361 results about "Large sample" patented technology

The invention discloses a physical knowledge-guided icing depth migration prediction method in a small sample scene, and the method comprises the steps: obtaining small sample icing data of a first region as a target domain, and obtaining large sample icing data of a second region as a source domain; constructing a physical information neural network and physical constraint loss thereof, and training the physical information neural network by using the source domain to predict the output icing thickness; a physical information neural network and self-migration learning are fused, knowledge of a source domain is effectively migrated to a target domain under guidance of physical knowledge, icing depth migration is carried out through iteration, an icing depth migration prediction model guided by the physical knowledge in a small sample scene is established, and the icing thickness of a first area is predicted. Physical knowledge is embedded into a transfer learning framework, a physical information neural network and transfer learning are fused, knowledge distillation loss based on cross entropy is used, and the physical knowledge in a teacher model is transferred into a student model, so that the student model adapting to a target domain is obtained.

The invention discloses an autophagy biomarker related to depression, a risk assessment model and application of the autophagy biomarker. A group of autophagy related biomarkers for auxiliary diagnosis of depression are screened out through multi-dimensional screening of large sample data and a machine learning algorithm, the autophagy related biomarkers comprise the following genes: GABARAPL2, RB1CC1, FOS and ULK1, and differential expression of the autophagy related biomarkers is proved in chronic stress induced depression-like mouse cortical brain tissues. The invention also provides a risk assessment model for auxiliary diagnosis of depression, a logistic regression model is constructed by taking the biomarker as a feature, a score formula and a risk boundary value of depression risk assessment are determined, and multi-aspect assessment is carried out on the prediction performance of the model. The prediction model has good prediction accuracy and calibration degree, can efficiently and conveniently realize early diagnosis of depression, and improves the accuracy of depression treatment.

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.

A video decoder is configured to determine a minimum sample value for one or more blocks of video data, the minimum sample value being greater than 0; determine a maximum sample value for the one or more blocks of video data, the maximum sample value being less than 2{circumflex over ( )}bd−1 and bd being a bit depth of samples in the one or more blocks; in response to determining that a process applied to a block of the one or more blocks of video data results in a sample value that is outside a range of the minimum sample value to the maximum sample value, clip the sample value to one of the minimum sample value or the maximum sample value to generate a clipped sample value; determine a decoded version of the block based on the clipped sample value.

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 discloses a potential landslide identification method and system based on a graph neural network, and the method comprises the steps: constructing a multi-level feature fusion graph attention network model which comprises a node layer, a subnet layer and a global layer, and employs a dual-branch encoder-decoder structure; the method comprises the following steps of: pre-training a dual-branch encoder-decoder structure model until convergence by adopting a combined data set of a data set 1 of a large sample and a data set 2 of a small sample to form a pre-training model; based on auxiliary set node features obtained by inputting the data set 1 and the data set 2 into the pre-training model, training and verifying the graph attention network model until model convergence training is completed; and inputting the pictures needing to be identified in the data set 2 into the trained picture attention network model to obtain a prediction result of the potential landslide. Through the technical scheme of the invention, effective extraction of the target features in the small data set 2 is realized, and the classification performance and generalization ability of the model are improved.

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 present application relates to a lung-cancer screening model construction apparatus, a lung-cancer screening apparatus, and a device and a medium. The lung-cancer screening model construction apparatus comprises: a sample acquisition and data collection module, which is used for acquiring benign pulmonary nodule patient samples, and collecting baseline parameters of the benign pulmonary nodule patient samples, wherein the baseline parameters comprise radial growths of pulmonary nodules and serum thymidine kinase 1; a model training module, which is used for performing model training by using the baseline parameters as independent variables and using, as dependent variables, risks of lung cancer newly diagnosed within three years after baseline visits; and a model evaluation and verification module, which is used for performing prediction efficiency evaluation and internal verification on the trained model, so as to obtain a risk prediction model for newly diagnosed lung cancer of benign pulmonary nodule patients within three years. The present application can be used for predicting the risk of progression from benign pulmonary nodule to lung cancer in a large-sample Chinese population.

The invention discloses a photovoltaic project natural disaster risk knowledge graph construction method, and relates to the field of key infrastructure risk assessment, and the method comprises the steps: S1, constructing a photovoltaic project natural disaster geographic information database based on text big data; s2, extracting a natural disaster risk causal relationship of the photovoltaic project based on machine learning; and S3, drawing a photovoltaic project natural disaster risk knowledge graph based on the photovoltaic project natural disaster geographic information base and the photovoltaic project natural disaster risk causal relationship. According to the method, the limitation that an existing method depends on traditional statistical data and is based on single disaster case analysis is solved, the data bottleneck is broken through from the source, the advantages of disaster text big data are fully played, and a big sample database for photovoltaic project natural disasters is built through natural language processing and a geographic information system platform; therefore, the natural disaster knowledge graph constructed by the method has higher universality and integrity.

