88results about How to "Improve fitting efficiency" patented technology

The invention relates to a virtual clothes fitting system. The virtual clothes fitting system comprises a clothes model database, a clothes selection module, a personal model database, a matching module and a combined display module. The invention further provides a virtual clothes fitting method. According to the virtual clothes fitting system and the virtual clothes fitting method, the clothes model database is established, a lot of clothes models are established and stored in the clothes model database, two-dimensional code information of the clothes is in index association with the clothes model database, one clothes model can be directly called by reading the two-dimensional code information of the clothes, the clothes model is matched with a user personal model, so on-site clothes model establishment is not needed, and clothes fitting efficiency is improved.

The present invention discloses a virtual fitting system in a shopping mall. The virtual fitting system in the shopping mall comprises: a three-dimensional virtual fitting module, a two-dimensional virtual fitting module, and a wireless communication module. The three-dimensional virtual fitting module comprises a color RGB camera, an infrared depth camera, a somatosensory device controller, a scene creation module, a posture identification module, a rigging module, a depth matching module, a logic module, and a rendering module, wherein the posture identification module comprises an infrared identification module and a gray identification module. According to a virtual scene fitting room disclosed by the present invention, the body bone and depth image information can be collected through somatosensory orientation and image identification and by using the depth camera sensor, the clothing is perfectly combined with realistic images, and display effects of different clothing in different scenes can be produced, so that the complicated procedure of traditional fitting is reduced, and fitting efficiency is improved.

The invention relates to a method of grabbing a shape symmetrical workpiece with a high precision by using a low precision depth camera. The method comprises the following steps: 1) measuring a position of a conveying workpiece tray in a grabbing area of a production line in a grabbing coordinate system O-XYZ; 2) carrying out hand-eye calibration on a grabbing system of a mechanical arm to obtaina homogeneous coordinate transfer matrix T1 between a camera coordinate system and the grabbing coordinate system; 3) generating a 3D workpiece model point cloud and marking the grabbing position; 4)constructing a workpiece detection network model based on deep learning and training the model; 5) carrying out 2D image target detection according to the trained workpiece detection network model; 6)integrating an image target detection result and tray position information, and cutting the 3D workpiece model point cloud to obtain a workpiece observation point cloud; and 7) estimating a grabbinggesture according to the cut workpiece observation point cloud. Compared with the prior art, visual guidance can be carried out by using the low precision depth camera to achieve the grabbing precision with visual guidance by a high precision depth camera.

A laminated arc heater high-enthalpy airflow parameter diagnosis system is used for obtaining the airflow parameters (particle number density and static temperature) of a high temperature flow field in the ground heat protection test of an arc heater, and online diagnosing high temperature disassociated air test airflow in the arc heater by using an atom O (777.19 nm) spectral line to obtain the plasma airflow temperature and the atom oxygen particle number density in the arc heater. The system can be used as a routine way for the arc heater high-enthalpy airflow parameter diagnosis.

The invention discloses a hearing aid self-fitting method based on a deep learning network. Through building a comprehensive patient information bank and through deeply analyzing the patient and the fitting process, an effective strategy in the fitting process is found out, a new intelligent hearing aid self-fitting model is thus built, and the fitting efficiency is improved. The deep learning network is introduced, through a large amount of data training, the network is made to have artificial intelligence, voice information consistent with the hearing loss characteristics of the patient canbe quickly generated according to the basic information of the patient, the input gains can be adjusted automatically according to the evaluation feedback of the patient, the fitting time is greatly shortened, and the satisfaction of the user is improved.

The invention discloses a remez algorithm based Sigmoid function hardware high-efficiency implementation method. The remez algorithm based Sigmoid function hardware high-efficiency implementation method is characterized in that: the Remez optimal consistency algorithm is firstly used to implement polynomial piecewise approximation on the Sigmoid function for four times to improve the fitting precision; and then the pipeline architecture optimization hardness is used to implement a module. The method effectively balances the contradiction between the resource consumption and the fitting precision, meets the real-time requirement, and meets different design indexes through the relatively strong flexibility.

