573 results about "Semi-supervised learning" patented technology

The invention relates to the technical field of printed circuit board assembly quality detection, and discloses a PCBA anomaly detection method and system based on three-dimensional modeling and AI fusion, and a medium. The method comprises the following steps: acquiring three-dimensional point cloud data of a PCBA board to be detected; generating a reference three-dimensional digital twin model according to a standard PCBA design drawing; carrying out spatial registration on the three-dimensional point cloud data and the reference three-dimensional digital twin model, obtaining the three-dimensional point cloud data, carrying out hierarchical processing on the obtained three-dimensional point cloud data, extracting geometric features of a welding spot region, contour features of an element region and surface features of a substrate region, and carrying out fusion to generate a feature vector group; constructing a generative adversarial model based on a preset semi-supervised learning framework and the normal PCBA sample vector group; and inputting the feature vector group into a generative adversarial model, and examining the abnormal vectors, the corresponding three-dimensional coordinates and the abnormal types in the feature vector group by the generative adversarial model to complete the abnormal detection of the PCBA board. The method is suitable for quality control of a high-density and miniaturized PCBA.

The invention discloses an industrial equipment intelligent control method and system, and the method comprises the following steps: S1, collecting and preprocessing equipment operation data, and constructing a state data set; s2, establishing a trend prediction model by adopting Gaussian process regression, and outputting a state prediction value; s3, constructing a graph structure representation model, and combining a graph convolutional network and a semi-supervised learning method to carry out joint training and identify a working condition category and an operation level; s4, based on the equipment operation data, calculating a fuzzy membership degree and a fuzzy entropy index, and generating an entropy distribution curve; s5, fusing the state prediction value, the working condition category, the operation grade and the fuzzy entropy index to generate a state evaluation result; s6, adjusting control strategy parameters according to a state evaluation result, and issuing a control instruction to an equipment control system; and S7, collecting feedback data, updating the state data set, and realizing closed-loop iterative optimization. The method has the advantages of accurate prediction, stable identification, flexible evaluation, fast control, self-learning and the like, and is suitable for complex and changeable industrial application scenes.

The invention discloses a semi-supervised medical image segmentation method based on uncertainty-driven dynamic correction and multi-scale consistency learning, and the method comprises the steps: carrying out the preprocessing of a medical image data set, and dividing the medical image data set into a training set and a test set; constructing a semi-supervised segmentation model of dynamic correction and multi-scale consistency learning based on uncertainty driving; inputting the training set into a semi-supervised segmentation model, and performing iterative training and parameter optimization to obtain a trained semi-supervised segmentation model; inputting the test set into the trained semi-supervised segmentation model to obtain a medical image segmentation result; wherein the semi-supervised segmentation model adopts a mean teacher model of a V-Net network, a prediction block is added behind each up-sampling block of a V-Net decoder, and a dropout layer is added; according to the method, the problems that the existing semi-supervised learning method is difficult to adapt to the complexity of annotated data and unannotated data distribution, so that effective information is lost; meanwhile, a traditional uncertainty estimation method needs multiple times of forward transmission, and the calculation cost is high.

The invention provides a semi-supervised image semantic segmentation method and system based on a visual basic model, and the method comprises the steps: constructing a multi-task model which comprises a visual basic model and a depth estimation basic model, and the visual basic model is connected with a task solution head, an adapter parameter efficient fine tuning module and a multi-modal cross fusion module; the task solution head comprises a semantic segmentation head and a depth estimation head; extracting semantic hierarchy features and a depth feature map of the RGB image, performing cross attention fusion on the semantic hierarchy features and the depth feature map, and inputting obtained fusion features into a semantic segmentation head and a depth estimation head respectively; semi-supervised learning is adopted to train a multi-task model, only parameters in the adapter parameter efficient fine tuning module and the multi-modal cross fusion module are trained, and a multi-task loss function is adopted. The image semantic segmentation model obtained through training can improve semantic segmentation performance, reduce training cost and is suitable for different tasks.

