User's manual for tooth contact analysis of face-milled spiral bevel gears with given machine-tool settings

Research was performed to develop a computer program that will: (1) simulate the meshing and bearing contact for face milled spiral beval gears with given machine tool settings; and (2) to obtain the output, some of the data is required for hydrodynamic analysis. It is assumed that the machine tool settings and the blank data will be taken from the Gleason summaries. The theoretical aspects of the program are based on 'Local Synthesis and Tooth Contact Analysis of Face Mill Milled Spiral Bevel Gears'. The difference between the computer programs developed herein and the other one is as follows: (1) the mean contact point of tooth surfaces for gears with given machine tool settings must be determined iteratively, while parameters (H and V) are changed (H represents displacement along the pinion axis, V represents the gear displacement that is perpendicular to the plane drawn through the axes of the pinion and the gear of their initial positions), this means that when V differs from zero, the axis of the pionion and the gear are crossed but not intersected; (2) in addition to the regular output data (transmission errors and bearing contact), the new computer program provides information about the contacting force for each contact point and the sliding and the so-called rolling velocity. The following topics are covered: (1) instructions for the users as to how to insert the input data; (2) explanations regarding the output data; (3) numerical example; and (4) listing of the program

Using Header Session Messages to Filter-out Junk E-mails

Due to the popularity of Internet, e-mail use is the major activity when surfing Internet. However, in recent years, spam has become a major problem that is bothering the use of the e-mail. Many anti-spam filtering techniques have been implemented so far, such as RIPPER rule learning algorithm, Naïve Bayesian classifier, Support Vector Machine, Centroid Based, Decision trees or Memory-base filter. Most existed anti-spamming techniques filter junk emails out according to e-mail subjects and body messages. Nevertheless, subjects and e-mail contents are not the only cues for spamming judgment. In this paper, we present a new idea of filtering junk e-mail by utilizing the header session messages. In message head session, besides sender\u27s mail address, receiver\u27s mail address and time etc, users are not interested in other information. This paper conducted two content analyses. The first content analysis adopted 10,024 Junk e-mails collected by Spam Archive (http://spamarchive.org) in a two-months period. The second content analysis adopted 3,482 emails contributed by three volunteers for a one week period. According to content analysis results, this result shows that at most 92.5% of junk e-mails would be filtered out using message-ID, mail user agent, sender and receiver addresses in the header session as cues. In addition, the idea this study proposed may induce zero over block errors rate. This characteristic of zero over block errors rate is an important advantage for the antispamming approach this study proposed. This proposed idea of using header session messages to filter-out junk e-mails may coexist with other anti-spamming approaches. Therefore, no conflict would be found between the proposed idea and existing anti-spamming approaches

l1-norm penalized orthogonal forward regression

A l1-norm penalized orthogonal forward regression (l1-POFR) algorithm is proposed based on the concept of leave-one-out mean square error (LOOMSE), by defining a new l1-norm penalized cost function in the constructed orthogonal space and associating each orthogonal basis with an individually tunable regularization parameter. Due to orthogonality, the LOOMSE can be analytically computed without actually splitting the data set, and moreover a closed form of the optimal regularization parameter is derived by greedily minimizing the LOOMSE incrementally. We also propose a simple formula for adaptively detecting and removing regressors to an inactive set so that the computational cost of the algorithm is significantly reduced. Examples are included to demonstrate the effectiveness of this new l1-POFR approach