98 results about "Small data sets" patented technology

The invention relates to a voltage sag source positioning method based on measured data. The method comprises the following steps: S1, acquiring monitoring data; s2, performing sag-fault matching based on time sequence matching; the voltage sag of one monitoring point is matched with the same bus fault and the external fault according to the time sequence of events, and the time difference between each pair of voltage sag and fault in the matching result is calculated; s3, dynamically adjusting a window to calculate a quantile detection abnormal value; s4, performing secondary matching and iteration termination based on the reduced data set; and S5, repeating the steps S2-S4 for other monitoring points to obtain all matching data in the region, and realizing accurate positioning of the voltage sag source. According to the method, accurate and efficient fault positioning and matching can be carried out on the voltage sag event.

The invention relates to a sensitive data identification and desensitization method and system based on an AI large model, and belongs to the technical field of data security processing and artificial intelligence. The method comprises the following steps: receiving a multi-source data processing request, establishing a sensitive data identification framework, analyzing elements of sensitive data, and extracting a sensitive data feature vector through a natural language processing algorithm; classifying the multi-source data, dynamically adjusting an identification threshold value of the sensitive data identification framework through a sensitive data feature vector, and generating a self-adaptive sensitive data identification strategy; establishing a terminal desensitization verification model, performing desensitization verification on the identified sensitive data, performing mask replacement or generalization processing according to privacy requirements, and outputting a desensitization retry processing scheme; the sensitive data is subjected to block desensitization, a large data set is divided into small data sets for independent desensitization, replaceable entities in information are removed according to data attributes, and the desensitized data is further subjected to secondary desensitization.

The invention relates to the technical field of radar signal processing, in particular to a ground penetrating radar reinforcing steel bar clutter suppression method based on physical constraints, and mainly solves the problem that a data set is difficult to obtain in an existing ground penetrating radar reinforcing steel bar clutter removal method based on deep learning. The method is an improved method based on the CUT network, the CUT network structure and a comparative learning mechanism determine that the requirement of the network for the data size of a data set is low, a waveform smoothness constraint is added on this basis, the clutter removal effect and generalization ability are improved by introducing physical prior, the physical constraint serves as a regularization item, and the regularization efficiency is improved. The problem that a CUT network is prone to model collapse under a small data set is solved. Finally, the improved model is compared with other models through different evaluation indexes, and the result shows that the improved model has more advantages.

The invention discloses a method and device for improving the fine tuning effect of a large model based on an iterative data enhancement strategy. The method comprises the following steps: manually generating a batch of seed data with different distributions according to requirements; enhancing artificially generated seed data based on a rule or a large model; checking the quality of the enhanced data; finely adjusting the model by using the checked data, and reasoning training data by using the trained model; performing comprehensive evaluation on the reasoning result in various aspects; whether the evaluated indexes meet requirements or not is judged; if the error sample does not meet the requirement, enhancing the error sample again, and iterating repeatedly; and if the evaluation result meets the requirement, ending the whole process. According to the method, the threshold of fine adjustment of the large model under the small data set is remarkably reduced, and the model is excellent in performance in a data scarce scene by generating the targeted synthetic data.

The invention discloses a dielectric constant prediction method and system and a network training method and system. The method comprises the following steps: acquiring material characteristics of a to-be-tested material; constructing a plurality of descriptors according to the material characteristics of the to-be-tested material; screening the plurality of descriptors through a genetic algorithm, determining M main descriptors, and determining the descriptors except the main descriptors as other descriptors; and inputting the main descriptor and other descriptors into a preset dielectric constant prediction model, and outputting dielectric constant prediction data. The dielectric constant of the microwave high-frequency material is efficiently and accurately predicted through the machine learning technology. Compared with a traditional neural network model and descriptor selection method, the prediction precision, the model efficiency and the generalization ability are remarkably improved, and the problem that high-dimensional descriptors are not matched with small data sets is solved.

