31results about How to "Solve difficult to extract" patented technology

The invention discloses a P-wave extracting method based on orthogonal projection and independent component analysis. The P-wave extracting method is used for analyzing and processing electrocardiograph signals, and includes the steps that collected electrocardiograph signals are subjected to signal preprocessing; a QRS wave group template is obtained with the time domain average method; QRS wavegroup ingredients are filtered out with the space orthogonal projection method through the wave group template, and therefore the signal to noise ratio of P waves and T waves in residual ingredients is increased; the P waves and T waves are separated; P waves after separation serve as reference signals, and the P waves are extracted from original aliasing signals again with the semi-blind method.

The invention discloses a method for characterization of target fluctuation characteristics based on coherent cumulative gain, including the following steps: Step 1, the echo data received by the radar is denoted as X, where X is a J×K-dimensional complex matrix, and J and K are respectively The number of distance units and the number of pulses of the radar echo data; step 2, divide the radar echo data X into blocks according to the length N in the pulse dimension, and step 3, divide the corresponding distance of the target to be analyzed on the lth pulse block The unit is marked as D l , and the remaining steps. Compared with the existing method based on statistical distribution modeling, the method of the invention solves the problem that the fluctuation characteristics of the weak target are difficult to extract, and enables the fluctuation characteristics of the weak target to be characterized, so that the weak target can be statistically analyzed.

The invention discloses a registration method of infrared and visible light images of electrical equipment based on a SURF operator. A morphological edge detection method based on multi-directions and different weights is used to obtain edge images of infrared and visible light images of electrical equipment respectively. , respectively extract the feature points of the edge image through the SURF operator, and then according to the prior knowledge of the slope direction consistency between the last correct matching point pair, a new matching strategy is proposed to find the correct matching point pair, finding The parameters of the affine transformation model are obtained, and finally the spatial coordinate transformation of the image to be registered is carried out by the method of bilinear interpolation to obtain the final registration image. The method adopted in the invention can realize rapid and high-precision matching of images, and can be widely used in the fields of electrical equipment health state detection and fault diagnosis.

The invention discloses a day / night image classification and object detection method based on an attention network. The method first collects street camera surveillance video and processes it into an image for labeling, and combines an open-source street data set to jointly construct an image data set; Pyramid’s deep convolutional neural network extracts the apparent features of the image; predicts the day / night attributes of the image on the extracted features, and captures attention maps that characterize day / night objects; weights the extracted feature maps based on the attention maps; finally According to the predicted day / night attribute, the weighted feature map is input into the detection head corresponding to day / night for position regression and object classification. The invention aims to make the network pay attention to the different features of day / night through the attention mechanism, and complete the detection of day / night objects respectively through two branches, which can improve the performance of day / night object detection, and can be used for street intelligent monitoring systems.

The invention discloses an adaptive matching control system and method for ovary handle removal based on peanut automatic selection. The control system includes a main controller, an elevator motor and an elevator motor driver, a fan and a fan driver, and a vibrating screen motor. And the vibrating screen motor driver, the disc cutter group motor and the disc cutter group motor driver; by collecting the data collected by the first stress sensor, the second stress sensor and the dry humidity sensor, and based on the current state of each motor through the main controller Analyze and process the working status of the peanut pods to realize adaptive adjustment and matching of wind speed, shank cutting speed, and sieve vibration frequency, etc., ensuring the accuracy of ovary stalk cutting and the reliable separation of pods and sundries, effectively improving the quality of peanut pods and solving the problem. It solves the problem of difficult extraction and feedback of operating data such as fruit, wind force, and speed, and realizes the intelligentization and informationization of efficient and accurate cutting of handles and separation of pods and sundries.