• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1100-1104
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• 2017

A detector-based approach shows the limited performances for the defect inspections such as shallow fine cracks and indistinguishable defects from background. Deep learning technique is widely used for object recognition and it's applications to detect defects have been gradually attempted. Deep learning requires huge scale of learning data, but acquisition of data can be limited in some industrial application. The possibility of applying CNN which is one of the deep learning approaches for surface defect inspection is investigated for industrial parts whose detection difficulty is challenging and learning data is not sufficient. VOV is adopted for pre-processing and to obtain a resonable number of ROIs for a data augmentation. Then CNN method is applied for the classification. Three CNN networks, AlexNet, VGGNet, and mofified VGGNet are compared for experiments of defects detection.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.379-383
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• 2005

This paper proposes a new steel strip surface inspection system. The system acquires bright and dark field images of defects by using a stroboscopic IR LED light and area camera system and the defect images are preprocessed and segmented in real time for feature extraction. 4113 defect samples of cold roll steel strips are used to develop KNN (k-Nearest Neighbor) classifier which classifies the defects into 8 different types. The developed KNN classifier demonstrates about 85% classifying performance which is considered very plausible result.

• Transactions of the Society of Information Storage Systems
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• v.2 no.1
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• pp.56-64
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• 2006

This paper proposes a new and simple input prediction method for robust servo system. A robust tracking control system for optical disk drives to reject disk runout was recently proposed based on both Coprime Factorization(CF) and Zero Phase Error Tracking(ZPET) control. The CF control system can be designed simply and systematically. Moreover, this system has not only stability but also robustness to parameter uncertainties and disturbance rejection capability. Since optical disk tracking servo systems can detect only racking error, it was proposed that the reference input signal for ZPET could be estimated from tracking errors. In this paper, we propose a new control structure for the ZPET controller. It requires less memory than the previously proposed method for the reference signal generation. Therefore, it is very effective in runout control. Furthermore, this method can be applied to defective optical disk like surface defects on disk. Numerical simulation and experimental result show the proposed method effective.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.209-211
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• 2018

A zinc oxide thin film doped with aluminum was deposited by RF sputtering. The deposition temperature of the sputter chamber was kept constant at $350^{\circ}C$, the power supplied to the chamber was 75 W, the oxygen flow rate was changed to 10 sccm and 20 sccm, and the thin film deposition time was changed to 120 and 180 minutes. The structures of the deposited zinc oxide thin films were analyzed by van der Waals method using an X-ray diffractometer. As a result of X-ray diffraction, the amount of oxygen supplied to the zinc oxide thin film increased, and the surface growth of the (002), (400), (110), and (103) planes showed a change with increasing deposition time. Moreover, as the amount of oxygen supplied to the zinc oxide thin film increased, their shape was observed to be coarse, and the thin film' s particles shape was correlated with the oxygen chemical defect introduced.

• Tribology and Lubricants
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• v.40 no.4
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• pp.133-138
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• 2024

This study evaluates the structural safety of wind turbine blades, analyzes the behavior of composite laminate structures with and without defects, and assesses surface erosion wear. The NREL 5 MW standard is applied to assign accurate composite material properties to each blade section. Modeling and analysis of the wind turbine blades reveal stable behavior under individual load conditions (gravity, motor speed, wind speed), with the web bearing most of the load. Surface erosion wear analysis in which microparticle impacts are simulated on the blade coating shows a maximum stress and maximum displacement of 14 MPa and 0.02 mm, respectively, indicating good initial durability, but suggest potential long-term performance issues due to cumulative effects. The study examines defect effects on composite laminate structures to compare the stress distribution, strain, and stiffness characteristics between normal and cracked states. Although normal conditions exhibit stable behavior, crack defects lead to fiber breakage, high-stress concentration in the vulnerable resin layer, and decreased rigidity. This demonstrates that local defects can compromise the safety of the entire structure. The study utilizes finite element analysis to simulate various load scenarios and defect conditions. Results show that even minor defects can significantly alter stress distributions and potentially lead to catastrophic failure if left unaddressed. These findings provide valuable insights for wind turbine blade safety evaluations, surface protection strategies, and composite structure health management. The methodology and results can inform the design improvements, maintenance strategies, and defect detection techniques of the wind energy industry.

