• Journal of Internet Computing and Services
• /
• v.21 no.6
• /
• pp.23-31
• /
• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Journal of the Korean Society of Radiology
• /
• v.5 no.5
• /
• pp.295-302
• /
• 2011

To n the field of nuclear medicine, with regard to checking regular patients, from the moment they register up to the doctor's diagnosis, the person in charge of the checks can find errors in the diagnosis, reexamine, reanalyze the results or save images to PACS. Through this process, the results obtained from the readings are delayed due to checks and additional tests which occur in hospitals, causing patient satisfaction and affected reliability. Accordingly, the purpose is to include visual inspection of the results to minimize error, improve efficiency and increase patient satisfaction. Nuclear medicine and imaging tests from examines at Asan Medical Center, Seoul, from March 2008 to December 2008, were analyzed for errors. The first stage, from January 2009 to December 2009, established procedures and know-how. The second stage from January 2010 until June 2010 conducted Pre-and Post-filtering assessment, and the third stage from July 2010 until October 2010 consisted of cross-checks and attaching stickers and comparing error cases. Of 92 errors, the 1st, 2nd and 3rd stage had 32 cases, and there were 46 cases after the 4th stage, with the overall errors reduced by 74.3% from 94.6%. In the field of general nuclear medicine, where various kinds of checks are performed according to the patient's needs, analysis, image composition, differing images in PACS, etc, all have the potential for mistakes to be made. In order to decrease error rates, the image can continuously Cross-Check and Confirm diagnosis.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.22 no.7
• /
• pp.846-853
• /
• 2016

This study prioritizes Noxious Liquid Substances (NLS) transported by sea via a risk-based database containing 596 chemicals to prepare against NLS incidents. There were 158 chemicals transported in Korean waters during 2014 and 2015, which were prioritized, and then chemicals were grouped into four categories (with rankings of 0-3) based on measures for preparedness against incident. In order to establish an effective preparedness system against NLS spill incidents on a national scale, a compiling process for NLS chemicals ranked 2~3 should be carried out and managed together with an initiative for NLS chemicals ranked 0-1. Also, it is advisable to manage NLS chemicals ranked 0-1 after considering the characteristics of NLS specifically transported through a given port since the types and characteristics of NLS chemicals relevant differ depending on the port. In addition, three designated regions are suggested: 1) the southern sector of the East Sea (Ulsan and Busan); 2) the central sector of the South Sea (Gwangyang and Yeosu); and 3) the northern sector of the West Sea (Pyeongtaek, Daesan and Incheon). These regions should be considered special management sectors, with strengthened surveillance and the equipment, materials and chemicals used for pollution response management schemes prepared in advance at NLS spill incident response facilities. In the near future, the risk database should be supplemented with specific information on chronic toxicity and updated on a regular basis. Furthermore, scientific ecotoxicological data for marine organisms should be collated and expanded in a systematic way. A system allowing for the identification Hazardous and Noxious Substances (HNS) should also be established, noting the relevant volumes transported in Korean waters as soon as possible to allow for better management of HNS spill incidents at sea.

• Journal of radiological science and technology
• /
• v.31 no.1
• /
• pp.17-23
• /
• 2008

Purpose : Because there is a difference depending on the environment as for an inspection equipment the important part of bone density scan and the precision/accuracy of a tester, the management of quality must be made systematically. The equipment failure caused by overload effect due to the aged equipment and the increase of a patient was made frequently. Thus, the replacement of equipment and additional purchases of new bonedensity equipment caused a compatibility problem in tracking patients. This study wants to know whether the clinical changes of patient's bonedensity can be accurately and precisely reflected when used it compatiblly like the existing equipment after equipment replacement and expansion. Materials and methods : Two equipments of GE Lunar Prodigy Advance(P1 and P2) and the Phantom HOLOGIC Spine Road(HSP) were used to measure equipment precision. Each device scans 20 times so that precision data was acquired from the phantom(Group 1). The precision of a tester was measured by shooting twice the same patient, every 15 members from each of the target equipment in 120 women(average age 48.78, 20-60 years old)(Group 2). In addition, the measurement of the precision of a tester and the cross-calibration data were made by scanning 20 times in each of the equipment using HSP, based on the data obtained from the management of quality using phantom(ASP) every morning (Group 3). The same patient was shot only once in one equipment alternately to make the measurement of the precision of a tester and the cross-calibration data in 120 women(average age 48.78, 20-60 years old)(Group 4). Results : It is steady equipment according to daily Q.C Data with $0.996\;g/cm^2$, change value(%CV) 0.08. The mean${\pm}$SD and a %CV price are ALP in Group 1(P1 : $1.064{\pm}0.002\;g/cm^2$, $%CV=0.190\;g/cm^2$, P2 : $1.061{\pm}0.003\;g/cm^2$, %CV=0.192). The mean${\pm}$SD and a %CV price are P1 : $1.187{\pm}0.002\;g/cm^2$, $%CV=0.164\;g/cm^2$, P2 : $1.198{\pm}0.002\;g/cm^2$, %CV=0.163 in Group 2. The average error${\pm}$2SD and %CV are P1 - (spine: $0.001{\pm}0.03\;g/cm^2$, %CV=0.94, Femur: $0.001{\pm}0.019\;g/cm^2$, %CV=0.96), P2 - (spine: $0.002{\pm}0.018\;g/cm^2$, %CV=0.55, Femur: $0.001{\pm}0.013\;g/cm^2$, %CV=0.48) in Group 3. The average error${\pm}2SD$, %CV, and r value was spine : $0.006{\pm}0.024\;g/cm^2$, %CV=0.86, r=0.995, Femur: $0{\pm}0.014\;g/cm^2$, %CV=0.54, r=0.998 in Group 4. Conclusion: Both LUNAR ASP CV% and HOLOGIC Spine Phantom are included in the normal range of error of ${\pm}2%$ defined in ISCD. BMD measurement keeps a relatively constant value, so showing excellent repeatability. The Phantom has homogeneous characteristics, but it has limitations to reflect the clinical part including variations in patient's body weight or body fat. As a result, it is believed that quality control using Phantom will be useful to check mis-calibration of the equipment used. A value measured a patient two times with one equipment, and that of double-crossed two equipment are all included within 2SD Value in the Bland - Altman Graph compared results of Group 3 with Group 4. The r value of 0.99 or higher in Linear regression analysis(Regression Analysis) indicated high precision and correlation. Therefore, it revealed that two compatible equipment did not affect in tracking the patients. Regular testing equipment and capabilities of a tester, then appropriate calibration will have to be achieved in order to calculate confidential BMD.