• Elastomers and Composites
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• v.42 no.4
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• pp.209-216
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• 2007

Fatigue life prediction methodology of the rubber component made of vulcanized natural rubber under variable amplitude loadings was studied. The displacement-controlled fatigue tests were conducted at different levels and the maximum Green-Lagrange strain was selected as damage parameters. A fatigue life curve of the rubber represented by the maximum Green-Lagrange strain was determined from the nonlinear finite element analysis. The transmission load history of SAE as variable amplitude loading was used to perform the fatigue life prediction. And then a signal processing of variable loading by racetrack and simplified rainflow cycle counting methods were performed. The modified miner's rule as cumulative damage summation was used. Finally, when the gate value is 30%, the predicted fatigue life of the rubber component agreed well with the experimental fatigue lives with a factor of two.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.9
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• pp.1400-1409
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• 2001

This paper describes a fiber optic sensor suitable for non-contact detection of ultrasonic waves. This sensor is based on a fiber optic Sagnac interferometer. Quadrature phase bias between two interfering laser beams in Sagnac loop is introduced by a polarization controller. A stable quadrature phase bias can be confirmed by observing the interferometer output versus phase bias. This method eliminates a digital signal processing for detection of ultrasonic waves using Sagnac interferometer. Interference intensity is affected by the frequency of ultrasonic waves and the time delay of Sagnac loop. Collimator is attached to the end of the probing fiber to focus the light beam onto the specimen surface and to collect the reflected light back into the fiber probe. Ultrasonic waves produced by conventional ultrasonic transducers are detected. This fiber optic sensor based on Sagnac interferometer is very effective for detection of small displacement with high frequency such as ultrasonic waves used in conventional non-destructive testing.

• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.237-243
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• 2012

In this paper, an atmospheric effect of repetitive measurements of X-band (9.65 GHz) arc-rail type GB-SAR (ArcSAR) system was quantitatively analyzed. Four artificial triangular trihedral corner reflectors as stationary targets for getting stable back scattered signal during 43 hours continually. The results of the analysis showed that the phase of those stationary targets had changed maximum of 5 radian (12.4 mm) and total RMS error had was 1.62 radian (4 mm) during 65 repeated measuring time. The refractive index of air which was calculated using the temperature;humidity and pressure of atmosphere showed very close relationship with the phase difference. We could check the atmospheric correction was fulfilled by the correction of an atmospheric effect using refractive index during the selected 16 hours period showed that RMS error was dropped from 1.74 radian (4.3 mm) to 0.10 radian (0.24 mm).

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.48 no.11
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• pp.748-754
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• 1999

Micromirrors supported by S-shape girders were fabricated and their angular deflections were measured using a laser-based system. A micromirror consists of a $50\mum\times50\mum$ aluminum plate, posts and an S-shape girder. Two electrodes were deposited on two corners of the substrate beneath the mirror plate. $50\times50$micromirror array were fabricated using the Al-MEMS process. The electrostatic force caused by the voltage difference between the mirror plate and one of the electrodes causes the mirror plate to tilt until the girder touches the substrate. Bial voltage of the mirror plate is between 25~35V and signal pulse voltage on both electrodes is $\pm5V$. A laser-based system capable of real-time two-dimensional angular deflection measurement of the micromirror was developed. The operation of the system is based on measuring the displacement of a HeNe laser beam reflecting off the micromirror. The resonant frequency of the micromirror is 50kHz when the girder touches the substrate and it is 25 when the micromirror goes back to flat position, since the moving mass is about twice of the former case. The measurement results also revealed that the micromirror slants to the other direction even after the girder touches the substrate.

• Journal of Sensor Science and Technology
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• v.14 no.5
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• pp.322-330
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• 2005

A new type of transducer, piezoelectric floating mass transducer (PFMT) which has advantages of piezoelectric and electromagnetic transducer has been proposed and implemented for the implantable middle ear hearing devices. By the uneven bonding of piezoelectric material to the inner bottom of transducer case, the PFMT can vibrate back-and-forth along the longitudinal axis of the transducer even though the piezoelectric material within the cylindrical case produces only the bilateral expansion and contraction according to the applied electrical signal. To improve efficiency of the PFMT, the multi-layered piezoelectric material has been adapted. The small number of components in the PFMT enables the simple manufacturing and the easy implanting into the middle ear. In order to examine the characteristics of vibration, mechanical modeling and finite element analyses of the proposed transducer have been performed. From the result of theoretical analyses and the measured data from the experiment, it is verified that the implemented PFMT can be used in implantable middle ear hearing devices.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.330-335
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• 2000

