• Smart Structures and Systems
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• 제5권4호
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• pp.381-395
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• 2009

The paper presents a case study in which the structural condition assessment of the East Bay bridge in Gibsonton, Florida is evaluated with the help of remote health monitoring techniques. The bridge is a four-span, continuous, deck-type reinforced concrete structure supported on prestressed pile bents, and is instrumented with smart Fiber Optic Sensors. The sensors used for remote health monitoring are the newly emerged Fabry-Perot (FP), and are both surface-mounted and embedded in the deck. The sensing system can be accessed remotely through fast Digital Subscriber Lines (DSL), which permits the evaluation of the bridge behavior under live traffic loads. The bridge was open to traffic since March 2005, and the collected structural data have been continuously analyzed since. The data revealed an increase in strain readings, which suggests a progression in damage. Recent visual observations also indicated the presence of longitudinal cracks along the bridge length. After the formation of these cracks, the sensors readings were analyzed and used to extrapolate the values of the maximum stresses at the crack location. The data obtained were also compared to initial design values of the bridge under factored gravity and live loads. The study showed that the proposed structural health monitoring technique proved to provide an efficient mean for condition assessment of bridge structures providing it is implemented and analyzed with care.

• Journal of Korean Neurosurgical Society
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• 제46권3호
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• pp.273-276
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• 2009

A 54-year-old man experienced injury to the second finger of his left hand due to damage from a paintball gun shot 8 years prior, and the metacarpo-phalangeal joint was amputated. He gradually developed mechanical allodynia and burning pain, and there were trophic changes of the thenar muscle and he reported coldness on his left hand and forearm. A neuroma was found on the left second common digital nerve and was removed, but his symptoms continued despite various conservative treatments including a morphine infusion pump on his left arm. We therefore attempted median nerve stimulation to treat the chronic pain. The procedure was performed in two stages. The first procedure involved exposure of the median nerve on the mid-humerus level and placing of the electrode. The trial stimulation lasted for 7 days and the patient's symptoms improved. The second procedure involved implantation of a pulse generator on the left subclavian area. The mechanical allodynia and pain relief score, based on the visual analogue scale, decreased from 9 before surgery to 4 after surgery. The patient's activity improved markedly, but trophic changes and vasomotor symptom recovered only moderately. In conclusion, median nerve stimulation can improve chronic pain from complex regional pain syndrome type II.

• 대한금속재료학회지
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• 제49권9호
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• pp.708-714
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• 2011

The premature failure of a servovalve used for six months in a thermal power plant has been analyzed. The servovalve was made of stainless steel, containing 16Cr-0.44Mo, along with other elements. An overload of oil-supply pumping and an abnormal increase in the oil flux were observed during operation. A study revealed that erosion and corrosion could be the main causes of the failure. The visual examination of the servovalve did not show any appreciable damage. However, corrosion and erosion of the servovalve were observed using scanning electron microscopy (SEM). Upon examination of the servovalve, the corrosion was found to have occurred throughout the bushing and spool; however, erosion occurred at only the edge-side. In addition, the condition of the electrohydraulic control system (EHC) oil was investigated with respect to its satisfaction of the management standard.

• Smart Structures and Systems
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• 제30권1호
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• pp.17-34
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• 2022

For steel structures, fatigue cracks are critical damage induced by long-term cycle loading and distortion effects. Vision-based crack detection can be a solution to ensure structural integrity and performance by continuous monitoring and non-destructive assessment. A critical issue is to distinguish cracks from other features in captured images which possibly consist of complex backgrounds such as handwritings and marks, which were made to record crack patterns and lengths during periodic visual inspections. This study presents a parametric study on image-based crack identification for orthotropic steel bridge decks using captured images with complicated backgrounds. Firstly, a framework for vision-based crack segmentation using the atrous convolution-based Deeplapv3+ network (ACDN) is designed. Secondly, features on crack images are labeled to build three databanks by consideration of objects in the backgrounds. Thirdly, evaluation metrics computed from the trained ACDN models are utilized to evaluate the effects of obstacles on crack detection results. Finally, various training parameters, including image sizes, hyper-parameters, and the number of training images, are optimized for the ACDN model of crack detection. The result demonstrated that fatigue cracks could be identified by the trained ACDN models, and the accuracy of the crack-detection result was improved by optimizing the training parameters. It enables the applicability of the vision-based technique for early detecting tiny fatigue cracks in steel structures.

• The Journal of Korean Physical Therapy
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• 제34권2호
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• pp.73-79
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• 2022

Purpose: This descriptive study aimed at physical therapists to determine the actual conditions of sexual harassment occurring in the workplace, their coping strategies, and effective coping methods. Methods: In total, 186 responses were collected through Google from June 28 to August 21, 2021. The questionnaire consisted of 102 questions about the subject's general characteristics, sexual harassment, psychological stress, physical stress, and sexual harassment prevention education. Statistical Package For The Social Sciences (SPSS) was used for analysis, frequency analysis, percentage, standard deviation, and corresponding sample t-test, and the significance level was set to 0.05. Results: The perception of sexual harassment was 7.1, which was lower than that of other occupations. The perpetrators of visual, verbal, and physical sexual harassment appeared in the order of patients, coworkers, and guardians. Psychological and physical stresses were higher than the average due to damage caused by sexual harassment, requiring some attention. Work stress showed an average level. With the higher perception, a negative correlation was observed in the face of mitigation (p<0.001). Conclusion: The number of victims of sexual harassment is increasing every year. To cope with sexual harassment, there should be a department capable of counseling and processing in the workplace, and what occurs should be analyzed. In addition, educational programs are needed to prevent sexual harassment in consideration of the characteristics of hospitals.

