- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO) - Kobe (2018.5.28-2018.5.31)] 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) - DEMON Spectrum Extraction Method Using Empirical Mode Decomposition
摘要： The noise radiated by a ship is modulated at a rate dictated by some parameters of the propeller and engine (number of blades, rotational speed). Evaluation of that modulation provides information on the ship, such as the shaft rotation frequency, that can be used for ship classification. The method for estimation of the envelope modulation is known as DEMON (Detection of Envelope Modulation on Noise). Traditionally, the ship noise is bandpass filtered in different frequency bands before the envelope analysis. The bandwidth and the number of the bandpass filters is not known. In this paper a new DEMON spectrum extraction method is proposed using empirical mode decomposition (EMD), in which the band number and width are automatically determined. In performance test, a feedforward neural network is used for 5 kinds ship noise classification, and the percentage of correct classification reaches 91.6%.
关键词： DEMON,empirical mode decomposition,Detection of envelope modulation on noise,feedforward neural network,EMD
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO) - Hangzhou (2018.8.13-2018.8.17)] 2018 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO) - Optimization of Phase Noise in Digital Holographic Microscopy
摘要： A phase noise optimizing method was proposed in this paper to improve the imaging quality of digital microscopic holography (DHM). In this method, bidimensional empirical mode decomposition (BEMD) was utilized to decompose the digital hologram. According to the principle of decomposition, the characteristics of first order intrinsic mode function (IMF1) are close to the gray value of ideal interference fringes. Therefore, after using BEMD method the digital hologram was optimized with enhanced interference fringes, from which phase image with reduced noise can be retrieved. The validity of the proposed method on noise reduction was verified by experimental results on a nano-step.
关键词： digital holography,phase,bidimensional empirical mode decomposition,noise
更新于2025-09-23 15:22:29
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[IEEE 2019 28th Wireless and Optical Communications Conference (WOCC) - Beijing, China (2019.5.9-2019.5.10)] 2019 28th Wireless and Optical Communications Conference (WOCC) - Fast Bi-dimensional Empirical Mode based Multisource Image Fusion Decomposition
摘要： Bi-dimensional empirical mode decomposition can decompose the source image into several Bi-dimensional Intrinsic Mode Functions. image decomposition, interpolation is needed and the upper and lower envelopes will be drawn. However, these interpolations and the drawings of upper and lower envelopes require a lot of computation time and manual screening. This paper proposes a simple but effective method that can maintain the characteristics of the original BEMD method, and the Hermite interpolation reconstruction method is used to replace the surface interpolation, and the variable neighborhood window method is used to replace the fixed neighborhood window method. We call it fast bi-dimensional empirical mode decomposition of the variable neighborhood window method based on research characteristics, and we finally complete the image fusion. The empirical analysis shows that this method can overcome the shortcomings that the source image features and details information of BIMF component decomposed from the original BEMD method are not rich enough, and reduce the calculation time, and the fusion quality is better.
关键词： fast bi-dimensional empirical mode decomposition,image fusion,Hermite interpolation,variable neighborhood window method
更新于2025-09-23 15:21:01
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Dynamic and quasi-static signal separation method for bridges under moving loads based on long-gauge FBG strain monitoring
摘要： Structural health monitoring is an important means of obtaining the state information of bridges, and the extracted quasi-static strain signal can reflect the stress state of bridges directly. However, the strain signals acquired during the operation stage of bridges are dynamic, and the strain gauges used in the health monitoring system are short (no more than 10 cm), which means they are easily affected by small damage at the installation parts of bridges and thereby the monitoring signal abnormalities occur. A type of externally affixed long-gauge fiber strain gauge is used to monitor the health of bridges, and the dynamic and quasi-static signal separation method for long-gauge strain sensors is studied under different vehicle loads; at the same time, the dynamic monitoring performance of the long-gauge sensor is investigated in this paper. The quasi-static strain signal extracted from the dynamic macro-strain signal can be used to directly monitor the stress status of the bridge. The results show that the method proposed in this paper is feasible for extracting the quasi-static macro-strain from a dynamic long-gauge strain signal.
