Most optical fiber sensors based on Brillouin/Rayleigh scattering, and discrete fiber Bragg gratings (FBGs) cannot offer the speed and distributed sensing with operation in a cryogenic environment < 80 K necessary ...
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Most optical fiber sensors based on Brillouin/Rayleigh scattering, and discrete fiber Bragg gratings (FBGs) cannot offer the speed and distributed sensing with operation in a cryogenic environment < 80 K necessary for superconducting magnet protection. An ultra-long fiber Bragg grating (ULFBG) comprised of quasi-continuous FBGs has the potential to rapidly detect the occurrence of a thermal hot spot in high-temperature super-conducting magnets. However, processing the data for detection purpose is challenging. This is because the spectrum of an ULFBG is complex, and the hotspot induced spectral changes are difficult to identify. In this paper, we present the novel Spectral Intensity Change (SIC) algorithm to 1) extract the region of interest within the spectrum based on unique change patterns, 2) identify the direction of temperature change, and 3) process the isolated spectral data to signal hotspot events. To demonstrate the effectiveness of the algorithm, three ULFBGs with sensing lengths of 0.2, 0.5 and 1.9 m are used to detect events of temperature variations. Using the algorithm, ULFBG sensors not only can identify a hot spot event rapidly and accurately but also achieve a significantly enhanced signal to noise ratio (SNR) at 80 K. The SIC algorithm has the potential to be implemented for real-time hotspot and quench detection in superconducting magnets.
algorithms for radar imaging of the underlying surface from aerospace carriers have been synthesized. In doing so, we have considered two possible ways of image reception based on methods of active and passive remote ...
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ISBN:
(纸本)9781665432993
algorithms for radar imaging of the underlying surface from aerospace carriers have been synthesized. In doing so, we have considered two possible ways of image reception based on methods of active and passive remote sensing. On this basis, a radar complex, which allows forming images proportional to either the specific effective surface scattering or the effective noise temperature of the underlying surface, has been created. The structural scheme of the complex in accordance with both operating modes is developed. The results of radio image simulation are given.
We aim at contributing to the reliability of the phase scintillation index on Global Navigation Satellite System (GNSS) signals at high-latitude. To the scope, we leverage on a recently introduced detrending scheme ba...
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We aim at contributing to the reliability of the phase scintillation index on Global Navigation Satellite System (GNSS) signals at high-latitude. To the scope, we leverage on a recently introduced detrending scheme based on the signal decomposition provided by the fast iterative filtering (FIF) technique. This detrending scheme has been demonstrated to enable a fine-tuning of the cutoff frequency for phase detrending used in the phase scintillation index definition. In a single case study based on Galileo data taken by a GNSS ionospheric scintillation monitor receiver (ISMR) in Concordia Station (Antarctica), we investigate how to step ahead of the cutoff frequency optimization. We show how the FIF-based detrending allows deriving adaptive cutoff frequencies, whose value changes minute-by-minute. They are found to range between 0.4 and 1.2 Hz. This allows better accounting for diffractive effects in phase scintillation index calculation and provides a GNSS-based estimation of the relative velocity between satellite and ionospheric irregularities.
This study describes a synchrophasor estimation method and its hardware implementation applicable at the distribution level of the power system, under steady-state or dynamic conditions. The method proposed is based o...
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This study describes a synchrophasor estimation method and its hardware implementation applicable at the distribution level of the power system, under steady-state or dynamic conditions. The method proposed is based on the discrete Fourier transform and uses the Savitzky-Golay filter in the frequency estimation process. The main goal of this study is to present the signal processing algorithm for phasor estimation and some important issues regarding its implementation in real-time systems. The performance of the micro-phasor measurement unit (mu-PMU) is validated using a waveform generator and real-time power system simulator.
This paper presents a new stochastic adaptive estimation-identification technique for nonstationary systems. The proposed method enhances the initial results from an on average estimation, and its identification, thro...
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This paper presents a new stochastic adaptive estimation-identification technique for nonstationary systems. The proposed method enhances the initial results from an on average estimation, and its identification, through a generalized adaptable function based on the Exponential Forgetting Factor (EFF), and the Sliding Mode (SM) regarding the error identification. In this form, the presented process includes the function implementation in three stages -estimation, adaptive estimation, and adaptive estimation-identification, allowing us to observe the gradual convergence to a nonstationary reference signal. Simulations first introduce convergence level checks obtained from the estimation and identification of artificial signals. After that, the algorithm is applied for real references, considering the Electroencephalogram (EEG) signals taken from a public database, finding their internal nonstationary gains, indirectly. Finally, the results include a performance comparison between the proposed strategy concerning the Recursive Least Square (RLS), the Least Mean Square (LMS), and the Kalman Filter (KF).
Fast nuclide identification with higher accuracy rate is a significant requirement within nuclear applications. So effective spectrum identification algorithms are developed for low resolution spectra of NaI(TI) scint...
