64 results about "Data resampling" patented technology

The invention discloses an InSAR (Interferometric Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) robust adaptive fusion method for high-precision three-dimensional surface deformation monitoring, which belongs to the technical field of space geodetic survey and remote sensing, and comprises the following steps of: constructing a time sequence deformation field based on a small baseline set time sequence InSAR processing technology, and resampling GNSS observation data to a grid consistent with the InSAR by using Kriging spatial interpolation; a Helmert variance component estimation method is adopted to adaptively estimate variance components of InSAR and GNSS data, and reasonable weight fixing of a multi-source observation value is achieved; iterative reweighted least square estimation is introduced, outlier influences in various observation data are dynamically restrained through a Tukey double-weight function, robust optimization of a fusion model is achieved, and therefore a high-precision three-dimensional deformation field is reconstructed. The method comprises the steps of Helmert weight fixing, IRLS robust, variance component updating and a closed-loop feedback mechanism of weight matrix optimization.

The invention relates to a high-precision data acquisition method and system for an MEMS sensor, and the method comprises the following steps: carrying out the multi-channel parallel sampling of an initial electric signal outputted by the MEMS sensor, and obtaining an original data flow matrix; performing adaptive denoising processing on the original data stream matrix to obtain a denoised data set; performing signal phase reconstruction on the noise reduction data set through a phase unwrapping technology to obtain a continuous phase feature sequence; performing dynamic drift compensation on the continuous phase feature sequence to obtain a calibration signal vector; performing transient characteristic extraction on the calibration signal vector to obtain a sensor response characteristic curve; and performing high-precision data resampling on the sensor response characteristic curve based on an adaptive quantization coding technology to obtain a high-precision data acquisition result, thereby solving the technical problem of how to effectively remove noise interference generated in a multi-channel parallel sampling process.

The invention discloses an accidental explosion concrete penetration depth prediction method based on a two-stage migration adversarial network, and the method comprises the steps: (1) an instance screening stage: resampling target domain test data to construct a plurality of sub-data sets, training a base learning device, and calculating a weight coefficient; and inputting the source domain simulation data into the base learner to obtain a weighted predicted value, screening out the simulation data conforming to a target domain rule through a threshold value, and optimizing the source domain data quality. (2) a prediction stage: constructing a dynamic adversarial network model (including a weight network, a task network and a domain discriminator), distributing dynamic weights through the weight network, and aligning cross-domain feature distribution in combination with domain adversarial learning; weighted task loss and adversarial loss are jointly optimized in back propagation, so that the model adaptively fuses effective information in a hidden space, and a cross-domain generalization prediction network is generated. According to the method, the knowledge migration problem of simulation data and test data is effectively solved, and the method has excellent performance in a concrete penetration depth prediction task.

The invention provides a plateau pulmonary edema risk prediction method based on resampling and ensemble learning, and relates to the technical field of plateau pulmonary edema disease risk prediction, and the method comprises the following steps: S1, information collection; the HAPE onset risk prediction model oriented to the plateau crowd is constructed by combining ensemble learning and a data resampling method, the global expression ability of the model is improved by integrating different types of features including demographic statistics, vital signs and biochemical indexes, and when the problem of data imbalance is solved, various sampling technologies are adopted, so that the prediction accuracy of the HAPE onset risk prediction model is improved. The prediction performance of the model is effectively improved through a grid search technology in feature optimization, in addition, the model integrating a multi-layer perceptron and a gradient boosting decision tree is further optimized through a soft voting mechanism to further optimize a final prediction result, health management in the plateau environment is improved, the incidence rate of HAPE and related health risks are reduced, and the prediction efficiency of the plateau environment is improved. The powerful support is provided for the formulation of clinical intervention and prevention strategies.

