69 results about "Random error" patented technology

The invention belongs to the technical field of precision machining and in-situ measurement, and discloses an in-situ measurement and bidirectional tool setting integrated system and method for an internal thread grinding machine. Various system errors and random errors are effectively eliminated through a non-hardware error compensation model and a high-fidelity data processing algorithm, in-situ measurement of the internal thread is completed in a modularized mode, and high-precision and high-efficiency in-situ automatic measurement of key parameters such as the pitch diameter, the thread pitch and the thread angle of the thread is achieved. The system can be directly integrated on the internal thread grinding machine, measurement is rapidly completed after machining or between procedures, workpieces do not need to be disassembled, secondary clamping errors are effectively avoided, and the detection efficiency and consistency are greatly improved.

The invention discloses an overlay error real-time correction system based on image recognition and dynamic fitting, and belongs to the field of semiconductor advanced packaging and manufacturing. The system comprises an image acquisition module, an error decoupling module and a dynamic compensation module to form closed-loop control. The image acquisition module adopts a high-resolution linear array CCD (Charge Coupled Device) and an AI (Artificial Intelligence) algorithm to realize high-speed and high-precision mark identification and error quantization; the error decoupling module performs decoupling and trend prediction on system errors and random errors by using PCA, a region weight matrix, LSTM and the like; and the dynamic compensation module realizes mu s-level partition graph compensation by a DMD optical system based on an error thermodynamic diagram through heterogeneous platform operation. The system supports the real-time processing of more than 100Hz, the average overlay error is less than or equal to 0.3 mu m and the response time is less than 5 seconds in the FOPLP and other processes, the problems of lag, low precision and the like of the traditional method are effectively solved, and the manufacturing yield and the production line efficiency are remarkably improved.

Provided is a memory system comprising a plurality of memory components. The ECC decoding is configured to construct first and second codewords from a single set of data within the plurality of memory components and perform error correction code (ECC) decoding on the first and second codewords received read from the plurality of memory components wherein the ECC decoding is configured to (i) detect random errors in the first received codeword and (ii) use data associated with the detected random errors to correct erasures in the second received codeword.

The invention discloses a GIS partial discharge fault positioning method based on multi-feature fusion and kernel density estimation optimization, and the method comprises the following steps: collecting a partial discharge original pulse signal, carrying out the signal processing, and outputting a first processing signal; performing adaptive robust baseline determination on the first processing signal, detecting a wave head position based on multi-feature fusion and a dynamic threshold voting mechanism, and outputting a wave head arrival time; carrying out time synchronization matching on the wave head arrival time, and calculating and outputting a preliminary positioning result based on a time difference of arrival method; and accumulating a plurality of preliminary positioning results, performing optimization by adopting a kernel density estimation method, and outputting a final partial discharge fault position. According to the method, impulse noise and white noise are effectively suppressed, stable signals are provided for subsequent processing, the detection precision of the wave head arrival time is remarkably improved, the problem that false detection and missing detection are caused by the fact that a traditional fixed threshold value or a single feature is prone to noise interference is solved, the random error of single-time positioning is greatly compressed to be within 0.07 m from 0.33 m on average, and the overall positioning precision is improved by 80%.

The invention relates to the technical field of industrial automation control, and discloses a multi-station cooperative control system for an elevator guide shoe manufacturing process, and the system comprises a data collection unit which obtains the dimensional deviation of a first workpiece; an interval mapping table is built in the cooperative controller, double-frequency control logic containing single-piece discrete feedforward and batch trend drift suppression is executed, and global reference offset based on finished product statistical data is superposed to basic correction parameters obtained through table look-up; according to the method and the device, through orthogonal cooperation of high-frequency random error compensation and low-frequency system drift suppression, dynamic counteracting of cross-station matching sizes is achieved, and the problem of recession caused by tolerance accumulation and equipment time-varying drift is solved.

The invention provides a distributed InSAR (Interferometric Synthetic Aperture Radar) satellite distance direction space synchronization on-orbit test method and system. The method comprises the following steps: acquiring echo data of the same region of a tropical rainforest by adopting a one-transmitting double-receiving mode; imaging the tropical rainforest SAR echoes of the main and auxiliary satellites respectively to obtain SAR images of the main and auxiliary satellites; obtaining a main satellite distance direction beam pointing test result RP02 and an auxiliary satellite distance direction beam pointing test result RP12; a distance direction pointing test result RP01 in a primary satellite ground test stage, and a distance direction pointing test result RP11 in an auxiliary satellite ground test stage; judging whether the RP01-RP02 and the RP11-RP12 exceed a threshold or not, and selecting a test sample pair which is in a threshold range at the same time; calculating a distance direction space synchronization test result; and calculating a plurality of groups of test sample results, averaging each group of calculation values, further reducing the random error, and obtaining a final test result.