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Fast nuclide identification with higher accuracy rate is a significant requirement within nuclear applications. So effective spectrum identification algorithms are developed for low resolution spectra of NaI(TI) scintillator and Si (Li) detectors. Identification of X-ray and gamma complex spectra is the main objective of current research. So the experimental gamma and X-ray data are trained and tested using artificial neural network (ANN), support vector machine (SVM) and similarity classifiers. The features of acquired X-ray and gamma spectra are extracted using eight algorithms. These algorithms depend on fusion of time-domain descriptors (FTDD), electromyography (EMG), multiscale wavelet packet (MWP), multiscale wavelet packet with statistics (MWPS), principle component analysis (PCA), multi-dimensional scale, the preserved linear projection (LPP) and diffusion map. Robustness of these algorithms is investigated in terms of noise degradations such as Gaussian, Rician, Rayleigh and other complex degradations. Classification accuracy is investigated with the source name. The recognized spectrum is analyzed from view of peak width calibration, efficiency calibration, sum peak analysis, peak-to-Compton ratio (PCR). The rapid identification process is conducted with the algorithm based on electromyography method for both gamma and X-ray spectra. However, the algorithm based on diffusion map realizes the slowest spectrum identification. It is concluded that, for gamma and X-ray spectra, the SVM classifier achieves the fastest identification with maximum rate of 99%. Finally, the ANN is observed to achieve better rate of 100% with slower identification process depending on FTDD, EMG, LPP methods. The proposed approach helps the realization of fastest spectrum identification and classification of gamma and X-ray spectra within nuclear applications with higher robustness and accuracy.
The paper describes electronic vibration monitoring module (EVM) that can be used as standalone diagnostic system or as a part of a complex modular concept of engine electronic control (EEC/FADEC) with aircraft health...
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ISBN:
(纸本)9788026108924
The paper describes electronic vibration monitoring module (EVM) that can be used as standalone diagnostic system or as a part of a complex modular concept of engine electronic control (EEC/FADEC) with aircraft health monitoring system. We present a new generation of EVM for modern small turboprop engines and their vibration diagnostics which has been developed by Unis, a.s. in cooperation with Brno University of Technology and Tomas Bata University in Zlin.
One of the major concerns of pipe equipment used for oil, gas and nuclear power generation, is their failure due to corrosion and abrasion of their inner walls. However, current methods have some problems detecting th...
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One of the major concerns of pipe equipment used for oil, gas and nuclear power generation, is their failure due to corrosion and abrasion of their inner walls. However, current methods have some problems detecting the inner wall of long and deep pipes, which has become an urgent safety problem. In this paper, we have designed and tested a suitable scanning and imaging method that offers high quality imaging, high efficiency and low cost. We applied the technique on remanufactured ceramic-lined steel composite pipe parts using a 4-module visualization nondestructive testing device. Digital signal and imaging algorithms were employed to superimpose multi-interface images to reconstruct 3D models of tested pipes with defects. Our findings suggest that ultrasonic scanning technique is feasible to analyze inner wall damages. The newly-designed hardware and software system shows efficiency and effectivity in the testing experiments on specimens with artificial markers and defects.
Recent studies in oncology have addressed the importance of detecting circulating tumor cell clusters because circulating tumor cell clusters might survive and metastasize more easily than single circulating tumor ***...
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Recent studies in oncology have addressed the importance of detecting circulating tumor cell clusters because circulating tumor cell clusters might survive and metastasize more easily than single circulating tumor *** with larger peak widths detected by in vivo flow cytometer(IVFC)have been used to identify cell clusters in previous ***,the accuracy of this criterion might be greatly degraded by variance in blood°ow and the rolling behaviors of circulating tumor ***,we propose a criterion and algorithm to distinguish cell clusters from single *** this work,we first used area-based and volume-based models for single°uorescent *** each model,we analyzed the corresponding morphology of IVFC signals from cell *** to the Rayleigh criterion,the valley between two adjacent peak signals from two distinguishable cells should be lower than 73.5%of the peak values.A novel signal processing algorithm for IVFC was developed based on this *** results showed that cell clusters can be reliably identied using our proposed *** imaging was also performed to further support our *** enhanced accuracy,IVFC is a powerful tool to study circulating cell clusters.
In order to help visually impaired people to avoid obstacles, a K-band millimetre wave (MMW) radar system based on the principle of frequency-modulated continuous wave (FMCW) is proposed in this study. The FMCW basic ...
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In order to help visually impaired people to avoid obstacles, a K-band millimetre wave (MMW) radar system based on the principle of frequency-modulated continuous wave (FMCW) is proposed in this study. The FMCW basic principle and the signal processing algorithm are elaborated in detail. The prototype of the radar system has been designed and manufactured. The experiment results show the qualified detection accuracy in view of low deviation. The maximum deviations from the range measurement experiments for a person and a car are 0.14 and 0.47m, respectively. As a wearable system, the radar system has the characteristics of low power and small size, which is very suitable for blind navigation application. In addition, the MMW radar has broad application prospects in the field of automotive blind spot detection and small robots.
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