211 results about "Large sample" patented technology

The invention relates to a circuit board on-line defect detection method and system, and the method comprises the steps: carrying out the synchronous collection and structural integration of multi-source technological parameters such as production line environment temperature and humidity, equipment operation states, material batches and the like, and defect detection images, and achieving the construction of large-sample original data in a production process; through standardization and de-noising preprocessing, multi-modal features are fused, and a distribution mapping model of process and defect features is established by using algorithms such as mutual information analysis and principal component analysis. Based on a feature distribution model and real-time data, a dynamic anomaly detection threshold is adaptively generated, Bayesian inference and evidence reasoning are combined, multi-level confidence levels and risk response suggestions are output, and self-learning evolution of the model and the threshold is realized through a closed-loop feedback mechanism. According to the scheme, the accuracy of anomaly detection, the response timeliness and the risk disposal intelligent level are improved, the method adapts to complex working conditions and batch changes, and the closed-loop optimization and safety control capability of the production process is remarkably enhanced.

The invention provides a bearing fault variable working condition diagnosis method based on a multi-scale convolutional network and MAML, and relates to the technical field of equipment fault diagnosis. The method comprises the following steps: firstly, introducing fast Fourier transform to pre-process an original time domain vibration signal; secondly, a fault diagnosis model based on a multi-scale convolutional network and MAML is applied; and then, an internal and external circulation updating method based on model-independent element learning is adopted, so that the model can quickly adapt to a new task, and a relatively good prediction effect can be achieved only through a small amount of fine adjustment. According to the method, multi-scale feature extraction and meta-learning are creatively combined, the generalization ability of the model under variable working conditions is remarkably improved, the problem that a traditional fault diagnosis method depends on a single working condition and a large sample size is effectively solved, and the method is particularly suitable for small-sample and multi-working-condition bearing fault diagnosis scenes on an industrial site.

The invention relates to the technical field of medical informatics and artificial intelligence technology cross application, in particular to a dynamic intelligent prediction system for HIV infected person immune reconstruction insufficiency. Comprising a data acquisition and management module, a data preprocessing and feature engineering module, a Bayesian joint modeling core module, a dynamic prediction calculation module, a multi-dimensional verification and evaluation module and a clinical application service module. According to the method, on the basis of large-sample multi-center longitudinal follow-up visit data, a Bayesian shared parameter dynamic joint model is adopted, the dependency relationship between dynamic trajectories of immune indexes such as cell counting and the like and IIR occurrence time is captured, real-time risk prediction is achieved through a sequential Bayesian updating mechanism, and through multi-dimensional verification, the risk prediction accuracy is improved. The method has the advantages that the performance is better than that of an expert-driven model, expert experience pre-judgment and 19 mainstream machine learning algorithms, an individualized conclusion with a confidence interval can be output, clinical decision is assisted, prediction is promoted to be clinical from scientific research, and the method has important application value and popularization prospect.

The invention discloses a method for acquiring grain orientation characteristics based on optical and height characteristics of an alloy etching structure. The method comprises the following steps: (1) acquiring grain orientation distribution and grain size of an alloy surface structure; (2) obtaining an etching structure on the surface of the alloy; (3) acquiring optical characteristics of the etched structure on the surface of the alloy; (4) obtaining height characteristics of an alloy surface etching structure; and (5) constructing a correlation model of structure grain orientation characteristics based on alloy surface optics and height characteristics, and realizing prediction. Aiming at the test of the alloy grain orientation, the optical and height characteristics of the alloy surface etching structure are synthesized, the measurement of the centimeter-level large sample size or full-area grain orientation of the alloy is easy to realize, and compared with the existing measurement aspect, the method is more efficient and convenient; and more comprehensive and reliable tissue information can be provided for alloy optimization and new material development, and the method has important significance for high-throughput characterization and intelligent manufacturing.

