34 results about "Total error" patented technology

The invention discloses a hand-eye calibration method suitable for a five-axis series-parallel mechanical arm, which comprises the following steps of: analyzing coordinate conversion error distribution characteristics according to an initial hand-eye calibration matrix calculation result, and separating a total error into two components, namely a translation error and a rotation error through an error vector decomposition algorithm, obtaining distribution rules and variation trends of the errors in different spatial directions; establishing a calibration precision evaluation system according to the verification result, calculating the repeated positioning precision and the absolute positioning precision of the system by counting and analyzing the position deviation data of multiple tests, and obtaining the system performance index in the current calibration state; a self-adaptive updating mechanism is established for system performance indexes, local updating of calibration parameters is automatically triggered when environment temperature change or small deformation of a mechanical structure is detected, and a compensation model is adjusted in an incremental learning mode to avoid comprehensive recalibration.

A measurement system for determining a noise figure of a device under test is described. The measurement system determines a first total error power based on the output signal by a first noise canceling technique, wherein the first total error power includes systematic errors originating in the device under test and in the measurement system. The measurement system determines a second total error power based on the output signal by a different, second noise canceling technique. The second total error power includes systematic errors originating in the measurement system. The second total error power further includes noise originating in the device under test and in the measurement system outside of the measurement instrument. The measurement system is configured to subtract the first total error power from the second total error power, thereby obtaining an external noise power.

The invention relates to the technical field of computers, in particular to a precision configuration method and system for multi-level calculation, a terminal and a storage medium, and the method comprises the steps: configuring initial precision for each level in the multi-level calculation; in the multi-level calculation process, when each level is executed, recording the absolute deviation between the theoretical value and the alternative value of the current level calculation as the error of the current level, meanwhile, accumulating to obtain the total error when the multi-level calculation is executed to the current level, and judging whether the total error meets a preset threshold condition or not; and if not, reconfiguring the precision of the corresponding level according to a preset precision adjustment strategy until the corrected total error meets a preset threshold condition. The method is used for solving the problem that the calculation result of the final calculation layer of the multi-level calculation with more levels is easy to increase and is not beneficial to the total error control target in the multi-level calculation scene.

The present invention provides a computer program, a method for generating a trained model, an information processing method, and an information processing device that can be expected to perform searching of time-series data related to substrate processing.　This computer program causes a computer to execute a process for generating a trained model that receives time-series data related to substrate processing as an input and outputs feature quantities of the time-series data, the computer program causing the computer to execute a process in which: training data in which time-series data related to substrate processing and a plurality of correct answer values related to a process performed on the basis of the feature quantities of the time-series data are associated with each other is acquired; time-series data of the acquired training data is inputted to the trained model, and feature quantities outputted by the trained model are acquired; a plurality of errors are calculated on the basis of the acquired feature quantities, the correct answer values for the training data, and a plurality of loss functions; the total error is calculated on the basis of the calculated plurality of errors; and the parameters of the trained model are updated on the basis of the calculated total error.

A method, apparatus and product comprising: obtaining alternative quantum circuits implementing a quantum program, each having a respective design error; identifying, for each quantum circuit, respective quantum resources that are used by the quantum circuit and are associated with respective hardware errors of a quantum computer; determining a total hardware error bound for the quantum circuit; determining a total error bound for the quantum circuit; applying a search algorithm over a plurality of total error bounds of the plurality of alternative quantum circuits, to identify an optimal total error bound that is associated with an optimal quantum circuit; and providing the optimal quantum circuit for execution on the quantum computer.

To provide a resolver decoding method, a system, a device, and a storage medium regarding the resolver technical field.SOLUTION: This resolver decoding method comprises: a step of generating a first sine reference signal and a first cosine reference signal; a step of calculating a difference between the first sine reference signal and a real time sine signal of a resolver to acquire a first difference signal and calculating a difference between the first cosine reference signal and a real time cosine signal of the resolver to acquire a second difference signal; a step of inputting the first and second difference signals to a preset error model to acquire an overall error; a step of repeating the step of generating first sine and cosine reference signals when it is detected that the overall error is larger than an error threshold value; and a step of setting an angle output from the resolver as a decoding result when it is detected that the overall error is smaller than or equal to the preset error threshold value. This method improves the decoding accuracy of the resolver.SELECTED DRAWING: Figure 1

A system and related method includes a memory and processing circuitry to receive data and corresponding expected error detection code values. The processing circuitry processes data in the at least two portions by calculating and storing error detection code values for the respective portions in the memory. The processing circuitry is then to calculate a total error detection code value based on the respective error detection code values of the at least two portions. When the total error detection code value does not match the expected error detection code value, the processing circuitry is to correct the at least one portion and to correct the at least one portion by calculating an updated error detection code value for a respective one of the corrected portions and calculating an updated total error detection code value based on the updated error detection code value for each corrected portion and the stored error detection code value. The corrected portion is processed.

