• Journal of the Institute of Convergence Signal Processing
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• v.21 no.4
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• pp.195-201
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• 2020

This study studied scoliosis, one of the most common modern diseases caused by lifestyle patterns of office workers sitting in front of computers all day and modern people who use smart phones frequently. Scoliosis is a typical complication that takes more than 80% of the nation's total population at least once. X-ray are used to test for these complications. X-ray, a non-destructive testing method that allows scoliosis to be easily performed and filmed in various areas such as the chest, abdomen and bone without contrast agents or other instruments. We uses NI DAQ to miniaturize digital X-ray imaging devices and image intensifier in self-shielding housing with Vision Assistant for drawing lines to the top and the bottom of the spine to acquire angles, i.e. curvature in real-time. In this way, the research was conducted to see scoliosis patients and their condition easily and to help rapid treatment for solving the problem of posture correction in modern people.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.1
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• pp.103-111
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• 2021

Naju Seokdanggan, Treasure No. 49, was dismantled and reconstructed due to poor performance. During construction, the crack area was reinforced and the inclination was improved. It is necessary to analyze the stiffness changes before and after the reconstruction of these cultural properties, and to establish a database of related information. In addition, there is a need for research on a scientific non-destructive testing method capable of predicting or evaluating the reinforcing effect. In this study, a simple equation for estimating the overall stiffness of the structural system was derived from information on the elasticity coefficient and the natural frequency measured by vibration tests before and after reconstruction work, and the applicability of the equation was examined. If the stiffness of important cultural properties is regularly investigated by the suggested method, it is judged that it can be used as data to estimate the time when structural safety diagnosis is necessary or when repair or reinforcement is necessary.

• Journal of the Korean Society of Radiology
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• v.16 no.7
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• pp.969-974
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• 2022

In order to maximize dynamic range and to minimize image lag in digital X-ray imaging, diminishing residual parasitic capacitance in photodiode in pixels is critically necessary. These requirements are more specifically requested in dynamic X-ray imaging with high frame rate and low image lag for industrial 2D/3D automated X-ray inspection and medical CT imaging. This study proposes duoPIX^TM X-ray imaging sensor for the first time that is composed of reset thin film transistor, readout thin film transistor and photodiode in a pixel. To verify duoPIX^TM X-ray imaging sensor, designing duoPIX^TM pixel and imaging sensor was executed first then X-ray imaging sensor with 105 ㎛ pixel pitch, 347 mm × 430 mm imaging area and 3300 × 4096 pixels (13.5M pixels) was fabricated and evaluated by using module tester and image viewer specifically for duoPIX^TM imaging sensor.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.746-753
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• 2022

Purpose: The purpose of this study is to review the safety of small tunnels for food storage excavation in the 1960s~1970s and to improve the stability of small tunnels. Method: A visual inspection and a hammer test were used to conduct safety tests, and the visual inspection is one of the tests conducted for non-destructive testing, and the hammer test is one of the types of hitting methods of rebound hardness. Result: According to the integrated analysis of the survey area data, there are generally good appearance, but there are many small cracks and complex geological conditions, requiring continuous observation and attention. Seven of the 23 tunnels require safety diagnosis, one collapse, one safe, and 14 require continuous observation and attention. Conclusion: All parts of small tunnels should be checked and recorded from time to time, and stability is expected to be improved when reinforcing small tunnels proposed in this study.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.625-631
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• 2023

The important requirement of industrial dynamic X-ray detector operating under high tube voltage up to 450 kVp for 24 hours and 7 days is to obtain significantly high radiation resistance. This study presents the radiation resistance characteristics of various thin film transistors (TFTs) with a-Si, poly-Si and IGZO semiconducting layers. IGZO TFT offering dozens of times higher field effect mobility than a-Si TFT was processed with highly hydrogenated plasma in between IGZO semiconducting layer and inter-layered dielectric. The hydrogenated IGZO TFT showed most sustainable radiation resistance up to 10,000Gy accumulated, thus, concluded that it is a sole switching device in X-ray imaging sensor offering dynamic X-ray imaging at high frame rate under extremely severe radiation environment such as automated X-ray inspection.