The first token prediction of a large language model is bottlenecked by compute and second token predictions onwards are bottlenecked by memory bandwidth. Inferences can be made more efficient through activation sparsity. An activation tensor is pruned using an importance threshold value. The mode of the activation tensor is centered in a lossless manner using an estimated mode value to improve activation sparsity further. Pruning and mode-centering mechanisms can be inserted into a neural network strategically and post-training to implement sparsification. A two-stage greedy grid search algorithm is implemented to determine the calibrated importance threshold values of various pruners and the estimated mode values using a small dataset. A modified neural network with pruning and lossless mode-centering can be deployed onto hardware.

The invention provides a reverse design method of a composite function metasurface based on transfer learning, relates to the field of metamaterial design and optimization, and solves the problem of low efficiency of a traditional design method of a metasurface with a complex structure. Comprising the following steps: generating a data set containing various metasurface structure parameters and corresponding spectral responses; building a deep prediction neural network taking the unit structure parameters as input and the spectral response as output; the trained network is combined with a genetic particle swarm optimization algorithm, metasurface phase distribution, transmissivity and other requirements determined in advance are used as objective functions, and optimal parameters are searched through multiple iterations; and migrating universal features learned on the large-scale data set by the source domain network model through a parameter fine tuning method, applying the universal features to a new small data set target domain, repeating the previous process until all parameters are determined, and completing the design. According to the method, the complex metasurface design efficiency can be improved, the neural network performance is ensured, meanwhile, the requirement of the deep learning method for the data collective quantity is reduced, the robustness of the model is improved, and a low-cost practical scheme is provided for metasurface inverse design.

The present invention relates to the field of organic polymers. Specifically disclosed is a method for predicting the properties of grafted polypropylene for a high-voltage power cable. In the method of the present invention, a property prediction model for grafted polypropylene is constructed on the basis of a multi-layer perceptron model and a random forest model. The model solves the defect that a conventional machine learning model mainly relies on a large amount of data and is not suitable for a small data set. Experimental data is used to quantitatively establish a relationship between a grafted structure and mechanical properties, the amount of data required is small, the data is easy to obtain, and the prediction accuracy is high, thereby providing a reliable grafted group for a grafted polypropylene material used for a high-voltage power cable.

By accurately predicting the industrial aging process (IAP), such as the slow deactivation of catalysts in chemical plants, maintenance events can be scheduled further in advance, ensuring the cost - effectiveness and reliable operation of the plant. So far, these degradation progressions have typically been described by mechanical models or simple empirical prediction models. To accurately predict the IAP, data - driven models are proposed, comparing some traditional stateless models (linear and kernel ridge regression, and feed - forward neural networks) with more complex state - recursive neural networks (echo state network and long - short - term memory network). In addition, variants of the stateful models are discussed. In particular, stateful models that use mechanical pre - knowledge about the degradation dynamics (hybrid models). Stateful models and their variants may be more suitable for generating near - perfect predictions when trained on a sufficiently large dataset, while hybrid models may be more suitable for better generalization in the case of smaller datasets under changing conditions.

The present invention discloses a classification method and system for the invasive evolution of lung adenocarcinoma based on a state space network, which relates to a classification method for lung adenocarcinoma infiltration. The purpose is to solve the technical problems of poor classification accuracy and low classification efficiency of the IA type classification of GGO imaging features in the prior art. The constructed GGO invasiveness classification model includes a teacher encoder, a student encoder, and a fine-tuning encoder, and the teacher encoder, the student encoder, and the fine-tuning encoder are all SSM state space networks; during training, a large sample data set without GGO classification labels is used to train the teacher encoder and the student encoder, and the output of the teacher encoder is used as the pseudo-label available to the student encoder, and the parameter update of the teacher encoder is guided by the exponential moving average of the output of the student encoder; a small data set with GGO classification labels is used to fine-tune and train the fine-tuning encoder. It can effectively improve the classification accuracy and classification efficiency of the IA type of GGO imaging features.

