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Detection algorithm

Simulations about Eddy Current Distributions and Crack Detection Algorithms for a SQUID Based NDE System. [Pg.255]

In this paper we present simulations and measurements of several types of excitation coils, which match the special requirements for a SQUID based eddy current NDE system. We note however that all calculations presented here on penetration depths, current distributions and crack-detecting algorithms are also useful for conventional eddy current testing systems. [Pg.255]

Two different types of calibration marks are used in our experiments, planar circles and circular balls. The accuracy of the calibration procedure depends on the accuracy of the feature detection algorithms used to detect the calibration marks in the images. To take this in account, a special feature detection procedure based on accurate ellipses fitting has been developed. Detected calibration marks are rejected, if the feature detection procedure indicates a low reliability. [Pg.488]

Two optimal edge detection algorithms are described here. [Pg.526]

Figure 13.3 Potential pharmacophore points can be generated with MOE s site detection algorithm. The white and red dots are the antomatically generated site points, and the ligand structure comes from the X-ray structure of the complex. See color plate. Figure 13.3 Potential pharmacophore points can be generated with MOE s site detection algorithm. The white and red dots are the antomatically generated site points, and the ligand structure comes from the X-ray structure of the complex. See color plate.
A 2D soft-sphere approach was first applied to gas-fluidized beds by Tsuji et al. (1993), where the linear spring-dashpot model—similar to the one presented by Cundall and Strack (1979) was employed. Xu and Yu (1997) independently developed a 2D model of a gas-fluidized bed. However in their simulations, a collision detection algorithm that is normally found in hard-sphere simulations was used to determine the first instant of contact precisely. Based on the model developed by Tsuji et al. (1993), Iwadate and Horio (1998) incorporated van der Waals forces to simulate fluidization of cohesive particles. Kafui et al. (2002) developed a DPM based on the theory of contact mechanics, thereby enabling the collision of the particles to be directly specified in terms of material properties such as friction, elasticity, elasto-plasticity, and auto-adhesion. [Pg.87]

Cherkassky, B. and Goldberg, A. (1996) Negative-cycle detection algorithms. Eur Symp Algorithm, 349-363. [Pg.90]

In a subsequent development, a novel, real-time peak detection algorithm known as intelligent automated LC/MS/MS (INTAMS) was developed for the analysis of samples generated by in vitro systems.27 INTAMS also requires two separate chromatographic runs for each sample. It allows the user to detect the two most abundant ions of all components and also any predetermined metabolite precursor ions. In the second chromatographic run, INTAMS conducts automatic product ion scanning of molecular ions of metabolites detected in the first full scan analysis. [Pg.145]

Richard, J.T., Dillard, G.M. Adaptive Detection Algorithms for Multiple Target Situations, IEEE Transactions on AES, 4, pp. 338-343, 1977. [Pg.322]

The behavior of the detection algorithm is illustrated by adding a bias to some of the measurements. Curves A, B, C, and D of Fig. 3 illustrate the absolute values of the innovation sequences, showing the simulated error at different times and for different measurements. These errors can be easily recognized in curve E when the chi-square test is applied to the whole innovation vector (n = 4 and a = 0.01). Finally, curves F,G,H, and I display the ratio between the critical value of the test statistic, r, and the chi-value that arises from the source when the variance of the ith innovation (suspected to be at fault) has been substantially increased. This ratio, which is approximately equal to 1 under no-fault conditions, rises sharply when the discarded innovation is the one at fault. [Pg.166]

When dealing with noise, one should consider use of a peak detection algorithm optimized for CE. This algorithm, which is readily available on some CE systems, will allow a much lower signal-to-noise ratio and an improvement of reproducibility by a factor 1.5—5, and thereby, of the LOD. [Pg.324]

Filters are sometimes derived from test cargo calibration runs to optimize the explosive detection. The PFNA detection algorithm can be totally automated but previous demonstrations (prior to the 2005 Ysleta Port of Entry testing) have used operator intervention to evaluate the basic scan alarm regions before a directed scan is made of the selected basic scan alarm regions. [Pg.80]

Schmidtke P, Souaille C, Estierme F et al (2010) Large-scale comparison of four binding site detection algorithms. J Chem Inf Model 50 2191-2200... [Pg.163]

Data analysis. We use a combination of two movement detection algorithms (23) written in Madab environment (The MathWorks, Inc.) to accurately track movement of the heart edges see Note 10). Measurements of diastolic and systolic diameters as well as diastolic and systolic intervals, % fractional shortening (% PS), arrhythmicity index, and contraction direction and velocity are obtained as output from this analysis program. Heart rate is calculated as the inverse of the heart period where one period corresponds to a single diastolic interval plus subsequent systolic interval. % FS is quantified based on diameter measurements and is calculated as ((diastolic diameter-systolic diameter)/diastolic diameter) x 100 (%). Heart beat rhythmicity is quantified based on the standard deviation of the mean heart period normalized to the median heart period for each fly providing a dimensionless arrhythmicity index. [Pg.243]

Reliability Large numbers of redundant sensors allow signal averaging to improve accuracy and the use of fault detection algorithms to detect the failure of individual array elements.36 For example, the variance of a measurement based on the average of N identical sensors is... [Pg.221]

Graphs of query and test molecules can be compared by graph matching (subgraph detection) algorithms or systematic comparison of inter-feature distances. Two molecules are considered similar if their pharmacophores match for at least one predicted conformation. In order to explore conformational space and generate conformational ensembles, multiple compound conformations are typically generated by systematic conformational search (in increments) around rotatable bonds. [Pg.20]

Since its first introduction in 1982, the DOCK software has been extended in several directions. The matching spheres can be labeled with chemical properties,61 and distance bins are used to speed up the search process.62,63 Recently, the search algorithm for distance-compatible matches was changed28 to the clique-detection algorithm introduced by Crippen and co-workers.55 Furthermore, several scoring functions are now applied in combination with the DOCK algorithm.64-68... [Pg.7]


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Algorithm change detection

Clique detection algorithms

Component detection algorithm

Peak detection algorithm

Peak detection integration algorithm

Termination detection algorithm

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