883results about How to "Improve fitting accuracy" patented technology

A method, apparatus, system, article of manufacture, and data structure provide the ability to extract a primitive quadric surface from point cloud data. Point cloud data is obtained in 3D space. The point cloud data is segmented to create a disjoined surface and a smooth surface segment based on spatial connectivity and surface smoothness. One or more shapes are extracted from the point cloud data using geometric fitting. The geometric fitting searches for one or more quadric surface parameters of a given type of model that provides a best agreement between selected points from the point cloud data and a resultant model.

The invention discloses a method for evaluating the reliability of a stepping stress quickened degradation experiment based on a time sequence. The method utilizes a correlation coefficient stationary sequence analytical method to describe non-equidistant stochastic information in stepping stress quickened degradation experiment data, establishes a regressive-non-equidistant autoregression degradation model on the basis of obtaining the respective advantages of a deterministic regression model and a stochastic correlation coefficient stationary sequence autoregression model, combines a grey theory to predict a product degradation trend and forms a function and a curve for predicting the life and evaluating the reliability of the stepping stress quickened degradation experiment based on the time sequence. The method obviously decreases the sample quantity of a product and shortens the time of the degradation experiment, thereby saving a large amount of expenses and resources, enhancing the fitting accuracy of the degradation model and enhancing the reliability of a reliability evaluating result.

The invention discloses a meter pointer angle identification method based on image processing, belonging to the field of image processing and solving the problem of large frame difference errors when a meter pointer is detected by adopting an absolute angle method. The meter pointer corner identification method comprises the following steps of: 1, acquiring a gray level image of a meter as an original image; 2, preprocessing to obtain a binarization image; 3, obtaining a preliminarily extracted meter panel scale image; 4, carrying out Hough circle transformation and determining a circle center of a meter panel; 5, according to the circle center of the meter panel, carrying out polar coordinate transformation on the original image to obtain a polar coordinate system image; 6, analyzing an angle value scale of the y axis of a polar coordinate system and establishing a scale angle sequence; 7, searching the position of a pointer in the polar coordinate system to obtain a pointer binarization image; and 8, obtaining an angle value of the pointer binarization image towards the y axis under the polar coordinate system according to the scale angle sequence obtained in the step 6, outputting actual data of pointing of the pointer of the meter panel, and finally identifying the angle of the meter pointer.

The invention discloses a transmission line oscillation positioning system based on micro inertial measurement combination. The transmission line oscillation positioning system comprises a monitoring center and a pole and tower monitoring host machine which are connected with each other, wherein the pole and tower monitoring host machine is wirelessly connected with at least two wireless inertialsensor nodes; and each wireless inertial sensor node comprises a triaxial acceleration sensor and a triaxial gyroscope. By the method for positioning by adopting the system, the wireless inertial sensor nodes acquire acceleration values and state space angles of a lead wire monitoring point in three directions; the monitoring center processes and analyzes the data of each monitoring point by adopting algorithms, such as Fourier transformation, a least square method, digital filtering, Kalman filtering, matrix coordinate transformation, frequency domain integral operation so as to fit to acquire an oscillation trace of a whole line, and corrects the oscillation line according to a digital model of a relation between an oscillation characteristic value and a micro meteorological condition; therefore, the precision of oscillation monitoring is improved and the most direct and intuitive monitoring of transmission line oscillation can be realized.

The invention provides a multiple-target operation optimizing and coordinating control method and a device of a garbage power generator. The multiple-target operation optimizing and coordinating control method includes the following steps. Operational parameters are downloaded from a data communication system (DCS), data judged as reasonable based on a threshold value are transmitted to a database. In terms of environmental protection, economy and safety of the power generator, three models are respectively set up by means of a support vector machine and a fuzzy neural network. A modified strength PARETO genetic algorithm is used for comprehensively optimizing multiple targets and then optimum operation parameters under the present working condition are worked out. Operational staff can adjust operation of corresponding parts based on the optimum operation parameters. The device comprises a data collecting module, a data filtering module, a database module, a data modeling module, an optimizing module, a forecasting module, a remote monitoring module, a monitor, an alarming module and a manual alarming module. The multiple-target operation optimizing and coordinating control method and the device of the garbage power generator achieve multiple functions of real-time forecasting, offline simulation, dynamic optimizing and the like and have the advantages of being strong in adaptability, good in self-learning ability, high in fitting precision, obvious in optimizing effect and the like.