A data processing method, applied to the field of artificial intelligence. The method comprises: acquiring data and a corresponding label, the label corresponding to an expert rule; and under the condition that the number of data sets corresponding to the label is smaller than a first threshold value, inputting into an algorithm model prompt information composed of the data sets, the label, and the expert rule to generate new data corresponding to the label, wherein the prompt information comprises the label, the data, and the expert rule; and there is a mapping relationship among the label, the data, and the expert rule. According to the present application, the prompt information prompt comprising the expert rule is used as the input of the algorithm model, the algorithm model can be guided to generate new data so as to expand small sample data, and the expanded small sample data set is fused with a preprocessed large sample data set, realizing the balance of data sets of different labels. Meanwhile, under the condition that the diversity and balance of the data are ensured, the performance of the model is improved by combining the dynamic optimization of the prompt information prompt.

The invention provides an engineering drawing steel bar bulk sample annotation text detection method, system and device based on deep learning, and a storage medium. The method comprises the following steps: S1, preprocessing an engineering drawing image and labeling a steel bar bulk sample annotation text region data set; s2, constructing a staged multi-scale feature map extraction backbone network framework; s3, introducing compression excitation into each scale feature map; s4, adopting an ACON adaptive activation function in nonlinear modeling; s5, constructing a multi-scale feature pyramid structure based on the RSE-FPN, and superposing scales to reinforce fusion; and S6, outputting the approximate binary image to realize accurate prediction of the bounding box. According to the method, the OCR detection precision is remarkably improved, small character and complex background interference is effectively overcome, and missing detection and false detection are reduced; the model is lightweight to facilitate efficient deployment of equipment; the output boundary is compact and accurate, the semantics is reasonable, and subsequent recognition and analysis are facilitated; the method has excellent generalization ability, adapts to steel bar large sample drawings of different formats, definitions and styles, and is high in engineering practicability.

The invention provides a celluloid nitrocotton stability quantitative determination method based on conductivity analysis, and the method comprises the following steps: determining the conductivity of standard nitrogen oxide gas dissolved in an aqueous solution by adopting a conductivity meter through a standard gas simulation mode according to the criterion of Beckmann-Lockg method 2mL / g; taking the numerical value as a stability index critical value of the celluloid nitrocotton; the method comprises the following steps: absorbing nitrocotton with the same mass by using ultrapure water, heating at 132 DEG C for 2 hours, releasing nitrogen oxide gas, recording the change value of the conductivity in the solution along with time in real time by using an oxidation stability tester, obtaining the change value of the conductivity after 2 hours, and determining the content of nitrogen oxide according to the comparison between the conductivity value and a critical value. And judging whether the stability of the celluloid nitrocotton sample is qualified or not. The method provided by the invention can solve the technical problem that accurate quantitative judgment is difficult to realize due to large sample quantity, high safety risk, tedious operation process and large color change error of test paper observed by human eyes in the traditional method.

The invention provides a data stream active sampling and predicting method for a product ash content index in a coal dense medium separation process. The method comprises the following steps: S1, preprocessing coal dense medium separation data; s2, establishing a dynamic cache window in the sorted data stream to perform synchronous clustering; s3, establishing a feature memory dictionary, and constructing a centralized kernel space by using data in the feature memory dictionary to obtain a dictionary data feature micro-mode direction and a feature main mode direction; s4, calculating the projection of the real-time data point in the feature micro-mode direction, and judging whether active sampling is carried out or not; s5, obtaining the projection of the dictionary data in the main mode direction by using the kernel principal component information in the S4, constructing an evaluation function, and judging whether to sort active sampling or not; s6, taking the maximum sample set of the value function as a new feature memory dictionary; and S7, strengthening the weight of the actively sampled sample, recursively constructing a random weight neural network through a Woodbury formula, and predicting the clean coal ash content in the label data stream until the data stream is finished.

The invention provides a gas sensor response fingerprint feature extraction method based on parameter fitting, and relates to the technical field of gas fingerprint feature extraction. According to the method, curve parameter fitting is carried out on response data of a gas sensor to obtain feature sequences, the information entropy contribution rate of the features is calculated based on the inter-class and intra-class divergence ratio of each feature, the feature sequences are sorted and screened according to the information entropy contribution rate, and therefore high-compactness and interpretable fingerprint features are obtained. The feature extraction method and the extracted fingerprint features have interpretability, and the method has lower calculation and development cost and is suitable for feature extraction of a large sample set and feature extraction of a small sample set.

