85 results about "Error analysis" patented technology

An instrument system that includes an elongate body, a first optical fiber, a second optical fiber, and a controller is provided. The first optical fiber is operatively coupled to the elongate body and has a first strain sensor provided on the first optical fiber. The second optical fiber is operatively coupled to the elongate body and has a second strain sensor provided on the second optical fiber. The controller is operatively coupled to the first optical fiber and the second optical fiber and is adapted to receive a first signal from the first strain sensor and a second signal from the second strain sensor and to compare the first signal with the second signal.

The invention relates to an on-line detection system and detection method of machining accuracy of parts for a numerically controlled lathe. The detection system comprises a chuck, a workpiece and a lathe probe, wherein the workpiece is arranged on the chuck, and the lathe probe is arranged at the side of the workpiece; and a signal output terminal of the lathe probe is connected with a controller of the detection system. The detection method comprises the following steps that 1) the lathe probe is arranged at the side of the workpiece; 2) the lathe probe is calibrated; 3) the measuring path and code generation of geometrical parts and curved-surface parts are planned; and 4) the heat distortion temperature error compensation and machining error analysis of parts are carried out by the controller of the detection system. The parts do not need to leave the lathe after being machined, a touch probe is used for replacing a turning tool, and the detection path of the probe is automatically planned and the detection code is generated according to the geometric outline and detection items of the machined workpiece; and through a communication interface of the numerically controlled lathe, the detection code is driven, so that the automatic measurement of the size and precision of size and form of the machined part is realized, and the detection accuracy and efficiency of the workpiece are improved.

The invention provides a device and method for automatically testing and calibrating parameters of battery management systems. The system comprises a battery model database unit, wherein the input end of the battery model database unit is connected with a testing man-machine interface; the battery model database unit is simultaneously connected with a parameter automatic adjustment unit; the output end of the battery model database unit is connected with a test data stream generation unit; the test data stream generation unit outputs signals to a signal output unit; one path of the signals of the signal output unit are output to a battery management system to be tested and the other path of the signals thereof are output to an error analysis unit; one path of input of the error analysis unit is the signal output unit and the other path of input thereof is the battery management system; the computation results of the error analysis unit are output to the parameter automatic adjustment unit; the adjustment amount is computed; the results after adjustment are output to the battery model database unit; then the battery model database unit outputs new data stream; and such a cycle is carried out until the parameter precision meets the design requirement. The device and the method have the following beneficial effects that: the parameters of the battery management systems can be automatically adjusted to optimize the parameters; and the device and the method have the advantage of automatic test and calibration of the parameters.

The invention discloses a method and system for monitoring rope arrangement faults of a drum steel wire rope on the basis of machine vision. An industrial camera is arranged on the back surface of a drum, the automatic calibration of internal parameters of a vision system is finished by sampling static images of a designated marker, the industrial camera is used for performing continuous image sampling on the steel wire rope arranged on the drum, and the horizontal coordinates of the center point of the steel wire rope just wound on the drum in an image coordinate system are acquired by virtue of a corresponding image processing algorithm; according to the parameters obtained by the automatic calibration, the horizontal coordinates are converted into corresponding position coordinates in an actual coordinate system, so that the center distance between the steel wire rope just wound on the drum and an adjacent steel wire rope is calculated; and whether a fault happens is judged according to the center distance, and a fault signal is transmitted to a monitoring center to give an alarm. Whether the drum steel wire rope has rope arrangement faults is quantitatively judged by virtue of the industrial camera, the data processing and error analysis are simple and convenient, the reliability is high, and the practicability is strong.

The invention relates to a combined method for predicting short-term wind speed. The method comprises the following steps: 1, extracting historical wind speed data from a related data acquisition and monitoring control system; 2, performing sequence analysis on the extracted wind speed data by adopting clustering empirical mode decomposition; 3, respectively establishing a least squares support vector machine model for each subsequence obtained through the clustering empirical mode decomposition, and comprehensively selecting three parameters which influence the prediction effect of the least squares support vector machine by adopting an adaptive disturbance particle swarm algorithm and learning effect feedback; 4, predicting by selecting the optimal parameters according to the learning effect of the least squares support vector machine; 5, superposing the prediction result of each subsequence, and obtaining a wind speed prediction result; and 6, performing error analysis on the wind speed prediction result. The modeling process is simple and practical, and the wind speed can be rapidly and effectively predicted, so that the wind power is effectively predicted, and the method has significance on safety, stability and scheduled operation of the power system and has wide popularization and application values.

