• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.4
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• pp.245-250
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• 2015

To verify the possibility of generating rattling noise from a motor on caliper brake system, a test was conducted using a caliper excited with vibrations similar to that in a vehicle running on actual roads; this test was conducted using a quiet shaker installed in an anechoic room. After several hours of external excitation, the test assembly was loosened, and the frequency of rattling noise generation increased. A microphone was used to record the generated noise. The measured signals were analyzed by conventional spectrum analysis. Since the noise is generated as an impact response, the advantages of employing Prony analysis was discussed, and the results were compared to those obtained using conventional fast Fourier transforms. The accuracy of Prony analysis was through endurance tests on different brake systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.3
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• pp.317-323
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• 1998

A synchronization is important element not only wire communication but also wireless communication. Especially, In SS(Spread Spectrum) communication method used GPS(Global Positioning System) synchronization is more important. A synchronous oscillator(SO) is a network which synchronizes, tracks, filter, amplifies and divides (if necessary) in a single process. Without an input signal, the SO is a free-running oscillator, oscillating at a frequency $w_0$, but phase changes $180^{\circ}$ within tracking range of SO. Therefore CPSO was used for this problem. The coherent phase synchronous oscillator(CPSO) is created by adding two external loops to the SO and has a wider tracking bandwidth and a zero-offset phase response (coherent) while maintaining the SO properties of high signal-to-rejection and fast frequency acquisition times. Therefore phase between input signal and output signal is synchronized. In this paper, GPS data recovery circuit has applied CPSO using front reference characters and has certified an excellent data recovery capability.

• Smart Structures and Systems
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• v.19 no.1
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• pp.1-10
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• 2017

The operation of subway trains induces secondary structure-borne vibrations in the nearby underground spaces. The vibration, along with the associated noise, can cause annoyance and adverse physical, physiological, and psychological effects on humans in dense urban environments. Traditional tethered instruments restrict the rapid measurement and assessment on such vibration effect. This paper presents a novel approach for Wireless Smart Sensor (WSS)-based autonomous evaluation system for the subway train-induced vibrations. The system was implemented on a MEMSIC's Imote2 platform, using a SHM-H high-sensitivity accelerometer board stacked on top. A new embedded application VibrationLevelCalculation, which determines the International Organization for Standardization defined weighted acceleration level, was added into the Illinois Structural Health Monitoring Project Service Toolsuite. The system was verified in a large underground space, where a nearby subway station is a good source of ground excitation caused by the running subway trains. Using an on-board processor, each sensor calculated the distribution of vibration levels within the testing zone, and sent the distribution of vibration level by radio to display it on the central server. Also, the raw time-histories and frequency spectrum were retrieved from the WSS leaf nodes. Subsequently, spectral vibration levels in the one-third octave band, characterizing the vibrating influence of different frequency components on human bodies, was also calculated from each sensor node. Experimental validation demonstrates that the proposed system is efficient for autonomously evaluating the subway train-induced ambient vibration of underground spaces, and the system holds the potential of greatly reducing the laboring of dynamic field testing.

• Applied Biological Chemistry
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• v.48 no.3
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• pp.267-272
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• 2005

trans-Cinnamaldehyde, a major component of Cinnamomum cassia, was quantitatively analyzed using the $^1H-NMR$ spectrometry. Applicability of this method was confirmed through observing the variation of chemical shift in the $^1H-NMR$ spectrum of t-cinnamaldehyde and the integration value according to various sample concentrations or running temperatures. When the $^1H-NMR$ spectrometry was run for t-cinnamaldehyde (7.1429 mg/ml) at 19, 25, 30, 40 and $50^{\circ}C$, the chemical shifts of the doublet methine signal due to an aldehyde group were observed at 9.7202, 9.7184, 9.7169, 9.7142 and 9.7124 ppm, respectively, to imply that the running temperature had no significant variation in the chemical shift of the signal. The integration values of the signal were $1.37\;(19^{\circ}C),\;1.37\;(25^{\circ}C),\;1.37\;(30^{\circ}C),\;1.37(40^{\circ}C)$ and $1.37(50^{\circ}C)$, respectively, to also indicate running temperature gave no effect on the integration value. When the sample solutions with various concentrations such as 0.4464, 0.8929, 1.7857, 3.5714, 7.1429 and 14.286 mg/ml were respectively measured for the $^1H-NMR$ at $25^{\circ}C$, the chemical shifts of the aldehyde group were observed at 9.7206, 9.7201, 9.7196, 9.7192, 9.7185 and 9.7174 ppm. Even though the signal was slightly shifted to the high field in proportion to the increase of sample concentration, the alteration was not significant enough to applicate this method. The calibration curve for integration values of the doublet methine signal due to the aldehyde group vs the sample concentration was linear and showed very high regression rate ($r^2=1.0000$). Meantime, the $^1H-NMR$ spectra (7.1429 mg/ml $CDCl_3,\;25^{\circ}C$) of t-cinnamaldehyde and t-2-methoxycinnamaldehyde, another constituent of Cinnamomum cassia, showed the chemical shifts of the aldehyde group as ${\delta}_H$ 9.7174 (9.7078, 9.7270) for the former compound and ${\delta}_H$ 9.6936 (9.6839, 9.7032) for the latter one. The difference of the chemical shift between two compounds was big enough to be distinguished using the NMR spectrometer with 0.45 Hz of resolution. The contents of cinnamaldehyde in Cinnamomum cassia, which were respectively extracted with n-hexane, $CHCl_3$, and EtOAc, were determiend as 94.2 \;mg/g (0.94%), 137.6 mg/g (1.38%) and 140.1 mg/g(1.40%) t-cinnamaldehyde in each extract, respectively, by using the above method.

• Nuclear Engineering and Technology
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• v.23 no.4
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• pp.444-455
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• 1991

An accurate and fast running NEDAR model for calculating radial power profile throughout fuel life in both solid and annular pellets for existing and advanced CANDU-PHWR-fuel was developed in this work. This model contains resultant flux depression equations and neutron depression data tables which have been developed for CANDU-PHWR fuel of pellet with the diameter 8.0 to 19.5 mm and enrichment 0.71-6.0 wt % U-235, over a bumup range of 0 to 840 MWh /kgU (35000 MWD/T). In order to obtain the neutron flux distribution in the fuel pellet, the CE-HAMMER physics code was run for a neutron flux spectrum appropriate to a CANDU-PHWR to give predictions of radial power profile for several ranges of fuel design parameters. The results, which were calculated by the CE-HAMMER physics code, were fitted to an equation which is solved in terms of Bessel and exponential functions in order to obtain the parameters, $textsc{k}$, $\beta$ and λ in the resultant equation. The present NEDAR model produce a radial profile which, when normalized to unity at the pellet surface, is slightly higher than the profile of the original ELESIM data table. The predictions of the fission gas release by KAFEPA-NEDAR are in slightly better agreement with the experiments than those of ELESIM. The NEDAR model described in this study has been shown to provide an effective, reliable, and accurate method for determining radial power profiles in CANDU-PHWR fuel rods without incurring a significant increase in computing time.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.