122 results about "Sample selection" patented technology

The invention discloses a multi-component catalyst active site prediction system and method fused with quantum embedding, and relates to the technical field of catalysis and material informatics, and the system comprises a structure and site enumeration module which generates candidate sites; the adaptive quantum embedding calculation module obtains key reaction microcosmic parameters; the unified site fingerprint and feature engineering module constructs and fuses standard site fingerprints; the physical consistency machine learning module predicts adsorption energy and other parameters and uncertainty thereof; the active learning and sample selection module selects a high-value sample optimization model; the microdynamics evaluation module calculates index values such as activity; and the multi-objective optimization and sorting module generates an optimization sorting list. According to the method, the unification of calculation precision and efficiency is realized, the problem of non-unification of locus characterization is solved, the model interpretability and extrapolation reliability are improved, and the comprehensive evaluation and optimization sorting of multi-target performance are completed.

The invention discloses a power system power flow self-adaptive regulation and control method based on deep transfer learning, and the method comprises the steps: 1, constructing a power system source domain power flow analysis model, and constructing a loss function for a power flow error in combination with the power flow data characteristics of a source domain and a target domain; step 2, designing a network parameter initialization method suitable for power flow analysis, realizing transferable representation from source domain data to a target domain, and constructing an initialized target domain power flow analysis model; 3, introducing a sample selection and incremental learning mechanism, and carrying out dynamic screening and retraining on key power flow samples in a target domain; and 4, carrying out load flow calculation and optimization control on the target power system by adopting the trained target domain load flow analysis model, and realizing self-adaptive optimization of the system operation state through iterative correction and parameter updating. According to the method, rapid calculation and dynamic optimization of the power flow of the power system are realized, and the intelligent level, stability and calculation reliability of power grid operation analysis are remarkably improved.

The invention provides a sample data processing and data auditing method and device, equipment and a storage medium, which can be applied to various scenes such as sample selection. The method comprises the following steps: acquiring N candidate samples; for each candidate sample in the N candidate samples, reasoning the candidate sample through the target model to obtain first feature information of the candidate sample extracted by a middle layer of the target model; encoding the first feature information of the candidate sample to a high-dimensional space to obtain second feature information of the candidate sample; and selecting K difficult samples from the N candidate samples based on the second feature information of each candidate sample. According to the method, the first feature information of the candidate samples is extracted from the middle layer of the target model, the first feature information is coded into the high-dimensional sparse second feature information, then the K difficult samples are accurately selected from the N candidate samples based on the second feature information, and the selection accuracy of the difficult samples is improved.

This invention relates to the field of agricultural intelligent equipment and machine vision technology, and discloses a method for weed detection and growth point localization based on shared features. The method constructs a collaborative dataset containing weed detection labels and growth point labels, utilizes a shared feature extraction network to extract multi-scale features, and sets detection task branches and growth point localization branches in a unified feature space to achieve joint modeling of weed detection and growth point localization. Then, adaptive positive sample selection is performed based on the joint cost between candidate sample points and labeled growth points, and joint training is conducted using detection loss, growth point localization classification loss, and growth point localization regression loss. During the inference stage, the method outputs weed detection results and growth point localization results, and generates target point information. This method can reduce redundant calculations and error cascading in the multi-stage processing, and improve the accuracy of growth point localization and the stability of target point output in complex farmland scenarios.

The application belongs to the field of shell structure design, and particularly discloses a shell stability prediction method and system based on combined proxy model sequence sampling, which comprises the following steps: obtaining initial samples and corresponding shell critical pressures, and putting them into a database; obtaining training samples from the database, and establishing a temporary combined proxy model based on the training samples; randomly obtaining candidate samples, calculating the prediction uncertainty and sparsity degree of the candidate samples based on the temporary combined proxy model and the sample distribution in the database, and then selecting part of the samples in the candidate samples to add to the database; repeating the sampling until a preset termination condition is reached, and ending the sampling process; and using the samples in the database and the corresponding shell critical pressures to establish a final combined proxy model, so as to realize shell structure stability prediction. The application uses the information provided by the model and data to guide sample selection, can reduce the number of samples required for establishing a shell structure proxy model, and improves the design efficiency.

