41results about How to "Comprehensive feature extraction" patented technology

The invention discloses a CNN-based SAR target recognition method, the implementation steps of which are as follows: 1. Perform multiple random translation transformations on the target to be recognized in each training image to obtain new samples, and mark these new samples with original The label of the image is expanded into the training sample; 2. Construct the CNN structure of the convolutional neural network in the caffe architecture; 3. Input the expanded training sample into the CNN for training to obtain the trained network model; 4. Test the sample Multiple times of translation and expansion to obtain the expanded test samples; 5. Input the expanded test samples into the trained CNN network model for testing to obtain its recognition rate. The invention has high recognition rate and stable performance for the target to be recognized at any position of the sample image, and solves the problem that the existing SAR target recognition method is greatly affected by the position of the target to be recognized in the sample image.

The invention discloses a database abnormality query detecting method based on target condition relevance rules. The method comprises a rule mining method and an abnormality detecting method, wherein the rule mining method comprises an initialization phase, a frequent-item generation phase and a target condition relevance rule generation phase; the abnormality detecting method adopts a rule bank obtained in the rule mining process to carry out abnormality detection; and just when the rule existing in the rule bank meets the rule support conditions, the operation is normal, otherwise, the operation is abnormal. For the invention, the rule mining efficiency, the detection efficiency and precision are high; the detection failure and the omission factor are low; and the universality and the expansibility are high.

The invention relates to a method for detecting a computer generated image and a natural image based on wavelet transformation, which belongs to the technical field of image detection. The method comprises the following steps: firstly, performing color channel transformation to an image to be detected, transforming a RGB color image into an HSV color image; secondly, transforming all channels of the image further to obtain a multi-dimensional eigenvector including statistic matrix characteristics and high-frequency filter characteristics; thirdly, extracting characteristics by utilizing characteristics of the computer generated image and the natural images; fourthly, rapidly judging the truth of the image by a support vector machine (SVM), and detecting whether the image is the computer generated image or the natural image. The invention adopts a technology of combining the statistic matrix characteristics and the high-frequency filter characteristics, and has the characteristics of high precision, complete characteristics extraction, complete detection and the like, thereby greatly improving the detection precision.

The invention discloses a fault diagnosis method of abnormal vibration, of a rotor, caused by a floating oil retainer. The fault diagnosis method of abnormal vibration, of a rotor, caused by a floating oil retainer includes the steps: importing shafting TSI data into a fault diagnosis system; setting a threshold, utilizing a spectrum analysis method, determining whether the property of shafting abnormity vibration belongs to common forced vibration, extracting typical characteristics of a fault, analyzing operating parameters of a set, and eliminating the influence of load, thermal deformation and field current; and with combination of shafting structure analysis structure, determining whether abnormal vibration of the rotor is caused by a floating oil retainer, and then obtaining a diagnosis result. The fault diagnosis method of abnormal vibration, of a rotor, caused by a floating oil retainer has the advantages of being comprehensive in the considered extracted characteristics, being suitable for field fault diagnosis, being quick and accurate in result diagnosis, and having practicality and operability.

The invention discloses a method for constructing a similarity matrix in the ultrasound image Ncut segmentation process. The method includes the following steps of firstly, preprocessing an ultrasound image; secondly, conducting over-segmentation on the ultrasound image through a simple linear iterative clustering method to generate even-medium sub-areas with irregular borders; thirdly, enabling average gray level information of all the sub-areas to serve as one characteristic for constructing the Ncut similarity matrix, and calculating the texture characteristics of all the sub-areas through a gray level co-occurrence matrix at the same time, wherein the texture characteristics totally include 24 characteristics with six characteristics in each of the four directions, and the 24 data are combined to form a texture characteristic vector, namely, another characteristic for constructing the Ncut similarity matrix; fourthly, calculating the distance between the characteristics of every two adjacent sub-areas, and combining the sub-areas according to a certain proportion to form a new Ncut similarity matrix calculation formula. When the method is used for segmenting the ultrasound image based on Ncut clustering, a good segmentation result is obtained, the problems that the ultrasound image is high in noise and low in contrast ratio are solved, and the tumor areas and the background areas can be effectively separated.