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Author

Ivan Petrunin

Other affiliations: National Technical University
Bio: Ivan Petrunin is an academic researcher from Cranfield University. The author has contributed to research in topics: Computer science & GNSS applications. The author has an hindex of 7, co-authored 50 publications receiving 129 citations. Previous affiliations of Ivan Petrunin include National Technical University.


Papers
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Journal ArticleDOI
25 Aug 2020-Robotics
TL;DR: It was shown that DOP coefficients, when considered together with a number of visible satellites and cut-off elevations specific to the urban environment carry valuable integrity information that is difficult to get using existing integrity monitoring approaches.

28 citations

Journal ArticleDOI
20 Jan 2020-Sensors
TL;DR: A wavelet transform-based algorithm is proposed that is able to detect and characterize defects (depth, size, and shape in 3D plots) and uses a smart thresholding technique based on the extracted statistical mean and standard deviation of the structural noise.
Abstract: In this paper, we present challenges and achievements in development and use of a compact ultrasonic Phased Array (PA) module with signal processing and imaging technology for autonomous non-destructive evaluation of composite aerospace structures. We analyse two different sets of ultrasonic scan data, acquired from 5 MHz and 10 MHz PA transducers. Although higher frequency transducers promise higher axial (depth) resolution in PA imaging, we face several signal processing challenges to detect defects in composite specimens at 10 MHz. One of the challenges is the presence of multiple echoes at the boundary of the composite layers called structural noise. Here, we propose a wavelet transform-based algorithm that is able to detect and characterize defects (depth, size, and shape in 3D plots). This algorithm uses a smart thresholding technique based on the extracted statistical mean and standard deviation of the structural noise. Finally, we use the proposed algorithm to detect and characterize defects in a standard calibration specimen and validate the results by comparing to the designed depth information.

26 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a new chirp-Wigner higher order spectra (CWHOS) for transient signals with any known nonlinear polynomial variation of instantaneous frequency.

16 citations

Journal ArticleDOI
TL;DR: In this article, the authors compared two fiber optic sensing techniques for vibration characterisation: (a) optical fibre Bragg grating (FBG) strain gauges and (b) a novel direct fibre optic shape sensing (DFOSS) approach based on differential interferometric strain measurements between multiple fibres within the same fibre arrangement.

13 citations


Cited by
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Book ChapterDOI
E.R. Davies1
01 Jan 1990
TL;DR: This chapter introduces the subject of statistical pattern recognition (SPR) by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier.
Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

1,189 citations

Book ChapterDOI
15 Jun 2006

180 citations

Journal ArticleDOI
TL;DR: In this article, the authors describe and evaluate the development and application of an intelligent diagnostic technique based on the integration of the empirical mode decomposition (EMD), kernel independent component analysis (KICA), Wigner bispectrum and support vector machine (SVM).
Abstract: The normal operation of marine diesel engines ensures the scheduled completion and efficiency of a trip. Any failures may result in significant economic losses and severe accidents. It is therefore crucial to monitor the engine conditions in a reliable and timely manner in order to prevent the malfunctions of the plants. This work describes and evaluates the development and application of an intelligent diagnostic technique based on the integration of the empirical mode decomposition (EMD), kernel independent component analysis (KICA), Wigner bispectrum and support vector machine (SVM). It is an extension of the previous work on the fault detection for a diesel engine using the instantaneous angular speed (IAS). In this study, in order to solve the underdetermined blind source separation (BSS) problem the combination of EMD and KICA is firstly presented to estimate IAS signals from a single-channel IAS sensor. The KICA is also applied to select distinguished features extracted by Wigner bispectrum. The SVM is then employed for the multi-class recognition of the marine diesel engine faults in an intelligent way. Numerical simulations using a 6-cylinder engine model and real IAS data measured on the ship named “Hangjun 20” are used to evaluate the proposed method. Both the numerical and experimental diagnostic results have shown high efficiency of the proposed diagnostic method. Distinct fault features of the IAS signals have been extracted by the EMD-KICA and Wigner bispectrum, and the fault detection rate of the SVM is beyond 94.0%. Thus, the proposed method is feasible and available for the fault diagnosis of marine diesel engines.

89 citations

Journal ArticleDOI
Sean Wu1
TL;DR: Results demonstrate that this hybrid NAH combines the advantages of HELS and inverse BEM, because a majority of the input data are regenerated but not measured, thus the efficiency of reconstruction is greatly enhanced.
Abstract: Hybrid near-field acoustical holography (NAH) is developed for reconstructing acoustic radiation from an arbitrary object in a cost-effective manner. This hybrid NAH is derived from a modified Helmholtz equation least squares (HELS) formula that expands the acoustic pressure in terms of outgoing and incoming waves. The expansion coefficients are determined by solving an overdetermined linear system of equations obtained by matching the assumed-form solution to measured acoustic pressures through the least squares. Measurements are taken over a conformal surface around a source at close range so that the evanescent waves can be captured. Next, the modified HELS is utilized to regenerate as much acoustic pressures on the conformal surface as necessary and take them as input to the Helmholtz integral formulation implemented numerically by boundary element method (BEM). The acoustic pressures and normal velocities on the source surface are reconstructed by using a modified Tikhnov regularization (TR) with its regularization parameter determined by generalized cross validation (GCV) method. Results demonstrate that this hybrid NAH combines the advantages of HELS and inverse BEM. This is because a majority of the input data are regenerated but not measured, thus the efficiency of reconstruction is greatly enhanced. Meanwhile, the accuracy of reconstruction is ensured by the Helmholtz integral theory and modified TR together with GCV method, provided that HELS converges fast enough on the measurement surface. Numerical examples of reconstructing acoustic quantities on the surface of a simplified engine block are demonstrated. [Work supported by NSF.]

76 citations

Journal ArticleDOI
TL;DR: A combined Helmholtz equation-least squares (CHELS) method is developed for reconstructing acoustic radiation from an arbitrary object that allows for reconstruction of the acoustic field radiated from a arbitrary object with relatively few measurements, thus significantly enhancing the reconstruction efficiency.
Abstract: A combined Helmholtz equation-least squares (CHELS) method is developed for reconstructing acoustic radiation from an arbitrary object. This method combines the advantages of both the HELS method and the Helmholtz integral theory based near-field acoustic holography (NAH). As such it allows for reconstruction of the acoustic field radiated from an arbitrary object with relatively few measurements, thus significantly enhancing the reconstruction efficiency. The first step in the CHELS method is to establish the HELS formulations based on a finite number of acoustic pressure measurements taken on or beyond a hypothetical spherical surface that encloses the object under consideration. Next enough field acoustic pressures are generated using the HELS formulations and taken as the input to the Helmholtz integral formulations implemented through the boundary element method (BEM). The acoustic pressure and normal component of the velocity at the discretized nodes on the surface are then determined by solving two matrix equations using singular value decomposition (SVD) and regularization techniques. Also presented are in-depth analyses of the advantages and limitations of the CHELS method. Examples of reconstructing acoustic radiation from separable and nonseparable surfaces are demonstrated.

64 citations