This invention provides a method and system for predicting the state of power equipment based on online testing adaptation, comprising: collecting power equipment state data in real time through sensors and forming test samples; filtering a set of adapted historical samples from a historical sample memory bank that meet preset conditions in terms of similarity to the test samples in the latent space through a transferable historical sample selection module, wherein the historical sample memory bank stores historical power equipment state data; performing time-frequency domain hybrid data augmentation on the test samples and the adapted historical sample set through a transferable online augmentation module to generate an augmented sample set; inputting the augmented sample set into a pre-trained power equipment state prediction model for batch training, dynamically adjusting the model parameters to adapt to the distribution shift; and fusing the output of the dual-stream predictor of the power equipment state prediction model to generate the power equipment state prediction result for the next time period. This invention can perform power equipment state prediction.

The application discloses a cross-working condition control equipment fault detection system based on causal correction and full-space modeling. The system comprises a control equipment detection instrument, a control equipment fault database, a data processing module, a full-space fault modeling module, a causal correction module and a control equipment fault display and control module. The full-space modeling module and the causal correction module respectively solve the problems of data sparsity and sample selection bias existing in traditional methods, and are intended to realize unbiased estimation of fault probability under multiple working conditions.

The application discloses a wireless federated learning method for large-scale Internet of Things cooperative intelligence, aiming at the problem of heterogeneous device computing capability in a large-scale Internet of Things scene, and integrating centralized learning and federated learning to form a unified architecture, so that devices with weak computing capability can participate in global model training. On the one hand, the application determines the data sample selection strategy by the data importance of the centralized learning user, which can reduce the communication overhead and transmission time of data uploading; on the other hand, the application prunes the model of the federated learning user, which can effectively reduce the local computing time under the premise of ensuring the learning performance. The federated learning method provided by the application can realize data sample selection, model pruning and user scheduling of different types of users, which is helpful to improve the utilization rate of wireless network resources and alleviate the problem of limited resources of the Internet of Things.

The invention relates to the technical field of computer security, in particular to a feature space backdoor attack method, system and device based on fusion data transaction model parameter constraint and a storage medium. Obtaining an original training data set, constructing a data transaction model, calculating malicious sample loss values, and screening samples with smaller loss values as target samples; poisoning features are randomly extracted from the original data set, discrete noise is generated through a noise generator to form a trigger, the trigger is added to a target sample, a label is turned over, and an attack data set is formed; original training data are sampled, and a toll-snow information matrix of each layer is calculated; using the original model parameters to initialize the backdoor model, adding the L1 norm of the parameter disturbance difference value and the parameter difference constraint term based on the Feisnow information matrix into a loss function, and training the backdoor model; performance fluctuation caused by random sample selection is avoided through sample screening based on loss values, so that the backdoor model maintains the classification capability of clean samples while learning trigger features.

The application discloses a wetland feature set optimization method fusing improved filtering and packaging strategies, and steps of the method comprise the following: according to the actual situation of a research area, sample selection and a classification system are completed; multi-source remote sensing data are selected, and corresponding pretreatment is carried out on each data; feature extraction is carried out on the pretreated data, and an original feature set is constructed; three single-criterion filtering algorithms, i.e., LS, DC and JM distance, are fused to obtain an MCF-LDJ algorithm, the original feature set is preliminarily selected based on the MCF-LDJ, and a preliminary selected feature set is obtained; the preliminary selected feature set obtained in step 4 is further optimized by using a packaging algorithm, and a final optimized feature subset is obtained; the application proposes a multi-criterion fusion filtering algorithm, so that the importance of calculated features is more reasonable. The complementarity of the filtering algorithm and the packaging algorithm is utilized to improve the operation speed and evaluation accuracy of the feature selection algorithm.