The invention discloses a high-speed motor full-speed-domain high-precision speed measurement method based on a gear encoder, and belongs to the technical field of high-speed motor control. The method comprises the steps that a gear encoder is coaxially installed at the shaft end of a motor, integer position pulses are obtained through an enhanced orthogonal encoding pulse module, decimal position pulses are obtained through an enhanced capturing module, and rotor mechanical position pulses are calculated by combining the integer position pulses and the decimal position pulses. And calculating a single-interrupt period position pulse increment through a main interrupt program, performing multi-period accumulation, and calculating the rotating speed of the motor in real time based on an accumulation result. According to the method, through a continuous interruption accumulation algorithm, integer and decimal position pulse information is fully utilized, truncation errors and random errors in a traditional pulse measurement method and a time measurement method are effectively reduced, high-precision and fast-response speed measurement of the high-speed motor in the full-speed domain range is achieved, and the method is suitable for a high-speed control system with the speed of more than 60000 revolutions per minute and has a wide application prospect. The system has the advantages of simple structure, strong anti-interference capability and low software and hardware overhead.

Provided are a method, system, and computer program product in whichdata is encoded for a plurality of tracks of a tape of a tape drive, by using an error correction code (ECC) scheme that applies a single code with code word symbols that are at least 10 bits. The code word symbols are interleaved along and across parallel tracks of the tape in the tape drive.

The present application belongs to the technical field of artificial intelligence, in order to solve the problems of random error and result adaptability defect existing in ChatBI process, the present application provides a dialogue business intelligence platform process optimization method, system and equipment, the output results of each link in the dialogue business intelligence platform process are obtained, the output results of each link are sequentially checked in multidimension according to the process execution sequence, the process link that may have problems can be found in time, and the error is corrected and optimized in time before it spreads to the next process link; moreover, the output results are checked from three aspects of adaptation to the original demand of user input problem, compliance with the business logic of the process link itself and adaptation to the use demand of the downstream process link, the adaptability of the result is guaranteed, and the problem that the output of each process link is disconnected with the downstream process link without obvious error is avoided.

The invention discloses an unmanned aerial vehicle interference detection method based on a wide-deep attention residual network and random perturbation enhancement, and belongs to the technical field of unmanned aerial vehicle communication security. The method comprises the following steps: firstly, extracting basic features of unmanned aerial vehicle received signals, and expanding feature dimensions through polynomial interaction; and then feature importance is calculated by using a set uniformity index and dynamic classification is carried out. In a network construction stage, a wide-deep fusion architecture is designed: a deep path establishes an independent SE-ResNet sub-network for each feature level, and high-dimensional nonlinear features are extracted by using residual connection and a channel attention mechanism; a wide-layer path directly processes full-amount characteristics and memorizes a low-order linear rule. And finally, fusing and classifying the two paths of features. In the reasoning stage, an enhancement mechanism during testing is introduced, and environmental accidental errors are eliminated by injecting tiny random noise into real-time signals and performing multi-step average prediction. The method effectively solves the problems of insufficient feature physical association mining and weak model generalization ability in the prior art, and significantly improves the accuracy and robustness of unmanned aerial vehicle interference detection in a complex electromagnetic environment.

The application discloses a track correlation method based on improved topology, which comprises the following steps: S1, converting two radar target tracks into a common coordinate system; S2, constructing a topology sequence of the radar target tracks; S3, combining historical topology information to calculate the topology average distance of the target at the current moment; S4, using the OSPA distance to measure the correlation degree of the target topology set to obtain a correlation matrix; and S5, using the Hungarian algorithm to perform optimal distribution on the correlation matrix to obtain a correlation result. The track correlation method based on the improved topology can effectively reduce the influence of random errors on the track correlation algorithm and improve the correlation performance.

The application provides a sensor error parameter estimation method and system, comprising: collecting measurement data of a sensor by using a six-position method, the sensor comprising a gyroscope and / or an accelerometer; modeling a sensor error, performing sensor deterministic error parameter fitting by using a least square method; performing error compensation according to the deterministic error parameter to obtain a random error of the sensor; taking the random error as a measurement value of a Kalman filtering algorithm, modeling the random error by using a Gaussian-Markov model to take the random error as a state value of the Kalman filtering algorithm; designing a random error matrix and a driving noise covariance matrix; performing time updating and measurement updating of the Kalman filtering algorithm based on the measurement value, the state value, the random error matrix and the driving noise covariance matrix to correct the random error of the sensor. In real-time measurement, estimation of the deterministic error parameter and the random error parameter can overcome error measurement caused by the instrument itself, and more accurate measurement data can be obtained.

