13 results about "Semi supervised clustering" patented technology

The application provides a charging facility fault early warning model construction method based on multi-source data perception, and belongs to the technical field of charging facility fault early warning.The application forms a multi-source time series data set by collecting charging pile operation state data and environmental data and performing time series alignment, expands fault samples by using an adversarial generative network and a semi-supervised clustering algorithm to form a balanced sample data set, constructs a fault early warning model containing a time series memory pool encoding layer and a frequency domain convolution feature extraction layer, cooperatively optimizes early warning accuracy and response time delay by using a double-layer game optimization framework to obtain an optimal parameter combination, deploys the optimized model to an edge computing module to realize real-time fault early warning, establishes an online incremental learning mechanism based on sliding time window detection data distribution offset, and uses an elastic weight consolidation technology to update model parameters and retain historical knowledge, thereby solving the problem of early warning performance degradation of charging facility fault early warning in a data distribution evolution environment.

The invention relates to a reservoir prediction method and device based on mixed probability principal component analysis fusion attributes, and relates to the technical field of oil and gas resource exploration and development, and the method comprises the steps: obtaining an original three-dimensional seismic data volume, well point logging data and known reservoir feature data of a target research area; preprocessing the original three-dimensional seismic data volume and then constructing an attribute matrix, and modeling the attribute matrix by using mixed probability principal component analysis to obtain a fusion attribute matrix; taking the fusion attribute matrix as an input feature, taking known reservoir feature data as a target variable, and establishing a mapping model by adopting semi-supervised K-means clustering in combination with well point label guidance classification; and calculating all data in the fusion attribute matrix of the research area according to the mapping model to obtain a global reservoir feature prediction result. The reservoir prediction method provided by the invention has the advantages of high reservoir prediction precision, high reliability and uncertainty quantification capability.

Techniques are disclosed herein that encompass image pre-processing and a semi-supervised clustering for optimization and analysis of immune-enriched single-cell proteomics data generated via multiplexed imaging technologies. This is achieved through an image pre-processing pipeline, which converts image data contained in one type of file (e.g., .mcd) into another type of file (e.g., .tiff) and removes artifact signals from the image data using various algorithms to generate improved image data. Thereafter, a semi-supervised clustering pipeline analyzes the improved image data using various techniques, including implementing a supervised algorithm to identify metaclusters such as general immune phenotypes (e.g., CD4−T-cells, Macrophages, Neutrophils, etc.) as well as non-immune phenotypes while implementing an unsupervised algorithm that enables the identification of specific subclusters and a more in-depth cellular status characterization.

The embodiment of the invention discloses an abnormality detection method and device for an aluminum electrolysis cell, belongs to the technical field of artificial intelligence detection, and is used for solving the problem that the accuracy of recognizing the abnormality of the aluminum electrolysis cell is relatively low. Comprising the steps that multi-dimensional operation data of the aluminum electrolysis cell are obtained, the operation data are preprocessed, a multivariable time sequence is obtained, and the operation data comprise data in the operation process of the aluminum electrolysis cell; detecting the time sequence by using an unsupervised learning method, and identifying abnormal candidate data; semi-supervised clustering processing is carried out on the abnormal candidate data according to different working condition categories, similar clusters of the abnormal candidate data under the different working condition categories are obtained, and the similar clusters comprise normal prediction data and abnormal prediction data; the abnormal prediction data in the similar clusters are input into a pre-trained classification model, abnormal data corresponding to the aluminum electrolysis cell are output, the classification model comprises a full supervision model, and the abnormal data comprise data points or subsequences deviating from a normal mode.

The invention discloses an adaptive graph learning fuzzy clustering method based on low-rank matrix decomposition in the medical field, and the method comprises the following steps: carrying out the preprocessing of collected original medical image data, and forming an image feature matrix X; establishing an LAGFC objective function f which is transformed by utilizing a Feychel conjugate technology; iteratively optimizing and updating parameters in the loss function of the LAGFC objective function f in sequence until convergence or reaching the maximum iteration number; and reading the position of the maximum value of the clustering indication matrix F to obtain a clustering label. Through an adaptive graph learning mechanism and a matrix decomposition-fuzzy clustering unified framework, and through low-rank matrix decomposition guided graph learning and fuzzy clustering joint optimization, the data representation discrimination and clustering robustness are synchronously improved, so that more stable, efficient and interpretable semi-supervised clustering is realized, the clustering precision and generalization ability are improved, and the clustering efficiency is improved. The method is especially suitable for medical image data analysis tasks of high-dimensional and complex structures.

The invention discloses a power transmission conductor galloping monitoring method and system, computer equipment and a storage medium. The method comprises the following steps: acquiring attitude monitoring data and surrounding meteorological data of a power transmission conductor in a jurisdiction range; acquiring operation state data related to the transmission line service, associating the attitude monitoring data, the peripheral meteorological data and the operation state data of the same transmission line acquired at the same monitoring node, and generating a data sample; performing iterative training on the semi-supervised clustering model by using the collected data samples, and outputting a clustering result; and determining whether the target transmission conductor corresponding to the newly collected data sample gallops or not according to the clustering result. According to the application, online learning training is carried out through the semi-supervised clustering model, monitoring of transmission conductor galloping is realized, especially monitoring of micro galloping can be realized, early discovery, early disposal and early regulation of transmission conductor galloping are realized, and the capability of a transmission line to resist natural disasters is improved.

