357 results about "Error correction model" patented technology

A fast geometric correction method for a satellite remote sensing image and based on a control point image database comprises the following steps: the geographical scope of an image to be corrected is determined; all control points which meet requirements are retrieved in the control point image database according to the determined geographical scope of the image to be corrected; automatic matching and point selection are conducted, and control point pairs used for geometric correction are determined; whether the number and the distribution of the matched control point pairs meet the requirements of the geometric correction is judged, if the judging result is yes, the next step is conducted, and if the judging result is no, the retrieval step is conducted again; a geometric correction model is selected according to the type and imaging features of a sensor; a transformational relation between a pixel coordinate and a geodetic coordinate is established based on the matched control point pairs and the selected geometric correction model; and the geometric correction is conducted based on a digital elevation model and the transformational relation so as to obtain a corrected digital orthoimage. According to the fast geometric correction method for the satellite remote sensing image and based on the control point image database, the automatic image matching technique is introduced into automatic selection of the control points, and automatic geometric correction processing of the satellite remote sensing image can be realized.

A computer-implemented method is performed at a device having one or more processors and memory storing programs executed by the one or more processors. The method comprises: selecting a target word in a target sentence; from the target sentence, acquiring a first sequence of words that precede the target word and a second sequence of words that succeed the target word; from a sentence database, searching and acquiring a group of words, each of which separates the first sequence of words from the second sequence of words in a sentence; creating a candidate sentence for each of the candidate words by replacing the target word in the target sentence with each of the candidate words; determining the fittest sentence among the candidate sentences according to a linguistic model; and suggesting the candidate word within the fittest sentence as a correction.

The invention discloses a multi-system dynamic PPP resolving method based on robust self-adaption Kalman smoothing. The method includes the steps that receiving machine outline coordinates and receiving machine clock bias of all systems are solved through selecting-weight-iteration pseudo-range single-point positioning, and accordingly all positioning error correction values are calculated according to an error correction model in combination with the satellite precise ephemeris and satellite precise clock bias; strict data quality control is conducted on observation data. Due to the fact that dynamic PPP accuracy is easily affected by undetected small cycle slips or the gross error and the like, an observation equation weight matrix is adjusted according to the observation value residual vectors, and the undetected small cycle slips or the gross error and other influence factors are removed; self-adaption factors are determined according to the state predictive information, and thus the influence on parameter estimation of the predictive information is controlled. By means of the method, when multi-system dynamic PPP is conducted through a single receiving machine, the feature that the number of multi-system satellites is increased greatly, on the basis that the stability of the satellite structure is guaranteed, the influence of the gross error is weaken effectively, the dynamic noise abnormity in dynamic positioning is improved, and finally the high-precision and high-stability multi-system dynamic PPP result is achieved.

The invention discloses a method for forming a personalized error correction module. The method comprises: collecting input information of a user, analyzing the input information, obtaining input habit information of the user, adjusting a present error correction module according to the input habit information, and obtaining the personalized error correction module. The invention also discloses a device which forms the personalized error correction module and an input method system of personalized error correction. The invention obtains the personalized error correction module which accords with user input habits through adjusting the present error correction module to realize more accurate personalized automatic error correction to user input sequences. Furthermore, many-sided information can be collected, the error correction range not only comprises cognitive errors which are similar to southern fuzzy sound, but also comprises non-cognitive errors, and the error correction coverage is wide. Since factors such as the input device distribution, the input device quality and the like are comprehensively considered, the invention can be applied in different input devices such as a PC keyboard, a mini-keyboard and the like, and has wide adaptability.

The invention provides an error correction method and device for a search query term. After the error correction device for the search query term receives the query term in a search request, firstly, the error correction candidate sub-term list of each sub-term in the query term is obtained; then, according to a pre-established N-Gram language model, each error correction candidate term spliced by each error correction candidate sub-term list is subjected to real-time on-line grading to obtain an error correction target term list; and finally, according to the score of each error correction target term in the error correction target term list, a final error correction result is output. By use of the method and the device, on the basis of on-line analysis and calculation, the search query term input by the user is subjected to error correction in real time, off-line mass data processing and historical error correction databases are not required, and large-scale search logs and user feedback are not depended. For application scenes including mobile application markets and the like, error correction capability can be effectively improved.