The application relates to the technical field of industrial automation control, and discloses a multi-station cooperative control system for an elevator guide shoe manufacturing process, which comprises the following steps: a data acquisition unit acquires a first workpiece size deviation; a cooperative controller is internally provided with an interval mapping table, executes double-frequency control logic containing single-piece discrete feedforward and batch trend drift suppression, superimposes a global reference offset based on finished product statistical data on basic correction parameters obtained through table lookup; and an adaptive execution unit adjusts a second workpiece machining reference in response to a synthesized instruction to generate a reverse bias amount. Through orthogonal cooperation of high-frequency random error compensation and low-frequency system drift suppression, the application realizes dynamic offset of cross-station matching sizes, and solves the problem of degradation caused by tolerance accumulation and equipment time-varying drift.

The application provides a kind of simulation analysis method of assembly characteristic based on surface feature topography, the surface topography of each part is digitally modeled, the surface topography of digital part is superimposed system error and random error, and meets the design tolerance band;Three-dimensional assembly model is constructed, the surface topography of part with error is stitched with the surface topography of error-free part;The assembly key characteristics and manufacturing cost model of three-dimensional assembly model are constructed;Through simulation analysis on three-dimensional assembly model, a plurality of three-dimensional assembly models and the surface topography and tolerance value of its parts meeting design requirements are obtained;Based on the simulation analysis result, the part tolerance is optimized, and the manufacturing cost is reduced.The application takes the real surface model as input to carry out simulation analysis, analyzes the rationality of actual assembly key size or tolerance, guides product tolerance design and manufacturing process, reduces manufacturing cost, and provides a set of feasible method for quantifying assembly key characteristics.

The invention discloses a reflector assembly, a method, a device and a system for reducing a light beam pointing temperature drift amount, and the thickness difference of an adhesive layer is converted into a controllable and preset design parameter from an uncontrollable random error through the introduction of an inclined bonding surface mirror seat. Therefore, when the ambient temperature changes, the reflector assembly can actively generate a pitch angle change opposite to the inherent temperature drift direction of the system by utilizing the characteristic that the thermal expansion coefficient of the ultraviolet glue is large, so that the autonomous, real-time and passive compensation of the light beam pointing drift is realized without depending on an external control system; and the stability of the optical system in a variable-temperature environment is greatly improved. The improved structure is simple, processing and manufacturing are convenient, cost is low, improvement and upgrading in an existing optical system and popularization and application of new products are facilitated, and the optical system has high cost performance and high practicability.

The invention discloses a five-axis numerical control machine tool machining error on-line compensation system based on deep learning, relates to the technical field of machine tool error compensation, and solves the problems that position correction difference is caused by neglecting a complex coupling relation between rotating shafts and linear shafts of different types of machine tools, and the error compensation accuracy is high. And deep learning models for machine tools with different requirement types are not designed, so that the model prediction precision is influenced. Determining the coupling relation of each axis error through the HTM homogeneous transformation matrix of the machine tool kinematic chain; aiming at kinematic chain structure differences of different types of machine tools, a differential correction strategy is designed. And constructing a zero drift time sequence model in combination with a historical drift trend term and a random error term predicted by the LSTM. Geometric error terms strongly related to machining errors are screened through grey correlation analysis, then the weight of the geometric error terms is quantified through random forest, the accuracy of key error recognition through a combinatorial algorithm is improved, and the model prediction precision is improved by training fusion models for machine tools with different requirement types.

The application provides a MEMS navigation system optimization control method and a MEMS navigation system, and relates to the technical field of inertial navigation. The method comprises the following steps: determining noise compensation parameters according to measurement data of a target MEMS inertial measurement chip, and performing vibration compensation on each MEMS inertial measurement chip according to the noise compensation parameters; determining the weight of each MEMS inertial measurement chip according to the measurement data of each MEMS inertial measurement chip; and performing weighted summation on the measurement data of each MEMS inertial measurement chip based on the weight of each MEMS inertial measurement chip to obtain target measurement data. The common vibration error of each chip is uniformly corrected based on the target chip measurement data, and the system error is reduced. Meanwhile, multiple chips are redundantly arranged, random errors are reduced through information fusion, the measurement accuracy of the navigation parameters is improved, and the positioning accuracy of the navigation system is further improved.

