Image detection is an important direction of fuze development nowadays, and laser imaging fuze is one of the main technologies. This paper carries out the research in simulation technology of the process with detection, scan and imaging, which is used in laser imaging fuze for tank target, and get the simulation images information of different intersection conditions, including tank spot information,distance information and power information. The target coordinate system is established with the movement characteristics,physical characteristics and existing coordinate system of tank target. And through transferring missile coordinates to the target coordinate system as well as the relative movement between the different time intervals, the model of missile-target in time and space is build up. The model is build up according to the tank target and diffusion properties of different background, including desert, soil, vegetation, and buildings. The relations of scattering power and bidirectional reflectance distribution function deduced the laser echo power calculation formula, which can calculate the echoes incidence to each surface of the laser.The design of laser imaging fuze simulation system is complicated ,which contains the technology of the process with detection, scan and imaging used in laser imaging fuze for tank target. The simulation system products the tank spot picture, the distance gradation picture, and the power gradation picture. The latter two contains two-dimensional information, the scanning distance as well as the value of echo power to meet the expected design effects.
In order to diagnose the compressors' fault on line, an intelligent checking method is presented in the paper. A vibration
sensor was put on the compressors that should be detected. The vibration signals obtained by the sensor contain a great
deal information, which reflects the compressors' qualities and their type of faults.
It has been proved that, the vibration signals obtained from compressors with different faults have different time domain
features and frequency domain features. We extract those features, and then get a feature vector which is sent to an
intelligent information processor.
In order to improve the generalization and robustness of the processor, we adopt a fuzzy clustering radial basis function
(RBF) neural networks as the information processor. A method of fuzzy C-means clustering based on minimized mean
square error rule is used to determine the RBF layer, and the shape factors of RBF neurons are determined by the grades
of membership.
The experimental results show that, fuzzy clustering RBF neural networks neural networks have powerful ability of
pattern recognition, and the faults diagnosis method is feasible to diagnose the fault of the revolving machinery.
An new enhancement method is proposed to the Stochastic Active Contour Scheme (STACS) for image segmentation
using Principle Component Analysis(PCA). STACS is a method developed for segmentation of cardiac Magnetic
Resonance Imaging(MRI) images and is based on the level set method in which the contour is driven by the
minimization of a function of four terms−region based, edge based, shape prior, and curvature. STACS derives each of
these forces from the original image that is to be segmented. In our method, PCA is performed on the entire set of eight
images of the same slice of the heart taken at different instants of time in the cardiac cycle and then segment each image
separately. The various terms in the energy functional in this new scheme are obtained from different principal
components(Eigenvectors). Thus, STACS is improved by emphasizing each term in the energy functional with the help
of the principal component that gives the most accurate result. Experimental results are presented with the proposed
scheme.
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