83 results about "Resampling" patented technology

The invention belongs to the field of power distribution automation, and relates to a power grid equipment frequency adaptive resampling method based on a dynamic scale factor, and the method comprises the steps: carrying out the point compensation through employing a linear interpolation method when a power grid equipment detects that the number of continuous lost points is smaller than or equal to 3; selecting a voltage signal channel or a current signal channel to carry out zero crossing point detection and real-time frequency calculation; when the real-time frequency obtained through calculation deviates from the nominal frequency, the power grid equipment introduces a dynamic scale factor for resampling, sampling points are increased when the real-time frequency is higher than the nominal frequency, and sampling points are reduced when the real-time frequency is lower than the nominal frequency; writing data obtained by resampling into a calculation annular cache region of the power grid equipment; and performing FFT calculation on the data in the calculation annular cache region to obtain power grid parameters. The sampling interval of the data acquisition unit is dynamically adjusted by increasing or reducing the sampling points, so that the sampling time sequence and the actual signal period are synchronized, the sampling frequency and the real-time frequency are adaptively matched, and the sampling distortion problem under the power grid frequency fluctuation is solved.

The invention discloses a small sample modeling stability evaluation method and system based on Bootstrap resampling, and particularly relates to the technical field of computers and data intellectualization, the method comprises the following steps: completing data preprocessing and stable stage identification under a unified time base, and forming a segment set; an average block length is used as a decision quantity, an SBB is optimized to construct a sample space, and an optimal block length is determined in combination with variance consistency and nominal coverage rate consistency criteria; carrying out re-sampling training around short / medium / long scales, and collecting layered indexes such as prediction, parameters and features; stability calculation and empirical coverage rate calibration are completed based on intra-scale statistics and inter-scale weighted mixing, and a pseudo-stationary diagnosis score is output; and finally, engineering judgment and backspacing optimization are carried out according to the coverage rate, the correlation maintenance and the risk threshold. The system records random seeds, block metadata and model version generation evidence pointers, has the characteristics of traceability and reverifiability, and is suitable for scenes of production lines, network traffic, finance and the like.

The invention relates to the technical field of numerical control machining measurement and correction, and discloses a machining surface correction method for a numerical control milling center. The method comprises the steps of establishing an original point cloud of a processing surface and extracting a feature structure; analyzing each feature structure to form a structured description list; inputting the list into a geometric growth model, and generating a virtual feature structure set according to rule iterative evolution; extracting and rasterizing the contour and the height field, carrying out registration fusion with a reference plane equation, and carrying out variation calculation to obtain a smooth middle geometric surface; performing adaptive resampling on the intermediate surface to generate an intermediate point cloud; and finally, according to the spatial frequency distribution of a topological residual field formed by the difference between the original point cloud and the intermediate point cloud, adaptively determining a weight, and carrying out weighted mixing on the original point cloud and the intermediate point cloud to generate an optimized surface point cloud. According to the method, intelligent evolution of the machined surface features and frequency domain self-adaptive data fusion are achieved, and the correction machining precision and the surface forming quality are improved.

The embodiment of the invention provides a large model distributed reasoning acceleration method and device based on speculation sampling, and relates to the field of artificial intelligence. And inputting the terminal prefix information into a draft small model for content generation to obtain a plurality of candidate tokens and corresponding small model probability distributions, and transmitting the parameter sequence, each candidate token and a small model probability value corresponding to the candidate token to a base station, so that the base station generates a large model probability distribution of each candidate token by using a target large model, and the candidate tokens are divided into accepted tokens and / or rejected tokens. And if the terminal receives the large model probability distribution corresponding to the first rejection token, resampling the first rejection token to obtain a resampling token, writing the resampling token into the parameter sequence, updating terminal prefix information according to the resampling token, and performing iterative execution until a reasoning result is obtained. The rejection / receiving process of the candidate tokens is executed on the base station, and the resampling process after rejection is executed on the terminal equipment, so that the cooperative reasoning efficiency is improved.