The invention relates to a micro-service architecture root cause positioning method based on heterogeneous graph modeling and active learning, and the method comprises the following steps: 1, carrying out the feature extraction and topological structure of indexes, logs, call chains and deployment information, and forming unified heterogeneous graph modeling; and step 2, clustering, label diffusion and boundary sample selection are carried out on the heterogeneous graph model constructed in the step 1, semi-supervised training driven by active learning is used to continuously optimize a root cause positioning model, and the model is deployed in a production environment online to realize real-time fault detection and positioning. According to the method, the structural characteristics of the micro-service system can be fully utilized, and the root cause positioning method with low labeling requirements is provided, so that the balance between the task performance and the labeling overhead is realized, and a better solution is provided for efficient root cause positioning of the micro-service system.

Data samples are selected for power consumption management. This comprises receiving samples associated with respective times, distributed in a sliding time window as current samples (23) and in a past period as past samples (25). Selected past samples are determined by keeping a first share (26) of the past samples, consisting in most recent ones, and a second share (29) through eliminating among the past samples deprived from the first share, called a complementary share (27), part of the past samples in function of at least some of the current samples and of elimination conditions (32) depending on similarity criteria (31) applied to at least the first and complementary shares. The selected past samples are provided with the current samples for power consumption management. Applications to power failure detection and power consumption dynamic adaptation.

This invention discloses a dual-constraint incremental axial piston pump fault diagnosis method based on sample selection and playback, belonging to the field of fluid pressure and electrical digital data processing. The method includes: acquiring fault signals at the current moment and labeling them to obtain fault samples, constructing a fault diagnosis model; merging the fault samples at the current moment with old fault samples to form an incremental dataset, where the selection of old fault samples is based on improved sample profile coefficients and thresholds; inputting the incremental dataset into the fault diagnosis models at the current and previous moments respectively, further calculating the total incremental loss function composed of cross-entropy loss, cosine contrast loss, and surrogate loss, updating the model parameters through backpropagation until convergence, obtaining the fault diagnosis model trained at the current moment. This invention overcomes the catastrophic forgetting problem of learned fault knowledge in existing fault diagnosis models, and improves the continuous learning capability of the axial piston pump fault diagnosis model through incremental learning.

The invention discloses a paper classification method based on graph matching and self-supervised graph learning, and relates to the technical field of document classification based on deep learning. According to the method, literature data is represented by adopting a literature relation graph, a graph learning model ConGM based on the literature relation graph is constructed, and reference and theme association between literatures are mined through sub-graph sampling and data enhancement, linear node matching, secondary edge alignment and double-layer negative sample selection, so that precise classification of fields to which papers belong is realized.

The invention discloses an intelligent drought disaster risk estimation system which comprises a data acquisition module, a feature dimension reduction module, a risk sample selection module, a risk interval loss function design module, a drought risk model design module, a hyper-parameter optimization module and a drought disaster risk estimation module. The invention belongs to the field of risk estimation, and particularly relates to an intelligent drought disaster risk estimation system, which introduces a dynamic risk sample selection mechanism to improve the drought risk capture capability. The risk disorder loss is designed in a targeted manner, and the critical risk missed judgment probability is reduced; designing a dynamic threshold to realize dynamic self-adaption of a sample screening threshold; the dynamic evolution trend of the drought risk is incorporated into the loss function design, and the early warning capability is improved; the gradient amplitude is dynamically adjusted in combination with drought risk prediction confidence, and an unknown risk mode is effectively explored; and difference traction search and focusing traction deviation optimization are performed on the hyper-parameters, so that the final drought disaster risk estimation effect is improved.