The invention relates to the voice detection field, especially to a multi-precision-fitting-based automatic detection method for a copied sample voice. The method comprises: S1, a voice feature base is established; S2, a target speaker identification model is trained; S3, a sample copied voice detection model is trained; S4, speaker identification is carried out; and S5, sample copied voice detection. According to the automatic detection method, a phenomenon that a fake user obtains a copied sample voice for a vocal print identification system training model and then tries to invoke a server side interface directly instead of carrying out voice testing collection so as to submit the copied sample voice to the server side of the vocal print identification system directly for intrusion can be avoided; and thus security of the vocal print identification system can be improved.

The invention relates to an optical fiber wavelength demodulating light spectrum peak value positioning method, belonging to the technical field of optical fiber sensing. The method comprises the following steps: firstly, carrying out maximum value detection on obtained diffraction light spectrum energy data; determining a suitable threshold value according to a maximum value and utilizing the threshold value to carry out peak searching treatment; constructing a fitting array by the processed data; and utilizing an LM peak value positioning algorithm to carry out accurate peak value positioning on the spectrum. Compared with the existing power weighted algorithm and Gaussian-polynomial fitting algorithm, the algorithm has the advantages of high-precision positioned light spectrum peak value; and the method is easy to realize on a digital television through a simplified data processing process.

The invention discloses a monitoring method of salt ion content in saline soil based on a hyperspectral technology. The method comprises the following main steps: (1) collecting a soil sample in the wild and pre-treating the sample; (2) drying and sifting the soil sample; (3) measuring the total salt and eight salt ions in the soil sample; (4) carrying out hyperspectral measurement on the soil sample to obtain the original spectral reflectance characteristic curve of the soil sample; (5) processing the original spectral value, and carrying out correlation analysis between the spectral value and the measured salt ion content; (6) introducing the construction method of a support vector machine model, and performing model building and precision evaluation on the screened spectral transformation form. The monitoring method is relatively high in the prediction accuracy for each salt ion content in soil, provides a novel approach for the indoor measurement of the salt ion content in saline soil, and provides a novel way for achieving quick and lossless monitoring of the harm types of regional soil salinization.

The invention discloses a multi-camera calibration and alignment fitting method. A posture of a target object is observed by using a first camera group; a posture of a fitting object is observed by using a second camera group; and rotating coordinate systems of the two camera groups coincide. A calibration process comprises a first calibration stage, a second calibration stage and a third calibration stage. In the first calibration stage, internal parameters and a camera mounting angle of each camera in the camera groups are calibrated; in the second calibration stage, the position, in an output coordinate system of each camera in the camera groups, of a rotating center of a correction platform is calibrated and the rotating coordinate systems are established according to the condition that the rotating center of the correction platform is a fixed point after returning to zero; and in the third calibration stage, mapping and calibration are performed: the first camera group is mapped and calibrated by the second camera group according to the condition that the rotating coordinate systems of the first camera group and the second camera group coincide.

The invention relates to a curvature detection device and method for a drill rod. The device comprises a drill rod clamping part, a sensor transmission part and a control and operation part, wherein a three-jaw chuck, a V-shaped block and a stepping motor A are arranged on the drill rod clamping part; the three-jaw chuck is used for clamping one end of the drill rod, and the other end of the drill rod is placed on the V-shaped block; a 2D (two-dimensional) laser sensor and a stepping motor B are arranged on the sensor transmission part; the control and operation part comprises a single-chip microcomputer and a host. The detection method comprises the following steps: acquiring the three-dimensional coordinate information of hemi-profile data points of a plurality of sections of the drill rod by using the 2D laser sensor; processing and calculating a section central point through the host, and fitting the central lines of n sections; determining the maximum curvature and coordinate value of a maximum bending point, comparing with the curvature limit value of the drill rod, judging whether the curvature of the drill rod is qualified or not, and providing a judgment result for a user. The device is scientific in structure, the detection method is simple, the curvature information of the drill rod can be obtained efficiently and accurately, and technical support is provided for the increase of the production accuracy of the drill rod.

