Singularity Detection Research Articles

ICONE19-43468 Detection of Graphite Radiation Damage in HTR Based on Wavelet Transform

According to wavelet singularity detection principle and using its multi-scale analysis function that can manifest the local characteristics of signals, the change of graphite X ray diffraction graph in HTR is analyzed and the correspondence between wavelet transform modelmaximum and original signals singularity is to find out. Then the macroscopic damage location of the irradiated graphite can be accurately detected, which is caused by the micro-scale crystal lattice damage of the irradiated graphite. And it can improve the accuracy of graphite radiation damage detection. This study demonstrated that wavelet transform shows outstanding advantages on diagnostic performance of graphite radiation damage. Wavelet transform as a tool provides a new way for the analysis of the signal with a local singularity, and further provides a viable security method for the safety of HTR reactor.

Application of Hermitian wavelet to crack fault detection in gearbox

The continuous wavelet transform enables one to look at the evolution in the time scale joint representation plane. This advantage makes it very suitable for the detection of singularity generated by localized defects in the mechanical system. However, most of the applications of the continuous wavelet transform have widely focused on the use of Morlet wavelet transform. The complex Hermitian wavelet is constructed based on the first and the second derivatives of the Gaussian function to detect signal singularities. The Fourier spectrum of Hermitian wavelet is real; therefore, Hermitian wavelet does not affect the phase of a signal in the complex domain. This gives a desirable ability to extract the singularity characteristic of a signal precisely. In this study, Hermitian wavelet is used to diagnose the gear localized crack fault. The simulative and experimental results show that Hermitian wavelet can extract the transients from strong noise signals and can effectively diagnose the localized gear fault.

Fault Diagnosis of Emulsion Pump Valve

The abruption of the valve spring will deteriorate the opening and closing hysteresis characteristic and reduce the volumetric efficiency of the emulsion pump further. With the singularity detection and the improved algorithm of the Lipschitz exponents based on least median of squares, the singularities in signals could be localized to find the impact hour between the discharge valve disc and the valve seat or the lift limiter. The diagnosis results show that the discharge valve spring abruption fault of the emulsion pump can be identified correctly and effectively.

DETEKSI PEMALSUAN CITRA BERBASIS DEKOMPOSISI NILAI SINGULIR

Normal 0 false false false IN X-NONE X-NONE MicrosoftInternetExplorer4 Image Fakery Detection Based on Singular Value Decomposition . The growing of image processing technology nowadays make it easier for user to modify and fake the images. Image fakery is a process to manipulate part or whole areas of image either in it content or context with the help of digital image processing techniques. Image fakery is barely unrecognizable because the fake image is looking so natural. Yet by using the numerical computation technique it is able to detect the evidence of fake image. This research is successfully applied the singular value decomposition method to detect image fakery. The image preprocessing algorithm prior to the detection process yields two vectors orthogonal to the singular value vector which are important to detect fake image. The result of experiment to images in several conditions successfully detects the fake images with threshold value 0.2. Singular value decomposition-based detection of image fakery can be used to investigate fake image modified from original image accurately. Keywords: image fakery, orthogonalized vector, singular value decomposition

Residual orientation modeling for fingerprint enhancement and singular point detection

This paper presents a novel method for fingerprint orientation modeling, which executes in two phases. Firstly, the orientation field is reconstructed using a lower order Legendre polynomial to capture the global orientation pattern in the fingerprint structure. Then the preliminary model around the region with presence of fingerprint singularities is dynamically refined using a higher order Legendre polynomial. The singular region is automatically detected through the analysis on the orientation residual field between the original orientation field and the orientation model. The method does not require any prior knowledge on the fingerprint structure. To validate the performance, the method has been applied to fingerprint image enhancement, fingerprint singularity detection and fingerprint recognition using the FVC 2004 data sets. Compared with the recently published Legendre polynomial model, the proposed method attains higher accuracy in fingerprint singularity detection, lower error rates in fingerprint matching.

