The grinding quality assessing is a complex decision-making process, which must optimize and balance multi-influence factors. A grinding quality assessing method based on fuzzy synthetic evaluation theory combined with analytic hierarchy process (AHP) is presented in this paper, and used to quality assessing for grinding process. The result of analyzing example indicates that this method can be used to estimate grinding quality based on the monitoring result of grinding process and grinding condition, and to assist grinding worker to select optimum grinding parameters for the steady grinding quality.

A grinding trouble on-line monitoring mode is presented based on the nonlinear building mode principle of neural network. The input units were the peak of the FFT, the peak of RMS, and the standard deviation of AE signals. The outputs were the troubles of the grinding burning, grinding chatter, and grinding wheel dull. The structure of neural network is established by self-configuration method. The network mode is trained and tested by using the experiment data, and the results indicate that the neural network mode obtained by self-configuration method has high recognize rate for grinding troubles, and can be used to monitor grinding troubles on-line.

In recent years, with the development of optical communication by leaps and bounds, promote the Micro-opto-electro-mechanical system (MOEMS) development. As a new technology, the MOEMS have been widely used in optical communication, optical switching, data storage, optical sensing and etc.. Compared with the traditional pressure sensors, the optical pressure sensor based on MOEMS has some unique advantages. In this paper, the structures, operation principles and fabrication processes of various MOEMS pressure sensors are described mainly. Finally, the structure and Key technology of a MOEMS pressure sensor array is presented in brief.

Seismic exploration is a effective methods to ascertain underground structure and looking for oil. As the data collected in the wild are often subjected to the interference of noise, is not conducive to the analysis and interpretation of seismic data, therefore removing noise is the premise of data interpretation. To use the Fourier transform and wavelet analysis denoising a synthetic seismogram in this paper, by comparing denoising results, wavelet soft threshold denoising waveform is smooth, and it can better reconstruction original waveform.

Improved and extended level set framework with a novel iso-neigborhood concept. In the new framework, driving forces are determined by the iso-neighborhood rather than only by some exterior field outside the propagating fronts. This hybrid driving forces make the propagation of the active contour more robust. And furthermore the new framework will be very flexible to various kinds of images by defining different type of sampling algorithm in the iso-neighborhood.