The invention provides a frequency-wavenumber domain-based matrix array full-matrix capture three-dimensional ultrasonic imaging method and system, and belongs to the technical field of ultrasonic imaging. Five-dimensional time-space domain radio frequency data are collected through a two-dimensional full-sampling matrix array and are converted into a frequency-wave number domain through five-dimensional fast Fourier transform, angular frequency is mapped into axial wave number through Stolt interpolation, data resampling is achieved, then three-dimensional image frequency spectrum data are synthesized by defining spatial wave number coordinates of an image and conducting coherence stacking, and the image frequency spectrum data are obtained. And finally, reconstructing a high-quality three-dimensional ultrasonic image through three-dimensional inverse fast Fourier transform. Compared with a traditional delay summation beam forming method, the method has the advantages that the calculation efficiency is remarkably improved, the speed is increased by several times to dozens of times, the spatial resolution, the contrast noise ratio and the spot signal-to-noise ratio are remarkably improved, and the method is suitable for high-resolution real-time three-dimensional and four-dimensional ultrasonic imaging application.

The application relates to the field of meteorological data processing and satellite navigation application, and provides a multi-source meteorological data fusion method and system based on a Beidou space-time reference. The method comprises the following steps: acquiring Beidou data and multi-source meteorological data to be fused, taking the coordinated universal time timestamp output by a Beidou ground-based enhancement station as a global time reference, and setting a sliding window; taking the world geodetic coordinate system corresponding to the Beidou positioning data as a space reference, resampling the multi-source meteorological data to a preset standard grid, and combining terrain elevation data to construct a unified space coordinate system; calculating the confidence degrees of fused static terrain features and dynamic change features based on Beidou satellite orbit residual and clock difference parameters, adjusting the feature weights of the fused static terrain features and dynamic change features, and generating an enhanced fusion feature matrix.

The invention relates to a touch screen resampling method and device, electronic equipment and a storage medium. The method comprises the following steps: performing scene recognition on the electronic equipment to obtain a scene recognition result; determining a corresponding target resampling time difference according to the scene recognition result; the target resampling time difference is a time difference between a sampling time point before a time point of a screen refreshing event and the time point of the screen refreshing event; after a screen refreshing event is monitored each time, determining a sampling time point according to the target resampling time difference and the time point of the screen refreshing event; and resampling target touch screen data reported before the screen refreshing event according to the sampling time point to obtain supplemented touch screen data. By adopting the method, the touch response effect can be improved.

The invention provides a named entity recognition method, device and equipment based on loop iteration, and relates to the technical field of computers.The named entity recognition method comprises the steps that to-be-processed data is preprocessed, and a first processed text is obtained; the preprocessing comprises text cleaning, truncation and deletion, part-of-speech reduction, unified coding, data resampling and stop word removal; inputting the first processing text into a primary recognition model, and determining a first entity label to which a to-be-recognized entity belongs; performing secondary identification on the to-be-identified entity based on the first entity tag and a secondary identification model, and determining a named entity and a target entity tag corresponding to the to-be-identified entity; the secondary recognition model comprises at least two stages of recognition models, and the granularity of the at least two stages of recognition models is smaller than that of the primary recognition model. According to the method, entity labels of multiple levels are obtained step by step by utilizing a loop iteration method, and the accuracy of geographic entity recognition is improved from a multi-dimensional angle.

This invention discloses a method and system for resampling time-domain data of electromechanical-electromagnetic simulations while preserving time-frequency domain features. It includes: constructing electromechanical and electromagnetic transient time-series trajectory samples of a new energy power grid system; processing the trajectory samples separately using an improved Proni method to obtain frequency domain response features; reconstructing the electromechanical and electromagnetic transient time-series trajectories separately using linear interpolation and adaptive resampling techniques based on the frequency domain response features; and fusing the time-domain and frequency-domain features in the reconstructed time-series trajectory data to form a comprehensive feature vector, thereby obtaining electromechanical-electromagnetic simulation time-domain data that preserves time-frequency domain features. This invention solves the asynchronous sampling problem of electromechanical-electromagnetic time-domain simulation data and can be used to support the feature analysis of inconsistent data at different time scales in electromechanical and electromagnetic systems. It has broad application prospects and significant practical implications in fields such as scheduling and control of new energy power grids.

