65 results about "Scale weight" patented technology

A deep learning model based on a multi-scale network and application in brain state monitoring are provided. A model establishing method comprises steps of: preprocessing and multi-scale transforming a measured multichannel signal; obtaining a multi-scale weighted recursive network and a cross-recursive rate matrix corresponding to the multi-scale weighted recursive network of the multichannel signal in all scales; extracting the network indexes of the multi-scale weighted recursive network at different scales; at each scale, retaining relatively large elements in the cross recursive rate matrix and obtaining an unweighted adjacent matrix and a multi-scale unweighted recursive network corresponding thereto; for each value of a variable in the set range, obtaining the multi-scale unweighted recursive network and the adjacent matrix corresponding to the multi-scale unweighted recursive network, extracting the network indexes of the multi-scale unweighted recursive network at different scales, calculating the integral of the network indexes when the variable is changed in the set range, and the integral as the final network index of the multi-scale unweighted recursive network under each scale; and training the deep learning model and monitoring a brain state.

The invention discloses a CT image liver segmentation method and system based on multi-scale weighting similarity measure and capable of accurately segmenting a liver area. The method comprises steps of: S101, reading a training image set and a to-be-segmented image; S102, preprocessing the read image data; S103, extracting superpixels from an area around an initial bound and a liver bound in the to-be-segmented image; S103, by using the central point of each superpixel in the to-be-segmented image as a center, selecting all pixels within a certain neighborhood as test blocks and selecting multi-scale image blocks with the same positions and sizes from training images as training blocks to obtain a training block set; S105, computing the similarity measure between the test blocks and the training block set to obtain the prior probability that each superpixel around the liver bound in the to-be-segmented image belongs to the liver; and S106, modifying a randomly moving graph model weight value in combination with a prior model and the to-be-segmented image so as to segment the liver in the to-be-segmented image.

The invention discloses an improved two-dimensional maximum entropy division night vision image fusion target detection algorithm which comprises the steps that a two-dimensional histogram is improved, that is the two-dimensional histogram is established according to the maximum gray scale of a gray scale-weighted area, a weight is selected, the maximum entropy is calculated by using the histogram, and infrared and low light images are divided. Compared with the traditional maximum entropy division algorithm, an effect in the aspect of target detection is obvious, and background suppression and target extraction functions are provided; and then a phase of the multi-dimensional characteristic and the validation of operation are verified, and the divided infrared and low light images are subjected to characteristic level fusion target detection. The detection algorithm has a better effect and better applicability in the aspects of target detection under a complicated background and multi-target detection.

The invention discloses a binocular visual multi-line projection structured light calibration method. The method comprises the following steps that 1) the sequence numbers of light stripes are determined; 2) left and right cameras collect a lattice image, four corners of a lattice are clicked, initial estimated positions of other intersection points in the lattice are obtained, and sub-pixel coordinates of intersection points are obtained by using an Ostu threshold method and a gray scale weighted average method in a k*k neighborhood of the initial estimated positions of intersection points; 3) 3D coordinates of the intersection points are obtained; and 4) the steps 1-3 are repeated, the 3D coordinates of the intersection points obtained in multiple times are classified according to the sequence numbers of the light stripes, for all points belonging to the same structured light plane, a least square method is used to carry out planar fitting on the 3D coordinates of the intersection points belongs to the same structured light to obtain a plane equation, and calibration is not completed until all the structured light planes are in the planar equation of the system coordinate system. The method has the advantages that a calibration board has no requirements for planarization, manufacture is simple, calibration is flexible, no auxiliary equipment is needed, the method is suitable for onsite calibration and the versatility is high.

The invention belongs to the image enhancement technology and relates to the improvement of the gamma correction method in the infrared image enhancement method. The steps of the enhancement method are as follows: Assuming that all pixel data of the kth frame image is read, first count the gray histogram of the frame image, and calculate the gray weighted average value (av) of all pixels according to the histogram, and then according to the image in the image The image grayscale sampling range [a, b] is combined with the grayscale weighted average to comprehensively determine the gamma correction curve, and finally use this curve to perform gamma correction enhancement on the image of the kth frame. The dynamic calculation of the gray scale sampling range in the method of the invention utilizes the information of the image scene and the gray scale distribution to the greatest extent. The dynamic correction of the correction curve makes the method not only suitable for image enhancement of specific target scenes, but also suitable for real-time adaptive image enhancement of different scenes.

A method of driving a PDP, in which discharge cells are defined in regions where a plurality of electrodes cross one another and a unit frame displaying an image is divided into a plurality of subfields, each subfield including a reset period for initializing all discharge cells, an address period for selecting discharge cells to be turned on, and a sustain period for performing a sustain discharge in the selected discharge cells according to a gray scale weight allocated to each subfield, including applying a falling pulse to first electrodes among the plurality of electrodes during the reset period, and changing a lowest level of the falling pulse according to a temperature of the PDP. An apparatus configured to drive a PDP, which includes zener diodes to implement the method, and a plasma display device driven using the method and apparatus.