Potential of Transient Tests to Diagnose Real Supply Pipe Systems: What Can Be Done with a Single Extemporary Test

In this paper, the reliability of a transient test-based technique as a powerful tool in the management of real supply pipe systems is shown. The pressure signal acquired during the first phase of the single completed test has been analyzed on the basis of extensive research activity performed in the previous years in the field of transient test-based techniques and refined on the basis of numerical and laboratory tests. Specifically, the wavelet transform, which allows the automatic detection of singularities in noisy pressure signals, and a Lagrangian model, which evaluates the causes of discontinuities, are efficiently coupled for analyzing the pressure signal acquired in the described pipe system. As a result, the topology of such a system has been checked, and its functioning conditions have been determined. Moreover, the unwanted status of an in-line valve—certified by the manager as fully open but actually partially closed—has been pointed out.

Pixel-level singular point detection from multi-scale Gaussian filtered orientation field

Singular point, as a global feature, plays an important role in fingerprint recognition. Inconsistent detection of singular points apparently gives an affect to fingerprint alignment, classification, and verification accuracy. This paper proposes a novel approach to pixel-level singular point detection from the orientation field obtained by multi-scale Gaussian filters. Initially, a robust pixel-level orientation field is estimated by a multi-scale averaging framework. Then, candidate singular points in pixel-level are extracted from the complex angular gradient plane derived directly from the pixel-level orientation field. The candidate singular points are finally validated via a cascade framework comprised of nested Poincare indices and local feature-based classification. Experimental results over the FVC 2000 DB2 confirm that the proposed method achieves robust and accurate orientation field estimation and consistent pixel-level singular point detection. The experimental results exhibit a low computational cost with better performance. Thus, the proposed method can be employed in real-time fingerprint recognition.

Singular points detection using fingerprint orientation field reliability

Singular Points detection is the most challenging and important process in biometrics fingerprint verification and identification systems. Singular points are used for fingerprint classification, fingerprint matching and fingerprint alignment. This paper overcomes problems of the previous methods of miss-deducting or deducting spurious singular points. We propose a novel algorithm for singular point detection based on the fingerprint orientation field reliability. The algorithm starts by enhancing the fingerprint image using the short time Fourier Transform analysis (STFT), followed by calculating the orientation field reliability and locating the singular points. Experimental results have proven that the proposed algorithm locates singular points in a fingerprint image with high accuracy and can even locate the secondary core and delta if they exist. Key words: Biometrics, security, fingerprint image, singular points, reliability.

Singular points detection method in tented arch fingerprints

Singular points detection method in tented arch fingerprints

Convective cloud identification algorithm based on weather radar image

Both the convective and stratiform regions take an important role in the precipitation. Correct recognition of them facilitates precise prediction of the rainfall amount and duration. An automatic algorithm for the partitioning of radar reflectivity into convective and stratiform rain classifications named WLS was developed. Theory of abrupt-change detection based on wavelet analysis was adopted in this algorithm. First,wavelet transform was carried out on preprocessed raw reflectivity data and echo top data. Second,the singularity detection of modulus maximum value was done and noise points were filtered too. Finally,the edge was detected and convective region was filled by using mathematical morphology. Experiment uses the representative squall line on 2150 UTC 25 August 2008 and mixed precipitation 2222 UTC 8 August 2008 at Hohhot. Compared with WLS algorithm,BL algorithm and SHY95 algorithm,the experimental results show that WLS algorithm is more effective,and it can exactly partition the regions of convective clouds and restrain noise points.

Machine vision system for singularity detection in monitoring the long process

We discuss a machine vision system (MVS) for singularity detection when monitoring the long process. The main application of this MVS is the automation of laboratory analysis for determining the number of gel particles in a polymer solution. Components and specifications of the system intended to solve the problem are proposed. As part of the MVS development process, we come forward with novel methods for binary image thresholding and analysis, which prove to be efficient for low contrast images and at fairly high crosstalk levels. The software to realize the aforesaid methods has been developed, allowing the number of gel particles in the polymer solution to be automatically determined. The use of the MVS instead of a human observer enables the psychovisual load on the laboratory staff to be essentially reduced, the accuracy and reliability of determining the number of gel particles in the polymer solution to be enhanced, as well as providing the automatic recording of the laboratory analysis data.

First-order Field-Induced Magnetization Processes and Magnetostriction in Tb2Co12Fe5

We report the results of magnetization and magnetostriction measurements performed by pulsed-magnetic-field techniques on the intermetallic compound Tb2Co12Fe5. The temperature dependence of the critical field at which the first-order magnetization process takes place was measured by a singular point detection technique on aligned powders. Magnetostriction measurements were performed by a strain-gauge-based technique at different temperatures. The obtained results are compared with observations recently made for the closely related compound Pr2Co12Fe5, allowing to clarify the crucial role of both the rare-earth and the transition-metal sublattices in the peculiar behaviour of the latter.