关键词： signal processing,bridge engineering,empirical mode decomposition method,macro-strain,Bridge health monitoring
更新于2025-09-19 17:15:36
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Eliminating Phase Drift for Distributed Optical Fiber Acoustic Sensing System with Empirical Mode Decomposition
摘要： Phase-drift elimination is crucial to vibration recovery in the coherent detection phase-sensitive optical time domain re?ectometry system. The phase drift drives the whole phase signal ?uctuation as a baseline, and its negative e?ect is obvious when the detection time is long. In this paper, empirical mode decomposition (EMD) is presented to extract and eliminate the phase drift adaptively. It decomposes the signal by utilizing the characteristic time scale of the data, and the baseline is eventually obtained. It is validated by theory and experiment that the phase drift deteriorates seriously when the length of the vibration region increases. In an experiment, the phase drift was eliminated under the conditions of di?erent vibration frequencies of 1 Hz, 5 Hz, and 10 Hz. The phase drift was also eliminated with di?erent vibration intensities. Furthermore, the linear relationship between phase and vibration intensity is demonstrated with a correlation coe?cient of 99.99%. The vibrations at 0.5 Hz and 0.3 Hz were detected with signal-to-noise ratios (SNRs) of 55.58 dB and 64.44 dB. With this method, when the vibration frequency is at the level of Hz or sub-Hz, the phase drift can be eliminated. This contributes to the detection and recovery of low-frequency perturbation events in practical applications.
关键词： phase drift elimination,phase recovery,phase-sensitive optical time domain re?ectometry,distributed acoustic sensing system,empirical mode decomposition
更新于2025-09-12 10:27:22
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EMD- PNN based welding defects detection using laser-induced plasma electrical signals
摘要： The plasma electrical signal has gained extensive attention for characterizing the behavior of the laser-induced plasma due to the advantages of easy acquisition and feedback control. In this paper, the electrical signals were measured by a passive probe based on the principle of plasma sheath effect. To explore the mutation characteristics of plasma electrical signals during defect generation in laser deep penetration welding, wavelet packet transform (WPT) and empirical mode decomposition (EMD) were used to compress data and extract features, respectively. Based on the analysis of the time-frequency spectrum of a typical plasma electrical signal, the approximate coefficients of 0?390 Hz frequency range were reconstructed. The residual term which characterizes the change trend of electrical signal was obtained by the further adaptive decomposition. For better identifying weld defects, another two statistical features, mean value and standard deviation, were extracted by carrying out statistical analysis in the time domain. The feature database is built with above features and used as inputs of the predictive model based on the probabilistic neural network (PNN). The result showed the average prediction accuracy was as high as 90.16% when recognizing five statuses of weld seam, including sound weld and four kinds of weld defects.
关键词： Wavelet packet transformation,Empirical mode decomposition,Laser welding,Plasma electrical signal,Probabilistic neural network
更新于2025-09-12 10:27:22
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Photovoltaic active power control based on BESS smoothing
摘要： The power fluctuation of photovoltaic (PV) is harmful to power systems, so the battery energy storage system (BESS) was applied to smooth power fluctuation in PV. At present, the main ways to get configuration of BESS are low-pass filter and spectrum compensation, which have some drawbacks. In this paper, a method that combines empirical mode decomposition (EMD) with wavelet analysis (WA) is proposed to get grid-connected active power expectation of PV properly. Based on simulation of PV output, the minimum sizing of BESS is determined by different batteries’ state-of-charging (SOC) and efficiency. Comparing traditional low-pass filter and spectrum compensation, this method not only acquires the capacity of BESS accurately, but also improves the effect to smoothing power fluctuation of PV effectively. Finally, a case is proposed to verify correctness of the theory.