• 인간식물환경학회지
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• 제23권2호
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• pp.171-178
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• 2020

The present study was conducted to determine the effects of electron beam irradiation on the postharvest quality of cut flowers. Cut flowers were irradiated with electron beam at 100, 200, 400, 600, 800, 1,000, and 2,000 Gy with a 10 MeV linear electron beam accelerator to evaluate their irradiation tolerance. Postharvest quality was determined by monitoring fresh weight loss, flower longevity, flower diameter, flowering rate, visual quality of flowers and leaves, and chlorophyll content. Cut flowers showed a radiation-induced damage with increasing the irradiation dose. Flower longevity and fresh weight of cut flowers decreased when the irradiation dose was increased. Flower bud opening was also inhibited in a dose-dependent manner. The effective irradiation doses for 10% reduction of postharvest quality (ED₁₀) values were 144.4, 451.6, and 841.2 Gy in the 'Medusa' lily, 'Montezuma' carnation, and 'Rosina White' eustoma, respectively. Although tolerance of cut flowers to electron beam irradiation vary according to species, cultivars, or maturity stage conditions, it is conceivable that 'Montezuma' carnation and 'Rosina White' eustoma could be tolerated and maintained overall postharvest quality up to 400 Gy, the generic irradiation dose approved by the Animal and Plant Health Inspection Service (APHIS) and the International Plant Protection Convention (IPPC) for postharvest phytosanitary treatments.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.1260-1265
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• 2009

Topographical features in South Korea is characterized that 70% of territory is composed of the mountains that can experience intense rainfall during storms in the summer and autumn. Efficient planning and management of landscape becomes utmost important since the cutting slopes in the mountain areas have been increased due to the limited construction areas for the roadway and residential development. This paper proposed an efficient way of slope management for the landslide risk by developing Web-GIS landslide risk management system. By deploying the Logistic Regression Analysis, the system could increase the prediction accuracy that the landslide disaster might be occurred. High resolution survey technology using GPS and Total-Station could extract the exact position and visual shape of the slopes that accurately describe the slope information. Through the proposed system, the prediction of damage areas from the landslide could also make it easy to efficiently identify the level of landslide risks via web-based user interface. It is expected that the proposed landslide risk management system can support the decision making framework during the identification, prediction, and management of the landslide risks.

• Smart Structures and Systems
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• 제30권4호
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• pp.385-395
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• 2022

Detecting cracks on a concrete structure is crucial for structural maintenance, a crack being an indicator of possible damage. Conventional crack detection methods which include visual inspection and non-destructive equipment, are typically limited to a small region and require time-consuming processes. Recently, to reduce the human intervention in the inspections, various researchers have sought computer vision-based crack analyses: One class is filter-based methods, which effectively transforms the image to detect crack edges. The other class is using deep-learning algorithms. For example, convolutional neural networks have shown high precision in identifying cracks in an image. However, when the objective is to classify not only the existence of crack but also the types of cracks, only a few studies have been reported, limiting their practical use. Thus, the presented study develops an image processing procedure that detects cracks and classifies crack types; whether the image contains a crazing-type, single crack, or multiple cracks. The properties and steps in the algorithm have been developed using field-obtained images. Subsequently, the algorithm is validated from additional 227 images obtained from an open database. For test datasets, the proposed algorithm showed accuracy of 92.8% in average. In summary, the developed algorithm can precisely classify crazing-type images, while some single crack images may misclassify into multiple cracks, yielding conservative results. As a result, the successful results of the presented study show potentials of using vision-based technologies for providing crack information with reduced human intervention.

• 국제초고층학회논문집
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• 제11권2호
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• pp.115-124
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• 2022

Welding is a significant part of the construction industry. Since most high-rise building construction structures rely on a robust metal frame welded together, welding defect can damage welded structures and is critical to safety and quality. Despite its importance and heavy usage in construction, the labor shortage of welders has been a continuous challenge to the construction industry. To deal with the labor shortage, the ultimate goal of this study is to design and develop an automated robotic welding system composed of a welding machine, unmanned ground vehicle (UGV), robotic arm, and visual sensors. This paper proposes and focuses on automated weaving using the robotic arm. For automated welding operation, a microcontroller is used to control the switch and is added to a welding torch by physically modifying the hardware. Varying weave patterns are mathematically programmed. The automated weaving is tested using a brush pen and a ballpoint pen to clearly see the patterns and detect any changes in vertical forces by the arm during weaving. The results show that the weave patterns have sufficiently high consistency and precision to be used in the actual welding. Lastly, actual welding was performed, and the results are presented.

• Smart Structures and Systems
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• 제30권5호
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.