290 results about "Model correction" patented technology

A method for generating a photolithography mask for optically transferring a pattern formed in the mask onto a substrate utilizing an imaging system. The method includes the steps of: (a) defining a set of calibration patterns, which are represented in a data format; (b) printing the calibration patterns on a substrate utilizing the given imaging system; (c) determining a first set of contour patterns corresponding to the calibration patterns imaged on the substrate; (d) generating a system pseudo-intensity function, which approximates the imaging performance of the imaging system; (e) determining a second set of contour patterns by utilizing the system pseudo-intensity function to define how the calibration patterns will be imaged in the substrate; (f) comparing the first set of contour patterns and the second set of contour patterns to determine the difference therebetween; (g) adjusting the system pseudo-intensity function until the difference between the first set of contour patterns and the second set of contour patterns is below a predefined criteria; and (h) utilizing the adjusted system pseudo-intensity function to modify the mask so as to provide for optical proximity correction.

A system and a method automate metrology, measurement, and model correction of a three dimensional (3D) model for 3D printability. Slices of the 3D model are received or generated. The slices represent 2D solids of the 3D model to be printed in corresponding print layers. Medial axis transforms of the slices are calculated. The medial axis transforms represent the slices in terms of corresponding medial axes. A local feature size at any point along a boundary of the slices is determined as the shortest distance from the point to a corresponding medial axis.

An adaptive CT data acquisition system and technique is presented whereby radiation emitted for CT data acquisition is dynamically controlled to limit exposure to those detectors of a CT detector assembly that may be particularly susceptible to saturation during a given data acquisition. The data acquisition technique recognizes that for a given subject size and position that pre-subject filtering and collimating of a radiation beam may be insufficient to completely prevent detector saturation. Therefore, the present invention includes implementation of a number of CT data correction techniques for correcting otherwise unusable data of a saturated CT detector. These data correction techniques include a nearest neighbor correction, off-centered phantom correction, off-centered synthetic data correction, scout data correction, planar radiogram correction, and a number of others. The invention is applicable with energy discriminating CT systems as well as with conventional CT systems and other multi-energy CT systems, such as dual kVp-based systems.

In order to improve recognition performance, a no-speech sound model correction section performs an adaptation of a no-speech sound model which is a sound model representing a no-speech state on the basis of input data observed in an interval immediately before a speech recognition interval for the object of speech recognition and the degree of freshness representing the recentness of the input data.

The invention relates to a vehicle driving economy evaluation system and a vehicle driving economy evaluation method. The vehicle driving economy evaluation system is characterized by comprising a data center and a plurality of vehicular terminals; the data center exchanges information with the vehicular terminals through wireless network communication technology; the data center comprises an archive data storage module and an evaluation model correction module; each of the vehicular terminal comprises a mode command acquisition module, an archive data acquisition module, a running data acquisition module composed of a GPS (global positioning system)navigation installation and a car recorder, an desired fuel consumption prediction module, an evaluation parameter storage module, a driving behavior evaluation module and a driving suggestion output module. The vehicle driving economy evaluation method includes acquiring various data needed for evaluation firstly, calculating to obtain a corresponding score through the desired fuel consumption prediction module and the driving behavior evaluation module and outputting the score to the driving suggestion output module to display, and storing various data of drivers to the data center for model correcting and training. The vehicle driving economy evaluation system and the vehicle driving economy evaluation method can be applied to driving economy evaluation of various road conditions and vehicle models.

The invention provides road test software supporting a TD-SCDMA wireless access network, which can meet the requirements of the planning and optimization of the TD-SCDMA network in combination with a receiver and a GPS. The invention provides a method for calculating the performance parameter of the TD-SCDMA network and can intuitively and correctly reflect the quality of the network. The invention also provides a structuring thought of the TD-SCDMA road test software which comprises: an interface layer, which communicates with the receiver and the GPS, sends a network parameter value measured on the spot and geographical position information to an analytics layer; the analytics layer corresponds the geographical position information to the network parameter value one by one and classifies and reorganizes the TD-SCDMA network parameter value according to the standard set by users, thereby being capable of analyzing and diagnosing the state of the network measured in an intelligent manner; a presentation layer visually presents the classified and reorganized network parameter value and the analytic and diagnostic results of users in many ways, such as map, report form, and diagnostic listing presenting and so on. The invention also provides key technologies of propagation model correction, network coverage and test, data analysis and diagnosis, etc.

