute dose distributions regardless of tracking mode.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.5
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• pp.608-616
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• 2015

In this study, we have estimated the storm surge heights using numerical modeling on coastal area, and then evaluated the vulnerability index by applying the vulnerability assessment techniques. Surge modelling for 27 typhoons affected from 2000 to 2014 were simulated by applying the ADCIRC model. The results of validation and verification was in significant agreement as compared with observations for the top 6 ranking typhoons affected. As results, the storm surge heights in Jinhae Bay, Sacheon Bay, Gwangyang Bay, Cheonsu Bay and Gyeonggi Bay were higher than other inner coastal areas, then storm surge vulnerability assessment was performed using a standardization, normalization and gradation of storm surge heights. According to results of storm surge vulnerability assessment, index of Jinhae Bay, Sacheon Bay, Gwangyang Bay etc. are estimated to be vulnerable(4~5) because of the characteristics of storm surge such as inner bay are vulnerable compared with exposed to the open sea areas. However, index in the inner bay of western Jeonnam are not vulnerable(1~3) relatively. It may not appear the typhoons affected significantly for the past 15 years. So, the long-term vulnerability assessment with the sensitivity of geomorphology are necessary to reduce the uncertainty.

• Journal of Korea Water Resources Association
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• v.51 no.1
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• pp.71-80
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• 2018

For the stream management, time of concentration is one of the important factors. In particular, as the requirement about various application of the stream increased, accuracy assessment of concentration time in the stream as waterfront area is extremely important for securing evacuation at the flood. the past studies for the assessment of concentration time, however, were only performed on the single hydrological event in the complex basin of natural streams. The development of a assessment methods for the concentration time on the complex hydrological event in a single watershed of urban streams is insufficient. Therefore, we estimated the concentration time using the rainfall- runoff data for the past 10 years (2006~2015) for the Oncheon stream, the representative stream of the Busan, where frequent flood were taken place by heavy rains, in addition, reviewed the reliability using artificial neural network method based on Matlab. We classified a total of 254 rainfalls events based on over unrained 12 hours. Based on the classification, we estimated 6 parameters (total precipitation, total runoff, peak precipitation/ total precipitation, lag time, time of concentration) to utilize for the training and validation of artificial neural network model. Consequently, correlation of the parameter, which was utilized for the training and the input parameter for the predict and verification were 0.807 and 0.728, respectively. Based on the results, we predict that it can be utilized to estimate concentration time and analyze reliability of urban stream.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.205-215
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• 2008

As static traffic assignment has reached its limitation with ITS policy applications and due to the increase of interest in studies of ITS policies since the late 1980's, dynamic traffic assignment has been considered a tool to overcome such limitations. This study used the Dynameq program, which simulates route choice behavior by macroscopic modeling and dynamic network loading and traffic flow by microscopic modeling in consideration of the feasibility of the analysis of practical traffic policy. The essence of this study is to evaluate the feasibility for analysis in practical transportation policy of using the dynamic traffic assignment technique. The study involves the verification of the values estimated from the dynamic traffic assignment with South Korea's expressway network and dynamic O/D data by comparing results with observed link traffic volumes. This study used dynamic O/D data between each toll booth, which can be accurately obtained from the highway Toll Collection System. Then, as an example of its application, exclusive bus-lane policies were analyzed with the dynamic traffic assignment model while considering hourly variations.