The invention provides a semi-supervised target detection method for a visible light-infrared multi-mode fusion scene. The method comprises the following steps: constructing a semi-supervised visible light-infrared multi-modal image data set based on an LLVIP data set; on the basis of a YOLOv11 model architecture, constructing a target detection model oriented to multi-modal image feature fusion, and training the target detection model by using a semi-supervised visible light-infrared multi-modal image data set in a deep learning end-to-end mode to obtain a trained target detection model; and inputting a to-be-detected multi-modal image into the trained target detection model, and outputting a target detection result of the to-be-detected multi-modal image by the trained target detection model. The method is based on a semi-supervised learning normal form, so that the precision and robustness of target detection in a multi-modal fusion scene are improved, and the requirements for high efficiency and reliability of target recognition in practical application scenes such as intelligent traffic and intelligent security and protection are met.

The invention relates to the technical field of network space security, and provides a DoH malicious tunnel traffic detection method and system based on feature fusion, and the method comprises the steps: carrying out the preprocessing of obtained to-be-detected DoH traffic data, carrying out the sequence segmentation processing, obtaining a Token sequence, and extracting features based on a byte sequence feature extractor; statistical features are extracted and standardized, features are extracted through a statistical feature extraction sub-network, and statistical feature vectors are obtained; fusing the byte sequence feature vector with the statistical feature vector to obtain a classification result; the byte sequence feature extractor and the statistical feature extraction sub-network extract features, and training is carried out by adopting a semi-supervised learning framework of dynamic pseudo-tag screening for non-tag training samples. According to the method, multi-modal features are fused, a semi-supervised learning mechanism is introduced, malicious DoH traffic generated by multiple DNS tunnel tools is effectively identified under a limited annotation data set, and the detection precision and generalization ability are improved.

The invention discloses a winter wheat LAI and SPAD estimation method based on a lightweight semi-supervised model, and relates to the technical field of agricultural remote sensing monitoring, and the method comprises the steps: obtaining multispectral image data of a winter wheat key growth period, and carrying out the preprocessing of the multispectral image data to generate a standardized multichannel vegetation index image; the method comprises the following steps: constructing a lightweight semi-supervised model MCVI-SANet, and carrying out self-supervised training on the MCVI-SANet by adopting a semi-supervised training strategy driven by VICReg; and inputting the multi-channel vegetation index image into the trained MCVI-SANet, and outputting quantitative estimation results of the LAI and SPAD of the winter wheat. Through combination of a saturation perception mechanism and semi-supervised learning, estimation deviation caused by dense canopy vegetation index saturation and data noise is effectively eliminated, and the estimation precision and generalization ability in a complex agricultural scene are improved while the lightweight deployment characteristic of the model is ensured.

The invention belongs to the field of geotechnical engineering, and provides a geotechnical engineering investigation data analysis method and system based on machine learning, and the method comprises the steps: obtaining geotechnical engineering investigation data; taking the sampling points as nodes of the rock-soil data graph; according to the spatial proximity relation and the geological similarity, determining edges of the rock-soil data graph, and endowing the edges with weights, so that nodes with similar spaces and similar geological characteristics are connected; carrying out sparse processing on the rock and soil data graph; performing weighted fusion according to the spatial distance weight and the geological similarity weight to form a final rock-soil data graph; a graph convolutional neural network is adopted, and the initial features and the adjacency relation of the nodes are used for information propagation; neighbor information of each node is aggregated through multi-layer graph convolution calculation, and node features are updated step by step; a semi-supervised learning or supervised learning method is adopted to train a GNN model, and prediction error minimization is taken as a target; and deducing rock and soil parameters of an unknown area through the trained GNN.

A dynamic adaptive learning method for mineral prediction includes: collecting a dataset including geological data and labels of the geological data; extracting features from the geological data, initializing parameters of a training model and optimizing the parameters to obtain training parameters; performing an associative training on the training model based on the training parameters and the labels in a dynamic adaptive learning framework to obtain a mineral prediction model, algorithms of the associative training including a variational expectation algorithm and a variational maximization algorithm, and the variational expectation algorithm including an unsupervised learning mode, a semi-supervised learning mode, and a fully supervised learning mode; and predicting, by using the mineral prediction model, a mineral to obtain a mineral prediction result. The method can break through limitations of the traditional machine learning technology, offering a more efficient, universal, and stable strategy for geophysical data analysis and mineral resource assessment.