The invention discloses a hearing aid self-fitting method. The method includes the steps that firstly, a knowledge base is established, a historical patient most similar to a current present is found out, and the optimal hearing aid algorithm parameters of the historical patient are obtained; the current patient conducts satisfaction evaluation on the sound quality, the hearing aid algorithm parameters are updated in an iteration mode according to evaluation indexes, and interaction with the knowledge base is conducted; test sounds are generated for the current patient according to the updated hearing aid algorithm parameters until the current patient feels satisfied. The knowledge base is introduced into the hearing aid self-fitting method, the matching range of the hearing aid algorithm parameters is greatly narrowed, and the fitting efficiency of a hearing aid is obviously improved. The expected value of the hearing aid of the current patient is met to the maximum degree due to the fact that the hearing aid algorithm parameters are updated in the iteration mode according to the evaluation indexes, and the accuracy of the hearing aid algorithm parameters is improved.

A first fitting part is in geared connection with a second fitting part, and a rotatably mounted eccentric drives a rolling movement of the first and second fitting parts on each other. The eccentric includes at least one driver and wedge segments. The components of the eccentric bear at least indirectly by way of their inner side and/or their outer side in each case against one of the fitting parts, at least one component of the eccentric is mounted with its inner side on the first fitting part and/or with its outer side on the second fitting part and interacts directly with the relevant fitting part, wherein the component of the eccentric or the associated fitting part, in the interacting region, is coated with a thin layer, and the thin layer has a lower coefficient of friction than the material of the component.

The invention provides a production data analysis method for a shale gas well with varying production systems, and belongs to the field of oilfield development. The method comprises: (1) establishinga shale gas well test analysis model; (2) obtaining production data and required basic data; (3) calculating output regularized pressure and an output regularized pressure differential coefficient, drawing a double logarithmic chart, the double logarithmic chart including two curves of a double logarithmic curve for the output regularized pressure and material balance time, and a double logarithmic curve for the output regularized pressure differential coefficient and material balance time; or calculating output regularized pressure integral and an output regularized pressure integral differential coefficient, drawing a double logarithmic chart, the double logarithmic chart including two curves of a double logarithmic curve for the output regularized pressure integral and material balancetime, and a double logarithmic curve for the output regularized pressure integral differential coefficient and the material balance time.

The invention provides an automatic oil deposit history fitting system and method based on a genetic algorithm, belonging to the field of oil deposit numerical simulation. In the invention, automatic history fitting is realized by taking a database as a data storage and computation center based on a generic algorithm flow. The system comprises an algorithm management module, a simulator pre-processing module, a simulator calling module, a simulator post-processing module a user interface module. Due to the adoption of the system and the method, automatic oil deposit history fitting is realized by organically combining the generic algorithm with a data technology, the indexing and storing process technology of a database is fully utilized, efficient storage of data is realized, a fitting evaluation value is calculated easily and rapidly, and the efficiency of automatic oil deposit history fitting is effectively increased.

The invention provides a trajectory fitting method and device, electronic equipment and a storage medium, and relates to the field of path data processing of traffic management. The trajectory fittingmethod comprises the steps of preprocessing trajectory data of a target vehicle to obtain a to-be-processed trajectory point set; acquiring first road grabbing information of the first track point set according to the road network mapping data of the monitoring area; obtaining a target path combination through which the target vehicle passes according to the first road grabbing information, wherein the target path combination comprises at least one road; and fitting the to-be-processed track point set in the target path combination to obtain a fitting track of the target vehicle in the monitoring area. By using the trajectory fitting method provided by the embodiment of the invention, the dense trajectory points of the vehicle can be removed, and the road network mapping data of the monitoring area can be called, so that the fitting of the vehicle trajectory is realized, and the trajectory fitting efficiency is improved.