The invention discloses a spectral model transmission method based on unary linear regression. The spectral model transmission method mainly comprises the steps of I. performing the identical preprocessing on sample spectrums of M spectrometers; II. classifying spectrum data into m correction sets and forecasting sets according to m kinds of components of samples, and respectively constructing m correction models on the M spectrometers; III. evaluating the correction models; IV. selecting the spectrometer with the best forecasting effect as a main instruments and the other spectrometers as auxiliary instruments; and V. selecting optimal samples for all the auxiliary instruments from a correction set of the main instrument, determining samples in conversion sets of all the auxiliary instruments according to the sequence numbers, calculating regression coefficients through the unary linear regression, correcting spectrums of the auxiliary instruments, and substituting the regression coefficients into the correction model of the main instrument to obtain a sample component content result. According to the method, the difference of the different instruments is effectively eliminated; the correction model constructed on the main instrument can be shared on the multiple auxiliary instruments; the analysis test workload is reduced, and the cost of the model construction is saved; furthermore, the number of calculated parameters is small, the models are simple, and the forecasting accuracy is relatively high.

A method for adaptive automatic error and mismatch correction is disclosed for use with a system having an automatic error and mismatch correction learning module, an automatic error and mismatch correction model, and a classifier module. The learning module operates by receiving pairs of documents, identifying and selecting effective candidate errors and mismatches, and generating classifiers corresponding to these selected errors and mismatches. The correction model operates by receiving a string of interpreted speech into the automatic error and mismatch correction module, identifying target tokens in the string of interpreted speech, creating a set of classifier features according to requirements of the automatic error and mismatch correction model, comparing the target tokens against the classifier features to detect errors and mismatches in the string of interpreted speech, and modifying the string of interpreted speech based upon the classifier features.

The invention, which relates to the scheduling, operating, and planning field of the power system, discloses a short-term load prediction based on meteorological similar day and error correction. According to the invention, a meteorological factor regression analysis is carried out by using SPSS software; meteorological factors affecting a load most obviously in all seasons are selected; and weights of all factors are determined and are used as a basis for selecting a meteorological similar day. A historical prediction error data sample set is established; for a certain prediction day, an error data sample establishment set of a similar day is extracted, and a probability density distributed fitting model is established. An error fluctuation situation of a prediction point is analyzed to obtain a compensation value of a predicted error; and an error sample value closest to the error compensation value is selected as an error fitting value of this time and is superposed on the prediction value. Therefore, the prediction precision is improved.

The invention discloses a foreign language writing automatic error correction method and system. The method comprises the steps of pre-building an error correction model used for foreign language statement automatic error correction; obtaining a foreign language statement written by a user; extracting word vectors of words in the statement and sentence vectors of the statement; inputting the word vectors of the words in the statement and the sentence vectors of the statement to the error correction model, obtaining corrected word vectors of the words output by the error correction model, and generating an error correction text according to the corrected word vectors of the words; and displaying the error correction text. By utilizing the method and the system, automatic error correction of statement errors in foreign language writing can be realized and the writing and learning effects of students can be improved.

The invention discloses a flood forecasting method based on depth learning model and BP neural network correction, including: 1.normalizing the historical flood process data, 2. analyzing the normalized historical flood process data to obtain the influence time of each monitoring point on the outlet flow; 3. establishing the input and output values of the forecasting model from the historical flood data by using the sliding window, and establishing the training data set TRSet1 of the model, 4. using TRSet1 to train and establish the flood forecasting model CNNFM; 5, establishing a real-time error correction model training data set TRSet2; 6. training and establishing BP neural network based error correction model BPCM by TRSet2, 7. forecasting the real-time data with CNNFM and correcting them with BPCM to get the final forecast value for the real-time monitoring flow and rainfall. The invention models the training data through the characteristics of automatic extraction of data features by depth learning, and carries out real-time correction through BP neural network to improve the accuracy of model prediction.