• Smart Structures and Systems
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• v.31 no.1
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• pp.1-11
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• 2023

Assessment of the compressive strength of concrete plays a major role during formwork removal and in the prestressing process. In concrete, temperature changes occur due to hydration which is an influencing factor that decides the compressive strength of concrete. Many methods are available to find the compressive strength of concrete, but the maturity method has the advantage of prognosticating strength without destruction. The temperature-time factor is found using a LM35 temperature sensor through the IoT technique. An experimental investigation was carried out with 56 concrete cubes, where 35 cubes were for obtaining the compressive strength of concrete using a universal testing machine while 21 concrete cubes monitored concrete's temperature by embedding a temperature sensor in each grade of M25, M30, M35, and M40 concrete. The mathematical prediction model equation was developed based on the temperature-time factor during the early age compressive strength on the 1^st, 2^nd, 3^rd and 7^th days in the M25, M30, M35, and M40 grades of concrete with their temperature. The 14^th, 21^st and 28^th day's compressive strength was predicted with the mathematical predicted equation and compared with conventional results which fall within a 2% difference. The compressive strength of concrete at any desired age (day) before reaching 28 days results in the discovery of the prediction coefficient. Comparative analysis of the results found by the predicted mathematical model show that, it was very close to the results of the conventional method.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.1
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• pp.97-105
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• 2009

This paper studied the cause of the deterioration of the four 3-Arch tunnels built in mid-1990. The common deteriorations of the four 3-Arch tunnels were longitudinal cracks, leakage and efflorescence at the same parts of lining concrete. Three fourths of 3-Arch tunnels, there was high percentage longitudinal cracks and a quarter was low frequency about longitudinal cracks. So the material reviewed to find out the differences between two groups in construction process and analysis was conducted such as non-destructive testing, precise visual survey and safety evaluation of one tunnel which had bad ground condition As the result, the tunnels were safety condition and the primary deterioration occurred during the construction process, namely, problems arrangement of rebar and the effects of the blast at middle tunnel.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.685-686
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• 2023

상수도관의 수질 및 부식도 검사에는 파이프에 손상을 입히지 않고 지속적인 방법이 필요하다. 초음파는 이를 만족하면서 상태를 확인할 수 있고 주파수가 높을수록 해상도가 좋아져 정밀한 측정이 가능하다는 장점이 있다. 이러한 특성을 이용해 상수도관 모니터링 시스템으로 초음파 기반의 Scanning Acoustic Microscopy(SAM)과 Convolutional Neural Network(CNN)을 사용하는 새로운 방법을 제안한다. 기존의 Non-Destructive Testing(NDT)방식의 단점을 보완하면서 더 높은 해상도로 상수도관을 점검하는 방식으로, SAM 을 이용하여 부식으로 인한 파이프 두께 변화와 부유물의 여부 및 수질을 동시에 감지하고 얻은 데이터를 CNN 으로 분석했다. CNN 의 높은 정확도 결과로 이 시스템의 파이프 부식도 및 수질 모니터링에 대한 적합성을 보여주었다.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.36-44
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• 2024

This paper presents the application of Convolutional Neural Networks (CNNs) and Region of Interest (ROI) techniques for concrete crack analysis. Surfaces of concrete structures, such as beams, etc., are exposed to fatigue stress and cyclic loads, typically resulting in the initiation of cracks at a microscopic level on the structure's surface. Early detection enables preventative measures to mitigate potential damage and failures. Conventional manual inspections often yield subpar results, especially for large-scale infrastructure where access is challenging and detecting cracks can be difficult. This paper presents data collection, edge segmentation and ROI techniques application, and analysis of concrete cracks using Convolutional Neural Networks. This paper aims to achieve the following objectives: Firstly, achieving improved accuracy in crack detection using image-based technology compared to traditional manual inspection methods. Secondly, developing an algorithm that utilizes enhanced Sobel edge segmentation and ROI techniques. The algorithm provides automated crack detection capabilities for non-destructive testing.

• Smart Structures and Systems
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• v.33 no.1
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• pp.55-64
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• 2024

This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zero-shot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a charge-coupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.