The present invention discloses a surface morphology data resampling method and a wafer thickness measurement method, the surface morphology data resampling method comprises the following steps: preprocessing the original morphology data of the object to be measured, removing abnormal data, and obtaining the preprocessed sampling data; performing regional segmentation on the preprocessed sampling data to obtain segmented grid data; based on the original sampling relative height value of the sampling points around each grid point and its contribution weight to the grid point, obtaining the relative height value of each grid point by weighted calculation; generating a resampling point array covering the object to be measured according to the resampling interval; based on each resampling point in the resampling point array, using the relative height values ​​of the four grid points around it and their corresponding contribution weights, calculating the relative height value of each resampling point. The method can realize effective denoising and data distribution uniformity of the original morphology data of the object to be measured, and accurately reflect the true morphology of the object to be measured.

A kind of coal mine personnel action recognition method applied in low-illumination environment belongs to video recognition field.The video data of personnel operation in low-illumination environment of coal mine is resampled, and then converted into video data under normal light by style conversion module.After global feature extraction module extracts global space-time features of video frame sequence, target detection module detects personnel in video, and extracts feature representation of personnel action pipeline from video frame sequence.Finally, pipeline features are respectively input into regression module and action classification module to calculate the position of each pipeline and the probability of belonging to each action.The advantages of the method are as follows: style conversion module does not need paired data for training, which reduces the difficulty of data acquisition;in target detection module, video target detection is converted into a set of set predictions, which does not need any prior knowledge and subsequent processing, and realizes end-to-end detection of personnel in video.

The invention provides an unbalanced drug response data resampling method and device based on swarm intelligence optimization, a storage medium and equipment, and belongs to the field of unbalanced data classification in data mining. The method comprises the following steps: firstly, generating an initial sampled drug reaction data candidate set; training the classification performance of a classifier according to each drug reaction data candidate set; dynamically adjusting a sample selection strategy, and iteratively updating samples contained in each candidate set through a position updating formula; and finally, outputting to obtain a globally optimal drug reaction resampling data set. The method can be compatible with continuous swarm intelligent optimization algorithms (such as a particle swarm optimization algorithm and a grey wolf optimization algorithm), and is suitable for drug reaction unbalanced data classification tasks of different scales and distribution characteristics.

A device includes a receiver to receive a packet over a channel at a first frequency and generate a sampled stream of data at a first sample rate corresponding to the first frequency. A data resampler circuit includes a re-timer engine to determine, using a ratio between the first sample rate and a crystal oscillator (XO)-divided sample rate, re-timer values. The data resampler circuit includes a time shifting circuit to re-sample data values of the sampled stream of data associated with locations of the plurality of re-timer values. A correlation circuit uses the re-sampled data values, and the re-timer values to match an expected data pattern to a corresponding data pattern detected in a frame of the packet.

The invention provides a domain generalization laser radar point cloud semantic segmentation method and system, and belongs to the field of laser radar semantic segmentation, and the method comprises the steps: obtaining point cloud data of a source domain, and carrying out the preprocessing; resampling the preprocessed source domain point cloud data to generate a resampling point cloud; generating a mixed point cloud through a cross-scene mixed enhancement strategy; inputting the initial point cloud data, the re-sampling point cloud data and the mixed point cloud data into a semantic segmentation network to extract point cloud features; unified alignment of point cloud features in a semantic space is realized by using a text encoder of a contrast language-image pre-training model; and carrying out model training by adopting a contrast loss function, selecting a target domain point cloud, inputting the target domain point cloud into the trained model, and outputting a point cloud semantic segmentation result. According to the method, through the synergistic effect of multi-density resampling, cross-scene mixing and unified semantic space alignment, the domain offset problem is effectively relieved, and the semantic segmentation precision and generalization ability of the model on an unknown target domain are remarkably improved.

The application discloses an exoplanet light variation signal classification method based on data enhancement and integrated learning, which comprises the following steps: splicing light variation signal data of different observation seasons of the same target, and pre-processing the data; resampling the data to a fixed length; dividing the resampled data set into a training set, a test set and a validation set; performing data enhancement on the training set by over-sampling and under-sampling respectively to obtain sub-training set 1 and sub-training set 2; building a one-dimensional CNN network model for the two training sets respectively, and optimizing the network by adjusting the number of hidden layers, the number of neurons, the activation function and the model optimizer; obtaining the best classification model as base classifier 1 and base classifier 2 by adopting N-fold cross-validation respectively; combining the base classifiers by adopting a weighted voting method to obtain a light variation signal classifier, and obtaining a classification result according to the signal classifier. The application solves the problem of unbalanced exoplanet light variation signal categories, and enables the model to obtain more features to improve the generalization ability.