An ultrasonic wire bonding wedge with multiple bonding wire slots is provided. The wire bonding wedge includes a pillar fixing part for providing fixed connection to the bonding device. One end of the fixing part includes a wire bonding wedge nozzle, with feature that the tip of the nozzle including at least two bonding wire slots. The tip of nozzle of the wire bonding wedge includes at least two bonding wire slots, and is able to realize bonding operations between two or more bonding wires and the chips to improve bonding operation efficiency. When bonding operation is performed on a smaller bonding area, or two or more parallel bonding wires, the wire bonding wedge is able to perform a single bonding operation in a smaller bonding operation so as to reduce defective rate of the chips and ensure sufficient bonding strength of the bonding operation.

The invention discloses a segmenting method for a surface electromyogram signal activity section based on sample entropy and a Gaussian model. The segmenting method comprises the steps that 1 electricpotential values of surface electromyogram signals are collected from the related muscles of the human body, widths u and step lengths f of sliding windows are set, and sliding segmentation is conducted on data; 2 sample entropy parameters m and r are set, and the sample entropy of each sliding window is calculated; 3 a DBSCAN cluster method is adopted for determining a Gaussian polynomial basicmodel, and then model parameters are initialized; 4 a nonlinear least square method is adopted for fitting of the Gaussian model; 5 according to the Gaussian model, an energy threshold is determined,and the activity section is segmented. According to the method, the characteristic can be utilized that the sample entropy has an inhibition effect on noise, the influence of the noise on signal segmentation is overcome, and therefore the foundation is provided for precision and reliability of further analysis for the surface electromyogram signals.

The invention discloses a health level evaluation method of a thermal power generating unit based on data analysis, belongs to the field of thermal power generation, The aim of invention is to providea health level evaluation method of a thermal power generating unit based on data analysis to evaluate the operation status. The main points of the technical scheme are as follows: S1, acquiring historical operation data for a period of time from the real-time information monitoring and management system of thermal power generating units, screening the historical operation data, and selecting thehealthy operation data; S2, determining the parameters to be measured, selecting the independent variables related to the parameters, and carrying out curve fitting on the parameters and the independent variables related to the parameters to establish a mathematical model of each parameter; S3: establishing the scoring rules based on the mathematical models of each parameter. The method can be used to realize the real-time prediction of parameters and to judge whether the predicted value is normal or not.

The invention provides an establishment method for a pipe network leakage prediction model based on EPR evolution polynomial regression, and belongs to the field of an urban water supply network. The method comprises: processing original data of modeling, determining pipe diameters, pipe lengths, and pipe age as influence factors; then respectively modeling five kinds of pipes, grouping each kind of pipes according to pipe diameters, pipe lengths, average weighted pipe age, and leakage frequency per year, establishing a leakage prediction model with pipe diameter, pipe length, and pipe age as independent variables, and leakage frequency per year as a dependent variable, using fitting degree of a CoD determination model; using actual leakage data to verify the leakage prediction model, using predictive ability of a RMSE judging model; finally, summing the leakage frequency per year which is predicted according to the leakage prediction models respectively established for the five kinds of pipes, to obtain final leakage frequency total amount of the pipe network in a region. Through the establishment of the model and determination of the leakage number of the region, the pipes which have leakage risks can be found out as early as possible in daily management, so as to reduce probability of accidents and reduce economical loss.

The invention relates to in-plane curve fitting methods, in particular to a geometric optimization approximation based minimum region fitting method for profiles of parabolas. The method comprises the steps: firstly, approximate coordinates of an initial focus and a vertex of a measured parabola are roughly estimated according to coordinates of measuring points, and a series of auxiliary points are arranged around the initial focus and the vertex according to certain rules; next, supposed that the auxiliary points around the initial focus and the vertex are focuses and vertexes of the parabolas to be fit, the supposed focuses and vertexes can be used for constructing a series of parabolas; then, range values from the measuring points to the parabolas are calculated, and finally, the minimum region fitting of the measured parabola is realized through comparing, judging and transforming reference points and establishing a new supposed auxiliary parabola. The method has the advantages that the fitting process is simple, the accuracy of fitting is high, and the minimum region fitting and evaluation of profile tolerance errors of the parabolas can be effectively realized.