The invention relates to an aircraft hanging-type fluxgate magnetic gradient tensor instrument correction method. The method comprises the following steps: 1) establishing a single fluxgate error model; 2) establishing a magnetic gradient tensor component error correction model; 3) collecting correction data in a uniform magnetic field region at high altitude; 4) correcting errors of tensor components; 5) collecting correction data in non-uniform magnetic field region on the ground; 6) solving scale factor error of the tensor components; 7) combining correction coefficients obtained through high-altitude correction and ground correction; and 8) fully calculating magnetic gradient tensor after correction. Compared with a conventional aircraft magnetic field gradient tensor correction algorithm, the correction method in the invention not only corrects a single tensor component, but also corrects the tensor as a whole, so that the tensor component and the whole correction results are allowed to be more accurate, and meanwhile, application conditions thereof meet actual aircraft magnetic gradient tensor detection region geological conditions better.

The invention discloses a point cloud error correction method based on self-made ground-based laser radar perpendicularity error, the method mainly aims at a scanning mode of self-made laser radar system 45 degrees mirror rotating cooperated with a tripod head, a self-made laser radar system point cloud error correction model is established through calculating influence from pitch axis and azimuth axis perpendicularity error to self-made system angle measuring error, a high precision 3D scanner is adopted to solve perpendicularity error for scanning point cloud coordinates as a true value, so that error correction from self-made laser radar system perpendicularity error to target point cloud error is realized. The method mainly comprises following steps: 1) a model of self-made laser radar 3D imaging system angle measurement error caused by perpendicularity error is established and simplified; 2) a self-made laser radar 3D imaging system point cloud error model is obtained; 3) the perpendicularity error in the self-made laser radar 3D imaging system point cloud error model is solved, and the self-made laser radar system target point cloud is corrected according to the model.

The invention discloses a text error correction method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the steps of obtaining a to-be-corrected text according to a text error correction demand; and according to the error correction model, carrying out error correction on the to-be-corrected text, and outputting a standard text corresponding to the to-be-corrected text, the error correction model being a trained attention-based sequence to a sequence model. According to the embodiment, the text is corrected by using the trained attention-based sequence-to-sequence model, so that the calculation complexity is reduced, and the text correction accuracy is improved.

By a simple computation, orthogonal errors from an orthogonal modulator and an orthogonal demodulator are separately corrected. Based on the amplitude of a demodulated signal, an orthogonal-error detection unit (320) detects orthogonal errors from an orthogonal modulation unit (140) and an orthogonal demodulation unit (230). Specifically, according to the distribution of transmission signal points on an I-Q plane, demultiplexers (321 and 322) in an orthogonal-error detection unit (320) separate a demodulated signal into a signal on the I-axis and a signal on the Q-axis. Zero-crossing detection units (325 and 326) detect the amplitudes of the separated signals at the intersection of the I- and Q-axes. The orthogonal-error detection unit (320) detects the orthogonal errors based on results from comparing the amplitudes at said intersection. A gain control unit (330) controls settings for a transmission orthogonal-error correction unit (120) and reception orthogonal-error correction unit (250) according to the detected orthogonal errors.

The invention discloses a wind power forecast method based on an error correction and promotion wavelet combination forecast model. According to the method, wind power history data is processed through utilization of a promotion wavelet decomposition technology, main characteristics of a power data sequence can be improved, frequency components with relatively obvious characteristics can be obtained, and the denoising effect can be achieved, thereby applying to various forecast algorithms. A suitable forecast model is selected according to the characteristics of high and low frequency components, the deficiency of a single forecast method can be removed, and the forecast precision can be greatly improved. According to the method, processing errors are corrected through utilization of an error layered analysis method. Compared with the method of obtaining a next moment error forecast value by directly using the forecast model, the method has the advantage that under the condition that relatively high fluctuation occurs in the errors, the next moment error condition and compensation strength can be analyzed precisely, the errors resulting from an error forecast process can be reduced, and the errors of the integrated forecast method can be reduced.

