41,320 research outputs found
Application of wavelet analysis in tool wear evaluation using image processing method
Tool wear plays a significant role for proper planning and control of machining parameters to maintain the product quality. However, existing tool wear monitoring methods using sensor signals still have limitations. Since the cutting tool operates directly on the work-piece during machining process, the machined surface provides valuable information about the cutting tool condition. Therefore, the objective of present study is to evaluate the tool wear based on the workpiece profile signature by using wavelet analysis. The effect of wavelet families, scale of wavelet and statistical features of the continuous wavelet coefficient on the tool wear is studied. The surface profile of workpiece was captured using a DSLR camera. Invariant moment method was applied to extract the surface profile up to sub-pixel accuracy. The extracted surface profile was analyzed by using continuous wavelet transform (CWT) written in MATLAB. The re-sults showed that average, RMS and peak to valley of CWT coefficients at all scale increased with tool wear. Peak to valley at higher scale is more sensitive to tool wear. Haar was found to be more effective and significant to correlate with tool wear with highest R2 which is 0.9301
On the Algorithmic Nature of the World
We propose a test based on the theory of algorithmic complexity and an
experimental evaluation of Levin's universal distribution to identify evidence
in support of or in contravention of the claim that the world is algorithmic in
nature. To this end we have undertaken a statistical comparison of the
frequency distributions of data from physical sources on the one
hand--repositories of information such as images, data stored in a hard drive,
computer programs and DNA sequences--and the frequency distributions generated
by purely algorithmic means on the other--by running abstract computing devices
such as Turing machines, cellular automata and Post Tag systems. Statistical
correlations were found and their significance measured.Comment: Book chapter in Gordana Dodig-Crnkovic and Mark Burgin (eds.)
Information and Computation by World Scientific, 2010.
(http://www.idt.mdh.se/ECAP-2005/INFOCOMPBOOK/). Paper website:
http://www.mathrix.org/experimentalAIT
Data compression for estimation of the physical parameters of stable and unstable linear systems
A two-stage method for the identification of physical system parameters from experimental data is presented. The first stage compresses the data as an empirical model which encapsulates the data content at frequencies of interest. The second stage then uses data extracted from the empirical model of the first stage within a nonlinear estimation scheme to estimate the unknown physical parameters. Furthermore, the paper proposes use of exponential data weighting in the identification of partially unknown, unstable systems so that they can be treated in the same framework as stable systems. Experimental data are used to demonstrate the efficacy of the proposed approach
On sample complexity for computational pattern recognition
In statistical setting of the pattern recognition problem the number of
examples required to approximate an unknown labelling function is linear in the
VC dimension of the target learning class. In this work we consider the
question whether such bounds exist if we restrict our attention to computable
pattern recognition methods, assuming that the unknown labelling function is
also computable. We find that in this case the number of examples required for
a computable method to approximate the labelling function not only is not
linear, but grows faster (in the VC dimension of the class) than any computable
function. No time or space constraints are put on the predictors or target
functions; the only resource we consider is the training examples.
The task of pattern recognition is considered in conjunction with another
learning problem -- data compression. An impossibility result for the task of
data compression allows us to estimate the sample complexity for pattern
recognition
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