The application discloses a kind of training sample selection methods for hyperspectral remote sensing image classification, belong to image classification technical field.The following steps are included: given unlabelled dataset, utilize pre-training GSCVIT model to extract classification features and multi-head attention weight, calculate spatial attention entropy and be spliced into enhanced features with classification features, through K-Center greedy algorithm screening core set sample subset, with the aid of group sampling loader loading data, using dynamic distribution balance loss (DDB Loss) training model to optimize classification performance.The application is verified on four datasets, and the results show that the method can effectively select key samples, dynamically optimize class distribution, significantly improve the classification accuracy and stability of the model under unbalanced data, and enhance the recognition ability of minority classes and weak class targets.Solve the problems of high labeling cost, class imbalance and complex feature expression of hyperspectral remote sensing image.

The invention discloses an MEMS inertial sensor reliability analysis method, which comprises the following steps: step 1, failure mode and mechanism analysis: carding environmental factors influencing the work of an MEMS inertial sensor, identifying failure modes caused by the environmental factors, and determining internal action mechanisms corresponding to the failure modes; 2, selecting a representative sample, and formulating a sample selection rule according to an application scene, a production batch, a structure type and performance parameter distribution of the sensor; the method has the beneficial effects that the system carding the association relationship between various environmental factors and failure modes, defining the internal mechanism of failure, breaking through the limitation that the existing method only pays attention to a single factor, enabling the analysis result to be more fit with the actual working scene of the sensor, and remarkably improving the comprehensiveness of reliability analysis; representative samples are selected based on scientific sample selection rules, key variables such as production batches, structure types and performance parameters are covered, and the problem of insufficient sample representativeness of an existing method is solved.

The invention discloses a landslide susceptibility evaluation method and system based on an optimized non-landslide sample selection strategy, and belongs to the field of geological disaster risk analysis. The method aims at solving the technical problem that the evaluation precision of the landslide susceptibility is not high due to unreasonable selection of traditional non-landslide samples. According to the technical scheme, the method comprises the steps that multi-source geographic information data and historical landslide data of a research area are collected and processed in a unified mode; extracting evaluation factors and constructing a susceptibility evaluation index system; performing susceptibility pre-partitioning based on an information quantity value method, and selecting a non-landslide sample in a low-susceptibility region; combining the landslide sample with the primarily selected non-landslide sample, eliminating a noise sample by adopting density-based spatial clustering analysis, and supplementing and generating a balanced optimized sample set by utilizing a synthetic minority oversampling technology; and inputting the optimized sample set into a support vector machine model for training and susceptibility evaluation drawing. By optimizing the sample selection strategy, the sample quality and the accuracy of the model evaluation result are improved.

The application discloses a kind of cultivation of smell and headspace analysis combination's odd nan hand string nondestructive identification method, it is related to precious timber true and false identification and quality detection technical field, including following step A: sample pool data model establishment: a1, sample selection: collect the cultivation of different producing areas, gram weight, shape and size odd nan hand string genuine sample not less than 200;Collect by high pressure injection paste, press oil, bubble medicine, high throwing, immersion paste smearing and non-lignum aquilariae imitated fake sample not less than 200;A2, electronic nose data acquisition: using electronic nose to the sample carries out smell data acquisition, acquisition condition includes;By being provided with electronic nose data acquisition and key sensor screening, the whole identification process does not need to be destructively treated to sample any cutting, grinding etc., completely maintain the integrity and value of hand string, and electronic nose detection can be completed within 1 hour, headspace mass spectrum analysis as supplementary means can be completed within 3 hours, improve detection efficiency.

The application relates to a self-step fuzzy clustering method and system for incomplete multi-view medical images, and relates to the field of intelligent analysis of medical images. The application solves the problems that existing multi-view clustering technologies cannot effectively process missing partial view data, fuzzy organization structure and lack a progressive dynamic sample selection mechanism. The method comprises the following steps: collecting multi-view medical image data, constructing a view missing label matrix, and performing spatial registration and feature standardization processing; based on a K nearest neighbor neighborhood weighting reconstruction strategy of a feature space, view features with missing data are completed; a standard multi-view fuzzy clustering is adopted, a joint fuzzy clustering objective function fusing a view missing label is constructed, an adaptive view weight distribution strategy and a self-step learning strategy are adopted, and the confidence of a clustering model of fuzzy membership entropy is defined; an alternating optimization strategy is used to update the membership, the clustering center and the view weight of the clustering model, and the self-step fuzzy clustering for incomplete multi-view medical images is completed.

