• Journal of the Korean Society for Nondestructive Testing
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• v.13 no.3
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• pp.24-30
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• 1993

The necessity of ultrasonic inspection to detect the cracks in turbine blade is being increased as the forced outage of nuclear power plants have been occurred due to blade failure in turbine components. However, the complex blade root geometry causes the ultrasonic inspection technique not to be established yet and much effort is required to set up a more reliable inspection. In this paper, the ultrasonic inspection technique for flaw detectability, skew angle effect, identification of flaw and geometric signal have been investigated with a test block and discussed the interpretation of ultrasonic signal through the acquisition and analysis of RF waveform. The experimental results show that the proper examination procedure can be established. It is required that the skew angle is essential to decrease the effect of signals from the complex blade geometry. The present results of this study can be applied to the site inspection without blade disassembly.

• Journal of the Korean Magnetics Society
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• v.20 no.2
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• pp.45-51
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• 2010

We suggest the algorithm that it detects volume and shape according with a variation of magnetic field in non-contact electromagnetic measurement system. It is possible to assess an object shape through a variation of magnetic field. The basic idea is compared a length difference with a variation of magnetic field in a detected object and a circle which modeled equivalent area. And the shape is detected to many calibration process that it is similar to signal pattern between a length difference and a variation of magnetic field in object and equivalent circle. This is the shape detection algorithm that use only the variation of magnetic field. In this paper, it has application to the shape detection algorithm about the object as hexagon, pentagon, rectangle, trigon. we can detect the object shape easily because the shape detection algorithm is only used to the variation of magnetic field.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.146-152
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• 2020

This study was initiated to develop an effective inspection method to detect defects such as corrosion in closed-cell steel members in steel-box girder bridges. The ultrasonic velocity method among various non-destructive method was selected as a rapid and effective method to derive the average propagation velocity in the medium by using the ultrasonic wave velocity method for specimens of different thickness. The regression analysis was performed based on the experimental results, and the results was interpolated to evaluate the prediction accuracy. If the material properties are identical, this ultrasonic velocity method can predict the thickness using the averaged transmitted velocity. In addition, a continuous scanning method moving at 200 mm/s was tested for scanning a wide area of a bridge. The results exhibited that the continuous scanning method was able to effectively scan the different thickness of a bridge.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.4
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• pp.360-366
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• 2011

The Elasto-magnetic sensor is applied to detect the local cross-sectional loss of steel cables in this study while it was originally developed for measuring the tensile force in the previous works. To verify the feasibility of the proposed damage detection technique, steel bars which have 4-different diameters were fabricated and the output voltage value was measured at each diameter by the E/M sensor. Optimal input voltage and working point are chosen so that the linearity and resolution of results can ensure through repeated experiments, and then the E/M sensor was measured the output voltage values at the damage points of steel bar specimen that was applied the 4 types of damage condition based on the selected optimal experimental condition. This proposed approach can be an effective tool for steel cable health monitoring.

• Journal of the Korean Geotechnical Society
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• v.39 no.7
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• pp.31-37
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• 2023

Since tunnels are built underground, it is impossible to check visually the location and degree of deterioration of steel ribs. Therefore, in tunnel maintenance, GPR images are generally used to detect steel ribs. While research on GPR image analysis employing artificial neural networks has primarily focused on detecting underground pipes and road damage, there have been limited applications for analyzing tunnel GPR data, specifically for steel rib detection, both internationally and domestically. In this study, a one-step object detection algorithm called YOLO, based on a convolutional neural network, was utilized to automate the localization of steel ribs using GPR data. The performance of the algorithm is then analyzed. Two datasets were employed for the analysis. A dataset comprising 512 original images and another dataset consisting of 2,048 augmented images. The omission rate, which represents the ratio of undetected steel ribs to the total number of steel ribs, was 0.38% for the model using the augmented data, whereas the omission rate for the model using only the original data was 7.18%. Thus, from an automation standpoint, it is more practical to employ an augmented dataset.