The invention provides a stretch-bending process model correction method based on workpiece three-dimension scanning. The stretch-bending process model correction method based on the workpiece three-dimension scanning comprises the steps of (1) carrying out comparison on a scanning model contour line of a fetched workpiece and a discrete design model contour line, obtaining the distance between every two endpoints of each section, (2) calculating the radius value of each discrete section of the scanning model contour line, carrying out difference calculation between the radius value of each discrete section of the scanning model contour line and the radius value of each subsection circular arc of the design model contour line to obtain the compensation values of subsection circular arcs of an existing process model contour line to obtain a corrected process model contour line, and (3) using the CAD software to carry out scanning on the corrected process model contour line to obtain a corrected process model. The stretch-bending process model correction method based on the workpiece three-dimension scanning carries out correction on the resilience value of numerical simulation calculation on the basis of the workpiece three-dimension scanning, solves the problem of errors generated by process model definition on the basis of numerical simulation, reduces manpower and material resources consumed by manual shape correction, analyzes the generation positions and the magnitude of the forming errors through comparison between a workpiece three-dimension scanning model and a design model, and can rapidly and accurately modify the process models of sectional material parts.

The invention discloses a hybrid prediction method for network traffic. The method is characterized in that a network traffic prediction method with an extreme learning machine (ELM) being compensated by autoregressive integrated moving average (ARIMA), namely, a network traffic prediction method with the ELM being compensated by a Farctal autoregressive integrated moving average model is provided through a self-similarity analysis of a network traffic sequence. The method comprises the following steps: firstly, predicting the network traffic sequence with the ELM; secondly, correcting an error sequence of network traffic prediction through an ARIMA model; and lastly, overlaying an ELM predicted value and an ARIMA model correction value to obtain a final predicted value. According to the method, prediction error data is fitted by the ARIMA model, and the predicted value of the ELM is overlaid with a residual error of ARIMA prediction to obtain the final predicted value. Residual error compensation is performed through the ARIMA model, thereby effectively increasing the prediction accuracy.

The invention relates to an image postprocessing method for removing a metal artifact from a computed tomography (CT) image, which comprises the following steps of: converting an original CT image into a polar coordinate image from an orthogonal coordinate image; determining a metal projection region in the polar coordinate image; establishing a model in the polar coordinate image; performing model correction by adopting the model; correcting front and back projection errors brought into the model correction; and converting the polar coordinate image into the orthogonal coordinate image. According to a model establishing method provided by the invention, the good model can be established, and finally, a good correction result can be obtained; the errors brought in by front and back projection are corrected, so that the result after the correction is better, and the processing speed is ensured.

The invention discloses a Beidou wide area timing system and method. The Beidou wide area timing method comprises the following steps: obtaining a phase observation value and a pseudo-range observation value of a China Beidou satellite navigation system of each ground Beidou tracking station network, time difference monitoring data of the time of the China Beidou satellite navigation system and the Beijing time of a laboratory atomic clock group, a satellite ephemeris of the China Beidou satellite navigation system, earth rotation and antenna phase center parameters; performing preprocessing of data check, gross error elimination and cycle slip detection on the obtained data; performing error model correction and fine data processing on the preprocessed data; encoding the corrected data togenerate a telegraph text, and uploading the telegraph text to a communication satellite for broadcasting; and performing, by a user, non-difference precise timing solution and time difference parameter correction on the received corrected data and the local observation data to obtain a user local time consistent with the standard time. By adoption of the Beidou wide area timing system and methoddisclosed by the invention, single-station wide area timing service can be achieved, the timing precision is high, the operability is high and the action range is limited.

The invention relates to a model correction based wind power forecasting system and a method. The model correction based wind power forecasting system comprises a numerical weather forecasting server, a fan, an anemometer tower, a wind power forecasting server, a database server and a wind power forecasting system client. The model correction based wind power forecasting method comprises the following steps: firstly using a numerical weather forecasting model provided by the meteorological department to forecast the weather situation of a wind power plant, and then building a real-time forecasting model, and converting the forecasting value of the numerical weather forecasting model to power output of the wind power plant. The system and the method are adopted to reduce various model errors and ensure the accuracy rate of forecasting results.