The application belongs to the field of data enhancement application, and particularly relates to a GAN model improvement method for small data set scenarios. The multi-discriminator fusion module is composed of multiple discriminators and a discriminator fusion submodule. Each discriminator is a network composed of multiple convolutional layers and fully connected layers, which receives a batch of pictures in a downsampling manner and converts the pictures into a tensor. The tensor has four dimensions, namely the number of batches of images batch, the number of channels c, the width w of each image and the height h of each image. The multi-discriminator fusion submodule is responsible for coordinating multiple discriminators, receiving the return values of the multiple discriminators, weighting the return values and then transmitting the weighted return values to the generator for back propagation. In order to avoid the occurrence of sample penetration, the weight of the discriminator responsible for enhancement is reduced. In this way, the probability of overfitting of the discriminator can be reduced, and the possibility of sample penetration can be reduced.

An improved multi-target regression algorithm based on target specific features is combined with a non-dominated sorting genetic algorithm to construct a double-layer structure multi-target algorithm, so that the problems of data distortion possibly caused by data enhancement, poor feature engineering generalization ability, negative migration caused by large inter-domain difference and the like are avoided, and the method is suitable for large-scale popularization and application. By capturing the correlation among a plurality of output targets, a high-precision model for a high-dimensional small data set is constructed, and the machine learning assisted hot-rolled ultrahigh-toughness steel component and process design with low data set cost is realized.

The application belongs to the technical field of unmanned aerial vehicle image detection, and in particular to an unmanned aerial vehicle image detection method based on an improved RT-DETR model. The method comprises the following steps: selecting a public unmanned aerial vehicle image dataset, dividing the training set, constructing a small dataset, training and evaluating the improved RT-DETR model on the small dataset, and then implementing the model on the complete dataset to obtain the final data. The application enhances the information of small targets in the fusion process without increasing the number of model parameters. While introducing S2, a new fusion module mainly composed of DSConv is used to replace the fusion module of the benchmark model, which balances the parameter quantity of the model while improving the small target detection accuracy. Compared with the previous algorithm, the improved model has higher accuracy and smaller parameter quantity, and can provide protection for accurate detection of unmanned aerial vehicle images.

The application relates to the technical field of archive construction, and provides a text attribute relation extraction method based on a small data set pre-training model, a pre-training model based on an ERNIE structure is publicly fine-tuned to a relation extraction model in a business background through a public pre-training model UIE which can be started by fine-tuning only a small amount of labeled data, and on the basis of the model, Before_pipeline data processing before prediction and After_pipiline data processing after prediction are constructed in combination with business text features to improve result reliability, the problems of lacking a labeled data set, UIE violent sentence splitting leading to errors, and low extraction result accuracy of the UIE are solved, and the problem that an existing traditional relation extraction model depends on a large amount of high-quality data sets manually labeled is solved.

Methods and systems for ultrasound imaging are provided. In one example, a method includes receiving, with a wireless handheld probe assembly, ultrasound signals of a region of interest; generating, within the wireless handheld probe assembly, a plurality of received digital signals based on the received ultrasound signals; generating each of a larger data set and a smaller data set from the plurality of received digital signals; transmitting the smaller data set from the wireless handheld probe assembly to a hub via a lower bandwidth wireless connection; transmitting the larger data set from the wireless handheld probe assembly to the hub via a higher bandwidth wireless connection; generating, at the hub, a low resolution image from the smaller data set and a high resolution image from the larger data set; and transmitting the low resolution image from the hub to a first display and the high resolution image from the hub to an electronic device.

