Virtek Vision Corporation

Other

Pattern Discovery Software Systems Ltd.

Virtek Vision Intelligence Robotics Technologies Corporation

Pattern Discovery Technologies Inc.

Andrew K Wong

Kurt D Rueb

Chung Lam Li

Mohamed Salem Kamel

John T King

Tom Tak Chau

Yang Wang

The present invention relates to control, monitoring, and automation. The present invention more specifically relates to pattern-based intelligent control, monitoring and automation. The invention performs pattern-based monitoring. It collects signal data from one or more signals. The signal data define signal data streams. It then transforms each of the signal data streams into trends. It also discovers patterns based on the trends within each signal data stream and/or across the signal data streams. The patterns are optionally used for diagnostics and root cause analysis, online plant monitoring and operation control, plant optimization, and other environments where a causal link or correlation may exist between related inputs, states and/or outputs.

System, method and computer program for pattern based intelligent control, monitoring and automation

In its broad aspect, the invention provides a method for analyzing relationships among patterns within a data set having a set of samples and associated attribute values defining each attribute of each said sample. The method comprises receiving at an input at least two patterns; defining a data cluster within the data set for each of said at least two patterns, each defined data cluster having samples with attribute values associated with a corresponding pattern of said at least two patterns; grouping at least some of the samples of each defined data cluster with one another to generate a resultant data cluster; and calculating a variation between the attribute values of a first set of samples and the attribute values of a second set of samples within said resultant data cluster, the attribute values of the first set of samples and the second set of samples corresponding to the same attribute.

System and method for detecting and analyzing pattern relationships

A 3D vision system utilizes a CCD camera to first generate a two dimensional image of the object. A computer analyzes the two dimensional image to determine areas of interest, such as high contrast areas which indicate sharp discontinuities. A laser range scanner, controlled by the computer, then scans the object at a variable resolution, increasing its resolution in the areas of interest to generate a computer model.

Image-directed active range finding system

Automatic discovery of qualitative and quantitative patterns inherent in data sets is accomplished by use of a unified framework which employs adjusted residual analysis in statistics to test the significance of the pattern candidates generated from data sets. This framework consists of a search engine for different order patterns, a mechanism to avoid exhaustive search by eliminating impossible pattern candidates, an attributed hypergraph (AHG) based knowledge representation language and an inference engine which measures the weight of evidence of each pattern for classification and prediction. If a pattern candidate passes the statistical significance test of adjusted residual, it is regarded as a pattern and represented by an attributed hyperedge in AHG. In the task of classification and/or prediction, the weights of evidence are calculated and compared to draw the conclusion.

Computational method for discovering patterns in data sets

A system combines a galvanometer system with a camera to provide information from a workpiece to the camera. The galvanometer rotates mirrors to change the image that is directed from the workpiece into the camera. The inventive method allows the use of the camera to identify the location of points on the workpiece. Further, the system facilitates the use of the camera to store information about a pattern on a workpiece. Also, the system can be utilized to create a computer aided design file of the surface of a workpiece. The system is utilized in the last two methods to serially store bit by bit information about the workpiece in a memory.

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Waterloo, Canada

Andrew K Wong