The invention discloses a human body tumble detection method and device, and belongs to the technical field of human body tumble detection, and the method comprises the steps: obtaining a radar point cloud time sequence which comprises continuous frame point clouds of all parts of a human body within a set time; performing relative coordinate coding on each frame of point cloud in the radar point cloud time sequence to obtain a spliced feature vector time sequence; and inputting the spliced feature vector time sequence into a trained human body tumble detection model to obtain a human body tumble detection result, and training is semi-supervised learning training based on a dynamic threshold. Relative coordinate coding is realized through construction of a local coordinate system and coordinate conversion of the point cloud, dynamic enhancement of four-limb micro-motion features in the falling process is realized, a better classification effect is shown, and thus the detection accuracy is improved; through semi-supervised learning training based on a dynamic threshold value, the labeling requirement is greatly reduced, and high detection precision in a small amount of training data scene is also realized.

The invention discloses a combined entity relationship extraction method based on semi-supervised learning and a large language model. The system comprises three modules, namely a data enhancement module, a semi-supervised joint extraction module and a large-scale language model fine adjustment updating module. The data enhancement module (1) is used for implementing a double enhancement strategy on unmarked data, weak enhancement retains syntactic structures and topological features of entities and relationships, and strong enhancement adopts a large language model which is finely adjusted in advance to generate semantic equivalent disturbance; (2) a semi-supervised joint extraction module which obtains a preliminary extraction model through training of marked data, and then generates prediction on unmarked data based on the preliminary extraction model; (3) a large-scale language model fine-tuning updating module which realizes parameter-efficient large-scale language model fine-tuning by using low-rank self-adaption and dynamically adjusts through a semi-supervised extraction result; and (4) fusing prediction results of the three modules to obtain a final joint extraction model. According to the method, semi-supervised learning, a large language model and joint entity relation extraction are creatively combined, the problem of scarcity of artificial label data in the professional field of an existing joint entity relation extraction method is solved, more efficient information extraction is provided, and therefore the overall performance of a joint extraction model is improved.

The invention discloses a semi-supervised learning method based on a self-adaptive threshold value, belongs to the field of radio communication, and aims to extract general semantic features of signals through comparative learning so as to improve generalization of downstream modulation identification tasks. A self-adaptive threshold mechanism is introduced to generate a high-quality pseudo tag, and confirmation deviation is reduced through additional category information, so that the robustness of the model is improved; besides, a hierarchical encoder capable of learning Fourier filtering is designed to capture multi-scale semantic features of large-scale long-sequence signals, improve the calculation efficiency of a model and research and verify an algorithm based on a plurality of large-scale data sets, so that the accuracy of automatic modulation recognition is remarkably improved, and particularly, under the condition of less label data, the accuracy of automatic modulation recognition is greatly improved. Compared with a mainstream method, the method has higher generalization ability and robustness, and can effectively cope with signal diversity and noise interference in a real communication environment.

The invention discloses a pavement disease target detection method based on semi-supervised learning. The method comprises the steps of obtaining an image unmarked sample set and an image marked sample set of pavement disease pictures; improving a YOLOv8 detection network to construct a pavement disease area detection model based on multilayer lightweight compression excitation and adaptive dynamic adjustment; obtaining an optimized pavement disease area detection model according to the image marking sample set; constructing a semi-supervised pavement disease area detection model based on a dual-threshold pseudo label distribution strategy; obtaining an optimal semi-supervised pavement disease area detection model according to the image marked sample set and the image unmarked sample set; and obtaining a to-be-detected pavement disease picture, and detecting a pavement disease region and a pavement disease type according to the optimal semi-supervised pavement disease region detection model. The problem that the working efficiency is low due to the fact that disease information in unlabeled samples cannot be utilized in an existing detection method is solved, and the problems that in reality, the scene pavement background is complex, interference is large, pavement disease detection tasks under the complex background cannot be well coped with, and false detection and missing detection exist are solved.