The invention discloses a tourism information recommending method and device. The method includes a user information obtaining step: acquiring the data of a first user, wherein the first user data comprises identity information of the first user and the history information of the user; a recommendation information generating step: inputting the data of the first user into a trained deep neural network, generating a travel information recommendation result which represents a probability that the first user will go to at least one tourist attraction, wherein the deep neural network is obtained through a training step, and the training step comprises a user data making step, an tourist attraction data acquisition step, a model prediction step, and a model correction step. The method adopts amachine learning method to automatically recommend the tourist attractions for the user by acquiring and analyzing the user's behavior data, which saves the user's time and brings a brand new and better user experience.

The present invention provides an environmental excitation data multiple-test-based Bayesian model correction method. The invention aims to eliminate the defects of a traditional method. With the method adopted, data obtained by multiple tests can be directly processed and analyzed; modal parameters obtained by the tests can be inputted by one step, and a model correction result is directly outputted. The method provided by the technical schemes of the invention can be used for solving the correction problem of actual test data-based finite element model. The method of the invention is divided into two stages. In the first stage, the acceleration data of a structure under environmental excitation which are acquired by the multiple tests are analyzed, so that the natural frequency and vibration mode of the structure which are measured by each test can be obtained; and the nondeterminacy of the modal parameters is calculated, and the modal parameters are represented by a covariance matrix. In the second stage, the optimal values of the model parameters of the finite element model requiring to be corrected are obtained through optimizing an objective function and based on the modal data parameters of the structure which are obtained based on the multiple tests and the covariance matrix thereof as well as the objective function built according to the Bayesian theory.

The embodiment of the invention discloses a power grid measurement data and power grid model correction method. The method comprises the following steps: acquiring power grid measurement data and power grid model data from a data acquisition and monitoring control system; performing state estimation of the actual operating state of the power grid according to the power grid measurement data and the power grid model data; matching the result of the state estimation with the preset conditions, and positioning the error in the power grid measurement data and the power grid model data according to the matching conditions; and correcting the positioned error according to the preset rule. The invention also discloses a power grid measurement data and power grid model correction device. According to the method, the accuracy of the power grid measurement data and the power grid model data can be improved, and a guarantee is provided for performing advanced application based on the data.

The invention discloses a highway real-time traffic monitoring method based on mobile phone signaling. The method comprises the following steps: first, performing wireless network coverage test along a highway, carrying out 'virtual monitoring point' modeling, and obtaining base station positioning reference data along the highway; then, acquiring and parsing Mc interface signaling data, obtaining call behavior information of users, and establishing a single-user traffic event tracking model; and using a highway user recognition model to screen out highway user data, performing correlation matching on the highway user traffic event tracking result and the base station positioning reference data to calculate the average driving speed of highway users, and outputting highway traffic monitoring results according to road sections. According to the method, by introducing network measured data in model correction, the accuracy of highway real-time traffic calculation based on the signaling positioning technology is greatly improved, fusion of the mobile communication technology and intelligent highway management is realized, and convenient and accurate real-time traffic information is provided for highway traffic and management.

According to an embodiment of the present invention, a material processing systeme (1) including a process tool (10) and a process performance control system (100). The process performance control system (100) includes a process performance controller (55) coupled to the process tool (10), where the process performance controller (55) includes a process performance prediction model (110), a process recipe correction filter (120), a process controller (130), and process performance model correction algorithm (150). The process performance prediction model (110) is configured to receive tool data from a plurality of sensors coupled to process tool (10) and to predict process performance data. The process recipe correction filter (120) is coupled to the process performance prediction model (110) and configured to receive predicted process performance data and generate a process recipe correction for run-to-run process control. The process controller (130) is coupled to the process recipe correction filter (120) and is configured to update a process recipe according to the process recipe correction.

A control apparatus which is capable of ensuring both high-level stability and accuracy of control, even when controlling a controlled object having extremal characteristics or a controlled object a controlled object model of which cannot be represented. The control apparatus 1 includes a cooperative controller 30, an onboard model analyzer 40, and a model corrector 60. The model corrector 60 calculates the model correction parameter matrix θ, so as to correct the controlled object model defining the relationship between the intake opening angle θlin and the exhaust reopening angle θrbl and the indicated mean effective pressure Pmi. The onboard model analyzer 40 calculates first and second response indices RI1 and RI2 representative of correlations between θlin, θrbl, and Pmi, based on the controlled object model corrected using θ. The cooperative controller 30 calculates the θlin and θrbl such that Pmi is caused to converge to a target value Pmi_cmd, and determines an increasing / decreasing rate and increasing / decreasing direction of θlin and θrbl according to RI1 and RI2.

