• Journal of Korean Dental Science
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• 제5권1호
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• pp.21-28
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• 2012

Purpose: In this study the bone healing ability of autogenous tooth bone graft material as a substitute material was evaluated in a mini-pig cranial defect model through histologic examinations and osteonectin reverse transcription polymerase chain reaction (RT-PCR) quantitative analysis. Materials and Methods: A defect was generated in the cranium of mini-pigs and those without a defect were used as controls. In the experimental group, teeth extracted from the mini-pig were manufactured into autogenous tooth bone graft material and grafted to the defect. The mini-pigs were sacrificed at 4, 8, and 12 weeks to histologically evaluate bone healing ability and observe the osteonectin gene expression pattern with RT-PCR. Result: At 4 weeks, the inside of the bur hole showed fibrosis and there was no sign of bone formation in the control group. On the other hand, bone formation surrounding the tooth powder granule was observed at 4 weeks in the experimental group where the bur hole was filled with tooth powder. Osteonectin gene expression; there was nearly no osteonectin expression in the control group while active osteonectin expression was observed from 4 to 12 weeks in the experimental group. Conclusion: We believe this material will show better results when applied in a clinical setting.

• Imaging Science in Dentistry
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• 제54권2호
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• pp.171-180
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• 2024

Purpose: This study was conducted to identify the typical sites and patterns of peri-implant bone defects on cone-beam computed tomography (CBCT) images, as well as to evaluate the detectability of the identified bone defects on panoramic images. Materials and Methods: The study population included 114 patients with a total of 367 implant fixtures. CBCT images were used to assess the presence or absence of bone defects around each implant fixture at the mesial, distal, buccal, and lingual sites. Based on the number of defect sites, the presentations of the peri-implant bone defects were categorized into 3 patterns: 1 site, 2 or 3 sites, and circumferential bone defects. Two observers independently evaluated the presence or absence of bone defects on panoramic images. The bone defect detection rate on these images was evaluated using receiver operating characteristic analysis. Results: Of the 367 implants studied, 167 (45.5%) had at least 1 site with a confirmed bone defect. The most common type of defect was circumferential, affecting 107 of the 167 implants(64.1%). Implants were most frequently placed in the mandibular molar region. The prevalence of bone defects was greatest in the maxillary premolar and mandibular molar regions. The highest kappa value was associated with the mandibular premolar region. Conclusion: The typical bone defect pattern observed was a circumferential defect surrounding the implant. The detection rate was generally higher in the molar region than in the anterior region. However, the capacity to detect partial bone defects using panoramic imaging was determined to be poor.

• 산업기술연구
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• 제21권B호
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• pp.67-74
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• 2001

According as the patterns of PCB (Printed Circuit Board) become denser and complicated, quality and accuracy of PCB influence the performance of final product. It's attempted to obtain trust of 100% about all of parts. Because human inspection in mass-production manufacturing facilities are both time-consuming and very expensive, the automation of visual inspection has been attempted for many years. Thus, automatic visual inspection of PCB is required. In this paper, we used an algorithm which compares the reference PCB patterns and the input PCB patterns are separated an object and a scene by filtering and edge detection. And than compare two image using pattern matching algorithm. We suggest an defect inspection algorithm in PCB pattern, to be satisfied low cost, high speed, high performance and flexibility on the basis of $640{\times}480$ binary pattern.

• 한국공작기계학회논문집
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• 제12권2호
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• pp.65-70
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• 2003

In this study, researchers developed the estimative algorithm for artificial defect in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages : Crack, Delamination and Normal. According to the results, we were confirmed that estimative algerian was provided the recognition rates of 75.7% (for Crack) and 83.4% (for Delamination) and 87.2 % (for Normal).

• 한국철도학회논문집
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• 제4권3호
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• pp.79-86
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• 2001

The signal pattern recognition method by acoustic emission signal is applied to detect and classify the defects of a journal bearing in a power plant. AE signals of main defects such as overheating, wear and corrosion are obtained from a small scale model. To detect and classify the defects, AE signal pattern recognition program is developed. As the classification methods, the wavelet transformation analysis, the frequency domain analysis and time domain analysis are used. Among three analyses, the wavelet transformation analysis is most effective to detect and classify the defects of the journal bearing..

• 전기학회논문지
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• 제57권2호
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• pp.307-313
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• 2008

In this paper, we propose a novel defects inspection algorithm for TFT-LCD panels. We first compensate the distorted image caused by the camera distortion and the uneven illumination environment using the least squares method and the bezier surface. We find a starting point of each pattern for restricting each pattern. A clean image is compared to each pattern to find defects using modified PCSR-G algorithm. The simulation example shows that our algorithm not only inspects the defects well, but also is robust to the 1-pixel error.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.585-590
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• 2000

Recently, It is gradually raised necessity that thickness of thin film is measured accuracy and managed in industrial circles and medical world. Ultrasonic Signal processing method is likely to become a very powerful method for NDE method of detection of microdefects and thickness measurement of thin film below the limit of Ultrasonic distance resolution in the opaque materials, provides useful information that cannot be obtained by a conventional measuring system. In the present research, considering a thin film below the limit of ultrasonic distance resolution sandwiched between three substances as acoustical analysis model, demonstrated the usefulness of ultrasonic Signal processing technique using information of ultrasonic frequency for NDE of measurements of thin film thickness, sound velocity, and step height, regardless of interference phenomenon. Numeral information was deduced and quantified effective information from the image. Also, pattern recognition of a defected input image was performed by neural network algorithm. Input pattern of various numeral was composed combinationally, and then, it was studied by neural network. Furthermore, possibility of pattern recognition was confirmed on artifical defected input data formed by simulation. Finally, application on unknown input pattern was also examined.

• Nuclear Engineering and Technology
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• 제36권3호
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• pp.219-228
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• 2004

[1] and [2] present an approach to diagnosing possible defects in the mechanical systems of a nuclear power plant. In this paper, by using a fault library as a database and training data, we develop a diagnostic algorithm 1) to decide whether an Air Operated Valve system is sound or not and 2) to identify the defect from which an Air-Operated Valve system suffers, if any. This algorithm is composed of three stages: a neural net stage, a non-neural net stage, and an integration stage. The neural net stage is a simple perceptron, a pattern-recognition module, using a neural net. The non-neural net stage is a simple pattern-matching algorithm, which translates the degree of matching into a corresponding number. The integration stage collects each output and makes a decision. We present a simulation result and confirm that the developed algorithm works accurately, if the input matches one in the database.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.1422-1423
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• 2007

This study deals with various neural network algorithms for the on-site partial discharge pattern recognition. For the purpose, the pattern recognition has been carried out on partial discharge data for the typical artificial defect using 9 different neural network models. In order to enhance on-site applicability, artificial defects were installed in the insulation joint box of extra-high voltage xLPE cables and partial discharges were measured by use of the metal foil sensor and a HFCT as a sensor. As the result, it is found out that the accuracy of pattern recognition could be enhanced through the application of the Sigmoid function, the Momentum algorithm and the Genetic algorism on the artificial neural networks. Although Multilayer Perceptron (MLP) algorism showed the best result among 9 neural network algorisms, it is thought that more researches on others would be needed in consideration of on-site application.

• 한국정밀공학회지
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• 제23권10호
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• pp.36-43
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• 2006