The invention relates to an expressway traffic flow parameter prediction method and a system based on a gated neural network GRU. The method comprises the following steps: according to the high-speedroute information of the collected data and the longitude and latitude information of a toll station of a section, the research section data is initially screened, then the abnormal data is cleaned according to the manifestation of the abnormal data, and then the velocity time series is calculated in a certain time period, and then the missing data is filled in the missing time series, the filledspeed time series data is divided into training data and test data, and the traffic flow prediction model is obtained by training the training data, finally, the error analysis is carried out by usingthe predicted data and test data. The invention utilizes the advantage of GRU long-time memory data characteristics to obtain higher prediction accuracy, relatively less prediction model parameters and good portability, and can provide technical support for traffic guidance and traffic accident management and dispatching of traffic management departments.

The invention relates to a short-term wind-speed combination forecasting method. The method comprises the following steps: 1, extracting the history data of wind-speed sequence; 2, allowing the extracted data to be subjected to sequential analysis through an EMO (Empirical Mode Decomposition) method; 3, reconstructing a phase space for each sequence obtained form the EMO; 4, adopting the reconstructed phase spaces of all sequences to train a built improved utmost learning machine predictive model, and stacking the forecasting results of all sequences to obtain the wind-speed forecasting result; and 5, allowing the result to be subjected to error analysis. The forecasting method is simple and practical in modeling process, and can effectively and quickly forecast the wind speed, to effectively forecast the wind power of a grid-connected wind farm. Therefore, the method has an important significance on the safety, stability and scheduling operation of the electric power system under power-wind grid-connection condition.

The invention discloses a global gravity field model inversion method. The method comprises the following steps: step 1: acquiring low-orbit gravity satellite data, and preprocessing the low-orbit gravity satellite data; step 2: calculating a pure Earth gravitation acceleration observation value by virtue of numerical differentiation; step 3: establishing a novel observation equation according toan observation value constant weight strategy; and step 4: estimating parameters, and obtaining an Earth gravity field model position coefficient. According to the acceleration domain residual error analysis of a reference model, a symmetric Toeplitz covariance matrix is established by directly adopting an autocorrelation sequence as a basic element, a complicated operation process of a high-dimensional huge matrix can be effectively avoided when the acceleration covariance matrix is calculated by an orbit prior dense array by virtue of an error propagation law, and the time for fixing the weight can be reduced, so that the calculation efficiency of the gravity field model can be improved. Compared with a classical method, an algorithm provided by the invention is simple and efficient in operation; and moreover, the stability and reliability of the inversion calculation efficiency of the gravity field model can be ensured.

The invention provides a displacement detection method for a moving device in a tunnel. The displacement detection method effectively corrects displacement of the moving device, and provides more accurate displacement data for obtaining tunnel images through the moving device. The tunnel is divided into a plurality of given sections, infrared geminate transistors are utilized for outputting displacement sensor distance measurement reset signals, and therefore the problem of continuous accumulation of errors caused by long-distance displacement measurement can be effectively solved; an image algorithm is utilized for detecting joint positions of pipe pieces, carrying out measurement many times, and carrying out error analysis, by analyzing a large number of data, a probability distribution model of the displacement measurement errors caused by slipping of a wheel track and S-shaped lines can be obtained, the error expectation is estimated, the total errors are distributed, and the measurement errors of each section are reduced.

Techniques for correcting gradient non-linearity bias in mean diffusivity measurements by MRI systems are shown and include minimal number of spatial correction terms to achieve sufficient error control using three orthogonal diffusion weighted imaging (DWI) gradients. The correction is based on rotation of system gradient nonlinearity tensor into a DWI gradient frame where spatial bias of b-matrix is described by its Euclidian norm. The techniques obviate time consuming multi-direction acquisition and noise-sensitive mathematical diagonalization of a full diffusion tensor for medium of arbitrary anisotropy.

