45 results about "Discriminant function analysis" patented technology

An input pattern feature amount is decomposed into element vectors. For each of the feature vectors, a discriminant matrix obtained by discriminant analysis is prepared in advance. Each of the feature vectors is projected into a discriminant space defined by the discriminant matrix and the dimensions are compressed. According to the feature vector obtained, projection is performed again by the discriminant matrix to calculate the feature vector, thereby suppressing reduction of the feature amount effective for the discrimination and performing effective feature extraction.

The invention discloses a method for discriminating reservoir fluid by establishing a gas logging chart on the basis of discriminant analysis, relating to the technical field of oil and gas exploitation and development. The method includes the steps of: a, collecting the gas logging peak value data and the oil test results in an oil test completion well; b, according to the categories of the oil test results, grouping the gas logging peak value data of the logging display sections which have the same category of oil test results; c, establishing discrimination factors by taking the gas logging peak value data of the logging display sections as a base; d, selecting the established discrimination factors, and formulating discrimination functions which can effectively discriminate the property of the fluid on the basis of discriminant analysis; and e, according to the coincidence rates of the formulated discrimination functions, preferentially selecting two discrimination functions with the highest coincidence rates to be used as x axis and y axis to establish a gas logging chart to discriminate the property of the reservoir fluid. The method disclosed by the invention solves the problem that conventional gas logging charts are difficult for effectively discriminating the reservoir fluid, so the dominant regions of different fluids can be well marked out.

Devices and methods of commodity usage monitoring determine the amount of commodity used by individual elements of multi-element systems having a common source. An amount of commodity drawn from the source by all the users is determined. A user-state that indicates use of the commodity by each user is determined. Individual user consumption of the commodity for each of the users is determined using repeated determinations of the amount of the commodity and the user-state, for a number of repetitions greater than or equal to the number of users. Determining individual consumption can be performed using minimum least square estimation, singular value decomposition, discriminant function analysis, or other computational or analytical solution method. Duration and consumption information can be used to calculate the total amount of the commodity consumed by the particular user. Useful commodities include, but are not limited to, gas, electricity, mechanical, pneumatic, hydraulic, and water.

The invention relates to a discriminant analysis-based high road real-time traffic accident risk forecasting method. The method comprises the following steps of: building a high road accident risk discrimination model for a detection area; substituting real-time traffic flow characteristic parameters into the high road accident risk discrimination model; and judging whether the risk of traffic accident exists or not. According to the method, traffic accidents can be forecasted in real time by using the real-time traffic flow characteristic parameters acquired by high road traffic detection equipment, the method has relatively high forecasting precision, and technical defects and shortages in the prior art for analyzing traffic safety by using aggregated statistics are overcome. The method has practical engineering application value in the aspects of discrimination of the risk of the high road traffic accidents and forecast of the traffic accidents.

A method and system, for detecting closely spaced objects with data from an imaging sensor. A CFAR detector performs detection on the image data at a threshold level. A processor runs a first algorithm on exceedances above the CFAR threshold. The first algorithm is for single target detection and includes a local-max algorithm that identifies multiple exceedances within a 2×2 matrix of image data and only keeps exceedance with the maximum value. All other exceedances in image are single target detections. The processor runs a second algorithm that identifies multiple exceedances (single target detections) within a local regional area across the whole image. The regional area size may be a 3×3, 4×4, or 5×5 matrix depending on the specified CSO separations. This algorithm may use a regional moving window across the whole image. An alternative method using the indices of all the exceedances' (x,y) coordinates is also disclosed.