The small sample anti-migration prediction method suitable for the metallurgical process end point component under the zero expansion characteristic comprises the steps that smelting report data of a large sample steel grade and a small sample steel grade in the metallurgical process are collected to serve as source domain data and target domain data; adopting median to fill and restore abnormal values existing in the report data; dividing the source domain data and the target domain data into continuous feature variables and classification feature variables; inputting the continuous feature data and the classification feature data of the source domain data into a TabNet coding and decoding self-supervising network for self-supervising training, and inputting the target domain data into the trained self-supervising network for feature extraction and reconstruction; introducing an adversarial network, and gradually aligning feature distribution of a source domain and a target domain; and inputting the high-dimensional reconstruction features processed by the TabNet coding and decoding self-supervised network in the source domain into the deep table network model for training, and inputting the small sample steel grade features aligned by the adversarial network distribution into the trained deep table network model for transfer learning.

This invention discloses a combination of SNP loci, a gene chip, and their applications for cotton variety identification, relating to the fields of bioinformatics and molecular breeding. From 100 cotton whole-genome resequencing data, this invention screened out 128 SNPs, which can be used as SNP fingerprints for cotton varieties. Using the SNP molecular markers and liquid-phase probes developed in this invention, cotton identification achieved a 100% detection rate, with a minimum allele frequency greater than 0.05, capable of distinguishing any two cotton varieties, enabling efficient and rapid cotton variety identification. Furthermore, this invention utilizes next-generation sequencing technology, resulting in a simple and standardized procedure that reduces human error, leading to extremely low unit costs for batch identification analysis. It is economical and efficient, particularly suitable for large-sample analysis and identification, and has promising application prospects.

The invention provides a crack pouring glue compression rebound recovery rate test system and method, and relates to the technical field of rebound recovery rate test.The crack pouring glue compression rebound recovery rate test system comprises a tester, a high-precision injection ball and a large sample containing vessel; the tester comprises a machine body, a touch control type control display screen is mounted on an inclined panel of the machine body, a group of function buttons are mounted on the inclined panel of the machine body, the machine body is provided with a stepped circular groove, and the large sample containing vessel is matched with an upper side circular groove of the stepped circular groove; the machine body is connected with a U-shaped frame, and the U-shaped frame is connected with a first laser distance measuring sensor. Aiming at the defects in the prior art, the invention develops the system and the method for testing the compression resilience recovery rate of the crack pouring glue, and the high-precision penetration ball is adopted, so that the contact area with a sample is increased, and the detection error is reduced. By optimizing a hardware structure (replacing a high-precision sensing assembly) and control logic, automation and high precision of a test process are realized.

The invention relates to the technical field of bridge structure health monitoring and damage diagnosis, and discloses a bridge damage identification method based on a physical information neural network. According to the bridge damage identification method based on the physical information neural network, efficient and accurate identification of bridge damage is realized by optimizing a data acquisition scheme, designing a bridge exclusive physical constraint, constructing a biterm loss function and standardizing a network training process; the invention provides a damage identification method fused with a physical information neural network, aiming at solving the problems of strong manual dependence, large sample labeling demand, weak anti-interference capability and low identification precision in the existing bridge damage identification technology. The bridge structural mechanics principle and the neural network technology are deeply combined, so that the accuracy, robustness and engineering practicability of damage identification are improved while the dependence of the labeled sample is reduced, and the actual demand of real-time health monitoring in the bridge operation stage is met.

The invention provides a turbine blade fatigue performance influence factor analysis method and system and a medium, and belongs to the technical field of turbine blade performance prediction.The turbine blade fatigue performance influence factor analysis method includes the steps that a small number of training samples are generated, and a back propagation neural network (BP neural network) describing the relation between the fatigue life of a turbine blade and input variables is constructed; a training sample is added to the sequence, and the BP neural network is updated until the calculated failure probability is converged; based on a failure sample obtained in the process of solving the failure probability, estimating conditional failure probability estimation values at different sample points by using a Bayesian inference theory; and obtaining a fatigue reliability sensitivity estimation value of each input variable according to an average difference between the fatigue failure probability estimation value of the turbine blade and the condition failure probability estimation value of each failure sample point. The method solves the problems that when an existing agent model method is used for solving the reliability and sensitivity of the turbine blade, the sample requirement is large, efficiency is low, and the method cannot be suitable for a high-dimensional problem.

