15 results about "Intensity coefficient" patented technology

The invention relates to the technical field of computer aided design, in particular to an automatic intelligent building design scene generation method and system.The automatic intelligent building design scene generation method comprises the following steps of analyzing a material text mapping high-dimensional semantic vector, executing dot product projection with a physical reference to generate an attribute module length, and resolving noise frequency and a normal intensity coefficient; the method comprises the following steps of: mapping a texture style description text to a high-dimensional vector space, projecting the texture style description text to a physical attribute reference dimension, constructing a height field matrix by driving gradient interpolation and fractal superposition, generating a normal and roughness map based on differential operation, covering the surface of a three-dimensional model, and outputting a design scene. According to the method, a quantitative conversion path from semantic features to geometric generation control variables is established, noise frequency and normal intensity generation of programmed textures are driven, normal and roughness maps meeting physical rendering standards are generated, automatic rendering of material details is achieved, and unification of microscopic geometric textures and macroscopic visual styles of a design scene is achieved.

This invention belongs to the field of medical image processing and deep learning inference technology, specifically relating to a method, device, and storage medium for denoising and enhancing low-dose CT images. The denoising and enhancement method includes the following steps: S1, acquiring a low-dose CT image; S2, calculating the noise level parameter N; S2.1, generating a uniform reference region mask; S2.2, robust noise estimation within the mask; S2.3, robust fusion of the noise level parameter; S3, generating a denoised image I_dn based on the CT image to be processed using a deep learning denoising network; S4, calculating the denoising intensity coefficient s according to the noise level parameter N, and performing weighted fusion of the CT image to be processed and the denoised image I_dn to obtain the output enhanced image. This invention has the advantages of strong adaptability, no need for manual ROI selection, good robustness, and easy deployment.

The application discloses a kind of distributed image denoising methods based on edge computing, it is related to edge computing technical field;Effectively improve image denoising;The application is by setting edge node model, based on edge node model acquisition target image target pre-processing image;And target pre-processing image is classified to obtain distributed morphological image by image form;Distributed morphological image is extracted to obtain the denoising intensity coefficient of distributed morphological image;Further, according to the denoising intensity coefficient, distributed morphological image is denoised to obtain distributed denoising image;Further, the image feature set of distributed denoising image is obtained, and distributed denoising image is spliced to generate target restoration image according to image feature set;Obtain the denoising coefficient of target restoration image;Judge the eligibility of denoising coefficient, if not qualified, then target restoration image is denoised to obtain qualified target restoration image and corresponding denoising times.

The application discloses an automatic control system and method, and relates to the technical field of industrial automatic control. The method comprises the following steps: collecting real-time data of a plurality of control points at a fixed period; calculating a multi-dimensional state feature vector containing mean value, standard deviation and slope for each control point; comparing the state feature vector with a preset stable operation interval to screen a point to be adjusted; obtaining a basic control action and an intensity coefficient by looking up a table; determining associated points according to a topological relationship, predicting state changes of the associated points by using a pre-trained influence relationship model, and iteratively reducing the intensity coefficient when the predicted state of the associated points exceeds the stable interval of the associated points, to generate a final control instruction; evaluating control convergence and system fluctuation after executing the instruction; and adjusting the weight of a strategy table according to the evaluation result. The application realizes rapid suppression of local deviation under coupling constraints, and helps to maintain stable operation of the whole system.

The invention provides an indoor space intelligent planning method and system based on a BIM technology and multi-source data fusion. The method comprises the steps of obtaining an interaction strength coefficient and a space partition demand identifier according to a teaching target text; and correcting the interaction intensity coefficient and the spatial partition demand identifier based on the classroom state scalar and the spatial variable capability scalar. And calculating a spatial configuration intensity parameter according to the corrected interaction intensity coefficient, the spatial partition demand identifier and the spatial variable capability scalar. And calculating the action starting strength of each action according to the spatial configuration strength parameter, the corrected spatial partition demand identifier and the implementation threshold, and adding the actions of which the action starting strength is greater than a preset strength threshold into a strategy set. Each target action in the strategy set corresponds to the operation type of one type of component. The execution proportion of each type of components is calculated according to the spatial configuration strength parameters and the action starting strength, a spatial configuration strategy is generated according to the components, the component parameters and the execution proportion, and the spatial configuration strategy is accurately generated.