The invention discloses a task information analysis method and device. The method comprises the following steps of: monitoring the running status of a submitted task; if report error information of the task is monitored, obtaining a log of a report error task corresponding to the report error information; carrying out text analysis on the log of the report error task to obtain report error reason information of the report error task; and comparing the report error reason information with a pre-stored report error reason analysis table to obtain a report error analysis result corresponding to the report error reason information, wherein the report error reason analysis table comprises pre-stored report error reason information and report error analysis results corresponding to the pre-stored report error reason information. The task information analysis method and device disclosed by embodiments of the invention can automatically analyze the running statuses of the tasks without manual operation.

The embodiment of the present invention discloses a method for measuring the grounding resistance of a grounding grid, comprising the following steps: setting a measuring loop, a voltage electrode and an auxiliary current electrode; measuring the distance between the voltage electrode and the auxiliary current electrode and the distance between the voltage electrode and the grounding grid According to the distance and the potential difference, the optimal parameter of the analytic function is obtained by the least square method; the grounding resistance value is calculated by using the optimal parameter and the analytic function. By measuring the auxiliary current electrode potential distribution, combined with the analytical calculation formula of the auxiliary current electrode potential, the least squares method is used to analyze the error of the fitting results and parameter selection, and determine the auxiliary current electrode measured potential distribution curve. The auxiliary current pole potential drops the potential of the grounding grid to be measured, and obtains the accurate value of the grounding potential rise of the grounding grid, so that a more accurate grounding resistance can be obtained.

The invention provides a method, system and device for carrying out online self-correcting on the optimal gain of a wind generating set. The method comprises the following steps of: acquiring the real-time temperature of the peripheral environment; acquiring the actually-measured data of unit running; drawing an actually-measured rotating speed-torque curve, comparing the actually-measured rotating speed-torque curve with a theoretical curve, and calculating a root-mean-square error; calculating an optimal gain value, and carrying out correction on the optimal gain value; calculating a real-time gain value, and carrying out gain modification on the optimal gain value; carrying out gain limit on the optimal gain output; and transmitting the optimal gain value back to a master control PLC (programmable logic controller) through network communication. The system comprises a temperature calculation module, a data acquisition module, an error analysis module, a gain correction module, a gain modification module and a gain limit module. The device mainly comprises a temperature sensor, a temperature processing circuit, a self-correcting system and a master control PLC. According to the invention, the online correction on the optimal gain can be realized, and the cost is low, thereby facilitating popularization.

A controller (e.g., a memory controller) includes initial error analysis logic (e.g., a section of a Reed Solomon or BCH codeword decoder) that determines an error count for a data element. The data element may be data stored in the memory of a memory device (e.g., a flash memory device) that incorporates the controller. Comparison logic in the controller determines when the error count exceeds a power control threshold. When the error count exceeds the power control threshold, control logic in the controller reduces the operational speed of subsequent error analysis logic (e.g., a different section of the Reed Solomon or BCH codeword decoder) for the data element. For example, the subsequent error analysis logic may be error locator logic, such as Chien search logic, that determines where the errors exist in the data element.

The invention provides an intelligent anti-error analysis method and system, providing an ''intelligent'' application for drawing up tickets. The method and system can realize ''one-button'' intelligent ticket issue by regulation and control personnel, standardize ticket issue terms, shorten ticket drawing-up time, mitigate the workload of the regulation and control personnel, and increase the work efficiency of the regulation and control personnel. The method and system meanwhile monitor intelligent operation of drawing up tickets, and safe related functions in operation ticket processes, and provide safe related functions of remote control for real-time monitoring and early warning. The method and system can automate the work of dispatchers and monitors, substantially reduce the workload of the dispatchers and monitors, increase work efficiency, and reduce safety risk.

The invention discloses a medium-term load forecasting system for regional power grid master devices. The medium-term load forecasting system comprises a data sheet maintenance module, a trend analysis module, a load forecasting module, an error analysis module and a temperature influence module. The data sheet maintenance module is used for managing a data sheet and processing data at an earlier stage. A user can visually know load development trends of the master devices by the aid of the trend analysis module. The load forecasting module builds a forecasting model base including five forecasting methods which are a month coefficient method, a trend ratio method, a link relative ratio method, a multiple proportion method and an increase rate method, and the user can voluntarily select the forecasting methods. The error analysis module is used for computing absolute percentage errors of forecasting results of the master devices and analyzing the optimal forecasting method suitable for the master devices according to error results. The temperature influence module is used for adding temperature influence factors based on load forecasting to obtain load forecasting results at different temperatures ranging from 35 DEG C to 40 DEG C in summer.