The invention discloses a speech enhancement method and device applicable to strong noise environments. The method comprises establishing an air conduction speech sensor speech detection and non-air conduction speech sensor speech detection combined model under a clean environment; estimating a noise model of a currently received air conduction speech sensor speech detection signal according to a speech signal detected by a non-air conduction speech sensor; correcting parameters of the combined model by using the obtained noise model; and enhancing the air conduction speech sensor speech detection signal through the combined model before and after correction, and outputting enhanced speech signals. The device comprises an air conduction speech sensor, the non-air conduction speech sensor, a multi-channel data acquisition module, a noise model estimation module, a combined model correction module, a speech enhancing module and a training and self-adaptive module for the combined model. Compared with the prior art, the speech enhancement method and device have the advantages of being strong in noise immunity, good in speech quality and the like.

A method for correction of thermal defects using tomographic scanning for additive manufacturing is provided. The method may include forming a portion of an object using an additive manufacturing system based on an intended three-dimensional (3D) model of the object that is in an additive manufacturing system format. The portion of the object is scanned using a tomographic scanner to obtain a model of the portion of the object in a tomographic scanner format. The model is converted from the tomographic scanner format into the additive manufacturing system format to obtain a converted tomographic model; and the converted tomographic model is compared to the intended 3D model to identify a defect in the portion of the object. A modified 3D model may be generated of the object correcting the intended 3D model to address the defect of the portion of the object.

The invention provides a satellite in-orbit micro-vibration simulation modeling correction method, which comprises the following steps of: analyzing satellite configuration to separately model a satellite body structure and an on-satellite rotating part; according to ground test data and a finite element principle, carrying out modeling of a satellite body structure finite element model; according to attitude control and ground micro-vibration test data of other on-satellite rotating parts, establishing an on-satellite rotating part simulation model; according to a ground test state, establishing a ground simulation model, deleting ground constraints and establishing an in-orbit micro-vibration simulation model; according to the ground test data, a moving part, a control mode of the moving part and a modal value order reduction method, correcting the in-orbit micro-vibration simulation model; carrying out micro-vibration simulation analysis on the ground test state and utilizing the ground micro-vibration test data to carry out model correction; and deleting the corrected model ground constraint conditions to complete modeling. According to the invention, micro-vibration interference of a flywheel to a satellite platform can be effectively reduced; and technical support is provided for development of a high-accuracy high-resolution high-stability extra-static platform.

The invention relates to a method for monitoring the current-carrying capacity of a power cable, which comprises the following steps that: 1, a sensor sends acquired data to a processor; 2, according to the data, the processor combines cable engineering basic data to establish a heat balance finite element model; 3, a comparison module of the processor compares data of the heat balance finite element model and data of a cable temperature field model, if the data difference is greater than a threshold value, the step 4 is executed, and if the data difference is less than the threshold value, the step 5 is executed; 4, after the heat balance finite element model is corrected by a model correction module of the processor, the step 3 is executed again; and 5, the comparison module outputs the data of the heat balance finite element model and sends the data to a mode identification module to carry out dynamic mode identification and simultaneously, the current-carrying capacity analysis, the trend analysis, the surge summation effect analysis, the early warning and warning identification and the optimization of a power transmission and distribution scheme are carried out. Compared with the prior art, the method has the advantages of high measurement accuracy, more measurement data and the like.

The invention, which belongs to the technical field of underground medium speed model inversion of seismic exploration, relates to a full-waveform inversion method based on a seismic record integral. The method comprises: an observed seismic record is obtained, denoising processing is carried out, and a seismic wavelet is extracted; a simulated seismic record is calculated; time-domain integrating processing is carried out on the observed seismic record after denoising and the simulated seismic record; difference processing is carried out two groups of data after integrating to form a back transmission source, and a back transmission wave field; a forward transmission wave field is calculated; a gradient is calculated; a conjugate gradient is calculated; according to a linear searching method, a step length of iterative updating is calculated; a model correction value is calculated and a speed model is updated; and whether the updated model meets a given condition is determined; if so, a result is outputted; and if not, the updated speed model is used as a new initial speed model and calculation is started again. Therefore, a problem that the inversion is trapped into a local limit value problem because of lack of low frequency seismic data and thus inversion fails during full waveform inversion is solved.