The invention provides a self-adaptive particle filtering method for a laser SLAM task. The problems that a traditional particle filtering algorithm is bottleneck in calculation efficiency, insufficient in dynamic environment adaptability and the like in a complex environment are solved. In the particle filtering prediction stage, particle swarm optimization (PSO) is introduced, particle distribution is optimized by using the global search capability of the PSO, and the inertia weight and the acceleration factor of the PSO are dynamically adjusted to enhance the adaptability of the algorithm in a complex dynamic environment, so that the algorithm is effectively prevented from falling into a local extreme value; in the resampling stage, KLD sampling and a selective resampling method are combined, a sampling strategy is dynamically switched according to the effective particle number (ESS) and the KL divergence, and the GPU is used for accelerating the resampling step to improve the calculation efficiency. The method has remarkable advantages in the aspects of improving the positioning precision and the mapping efficiency, and is suitable for robot navigation and autonomous movement tasks in a complex environment.

The invention discloses a data classification method and system based on hierarchical clustering and resampling, and a storage medium. The method comprises the following steps: dividing samples in a data set into a minority class and a majority class; one part of samples in the minority class are distributed to a training set, and the rest part is distributed to a test set; dividing samples in the training set into a plurality of clusters to form a corresponding number of sub-training sets; training a test set classifier by using the cluster labels of the sub-training sets, and classifying the test set to form sub-test sets; the samples in the clusters are resampled, so that the proportion of minority class samples to majority class samples in the training sub-sets is 1: 1, and an enhanced training sub-set is formed; and training a data classifier by using the enhanced sub-training set, and testing by using the sub-test set. According to the method, the inter-class overlapping degree is reduced through the clustering algorithm, the sample proportion is balanced through the resampling technology, a classifier is made to pay more attention to minority class sample features, the recognition capacity of minority classes such as financial abnormal conditions is enhanced, and the misjudgment risk is reduced.

The invention discloses a physical field data restoration method and system based on diffusion and iterative resampling, and the method comprises the steps: initialization: filling a missing region with random noise in to-be-restored physical field data, and obtaining an initial noisy field; forward diffusion: gradually adding noise into the complete data until the complete data becomes complete random noise data; noise model pre-training: training random noise added in each step in forward diffusion; inverse diffusion denoising: gradually restoring the image from the initial random noise through the noise predicted by the model and an iterative formula; and iterative resampling correction: in the reverse diffusion process, iterative circulation is carried out in a de-noising-noise adding-re-de-noising mode, so that the model can be effectively forced to spread information of a known area to a missing area, and the physical consistency and smooth transition of a repair result and a known data boundary are ensured. Any prior information of the missing area does not need to be preset, the repairing precision is high, and the physical consistency is good.

The invention discloses an alluvial island instantaneous waterline segmentation method fusing MobileViT and dynamic resampling. The method comprises the steps that a remote sensing image, shot by an unmanned aerial vehicle, of an alluvial island area is acquired and preprocessed; and inputting the preprocessed remote sensing image into the trained semantic segmentation model for processing, and accurately and efficiently obtaining a segmentation result of the instantaneous waterline in the remote sensing image. The semantic segmentation model is a lightweight ALISEg model constructed based on a DeepLabv3 + framework, a MobileViT structure is introduced into an encoder part, local modeling advantages and global perception capability are integrated, so that local detail recognition capability and global context perception capability are greatly enhanced, a DSC-ASPP module is designed, depth separable cavity convolution and a CBAM attention mechanism are combined, and the semantic segmentation model has the advantages of being high in robustness, high in robustness and high in robustness. And multi-scale spatial features can be fully captured. And meanwhile, a DySample module and an H-RAMi module are introduced into a decoder part, so that the up-sampling precision and the multi-scale feature fusion effect are improved, and the details of the water edge line are better reduced.

The invention discloses a sensitivity-based network intrusion detection resampling method, system and device and a medium, and relates to the technical field of data detection, and the method comprises the steps: obtaining network data containing normal traffic and attack traffic; prior probabilities and class condition probabilities of the two classes of data are calculated respectively, then posterior probabilities are obtained, and the sensitivity weight of each sample is calculated; carrying out putting-back undersampling on a normal flow sample according to the sensitivity weight; carrying out replacement oversampling on the attack traffic sample according to the sensitivity weight; a sampling process is repeated to generate a plurality of balance training subsets, and a base classifier is trained based on each subset; and calculating voting weights based on the performance evaluation indexes of the base classifiers on the verification set, and obtaining weighted voting results based on the voting weights to classify new data. Random undersampling neglects the importance of boundary samples to decision boundary learning, and sensitivity weighted sampling can guide the model to sample the boundary samples, so that important information is prevented from being lost.