The invention is a container having at least two product chambers connected to form a common multi-chamber container system. Each of the product chambers includes at least one product discharge opening and the product discharge openings open into a common outlet channel. At least one of the product chambers includes at least one discharge valve device provided in the area of the product discharge opening that permits the discharge of a substance located in the product chamber but prevents return flow of the discharged substance into the product chamber. The discharge valve device also permits the admission of air into the product chamber.

The invention discloses a method for establishing a rock damage constitutive model based on least energy consumption principle, the method is as follows: a damage evaluation equation is deduced based on the least energy consumption principle; the rock damage constitutive model is established in a continuum damage theoretical framework by combination of the damage evaluation equation; the theoretical relation between deformation parameters of and parameters of the model is deduced, and conventional triaxial compression tests of the rock under different confining pressures can be performed to obtain strain-axial and radial strain relation curves of a specimen under corresponding confining pressures and mechanical strength parameters and strain parameters of the rock specimen; and by experimental results damage threshold can be determined, and the model parameters can be identified. The damage threshold in the rack damage evolution law can be determined by combination of the model and the dilatation startup property of the rock, the proposed model can better reflect the nonlinear mechanical behavior of a rock material under complex stress conditions, the constitutive model influenced by the damage threshold has higher fitting precision on the post-yielding section of the rock material, and the constitutive model has great reference significance and application value to the safety analysis of rock engineering under the complex stress conditions.

The invention discloses a method of performing a stress sensitivity experiment with artificial fractured carbonate rock cores, which includes following steps: a) measuring the content of minerals in natural carbonate rock and performing proportion to obtain a mineral mixture; b) determining the use amount of a cement, mixing the cement with the mineral mixture to manufacture an artificial substrate rock body, and measuring the stress sensitivity characters of the substrate rock body; c) forming fractures on the artificial substrate rock body to obtain fractured rock cores; d) measuring the stress sensitivity characters of the fractured rock cores in a non-filled mode; e) with a crude quartz sand cement, filling the fractured rock cores to simulate a semi-filled fractured rock core, and measuring the stress sensitivity characters of the rock cores in the semi-filled mode; and f) with a calcium carbonate cement, filling the fractured rock cores to simulate a full-filled fractured rock core and measuring the stress sensitivity characters of the rock cores in the full-filled mode. The method can satisfy the requirement of the stress sensitivity experiment better.

The invention discloses a sigmoid function fitting hardware circuit based on a Remez approximating algorithm. A sigmoid function fitting method is characterized by including the following steps that (1), the order of a fitting polynomial is determined; (2), a fitting interval of a sigmoid function is obtained; (3), a piecewise interval is obtained; (4), the fitting polynomial is obtained; (5), a coefficient storing module is designed; (6), a polynomial calculation module is designed; (7), a judgment module is designed; (8), the fitting hardware circuit is obtained; (9), a fitting execution interval where operands are located is judged; (10), a coefficient of the fitting polynomial is read; (11), fitting calculation is carried out in the polynomial calculation module. On the basis of reducing hardware resource consumption, the calculation accuracy is improved, the calculation speed is increased, and the flexibility of a calculation structure is promoted.

A method for manufacturing a golf club head includes providing a body having an opening delimited by an inclined surface, providing a striking plate with an inclined surface, engaging the inclined surface of the striking plate with the inclined surface of the body, applying a force to the striking plate to tightly embed the striking plate in the opening of the body, moving a rotating pin along an engaging area between the striking plate and the body to proceed with friction welding, and surface finishing the engaging area between the striking plate and the body. For carrying out the method, a friction welding structure for a golf club head includes a body having an opening delimited by an inclined perimeter surface and a striking plate having an inclined perimeter surface.