The invention discloses a cross-domain high-precision impact-free leap second processing method which is suitable for all safety-critical systems (including cross-domain scenes of finance, aerospace, medical treatment, industry and the like), wherein the time synchronization error of the safety-critical systems is less than or equal to 1ns and the time continuity is not interrupted. The core scheme is as follows: pre-calibration before leap second and enabling an initial synchronization error to meet the subsequent calibration requirement, uniformly dividing the total step number corresponding to a transition period into M (a positive integer greater than or equal to 2) sections, calculating a uniform step length, executing adjustment section by section, dynamically sharing an error (an absolute value of a delta E section is less than or equal to 0.000001 [mu] s * the step number of each section), and combining two-way closed-loop calibration (adjusting a time sequence of related steps). The total error in the whole process is smaller than or equal to 0.2 ns (preferably smaller than or equal to 0.1 ns), and the step length is not changed suddenly (the difference value of adjacent steps is smaller than or equal to 0.000001 microseconds). The method supports bidirectional compensation and cross-field seamless adaptation, can meet compliance requirements of different fields without modifying a core scheme, is high in anti-interference capability, low in landing cost and comprehensive in protection range, can effectively prevent various evasion behaviors, and is suitable for various high-precision scenes such as high-frequency transactions, aerospace navigation, medical surgical robots and the like.

The invention discloses an aero-engine test bed performance test analysis method and system, and relates to the technical field of test bed performance tests.The aero-engine test bed performance test analysis method comprises the steps that the error times in error records caused by error objects representing test bed parts and the error total value representing the sum of error values generating all error values are obtained; evaluation values of the error objects are obtained after calculation, and high-frequency labels, conventional labels and low-frequency labels are sequentially marked on the error objects according to a set proportion according to the descending order of the evaluation values; the problems of the test bed, which are long-term or high-frequency problems, can be determined; then, for the test object capable of detecting the fault of the error object, determining verification detection according to the data distribution condition of the previous detection time of the test object, and giving different weights in combination with the detection accuracy of the test object and a compatible value representing the number of the error objects capable of being detected by the test object, so as to obtain an available value of the test object; and marking the test object with the maximum available value as the selected test object.

The invention relates to a method and a device for determining the quality of a collection bobbin (14) containing a synthetic thread (30), in which during the production of the collection bobbin (14), a total error (61) is compiled, in which the total error (61) is classified as a quality-dependent error (63) or a quality-independent error (62), in which the quality-dependent error (63) is used to determine a quality class (7) of the collection bobbin (14).

This application discloses a method for obtaining a similarity threshold, a data processing method, and a product, all belonging to the field of software development. The method includes: obtaining at least one historical similarity; obtaining a first correct count and a first error count corresponding to the first historical similarity, where the first historical similarity is any one of the at least one historical similarity; obtaining a total correct count and a total error count; and determining a similarity threshold based on the at least one historical similarity, the at least one first correct count, the at least one first error count, the total correct count, and the total error count. This method can improve the accuracy of software version repairs.

Techniques are described for quickly finding a compression mode for BC7 encoding by modelling three sources of error in compression, namely, projection error, endpoint quantization error, and interpolation index quantization error. The mode with the lowest total error is selected for a block to be compressed.

A receiver including an equalization component to receive a signal comprising a sequence of samples corresponding to symbols and generate an equalized signal with an estimated sequence of symbols corresponding to the signal. The receiver further includes a decision generation component to detect that an aggregate error level associated with the equalized signal exceeds a saturation threshold level. The decision generation component identifies a set of errors including a first error associated with a first symbol having a highest error level and a last error associated with a last symbol. The decision generation component generates, based on the equalized signal, a decision including a sequence of one or more bits that represent each symbol of a first subset of the sequence of symbols and a confidence level corresponding to the decision, where the confidence level is based at least in part on a distance between an error level of each symbol and a level of the first error.