The invention discloses a burst point detection system and method based on an FPGA. The system includes a signal control and data processing module, a decoding circuit, a data caching circuit, an Ethernet circuit, and a peripheral circuit. The decoding circuit obtains original image data and caches the original image data to the large-capacity data caching circuit, an improved three-frame difference method in the signal control and data processing module is used to perform burst point target detection to obtain a burst point area, then a gray-scale weighted algorithm is used to obtain center pixel coordinates of a burst point after the connected components labeling, and finally the detected burst point image and the center pixel coordinates of the burst point are sent to a PC via the Ethernet circuit. The system and method of the invention implement the pipeline design of the algorithm steps, obtain high-speed operation results, and can realize the real-time detection of the results ofthe burst point on an image with a resolution of 1024*1024; can accurately obtain the burst point area and can quickly and accurately label the center point coordinates of the burst point; and can still maintain a high detection rate under the premises of light change and background movement.

The invention relates to a method for classifying electroencephalogram (EEG) signals based on a multi-scale brain function network. The method comprises the steps that data are acquired, specifically,the EEG signals in a resting state are collected and preprocessed; multi-scale time series are calculated, specifically, multi-scale processing is conducted on preprocessed time series of all leads,and thus the generalized multi-scale coarse-grained time series are obtained; the multi-scale brain function network is built, specifically, the amplitude correlation degree and the phase correlationdegree of all the leads of the EEG signals are taken as the quantitative standards to calculate multi-scale weighted brain function networks; a multi-scale convolutional neural network capable of learning the multi-scale brain function network is built; and neural network training is conducted, specifically, the cross-correlation degree of decision errors generated by all convolutional neural network paths is taken as a penalty term and a penalty loss function to accelerate decision-making of the neural network and reduce the generalization error.

The invention relates to a multi-scale segmentation-based saliency detection method. The method includes the following steps that: 1: smoothing image processing is performed on an input image through using bilateral filtering parameters, super-pixel segmentation of different segmentation scales is performed on the processed input image, global smoothness is calculated according to super-pixels obtained through segmentation, the global smoothness and the bilateral filtering parameters are combined to build an adaptive algorithm function adopting a segmentation effect as an objective, bilateral filtering parameters under different scales are solved, and super-pixel points in the optimal smoothed image are obtained; sep 2, initial foreground seeds are obtained through using a target likelihood graph technique, the boundary of the image is adopted as initial background seeds, background seeds and foreground seeds are selected from the initial background seeds and the initial foreground seeds by using a cross-validation method, and a background-based RBB saliency map and a foreground-based RFB saliency map are generated; and step 3, the scale weights of the super-pixels and the seed weights of the background seeds and the foreground seeds are calculated, and the obtained RBB saliency map and RFB saliency map are combined, so that a final saliency map can be obtained.

A plasma display panel (PDP) driving apparatus and a method for displaying pictures on the PDP increases the ability to express low gray scales without increasing the number of sub-fields. Input picture signal data are delayed by one frame and the number of high-order bits, from most significant bits not commonly used by all picture signal data within one frame, is detected. When the number of unused bits is detected, inverse gamma-corrected decimal point bits are shifted to an actual data position by the number of unused bits so as to increase power of expression of low gray scale. The decimal point bits are converted into sub-fields having low gray scale weights, in consideration of the shifting to the actual data position, for display on the PDP.

A beam scale for measurement of body weight and height according to the present invention includes a frame and a weighing mechanism. The frame includes a base assembly, a pillar assembly, and a horn assembly. The weighing mechanism is housed within the frame and includes a beam, weights, and a lever assembly. The horn assembly may include a transparent holding bracket having a level indicator. The scale may include a height measurement assembly having a height rod, a sheath supported by the horn assembly and the pillar assembly, a measuring arm, and a transparent height-measuring window having a line indicator. The frame may include a quick assembly mechanism to secure the pillar assembly to the base assembly during final assembly, quiet wheels for transport, and/or handle bars to support the user. Much of the frame may be made of pigmented thermoplastic or thermoset materials to reduce manufacturing costs and scale weight.

An adaptive weighting method for layout optimization differentiates different priorities by assigning the weight of a higher priority (p_i) to be multiple of the weight of a lower priority (p_i−1) where W(p_i)=m_i % W(p_i−1. To avoid numerical imprecision, this method keeps the total cost in the objective function within a trustable range by scaling the initial weights in the objectives, while maintaining relativity, to produce the scaled weights.

A viscosified miscible enhanced oil recovery fluid includes (a) a hydrocarbon fluid and viscosifying agent, wherein the viscosified fluid is a Newtonian fluid, (b) a hydrocarbon fluid and gelling agent, wherein the viscosified fluid is a power law fluid, (c) a hydrocarbon fluid, a gelling agent, and a rheological additive, wherein the viscosified fluid is a yield power law fluid, or (d) a hydrocarbon fluid, a gelling agent, a rheological additive, and solvent for the rheological additive, wherein the viscosified fluid is a yield power law fluid. The hydrocarbon fluid is preferably weighted with nano-scale weighting agents. The viscosified fluids are prepared and pumped into a subterranean petroliferous formation to recover a majority of the oil, heavy oil, condensate, or gas originally in place. A method for subsequently recovering a substantial fraction of the miscible injectant conventionally or by applying a viscosity breaker and recovering the viscosity-broken miscible injectant conventionally.