Singular Candidate Method: Improvement of Extended Relational Graph Method for Reliable Detection of Fingerprint Singularity

The singular points of fingerprints, viz. core and delta, are important referential points for the classification of fingerprints. Several conventional approaches such as the Poincare index method have been proposed; however, these approaches are not reliable with poor-quality fingerprints. This paper proposes a new core and delta detection employing singular candidate analysis and an extended relational graph. Singular candidate analysis allows the use both the local and global features of ridge direction patterns and realizes high tolerance to local image noise; this involves the extraction of locations where there is high probability of the existence of a singular point. Experimental results using the fingerprint image databases FVC2000 and FVC2002, which include several poor-quality images, show that the success rate of the proposed approach is 10% higher than that of the Poincare index method for singularity detection, although the average computation time is 15%-30% greater.

Büchse der Pandora: Krankheitserreger in Ixodes ricinus-Zecken in Mitteleuropa

Among the various species of hard ticks, Ixodes ricinus is the most frequently found tick throughout Europe. As with other ixodid ticks, the developmental cycle runs through three stages. In each stage a blood meal is required in order to develop to the next stage. Ixodes ricinus has been found to feed on more than 300 different vertebrate species. Usually, larval ticks feed on small mammals such as mice and become infected with various microorganisms and viruses, of which some are substantial pathogens to humans. The pathogens remain in the tick during molting and are thus transstadially transmitted to the next developmental stage. Pathogens transmitted to humans are the agents of Lyme borreliosis, the tick-borne encephalitis virus, Rickettsia species, Anaplasma phagocytophilum, occasionally Francisella tularensis, and protozoal Babesia species. Within the scope of an EU project Ixodes ricinus ticks from all federal states of Austria were searched by means of PCR methods for bacterial pathogens such as Anaplasma phagocytophilum, Borrelia burgdorferi sensu lato, Coxiella burnetii, Ehrlichia spp., Francisella tularensis, Rickettsia spp., and protozoal Babesia. Additionally, the prevalence of Bartonella spp. in this tick species was also determined. Besides the singular detection of Coxiella burnetii and Francisella tularensis in one tick collection site the overall prevalence of Anaplasma phagocytophilum, borreliae, rickettsae and babesiae in Ixodes ricinus amounted to 15%, 14%, 6% and surprising 36% and 51%, respectively. Bartonellae were detected in about 7%.

Automatic Monitoring System for Singularity Detection in Dikes By DTS Data Measurement

The development of automated monitoring systems for the detection of singularities, such as leakages in dikes, is indispensable to avoid mass disaster. An efficient solution for dike survey is the use of distributed temperature sensors (DTSs) based on optical fiber, offering a multitude of advantages such as low cost, extreme robustness, long-range measurement, etc. However, the temperature data acquired with DTSs, being not directly interpretable, require intervention of signal processing techniques. This paper addresses this signal processing aspect, exploiting the key idea that the temperature variations over the course of a day for singular zones are quite different from those for nonsingular zones. A daily reference temperature variation, which is representative of the nonsingular zones, is estimated using singular value decomposition (SVD). The residue subspace of SVD contains information linked to the deviations from this reference, thus allowing the degree of singularity to be quantified by a dissimilarity measure such as the L2-norm. To detect only the singularities in dikes, such as leakages or drains, a constant false alarm rate (CFAR) detector is proposed by modeling each daily dissimilarity measure with a mixture of Gamma and uniform distributions. The proposed automatic singularity detection system was validated under different scenarios on real data over periods from 2005 to 2007. The first scenario depicted the detection of percolation-type artificial leakages with their detection strength depending on their flow rates. Another scenario allowed detecting the presence of a real water leakage at the site, which was previously unobserved during manual inspections. The repeatability of the system was also verified by periodic analysis.

Nondetection, False Alarm, and Calibration Insensitivity in Kurtosis- and Pseudofractal-Based Singularity Detection

This work isolates cases of nondetection, false alarm, and insensitivity for a general class of problems dealing with the detection and characterization of existence, location, and extent of singularities embedded in signals or in their derivatives when employing kurtosis- and pseudofractal-based methods for the detection and characterization process. The nondetection, false alarm, and insensitivity for these methods are illustrated on an example problem of damage identification and calibration in beams where the singularity to be identified lies in the derivative of the measured signal. The findings are general, not constrained to linear systems, and are potentially applicable to a wide range of fields including engineering system identification, fault detection, health monitoring of mechanical and civil structures, sensor failure, aerospace engineering, and biomedical engineering.