The present invention relates to an audio data resampling method, an electronic device and a storage medium, comprising: S1, obtaining initial audio data; S2, obtaining an initial sampling rate and a target sampling rate as well as target audio data and a target number of sampling points; S3, when the target number of sampling points is not an integer multiple of an audio unit, obtaining the minimum number of remaining sampling points to obtain a cumulative offset; S4, executing S5 when the cumulative offset is less than the audio unit, otherwise executing S6; S5, obtaining the next data packet as the initial audio data, and executing S2; S6, obtaining compensation audio according to the last data of the previous target audio data and the first data of the current target audio data, and adding the compensation audio before the first data of the current target audio data and transmitting it; S7, subtracting the number of bytes corresponding to the compensation audio from the cumulative offset to obtain an updated cumulative offset, and executing S2 after obtaining new initial audio data. The implementation of the present invention can ensure the synchronization of the time series of audio before and after resampling.

The invention belongs to the technical field of Internet of Things card detection, and particularly relates to an Internet of Things card detection method, which specifically comprises the following steps of data collection, feature extraction, model training, real-time monitoring, anomaly detection, response processing and feedback optimization. An incremental learning method is adopted, a model is gradually updated when new data arrives, and the method specifically comprises the following steps of model initialization, data monitoring, model updating, performance evaluation and loop iteration. The invention discloses an Internet of Things card detection device which comprises a data acquisition module, a preprocessing module, a feature extraction module, an incremental learning module, a data resampling module, a weighting module, a model evaluation module and a decision support module. Risks caused by abnormal behaviors can be effectively reduced, new data can be adapted by updating model parameters step by step, the risk of concept drift is avoided at the same time, in addition, incremental learning methods in the detection device comprise regular model updating, data resampling and weighting methods, and new data and environment changes can be detected.

This invention relates to a small target detection method based on multi-scale information fusion, comprising the following steps: acquiring an existing dataset and preprocessing the dataset, with all preprocessed images constituting a training set; constructing a small target detection model, which uses the YOLOv5 backbone feature extraction network as the base model, replacing several modules within it; introducing data resampling operations on the input dataset and training it, saving the model parameters after optimal convergence to obtain the optimal small target detection model; calling the optimal small target detection model, inputting the test image for detection, and outputting the recognition result. Experimental results demonstrate that this method improves the ability to extract multi-scale features and effectively improves the detection performance of small-scale targets, with test accuracy improved compared to existing methods.

This application proposes a method and device for extracting amplitude anomalies based on formation energy gradients, belonging to the field of geophysical exploration. The method comprises: resampling post-stack 3D seismic data; extracting an amplitude envelope from the resampled 3D data; extracting formation reflection time differences from the amplitude envelope; extracting the strike energy gradient of each sample point in the resampled 3D data and constructing a tensor matrix of the strike energy gradient; smoothing the tensor matrix and calculating at least one eigenvalue of the smoothed tensor matrix; and determining the presence of an amplitude anomaly in the post-stack 3D seismic data if at least one eigenvalue does not meet a predetermined condition. The device comprises: a data acquisition module, a data resampling module, an envelope extraction module, a reflection time difference extraction module, a tensor matrix construction module, an eigenvalue calculation module, and an amplitude anomaly detection module. Compared with traditional methods, the present application can detect amplitude anomalies and improve the accuracy of energy difference characterization.

The invention discloses an out-of-distribution data learning method and system for agent navigation. The method aims at improving the capability of utilizing out-of-distribution data in a navigation task of a machine learning model. By optimizing the navigation strategy, a large amount of distributed external navigation data which is easy to obtain but does not reach the target position can be effectively utilized. According to the main technical scheme, the method comprises the steps that a navigation data set containing expert demonstration and out-of-distribution demonstration is collected; training a reverse environment model and a reverse navigation strategy; reverse data enhancement and data resampling are executed through self-paced learning; and finally, combining off-line behavior cloning and reinforcement learning, adopting a behavior cloning method for marked data containing expert behaviors, implementing approximate dynamic planning on unmarked data, and establishing an optimization target and an optimization navigation strategy in an out-of-distribution state. The method has the characteristics of easiness in data acquisition, easiness in algorithm implementation and the like, and based on few expert demonstration, the decision-making ability of the intelligent agent in a complex navigation environment is steadily improved.