The invention discloses a nested type electronic hoist scale counterweight loading system comprising an outer ring weight group, an inner ring weight group, an outer ring weight driving mechanism andan inner ring weight driving mechanism; the inner and outer ring weight groups are made to order according to electronic scale detection requirements, up and down displacement of the weight groups iscontrolled via an electric motor so as to realize electronic hoist scale weight loading effects, complex manual operation of a traditional loading mode is not needed, and security and work efficiencycan be ensured. The inner and outer ring weight groups are adopted at the same time; compared with a traditional single weight group mode, the mode of the inner and outer ring weight groups is advantageous in that a large number of weight points are available to choose from, safety can be improved while height of a weight mechanism is reduced properly, and loading work efficiency is improved via synchronous inner and outer ring loading.

The invention discloses a variable mass weight automatic carrying robot for elevator detection. The variable mass weight automatic carrying robot for the elevator detection comprises rock arm type caterpillar drive systems and a machine body system. The machine body system comprises a machine body, a weight storage tank is formed in the machine body to store weights, the weight storage tank is slidably connected with a tank cover, and a plurality of rock arm type caterpillar drive systems are arranged on two sides of the machine body. During the elevator detection, heavy detection weights aredifficult to carry by workers; and the weight carrying robot is remote-controlled by an operator and can move quickly, turns flexibly, and climbs obstacles such as stairs. The variable mass weight automatic carrying robot realizes large-scale weight carrying, the operation is convenient, simple, fast and efficient, the labor cost is fundamentally saved and carrying efficiency is greatly improved;and the variable mass weight automatic carrying robot can be used in staircase detection, and has extensive applicability in other transportation fields.

A transportable and repositionable portion control system 10 has an integral portion weight sensing system 90 configured for use with an optional fry ribbon bridge assembly 190 in a portion control method. The portion 14 is held in a portion indexing area, or corral 20 on an upper surface 22 of the scale 10. A portion indexing area 20 is defined between spaced, upstanding container supports 24 and 26 which diverge from one another in a “V” shape and the spacing between the rails is selected to allow a selected variety of known containers such as a cup to be placed on surface 22 between the rails to ensure that the carton is properly located for the weight measurement that is to be carried out by the scale 10.

The invention relates to a belt scale weight calibration method equivalent to material calibration, comprising the following steps: S1, testing the integrating factors Ks and Km of a belt scale under material calibration and weight calibration modes respectively; S2, calculating the proportionality coefficient K (K=Ks/Km) of the integrating factors Ks and Km according to Ks and Km; S3, calculating the current material calibration integrating coefficient Kxs (Kxs=K*Kxm) corresponding to the current weight calibration integrating coefficient Kxm according to the integrating coefficient Kxm of the belt scale under the current weight calibration mode and the proportionality coefficient K; and S4, using the current material calibration integrating coefficient Kxs to replace the current weight calibration integrating coefficient Kxm. The determined relationship of the same calibration precision parameter under different calibration modes is found out through research on the equivalence of a belt scale under the same internal and external conditions and different calibration modes and precision, and a calibration mode with low requirement on calibration conditions is used to replace a calibration mode with high requirement on calibration conditions. Convenient, timely, accurate and low-cost calibration is realized.

The invention discloses a moving vehicle tracking method for resisting strong shadow interference based on a non-subsampled shear wave domain zero tree structure, which is high in accuracy and robustness and has self-adaptive capability, and comprises the following steps of: after a video frame is converted into an HSV color space from an RGB color space, carrying out non-subsampled shear wave transformation; assuming that the transformation coefficient obeys Gaussian distribution, calculating a weighted mask of each scale by adopting a mean value and a standard deviation of the transformationcoefficient; according to the zero tree distribution characteristic of the multi-scale transformation coefficient, correcting a fine-scale weighted mask by utilizing a coarse-scale weighted mask, andcarrying out linear combination on the weighted masks of each scale and each color channel to obtain a public mask; calculating a self-adaptive segmentation threshold value by using a maximum entropymethod based on least square fitting, and binarizing the public mask; and determining a moving vehicle area in a voting mode, and tracking the target vehicle by adopting a mean shift algorithm.

The invention is a method to test the pectin content in ramie based on MAE. The detailed method is that dried small sections of ramie with the weight of W0 is arranged into a vessel holding distilled water, then is put into a microwave oven for pretreatment of MAE so as to remove the pigment and other water-soluble matter, after being filtered and washed, the ramie is dried to a constant weight and the scaled weight is recorded as W1 for backup; the spare dried ramie is marinated into water solution extraction agent (NH4)2C2O4 for a certain period and then are put into the microwave oven for pectin extraction by MAE. After being filtration washed, the ramie is dried to the constant weight and the scaled weight is recorded as W2; the calculation is made according to the following formula which is pectin content (percentage)= (W1-W2)/W0 by one hundred percent, the value obtained is the pectin content in ramie. The method is easy to operate, rapid to extract, convenient to analyze and saves water and electricity.