Proc. SPIE. 7130, Fourth International Symposium on Precision Mechanical Measurements
KEYWORDS: Fluctuations and noise, Digital filtering, Surface roughness, Fourier transforms, Linear filtering, Gaussian filters, Precision measurement, Lutetium, Communication engineering, Current controlled current source
A new approach for decreasing the amplitude characteristic deviation of Guassian filter in surface roughness measurements is presented in this paper. According to Central Limit Theorem, many different Guassian approximation filters could be constructed. By using first-order Butterworth filter and moving average filter to approximate Guassian filter, their directions of amplitude deviation are opposite, and their locations of extreme value are close. So the linear combination of them could reduce the amplitude deviation greatly. The maximum amplitude deviation is only about 0.11% through paralleling them. The algorithm of this new method is simple and its efficiency is high.
Sequential pattern mining is the mining of frequent sequences related to time or other orders from the sequence
database. Its initial motivation is to discover the laws of customer purchasing in a time section by finding the frequent
sequences. In recent years, sequential pattern mining has become an important direction of data mining, and its
application field has not been confined to the business database and has extended to new data sources such as Web and
advanced science fields such as DNA analysis.
The data of sequential pattern mining has characteristics as follows: mass data amount and distributed storage.
Most existing sequential pattern mining algorithms haven't considered the above-mentioned characteristics synthetically.
According to the traits mentioned above and combining the parallel theory, this paper puts forward a new distributed
parallel algorithm SPP(Sequential Pattern Parallel). The algorithm abides by the principal of pattern reduction and
utilizes the divide-and-conquer strategy for parallelization. The first parallel task is to construct frequent item sets
applying frequent concept and search space partition theory and the second task is to structure frequent sequences using
the depth-first search method at each processor. The algorithm only needs to access the database twice and doesn't
generate the candidated sequences, which abates the access time and improves the mining efficiency.
Based on the random data generation procedure and different information structure designed, this paper simulated
the SPP algorithm in a concrete parallel environment and implemented the AprioriAll algorithm. The experiments
demonstrate that compared with AprioriAll, the SPP algorithm had excellent speedup factor and efficiency.
This paper observed the chip forming and effusing process when high speed hard turning hardened steel using PCBN
tools under two-dimensional longitudinal turning and transverse turning by high-speed photography, and obtained the
chip formation and efflux states with different cutting edge preparation and parameters. The experiment results showed
that the sharp-edged tool was useful for chip forming, but strength of its edge is low and the tool life is short, and that the
tool has longer life under the chamfered edge, but too small or too large cutting thickness goes against the chip forming