The invention relates to a deep learning springback and audio-based rock compressive strength determination method. The method comprises the following steps: S1, collecting rock springback, springback instrument striking audio recording and rock uniaxial compressive strength test results through a large sample test; s2, carrying out data processing on the collected rock springback data and the springback striking audio set; s3, establishing a neural network calculation model based on the collected rock springback value, the characteristic value of the springback striking audio spectrum and the corresponding data of the uniaxial compressive strength of the rock; and S4, by collecting a rock rebound value and a characteristic value of a rebound strike audio spectrum, based on the established neural network calculation model, substituting the rock rebound value and the characteristic value of the rebound strike audio spectrum obtained by testing into the prediction model, and calculating the rock uniaxial compressive strength of the rock. The method performs deep learning training to obtain information and summarizes rules to establish the rock strength prediction model, and is suitable for various types of rocks.

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.

The invention discloses a semi-physical simulation experiment online verification system and method, and belongs to the technical field of digital simulation. The system comprises a Monte Carlo equipment experiment platform used for configuring experiment parameters, providing digital simulation key sample data and generating random interference parameters; the semi-physical simulation integrated management system is used for carrying out scheduling management on the semi-physical simulation system; performing a semi-physical simulation experiment on the semi-physical simulation system by using the digital simulation key sample data according to the experiment plan; random interference parameters are loaded in the experiment operation process; and the digital simulation system is used for realizing visualization of the experiment process. Seamless integration of an experimental equipment key parameter module, a pneumatic analysis module and an equipment simulation model in the environment is achieved, a Monte Carlo equipment test platform is developed and designed to complete equipment Monte Carlo experiment rapid design, and then a systematic large sample simulation test is completed.

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 relates to the technical field of network security, in particular to an update anomaly detection method. The method comprises the steps of receiving update data of a first time window; determining a plurality of abnormal feature values of the update data of the first time window, wherein the plurality of abnormal feature values are respectively used for representing data transmission states of the update data in a plurality of dimensions; processing the plurality of abnormal characteristic values based on an abnormal detection model, and determining whether the update data of the first time window is abnormal or not; wherein the anomaly detection model is obtained by performing multiple rounds of training based on a training sample set, training samples are obtained from the training sample set based on the maximum sample number in each round of training process, and each training sample comprises a feature sample of training data corresponding to a preset duration and an anomaly label; the maximum number of samples used in each round of training is determined based on the training samples used in the last round of training or the model error rate after the last round of training.

The invention discloses a metasurface multi-beam rapid generation method based on semi-supervision, and the operation process of the method comprises the following steps: 1, introducing an attention mechanism based on a convolutional neural network, and designing a cross entropy loss function; step 2, training the model by using a large number of samples obtained by caliber field superposition, migrating trained model parameters to a new model, and training based on the samples optimized by a traditional algorithm; step 3, semi-supervised learning is introduced, a model obtained through initial training is utilized to label unlabeled data samples, labeled sample data is added to label data samples, then the network model is retrained, the process is continuously repeated, and the number of the samples is iteratively increased; and 4, inputting required directional diagram data to obtain a corresponding coding matrix. Based on the method, a multi-beam coding problem is converted into a multi-label classification problem, the condition of repeated traversal in a codebook set in a traditional algorithm is avoided, and the time complexity is reduced.

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.

A method and apparatus for scanning probe-based surface characterization is disclosed which allows high scanning velocities and data throughput and is suitable for large samples. A combination of rotational and linear translation is used for scanning the probe by concentric circle or spiral trajectories in overlapping ring patterns. A method and apparatus for rapid control of tilt-related probe-sample separation distance and suitable for such scanning pattern is described.

The invention relates to the technical field of deep learning, and particularly provides a whale optimization-based power grid fault positioning method and system, a terminal and a medium, and the method comprises the steps: obtaining detection data of a plurality of power devices in a first time period, the detection data comprising feature values of detection signals of a plurality of sensors; performing denoising and dimension reduction processing on the detection data to obtain sample data; training a pre-constructed convolutional neural network based on the sample data by using an improved whale optimization method to obtain a fault positioning model; wherein the fault positioning model is used for determining the fault position of the power grid according to the detection data of the multiple pieces of power equipment within the second time period; the first time period is before the second time period. According to the method, the adaptability of the convolutional neural network in processing large sample data is improved, so that accurate positioning of a power grid fault is realized.