The invention belongs to the technical field of image segmentation, and relates to a semi-supervised pancreatic image segmentation method based on prototype estimation and prototype consistency. According to the method, based on a semi-supervised segmentation framework of an average teacher, V-Net is adopted as a deep learning segmentation model, and a projection head is inserted in the last but one stage of a decoder as a prototype branch; prototype learning design prototype estimation is introduced, unmarked data prediction is divided into determined areas of fuzzy areas, and different losses are designed for areas with different reliability of the unmarked data. The problems that in an existing semi-supervised learning segmentation method, unmarked data cannot be fully utilized, potential information of marked data is insufficient in utilization and the like are effectively solved, and a higher-performance solution is provided for an abdominal CT image pancreatic organ segmentation task.

The invention discloses an unmanned aerial vehicle autonomous inspection system and method based on AI identification, the system comprises an unmanned aerial vehicle body carrying a high-power optical zoom lens, an edge computing device and a function module, and the autonomous inspection of a power distribution tower is realized by fusing front-end AI identification and edge computing technologies. A Yolo structure is adopted to construct a lightweight tower recognition model, a dynamic route planning module is combined to realize single-point reference route generation and three obstacle crossing modes, and real-time coordinate correction in an RTK-free environment is supported. The edge computing device integrates a semi-supervised learning engine and a multi-sensor data fusion module, meets miniaturization design, and supports breakpoint continuous flight control and precise landing. The method covers automatic route generation, visual tracking, zoom cooperative control and self-adaptive task scheduling, solves the problems that a traditional unmanned aerial vehicle depends on manual operation, the data quality is poor and the efficiency is low, realizes whole-course automation of tower inspection in a complex environment, and is high in inspection efficiency, and the picture definition reaches the pin level.

The invention relates to a semi-supervised learning data anomaly intelligent identification and treatment system, which is applied to a hydrogen energy commercial vehicle, and comprises a data acquisition and preprocessing module, which is arranged on a vehicle-mounted terminal and is used for acquiring a hydrogen storage system signal, a hydrogen supply system signal, a fuel cell system signal and a whole vehicle system signal in real time, processing the acquired signal data; the semi-supervised anomaly recognition module is arranged on a cloud platform, is connected with the data acquisition and preprocessing module, and is used for fusing the processed signal data into a rule engine and semi-supervised learning, constructing a semi-supervised training data set, and performing deep auto-encoder model training through the data set so as to realize accurate anomaly recognition of few sample scene writing; and the exception treatment and feedback module is arranged at an edge node, is connected with the semi-supervised exception recognition module, carries out exception recognition through a trained model, carries out graded treatment according to the exception severity, and establishes a model evolution mechanism to realize continuous optimization of the system.

The invention provides a semi-supervised spine segmentation method based on a global-local semantic constraint visual language model. Comprising the following steps: respectively extracting spine MR slice image embedding and structure description text embedding by using an image encoder and a text encoder; learning the semantic relationship between the image blocks and the text in the local level to measure the uncertainty of the local view; the Wasserstein 2 distance between the cross-modal distribution representations is measured in a global level so as to standardize the global semantic similarity; generating a prompt guide mask based on a pre-trained GLsc model in combination with a text and an unmarked slice, and reinforcing semantic constraint through feature supervision and region alignment loss; predicting results of student and teacher models are fused to generate a high-quality pseudo label, and a segmentation network is optimized by means of supervised and consistent loss. According to the method, the prompt guide mask is introduced into spine segmentation semi-supervised learning, the VLM cross-modal uncertainty perception capability is enhanced through the two-dimensional semantic constraint, the pseudo tag quality and the segmentation precision are effectively improved, and an innovative technical normal form is provided for medical image semi-supervised segmentation.