The invention discloses a large-scale composite member deformation gap distributed measurement and full-field reconstruction method, and the method comprises the following steps: setting a plurality of sets of technological parameters, and carrying out the mold pasting of a composite member based on the technological parameters, so as to enable the surface of the composite member to form a skin; determining an optimal arrangement scheme of the sensors, arranging the sensors according to the scheme, and obtaining a gap value matrix; obtaining a plurality of groups of gap value matrixes under different process parameters, and constructing a finite element model small sample training set; training the finite element model by using the finite element model small sample training set to obtain a full-field gap prediction finite element model; and generating a convolutional neural network large sample training set through the full-field gap prediction finite element model, and training the convolutional neural network by using the large sample training set to obtain a full-field gap prediction convolutional neural network model. The method has high systematicness and engineering implementability, and is more suitable for film pasting quality evaluation of a complex shell surface structure compared with a traditional method.

The invention relates to a large sample exploratory experiment conclusion macro-micro interaction verification method and system, the method is used for constructing a'macro-interaction-micro 'three-layer linkage analysis framework, and the framework comprises a macro conclusion layer, an interaction analysis layer and a micro instance layer; the interaction analysis layer is used for connecting the macroscopic conclusion layer and the microscopic instance layer; the method comprises the four steps of automatic discovery and presentation of a macroscopic conclusion, interactive deep exploration and verification based on the macroscopic conclusion, macro-micro bidirectional linkage and sample screening, and concrete redisk and causal verification of micro instances. Through the method, interactive verification of a macroscopic conclusion and a microcosmic instance is realized, and the interpretability and credibility of the conclusion are remarkably improved. According to the method, a macroscopic and microcosmic barrier is broken through, a cognitive closed loop from'knowing the course 'to'knowing the course' is constructed, and a more robust basis is provided for decision making.

Systems, methods, and tools associated with Enhanced Object Mask (SEI) messages are disclosed herein. In an example, a video device may obtain one or more syntax elements associated with video data. The one or more syntax elements may specify a parameter (e.g., a location parameter) associated with an object mask associated with the picture. The parameter may correlate with at least a sample range of object mask samples around the object mask ID value. In an example, the parameters associated with the object mask may include a maximum sample value and a minimum sample value of the object mask that define a range of the object mask samples around the object mask ID value. In an example, the parameter associated with the object mask may be a bounding box parameter associated with the object mask.

This invention discloses an efficient RNA data preprocessing method to address the problems of low processing efficiency and low information density in high-throughput sequencing data. Its core steps include: (1) introducing a parallel processing scheme for high-throughput sequence data, rapidly mapping RNA-seq data to a reference genome to generate a BAM file; (2) extracting base sequences and expression levels and storing them as compact PKL format files; (3) extracting all exon position information by parsing the genome annotation file; (4) combining multi-sample expression level data to screen for highly variable exons and constructing a high-information-density feature list based on the sample set; and (5) accurately extracting target sequences from the preprocessed file based on this list. Compared to traditional methods, this innovative approach achieves triple optimization: full-process parallel processing for accelerated computation, high-compression data storage, and adaptive feature selection. Processing speed is increased by 3-5 times, and data volume is reduced by more than 90%, making it suitable for high-throughput RNA-seq data analysis with large sample sizes.

The application provides a gas sensor response fingerprint feature extraction method based on parameter fitting, and relates to the technical field of gas fingerprint feature extraction.The method obtains a feature sequence by performing curve parameter fitting on response data of a gas sensor, calculates information entropy contribution rates of the features based on inter-class and intra-class divergence ratios of each feature, sorts and screens the feature sequence according to the information entropy contribution rates, and thus obtains high compactness and interpretable fingerprint features.The feature extraction method and the extracted fingerprint features both have interpretability, the method has smaller calculation and development costs, and is suitable for feature extraction of large sample sets and feature extraction of small sample sets.