This application relates to the field of performance testing technology, providing an adaptive sampling method and apparatus for mixed responses based on uncertainty reduction. By constructing a Gaussian process model of multiple types of mixed responses, it models and quantifies the uncertainty of these responses and their potential correlations, thereby solving the problem of difficulty in constructing surrogate models for mixed responses. By generating a reduced candidate sample set based on ROI, it screens experimental samples, improving algorithm efficiency. Adaptive sampling based on the uncertainty reduction of the forward error of the mixed response is used to comprehensively address the uncertainty of the predicted variance and the uncertainty of the predicted mean. The sampling process follows a dynamically adjusted boundary-variance-distance criterion, ensuring the auxiliary role of the boundary-variance-distance criterion in the initial sampling stage. As the number of samples increases, the model stability improves, and the selection of samples gradually becomes dominated by the uncertainty reduction of the forward error of the mixed response with forward capability.

The invention relates to the technical field of sample labeling, in particular to a quantum inspired active learning type defect detection sample labeling method which can reduce the manual labeling amount and the labeling cost. Specifically, sample representativeness and uncertainty are balanced through unified process design, firstly, a center cluster set is obtained based on Hamiltonian operator clustering, secondly, the quantum entanglement degree is calculated, a representative sample set is screened, residual unlabeled sample sets are processed in combination with virtual time evolution and a Schrodinger equation, and a to-be-labeled sample set is obtained through a fusion result; a budget scene does not need to be distinguished, sample selection can be adaptively optimized, the labeling efficiency is improved, and the problem of strategy simplification is effectively solved; in addition, samples are mapped into quantum states through self-supervised learning, clustering precision is optimized based on Hamiltonian operators, quantum entanglement is utilized to quantify sample association, uncertainty is captured by means of virtual time evolution and a Schrodinger equation, the quality of labeled samples can be improved, and the problem of representation limitation of a classical framework is solved.

The invention discloses an example sample selection system based on a Shapley value, and aims to solve the problems that a current example selection method is long in calculation time and a finally selected example is not high in matching degree with the actual demand of a user. The system comprises a CSV model construction module, a candidate example set acquisition module and an example sample acquisition module. And the CSV model construction module is used for constructing a CSV model based on the Shapley value. The candidate example set acquisition module is used for acquiring a historical candidate example set according to a financial big data arrangement task and a big language model, setting the number of example samples required by a user, and storing the acquired candidate example set and the set number of example samples in the cloud. And the example sample obtaining module is used for obtaining a set number of example samples from a candidate example set by utilizing a CSV model based on the large language model at the cloud. The invention belongs to the technical field of example sample selection.

The application discloses a neighborhood density minimum uncertainty sample selection method based on an improved shadow set, and relates to the technical field of data preprocessing.The method comprises the following steps: calculating the neighborhood density of each sample in a data set; applying an improved shadow set balance factor optimization algorithm to determine an optimal threshold by establishing a target function and minimizing the function, wherein the target function quantifies the information loss of the neighborhood density in the division process, and introduces an adjustable balance factor to balance the loss of the determined area and the uncertain area; dividing the data set into core samples and boundary samples based on the optimal threshold, removing the core samples, and retaining the boundary samples; and training a classifier model using the retained boundary samples.The application replaces the traditional membership degree with the neighborhood density, optimizes the shadow set threshold, and constructs a sample selection framework, accurately retains key boundary samples, efficiently removes redundant data, significantly improves the training efficiency of the classifier, the generalization performance, and reduces the calculation and storage overhead.