The application discloses a kind of based on mechanism and data driven's sugarcane squeezing process process index prediction method, comprising the following steps: (1) multi-sensor field data acquisition and preprocessing, establish original data set;(2) establish sugarcane material elastic-plastic constitutive model;(3) establish sugarcane squeezing process porous medium control equation;(4) sugarcane squeezing process fluid-solid coupling model and its simulation, and establish new data set;(5) the establishment of physical guide neural network model;(6) the training details of physical guide neural network, obtain based on mechanism and data driven's sugarcane squeezing process process index prediction model.The method of the application is mainly used to predict the key process indexes such as sugarcane juice precipitation amount and sugarcane juice precipitation speed in the sugarcane squeezing production process.The cost of data acquisition in the production process is greatly reduced.High-dimensional system is characterized by small data set;The consistency of network can be greatly improved, and the generalization ability is enhanced.

The application provides a semi-supervised surface defect instance segmentation method based on pseudo label enhancement, comprising the following steps: obtaining a surface defect image dataset; training an initial instance segmentation network with a small amount of labeled defect image data; inputting the labeled defect image data into a student instance segmentation network for training; inputting unlabeled defect image data into a teacher instance segmentation network and the student instance segmentation network to obtain the mask and features of each defect instance; then inputting the mask and features into a cross-supervised contrast learning module and a general distribution fusion module; constructing a contrast learning loss function, obtaining fused pseudo labels, training the student instance segmentation network with the pseudo labels, and updating the parameters of the teacher instance segmentation network using the parameters of the student instance segmentation network. The application can cope with the challenges of small data set size and fuzzy defect boundary, estimate complex class feature distribution through the general distribution fusion module, and improve the quality of the mask pseudo labels from the teacher model.

The present invention discloses a mobile target tracking method based on a fusion network of MHS and GRU, belongs to the technical field of mobile target tracking, and is used for mobile target tracking, including inputting a training set into a fusion network based on MHS and GRU for iterative training, inputting a verification set into a fusion network based on MHS and GRU after each iteration, returning to iterative training if the accuracy requirement is not met, and inputting a test set into a fusion network based on MHS and GRU if the accuracy requirement is met, obtaining a fusion gain FG, and inputting the local filter of each sensor together with the fusion gain FG into a mobile target tracking model to obtain a mobile target tracking result. The present invention automatically corrects and adapts to errors in the process of time registration, effectively reduces error accumulation, and achieves improvements in tracking accuracy, convergence speed, and stability; using a relatively compact RNN, relatively small data sets can be used for training.

The application discloses a kind of multi-dimensional dynamic model under the evaluation method of soil salinization to cultivated land productivity, it is related to the field technical field of agricultural resources and environment, and the multi-source data set of fusing remote sensing, ground monitoring, meteorological and farmer management data is first constructed in the scheme, and the minimum data set of soil physical, chemical, biological properties is screened after fusion preprocessing;Soil quality index (SQI) and cultivated land productivity index (CPI) are calculated again;Subsequently, the multi-dimensional dynamic evaluation model containing key salinization index screening, effect quantification, mechanism model and crop sub-model is constructed;Finally, the productivity influence dynamic update is realized in combination with real-time data of Internet of Things, and the optimal improvement technology mode is simulated and screened by structural equation model simulation management scene. The method is comprehensive in data coverage, accurate in evaluation, dynamic in model and targeted, can reveal the influence mechanism of salinization on cultivated land productivity, provide practical decision support, and help efficient and sustainable use of saline-alkali soil.

The invention provides a marine mammal individual identification method, which belongs to the field of computer image identification, is based on a convolutional neural network model, combines an attention mechanism, multi-scale feature processing and a depth separable convolution technology, and comprises four steps of data acquisition, data enhancement, model construction and model evaluation. Through the application of the method, the facial key features of the marine mammals can be accurately captured, the parameter quantity and the calculation demand of model operation are reduced, and efficient and accurate identification of the marine mammal individuals under the background of a small data set is realized; and the marine mammal individual population identification challenges of high cost, low efficiency, unfriendliness and the like in the traditional method are solved. Therefore, the marine mammal individual identification method has good marine mammal individual identification capability, and can be widely applied to the fields of marine species investigation monitoring and protection management.

