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1546 results about "A-weighting" patented technology

A-weighting is the most commonly used of a family of curves defined in the International standard IEC 61672:2003 and various national standards relating to the measurement of sound pressure level. A-weighting is applied to instrument-measured sound levels in an effort to account for the relative loudness perceived by the human ear, as the ear is less sensitive to low audio frequencies. It is employed by arithmetically adding a table of values, listed by octave or third-octave bands, to the measured sound pressure levels in dB. The resulting octave band measurements are usually added (logarithmic method) to provide a single A-weighted value describing the sound; the units are written as dB(A). Other weighting sets of values – B, C, D and now Z – are discussed below.

Assessment of corporate data assets

InactiveUS20100228786A1Improve corporate information technology managementDigital data processing detailsSpecial data processing applicationsData valueA-weighting
The present invention provides a data processing system and a method of assessing the data value of a data assets inventory which comprises:
    • a) preparing a data map on a computer database comprising inputting data types and data subtypes into said database, connecting a data storing location to the data subtypes and recording the data subtype occurrences in said database;
    • b) assigning a weighting to each data subtype occurrence in said database to provide a data assets inventory and recording the data assets inventory in said database;
    • c) preparing evaluation types on said database wherein the evaluation type has a calculation type attribute and wherein the evaluation type is either quantity independent or quantity dependent;
    • d) connecting at least one evaluation type to each data subtype with a reference value and recording the reference value in said database;
    • e) determining the data value of the data assets inventory and recording the data value in said database wherein when the evaluation type is quantity dependent then the value is the product of the weighting, the reference value and the quantity at the data storing location for each data subtype occurrence or wherein when the evaluation type is quantity independent then the value is the product of the weighting and the reference value for each data subtype occurrence.

Remote sensing image classification method based on multi-feature fusion

The invention discloses a remote sensing image classification method based on multi-feature fusion, which includes the following steps: A, respectively extracting visual word bag features, color histogram features and textural features of training set remote sensing images; B, respectively using the visual word bag features, the color histogram features and the textural features of the training remote sensing images to perform support vector machine training to obtain three different support vector machine classifiers; and C, respectively extracting visual word bag features, color histogram features and textural features of unknown test samples, using corresponding support vector machine classifiers obtained in the step B to perform category forecasting to obtain three groups of category forecasting results, and synthesizing the three groups of category forecasting results in a weighting synthesis method to obtain the final classification result. The remote sensing image classification method based on multi-feature fusion further adopts an improved word bag model to perform visual word bag feature extracting. Compared with the prior art, the remote sensing image classification method based on multi-feature fusion can obtain more accurate classification result.

Dynamic contrast enhancement device and method

The invention relates to a dynamic contrast enhancement device and a dynamic contrast enhancement method. The device comprises a color space switching module, a histogram statistical module, an enhanced mapping function module and a brightness transformation module, wherein the color space switching module is used for switching an input image data from a red, green and blue (RGB) color space to aluma and chroma (YUV) color space; the histogram statistical module is used for counting a gray histogram of an image according to a brightness component Y; the enhanced mapping function module is used for calculating weights of a plurality of brightness component intervals in the histogram and obtaining an adaptive mapping function f according to the designed mapping table of each brightness component interval and the weights; and the brightness transformation module is used for transforming the brightness Y of the image to a novel brightness Y' according to the adaptive mapping function f, and outputting the brightness in the RGB color space through the color space switching module. By the device, the image is divided into the plurality of brightness component intervals, the weights arecalculated respectively, and the adaptive mapping function is obtained by a weighting method to transform the image brightness; therefore, the processed image can keep a mean brightness, the contrastis effectively improved, and the problems of level reduction and details loss caused by the traditional histogram equalization are solved.

Vehicle flow predicting method based on integrated LSTM neural network

The invention relates to a vehicle flow predicting method based on an integrated LSTM neural network. On the basis of historical data obtained by vehicle flow detection, an integrated LSTM neural network vehicle flow prediction model is established to carry out vehicle flow prediction, so that the generalization error of the prediction model is reduced and the accuracy is improved. The method comprises the following steps that: data preprocessing is carried out; according to a preprocessed vehicle flow time sequence value, a vehicle flow matrix data set is constructed and the vehicle flow of an (n+1)th period of time is predicted by using first n periods of time, wherein each period of time is delta t expressing the time length and the unit is min; a plurality of different LSTM neural network models are constructed by using different initial weights; on the basis of a bagging integrated learning method, a training set and a verification set are constructed; a plurality of LSTM neural networks are trained to obtain an optimized module; a weighting coefficient of the single LSTM model is calculated by using the verification set; and inverse transformation and reverse normalization are carried out on a predicted vehicle flow value to obtain a predicted vehicle flow and integrated weighting is carried out to obtain a vehicle flow value predicted finally by the model.
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