This paper deals with sensing ability of smart sensor that has a sensing ability of distinguish materials. We have developed new signal processing method that have distinguish different materials. We made the two type of smart sensors for experiment. The first type of smart sensor is H2 type. The second type of smart sensor is HH type. The smart sensor was developed for recognition of material. And then we developed estimation method of sensing ability of smart sensors. The first method(Sensing Ability Index) is developed for H2 smart sensor. The second method($R_{SAI}$ Index) is developed for HH smart sensor. We estimated sensing ability of smart sensor with new SAI and $R_{SAI}$ method. This paper describes our primary study for a new method of estimate sensing ability of smart sensor, which is need for precision work system. This is a study of dynamic characteristics of smart sensor according to frequency and displacement changing with new SAI and $R_{SAI}$ method. Experiment and analysis are executed for proper dynamic sensing condition. First, we developed advanced smart sensors. Second, we develop new SAI and $R_{SAI}$ methods that have a sensing ability of distinguish materials. Dynamic characteristics of smart sensor are evaluated through new SAI and $R_{SAI}$ method relatively. We can use the new SAI and $R_{SAI}$ method for finding materials. Applications of this method are finding abnormal condition of object(auto-manufacturing), feeling of object(medical product), robotics, safety diagnosis of structure, etc.

• Smart Structures and Systems
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• v.23 no.3
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• pp.307-318
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• 2019

The frequently excessive vibrations presented in civil structures during seismic events or service conditions may result in users' discomfort, or worst, in structures failure, producing economic and even human casualties. This work contributes in proposing the synthesis of a nonlinear optimal control strategy for semiactive structural control, with the main characteristic that the synthesis considers both the structure model and the semiactive actuator nonlinear dynamics, which produces a nonlinear system that requires a nonlinear controller design. The aim is to reduce the unwanted vibrations in the response of civil structures, by means of intelligent fluid semiactive actuator such as the Magnetorheological Damper (MRD), which is a device with a low level of power consumption. The civil structures for which the proposed control methodology can be applied are those admitting a state-dependent coefficient factorized representation model, such as buildings, bridges, among others. A scaled model of a three storey building is analyzed as a case study, whose dynamical response involves displacement, velocity and acceleration of each one of the storeys, subjected to the North-South component of the September 19th., 2017, Puebla-Morelos (7.1M), Mexico earthquake. The investigation rests on comparing the structural response over time for two different conditions: with no control device installed and with one MRD installed between the first floor and the ground, where a nonlinear optimal signal for the MRD input voltage is determined. Simulation results are presented to show the effectiveness of the proposed controller for reducing the building's dynamical response.

• Smart Structures and Systems
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• v.31 no.5
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• pp.469-483
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• 2023

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.

• Journal of the Korean Society of Radiology
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• v.83 no.2
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• pp.439-443
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• 2022

Hibernoma is a rare benign tumor of brown adipose tissue. Herein, we report a case of axillary hibernoma in a 53-year-old female and discuss the various radiologic findings. The US revealed a 4.5-cm well-defined oval heterogenous hyperechoic mass in the right axilla with anterior displacement of the axillary vessels. Non-enhanced chest CT showed a 5.0-cm well defined, oval, and low-attenuated mass. MRI demonstrated a 5.5-cm mass with heterogeneous intermediate-to-high signal intensity on T1-and T2-weighted images and irregular enhancement at the peripheral portion. The patient underwent an US-guided core needle biopsy and the final diagnosis was hibernoma.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.4
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• pp.183-191
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• 2024

Accurate seismic vulnerability assessment requires high quality and large amounts of ground motion data. Ground motion data generated from time series contains not only the seismic waves but also the background noise. Therefore, it is crucial to determine the high-pass cut-off frequency to reduce the background noise. Traditional methods for determining the high-pass filter frequency are based on human inspection, such as comparing the noise and the signal Fourier Amplitude Spectrum (FAS), f² trend line fitting, and inspection of the displacement curve after filtering. However, these methods are subject to human error and unsuitable for automating the process. This study used a deep learning approach to determine the high-pass filter frequency. We used the Mel-spectrogram for feature extraction and mixup technique to overcome the lack of data. We selected convolutional neural network (CNN) models such as ResNet, DenseNet, and EfficientNet for transfer learning. Additionally, we chose ViT and DeiT for transformer-based models. The results showed that ResNet had the highest performance with R² (the coefficient of determination) at 0.977 and the lowest mean absolute error (MAE) and RMSE (root mean square error) at 0.006 and 0.074, respectively. When applied to a seismic event and compared to the traditional methods, the determination of the high-pass filter frequency through the deep learning method showed a difference of 0.1 Hz, which demonstrates that it can be used as a replacement for traditional methods. We anticipate that this study will pave the way for automating ground motion processing, which could be applied to the system to handle large amounts of data efficiently.