Study on the characteristic points of boiling curve by using wavelet analysis and genetic neural network

Local singularity of a signal includes a lot of important information. Wavelet transform can overcome the shortages of Fourier analysis, i.e., the weak localization in the local time- and frequency-domains. It has the capacity to detect the characteristic points of boiling curves. Based on the wavelet analysis theory of signal singularity detection, Critical Heat Flux (CHF) and Minimum Film Boiling Starting Point ( q min) of boiling curves can be detected by using the wavelet modulus maxima detection. Moreover, a genetic neural network (GNN) model for predicting CHF is set up in this paper. The database used in the analysis is from the 1960s, including 2365 data points which cover a range of pressure ( P), from 100 to 1000 kPa, mass flow rate ( G) from 40 to 500 kg m −2 s −1, inlet sub-cooling (Δ T sub) from 0 to 35 K, wall superheat (Δ T sat) from 10 to 500 K and heat flux ( Q) from 20 to 8000 kW m −2. GNN mode has some advantages of its global optimal searching, quick convergence speed and solving non-linear problem. The methods of establishing the model and training of GNN are discussed particularly. The characteristic point predictions of boiling curve are investigated in detail by GNN. The results predicted by GNN have a good agreement with experimental data. At last, the main parametric trends of the CHF are analyzed by applying GNN. Simulation and analysis results show that the network model can effectively predict CHF.

Jump Detection in a Regression Curve and Its Derivative

Curve estimation from observed noisy data has broad applications. In certain applications, the underlying regression curve may have singularities, including jumps and roofs/valleys (i.e., jumps in the first-order derivative of the regression curve), at some unknown positions, representing structural changes of the related process. Detection of such singularities is important for understanding the structural changes. In the literature, a number of jump detection procedures have been proposed, most of which are based on estimation of the (one-sided) first-order derivatives of the true regression curve. In this paper, motivated by certain related research in image processing, we propose an alternative jump detection procedure. Besides the first-order derivatives, we suggest using helpful information about jumps in the second-order derivatives as well. Theoretical justifications and numerical studies show that this jump detector works well in applications. This procedure is then extended for detecting roofs/valleys of the regression curve. Using detected jumps/roofs/valleys, a curve estimation procedure is also proposed, which can preserve possible jumps/roofs/valleys when removing noise. Note that the printed paper only provides outlines of proofs of several theorems in the paper. Complete proofs of the theorems are available online as supplemental material.

A Statistical Measure for Wavelet Based Singularity Detection

This paper presents a statistical measure for the identification of the presence, the location, and the calibration of the strength of singularity in a signal or in any of its derivatives in the presence of measurement noise without the requirement of a baseline using a wavelet based detection technique. For this proposed wavelet based detection of singularities present in a signal, the problem of false alarm and its significant reduction by use of multiple measurements is presented. The importance of the proposed measure on baseline and nonbaseline damage calibration has been discussed from the aspect of structural health monitoring. The findings in this paper can also be used for cross-checking of background noise level in an observed signal. The detection of the existence, location, and extent of an open crack from the first fundamental modeshape of a simply supported beam is presented as an example problem.

Approximating piecewise-smooth functions

We consider the possibility of using locally supported quasi-interpolation operators for the approximation of univariate nonsmooth functions. In such a case, one usually expects the rate of approximation to be lower than that of smooth functions. It is shown in this paper that prior knowledge of the type of 'singularity' of the function can be used to regain the full approximation power of the quasi-interpolation method. The singularity types may include jumps in the derivatives at unknown locations or even singularities of the form (x — s) α , with unknown s and α. The new approximation strategy includes singularity detection and high-order evaluation of the singularity parameters, such as the above s and α. Using the acquired singularity structure, a correction of the primary quasi-interpolation approximation is computed, yielding the final high-order approximation. The procedure is local, and the method is also applicable to a nonuniform data-point distribution. The paper includes some examples illustrating the high performance of the suggested method, supported by an analysis proving the approximation rates in some of the interesting cases.