• International conference on construction engineering and project management
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• The 10th International Conference on Construction Engineering and Project Management
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• pp.311-318
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• 2024

Quality control in construction projects necessitates the detection of defects during construction. Currently, this task is performed manually by site supervisors. This manual process is inefficient, labor-intensive, and prone to human error, potentially leading to decreased productivity. To address this issue, research has been conducted to automate defect detection using computer vision-based object detection technologies. However, these studies often suffer from a lack of data for training deep learning models, resulting in inadequate accuracy. This study proposes a method to improve the accuracy of deep learning models through the use of virtual image data. The target building is created as a 3D model and finished with materials similar to actual components. Subsequently, a virtual defect texture is produced by layering three types of images: defect information, area information, and material information images, to fabricate materials with defects. Images are generated by rendering the 3D model and the defect, and annotations are created for segmentation. This approach creates a hybrid dataset by combining virtual data with actual site image data, which is then used to train the deep learning model. This research was conducted on the tile process of finishing construction projects, focusing on cracks and falls as the target defects. The training results of the deep learning model show that the F1-Score increased by 12.08% for falls and cracks when using the hybrid dataset compared to the real image dataset alone, validating the hybrid data approach. This study contributes not only to unmanned and automated smart construction management but also to enhancing safety on construction sites. To establish an integrated smart quality management system, it is necessary to detect various defects simultaneously with high accuracy. Utilizing this method for automatic defect detection in other types of construction can potentially expand the possibilities for implementing an integrated smart quality management system.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.1-10
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• 2022

Nowadays software defect prediction (SDP) is most active research going on in software engineering. Early detection of defects lowers the cost of the software and also improves reliability. Machine learning techniques are widely used to create SDP models based on programming measures. The majority of defect prediction models in the literature have problems with class imbalance and high dimensionality. In this paper, we proposed Centroid and Nearest Neighbor based Class Imbalance Reduction (CNNCIR) technique that considers dataset distribution characteristics to generate symmetry between defective and non-defective records in imbalanced datasets. The proposed approach is compared with SMOTE (Synthetic Minority Oversampling Technique). The high-dimensionality problem is addressed using Ant Colony Optimization (ACO) technique by choosing relevant features. We used nine different classifiers to analyze six open-source software defect datasets from the PROMISE repository and seven performance measures are used to evaluate them. The results of the proposed CNNCIR method with ACO based feature selection reveals that it outperforms SMOTE in the majority of cases.

• Journal of the Korean Society for Nondestructive Testing
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• 제30권3호
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• pp.261-270
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• 2010

This paper presents an analytical investigation for a baseline-free imaging of a defect in plate-like structures using the time-reversal of Lamb waves. We first consider the flexural wave (A0 mode) propagation in a plate containing a defect, and reception and time reversal process of the output signal at the receiver. The received output signal is then composed of two parts: a directly propagated wave and a scattered wave from the defect. The time reversal of these waves recovers the original input signal, and produces two additional side bands that contain the time-of-flight information on the defect location. One of the side band signals is then extracted as a pure defect signal. A defect localization image is then constructed from a beamforming technique based on the time-frequency analysis of the side band signal for each transducer pair in a network of sensors. The simulation results show that the proposed scheme enables the accurate, baseline-free detection of a defect, so that experimental studies are needed to verify the proposed method and to be applied to real structure.

• KIPS Transactions on Software and Data Engineering
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• 제9권10호
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• pp.297-302
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• 2020

Software design are an important phase in the software developments. In order to manage software design, we propose additional information. Additional information suggests a standard and quantitative methods. In this study, we propose additional information at the design stage for defect management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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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.

• Journal of Institute of Control, Robotics and Systems
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• 제19권12호
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• pp.1094-1098
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• 2013

AVI (Automatic Vision Inspection) systems automatically detect defect features and measure their sizes via camera vision. Defect detection is not an easy process because of noises from various sources and optical distortion. In this paper the acquired images from a TFT panel are enhanced with the adoption of an HVS (Human Visual System). A human visual system is more sensitive on the defect area than the illumination components because it has greater sensitivity to variations of intensity. In this paper we modified an MTF (Modulation Transfer Function) in the Wavelet domain and utilized the characteristics of an HVS. The proposed algorithm flattens the inner illumination components while preserving the defect information intact.

• Proceedings of the KIEE Conference
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• 대한전기학회 2003년도 하계학술대회 논문집 B
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• pp.702-704
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• 2003

In this paper, analysis of bobbin probe signal in steam generator tube with bulge defect on CE system 80 nuclear power plant is represented. The CE system 80 steam generator is adopted in ULJIN-4 nuclear power plant. From Maxwell's equation, the electromagnetic governing equation for eddy current problem is derived and by performing the finite element formulation the 3-dimensional finite element code with brick element is developed. For the ease of the comparison the numerical results with experimental ones, the calculated signals are adjusted by using the ASME standard 100[%] through hole signal. For analysis of the effect of variation of the bulge depth on the impedance signal 0.2[mm] and 0.4[mm] depth of bulge defect signals are calculated and analyzed. As the depth of the bulge defect is increased, the magnitude of the signal is increased, too. But the rate of the increment of the signal is less than that of the depth of defect. From the result of this paper, we can obtained the information of the effect of bulge defect on the impedance signal.

• Korean Journal of Construction Engineering and Management
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• 제10권5호
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• pp.16-27
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• 2009

Recently construction companies expand position for apartment house in construction market because of house supply rate increase and its amount. In addition, because the focus of house sale market has moved to customer from supplier, the importance of defect management is being issued currently. However, current apartment defect management is not satisfied with customer's demand for the lack of business process management, management organization, information feedback, and readiness for defect in construction phase. To solve this problem, this study proposed business process management improvement model for defect management. Proposed improvement model make information feedback, defect management business quality improvement, and improper process improvement through integrating defect management and quality management. It is expected to contribute to customer's satisfaction improvement and reliance improvement for construction companies.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• 제16권6호
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• pp.369-377
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• 2023

In the industrial manufacturing sector, quality control is pivotal for minimizing defect rates; inadequate management can result in additional costs and production delays. This study underscores the significance of detecting texture defects in manufactured goods and proposes a more precise defect detection technique. While the DFR(Deep Feature Reconstruction) model adopted an approach based on feature map amalgamation and reconstruction, it had inherent limitations. Consequently, we incorporated a new loss function using statistical methodologies, integrated a skip connection structure, and conducted parameter tuning to overcome constraints. When this enhanced model was applied to the texture category of the MVTec-AD dataset, it recorded a 2.3% higher Defect Segmentation AUC compared to previous methods, and the overall defect detection performance was improved. These findings attest to the significant contribution of the proposed method in defect detection through the reconstruction of feature map combinations.

• Transactions on Electrical and Electronic Materials
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• 제18권2호
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• pp.70-73
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• 2017

We investigated n-i-p type single junction hydrogenated amorphous silicon oxide solar cells. These cells were without front surface texture or back reflector. Maximum power point efficiency of these cells showed that an optimized device structure is needed to get the best device output. This depends on the thickness and defect density ($N_d$) of the active layer. A typical 10% photovoltaic device conversion efficiency was obtained with a $N_d=8.86{\times}10^{15}cm^{-3}$ defect density and 630 nm active layer thickness. Our investigation suggests a correlation between defect density and active layer thickness to device efficiency. We found that amorphous silicon solar cell efficiency can be improved to well above 10%.