• Smart Structures and Systems
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• v.6 no.3
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• pp.239-262
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• 2010

Tube Lines has carried out a "knowledge and investigation programme" on the deep tube tunnels comprising the Jubilee, Northern and Piccadilly lines, as required by the PPP contract with London Underground. Many of the tunnels have been in use for over 100 years, so this assessment was considered essential to the future safe functioning of the system. This programme has involved a number of generic investigations which guide the assessment methodology and the analysis of some 5,000 individual structures. A significant amount of investigation has been carried out, including ultrasonic thickness measurement, detection of brickwork laminations using radar, stress measurement using magnetic techniques, determination of soil parameters using CPT, pressuremeter and laboratory testing, installation of piezometers, material and tunnel segment testing, and trialling of remote photographic techniques for inspection of large tunnels and shafts. Vibrating wire, potentiometer, electro level, optical and fibre-optic monitoring has been used, and laser measurement and laser scanning has been employed to measure tunnel circularity. It is considered that there is scope for considerable improvements in non-destructive testing technology for structural assessment in particular, and some ideas are offered as a "wish-list". Assessment reports have now been produced for all assets forming Tube Lines' deep tube tunnel network. For assets which are non-compliant with London Underground standards, the risk to the operating railway has to be maintained as low as reasonably practicable (ALARP) using enhanced inspection and monitoring, or repair where required. Monitoring techniques have developed greatly during recent years and further advances will continue to support the economic whole life asset management of infrastructure networks.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.257-258
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• 2009

• Steel and Composite Structures
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• v.27 no.4
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• pp.417-426
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• 2018

This paper presents the seismic behaviors and restoring force model of ring beam joints of steel tube-reinforced concrete column structure under cyclic loading. First, the main failure mode, ultimate bearing capacity, stiffness degradation and energy dissipation capacity are studied. Then, the effects of concrete grade, steel grade, reinforcement ratio and radius-to-width ratios are discussed. Finally, the restoring force model is proposed. Results show that the ring beam joints of steel tube-reinforced concrete column structure performs good seismic performances. With concrete grade increasing, the ultimate bearing capacity and energy dissipation capacity increase, while the stiffness degradation rates increases slightly. When the radius-width ratio is 2, with reinforcement ratio increasing, the ultimate bearing capacity decreases. However, when the radius-to-width ratios are 3, with reinforcement ratio increasing, the ultimate bearing capacity increases. With radius-to-width ratios increasing, the ultimate bearing capacity decreases slightly and the stiffness degradation rate increases, but the energy dissipation capacity increases slightly.

• Magazine of the Korean Society of Agricultural Engineers
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• v.16 no.2
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• pp.3410-3419
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• 1974

The purpose of this study is to find out the correlation between pumping rates and influential factors in the tube wells for irrigation through the analysis of various statistical data of the existing tube wells for irrigation and pumping tests. Statistical data of the existing tube wells for irrigation were collected from the authorities concerned, and pumping tests were carried out for twelve tube wells. The results of this study are summarized as follows: 1. The drilled tube wells are the most useful among various tube wells in securing pumping rates. 2. The enlargement of well diameter or the improvement of pumping equipments is necessary in drilled tube wells with pumping rates more than 806 ㎥/day, and the adjustment of foot valves or the special control of pumping equipments is necessary in tube wells with pumping rates less than 300 ㎥/day. 3. The choking of aquifer and slits can be prevented by removing earth and sand piled in tube wells. 4. The increase of well loss and the destruction of aquifer can be prevented by determining the optimum pumping rates through the step draw down tests. 5. The thickness of gravel packing is rather thin in drilled tube tube wells. 6. High pamping rates can be gained by deepening the depth of tube wells in the place the ground water storage is abundant, the thickness of aquifer is thick. and the depth of tube wells is deep. 7. Higher pumping rates can be obtained by constructing tube wells in the place where the drawdown is little and the coefficient of transmissibility is large.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.213-222
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• 2024

This study analyzed the volumes generated by deep learning and atlas-based automatic segmentation methods, as well as the Dice similarity coefficient and 95% Hausdorff distance, according to the conditions of conduction voltage and conduction current in computed tomography for lung radiotherapy. The first result, the volumes generated by the atlas-based smart segmentation method showed the smallest volume change as a function of the change in tube voltage and tube current, while Aview RT ACS and OncoStudio using deep learning showed smaller volumes at tube currents lower than 100 mA. The second result, the Dice similarity coefficient, showed that Aview RT ACS was 2% higher than OncoStuido, and the 95% Hausdorff distance results also showed that Aview RT ACS analyzed an average of 0.2-0.5% higher than OncoStudio. However, the standard deviation of the respective results for tube current and tube voltage is lower for OncoStudio, which suggests that the results are consistent across volume variations. Therefore, caution should be exercised when using deep learning-based automatic segmentation programs at low perfusion voltages and low perfusion currents in CT imaging conditions for lung radiotherapy, and similar results were obtained with conventional atlas-based automatic segmentation programs at certain perfusion voltages and perfusion currents.