The invention discloses a method and a system for predicting the service life of a rolling bearing in multiple failure modes based on Weibull distribution, and relates to the technical field of rolling bearing service life prediction. In order to solve the problem that the service life discreteness of a rolling bearing is large due to multiple failure modes such as early failure, random failure and fatigue failure under different working conditions, the method comprises the following steps: firstly, determining first prediction time by using a random convolution kernel binary regression model, and dividing failure time data based on the first prediction time; a plurality of failure modes are identified by using Weibull distribution; secondly, through a particle filter (PF) dynamic updating mechanism and a soft resampling technology, improving a cell gate and a hidden state of a long and short term memory network (LSTM), constructing a particle filter and long and short term memory network (PF-LSTM) fusion prediction model, and enhancing adaptive capacity to complex working conditions; and finally, constructing a multi-criterion loss function by using the probability density function of the Weibull distribution and the mean square error again, and further improving the prediction precision of the PF-LSTM by integrating the traditional mean square error and the priori knowledge of the Weibull distribution. Experiments prove that the method provided by the invention can predict the service life of the rolling bearing in multiple failure modes, compared with a traditional LSTM method, the average absolute error is reduced by 13.69%, and in addition, compared with a CNN-LSTM model, the prediction average score is improved by 0.169.

The invention discloses a short text unbalanced classification method based on semi-supervised clustering and dynamic resampling, which comprises the following steps of: S1, performing cleaning and feature extraction on original short text data, removing features of which the missing rate exceeds 20%, performing vectorization expression on residual texts, and constructing an initial feature space; s2, under a'compact cluster 'assumption, based on the guidance of marked data, performing iterative segmentation on majority class and minority class samples by adopting a semi-supervised hierarchical clustering algorithm, generating a plurality of discontinuous clusters, and revealing inter-class and intra-class unbalanced distribution characteristics; s3, based on a clustering result, performing dynamic under-sampling on majority of samples; s4, based on a clustering result, carrying out dynamic oversampling on minority class unmarked data; according to the method for introducing semi-supervised clustering into short text unbalanced classification for mixed sampling, the marked data and the unmarked data are clustered, the basic distribution characteristics of the short text data are effectively captured, and follow-up sampling is facilitated.

Some embodiments are directed to a resource allocation analysis system implemented via a back-end application computer server. A resource data store may contain electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter. The back-end application computer server may receive, from the resource data store, information about a set of resource types to be analyzed, including the associated resource parameters. The computer server may then execute a differential evolutionary algorithm to optimize the set of resource types based on at least one non-linear constraint and generate resource analysis results. The back-end application computer server may, according to some embodiments, perform a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching.

The application discloses a kind of power electronic system fault diagnosis methods combined with resampling and integrated learning, belong to power electronic equipment fault diagnosis technical field, the method includes real-time sampling electric power electronic system in the current data of one-half fundamental period, current data is normalized, and normalized data is obtained;According to the normalized data, based on fast fourier transform algorithm, the frequency domain characteristics of normalized data are obtained;According to the frequency domain characteristics, the feature vector of normalized data is obtained by feature extraction selector;According to the feature vector of normalized data, the fault class label is obtained using integrated classification model, and the electric power electronic system fault diagnosis is completed.The application solves the technical problems that the original data sample is unbalanced in the existing fault diagnosis method, which leads to inaccurate diagnosis and even wrong judgment of the existing machine learning model, effectively realizes the diagnosis of various fault types of sensors and power devices of electric power electronic system.

The invention discloses an injection molding quality soft measurement method for staged resampling and quality enhancement, which belongs to the technical field of injection molding and comprises the following steps: acquiring a process data set and a quality data set; normalizing the process data set and the quality data set to obtain a normalized process data set and a normalized quality data set; performing clustering, staged resampling and merging on the normalized process data set to obtain a low-resolution process data set; and inputting the low-resolution process data set and the normalized quality data set into a quality enhancement prediction network to obtain a prediction result of the injection molding quality. The accuracy of injection molding quality soft measurement is improved.