The invention relates to the technical field of data transmission, in particular to a sensitive data desensitization method applied to a green supply chain platform. The method comprises the steps of collecting data of a green supply chain platform; constructing an identity vector based on links and batches so as to determine the correlation of the data; obtaining a historical correlation coefficient based on historical risk analysis; obtaining a correlation coefficient by combining the two, obtaining a sensitive degree by combining a data risk, and dividing into different sensitive layers; based on the page view, the access repetition rate and the permission complexity, obtaining the disclosure degree, and determining the link risk in combination with the data risk, thereby correcting the sensitivity degree to obtain an initial desensitization intensity coefficient; and determining timeliness based on the time label and the batch, adjusting the initial desensitization intensity coefficient, obtaining the desensitization intensity coefficient in combination with the sensitivity degrees of different sensitive layers, and completing layered desensitization based on the desensitization intensity coefficient. According to the invention, the timeliness and effectiveness of the data are improved.

This invention belongs to the field of display technology and discloses a method for compensating for brightness attenuation at the edge of an LCD panel. The method includes the following steps: S1: Acquiring attenuation parameters of the edge region using a data acquisition device. This invention employs a three-level response mechanism with an attenuation score threshold. At the compensation execution level, it dynamically calculates the compensation intensity coefficient based on the attenuation score and synchronously adjusts the backlight drive current and pixel data voltage. PID closed-loop control is formed through feedback from an optical sensor. Based on a predictive model-driven preventative compensation mode, when it is predicted that an edge region will reach a severe attenuation standard within a period below a time threshold, a series of preventative measures are initiated in advance. For the special application scenario of splicing screens, a collaborative compensation strategy for adjacent panels is proposed. When the brightness of the edge of a panel changes due to compensation, the brightness output of the corresponding edge of the adjacent panel is synchronously fine-tuned through the splicing controller, utilizing the visual characteristics of the human eye to achieve a smooth transition of the brightness field.

The invention discloses an intelligent analyzing and testing method and system for an IGBT (Insulated Gate Bipolar Translator) packaging structure, and relates to the technical field of packaging test.A current signal set Cur is constructed, normalized features of the current signal set Cur are extracted to form a feature set Feaa, an autocorrelation curve set COR is generated through autocorrelation operation, a periodic jitter intensity coefficient Jit is calculated, and the periodic jitter intensity coefficient Jit is obtained; the cross-domain mapping from electric signal change to mechanical loosening state is realized, and the early loosening symptom of the pin can be identified through the tiny change of the periodic jitter intensity coefficient Jit only under the conventional electrical characteristic test condition. Furthermore, by comparing with a normal jitter standard value NorJ of a normal model, a deviation ratio Rat is calculated, and an anomaly judgment set Abn is formed according to a set judgment threshold Thr, so that automatic anomaly recognition and result classification are realized. And the problems of large size, long detection delay, insufficient sensitivity and the like of external excitation equipment are avoided.

The application provides an element measurement method based on an improved multi-energy correction method, the Boltzmann expression is linearized by using a Taylor expansion, the spectral line intensity and the product of the spectral line intensity and the excitation energy are used as independent variables, and then the multi-energy correction method is used to calculate the unknown sample element concentration according to the spectral line intensity coefficient, so that the quantitative error caused by the change of the light source temperature is reduced, the process of calculating the plasma temperature is avoided, and the quantitative interference error caused by the calculation error of the plasma temperature is avoided.

The invention discloses a nighttime unsupervised domain adaptive target detection method based on causal inference and privacy protection, and the method comprises the steps: generating a mask matrix through marking a sensitive region of an original traffic scene image, calculating the visual feature entropy of an image window to quantify the visual disorder degree of the region, dynamically dividing sub-windows, and determining a privacy intensity coefficient, gradient pixel shuffling is carried out on different areas according to the coefficients; constructing a teacher-student network architecture, respectively generating a space attention map and a channel attention map, and constraining feature alignment of the student model and the teacher model by fusing attention masks; a structural causal model containing variables such as a target domain data set, a detection result and a pseudo tag is constructed, a knowledge distillation loss function is reconstructed through backdoor adjustment, foreground-background confusion interference is decoupled, and systematic deviation of a teacher model is eliminated. The method is stable in performance in a night scene, the detection precision is improved, and meanwhile sensitive information is effectively protected.