The invention discloses a wall surface heat flow prediction method considering material ablation response under a semi-supervised learning framework, and relates to the field of aerospace, and the method comprises the following steps: constructing a data set; building a neural network model, and introducing the ablation physical information loss item into a loss function of the neural network model; pre-training the neural network model; predicting the label-free data through a pre-trained neural network model, and generating a pseudo label; mixing the labeled data and the unlabeled data, and performing semi-supervised self-training on the pre-trained neural network model to obtain a final neural network model; and wall surface heat flow prediction of material ablation response is carried out through the final neural network model. According to the method, the requirement of the model for a labeled data set is reduced, the model generalization ability under the small sample size training condition is enhanced, the physical interpretability of the network model is improved, and the wall surface heat flow prediction precision is improved.

The invention provides an equipment fault diagnosis model training method and device based on semi-supervised learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the collection of equipment vibration data in a preset scene, and carrying out the training of an equipment fault diagnosis model based on the feature similarity between a target sample in a corresponding target feature vector and a preset training sample set; and generating a pseudo tag corresponding to the target sample. Limited high-quality labeled samples (supervised learning) and a large number of unlabeled samples (unsupervised learning) are organically combined, the method does not depend on manually set working condition rules, and corresponding pseudo labels are generated after sample similarity analysis is performed from feature level analysis. According to the method, knowledge migration with points as surfaces can be achieved, and on the basis, after an equipment fault diagnosis model is constructed, the model can effectively cope with sample differences under different working conditions, sensor channels and sampling frequencies, adapt to complex and changeable industrial environment requirements and improve the equipment fault diagnosis precision.

The invention discloses a semi-supervised learning positioning method based on channel mapping, belongs to the field of signal processing, and is applied to the problem of wireless positioning of user equipment in a communication and sensing integrated network. According to the method, mass unlabeled channel data which is easy to obtain is fully utilized, and the implicit relationship between the channel and the position is learned from the unlabeled data and the labeled data together. The semi-supervised learning scheme provided by the invention can be divided into two stages: firstly, pre-training an unsupervised dual-branch channel mapping twin network by using an unlabeled data set, and then performing supervised training fine tuning on the dual-branch channel mapping twin network and an overall positioning network of a positioning head module by using a labeled data set. According to the method, only a small part of labeled data is used, and robustness and positioning performance superior to a supervised learning algorithm and other baseline algorithms are obtained.

Implementations are provided for leveraging training data that is less costly to collect than state-action sequences to perform semi-supervised training of robot control policies. In various implementations, a first input prompt may be assembled with representations of an observed initial state of a robot and a goal state of the robot. The first input prompt may be processed using a goal-conditioned trajectory model to generate first output indicative of a sequence of predicted states to be reached by the robot between the observed initial and goal states. A second input prompt may be assembled to include representations of the sequence of predicted states. The second input prompt may be processed using an action prediction model to generate second output indicative of a sequence of predicted actions to be performed by the robot to reach the sequence of predicted states.

The invention discloses a semi-supervised graph fraud detection model based on double-space heterogeneity learning. According to the method, the attribute space heterogeneity and the structure space heterogeneity are fused, node representation is optimized by using an attention mechanism and a multi-relation aggregation strategy, and classification of unlabeled nodes is guided through labeling and embedding of labeled nodes to construct a category prototype, so that the classification efficiency is improved. The challenges of imbalance of positive and negative samples and scarcity of labeled data are respectively coped with by adopting label balance sampling and semi-supervised learning, the fraud detection performance under complex heterogeneous graph data is remarkably improved, a heterogeneous relationship in a fraud graph can be quickly processed, the detection efficiency is improved, and the detection efficiency is improved based on a double-space heterogeneity learning mechanism. According to the method, node individual feature differences are disclosed, structural space heterogeneity is learned through label directed propagation, node network relation differences are disclosed, the individual feature differences and the network relation differences of the nodes in double spaces are disclosed by means of heterogeneity fusion, the omission ratio and the false detection rate of fraud detection are reduced, and the accuracy is improved.