The chain system fixed-point resampling method belongs to the technical field of rotating machinery signal processing, and aims to solve the problems that the time domain signal regularity of a single chain link is poor, the dynamics characteristics of the whole system are difficult to directly reflect, and the frequency domain feature extraction cannot be performed. The method comprises the following steps: extracting original data of discrete motion units from multi-body dynamics simulation results; at the preset data monitoring point position of the system, the nearest discrete motion unit is searched as a monitoring unit in real time by calculation; according to the data type, one of a direct method, a synthetic method and an average method is adopted to process the original data of the monitoring unit, and feature data representing the dynamics state of the fixed position are generated; and finally, the feature data are written into a file in time sequence. The time sequence data of the fixed position are collected through the fixed-point resampling method, and the problems that the data is strong in contingency, poor in regularity, and difficult to perform in-depth frequency domain and statistical analysis due to tracking a single motion unit are solved.

The invention discloses a fixed-point resampling method for a chain system, belongs to the technical field of signal processing of rotating machinery, and aims to solve the problems that the regularity of a time domain signal of a single chain link is poor, the dynamic characteristics of the whole system are difficult to directly reflect, and frequency domain feature extraction cannot be carried out. The method comprises the following steps: extracting original data of a discrete motion unit from a multi-body dynamics simulation result; searching the nearest discrete motion unit as a monitoring unit in real time by calculating the distance at a preset data monitoring point position of the system; according to the data type, processing the original data of the monitoring unit by adopting one of a direct method, a synthesis method and an average method, and generating characteristic data representing the dynamic state of the fixed position; and finally, writing the feature data into a file according to a time sequence. According to the method, time sequence data of a fixed position is collected through a fixed-point resampling method, and the problems that due to tracking of a single motion unit, data contingency is high, regularity is poor, and frequency domain deepening and statistical analysis are difficult to carry out are solved.

An anti-robust adaptive UFastSLAM autonomous navigation method based on KLD resampling, comprising the steps of: 1. In the carrier pose estimation stage, an anti-robust adaptive factor is incorporated, and the carrier pose is estimated using the robust adaptive unscented particle filter algorithm (RAUPF); 2. In the feature state estimation stage, the robust adaptive unscented filter algorithm (RAUKF) is used to estimate and update the position information of environmental features; 3. In the particle resampling stage, an adaptive particle resampling method based on KL divergence is adopted to online and real-time adjust the required number of particles, improving the real-time performance of the algorithm while ensuring accuracy. This method can be applied to environments where the measurement noise is time-varying and there are abnormal interferences in the measurement information, improving the problems of decreased carrier positioning accuracy and low real-time performance caused by factors such as abnormal disturbances and inaccurate noise statistical characteristics.

This invention discloses a deep learning-based engine condition monitoring method, comprising the following steps: collecting multi-source sensor data and corresponding speed data during engine operation; preprocessing the original multi-source data to obtain preprocessed multi-source data; performing condition-locked resampling to obtain a set of window sequences; constructing a Krylov subspace and performing orthogonalization to obtain an orthogonal basis for the subspace; performing Krylov subspace change point and state transition detection to obtain the current monitoring state; generating a subspace fingerprint and selecting a deep learning monitoring branch based on the subspace fingerprint routing structure to obtain a health index and anomaly score; writing the health index and anomaly score into a candidate alarm queue, switching the current monitoring state, and outputting alarm results based on the candidate alarm queue. This invention improves the accuracy of engine condition monitoring by combining condition-locked resampling with Krylov subspace change point detection.

Apparatuses, methods, and systems for determining winning arms of electronic testing. One method includes obtaining historical data values related to the A / B test of a user, storing the historical data values, determining a historical weight for the historical data values, receiving new data values from the plurality of computing devices collected based on recipient actions during execution of the A / B, constructing a Dirichlet distribution, inferring corresponding central tendencies of samplings of a metric distribution, wherein each central tendency of the corresponding central tendencies is determined by sampling the Dirichlet distribution, constructing an overall utility distribution for each arms of the A / B test by combining the central tendency of each sampling of the metric distribution with a corresponding sampling of a conversion probability distribution, determining a winning arm of the A / B testing by comparing the overall utility distribution of each arm with each other arm of the A / B test.