• Journal of the Korean Society of Propulsion Engineers
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• v.3 no.1
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• pp.24-33
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• 1999

For performance prediction and diagnostics of gas turbine engines, linear and non-linear gas path analysis are applied. In order to find optimal instrument parameters to detect the physical faults such as (outing, erosion and corrosion, non-linear gas path analysis is used. A typical industrial gas turbine engine, TB5000, is used to study the effect of physical faults on engine performance. Through comparison of RMS error between linear and non-linear gas path analysis, the optimal instrument parameters can be defined. As a result, it is found that the linear GPA has the level of error introduced by the assumption of the linear mode: can be of the same order of magnitude as the fault being soughtwhile the non-linear GPA can be solved the non-linear relationships between dependent and independent parameters using an iterative method such as the Newton-Raphson method with sufficient accuracy.

• Journal of the Korean Wood Science and Technology
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• v.43 no.1
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• pp.104-111
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• 2015

The three-year inspection of 20 tree stumps in the fire prevention area around the wooden building confirmed that termite colonies had been rapidly spread. In particular, four buildings among thirty one wooden buildings of Songgwang-sa temple were infected by the termite, indicating that the habitate of termite has been spread across the fire prevention area over the temple area. However, a non-destructive microwave diagnosis showed that internal damages have been progressed until now, suggesting a high risk to the building. These results suggest that the fire prevention area should be properly maintained to have harmful element controlled. Therefore, effective methods are required to eliminate tree stumps or wood materials used to establish fire prevention area near wooden buildings.

• Journal of Aerospace System Engineering
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• v.16 no.4
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• pp.109-115
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• 2022

The CNN (Convolution Neural Network) algorithm which combines a deep learning technique, and a computer vision technology, makes image classification feasible with the high-performance computing system. In this thesis, the CNN algorithm is applied to the classification problem, by using a typical deep learning framework of TensorFlow and machine learning techniques. The data set required for supervised learning is generated with the same type of bolts. some of which have undamaged threads, but others have damaged threads. The learning model with less quantity data showed good classification performance on detecting damage in a bolt image. Additionally, the model performance is reviewed by altering the quantity of convolution layers, or applying selectively the over and under fitting alleviation algorithm.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.1-9
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• 2023

Malware files containing concealed malicious scripts have recently been identified within MS Office documents frequently. In response, this paper describes the design and implementation of a system that automatically detects malicious digital files using machine learning techniques. The system is proficient in identifying malicious scripts within MS Office files that exploit the OLE VBA macro functionality, detecting malicious scripts embedded within the CDH/LFH/ECDR internal field values through OOXML structure analysis, and recognizing abnormal CDH/LFH information introduced within the OOXML structure, which is not conventionally referenced. Furthermore, this paper presents a mechanism for utilizing the VirusTotal malicious script detection feature to autonomously determine instances of malicious tampering within MS Office files. This leads to the design and implementation of a machine learning-based integrated software. Experimental results confirm the software's capacity to autonomously assess MS Office file's integrity and provide enhanced detection performance for arbitrary MS Office files when employing the optimal machine learning model.

• Geophysics and Geophysical Exploration
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• v.27 no.2
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• pp.144-153
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• 2024

Recent research is increasingly focused on utilizing seismic waves for structure health monitoring (SHM). Specifically, seismic interferometry, a technique applied in geophysical surveys using ambient noise, is widely applied in SHM. This method involves analyzing the response of buildings to propagating seismic waves. This enables the estimation of changes in structural stiffness and the evaluation of the location and presence of damage. Analysis of seismic interferometry applied to SHM, along with case studies, indicates its highly effective application for assessing structural stability and monitoring building conditions. Seismic interferometry is thus recognized as an efficient approach for evaluating building integrity and damage detection in SHM and monitoring applications.