The present invention discloses a lake-reservoir cyanobacteria water bloom recognition method based on remote sensing monitoring and evidence fusion technology improvement, and belongs to the technical field of environmental projects. The method comprises: establishing a remote sensing inversion model, determining cyanobacteria water bloom breaking-out degree recognition indexes, pre-treating monitoring data, assigning belief function values, and recognizing monitoring area cyanobacteria water bloom. According to the present invention, an ensemble learning Bagging algorithm is fused into regression analysis of the remote sensing inversion model (ie., the normalization vegetation index model) to correct parameters of the model to obtain the corrected remote sensing inversion model so as to improve fitting accuracy; the problem of selection of indexes for lake-reservoir cyanobacteria water bloom recognition and value ranges thereof is solved; the problems of selection of the monitoring area and pretreatment of the monitoring data are solved; the problem of assignment of the belief function values is solved; and effective cyanobacterial water bloom recognition is achieved.

The invention relates to an electronic complete machine storage life assessment and prediction method. On the basis of analyzing a typical complete machine failure mechanism, an Arrhenius model-basedcomplete machine level acceleration factor calculation method is proposed; the method can perform electronic complete machine acceleration factor calculation by only three parameters; an accelerationtest is performed according to a calculation result and data is obtained; by utilizing an optimized GM(1,1) grey model-based prediction algorithm, a degradation law of natural storage and accelerationtest data is researched; the consistency of degradation trends of two groups of data is analyzed; closed-loop correction of acceleration factor calculation model parameters is realized by utilizing aconsistency analysis result; and finally electronic complete machine storage life prediction is realized by utilizing a corrected model. The method is simple and easy to understand and relatively high in prediction precision, and is suitable for engineering popularization.

The invention discloses a method for identifying and predicting a bus passenger flow influence factor based on geographically and temporally weighted regression (GTWR). The method comprises steps of: extracting a traffic zone hour bus passenger flow and calculating built environment density; 2, constructing a space-time three-dimensional coordinate system according to the time of a passenger flow observation point and latitude and longitude to calculate space-time distance, and reckoning a spatial regression weight matrix according to a Gaussian function and the distance; 3, calculating a relation between a passenger flow volume and land utilization under different space-time conditions based on the GTWR; and 4, obtaining a change of a relevant parameter to a coefficient according to the calculation to perform visualization processing in time and space, and analyzing an inherent law. The method takes account of the influence of a time factor on the bus passenger flow and the built environment relation, can deeply excavate an internal relation between the passenger flow and the land utilization, accurately predicts the bus passenger flow, and provides the scientific theoretical guidance for the bus line planning and operation management.

The invention discloses a high-speed press base reliability robust design method considering dynamic characteristics. The method comprises the following steps of establishing a reliability robust design model for the dynamic characteristics of a high-speed press base; sampling design variables and uncertain parameters by adopting a Latin hypercube sampling method and acquiring a response value of a sample point by a cooperation simulation technology; according to input-output information of the sample point, establishing an RBF (Radial Basis Function) neural network model for forecasting index values of the dynamic characteristics of the base in objective and constraint functions; carrying out iterative refinement by utilizing a double-layer nested genetic algorithm on the basis of interval constraint violation, a uniform interval dominance degree and an interval sequence vector so as to obtain the optimal solution of a base design scheme. The method carries out dynamic characteristic reliability robust design on the high-speed press base on the basis of the RBF neural network model according to the actual design requirements of the high-speed press base and can conveniently and rapidly obtain the design scheme of the high-speed press base which accords with the reliability requirement and has robust.

The invention relates to a streaming time series data dimensionality reduction and simplified representation method based on piecewise linear representation. The method comprises the following steps that: S1: presetting data segments and compression parameters; S2: carrying out data scanning on streaming time series data in a slide window way, and entering a streaming data buffer zone; S3: judging whether the fitting error of an initial segmented data segment exceeds an ME_ES (Maximum Error for Entire Segment) or not, carrying out reservation if the fitting error of the initial segmented data segment exceeds the ME_ES, and marking the initial data segment as "inseparable", and if the fitting error of the initial segmented data segment does not exceed the ME_ES, carrying out secondary optimal segmentation; and S4: moving the data segment which is marked as "inseparable" in the streaming data buffer zone out of the streaming data buffer zone, judging whether the streaming time series data to be processed is in the presence or not, if the streaming time series data to be processed is in the presence, returning to the S2, and otherwise, ending. By use of the method, data dimensionality reduction execution efficiency is guaranteed to a high limit, the fitting accuracy of data simplified representation is optimized to a certain range, and accuracy and the execution efficiency of data representation can be improved.