Methods, apparatus, and systems for semantic image segmentation using small data sets are disclosed. An example apparatus comprises: at least one memory, machine-readable instructions, and processor circuitry for instantiating or executing the machine-readable instructions to identify a gradient vector flow associated with an input image and / or generate a spatial feature map based on pixels of the input image using a two-stream neural network architecture and / or generate a field feature map based on the gradient vector flow using the two-stream neural network architecture and / or fuse the spatial feature map and the field feature map and / or output a segmented image of the input image based on the fused feature map.

Systems and methods are provided for an electronic match engine of an exchange that distributes a minimum data set to an external market data generation (MDG) processor. The electronic match engine derives a minimum data set from data already known to the electronic match engine. The MDG processor, which may be outside of the electronic match engine, may extract the minimum data set and uses it to generate market data. The electronic match engine may append the minimum data set to an order entry message sent to the MDG processor.

The application provides a process parameter screening method and device for an anti-penetration high-entropy alloy, and relates to the field of armored materials.The method comprises the following steps: obtaining performance indexes based on theoretical and target shooting experiment analysis of an alloy material target plate under normal penetration, and taking heat treatment process parameters of solid solution and aging as characteristic factors to design an orthogonal experiment to obtain a data set; performing standardization processing on characteristic values in the data set, and performing transformation on the characteristics; performing redundancy judgment and selection on time characteristics and temperature characteristics before and after the transformation; establishing a machine learning model, and performing generalization performance evaluation on the model through a root mean square error and a determination coefficient; and establishing a multi-objective optimization method based on a Pareto front and a multi-dimensional joint probability distribution function to realize multi-objective fusion and screening of process parameters.The data set in the application is completely obtained from experiments, which reduces generalization errors generated when a machine learning model is built for a small data set, and can accurately screen required process parameters in a short time.

The application provides an efficient unbalanced PSI based on a Bloom filter and a hash, wherein in a PSI calculation process, a Bloom filter is used to perform one round of screening on a large data set to reduce the overall complexity; a step of sending Bloom filter parameters by a large data set party to a small data set party; a step of calculating, by the small data set party, an index set of bit positions required to be set to 1 in the Bloom filter according to the data set of the small data set party, and sending the index set to the large data set party; a step of initializing the Bloom filter by the large data set party according to the Bloom filter parameters and the index set of the small data set party; a step of screening, by the large data set party, using the Bloom filter to obtain an element set that may be in a private intersection in the data set of the large data set party, wherein the screened set is used as a new private data set for subsequent calculation of the private intersection; and compared with an original PSI based on a hash and a semi-honest third party, the efficiency is greatly improved in an unbalanced scenario.

The application provides a method for predicting the band gap of porphyrin MOF based on deep transfer learning. First, a small data set of porphyrin MOF is constructed, and the Average SOAP Kernel is used to expand the data set of porphyrin MOF. Then, the GCN is pre-trained by using the QMOF database to obtain a pre-training model. The pre-training model is fine-tuned by using a transfer learning method to obtain a porphyrin MOF band gap prediction model. Finally, the band gap is predicted by using the porphyrin MOF band gap prediction model. The application can predict the band gap of porphyrin MOF with high accuracy. The application combines deep learning and transfer learning to predict the band gap of porphyrin MOF, and only depends on crystallographic information files instead of manually made geometric descriptors and chemical descriptors. The band gap of a given porphyrin MOF is efficiently and accurately predicted, the prediction accuracy and efficiency are improved, and the time and economic cost of measuring the band gap of porphyrin MOF are reduced.