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 discloses a patterned hydrophilic and hydrophobic fluorescence enhancement chip, a preparation method thereof and a protein detection method, and relates to the technical field of protein detection. Hydrophilic and hydrophobic patterns are designed on the basis of the plasma fluorescence enhancement chip with gold as a substrate, protein micro-droplets and to-be-detected sample droplets are confined through the hydrophilic and hydrophobic patterns, the method does not depend on large sample application equipment and expensive chip fences, and the method has the advantages of being easy to operate and high in sensitivity. Experimental results show that when the patterned hydrophilic and hydrophobic fluorescence enhancement chip provided by the invention is used for detecting protein, the use amount of a sample for manual sample application is small, and the detection sensitivity is high.

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 invention discloses a section infiltration type imaging system suitable for large-sample high-resolution three-dimensional imaging. The section infiltration type imaging system comprises an imaging medium liquid infiltration circulation module, an illumination imaging module and a cutting module. Through the imaging medium liquid infiltration circulating module, a stable imaging medium liquid infiltration environment is only formed between the shooting unit and a sample in the process of using a mechanical cutting imaging method when three-dimensional imaging of a biological large sample is carried out; compared with an existing method of soaking a sample embedded with biological tissues in imaging medium liquid, the method provided by the invention has the advantages that high-resolution imaging can be realized only by using a small amount of imaging medium liquid capable of forming an imaging medium liquid infiltration environment; the condition that the cutting performance is affected due to sample deformation caused by long-term soaking of the sample in the high-refractive-index imaging medium liquid is effectively avoided, the amount of the imaging medium liquid used in the water tank is further reduced, the use cost is reduced, and meanwhile the load requirement for the three-dimensional translation table is lowered.

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.

The invention provides a ceramic glue discharging and sintering integrated process and a ceramic high-temperature deformation and thermal weight loss synchronous analysis method, and the process provided by the invention has the following advantages: 1) through integration of glue discharging and sintering processes, the production efficiency is improved; 2) the production cost is reduced; 3) thermal behaviors of the ceramic sample in different stages can be better understood through accurate quality and size change data monitoring, so that the process control is optimized, and the ceramic quality is improved; and (4) large-size sample application is supported: the system breaks through the limitation of traditional differential thermal analysis (DTA) in large-size sample testing, so that the glue discharging and sintering processes of large-size ceramic samples can be accurately controlled, and the requirements on large samples in industrial production are met.

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 relates to the field of signal processing, and discloses a noise reduction method and system based on millimeter wave radar signals, and the method comprises the following steps: collecting millimeter wave radar signals to be denoised; the method comprises the following steps: constructing a fitness function of a ratio of a mean value to a variance of a multi-scale Kolmogorov entropy based on a millimeter wave radar signal; optimizing a decomposition mode number K and a penalty factor alpha of variational mode decomposition by using a starfish optimization search algorithm; variational mode decomposition is carried out on the millimeter wave radar signal; the sample entropy of each intrinsic mode component is calculated, the mode position with the maximum sample entropy break variable is determined, the intrinsic mode components with the sample entropy smaller than the position serve as signal modes, and the rest serve as noise modes; carrying out wavelet threshold denoising on the noise mode; and performing signal reconstruction by using the signal mode and the de-noised noise mode, and outputting a de-noised radar signal. The millimeter-wave radar gas leakage signal noise reduction method solves the problem that the noise reduction effect of millimeter-wave radar gas leakage signals is poor in the prior art, and has the advantage of being capable of improving the signal-to-noise ratio of the signals.

The invention provides a raindrop spectrometer correction method and a related device, and relates to the technical field of meteorological measurement. In the method, the electronic equipment measures the falling speeds of raindrops with different sizes by using an optical rain spectrometer, and calculates a speed compensation coefficient according to the actual falling speed and the theoretical speed of the raindrops; in addition, for each preset rainfall intensity, a concentration correction coefficient sequence of various raindrops under the intensity is obtained, and the concentration correction coefficient sequence is ranked according to the size. And then, constructing a large sample data set, and carrying out multiple iterative training on a median of a sorting result of each raindrop correction coefficient in the data set to obtain a concentration correction coefficient as a target concentration correction coefficient. Therefore, the measurement precision of the optical rain spectrometer on the raindrop speed and concentration is improved through the speed compensation coefficient and the target concentration correction coefficient obtained through calculation.

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.