The invention provides a bandwidth characteristic curve fitting system, method and device, and belongs to the technical field of bandwidth data, and the method comprises the steps: generating a digital quantity sequence according to initial reference point information, wherein the reference point information comprises a frequency and an amplitude corresponding to the frequency; controlling broadband signal generation equipment to convert the digital quantity sequence into a digital calibration signal, and converting the digital calibration signal into an initial waveform; controlling a spectrum analyzer to perform fast Fourier transform on the initial waveform to obtain a target amplitude-frequency response curve; acquiring a target reference point amplitude in the amplitude-frequency response curve, and fitting a bandwidth characteristic curve according to the amplitude of the initial reference point in the digital calibration signal and the target reference point amplitude, wherein the target reference point amplitude and the amplitude of the initial reference point correspond to reference points with the same frequency. According to the invention, the calibration frequency is reduced, and the fitting efficiency is improved.

The present application discloses a fast iterative line-circle optimal fitting method, which comprises: constructing candidate fitting results according to all data points; iteratively optimizing thecandidate fitting results; selecting an optimal curve. The candidate fitting results are obtained by full traversal in a basic search space to obtain a set of curves containing the optimal curve or the curves near the optimal curve and with the smallest error; the iterative optimization utilizes the prior knowledge that the outlier removed from the optimal fitting result must be the farthest datapoint to the curve, and iteratively modifies the outlier in the candidate fitting result, so that the candidate fitting result becomes stable and the RMS error is further reduced. Screening the optimal curve is to compare the RMS error of the iteratively optimized fitting curves and screen is the curves with the smallest error as the best fitting results. The line-circle optimal fitting method provided by the present application greatly shortens the fitting time, and the fitting process is non-random, so that the global optimal fitting result can be obtained quickly, stably and accurately.

A Doppler parameter quadratic fitting method based on time division multiplexing comprises the steps of (101) establishing a quadratic fitting unit which comprise a multiplying unit a2, a multiplying unit b2, a multiplying unit c2, a multiplying unit d2, a multiplying unit e2, a divider a2, a summator a2, a mean value module a2, a mean value module b2 and a mean value module c2; (102) calculating fitting parameters m, m21, m22, m23, m24 and m25 by means of the quadratic fitting unit with the abscissa xi of a scatter signal as the input end; (103) calculating the fitting coefficient summation by means of the quadratic fitting unit based on the fitting parameters m, m21, m22, m23, m24 and m25; (104) achieving quadratic curve fitting of the scatter signal based on the fitting coefficient summation. Compared with traditional quadratic fitting, the Doppler parameter quadratic fitting method based on time division multiplexing has the advantage that the number of hardware resources needed is reduced greatly by means of time division multiplexing of hardware resources in the quadratic fitting unit.

The invention discloses a threshold distribution fitting method, device and system for a flash memory, and the method comprises the steps: carrying out the fitting of the distribution of storage units in a plurality of voltage intervals in initial threshold voltage distribution, obtaining a first fitting normal distribution parameter, and then carrying out the fitting of one of two adjacent threshold voltage distribution states; if the fitted threshold voltage distribution state and the to-be-fitted threshold voltage distribution state are two adjacent threshold voltage distribution states, obtaining a second fitted normal distribution parameter of the fitted threshold voltage distribution state; and according to the second fitting normal distribution parameter, performing fitting to obtain a third fitting normal distribution parameter of the threshold voltage distribution state to be fitted. In the embodiment of the invention, the fitting of the threshold voltage distribution state can be carried out in sequence, and only the fitting normal distribution parameter of the threshold voltage distribution state adjacent to the threshold voltage distribution state obtained by previous fitting is referred to in the fitting process of the threshold voltage distribution state, so that the data calculation amount is reduced, and the fitting efficiency is improved.