Training an artificial intelligence model to categorize data by sensitivity and for applying the model to selectively protect sensitive portions of multimodal datasets. Sensitive training data can be obfuscated with synthetic noise or randomized errors to preserve confidentiality while enabling the model to learn patterns correlated with sensitivity. The trained model is validated on labeled data and can be refined as classification standards evolve. In operation, the classifier assigns sensitivity levels to data elements and directs tiered protection. Elements assigned to a higher relative sensitivity classification level are protected using post-quantum key establishment, for example a key encapsulation mechanism, combined with symmetric authenticated encryption of payloads, and associated metadata is authenticated using a post-quantum digital signature scheme. Less sensitive elements can be protected using conventional symmetric encryption for efficiency. This approach automates sensitivity classification, optimizes cryptographic resource allocation, and improves confidentiality and integrity for simulation and mission data.

The utility model relates to glass steel product assembly technical field, concretely relates to a kind of positioning tool for positioning column center distance assembly, the positioning column at least two, the parallel arrangement between the positioning column, the positioning tool includes connecting sleeve head and connecting rod, the connecting sleeve head one end is matched with positioning column, and the other end is provided with connecting rod;The clearance fit between the connecting sleeve head and positioning column, the positioning column includes column stem, protruding portion and column head from below to above in order, the width of protruding portion is greater than the width of column head, and the width of column head is greater than the width of column stem;The bottom of connecting sleeve head is provided with positioning slot, and the positioning slot is matched with column head, and at least two connecting sleeve heads are provided on the connecting rod.The utility model is convenient and fast, and positioning accuracy is high, reduces random error, improves assembly efficiency and assembly qualification rate, meets the requirement of product batch consistency, with remarkable engineering application value.

This application relates to the field of wireless communication technology, providing an antenna robustness design method and apparatus based on hybrid deep learning. This invention simulates manufacturing process errors by combining sampling methods within the range of process errors, obtaining design variables and... S Sensitivity analysis was performed on the Gaussian distribution curves of the statistical mapping relationship between parameter responses to identify variables more sensitive to manufacturing errors, thereby reducing the dimensionality of variables and decreasing the complexity of subsequent antenna robustness optimization. A hybrid deep learning model was used to construct an antenna response substitution model, replacing traditional electromagnetic simulation, significantly shortening the optimization cycle while ensuring the accuracy of response prediction and avoiding distortion of optimization results due to model errors. By constructing a robustness objective function and employing a genetic algorithm to optimize antenna parameter robustness, a highly robust optimal solution was found, avoiding premature convergence of the gradient method in multi-peaked environments caused by random errors.

This invention claims protection for an improved wavelet thresholding denoising method. The method decomposes a noisy signal through several layers of wavelet decomposition, obtaining the corresponding high-frequency wavelet coefficients for each layer. The amplitude of the corresponding wavelet coefficients is analyzed; those for useful signals are lower, while those for noise are higher. An improved threshold function is applied to process the wavelet coefficients at each layer, retaining coefficients below the threshold and discarding those above. These wavelet coefficients are then reconstructed using inverse wavelet transform, outputting the denoised signal. This invention constructs a new threshold function to address the shortcomings of traditional wavelet denoising algorithms in noise reduction. Experimental data processing results show that the new threshold function effectively suppresses the influence of random errors in the inertial measurement unit. Compared to the original compromise threshold algorithm, the signal-to-noise ratio is improved, the root mean square error is reduced, and the scheme of this invention achieves better signal smoothness and excellent denoising effect compared to traditional denoising schemes.

The invention discloses a manufacturing method of a double-side cylindrical surface array convex lens, and relates to the technical field of precise optical element manufacturing, and the method comprises the steps: calculating an assembly standard deviation based on the historical production data of the double-side cylindrical surface array convex lens; based on a normal distribution model, calculating a preset eccentricity according to a preset qualified tolerance zone and the assembly standard deviation; preparing an upper lens plate and a lower lens plate in batches based on the preset eccentricity; and sequentially folding and bonding the upper lens plate and the lower lens plate to prepare the double-side cylindrical array convex lens in batches. According to the method, random errors which cannot be eliminated are converted into controllable systematic errors, so that the effective yield is greatly improved.