The invention relates to a signal structure integrating navigation and communication, which comprises messages, spreading codes, carrier waves and the like, wherein the messages comprise communication messages to be transmitted, navigation messages for positioning satellite track, time, coordinates and the like required, parameters relevant to an error correction model, and the like. The spreading codes comprise an m sequence code, a chaos sequence code or other pseudo-random codes, wherein the pseudo-random codes have favorable auto-correlation property and cross-correlation property to be available for a plurality of users, and also have favorable acquisition property, traceability and the like; the pseudo-random codes not only have the function of spectrum spread, but also can be used as ranging codes to realize precision measurement of a pseudo range between a sending end and a receiving end and phases of the carrier waves. The carrier waves can be selected from frequency ranges of VHF, UHF, L, S, C, Ku, Ka, and the like, preferably C, S and L according to the traditional satellite resources. The signal structure can be applied to the fields with requirements for navigation positioning and communication transmission, and has multiple functions, good properties and good anti-interference performance.

An embodiment of the invention provides a model generation method, an image processing method and medical imaging equipment. The model generation method includes: under specified imaging parameters, acquiring first image data with scattering component and second image data without scattering component, selecting input data from the first image data or related data thereof, and selecting label data from the second image data or related data thereof; according to the input data and the label data, performing machine learning by adopting a neutral network to generate a scattering-correction model. Machine learning is performed by the aid of the neutral network to generate the scattering-correction model, the scattering-correction model is used for performing scattering correction on DR (digital radiography) images, increasing of radiation dose of X-rays is not needed, better safety is achieved, adding of a grid in DR equipment is not needed, cost of the DR equipment can be reduced, and problems that the DR equipment with the grid is poor in safety and high in cost in the prior art are solved to some extent.

The invention relates to a method for calibrating the geometrical parameter error of an industrial robot based on a two-step method. A calibration coordinate system is established and comprises a measuring coordinate system and a constraint coordinate system, an error model is established according to the interconversion relationship between a robot connecting rod coordinate system and the calibration coordinate system, and thus the preliminary calibration result of a conversion matrix between the robot connecting rod coordinate system and the calibration coordinate system is obtained; and then the preliminary calibration result is utilized to establish an error correction model including the robot connecting rod geometrical parameter error and the conversion matrix error between the robotconnecting rod coordinate system and the calibration coordinate system according to a differential perturbation method, the corrected robot geometrical parameter error is obtained, and the calibration method is simple and precise.

The invention discloses a wind power prediction method. The method comprises the following steps: collecting sample data and processing; establishing a BP (Back Propagation) neural network model by using the processed sample data, and training the BP neural network model to obtain a final weight, a threshold value and a relative error sequence of a prediction value relative to a sample value; solving an initial prediction value of output power according to the weight and the threshold value after the training is completed; calculating a calculation relative error state corresponding to the initial prediction value of the output power by using a markov chain error correction model; combining the initial prediction value of the output power with the corresponding calculation relative error state to calculate corrected power. According to the method, the accuracy of wind power prediction is further improved, and an accurate wind power prediction value is provided for daily power generation scheduling and secure economic dispatch of a wind power generation power grid.

The invention provides a correction method for mass concentration data of atmospheric particulates. According to the method, an on-line correction model is established according to meteorological data of a reference station and the mass concentration data, measured by the reference station, of the atmospheric particulates, and the correction model is adopted to correct the measured mass concentration data, measured by a non-reference station, of the atmospheric particulates. The correction method improves the accuracy of the mass concentration data of the atmospheric particulates and increases the monitoring efficiency of monitoring stations.