The present application discloses an information processing method, including: acquiring data to be processed in a target field corresponding to a small sample; processing the data to be processed using an initial content understanding model to obtain a processing result, wherein the initial content understanding model includes a plurality of sub-models, each of the plurality of sub-models is obtained based on sample data in a plurality of other fields, each of the plurality of other fields is a field corresponding to a large sample, and the processing result includes a result of processing the data to be processed by each of the plurality of sub-models; and then, determining a final result of processing the data to be processed based on the result of processing the data to be processed by each sub-model and a weight of each sub-model.

The invention discloses a remote sensing image large sample library generation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a to-be-purified sample set and a remote sensing image of a target research area; performing image segmentation on the remote sensing image to obtain an initial sample set; based on the initial sample set, obtaining an optimized sample set through unsupervised sample optimization based on a residual network; and based on the optimized sample set, performing feature similarity sample optimization based on Euclidean distance measurement on the to-be-purified sample set to obtain a purified sample set, and sorting based on the purified sample set to obtain a target sample library. According to the embodiment of the invention, samples are filtered and purified layer by layer through two-stage optimization, so that the purity and accuracy of the finally generated target sample library are ensured fundamentally, a solid foundation is laid for training a high-precision deep learning model, and the method can be widely applied to the technical field of image processing.

Provided are a structure evaluation method, a structure evaluation device, and a structure evaluation program with which it is possible to distinguish between diffracted X-rays generated for each region and evaluate respective structures. A structure evaluation method performed by measuring diffracted X-rays transmitted through a sample includes a step for simultaneously irradiating a plurality of regions of a large sample S11 in a single-crystal state with a white X-ray R11, and a step for detecting a diffracted X-ray produced by the irradiation. A measurement composed of the series of steps is performed a plurality of times. In at least one of the plurality of measurements, a slit 11 that selectively passes only a diffracted X-ray produced in a specific region among a plurality of regions C11, C12 irradiated with the X-ray is arranged on the back side of the large sample S10. Diffracted X-rays produced in the plurality of regions C11, C12 of the large sample S10 are each detected by using the slit 11, and the structure is evaluated for each of the regions.

The present application provides a method for quantitatively detecting water content in concrete, a device, a medium, and a product, and relates to the technical field of detection for water content in concrete. The method includes: obtaining an X-ray image of a to-be-detected sample, where the to-be-detected sample is a to-be-detected concrete sample, and the X-ray image is an image obtained after an X-ray travels through the to-be-detected concrete sample; determining X-ray light field distribution information of the X-ray image according to the X-ray image; determining a water density of the to-be-detected sample according to the X-ray light field distribution information; and determining water content of the to-be-detected sample according to the water density. The present application can improve precision and efficiency of detection for water content in concrete, reduces detection costs, and is applicable to a large sample.

This invention discloses an automatic method for extracting ionospheric TEC anomalies based on global seismic events. The method acquires a global seismic event catalog, global ionospheric TEC data, and space weather indices, performs data preprocessing, and for each seismic event, selects those occurring during quiescent periods based on space weather conditions. Ionospheric TEC data within fixed observation windows and space windows are extracted and differiated with contemporaneous background TEC data to obtain ionospheric TEC difference values. Normalized statistical analysis is then performed on the quiescent period TEC difference values ​​and their frequency corresponding to global seismic events to obtain statistical characteristics of ionospheric TEC anomaly rates and frequencies. Random seismic events are generated globally for comparison. This method is comprehensive, programmable, and suitable for automated processing of large global samples, possessing good scientific rigor, scalability, and engineering application value.

The invention belongs to the technical field of sonar data processing, and particularly relates to a small sample sonar reverberation data enhancement and target detection method and system. According to the small sample sonar reverberation data enhancement and target detection method, spectrum sensing loss and multi-scale short-time Fourier transform feature constraints are introduced through WGAN-GP; the cosine similarity between the time-frequency domain distribution of the generated reverberation signal and real data is greater than or equal to 0.92, so that the problem of mode collapse of a traditional generative adversarial network model under a small sample is solved, and the data diversity is improved; a sequential structure and a state transition rule of a sonar detection data set are coded into a two-dimensional image through a Gramer angle field and a Markov transition field, the feature discrimination degree of a target and reverberation is improved in combination with a CBAM attention mechanism, the detection accuracy can be improved by a multi-modal convolutional network in a small sample data scene, the multi-modal convolutional network does not depend on a large sample data volume any more, and the detection efficiency is improved. And overfitting caused by less sample data is avoided.