The invention discloses a fragment consistency verification method, device and equipment for erasure code data and a medium, and relates to the technical field of data verification, and the method comprises the steps: obtaining data blocks with preset sizes for each erasure code data fragment and each verification fragment, and carrying out byte-by-byte summation on the obtained data blocks, check codes of the erasure code data fragments and the check fragments are obtained; and based on linear matching verification between the check code of the erasure code data fragment and the check code of the check fragment, performing fragment consistency verification of the erasure code data. The erasure code data fragment consistency verification method has high data verification capability, has strong verification capability on byte interaction, random errors and multi-bit hopping, and effectively improves erasure code data fragment consistency verification efficiency.

The invention provides a variable-diameter shield steel pipe piece joint design method based on random error accumulation, and precise control is realized through a double-path design method: a path 1 is based on a determined pipe piece joint form, the splicing precision of a single pipe piece is input, the bolt position and error redundancy of a top sealing block are optimized in a targeted manner, and a path 2 is obtained; error accumulation is controlled from the key component design level; in the path 2, the design of a capping block and a corresponding accumulative error tolerance are firstly determined, and then a standard segment joint connection mode is optimized so as to refine design and control splicing random errors. Meanwhile, a Monte Carlo method is introduced, and balance of construction efficiency and structural quality is realized by randomly generating segment assembly errors, simulating an accumulation process, comparing and verifying with design error redundancy, judging a ring forming probability and iteratively optimizing path design parameters to improve the segment ring forming probability.

The application relates to an industrial process system data online coordination method based on dynamic data coordination, which comprises the following steps: acquiring random errors of each measurement variable of a system, establishing linear constraints between coordination values corresponding to measurement values of the measurement variables and unmeasured variables based on system mechanism, constructing a dynamic data coordination model based on the linear constraints, remodeling and augmenting a model data matrix and a vector, solving the model to obtain a least square solution, and reasonably setting a sliding window length and constraint equations to obtain high-precision and high-timeliness system online operation data during actual operation. When the method faces dynamic operation of a thermal power unit, the precision of high-pressure heater measurement data can be greatly improved in real time and accurately, and high-quality data are provided for dynamic performance monitoring.

The application discloses an error compensation processing method based on an LCU switch station analog signal and relates to the technical field of industrial automation.The application classifies the working conditions of real-time original signals of each analog quantity acquisition channel of the LCU switch station, removes invalid interference sections, retains steady-state working condition sections and transient working condition sections, calibrates and fuses a dedicated error sequence of the channel based on a preset dedicated value traceability reference library, separates systematic errors and random error components from the dedicated error sequence, solves a channel basic compensation coefficient, executes point-by-point error compensation on the real-time original signals based on the compensation coefficient, and reverses the basic compensation coefficient through the residual error sequence of the compensated signals.The application effectively improves the LCU analog signal acquisition accuracy and the full-working-condition adaptability, and guarantees the stability of the switch station operation.

The invention discloses a mutual inductor error self-adaptive correction method and system. The method comprises the steps that group cooperation features are constructed, and the real-time deviation degree is calculated to detect system abnormity; when abnormity is found, the individual contribution degree of each mutual inductor is further calculated to realize accurate fault positioning; for the positioned abnormal mutual inductor, extracting residual error characteristics of the abnormal mutual inductor, fusing the residual error characteristics with the macroscopic deviation degree, and identifying that the abnormal type of the abnormal mutual inductor is fixed deviation or random error mutation through a classification algorithm; dynamically determining an optimal prediction step length by adopting a machine learning model according to the anomaly type, and adaptively selecting a long-short-term memory network or a hybrid model combined with an attention mechanism to perform error prediction according to the optimal prediction step length; and finally, performing on-line compensation on real-time measurement data by using an error value obtained by prediction. The full-process closed-loop management from anomaly detection, intelligent diagnosis and self-adaptive prediction to active compensation is realized, and the accuracy, the fault identification capability and the long-term operation measurement precision of the online monitoring of the mutual inductor are remarkably improved.

One aspect of the invention is to provide systems and methods for improving the accuracy of an assay comprising at least one or more parameters each having a random error.

A kind of IMU gyroscope random error compensation method and device based on deep learning, comprising: obtaining IMU data;The IMU data is input into the random error compensation model constructed in advance, to obtain clean gyroscope data;Wherein, the random error model is based on the Gyro-Net network training obtained from IMU data;The present application extracts and selects IMU data features by IFESblock, can adjust feature flow by semi-dense network structure and carry out reuse to reach depth supervision and sufficient use of features, and utilizes relative loss and absolute loss function to train to reach attention local precision and global precision, with better performance in direction estimation.