• Archives of Plastic Surgery
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• v.40 no.6
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• pp.711-714
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• 2013

Background The most common cause of skin graft failure is the collection of blood or serous fluid underneath the graft. In our study, we describe the use of silicone tube for tie-over dressing to secure the skin graft margins with the aim of decreasing loss of the skin graft, particularly in grafting of deep wounds. Methods Between March 2008 and July 2011, we used this technique in 17 patients with skin defects with depths ranging from 3.5 to 8 mm (mean, 5.5 mm). First, the skin graft was sutured with 3/0 silk suture material from its corners. Then, a silicone round drain tube was sutured with 3/0 absorbable polyglactin 910 over the margins of the graft. Finally, long silk threads were tied over the bolus dressing, and the tie-over dressing was completed in the usual fashion. Results The mean follow-up was 7 months (range, 2-10 months) in the outpatient clinic. Graft loss on the graft margins due to hematoma or seroma was not developed. The results of adhesion between the graft and wound bed peripherally was excellent. Conclusions In our study, we suggest that use of a silicone tube for additional pressure on the edges of skin grafts in case of reconstruction of deep skin defects.

• Korean Journal of Bronchoesophagology
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• v.14 no.1
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• pp.38-41
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• 2008

Deep neck infection is an infection in the potential spaces and fascial planes of the neck, either with abscess formation or cellulitis. In the preantibiotics era most cases of deep neck infection were secondary to an oropharyngeal infection. Moreover, today manupulation of intubation tube and gastroenteroscopy may cause deep neck infection by iatrogenic trauma. We experience 1 case of deep neck infection which originate from pharyngeal penetrating injury following gastroenteroscopy.

• Nuclear Engineering and Technology
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• v.51 no.7
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• pp.1784-1790
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• 2019

Accurate and consistent characterization of defects in steam generator tubes (SGT) in nuclear power plants is one of the key issues in the field of nondestructive testing since the large number of signals to be analyzed in a time-limited in-service inspection causes a serious problem in practice. This paper presents an effective approach to this difficult task of automated classification of motorized rotating pancake coil (MRPC) eddy current flaw acquired from tube specimens with deliberated defects using deep neural networks (DNN). This approach consists of five steps, namely, the data acquisition using the MRPC probe in the tube, the signal preprocessing to make data more suitable for training DNN, the data augmentation for boosting a training performance, the training of DNN, and finally demonstration of the trained DNN for discriminating the axial and circumferential defects. The high performance obtained in this study shows that DNN is useful for classification of defects in tubes from the MRPC eddy current signals even though the number of signals is very large.

• The Journal of Information Systems
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• v.32 no.2
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• pp.87-108
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• 2023

Purpose The main purpose of this study is to improve fake news detection performance by using video information to overcome the limitations of extant text- and image-oriented studies that do not reflect the latest news consumption trend. Design/methodology/approach This study collected video clips and related information including news scripts, speakers' facial expression, and video metadata from YouTube to develop fake news detection model. Based on the collected data, seven combinations of related information (i.e. scripts, video metadata, facial expression, scripts and video metadata, scripts and facial expression, and scripts, video metadata, and facial expression) were used as an input for taining and evaluation. The input data was analyzed using six models such as support vector machine and deep neural network. The area under the curve(AUC) was used to evaluate the performance of classification model. Findings The results showed that the ACU and accuracy values of three features combination (scripts, video metadata, and facial expression) were the highest in logistic regression, naïve bayes, and deep neural network models. This result implied that the fake news detection could be improved by using video information(video metadata and facial expression). Sample size of this study was relatively small. The generalizablity of the results would be enhanced with a larger sample size.

• Journal of Distribution Science
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• v.20 no.11
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• pp.121-129
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• 2022

Purpose: Artificial intelligence is widely used, particularly in the popular neural network theory called Deep learning. The improvement of computing speed and capability expedited the progress of Deep learning applications. The application of Deep learning in education has various effects and possibilities in creating and managing educational content and services that can replace human cognitive activity. Among Deep learning, Deep fake technology is used to combine and synchronize human faces with voices. This paper will show how to develop e-Learning content videos using those technologies and open-source tools. Research design, data, and methodology: This paper proposes 4 step development process, which is presented step by step on the Google Collab environment with source codes. This technology can produce various video styles. The advantage of this technology is that the characters of the video can be extended to any historical figures, celebrities, or even movie heroes producing immersive videos. Results: Prototypes for each case are also designed, developed, presented, and shared on YouTube for each specific case development. Conclusions: The method and process of creating e-learning video contents from the image, video, and audio files using Deep fake open-source technology was successfully implemented.