The invention provides a deep belief network temperature prediction method based on a selective update strategy. The method comprises the following steps: collecting and standardizing historical and real-time sample data; calculating mutual information of the input variable and the target temperature, and weighting the sample distance according to the mutual information to select a local sample; calculating a variable weighting matrix based on the local sample, and constructing a local weighting data set; and calculating the sample center distance of the query sample, multiplexing the existing model when the sample center distance is smaller than a set threshold value, otherwise, retraining the deep belief network model for prediction. The system comprises a data acquisition module, a mutual information calculation module, a sample selection module, a distance determination module and a modeling prediction module. According to the method, through mutual information weighting and selective updating strategies, the real-time performance and prediction precision of the model are improved, the online modeling time is remarkably shortened, and the method has high anti-interference capacity and engineering applicability.

The application relates to a kind of unmanned aerial vehicle ground small target detection methods based on improved YOLOX, belong to unmanned aerial vehicle technical field.Add channel attention mechanism (DW-CBAM) in the Neck layer of YOLOX neural network, strengthen the feature extraction capability of small target of network, inhibit non-important feature, increase a layer of detection head for detecting small target in head layer and carry out light weight to it.In the selection of positive and negative samples, a label assignment strategy (LB-SimOTA) with position guidance is proposed, so that the network pays more attention to the bounding box with poor positioning accuracy.Finally, an improved YOLOX unmanned aerial vehicle small target detection model is formed.The unmanned aerial vehicle small target detection model is applied to obtain the corresponding labels of vehicles and people in the image.The attention mechanism module and positive and negative sample selection strategy proposed in the application are used in the YOLOX detection method, and the detection accuracy is obviously improved in the unmanned aerial vehicle scene.

This invention belongs to the field of wind power equipment diagnosis, specifically relating to a semi-supervised acoustic signature diagnosis method for wind turbine blades based on meta-optimization. The method includes the following steps: acquisition and preprocessing of wind turbine blade acoustic signature signals; construction of labeled and unlabeled datasets; initialization and baseline training of the semi-supervised diagnostic model; pseudo-label correction and construction of a quality scoring network; virtual model update based on corrected pseudo-labels; meta-loss calculation based on labeled data; determination of the training value of sample-level pseudo-labels; training of the pseudo-label quality scoring network and sample selection; iterative training of the semi-supervised model and diagnostic output. This invention achieves dynamic evaluation and adaptive correction of pseudo-label quality by explicitly modeling the impact of unlabeled sample pseudo-labels on the model optimization objective. This improves the accuracy and robustness of wind turbine blade fault diagnosis under limited labeled data conditions, enhances stability during training convergence, and improves the accuracy of the final model.

This invention provides an artificial intelligence-based method for identifying gust front wind shear, comprising: performing noise filtering, missing value imputation, data smoothing, wind shear value calculation, and sample selection and extraction on collected radial velocity data to obtain gust front wind shear samples; based on the gust front wind shear samples, performing coordinate system transformation, sample set partitioning, and data annotation to obtain an expanded dataset; designing and training a Mask R-CNN model architecture to obtain a gust front wind shear identification model; and inputting the expanded dataset into the gust front wind shear identification model for gust front wind shear detection. This invention reduces the dependence on reflectivity factor data, focusing on building the identification model based on gust front radial velocity data, which can not only accurately identify gust front wind shear but also achieve pixel-level segmentation and localization of wind shear regions, improving identification efficiency.

The invention discloses a low-temperature transistor modeling gold sample screening method. Firstly, a transistor test structure is designed; secondly, simulating the series of transistors with specific gate lengths and gate widths to obtain a simulation result of a business model under a type corner, and counting a median value used for determining a parameter of a gold sample; and testing the plurality of transistor test structures die at room temperature, comparing the simulation result of each parameter with the actual measurement result, and obtaining an alternative sample of the gold sample according to the standard that each parameter meets the screening criterion. The method is suitable for the gold sample selection process before the low-temperature integrated circuit establishes the intensive model, the gold sample screening efficiency can be greatly improved, and a large amount of manpower and material resources and time cost are saved.