A machine for evaluating failing software programs, a non-transitory computer-readable storage medium with an error analysis program stored thereon and an error analysis program executed by a microprocessor are disclosed. In one embodiment a machine for investigating an error source in a software program includes a microprocessor coupled to a memory, wherein the microprocessor is programmed to determine whether a failure of an error-prone program step occurs reproducibly by providing the software program with the error-prone program step, executing program steps preceding the error-prone program step, executing the error-prone program step a number of times and calculating a failure probability for the error-prone program step.

The invention relates to a short-term wind power combination prediction method which comprises steps of 1, extracting wind power sequential data from supervisory control and data acquisition (SCADA) related to a wind power plant; 2, performing sequence analysis on the extracted wind power sequential data through ensemble empirical mode decomposition; 3, reconstructing phase space of sequences obtained through the ensemble empirical mode decomposition; 4, according to the data obtained by reconstructing the phase space of the sequences, training the established wavelet neural network prediction model, and superposing predication results of the sequences to obtain a wind power prediction result; 5, performing error analysis on the wind power prediction result. By means of the short-term wind power combination prediction method, the modeling process is simple and practical, and wind power predication can be quickly and effectively performed. Therefore, the short-term wind power combination prediction method has great significance in safety, stability, management and running of a power system, and has wide popularization and application value.

The embodiment of the invention discloses a method and device for correcting a differential rotary full tensor instrument, and belongs to the technical field of correction of tensor instruments. According to the method for correcting the differential rotary full tensor instrument, according to the tensor transformation relation between a sensor coordinate system and an instrument coordinate system, an error model is established, a coefficient matrix in the error model is calculated, thus the corrected tensor value can be calculated through the error model, and the problem that an error analysis and correction method of a differential rotary full tensor instrument in the prior art does not exists is solved.

The invention discloses an in-site measurement system of an aspheric die. The system comprises a contact probe, an air-floatation bearing, an optical interference system, an image collecting and processing system and software processing system; the optical interference system comprises a light source, a laser beam expanding system, an cat eye reflector, two reflectors, a polarized spectroscope, a 1/2 lambda wave plate, three 1/4 lambda wave plates, a beam splitting grating or a holographic beam splitting unit, a space filter and a Polaroid group; the image collecting and processing system comprises a CCD camera, an image collection card and a computer; and the software processing system comprises a phase calculation module, a surface coordinate conversion module, a curved surface fitting module and an error analysis module. The system and methods thereof have the advantages that the displacement measuring precision can reach the nanometer magnitude, real-time in-site measurement can be realized, interference of vibration noises can be avoided in the measurement process, and the measuring accuracy and stability are high.

The invention discloses a method for measuring the harmonic accuracy of an electronic voltage transformer, and the method comprises the steps: enabling a high-voltage side of a capacitive voltage divider to be in parallel connection with a primary side of the electronic voltage transformer, applying a harmonic voltage, carrying out the Fourier transform of digital signals of two sides through a data processing unit, and obtaining the spectrums of the digital signals, wherein the spectrums comprise amplitude spectrums and phase spectrums; taking the digital signal of the capacitive voltage divider as the reference, calculating the amplitude error and phase angle error of the digital signal of the electronic voltage transformer, and obtaining the harmonic accuracy of a to-be-detected electronic voltage transformer. The method can achieve the overall verification of the harmonic accuracy of the electronic voltage transformer, meets the test demands of electronic transformers, and detects the harmonic measurement capability of the electronic transformers truly and effectively.

The invention discloses a railway and wind power plant environment wind speed machine learning prediction method. The method includes: selecting various neural network models; establishing 100 wind speed prediction characteristic pre-selection models for each wind speed; carrying out mean square error analysis and correlation analysis; selecting 10 models with good performance and strong feature independence; establishing a plurality of wind speed prediction integrated models; integrating the wind speed prediction characteristics; finally, establishing a wind speed prediction normalization model; carrying out Normalization processing on the wind speed integrated value, calculating the correlation between the predicted wind speed vector and the wind speed vector of the training sample, restoring and predicting the wind speed value through wind speed noise at the corresponding moment with high correlation. The method can effectively predict the non-stable wind speed and has the accurateprediction effect on complex and non-linear wind speed values.