• 지질공학
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• 제5권3호
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• pp.277-288
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• 1995

최근, 암반내에 형성된 절리 및 단층에 대한 정확한 규명은 무엇보다 암반분류 내지 암반내 용질유동연구에어 대단히 중요한 과제로 되고 있다. 본 연구에서 소개된 텔레뷰어 검층장치는 주사되는 초음파를 초점화함으로서 절리상태를 고분해능으로 파악할 수 있는 기능을 갖고 있다. 즉, 초음파 발생원이 시추공 축상에서 선회하는 동안 시추공내벽으로 조밀하게 초음파빔을 주사하고 그로 인해 반사되는 초음파의 $\circled1$ 진폭변화는 바로 절리 및 단층의 크기, 경사, 및 방향은 물론 상대적인 암석강도변화도 정확하게 추출하게 하며, $\circled2$ 주시변화는 바로 고분해능 공경검층기능을 대변하게 되어 시추공 내벽상태 내지 암석의 응력장 분포도 쉽게 판단하게 하는 것이다. 본 논문은 국내 청양군 실험시추공에서 얻게 된 텔러뷰어 현장탐사결과를 예시함으로써 텔레뷰어의 다양한 응용성을 입증하고 있다.

• Korean Journal of Radiology
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• 제21권7호
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• pp.891-899
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• 2020

Objective: To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT. Materials and Methods: We retrospectively identified all whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455). Results: All CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level [per CT scan: rib fracture(s): yes/no]. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity: 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT reports. A major factor associated with correct detection was displacement. Conclusion: We found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.

• Imaging Science in Dentistry
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• 제54권2호
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• pp.139-145
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• 2024

Purpose: This study examined the influence of a metal artifact reduction (MAR) tool, sharpening filters, and their combination on the diagnosis of vertical root fracture (VRF) in teeth with metallic posts using cone-beam computed tomography (CBCT). Materials and Methods: Twenty single-rooted human premolars - 9 with VRF and 11 without - were individually placed in a human mandible. A metallic post composed of a cobalt-chromium alloy was inserted into the root canal of each tooth. CBCT scans were then acquired under the following parameters: 8 mA, a 5×5 cm field of view, a voxel size of 0.085 mm, 90 kVp, and with MAR either enabled or disabled. Five oral and maxillofacial radiologists independently evaluated the CBCT exams under each MAR mode and across 3 sharpening filter conditions: no filter, Sharpen 1×, and Sharpen 2×. The diagnostic performance was quantified by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. These metrics were compared using 2-way analysis of variance with a significance level of α=5%. Intra- and inter-examiner agreement were assessed using the weighted kappa test. Results: Neither MAR nor the application of sharpening filters significantly impacted AUC or specificity (P>0.05). However, sensitivity increased when MAR was combined with Sharpen 1× and Sharpen 2× (P=0.015). The intra-examiner agreement ranged from fair to substantial (0.34-0.66), while the inter-examiner agreement ranged from fair to moderate (0.27-0.41). Conclusion: MAR in conjunction with sharpening filters improved VRF detection; therefore, their combined use is recommended in cases of suspected VRF.

• 대한기계학회논문집A
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• 제21권6호
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• pp.988-994
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• 1997

The demands for robotic and automatic system are continually increasing in manufacturing fields. There have been many studies to monitor and predict the system, but they have mainly focused upon measuring cutting force, and current of motor spindle, and upon using acoustic sensor, etc. In this study, digital image of time series sequence was acquired by taking advantage of optical technique. Mean square error was obtained from it and was available for useful observation data. The parameter was estimated using PAA(parameter adaptation algorithm) from observation data. AR(auto regressive) model was selected for system model and fifth order was decided according to parameter estimation. Uncorrelation test was also carried out to verify convergence of parameter. Through the proceedings, it was found that there was a system stability.

• 비파괴검사학회지
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• 제22권6호
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• pp.609-620
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• 2002

It is well known that during tensile testing, a part of the mechanical work done on the specimen is transformed into heat energy. However, the ultimate temperature rise and the rate of temperature rise is related to the nature of the material, conditions of the test and also to the deformation behaviour of the material during loading. The recent advances in infrared sensors and image/data processing techniques enable observation and quantitative analysis of the heat energy dissipated during such tensile tests. In this study, infrared imaging technique has been used to characterise the tensile deformation in AISI type 316 nuclear grade stainless steel. Apart from identifying the different stages during tensile deformation, the technique provided an accurate full-field temperature image by which the point and time of strain localization could be identified. The technique makes it possible to visualise the region of deformation and failure and also predict the exact region of fracture in advance. The effect of thermal gradients on plastic flow in the case of interrupted straining revealed that the interruption of strain and restraining at a lower strain rate not only delays the growth of the temperature gradient, but the temperature rise per unit strain decreases. The technique is a potential NDE tool that can be used for on-line detection of thermal gradients developed during extrusion and metal forming process which can be used for ensuring uniform distribution of plastic strain.

• Geomechanics and Engineering
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• 제25권4호
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• pp.295-302
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• 2021

Cross-borehole electrical resistivity tomography (ERT) is an effective groundwater detection tool in geophysical investigations. In this paper, an artificial water injection test was conducted on a small clay sample, where the high-resolution cross-borehole ERT was used to investigate the moisture migration law over time. The moisture migration path can be two-dimensionally imaged based on the relationship between resistivity and saturation. The hydraulic conductivity was estimated, and the magnitude ranged from 10^-11 m/s to 10^-9 m/s according to the comparison between the simulation flow and the saturation distribution inferred from ERT. The results indicate that cross-borehole ERT could help determine the resistivity distribution of small size clay samples. Finally, the cross-borehole ERT technique has been applied to investigate the self-sealing characteristics of clay.

• Imaging Science in Dentistry
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• 제54권3호
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• pp.232-239
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• 2024

Purpose: The use of artificial intelligence (AI) and deep learning algorithms in dentistry, especially for processing radiographic images, has markedly increased. However, detailed information remains limited regarding the accuracy of these algorithms in detecting mandibular fractures. Materials and Methods: This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specific keywords were generated regarding the accuracy of AI algorithms in detecting mandibular fractures on radiographic images. Then, the PubMed/Medline, Scopus, Embase, and Web of Science databases were searched. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool was employed to evaluate potential bias in the selected studies. A pooled analysis of the relevant parameters was conducted using STATA version 17 (StataCorp, College Station, TX, USA), utilizing the metandi command. Results: Of the 49 studies reviewed, 5 met the inclusion criteria. All of the selected studies utilized convolutional neural network algorithms, albeit with varying backbone structures, and all evaluated panoramic radiography images. The pooled analysis yielded a sensitivity of 0.971 (95% confidence interval [CI]: 0.881-0.949), a specificity of 0.813 (95% CI: 0.797-0.824), and a diagnostic odds ratio of 7.109 (95% CI: 5.27-8.913). Conclusion: This review suggests that deep learning algorithms show potential for detecting mandibular fractures on panoramic radiography images. However, their effectiveness is currently limited by the small size and narrow scope of available datasets. Further research with larger and more diverse datasets is crucial to verify the accuracy of these tools in in practical dental settings.