The present application relates to an indoor dynamic positioning method and device based on adaptive hierarchical resampling, belonging to the technical field of indoor positioning. The method includes: dividing an indoor map into at least two sub-map regions; initializing the particle distribution to obtain initial particles; performing position transfer on the initial particles to obtain a first subset of sub-particles that exceed the boundary of the sub-map region and a second subset of sub-particles that do not exceed the boundary of the sub-map region; performing position correction on the first subset of sub-particles to obtain a third subset of sub-particles; updating the weights of the second subset of sub-particles and the third subset of sub-particles; determining at least two hierarchical intervals based on the particle weights of the particles in the third subset of sub-particles and the second subset of sub-particles; collecting target particles based on the hierarchical intervals and the particle weights of the respective particles to obtain a target particle set; determining the positioning position of the positioning tag based on the target particle set; the present application can improve the accuracy of indoor positioning and achieve real-time positioning.

The embodiment of the invention provides a transfer prediction method and system based on resampling and three-dimensional attention convolution, and relates to the technical field of medical image artificial intelligence. According to the method, after PET image data is acquired, resampling is carried out on PET images in the PET image data to obtain balanced image data, and then image key features are extracted from the balanced image data through a three-dimensional residual convolutional neural network and based on a three-dimensional convolutional attention mechanism. And training a prediction model by using the image basic features and the image key features to output a transition probability by using the prediction model. According to the method, the distribution of the positive and negative samples can be balanced through resampling, and model deviation caused by height imbalance of the positive and negative samples faced by 3DPET image data is relieved. A three-dimensional attention convolutional network is adopted to deeply mine spatial features, three-dimensional spatial features of 3D PET are fully extracted, and a weighted fusion loss function is combined to optimize a model training process, so that the prediction precision of the lymph node metastasis probability is improved.

The invention discloses a minimum region method flatness error evaluation algorithm based on improved teaching and learning optimization, and belongs to the technical field of geometric quantity measurement. In order to solve the problems that a traditional minimum region method is slow in convergence and prone to falling into local optimum, a teaching and learning optimization algorithm is improved through a Sine chaotic mapping population, a self-adaptive learning factor and local resampling search, and the method is applied to minimum region evaluation of flatness errors. The method comprises the specific steps of point cloud data preprocessing, Sine chaos population initialization, teaching stage adaptive updating, local resampling, convergence judgment and result output. Experiments show that compared with traditional TLBO, particle swarm optimization and other algorithms, the algorithm is higher in convergence speed and evaluation precision, higher in robustness and suitable for flatness detection scenes in high-precision manufacturing.

The embodiment of the invention provides a robot control method and device, electronic equipment, a storage medium and a computer program product, which are at least applied to the field of artificial intelligence or robot control, and the method comprises the following steps: determining at least one new approach point based on a plurality of original approach points of a to-be-controlled robot in a target space; performing linear interpolation on a path point mixed sequence formed by the original path points and the newly added path points to obtain a planned trajectory; in the process of controlling the to-be-controlled robot to move according to the planned trajectory, if it is detected that the current motion state of the to-be-controlled robot meets a preset resampling condition, performing approach point resampling on a target approach point on the planned trajectory; updating the target approach point by adopting the resampling approach point to obtain an updated planning track; and controlling the to-be-controlled robot to continuously move according to the updated planning track. According to the invention, the real-time performance of robot control can be improved.

The present invention discloses a chemical plating process simulation optimization system and method for metal processing, which specifically relates to the technical field of metal processing simulation process, and comprises the following steps: before simulation, multiple data dimensions are collected to obtain an initial data set, and data dimensions with hidden dangers of insufficient data volume are identified through preliminary analysis; for these dimensions, whether to resample is determined through deductive analysis, and collection is performed according to the simulation sampling volume; when resampling is required, the collected data replaces the corresponding dimensional data in the initial data set to generate a simulation input data set; if resampling is not required, the initial data set is directly used for simulation, and finally the simulation results are output and applied to actual operation; the present invention can automatically adjust the data collection volume of data dimensions by introducing a dynamic data source optimization mechanism, ensure that the data volume of each parameter is sufficient, and ensure the stability and reliability of the simulation results.