The present application discloses a resolver decoding method, system, device and storage medium, and relates to the technical field of resolvers. [Solution] A resolver decoding method is disclosed, which includes the steps of generating a first sine reference signal and a first cosine reference signal, calculating a difference between the first sine reference signal and a real-time sine signal of the resolver to obtain a first difference signal and calculating a difference between the first cosine reference signal and the real-time cosine signal of the resolver to obtain a second difference signal, inputting the first difference signal and the second difference signal into a predetermined error model to obtain an overall error, repeating the step of generating the first sine reference signal and the first cosine reference signal when it is detected that the overall error is greater than an error threshold, and taking the angle output by the resolver as the decoding result when it is detected that the overall error is equal to or less than the predetermined error threshold. The technical solution of this application aims to solve the technical problem of how to improve the accuracy of resolver decoding.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an injector time calibration method, device and equipment and a medium, the method is applied to an MCU of an injector, and the method comprises the steps that whether a PPS IN signal exists or not is detected, and a detection result is obtained; based on a detection result, determining a timing condition; obtaining an error coefficient, wherein the error coefficient is a first time difference expanded by the equipment per second; based on the error coefficient and the timing condition, total error time needing to be compensated is calculated; and based on the total error time, a compensation mechanism is executed, so that the synchronization precision and the travel time precision can be improved.

Embodiments of the present application provide a neural network input adjustment method, device, medium and equipment based on an RRAM array. The method comprises: obtaining input data comprising to-be-input values corresponding to each row in the RRAM array; determining ideal unit current values corresponding to each row in the RRAM array and actual unit current values after weight writing according to a plurality of weights of the neural network; determining a total error of the RRAM array between actual output and ideal output according to the to-be-input values, the ideal unit current values and the actual unit current values corresponding to each row in the RRAM array; if the absolute value of the total error is greater than or equal to a first threshold, calculating a cumulative error corresponding to each row in the RRAM array row by row, and adjusting the to-be-input value corresponding to each row according to the cumulative error corresponding to each row to obtain target input data. The technical solution of the embodiments of the present application improves the accuracy of the calculation result based on the RRAM device, and further ensures the accuracy of the recognition result of the neural network.

Devices, systems, and methods for improving performance of an iterative decoder in a non-volatile memory are described. An example method includes obtaining a hard error percentage, a spare byte percentage, and a total error count associated with a first page in the memory device, determining that the total error count is greater than a threshold value that is determined based on the hard error percentage and the spare byte percentage, and performing, based on the determining, a data relocation operation that relocates at least some data from the first page to a second page different from the first page.

The present invention discloses a calibration method for estimating parameters of a stochastic microscopic traffic simulation model. First, the number of repetitions N is determined for different models and data sets; the calibration range of the parameters is determined, and the calibration range of the parameters is consistent with the physical meaning of the parameters; parameters are extracted and iterated within the calibration range of the parameters, and the speed and spacing information of the simulation output by the model is used; multiple rounds of simulations are repeated, and the total error is calculated for the speed and spacing information output by each simulation and the actual experimental data, and the minimum value of the total error of N simulations is taken as the objective function; the speed and spacing are respectively selected as performance metric indicators, and an optimization algorithm is used to solve for the optimal parameters; the performance of the two sets of parameters is verified, the error on the data set is calculated, and the smaller one is selected as the calibration result. The present invention realizes the calibration of parameters by establishing an optimization framework capable of calibrating the parameters of the stochastic model.

The present invention provides a mesh encryption method, a computer device, a storage medium and a program product, including: obtaining an input initial mesh, frequency, maximum number of iterations and a preset S-parameter change threshold; reading the initial mesh and performing mesh boundary recognition; constructing a finite element matrix and solving it to obtain the S-parameter matrix and the current S-parameter change amount at the current iteration; constructing the mapping relationship between the four faces of each tetrahedron and adjacent tetrahedrons when the current S-parameter change amount is greater than the preset S-parameter change threshold; calculating the volume charge, volume current density, surface charge and surface current density of each tetrahedron; calculating the total error of each tetrahedron, calibrating the tetrahedrons that need to be encrypted and performing encryption. This solution can identify the mesh regions that have the greatest impact on the S-parameters by introducing volume element error indicators and surface element error indicators, and perform local encryption on them, thereby reducing the consumption of computing resources and improving the solution accuracy.