This invention relates to the field of data processing technology, and more particularly to a data link optimization method. The method includes parameter acquisition, noise interference determination, interference mode identification, frame length and urgency adjustment, frame upgrade selection, iterative optimization, output optimization results, and dynamic threshold correction. This invention determines whether noise interference occurs by utilizing the power change rate and a preset threshold, and identifies interference modes by combining the number of verification failures and the interference intensity coefficient. For pulse-type interference, the frame length is dynamically adjusted, and the transmission urgency is calculated based on the time pressure coefficient and the original priority. Candidate upgrade frames are selected using the urgency threshold, and the upgrade duration increment is quantified. The pulse coverage probability and the upgrade duration increment are fused into a joint transmission duration, and the frame length and candidate upgrade frames are iteratively optimized. The output is based on the optimal frame length and the target upgrade frame, and the preset change threshold is dynamically corrected according to the interference mode switching frequency within the preset adjustment duration.

This invention discloses a method for eliminating diffraction calculation errors in a checkerboard grating in a transverse shearing interferometer. The optical system of the transverse shearing interferometer includes: fixing the optical system under test on the optical platform of the transverse shearing interferometer; emitting incoherent light through an optical illumination system; the light rays sequentially passing through an object grating, the optical system under test, and an image grating, and being received by an optical detection element; uniformly shifting the phase of the object grating or the image grating to obtain shearing interference patterns in two perpendicular directions; using the least squares method to separate and solve for the shearing phases of the 0th and +1st order or 0th and -1st order diffracted light in the two perpendicular directions, and reconstructing the wavefront. This invention takes into account the different intensity coefficients of each diffraction order, accurately solves for the shearing phase, eliminates the errors introduced by the checkerboard grating in the whole-path diffraction calculation, and improves the wavefront reconstruction accuracy.

The invention discloses an automatic marking method and system for a focus in a medical image, and relates to the technical field of medical equipment, and the method comprises the following steps: collecting and preprocessing medical images of different modal types, and manually marking a focus region to obtain a marked medical image; calculating feature parameters of the grayed labeled medical image to obtain a classification intensity coefficient, thereby dividing initial training data and dynamic training data, constructing a focus recognition sub-model for each modal type, training by using the initial training data, performing forward propagation reasoning, reading a focus prediction probability value, and performing prediction on the focus. And calculating the pixel prediction offset degree based on the prediction probability value, adjusting the learning rate, inputting the dynamic training data into the sub-model, and carrying out iterative execution until the offset degree is lower than a preset threshold value, thereby finally realizing automatic focus marking of the to-be-marked medical image, providing a more accurate marking result, and enhancing the recognition precision of a fuzzy focus.

The application provides a ground subsidence prediction method and system based on multi-source data fusion, and relates to the technical field of ground subsidence, and the specific steps comprise: dividing a lower passing section, determining a subsidence time interval; collecting construction parameters and calculating a disturbance intensity coefficient, and screening out a double-line intersection influence section; obtaining daily cumulative subsidence simulation values, comparing with measured values to obtain a cumulative time sequence deviation sequence; screening time nodes with cumulative subsidence deviation values less than a preset threshold value, constructing a time sequence feature matrix, setting parameters of time nodes not less than the preset threshold value to zero and supplementing to the time sequence feature matrix to obtain next-day intersection influence section cumulative subsidence prediction values. The application not only fully considers double-line intersection disturbance effects, but also distinguishes subsidence differences of the intersection influence section and the single-line lower passing section, avoids blindness of global fitting of the prior art, realizes accurate capture of the subsidence law of the intersection influence section, and greatly improves subsidence prediction accuracy of the region.

The invention discloses an earthquake economic loss optimization reinforcement inversion method. The method comprises the following steps: extracting initial shear strength and rigidity of each floor of a target building from design software (such as PKPM); determining small, medium and large earthquake intensity and occurrence probability of the site; the economic loss of each intensity level is calculated based on a loss model, and an optimization design problem taking weighted annual loss minimization as a target is constructed in combination with the occurrence probability; designing variables and constraint conditions: taking the ratio (strength coefficient) of the shear strength of each floor to an initial value as a design variable, and keeping the total strength and the floor rigidity unchanged as constraints; carrying out efficient solution by adopting an artificial intelligence optimization algorithm and carrying a real-time online updated proxy model to obtain optimal strength coefficient distribution; and a result is fed back to original design software to automatically complete reinforcement correction and specification review. The method can be directly embedded into an existing design process, and real-time and dynamic post-earthquake loss minimization design is achieved on the premise that the manufacturing cost is hardly increased.