关键词： Sizing energy storage capacity,Empirical mode decomposition (EMD),Wavelet analysis,Photovoltaic output
更新于2025-09-12 10:27:22
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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI, USA (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Robust Estimation of Pulse Rate from a Wrist-type PPG During Intensive Exercise
摘要： Estimation of pulse rate from a wrist-type PPG during motion is a notoriously difficult problem because of the presence of motion artifact (MA) which corrupts the signal in both the time and frequency domains. In this paper, we propose a new method for deriving pulse rate under intense exercise conditions which employs Ensemble Empirical Mode Decomposition and power spectral analysis to extract the pulsatile component of the signal. The method was validated on an openly available database containing PPG and ground-truth ECG-derived pulse rate measurements from 12 subjects during a running experiment. Our proposed technique showed a high estimation accuracy with a mean absolute error of 2.14 bpm over the entire database and a correlation coefficient between the estimates and the ground truth of 0.98. Our approach matched the performance of the state-of-the-art TROIKA framework without utilizing simultaneously recorded accelerometry data to remove the MA component. With over 97.5% of estimates within a 10% margin from the ground truth, our technique shows a lot of potential for inclusion in next generation wrist-worn wearable monitors in both sports and clinical settings.
关键词： Pulse rate,PPG,Ensemble Empirical Mode Decomposition,power spectral analysis,motion artifact
更新于2025-09-10 09:29:36
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[ASME ASME 2018 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems - San Francisco, California, USA (Monday 27 August 2018)] ASME 2018 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems - Assessment of Damage Progression in Automotive Electronics Assemblies Subjected to Temperature and Vibration
摘要： Electronics in automotive underhood environments is used for a number of safety critical functions. Reliable continued operation of electronic safety systems without catastrophic failure is important for safe operation of the vehicle. There is need for prognostication methods, which can be integrated, with on-board sensors for assessment of accrued damage and impending failure. In this paper, leadfree electronic assemblies consisting of daisy-chained parts have been subjected to high temperature vibration at 5g and 155°C. Spectrogram has been used to identify the emergence of new low frequency components with damage progression in electronic assemblies. Principal component analysis has been used to reduce the dimensionality of large data-sets and identify patterns without the loss of features that signify damage progression and impending failure. Variance of the principal components of the instantaneous frequency has been shown to exhibit an initial damage progression, increasing trend during the attaining a maximum value and decreasing prior to failure. The unique behavior of the instantaneous frequency over the period of vibration can be used as a health-monitoring feature for identifying the impending failures in automotive electronics. Further, damage progression has been studied using Empirical Mode Decomposition (EMD) technique in order to decompose the signals into Independent Mode Functions (IMF). The IMF’s were investigated based on their kurtosis values and a reconstructed strain signal was formulated with all IMF’s greater than a kurtosis value of three. PCA analysis on the reconstructed strain signal gave better patterns that can be used for prognostication of the life of the components.
关键词： high temperature vibration,prognostication,Empirical Mode Decomposition,spectrogram,kurtosis,automotive electronics,principal component analysis
更新于2025-09-09 09:28:46
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A research on fiber-optic vibration pattern recognition based on time-frequency characteristics
摘要： To detect and recognize any type of events over the perimeter security system, this article proposes a fiber-optic vibration pattern recognition method based on the combination of time-domain features and time-frequency domain features. The performance parameters (event recognition, event location, and event classification) are very important and describe the validity of this article. The pattern recognition method is precisely based on the empirical mode decomposition of time-frequency entropy and center-of-gravity frequency. It implements the function of identifying and classifying the event (intrusions or non-intrusion) over the perimeter to secure. To achieve this method, the first-level prejudgment is performed according to the time-domain features of the vibration signal, and the second-level prediction is carried out through time-frequency analysis. The time-frequency distribution of the signal is obtained by empirical mode decomposition and Hilbert transform and then the time-frequency entropy and center-of-gravity frequency are used to form the time-frequency domain features, that is, combined with the time-domain features to form feature vectors. Multiple types of probabilistic neural networks are identified to determine whether there are intrusions and the intrusion types. The experimental results demonstrate that the proposed method is effective and reliable in identifying and classifying the type of event.
关键词： time-frequency domain features,time-frequency analysis,empirical mode decomposition,time-frequency entropy,Event recognition,pattern recognition,center-of-gravity frequency,time-domain features
更新于2025-09-04 15:30:14