The invention belongs to the field of medical image analysis and processing systems, and discloses a medical image segmentation system based on dual-scale consistency, and the specific system scheme is as follows: taking a breast ultrasound image as an example, adopting a semi-supervised learning mechanism, carrying out supervised training on a small amount of labeled data, and carrying out unsupervised training on a large amount of unlabeled data; the backbone network is a dual-scale consistent teacher-student network, and accurate segmentation of a medical target is realized through feature extraction and feature interaction of a dual-scale medical image; a common constraint network of supervision loss, scale consistency loss and reliable pseudo-label loss is adopted for training, the scale consistency loss is used for implementing constraint on images before and after disturbance, more reliable pseudo-labels are obtained from prediction probability distribution of two teacher networks, and the reliable pseudo-label loss can guarantee smooth implementation of unsupervised training. According to the method, the dependence of the model on the annotation data can be reduced on the premise of ensuring the model segmentation precision.

The invention discloses a multi-modal wood classification method based on semi-supervised learning, and the method comprises the following steps: S1, data set construction: collecting unidentified wood samples, and constructing a label-free data set; wood samples identified by experts are collected, and a data set with labels is constructed; s2, network construction and training: in network construction, spectrum data is processed by a full-connection network, image data is subjected to feature extraction through an improved residual network, after the spectrum data and the image data are spliced, a learnable CLS mark is added, and multi-modal features are fused through a Transform encoder; s3, wood classification prediction: inputting labeled samples into the trained network, extracting features and storing the features in a database; and performing spectrum and image acquisition on a to-be-detected sample, inputting a network to extract features, comparing the features with database features, and outputting a classification result. The invention provides a multi-modal wood classification method capable of utilizing semi-supervised learning of a small amount of annotated data and a large amount of unannotated data.

The invention discloses a wheat seedling missing detection method for performing semi-supervised learning by fusing spatio-temporal information, and belongs to the technical field of seedling missing detection methods. The invention discloses a wheat seedling missing detection method for performing semi-supervised learning by fusing spatio-temporal information. According to the method, a set of semi-supervised learning framework is constructed; an initial model is trained by using a small number of precisely-labeled frames, and pseudo labels are generated for unlabeled frames; and'time sequence consistency 'and confidence coefficient are introduced to form a joint screening mechanism, and high-quality spatio-temporal data are obtained at low cost. In order to break through the limitation of single-frame detection, a space-time attention module is integrated in a detection network, and multi-frame information is aggregated to improve the robustness to motion blur and the like. The invention also provides a new normal form of space anomaly detection, which does not directly identify the missing seedlings, but learns a normal seedling space distribution rule, and identifies a low-density area as a seedling missing area by using a K-nearest neighbor (KNN) algorithm. According to the method, an unstable classification task is converted into a robust statistical problem, and the method has the advantages of low cost, high robustness and strong generalization.

The invention relates to the technical field of underwater image processing, in particular to a semi-supervised underwater image enhancement method based on multi-scale context awareness, which comprises the following steps: establishing an underwater image enhancement network model which comprises parallel image restoration branches and detail restoration branches, the image restoration branch comprises a multi-scale input-level feature fusion module, a multi-branch hybrid convolution attention residual module, a down-sampling module and an up-sampling module; the detail repair branch comprises a pixel difference convolution layer, a normalization layer and a channel perception feedforward neural network; adding the output of the image restoration branch and the output of the detail restoration branch element by element to obtain the output of the underwater image enhancement network model; training an underwater image enhancement network model through a semi-supervised learning strategy and a joint loss function; and inputting the underwater image into the trained underwater image enhancement network model to obtain an enhanced underwater image. According to the invention, the enhancement effect and the cross-scene generalization performance of the underwater image are improved.

The invention discloses an idle land recognition method, device and equipment and a storage medium, and belongs to the technical field of machine learning, and the method comprises the steps: obtaining a first land feature set based on the land text data of a plurality of pieces of land; screening the first land feature set to obtain a second land feature set; semi-supervised learning cooperative training is carried out on a first classifier and a second classifier based on the second land feature set, the first classifier and the second classifier are used for idle land identification, the first classifier is realized based on a rotating forest algorithm, and the second classifier is realized based on an extreme gradient lifting algorithm; wherein in the semi-supervised learning cooperative training process, the prediction labels are screened based on a preset idle land classification rule. The method can realize accurate and efficient idle land identification.