The invention belongs to the field of electric power system distribution automation, and particularly relates to a pattern and model verifying method of a distribution automation master station system and a geographical information system. The method comprises the following steps that firstly, a model and a pattern file are guided out of the geographical information system; secondly, the distribution automation master station system obtains the pattern and the pattern file of the geographical information system through an information interaction bus; thirdly, the distribution automation master station system automatically analyzes a pattern and a model file online based on an adjacent matrix according to the depth-first search algorithm, and the pattern and model verification is carried out. A power grid topological structure is simplified through the depth-first search algorithm, accordingly, the pattern and model verification between the distribution automation master station system and the geographical information system is carried out, the working efficiency is improved, the pattern and model correction period is shortened, and the defect that a traditional topological method is low in searching efficiency is overcome.

The invention discloses a high-pile wharf foundation pile damage diagnosis method based on structural residual modal force. The method includes the steps that a finite element model of a high-pile wharf foundation pile structure is established, the structure is divided into a plurality of units, and each unit comprises two nodes; an acceleration sensor is arranged on each unit, and modal test data detected under natural environment excitation are recorded and saved; modal parameter recognition is performed on the modal test data, and a low-order modal frequency and a vibration mode of previous orders are recognized; model correction is performed, and a reference finite element model is obtained; under the non-damage and damage condition, a rigidity matrix and a mass matrix of each unit are extracted; and each freedom-degree residual modal force vector in each unit is calculated, when the absolute value of each freedom-degree residual modal force vector in each unit is larger than a set value, it is judged that damage exists, and the position where structural damage is located is positioned according to the nodes corresponding to freedom degrees. Only through low-order modal information generated before and after structural damage, damage diagnosis can be performed under the condition that the structure has single damage and multi-site damage.

The invention discloses a cloud computing resource distributing method based on energy consumption optimization. The cloud computing resource distributing method includes the steps that a cloud computing experiment platform is deployed; cloud computing resource distribution describing and modeling are conducted; different energy consumption optimization factors are selected; simulation platform construction is conducted; a test experiment is conducted on performance; model correction is conducted; a performance simulation experiment is conducted; a performance analysis model and key factors are determined; an energy consumption optimizing model for resource distribution under the cloud computing environment is studied; a heuristic resource distribution algorithm for achieving load balancing and meeting energy consumption minimization is designed; a performance simulating and testing experiment is conducted. According to the cloud computing resource distributing method based on the energy consumption optimization, the practical situation of resources under the cloud computing environment can be accurately and truly reflected, the energy consumption optimizing model is provided, and the limitation that only user task time demands are considered in an existing research method is broken through. In addition, the heuristic resource distribution algorithm achieving resource load balancing and energy consumption optimization is provided, and the limitation that global and local search balancing cannot be ensured in an existing resource distribution algorithm is broken through.

The invention discloses a structure frequency response dynamic model correction method based on deep learning. The method includes transforming the frequency response value of the experimental measured structure and the frequency response value of the dynamic model simulation into an image mode for storing and feature extraction, taking the corresponding parameters of the training sample images aslabels of the training set, and establishing the training sample sets of multi-frequency points, multi-observation points and multi-observation directions, and based on this, building a deep neural network and other processes. The method combines the advantages of deep learning in the field of image recognition, establishes the fast mapping relationship between the dynamic output and the parameters to be corrected, inputs the experimental measurement image into the trained neural network, outputs the model correction result, and effectively solves the problems of poor model representation ability of the manual feature extraction method and the like. In addition, considering the possibility of over-fitting caused by less training samples, the method of adding fast connection structure in the network forward transmission and adding noise expansion samples are adopted to reduce the parameter correction error.

The present invention discloses a four-line one-storehouse spare part inventory prediction system which comprises a data acquisition module, a data analysis processing module, a fault online diagnosis module, an inventory prediction module and a model correction module, wherein the model correction module is respectively and bidirectionally connected with the fault online diagnosis module and the inventory prediction module; the fault online diagnosis module is also connected with an early warning and alarming module; and the early warning and alarming module is connected with a fault processing knowledge base. The present invention also discloses an establishing method of the four-line one-storehouse spare part inventory prediction system, which comprises: establishing a four-line one-storehouse system fault online diagnosis module carrying out extraction and analysis on fault diagnosis information; establishing a spare part inventory prediction model; and correcting the spare part inventory prediction model. According to the present invention, not only can fault online diagnosis be carried out on production equipment and reliability of production facilities and the operation and maintenance level of a measurement center are improved, but also an inventory of spare parts can be minimized when the production requirements are met.