The application provides a camera and IMU joint calibration method, which comprises the following steps: step 1, initializing a target camera and a target IMU to obtain initialization parameters; step 2, constructing a first error L1 and performing one-time iterative optimization by using an LM algorithm; step 3, updating the initialization parameters and the bias of the target IMU acceleration, and determining whether the total error determined by the first error is less than a set threshold value; if yes, entering step 7, otherwise, entering the next step; step 4, obtaining gyro noise and acceleration noise according to the bias of the gyroscope and the acceleration of the target IMU at each time after updating; step 5, constructing a second error according to the acceleration noise and the first error L1; step 6, performing iterative optimization on the second total error L2 by using the LM algorithm, and determining whether the error is less than a preset threshold value; if yes, entering the next step, otherwise, entering step 3; and step 7, obtaining a calibration quantity. The application realizes joint calibration of the camera and the IMU, and has high efficiency and high accuracy.

The invention discloses a leveling example generation method and a teaching auxiliary system, and belongs to the technical field of surveying and mapping teaching. The core of the method comprises the following steps: deducing a theoretical observation value through reverse calculation based on a known benchmark elevation and a measurement section distance; dynamically generating an observation record with a random error in combination with an error parameter within a standard tolerance; for an attachment route, a closed loop and a leveling network, a closing error is automatically distributed and a full-mean error / accidental-mean error is calculated. The system module comprises a route design module which supports diversified format data import and dynamically adjusts route levels and structures; the observation simulation module is used for realizing height difference and distance reverse derivation and error simulation; the handbook output module is used for generating a training handbook containing key field spaces according to a standard format; and the intelligent scoring module automatically verifies the submitted result of the user and scores the submitted result. The method solves the problem of lack of examples in teaching and competition, realizes automatic generation and intelligent evaluation of diversified and standardized leveling training examples, provides an efficient tool for surveying and mapping major teaching and skill competition, and has outstanding practical value.

The invention provides a machine tool expected running track error compensation method and device and a medium, and relates to the technical field of machine tool expected running track error compensation. The method comprises the steps that the complexity of an original running track is determined; if the complexity of the original moving trajectory is higher than a first preset complexity threshold value, performing total error prediction on an expected moving trajectory corresponding to the original moving trajectory subsequently; if the complexity is between a second preset complexity threshold value and a first preset complexity threshold value, dividing the original running track into a plurality of original running sub-tracks; performing error prediction on a track segment corresponding to each target original operation sub-track in the expected operation track; if the complexity of the original moving trajectory is smaller than or equal to a second preset complexity threshold value, error prediction and compensation are not carried out on the expected moving trajectory; on the premise of ensuring the compensation precision of the key part, the error prediction calculation amount of the non-key sub-track can be greatly reduced, and the computing power consumption of a machine tool is reduced.

Techniques are described for quickly finding a compression mode for BC7 encoding by modelling three sources of error in compression, namely, projection error, endpoint quantization error, and interpolation index quantization error. The mode with the lowest total error is selected for a block to be compressed.

The invention discloses a precise error quantification and traceability analysis method and system, and relates to the technical field of precise measurement and error analysis, and the method comprises the following steps: carrying out the layering preprocessing of original data, so as to remove abnormal values, and constructing a comprehensive multi-dimensional error quantification model; based on the multi-dimensional error quantification model, positioning a key error source with the highest contribution to a total error in a mode of combining probabilistic simulation and sensitivity analysis; and analyzing the coupling relationship between different error sources and the influence of the coupling relationship on the total error. According to the accurate error quantification and traceability analysis method provided by the invention, a layered preprocessing strategy is adopted, abnormal values in original data are effectively eliminated, a solid foundation is laid for follow-up analysis, a built multi-dimensional error quantification model comprehensively covers random errors, system errors and truncation errors, and the accuracy of the analysis is improved. The integrity and accuracy of an error quantification result are ensured, and a clear direction is provided for error control.