• Journal of Periodontal and Implant Science
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• v.53 no.4
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• pp.295-305
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• 2023

Purpose: Various methods have been proposed to achieve the nearly complete decontamination of the surface of implants affected by peri-implantitis. We investigated the in vitro debridement efficiency of multiple decontamination methods (Gracey curettes [GC], glycine air-polishing [G-Air], erythritol air-polishing [E-Air] and titanium brushes [TiB]) using a novel spectrophotometric ink-model in 3 different bone defect settings (30°, 60°, and 90°). Methods: Forty-five dental implants were stained with indelible ink and mounted in resin models, which simulated standardised peri-implantitis defects with different bone defect angulations (30°, 60°, and 90°). After each run of instrumentation, the implants were removed from the resin model, and the ink was dissolved in ethanol (97%). A spectrophotometric analysis was performed to detect colour remnants in order to measure the cumulative uncleaned surface area of the implants. Scanning electron microscopy images were taken to assess micromorphological surface changes. Results: Generally, the 60° bone defects were the easiest to debride, and the 30° defects were the most difficult (ink absorption peak: 0.26±0.04 for 60° defects; 0.32±0.06 for 30° defects; 0.27±0.04 for 90° defects). The most effective debridement method was TiB, independently of the bone defect type (TiB vs. GC: P<0.0001; TiB vs. G-Air: P=0.0017; TiB vs. GE-Air: P=0.0007). GE-Air appeared to be the least efficient method for biofilm debridement. Conclusions: T-brushes seem to be a promising decontamination method compared to the other techniques, whereas G-Air was less aggressive on the implant surface. The use of a spectrophotometric model was shown to be a novel but promising assessment method for in vitro ink studies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.83-88
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• 2024

In this research, we propose a Semi-Supervised learning based railroad surface defect detection method. The Resnet50 model, pretrained on ImageNet, was employed for the training. Data without labels are randomly selected, and then labeled to train the ResNet50 model. The trained model is used to predict the results of the remaining unlabeled training data. The predicted values exceeding a certain threshold are selected, sorted in descending order, and added to the training data. Pseudo-labeling is performed based on the class with the highest probability during this process. An experiment was conducted to assess the overall class classification performance based on the initial number of labeled data. The results showed an accuracy of 98% at best with less than 10% labeled training data compared to the overall training data.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.11
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• pp.1060-1066
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• 2007

When a high-energy ultrasound propagates through a solid body that contains a crack or a delamination, the two faces of the defect do not ordinarily vibrate in unison, and dissipative phenomena such as friction, rubbing and clapping between the faces will convert some of the vibrational energy to heat. By combining this heating effect with infrared imaging, one can detect a subsurface defect in material efficiently. In this paper a detection of the welding defect of thin SUS 304 plates using the UIR (ultrasonic infrared imaging) technology is described. A low frequency (20kHz) ultrasonic transducer was used to infuse the welded thin SUS 304 plates with a short pulse of sound for 280ms. The ultrasonic source has a maximum power of 2kW. The surface temperature of the area under inspection is imaged by a thermal infrared camera that is coupled to a fast frame grabber in a computer. The hot spots, which are a small area around the defect tip and heated up highly, are observed. From the sequence of the thermosonic images, the location of defective or inhomogeneous regions in the welded thin SUS 304 plates can be detected easily.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.11
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• pp.55-61
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• 2009

The wall thinning defect of nuclear power pipe is mainly occurred by the affect of the flow accelerated corrosion (FAC) of fluid. This type of defect becomes the cause of damage or destruction of in carbon steel pipes. Therefore, it is very important to measure defect which is existed not only on the welding part but also on the whole field of pipe. This study use dual-beam Shearography, which can measure the out-of-plane deformation and the in-plane deformation by using another illuminated laser beam and simple image processing technique. And this study proposes Infrared thermography, which is a two-dimensional non-contact nondestructive evaluation that can detect internal defects from the thermal distribution by the inspection of infrared light radiated from the object surface. In this paper, defect of nuclear power pipe were, measured using dual-beam shearography and infrared thermography, quantitatively evaluated by the analysis of phase map and thermal image pattern.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6043-6062
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• 2019

Low-rank matrix decomposition has shown its capability in many applications such as image in-painting, de-noising, background reconstruction and defect detection etc. In this paper, we consider the texture background of rail track images and the sparse foreground of the defects to construct a low-rank matrix decomposition model with block sparsity for defect inspection of rail tracks, which jointly minimizes the nuclear norm and the 2-1 norm. Similar to ADM, an alternative method is proposed in this study to solve the optimization problem. After image decomposition, the defect areas in the resulting low-rank image will form dark stripes that horizontally cross the entire image, indicating the preciselocations of the defects. Finally, a two-stage defect extraction method is proposed to locate the defect areas. The experimental results of the two datasets show that our algorithm achieved better performance compared with other methods.