• Structural Engineering and Mechanics
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• v.91 no.1
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• pp.49-61
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• 2024

In recent decades, the majority of studies have concentrated on the utilization of Steel Square Hollow Section (SHS) columns, with minimal attention given to reinforcing columns exhibiting inherent defects. This study addresses this gap by introducing initial vertical and horizontal defects at three distinct locations (top, middle, and bottom) and employing Carbon-FRP for reinforcement. The research investigates the dimensional and positional impacts of these defects on the axial behavior of SHS columns. A total of 29 samples, comprising 17 with defects, 11 strengthened, and 1 defect-free control, underwent examination. The study employed ABAQUS modeling and conducted experimental testing. Results revealed that defects located at different positions significantly diminished the load-bearing capacity and initial performance of the steel columns. Axial loading induced local buckling and lateral rupture, particularly at the defect side, in short columns. Notably, horizontal (across the column's width) and vertical (along the column's height) defects in the middle led to the most substantial reduction in strength and load-bearing capacity. The axial compressive failure increased with the length-to-width ratio of the defect. Moreover, the application of four carbon fiber layers to strengthen the steel columns resulted in increased Energy Dissipation and a delayed onset of local buckling in the face of axial ruptures.

• 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%.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2218-2223
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• 2014

A crack which is axially oriented with small size is hard to detect in conventional system. CMFL(Circumferential Magnetic Flux Leakage) type PIG(Pipelines Inspection Gauge) in the NDT(Nondestructive Testing), is operated to detect this defect called axially oriented cracks in the pipe. It is necessary to decompose the size and shapes of cracks for the maintenance of underground pipelines. This article is mainly focused on the decomposing method of the size and shape of the axially oriented cracks by using inspection signal data for defect.

• Journal of Ocean Engineering and Technology
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• v.20 no.6 s.73
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• pp.1-6
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• 2006

Corrosion of reinforcing steel bars is a major concern for ocean engineers when reinforced concrete structures are exposed to marine environments. Evaluating the degree of corrosion and corrosion-induced defects is extremely necessary to pursue a proper retrofit or rehabilitation plan for reinforced concrete structures. A promising inspection should be carried out for the evaluation, otherwise the retrofit or rehabilitation process would be useless. Nowadays, ultrasonic guided wave-based inspection techniques become quite promising for the inspection, mainly because of their long-range propagation capability and their sensitivity to different types of defects or conditions. Evaluating haw the guided waves response to the different types of defects or conditions is quite challenging and important. This study shows how surface conditions of reinforcing bars and a corrosion-induced defect, separation, affect guided wave propagation in reinforced concrete. Experiments and associated signal analysis show the sensitivity of guided waves to the surface conditions, as well as the amounts of separation at the interface between. concrete and steel bar.

• Journal of the Korean Vacuum Society
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• v.12 no.S1
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• pp.40-45
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• 2003

Ru interfacial layer was inserted into Mo-on-Si interface to enhance the extreme ultra-violet (EUV) reflective multilayer properties. The stacking status and optical properties are analyzed using cross-sectional transmission electron microscope (TEM), and reflectometer. About 1.5% of maximum reflectivity can be acquired as predicted in optical simulation, which is thought to be originated from the diffusion inhibition property. Phase defect correctability of the multilayer can be enhanced by the insertion of Ru barrier layer.

• Transactions on Electrical and Electronic Materials
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• v.13 no.3
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• pp.129-135
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• 2012

A number of surface mount technology (SMT) process variables including land design are considered for minimizing tombstone defect in flexible printed circuit assembly in high volume manufacturing. As SMT chip components have been reduced over the past years with their weights in milligrams, the torque that once helped self-centering of chips, gears to tombstone defects. In this paper, we have investigated the correlation of the assembly process variables with respect to the tombstone defect by employing statistically designed experiment. After the statistical analysis is performed, we have setup hypotheses for the root causes of tombstone defect and derived main effects and interactions of the process parameters affecting the hypothesis. Based on the designed experiments, statistical analysis was performed to investigate significant process variable for the purpose of process control in flexible printed circuit manufacturing area. Finally, we provide beneficial suggestions for find-pitch PCB design, screen printing process, chip-mounting process, and reflow process to minimize the tombstone defects.

• Journal of Ocean Engineering and Technology
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• v.22 no.2
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• pp.72-77
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• 2008

The application of infrared thermography for detecting defects under the surface of a material was studied. Defects in a specimen were made by back-drilled circular holes. To get alarge temperature difference at the surface, a halogen lamp was used for surface heating. We confirmed that the defect location had a good relationship with the maximum temperature difference. The sizes of the defects could be calculated by means of the FWHM. The value of the FWHM of a temperature difference decreased with time. Therefore in an extremely short time after the heating, the true defect size could be measured.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4763-4775
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• 2020

This research aims to develop a deep learning-based method that automatically detects and segments the defects on railroad surfaces to reduce the cost of visual inspection of the railroad. We developed our segmentation model by modifying a fully convolutional network model [1], a well-known segmentation model used for machine learning, to detect and segment railroad surface defects. The data used in this research are images of the railroad surface with one or more defect regions. Railroad images were cropped to a suitable size, considering the long height and relatively narrow width of the images. They were also normalized based on the variance and mean of the data images. Using these images, the suggested model was trained to segment the defect regions. The proposed method showed promising results in the segmentation of defects. We consider that the proposed method can facilitate decision-making about railroad maintenance, and potentially be applied for other analyses.

• Design & Manufacturing
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• v.14 no.4
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• pp.52-57
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• 2020

In this study attempted to prevent defects due to blow holes among defects of sand casting products. It was intended to reduce the defect rate by reducing the blow hole of the inner surface. Currently, expectations and requirements for the quality level of non-ferrous aluminum casting in the casting industry are increasing. In addition, the shape is complex and the shrinkage precision is required. Among them, the test prototype is expensive to manufacture the mold, and the production time is also long, and the product is manufactured by sand casting. At this time, the highest defect rates are defects caused by shrinkage defects, surface defects, and blow holes.. At this study, the manufacturing time was shortened by using the shape of the fluid movement path in advance. Also, it is possible to reduce defects due to blow holes.

• Journal of information and communication convergence engineering
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• v.22 no.3
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• pp.231-236
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• 2024

This paper presents a method used to identify partial discharge defects in cast-resin power transformers using a back-propagation algorithm. The Rogowski-type partial discharge (PD) sensor was designed with a planar and thin structure based on a printed circuit board to detect PD signals. PD electrode systems, such as metal protrusions, particle-on-insulators, delamination, and void defects, were fabricated to simulate the PD defects that occur in service. PD characteristics, such as rising time, falling time, pulse width, skewness, and kurtosis without phase-resolved partial discharge patterns, were extracted to intuitively analyze each PD pulse according to the type of PD defect. A backpropagation algorithm was designed to identify PD defects using a virtual instrument (VI) based on the LabVIEW program. The results show that the accuracy rate of back-propagation (BP) algorithm reaches over 92.75% in identifying four types of PD defects.