The invention discloses a laparoscopic surgery robot tail end position error compensation control method which comprises the steps that S1, a laparoscopic holding arm and an instrument arm of a laparoscopic surgery robot are dragged to a plurality of random positions in a working space, and errors of the tail end positions of all mechanical arms at all the random positions are obtained; s2, according to the error obtained in S1, obtaining the error of each connecting rod parameter; s3, calculating the tail end position of the endoscope holding arm and the tail end position of the instrument arm after the error is considered according to the error obtained in the S2; acquiring a target end position of the instrument arm, thereby calculating a target value of a relative motion joint of the instrument arm; and S4, calculating the total error of the relative motion joint of the instrument arm, calculating the execution value of the relative motion joint of the instrument arm after the error is considered in combination with the target value obtained in the S3, and inversely solving the execution value and issuing the execution value to a driver for execution. According to the method, the total error can be compensated under the condition of not iteratively solving the error joint angle.

The invention discloses an error calibration compensation method and system for a three-coordinate measuring instrument, and relates to the technical field of precision measurement. Comprising the following steps: S1, fusing a geometric error compensation sub-model, a thermal deformation error sub-model and a dynamic load error sub-model to determine a total error compensation amount; s2, correcting the original target instruction according to the total error compensation amount, obtaining the actual position coordinates of each axis, and obtaining the final corrected position coordinates through a feedback compensation algorithm; and S3, determining a confidence interval corresponding to each final corrected position coordinate through the digital twin model of the three-coordinate measuring instrument and the uncertainty variable, and determining a corresponding monitoring result through a comparison result between an interval width corresponding to the confidence interval and an interval threshold range. According to the invention, systematic compensation of various error sources of the three-coordinate measuring instrument under different working conditions is realized, a motion instruction can be corrected in advance, and errors are reduced from the source.

To provide functional system and method for data filtering for reducing outlier bias of a trend line.SOLUTION: The present invention is directed to an objective, statistic method of eliminating an outlier from a data set. The method has the steps of determining a bias based on an absolute error, a relative error or both of them, calculating an error value from calculation of data, model coefficients or trend lines, and eliminating an outlier data record if the error value exceeds a reference set by a user. For the purpose of a repeated calculation such as an optimization method, the eliminated data is re-applied to the model for use in calculation of a new result upon each repeated calculation. With use of a model value for a completed data set, a new error value is calculated, and an outlier bias reduction process is re-applied to minimize the total error for the model coefficient and the outlier eliminated data repeated until the value reaches a user definition error improvement limit. Filtered data is used for the purpose of authentication, the outlier bias reduction, and data quality operation.SELECTED DRAWING: Figure 1

The present disclosure relates to a cloud service control method and device, a storage medium and an electronic device, to specifically handle memory errors and improve the processing efficiency of memory errors. The method comprises: when a memory error is detected, determining a memory address at which the memory error occurs, and determining an error type of the memory error according to the memory address, the error type being used to identify whether the memory error will cause a virtual machine to crash; determining a total error number of the detected memory error; and according to the error type of the memory error and the total error number, controlling a cloud scheduler to schedule a corresponding scheduling service from a scheduling service library, the scheduling service library storing a first scheduling service and a second scheduling service, the first scheduling service being used to control restarting a virtual machine on an original host to which the virtual machine belongs, and the second scheduling service being used to control migrating the virtual machine from the original host to which the virtual machine belongs to another host.

The invention discloses a laboratory data fault-tolerant control method considering total errors, and belongs to the field of medical data statistics, and the method comprises the steps: building a fault-tolerant mechanism which is used for defining a fault-tolerant object and a fault type; inputting the fault-tolerant mechanism and related original data of the fault-tolerant mechanism into a first prediction model to establish a data set, and dividing the data set into a training data set and a test data set in proportion; calculating a total error of the data samples in each training data set, wherein the total error comprises a proportional error, a constant error and an index error; inputting the proportional error, the constant error and the index error into a data set of the first prediction model to obtain a second prediction model; the fault-tolerant object or fault type is redefined when the predicted value error of both the first prediction model and the second prediction model is above a threshold. According to the method, the reliability of the algorithm can be ensured, a fault-tolerant mechanism can be adaptively adjusted, and the fault-tolerant capability is further expanded.

A low-complexity AGENT error precision control method solves the problems of difficult control, complex compensation, and poor real-time performance of AGENT full-process error + core Jiang's initial error decomposition formula Δ=Δ₁+Δ₂+Δ₃, combined with phase compensation and closed-loop verification, total error ≤1.1%, calculation period ≤1s + for industrial / creative / public service AGENT error control.