The present application discloses a method for tracing the source of faults in the operation of power secondary equipment based on supervised learning. The method includes: establishing a data storage unit, storing fault types and constructing a fault propagation map, and using a relational database and data processing and integration tools to update the data in the data storage unit. After processing the stored data, the data set is divided to construct and verify a fault tracing model. By constructing a fault tracing credibility index and a fault propagation perception confidence interval, the accuracy of the fault tracing model and whether the data is unbalanced are judged. If the data is unbalanced, resampling and data enhancement are used for processing. The processed fault tracing model can effectively realize the tracing of the source of faults in the operation of power secondary equipment, improve the ability to handle faults, and provide guarantees for the stable operation of the power system.

The invention provides a multi-model multi-resolution self-adaptive image recognition and extraction method based on deep learning, and the method comprises the following steps: an unmanned plane collects an aviation picture, and generates a digital orthoimage through a picture splicing algorithm; judging whether natural language analysis needs to be performed or not according to a processing requirement input by a user; obtaining a target category to be recognized and extracted, and adaptively selecting an optimal deep learning algorithm; according to the overall contour pixel size of the target category, selecting a self-adaptive spatial resolution to carry out reprojection resampling processing; obtaining the recognized and extracted original contour; according to the method, a complex natural language can be analyzed, an unknown target category can be recognized and extracted, a high-precision specific category can also be matched, generalization and expandability are enhanced, and the method has the advantages of being high in robustness, high in robustness and the like. And optimal algorithm parameters can be adaptively selected, and the precision requirement of the result is ensured to the greatest extent.

Systems and methods for dynamic applicative session separation and grouping. The system receives logs from one of a plurality of machines. The system can receive and manage logs from any number of machines in the cloud-based system. The system resamples the received logs by a given time frame. The system determines time intervals between activities based on the resampling of the logs. After determining the time intervals, the system determines a probability of a new log to be received after a time interval. The system then defines a session separation breaker and defines one or more logs as opening or closing a session.

The application discloses a small sample modeling stability evaluation method and system based on Bootstrap resampling, and particularly relates to the technical field of computer and data intelligence, and the method comprises the following steps: completing data preprocessing and stable stage identification under a unified time base, and forming a segmented set; taking the average block length as the decision quantity to optimize SBB to construct a sample space, and determining the optimal block length in combination with the variance consistency and nominal coverage consistency criteria; performing resampling training around three scales of short, medium and long, and collecting layered indexes such as prediction, parameters and features; completing stability calculation and empirical coverage calibration based on scale-in statistics and scale-weighted mixing, and outputting a pseudo-stationary diagnosis score; finally, engineering judgment and rollback optimization are performed according to the coverage, correlation retention and risk threshold. The system records random seeds, block metadata and model version generation evidence pointers, has traceable and retestable characteristics, and is suitable for scenes such as production lines, network traffic and finance.

The application discloses a kind of synthetic and real data's adaptive resampling method and device, electronic equipment, comprising: obtaining training dataset from multiple data sources;Uniformly pre-process multiple-source training data, and construct data source identification and sample basic feature representation;Based on basic feature representation, calculate the distribution deviation between different data sources and sample bias feature;According to distribution deviation and sample bias feature, the weight of each sample is calculated or resampling probability is calculated;Based on the weight of removing bias or resampling probability, adaptive resampling is executed to multiple-source training data, and the training sample set of removing bias is constructed;Training or updating is carried out to target model using the training sample set of removing bias, and resampling strategy is dynamically adjusted based on model training feedback, to realize multiple rounds of adaptive debiasing and resampling optimization.

The invention provides a three-dimensional medical image segmentation method without resampling, and relates to the field of computer vision, in particular to the three-dimensional medical image segmentation method without resampling. The three-dimensional medical image segmentation framework does not need to be resampled in training and reasoning stages, information of original medical images can be reserved to a greater extent, detail loss caused by resampling is avoided, and therefore segmentation precision is improved. Convolutional low-rank decomposition is introduced into the attention module, so that global attention calculation which originally needs to be subjected to high-dimensional matrix product can be approximated to low-rank matrix operation, and the calculation complexity is effectively reduced. Meanwhile, the corresponding self-adaptive parameterized European matrix is also compressed to a small scale, video memory occupation and computing resource consumption are greatly reduced under the condition that global dependence modeling capability is guaranteed, and efficient operation on large-scale three-dimensional medical image data can be achieved.