A simple methodology for identification and quantification of nonlinear effects such as Coulomb friction and backlash is desired for use in condition based maintenance programs for both structural and machine based applications. Typically, structural applications are passive and undergo small vibratory motion when an external excitation is presented to the system. A spring-mass system was used as the structural example. Machine applications are typically active and motion is excited by internal actuation of large motion within the system. An industrial SCARA robot was used as the machine based example. The Hilbert transform was tested for detection and quantification of Coulomb friction in both systems.
We present here the development of a texture-like measure to aid the quantification of rock face stability using two familiar transforms in a novel combination. It is shown that the Fourier and Hough transforms together can yield accurate quantitative information relating to the texture of an image. With respect to rock faces, the textural quality of the image is a direct measure of the stability index, since the orientation, distribution, and number of fissures indicate its stability. Stability of rock faces for mining operations is currently estimated manually, prior to further excavation. Manual inspection is often undesirable due to the subjective nature of, and potential hazard to, the human inspector. This provides the motivation to develop an automated system which can survey the scene via some sensors and process the resulting data to compute a preliminary stability index before further detailed inspection and subsequent excavation.
Experimental measurement of position and attitude (pose) of a rigid target using machine vision is of particular importance to autonomous robotic manipulation. Traditionally, the monocular four-point pose problem has been used which encompasses three distinct subproblems: inverse perspective; calibration of internal camera parameters; and knowledge of the pose of the camera (external camera parameters). To this end, a new unified concept for monocular pose measurement using computational neural networks has been developed which obviates the need to estimate camera parameters and which provides rapid solution of inverse perspective with compensation for nonhomogeneous lens distortion. Input neurons are (x, y) image coordinates for target landmarks. Output neurons are (X, Y, Z, roll, pitch, yaw) target position and attitude relative to an external reference frame. Modified back-propagation has been used to train the neural network using both synthetic and experimental training sets for comparison to current four-point pose methods. Recommendations are provided for number of neural layers, number of neurons per layer, and richness versus breadth of pose training sets.
A new printed circuit board (PCB) inspection scheme designed to perform automated inspection of component sites is presented. A digitized image of the artwork for a board is stored after digitization via a scanner (300 dots per inch) in a tag image file format (TIFF) file. At inspection time, after reading in the compressed format TIFF file and decoding the compressed image, the PCB is graphically displayed on a PC screen together with a command menu. The user is then taken through a series of menu-driven commands to ultimately check the dimensions of center-to-center distance between holes and pads for electronic components on the PCB. The significance of this research is in the adaptation of the UNION-FIND procedure to develop a robust algorithm to segment an image into component objects and background to facilitate dimensional analysis and inspection of the artwork.
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