The invention discloses a network planning method and device based on a hybrid reinforcement learning strategy, and the method comprises the steps: firstly, providing a basic network structure and an adjustment strategy through building a data link network topological structure model and a strategy support library; and then, an optimization strategy model is constructed based on multi-relation constraints, and efficient network planning decision optimization is performed through a hybrid reinforcement learning strategy so as to cope with a complex network environment. And then, generating an initial network planning structure through small sample data input, and finally, performing security review through the trusted network planning decision model to obtain a target network planning structure conforming to the standard. According to the method, a mixed strategy reinforcement learning method is adopted, high-dimensional target optimization and agile and efficient decision making under the condition of dynamic environment change are achieved, an intelligent planning technology based on a small data set and empirical knowledge is introduced, and the credibility and safety of network planning decision making are enhanced.

The application relates to the technical field of battery management, and specifically discloses a kind of energy storage battery power estimation method and device based on multiple incremental features, electronic equipment and medium, comprising the following steps: obtaining test data of battery under multiple ambient temperatures and operating conditions;Incremental feature extraction is carried out from the obtained test data, and a joint feature matrix is formed with the original test data;The joint feature matrix is pretreated to obtain pretreated data;The pretreated data is processed using a sliding window technique to obtain power evaluation data;The power evaluation data is input into a pre-constructed energy storage battery power estimation model based on multiple incremental features for iterative training to obtain a pre-trained SOC estimation model;The pre-trained SOC estimation model is migrated to a small data set battery to obtain the power of the small data set battery.The application not only improves the accuracy of SOC estimation, but also has strong adaptability and stability, providing strong support for the practical application of the battery management system.

The present invention provides a breast mass image segmentation method and system based on pruned U-Net++, which relates to the technical field of ultrasonic image segmentation. The breast mass image segmentation model of pruned U-Net++ constructed in the present invention is based on the U-Net++ network, uses skip connections to fuse the feature expressions of each branch U-Net, and introduces residual connections to replace the dense connections in the U-Net++ method, and uses this model to segment the obtained original breast ultrasound images. The pruned U-Net++ breast ultrasound image segmentation model constructed in the present invention can avoid the problem of model overfitting caused by the small data set problem in medical image data when segmenting the masses in breast ultrasound images, and improves the generalization ability of the ultrasound image breast mass segmentation model; at the same time, the scale of the model parameters is much smaller than that of the prior art, reducing the complexity of the network model, shortening the calculation time of network training, reducing the memory occupation of network training, and improving the model training efficiency.

The application discloses a patent technology function extraction method for technical literature, analyzes the features of patent technology terms and function terms in the field of high-end equipment, and improves the accuracy of technology term and function term extraction in Chinese patents. The application constructs a deep learning model for technology term and function term extraction, combines the sentence pattern rules of patents, constructs the heuristic features of technology terms, positions the function sentences by constructing a function term feature dictionary, accelerates the training speed of the model, and improves the extraction accuracy. In order to reduce the cost of manual sample labeling and avoid the model overfitting phenomenon caused by too small data set, a self-training algorithm is used to realize weak supervision learning of the model. The technology terms most similar to the theme of the patent text are selected from the word clustering, the cosine similarity is used to combine the similar semantic terms, and a technology function matrix is constructed.

The invention discloses an automatic attack countermeasure method based on a multi-target memetic algorithm, which comprises the following steps: sampling after sorting loss values from a complete data set, and selecting a small data set for rough evaluation; initializing a population, generating an initial population containing a plurality of random attack sequences, and evaluating the robust precision and time consumption of each individual by using a small data set; generating a progeny population through crossover and mutation operations, and evaluating individuals in the progeny population; performing non-dominated sorting and crowding degree distance calculation on individuals of the parent population and the descendant population, and selecting a certain number of excellent individuals to form a current population; optimal individuals in the current population are randomly selected, local search is carried out by adjusting a loss function, an iteration step length or a restart point, and iteration is continuously carried out until a preset termination condition is not met; and carrying out local search on a better individual by utilizing the complete data set, adjusting a loss function, an iterative step length or a restart point, and outputting an optimal adversarial attack combination mode.