The invention discloses an I-V (Current-voltage) model parameter extraction method based on a gallium nitride high electronic mobility crystal valve. The method comprises the following steps of according to the physical meaning of the parameters of an I-V model, blocking the model parameters, decreasing the complicated degree of the I-V model, fitting transfer characteristic curves of actually-measuring pulse I-V and static I-V, extracting the model parameters of the corresponding block. Compared with the traditional parameter extraction method, the method disclosed by the invention has the advantages that the parameter extraction of the I-V model can be quickly and accurately completed, and the modeling efficiency of devices is greatly improved.

The invention relates to a method for measuring a QoE (quality of experience), which comprises the following steps: establishing a neural network of cascade structure according to a QoE evaluation model and training the neural network according to a tested and acquired sample database; judging whether the training output of the neural network meets the requirement, if not, updating the speed and position of particles by a PSO (particle swarm optimization) way, adjusting the weight of the neural network, and judging whether the test error of the QoE meets a preset value, and if so, completing the establishment of the QoE evaluation model and finally completing the measurement of the QoE. According to the method, a neural network model of cascade structure is adopted and whether to start a PSO algorithm is determined according to the training result of the neural network, so that the computing complexity of the model is minimized and the fitting precision is improved; and furthermore, the problems that the conventional neural network is slow in convergence speed, liable to fall into local minimum, too complicated in model and poor in precision are solved.

The invention discloses a force feedback-based industrial robot auxiliary assembling and flexible docking method which is characterized in that a low-six-dimensional-force sensor is manually pushed or twisted to enable a workpiece at the tail end of an industrial robot to do corresponding instant motion; the workpiece docking posture is subjected to fine adjustment to realize posture adjustment for mounting the workpiece; and meanwhile, the moving track of the industrial robot is corrected in real time through a force / posture mixing control method according to feedback information of a high-six-dimensional-force sensor and an error between actual acting force and ideal acting force, so that contact force can be retained in a desired range, and flexible docking is realized. According to the force feedback-based industrial robot auxiliary assembling and flexible docking method, by means of force feedback and algorithm control, mounting equipment can be completely fitted with the mounting surface, and the fitting precision is much higher than that obtained by visual observation and manual following adjustment fitting; and repeated visual observation of the docking condition and repeated adjustment of the equipment posture are not needed, so that the docking efficiency is greatly improved.

The invention discloses a method for detecting composite insulator interface defects; according to the method, DC voltage leakage current characteristic tests of a composite insulator before and after a water boiling test are performed; power function fitting of the DV voltage and corresponding leakage current data and curves obtained in the measurement is performed; interface defects of the composite insulator are discriminated according to the fitting results; and discrimination determination can be performed by using characteristic coefficient ratios or characteristic index change rates. The method provided by the invention overcomes the disadvantage that withstand voltage tests and steep impulse tests can not sensitively reflect composite insulator interface defects; the method of the invention is simple and effective, and is applicable to the detection of 35-1000 kV AC-DC composite insulator interface defects.

The invention discloses a ZigBee-based system for monitoring the wind drift of a power transmission line on line, which comprises a ZigBee wireless wind drift detector, a rod tower monitoring sub-machine, a general packet radio service (GPRS) communication module and a monitoring center, which are connected in turn, wherein the rod tower monitoring sub-machine and the GPRS communication module are connected with a power supply module respectively; and the rod tower monitoring sub-machine is connected with a wired wind drift detector. In a method using the system for monitoring, the actual wind drift condition of an insulator string of the power transmission line can be obtained accurately by acquiring the wind drift angles of the topmost and bottommost insulators of the insulator string of the power transmission line in real time or fixed time and by quantitative calculation and qualitative analysis, and thus, the most direct and efficient monitoring of the wind drift of the insulator string of the power transmission line is realized.