The application discloses a kind of based on the incremental semi-supervised image clustering method and system of double-layer label propagation, mainly used to solve the problem of low efficiency caused by repeated calculation when static semi-supervised clustering method faces incremental image data and incremental pairwise constraint.This application uses double-layer label propagation to process the clustering problem of increasing image data and constraint condition.In the first layer label propagation, propagate and diffuse pairwise constraint information in image data samples, and combine the membership matrix of component of image data samples at the last time, incrementally calculate the membership matrix of component of image samples at the current time.In the second layer label propagation, use the clustering result at the last time to mark cluster label information in component, and let known cluster label information propagate in component structure, then gradually expand cluster label information to the whole image dataset through the membership relationship of image sample pair component, so as to realize effective semi-supervised clustering of incremental image data.

The application discloses a tobacco slitting method based on multi-view weight learning and a storage medium, obtains a hyperspectral image of target batch tobacco and performs division; an effective area of the tobacco is obtained by using a threshold segmentation method, the tobacco is divided into a set number of subareas with the same longitudinal length, characteristic spectra of the subareas corresponding to the tobacco are calculated, and a tobacco spectrum database is constructed; spectrum data in the tobacco spectrum database is subjected to band division, different band combinations are obtained, and a multi-view tobacco spectrum database is constructed; a semi-supervised clustering algorithm model is constructed based on multi-view weight and similarity learning, the semi-supervised clustering algorithm model is trained through the multi-view tobacco spectrum database, and a semi-supervised clustering algorithm model with an optimized target function is obtained; and a tobacco segmentation result is obtained through the semi-supervised clustering algorithm model. Through construction of the multi-view of the tobacco, weights are allocated to each view, differences between different tobaccos are accurately quantified, and the tobacco slitting effect is ensured.

The invention provides a semi-supervised clustering method and device, a computer program product and a clustering system. The method comprises the steps that samples and constraint pairs are obtained, the samples are records using credit cards, and the constraint pairs are rule representations that the two samples should be clustered into the same cluster or different clusters; expanding the constraint pair by adopting a union search set technology to obtain an expanded constraint pair; the influence degree of the samples is calculated to obtain an influence score, and the influence score is the influence degree of the samples on clustering; the influence scores are ranked from large to small, a preset number of samples before the ranked influence scores are determined as centroids, semi-supervised clustering is carried out according to the centroids and the expanded constraint pairs, clustering results are obtained, and the clustering results are used for identifying groups with different credit risks. According to the scheme, the problem that the accuracy of a clustering result is low due to the fact that the accuracy of mass center selection is low in the clustering process in the prior art is solved.

This invention discloses a multi-level clustering algorithm for pedestrian trajectory flows that takes into account information from multiple cameras. It relates to a semi-supervised multi-level clustering algorithm for video target geographic flows. The method is based on a flow space and uses a hierarchical representation of video target trajectories to obtain geographic flow segment sets at different levels. Using camera IDs as labels, semi-supervised clustering is performed on these segments at different levels to obtain a set of cluster centers for each level. These cluster centers effectively describe the overall trend of a large number of geographic flows. This method overcomes the limitations of traditional algorithms, which can only perform single-level clustering and do not consider camera information. A series of experiments demonstrate the effectiveness of this algorithm.

The invention relates to a working condition discriminating and dosing method based on flotation froth image recognition, belongs to the technical field of flotation working condition intelligent recognition, and solves the problems that industrial field labels are scarce, and an existing clustering model is difficult to reflect actual working conditions. Comprising the following steps: collecting a flotation froth image, dividing a data set, and carrying out unsupervised clustering on an unsupervised training set to obtain an unsupervised clustering result; constructing a working condition coupling function, subdividing an unsupervised clustering result according to the working condition coupling function value of each image, and endowing a working condition false label; taking the subdivision result as a semi-supervised clustering initial center, and carrying out semi-supervised clustering on the semi-supervised training set according to a working condition pseudo label to obtain a semi-supervised clustering result to form a new clustering center; acquiring a real-time flotation froth image, and comparing the real-time flotation froth image with the new clustering center to obtain a working condition discrimination result; and adjusting dosing parameters according to the working condition judgment result. Working condition identification and intelligent dosing of the foam image are realized, and the stability and working condition interpretability of the model are improved.

The application discloses a kind of self-adapting semi-supervised deep clustering method, belong to image data clustering analysis technical field, by using labeled data training model;The trained model is again trained to the unlabeled training data, and the classification features of the data are obtained, and its classification features are converted into probability value;The features obtained by labeled data and unlabeled training data in the last hidden layer of model are semi-supervised clustering to obtain the similarity between unlabeled training data and cluster center, and the probability value and the similarity corresponding label consistent are given its pseudo label;Self-adaptively select the data with high confidence from pseudo label data and add to labeled data to participate in the next model iteration training, until model converges, then training ends;Finally, new test data is clustered and tested.Compared with other clustering methods, the application can improve the accuracy of model clustering on different data sets.

This invention discloses a semi-supervised clustering modeling method and system for reservoir prediction. Addressing the challenges of large seismic attribute data volume, high redundancy, scarce labels, and low signal-to-noise ratio, this invention embeds a modified K-means framework simultaneously with pairwise constraint guidance and sparse feature weighting. It iteratively optimizes cluster assignment and attribute weights, achieving a balance between maximizing inter-class differences and minimizing intra-class differences. Differential privacy noise is introduced to ensure data security without significantly reducing accuracy. It supports downsampling acceleration and multi-layer 3D label alignment, enabling efficient processing of millions of data points. Compared to conventional K-means and waveform clustering, this invention significantly improves the accuracy of blind well testing, clearly characterizing micro-structures such as channels and riverbeds, providing a high-resolution, highly interpretable integrated solution for reservoir distribution, thickness, hydrocarbon content, and sedimentary facies analysis.