• Structural Engineering and Mechanics
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• v.80 no.5
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• pp.503-522
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• 2021

This paper investigates experimentally and numerically the influence of drilling process on the mechanical and thermomechanical behaviors of woven glass fiber reinforced polymer (GFRP) composite plate. Through the experimental analysis, a CNC machine with cemented carbide drill (point angles 𝜙=118° and 6 mm diameter) was used to drill a woven GFRP laminated squared plate with a length of 36.6 mm and different thicknesses. A produced temperature during drilling "heat affected zone (HAZ)" was measured by two different procedures using thermal IR camera and thermocouples. A thrust force and cutting torque were measured by a Kistler 9272 dynamometer. The delamination factors were evaluated by the image processing technique. Finite element model (FEM) has been developed by using LS-Dyna to simulate the drilling processing and validate the thrust force and torque with those obtained by experimental technique. It is found that, the present finite element model has the capability to predict the force and torque efficiently at various drilling conditions. Numerical parametric analysis is presented to illustrate the influences of the speeding up, coefficient of friction, element type, and mass scaling effects on the calculated thrust force, torque and calculation's cost. It is found that, the cutting time can be adjusted by drilling parameters (feed, speed, and specimen thickness) to control the induced temperature and thus, the force, torque and delamination factor in drilling GFRP composites. The delamination of woven GFRP is accompanied with edge chipping, spalling, and uncut fibers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.570-590
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• 2024

Breast cancer ranks among the most prevalent forms of malignancy and foremost cause of death by cancer worldwide. It is not preventable. Early and precise detection is the only remedy for lowering the rate of mortality and improving the probability of survival for victims. In contrast to present procedures, thermography aids in the early diagnosis of cancer and thereby saves lives. But the accuracy experiences detrimental impact by low sensitivity for small and deep tumours and the subjectivity by physicians in interpreting the images. Employing deep learning approaches for cancer detection can enhance the efficacy. This study explored the utilization of thermography in early identification of breast cancer with the use of a publicly released dataset known as the DMR-IR dataset. For this purpose, we employed a novel approach that entails the utilization of a pre-trained MobileNetV2 model and fine tuning it through transfer learning techniques. We created three models using MobileNetV2: one was a baseline transfer learning model with weights trained from ImageNet dataset, the second was a fine-tuned model with an adaptive learning rate, and the third utilized early stopping with callbacks during fine-tuning. The results showed that the proposed methods achieved average accuracy rates of 85.15%, 95.19%, and 98.69%, respectively, with various performance indicators such as precision, sensitivity and specificity also being investigated.

• Economic and Environmental Geology
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• v.45 no.4
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• pp.397-406
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• 2012

During the 2nd Ulleung Basin Gas Hydrate Drilling Expedition (UBGH2) in 2010, gas-hydrate-bearing sediment cores were recovered at 10 drill sites. Base, on Infrared (IR) thermal image and grain-size analysis of the cores, three distinct types of gas hydrate are classified: Type I (fracture-filling in mud layers), Type II (disseminated in mud layers), and Type III (pore-filling in sand layers). Types I and II gas hydrates occur in mud as discrete veins, nodules or disseminated particles. Type III fills the pore spaces of the sand layers encased in mud layers. In this case, the sand content of hosting sediments shows a general linear relationship with gas hydrate saturation. The degrees of temperature anomalies (${\Delta}T$) from IR images generally increase with gas hydrate saturation regardless of gas hydrate occurrence types. Type I is dominantly found in the sites where seismic profiles delineate chimney structures, whereas Type II where the drill cores are composed almost of mud layers. Type III was mainly recovered from the sites where hemipelagic muds are frequently intercalated with turbidite sand layers. Our results indicate that gas hydrate occurrence is closely related to sedimentological characteristic of gas hydrate-bearing sediments, that is, grain size distribution.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1323-1337
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• 2020

GEO-KOMPSAT-2A AMI (Advanced Meteorological Imager) radiometric calibration evaluation is an essential element not only for functional and performance verification of the payload but for the quality of the sensor data. AMI instrument consists of six reflective channels and ten thermal infrared ones. One of the key parameters representing radiometric properties of the sensor is a SNR (Signal-to-Noise Ratio) for the reflective channels and a NEdT (Noise Equivalent delta Temperature) for the IR ones respectively. Other important radiometric calibration parameters are a dynamic range and a gain value related with the responsivity of detectors. To verify major radiometric calibration performance of AMI, an offline radiometric evaluation tool was developed separately with a real-time AMI data processing system. Using the evaluation tool, validation activities were carried out during the GEO-KOMPSAT-2A In-Orbit Test period. The results from the evaluation tool were cross checked with those of the HARRIS, which is the AMI payload vendor. AMI radiometric evaluation activities were conducted through three phases for both sides (Side 1 and Side 2) of AMI payload. Results showed that performances of the key radiometric properties were outstanding with respect to the radiometric requirements of the payload. The effectiveness of the evaluation tool was verified as well.

• Journal of the Korean Society for Nondestructive Testing
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• v.26 no.4
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• pp.211-219
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• 2006

The UET(ultrasound excited thermography) for the ,eat-time diagnostics of the object employs an infrared camera to image defects of the surface and subsurface which are locally heated using high-frequency putted ultrasonic excitation. The dissipation of high-power ultrasonic energy around the feces of the defects causes an increase In temperature. The defect's image appears as a hot spot (bright IR source) within a dark background field. The UET for nondestructive diagnostic and evaluation is based on the image analysis of the hot spot as a local response to ultrasonic excited heat deposition. In this paper the applicability of VET for fast imaging of defect is described. The ultrasonic energy is injected into the sample through a transducer in the vertical and horizontal directions respectively. The voltage applied to the transducer is measured by digital oscilloscope, and the waveform are compared. Measurements were performed on four kinds of materials: SUS fatigue crack specimen(thickness 14mm), PCB plate(1.8 mm), CFRP plate(3 mm) and Inconel 600 plate (1 mm). A high power ultrasonic energy with pulse durations of 250ms Is injected into the samples in the horizontal and vertical directions respectively The obtained experimental result reveals that the dissipation loss of the ultrasonic energy In the vertical injection is less than that in the horizontal direction. In the cafe or PCB, CFRP, the size of hot spot in the vortical injection if larger than that in horizontal direction. Duration time of the hot spot in the vertical direction is three times as long as that in the horizontal direction. In the case of Inconel 600 plate and SUS sample, the hot spot in the horizontal injection was detected faster than that in the vertical direction