The invention provides a primary wave azimuthal anisotropy based fracture predicting method. The method comprises the steps of amplitude preservation preprocessing, azimuth angle grouping, super surface element processing, sub-azimuth velocity analyzing, pre-stack amplitude equalization processing, sub-azimuth migration imaging, seismic attribute extracting, fracture ellipse fitting, fracture development intensity predicting and the like. The step of azimuth angle grouping is carried out before the step of super surface element processing so as to ensure that azimuth angle groups are relatively consistent in variation amplitude and that trace gather data of the azimuth angle groups is relatively consistence and uniform in coverage times; dynamic correction is carried out on the anisotropy through introducing anisotropy parameters in sub-azimuth velocity analysis, far-offset data with azimuthal anisotropy characteristics being the most obvious is reserved, and the far-offset data is enabled to participate in sub-azimuth migration imaging and fracture ellipse fitting calculations. In addition, a Thiessen polygon based pre-stack amplitude equalization processing method is adopted to improve the amplitude preservation performance of migration results. The fracture predicting method provided by the invention is high in acquired fracture ellipse fitting precision, and fracture prediction results are more accurate.

A structure reliability dynamic response surface method based on discriminant analysis, comprising the steps as follows: determining a random variable; sampling via using Markov chain Monte Carlo method, determining an initial training sample point, calculating the function value of the initial training sample point, and determining status value thereof; establishing a training sample set and training a classification response surface to obtain a trained classification response surface; randomly sampling N sample points and estimating the status value, and then calculating failure probability; judging whether the failure probability meets condition of convergence, and stopping if the failure probability meets condition of convergence, otherwise, finding the failed sampling point, finding and calculating the function value of the most probable failure point and determining the status value; adding to the training sample set by using the most probable failure point and status value thereof as a new sample, and repeating the following steps until the condition of convergence is met. The method of the invention is simple in theory and efficient in calculation, which provides an efficient path for analyzing the reliability and precision of the complex structure that the single calculation thereof is time-consuming.

The invention discloses a wind power unit commitment contained modeling method considering predication error timing sequence distribution. A predication error segmentation fitting method under a timing sequence based on error characteristic analysis is proposed; fitting is performed based on t location-scale distribution to reduce a heavy-tail effect and improve the fitting precision, and the fitting method can be matched with a UC decision in timing sequence; next, a dual-quantile type UC decision model capable of taking conventional cost, extra standby cost and risk cost into considerations is established; the selection of confidence levels can be balanced through the restrictive relations among different costs; the standby classification can be guided by the division of the different confidence levels; the error timing sequence segmentation distribution is adapted by time varying confidence levels, so that the model is more economical, targeted and applicable; and finally, an improved hybrid particle swarm algorithm with a heuristic searching principle is adopted to solve a multi-variable mixed integer programming model of a text, and the effectiveness of the method is verified by example results.

The invention provides an automatic method for external parameter combined calibration of a vehicle-mounted three-dimensional laser radar and a camera. A laser radar and a camera are used to measure amoving plane target, a plurality of groups of point cloud and image data belonging to a target characteristic region are extracted from the measurement data; the characteristic region is fit, the external parameters of the laser radar and the camera are calibrated from coarse to fine by using a fitting result, and in coarse calibration, an initial conversion matrix between the laser radar and thecamera is obtained by using a PnP algorithm by using coordinates of a mass center point of the feature region fitted by the point cloud and the image as feature points; and fine calibration is carried out on the basis of coarse calibration, a feature region edge contour of point cloud fitting is projected to a camera normalization plane, a least square optimization function is constructed according to the similarity of a camera fitting edge and a projection edge, an initial conversion matrix is optimized, and a high-precision calibration relationship is obtained. Only the plane target with the special characteristic area is used, manual selection is not needed in the calibration process, and good calibration efficiency and accuracy are achieved.