The invention relates to the field of artificial intelligence and point cloud segmentation, and provides a segmentation method and system for disaster scene point cloud data, and the method can effectively promote the network to improve the discrimination capability of new objects through carrying out the data resampling of new objects in a secondary subset in the network continuous learning of students. The teacher network prediction result is combined with the new-class original real label to generate a pseudo label, and training loss is obtained based on the difference between the generated pseudo label and the student network prediction result; point cloud features of a teacher network and a student network are subjected to synchronous layered pooling, and distillation loss is obtained based on the difference between the pooling features of the teacher network and the student network, so that the network does not need to construct total loss based on an old class, and the problem that a computer memory is greatly increased due to old class continuous storage is solved; and meanwhile, the trained teacher network on the main subset is utilized to guide the students to learn on the network, so that the post-disaster objects in the new class can be quickly identified.

The application relates to a touch screen resampling method and device, electronic equipment and a storage medium. The method comprises the following steps: performing scene recognition on the electronic equipment to obtain a scene recognition result; determining a corresponding target resampling time difference according to the scene recognition result; the target resampling time difference is a time difference between a sampling time point and a time point of a screen refresh event; after listening to the screen refresh event each time, determining the sampling time point according to the target resampling time difference and the time point of the screen refresh event; resampling target touch screen data reported before the screen refresh event according to the sampling time point to obtain supplementary touch screen data. The method can improve the response effect of touch.

The invention discloses a method for reducing resource overhead in a data resampling process. The method comprises the following steps: calculating a ratio of an input sampling rate to an output sampling rate of a rate matching module and an absolute difference value between 1 and the ratio; when the input sampling rate is smaller than the output sampling rate, data are read from the FIFO buffer according to a read enable signal and a read pointer, and after each time of reading, the read pointer is moved forwards by one bit, and absolute difference values are accumulated; when the accumulation result of the absolute difference is greater than or equal to 1, keeping the read pointer unchanged and reading again for interpolation; when the input sampling rate is greater than or equal to the output sampling rate, writing data into the FIFO buffer according to the write enable signal and the write pointer, and after each time of writing, forwards moving one bit of the write pointer and accumulating absolute difference values; and when the accumulation result of the absolute difference is greater than or equal to 1, pulling down the write enable signal and discarding the current write-in data to perform value extraction. According to the invention, under the condition of not depending on a high-frequency clock and a large-depth FIFO, the resource overhead in the data resampling process is reduced.

The invention provides a suspension road spectrum reduction method for accelerating a rack endurance test, and belongs to the field of rack endurance tests. The method comprises the following steps: step 1, recognizing a suspended road spectrum characteristic pavement and splitting load data; step 2, deleting burrs of the suspension time domain load data; 3, determining the resampling frequency of the suspension time domain load data; step 4, executing the resampling of the suspension time domain load data, and correcting the resampled data; step 5, deleting the data of the time period less than the threshold load to obtain reduced time domain load data of each characteristic pavement suspension; step 6, obtaining complete reduced suspension time domain load data by splicing the suspension time domain load data of each characteristic pavement; step 7, calculating a false damage amount after the suspension time domain load data is reduced, and judging whether the false damage amount is greater than or equal to a threshold value or not: if so, turning to step 8; if not, turning to the step 3; and step 8, carrying out a rack endurance test. According to the method, the bench test period is shortened, and the accuracy is improved.

