• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.5 no.3
• /
• pp.199-212
• /
• 2007

If a type IP-2 transport package were to be subjected to a free drop test and a penetration test under the normal conditions of transport, it should prevent a loss or dispersal of the radioactive contents and a more than 20% increase in the maximum radiation level at any external surface of the package. In this paper, we suggested the analytic method to evaluate the structural safety of a type IP-2 transport package using a thick steel plate for a structure part and a bolt for tying a bolt. Using an analysis a loss or dispersal of the radioactive contents and a loss of shielding integrity were confirmed for two kinds of type IP-2 transport packages to transport radioactive waste drums from a waste facility to a temporary storage site in a nuclear power plant. Under the free drop condition the maximum average stress at the bolts and the maximum opening displacement of a lid were compared with the tensile stress of a bolt and the steps in a lid, which were made to avoid a streaming radiation in the shielding path, to evaluate a loss or dispersal of radioactive waste contents. Also a loss of shielding integrity was evaluated using the maximum decrease in a shielding thickness. To verify the impact dynamic analysis for free drop test condition and evaluate experimentally the safety of two kinds of type IP-2 transport packages, free drop tests were conducted with various drop directions. For the tests we examined the failure of bolts and the deformation of flange to evaluate a loss or dispersal of radioactive material and measured the shielding thickness using a ultrasonic thickness gauge to assess a loss of shielding integrity. The strains and accelerations acquired from tests were compared with those by analyses to verify the impact dynamic analysis. The analytic results were larger than the those of test so that the analysis showed the conservative results. Finally, we evaluated the safety of the type IP-2 transport package under the stacking test condition using a finite element analysis. Under the stacking test condition, the maximum Tresca stress of the shielding material was 1/3 of the yielding stress. Two kinds of a type IP-2 transport package were safe for the free drop test condition and the stacking test condition.

• Journal of Oral Medicine and Pain
• /
• v.33 no.4
• /
• pp.305-316
• /
• 2008

The mandibular advancement device(MAD) has been used to help manage snoring and obstructive sleep apnea. The aims of this study were to specify the demographic and clinical characteristics of the patients receiving long-term treatment with MAD and to quantify the compliance with and side effects of the use of the device. Of 103 patients who were treated with MAD for at least one full year after delivery date, 49 were able to be contacted with telephone and complete follow-up questionnaires were obtainable. They were telephoned to determine whether they were still using the device. If not, they were asked when and why they stopped using it. Patients were also asked how much effectiveness of the MAD in decreasing snoring and how much they and their bed-partners were satisfied with the MAD therapy. The initial respiratory disturbance indices and pre-treatment snoring frequency and intensity were obtained from the medical records of initial visit. All the data were compared between users and nonusers. The results were as follows: 1. Of 49 patients 25 are still using the device, but 24 stopped using it. Among nonusers nobody stopped wearing the device within first 1 month, but 37.5% of nonusers stopped wearing it in the following 6 months, and another 4.2% before the end of the first year. 2. The one-year compliance of the MAD therapy was 79.59%. 3. There were no significant differences in mean age, mean body mass index, and gender distribution between users group and nonusers group. 4. There was no significant difference in mean respiratory disturbance index at initial visit between users group and nonusers group. 5. There was no significant difference in pre-treatment snoring frequency and intensity between users group and nonusers group. 6. The degree of decrease in snoring with use of MAD was significantly higher in the users when compared to nonusers. 7. Patient's overall satisfaction with treatment outcome was significantly higher in the users when compared to nonusers. 8. Bed partner's satisfaction with treatment outcome tended to be higher in the users when compared to nonusers. 9. The most frequent reasons why patients discontinued wearing the MAD were: jaw pain(25%), dental pain(20.83%), broken appliance(20.83%), hassle using(16.67%), lost weight(8.3%), dental work(8.3%), no or little effect(4.17%), sleep disturbance(4.27).

• 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.