31 results about "Eigenvalue assignment" patented technology

A method, computer program product, and computer system for identifying, by a computing device, a plurality of sources. One or more feature values of a plurality of features may be assigned to a first source of the plurality of sources. One or more feature values of the plurality of features may be assigned to a second source of the plurality of sources. A first score for the first source and a second score for the second source may be determined based upon, at least in part, the one or more feature values assigned to the first source and the second source. One of the first source and the second source may be selected for spatial processing based upon, at least in part, the first score for the first source and the second score for the second source.

The invention discloses a turbo generator rotor interturn short circuit fault diagnosis method based on multi sensor joint. According to the method, a variety of fault characteristic parameters acquired by different types of sensors arranged at a number of parts of a generator and respective thresholds are used to carry out calculation, so as to acquire fault characteristic values when diagnosis is carried out according to each fault characteristic parameter; according to the reliability of different diagnostic methods, weight assigning is carried out on each fault characteristic value; the weighted sum of a variety of fault characteristic values is acquired; and finally by comparing the weighted sum with a comprehensive threshold, whether the turbo generator has a rotor winding interturn short circuit fault is identified. According to the invention, comprehensive analysis is carried out on the diagnosis information acquired by different types of sensors to judge whether the turbo generator has the rotor winding interturn short circuit fault; the method overcomes the shortcomings of the traditional single diagnosis method; and the sensitivity and the reliability of the rotor winding interturn short circuit fault detection of the turbo generator are improved.

The invention provides a method and a device for sample distributed clustering calculation. The method comprises that characteristic values of all the samples to be clustered are acquired to constitute a characteristic value set; a calculation speed of each kind of usable calculation equipment is estimated; and before a similarity degree between any two characteristic values in the characteristic value set is less than a preset threshold value, the following steps are repeated, wherein all the characteristic values in the characteristic value set are distributed to at least one kind of calculation equipment according to the calculation speed of each usable calculation equipment, so that the at least one kind of calculation equipment can screen the distributed characteristic values on the premise that processing time satisfies a preset condition, and the similarity degree between any two characteristic values is less than the preset threshold value. The method and the device for the sample distributed clustering calculation provided by the invention can solve the problem that existing clustering algorithms can hardly deal with a large scale of data, can realize the distributed clustering calculation of the large scale of data which can hardly be handled by the prior art, and can effectively increase efficiency of the clustering calculation.

The invention provides a method for user identification. The method comprises the following steps of receiving a user login request, wherein the user login request comprises more than one item of characteristic data of a user; computing a characteristic value corresponding to each item of characteristic data according to a preset function corresponding to each item of characteristic data in more than one item of characteristic data; computing a threshold corresponding to each item of characteristic data according to pre-stored sample data, wherein the threshold is used for distinguishing a characteristic value of a normal account and a characteristic value of an abnormal account; distributing a coefficient to the characteristic value corresponding to each item of characteristic data according to the threshold and the characteristic value of each item of characteristic data; and computing a first risk value corresponding to the user according to the characteristic value corresponding to each item of characteristic data, the coefficient distributed by the characteristic value corresponding to each item of characteristic data and a preset weight value corresponding to each item of characteristic data. The invention also provides a device for user identification. According to the method and the device, the risk of the user can be effectively identified, and the accuracy rate is high.

The embodiment of the present invention discloses a task data processing method, device, readable storage medium and electronic device. By determining the current pressure attribute of the target object and the matching feature value of each task information corresponding to the target object, the target is determined according to the current pressure attribute. Diffusion threshold, and then assign each task information according to the target diffusion threshold and the matching feature value of each task information. The embodiment of the present invention can dynamically determine the target diffusion threshold for task classification according to the current pressure condition of the target object, so as to distribute the classified task information through different distribution methods, so as to ensure the task connection of the target object corresponding to the task information. The starting rate further improves the task processing efficiency of the system.

A large-scale image library retrieval method based on the optimal K-means hash algorithm, comprising: selecting some images from the image library to be retrieved as a training image set, first extracting the GIST features of the images in the training set; Preprocessing of feature value assignment; dividing the preprocessed feature data into multiple subspaces; training the codebook and codebook encoding of the subspace for each subspace; processing the feature data in the image library to be retrieved and the query image Corresponding to the training process, extract the GIST features of the retrieval image and the query image respectively, then calculate the hash codes of the features of the image to be retrieved and the query image, and then calculate the Hamming distance between the feature codes of the image to be retrieved and the feature code of the query image , so as to quickly retrieve similar images. The invention has good universality, not only reduces the storage space of data but also improves the retrieval efficiency of query.

The present application provides an image processing method and device, wherein the method includes: extracting X magnitude images of the image to be processed, and fusing multiple magnitude images in different directions on the same scale to obtain Y fused Amplitude image; encode the Y fused amplitude images, assign weights to each feature value generated after encoding according to the local characteristics of each amplitude image, and obtain the Y fused amplitude images corresponding to Y statistical histograms with weight information, cascading the Y statistical histograms to form a feature vector of the image to be processed. The technical solution provided by the application is applicable to image recognition in a complex environment, and realizes a more universal image recognition mechanism.

The invention discloses a real-time super-resolution method and system based on an FPGA, and relates to the technical field of image processing. The method comprises the steps of splitting each frameof image in a medium with a relatively low resolution into sub-images for performing super-resolution processing; allocating a processing module according to an eigenvalue of the current frame of thesub-image; if the eigenvalue is relatively high, performing calculation by a neural network module; and if the eigenvalue is relatively low, performing calculation by an interpolation module. An output buffer outputs the high-resolution sub-images obtained by calculation and recombines the sub-images into a high-resolution image, so that a user can see the real-time ultra-high-definition image.