The invention discloses an automobile multi-source data time alignment and fusion method and system. The method comprises the steps of obtaining first data and second data with non-uniform time references, and performing standardization processing on time; in the second data, window data are extracted from each candidate position through sliding window search, time coverage is judged, the window data are resampled to a first data time point, and a similarity distance is calculated by adopting a dynamic time warping algorithm; selecting the position with the minimum distance to establish a time mapping relation; and performing data fusion to generate fused data covering the complete or partial time range of the second data. The system comprises a data acquisition and preprocessing module, a sliding window matching module based on dynamic time warping, a time alignment relationship establishment module, a data fusion module and a data output module. According to the method, the relation between the time type and the sampling frequency of the data is not limited, universality is achieved through self-adaptive resampling, and the problems that in the prior art, the requirement for data characteristics is harsh, and the application range is narrow are solved.

The application discloses a waveform data resampling method, device, functional module and system. The waveform data resampling method comprises the following steps: using target waveform sampling data to initialize a jump point detection data, and identifying a jump sampling point in the jump point detection data; using a current interpolation algorithm to perform interpolation processing between the jump sampling point and a previous adjacent sampling point of the jump sampling point; after updating the jump point detection data by using the interpolation data, returning to perform the operation of identifying the jump sampling point until the use of the interpolation algorithm is completed for a preset number of times; performing resampling processing according to interpolation positions recorded by each interpolation algorithm in the interpolation algorithm set before interpolation processing to obtain resampling data of a to-be-processed channel; performing data waveform recovery according to the resampling data to obtain to-be-stored data, and storing the to-be-stored data. The technical scheme of the embodiment of the application can greatly improve the sampling precision of the waveform data and meet the occasions with high data precision requirements.

The invention relates to a head and neck tumor multi-modal image database construction method and a computer storage medium, and relates to the technical field of multi-modal image database construction.The method comprises the steps that S1, CT and MRI data of head and neck tumors and related clinical, pathological and gene cross-scale multi-modal data are collected through an image archiving and communication system (PACS) and a hospital information system (HIS); data quality control is carried out; s2, performing data anonymization and data desensitization processing on the acquired CT and MRI images; s3, carrying out standardization processing on all CT and MRI images, wherein the standardization processing comprises data resampling and data annotation; s4, multi-modal CT and MRI images and corresponding marks are classified, arranged and updated in real time; and S5, carrying out cleaning, conversion and quality control on the unstructured data. The system has the advantages that the blank of head and neck tumor image artificial intelligence databases at home and abroad is filled; a solid foundation is laid for research, development and training of an artificial intelligence model, and the method has great value for promoting precise diagnosis and treatment, mechanism research and clinical transformation of head and neck tumors.

The invention belongs to the field of civil engineering, and relates to an arch dam pouring bin concrete temperature online prediction method, which comprises the following steps: 1) collecting arch dam pouring bin concrete temperature data; (2) the arch dam pouring bin concrete temperature data are preprocessed; (3) data resampling is conducted on the preprocessed arch dam pouring bin concrete temperature data, and a sample data set after dimension reduction is obtained; and 4) establishing an SSA-ISVR concrete temperature online prediction model according to the sample data set after dimension reduction, and predicting the concrete temperature of the arch dam pouring bin according to the SSA-ISVR concrete temperature online prediction model. According to the online prediction model for the temperature state of the arch dam after concrete pouring, boundary conditions and the time-varying characteristics of concrete properties are considered in the model, and compared with an offline model, the online prediction model has higher prediction precision and can provide support for concrete temperature control.

The invention discloses a surface nitrogen dioxide concentration estimation method based on distributed extreme gradient lifting, and belongs to the technical field of multi-source remote sensing data processing and application, and the method comprises the following steps: 1) data acquisition; 2) data resampling; 3) space-time matching; 4) selecting data variables; 5) data standardization; 6) building a cluster environment; 7) data cleaning; 8) constructing a satellite data reconstruction model; (9) constructing a surface NO2 estimation model; and 10) evaluating the performance of the model. The method has the beneficial effects that a multi-source data fusion model is constructed based on a big data technology and a distributed machine learning model in combination with auxiliary variables such as meteorological data, population density and land utilization, missing values in troposphere NO2 column concentration data acquired by a satellite are filled up, and the integrity and effectiveness of the data are improved. And further constructing a multi-source data fusion model by using the reconstructed troposphere NO2 column concentration data, and accurately predicting the ground NO2 concentration.