The invention discloses a marker combination for thyroid nodule risk assessment and application thereof. Based on large-sample multi-omics sequencing data, papillary thyroid carcinoma (PTC) and benign nodules are found to have a common epithelial spectrum origin, epithelial intermediate state cells (EICs) with malignant potential are found in the benign nodules, and typical molecular markers of the EICs are further identified to comprise ZCCHC12, CDLN1 and NPC2. The three genes participate in proliferation and differentiation of cells, the expression in PTC is remarkably up-regulated, and the malignant risk of the benign thyroid nodules can be evaluated by detecting the expression level of the markers in a biological sample. According to the method, a means for recognizing the high-risk benign nodules before malignant transformation occurs is provided for the first time, risk perspectiveness is achieved, and higher specificity and sensitivity are possibly achieved for recognizing the benign nodules with malignant potential.

This invention relates to the construction and application of an exosome purification device. The purification device utilizes the differences in size and charge between exosomes and other substances, combining the tangential flow of the sample within the device with the dual effects of membrane sieving and an electric field to remove contaminants and achieve high-purity exosome enrichment. The device includes a pre-filtration section and an exosome purification section. This device is simple to operate, can handle large sample volumes, has a short processing time, is less prone to membrane clogging by contaminants, and achieves high purity exosome separation with minimal structural damage. It can meet the separation and purification needs of exosomes from different sources and can be further applied to research on exosome proteomics, drug loading, activity, and biological functions.

This invention discloses a method for high-dimensional uncertainty aggregation modeling of power systems based on Gaussian mixture models (GMMs). The method comprises two stages: characterization of the original uncertainty distribution and GMM aggregation modeling. The GMM aggregation modeling specifically includes three steps: determining the dimension of aggregation variables, calculating aggregation parameters, and integrating aggregation results. This method achieves probability-preserving aggregation of high-dimensional uncertainty variables in power systems while significantly reducing the required sample size, effectively solving the problem of the large sample size required by Monte Carlo sampling when processing high-dimensional data. This invention can be applied to the dimensionality reduction aggregation of high-dimensional variables in uncertain power systems with a high proportion of renewable energy integration, achieving sample space reduction while maintaining probabilistic characteristics, and can be further applied to power system risk assessment, reliability analysis, and operation planning.

The invention provides a construction method of a hypertension-cerebral hypoperfusion composite cerebral small vascular disease rat model. The construction method comprises the steps of experimental animal and grouping, hypertension model induction, cerebral hypoperfusion model induction and model evaluation system. According to the model, hereditary hypertension is replaced by 'L-NAME-induced drug-induced hypertension', and the model is combined with bilateral carotid artery occlusion surgery, so that clinical common hypertension and cerebral hypoperfusion dual pathological processes are successfully simulated. Compared with an SHR model, drug-induced hypertension is adopted to replace hereditary hypertension, and the method has the remarkable advantages in the aspects of period-cost-controllability-flux, 1, the modeling period is shortened to 8 weeks, and about 2 / 3 of time is saved; (2) the cost of a single animal is less than 1 / 3 of that of SHR, and the method is suitable for large-sample and multi-batch research; (3) the dosage of the L-NAME is adjustable, and the two-way intervention research of'hypertension driving 'and'blood pressure normalization' is supported; background data of the SD rats are complete and sufficient in supply, and multi-center repeated experiments are facilitated.

The application discloses a dynamic main shaft thermal error prediction method based on a multi-view space-time graph neural network, which jointly considers multi-angle evolution characteristics of thermal errors in axial, radial and bending directions, constructs a multi-dimensional graph structure and fuses space-time characteristics to model; the method cooperatively extracts local and global space-time dependent relationships by introducing graph convolution and a bidirectional long short-term memory network, combines modeling strategies and attention mechanisms of short-term, medium-term and long-term time scales, and effectively captures long-term dynamics and multi-scale thermal response characteristics in large sample thermal information data; comparative experiments show that the model exhibits superior prediction accuracy and robustness under various working conditions, and the average thermal error prediction accuracy is maintained at 92%-97%, which is significantly better than other models and variants.