39 results about "Fcm clustering" patented technology

The invention relates to the field of agricultural fertilization, in particular to a corn straw returning-to-field and fertilization operation path collaborative scheduling optimization method, which comprises the following steps: performing standardization processing on farmland multi-source original data to obtain a field piece unit standardization feature vector; performing imbalance degree analysis on the feature dimension distance component to obtain a feature contribution balance factor; performing activeness evaluation on neighborhood membership distribution of the field units to obtain a spatial membership smoothing factor; obtaining adaptive distance measurement by combining a feature contribution balance factor and a space membership smoothing factor; and performing hardening processing on the final membership matrix to obtain an agricultural machinery path collaborative scheduling instruction set, thereby solving the problems of misalignment of a zoning result, fragmentation of a management boundary and unreasonable agricultural machinery path scheduling in an abnormal straw accumulation region based on a standard FCM clustering algorithm in the prior art.

The invention discloses a regulation and control quota distribution method and system fusing subjective and objective multi-dimensional features. The method comprises the following steps: acquiring electrical load data, historical response behavior data and subjective response intention information of a user; firstly, a Ward system is used for clustering, users are clustered according to active power, then a primary clustering center is used as an initial clustering center of secondary clustering, FCM clustering is carried out, and a typical load curve of the users is described based on a secondary clustering method; load prediction is carried out based on an NARX neural network, a predicted load curve is compared with a typical load curve, and adjustable potential is calculated; constructing a DR feature data set; and the DR feature data set is fused with an entropy weight method and an analytic hierarchy process to obtain a combined weight, a fuzzy relation matrix is constructed, a user comprehensive score is quantified, and a comprehensive response potential score of the user is formed.

The invention belongs to the technical field of asphalt paving segregation detection, and particularly relates to an asphalt temperature segregation detection method, system and device. The method comprises the following steps: S1, acquiring an asphalt infrared image; s2, performing super-pixel processing on the asphalt infrared image by adopting a multi-scale morphological watershed algorithm to obtain a super-pixel image; s3, performing clustering processing on the super-pixel image by adopting a K-Means clustering algorithm to obtain a plurality of segmented regions; s4, processing each segmented region by adopting an FCM clustering algorithm to generate a fuzzy label, and merging the fuzzy label into the superpixel image to serve as a segmentation result; and S5, extracting temperatures of different points in the subareas, and judging whether segregation exists in the corresponding subareas or not according to the temperatures. According to the invention, the positioning precision can be improved, so that the asphalt temperature detection requirement in the paving process is met.

The invention relates to the technical field of remote sensing image processing, in particular to a joint sparse representation hyperspectral image classification method based on dual domain constraints, which comprises the following steps: preprocessing: carrying out spectral feature-based wave band grouping on hyperspectral image data, and then carrying out MNF data dimension reduction on the grouped hyperspectral image data; extracting a main component feature map by using morphology; performing superpixel segmentation on the hyperspectral image by using an improved watershed algorithm, and performing FCM clustering on the hyperspectral image; adaptive selection of the neighborhood is carried out through weight calculation under double constraints of the obtained superpixel neighborhood and the clustering field; multi-view angles of the superpixel field, the clustering field and the constraint field are used for joint sparse representation; and a majority voting method is adopted to integrate classification results, and the classification results are adjusted through a correction rule, so that the classification effect of the hyperspectral image is improved, and the classification precision of edge pixels is improved.

The invention belongs to the technical field of intelligent traffic, and discloses a traffic state estimation and queuing state discrimination method based on a fuzzy traffic wave model, comprising the following steps: S1, collecting and preprocessing ETC gantry data, thunder-vision fusion trajectory data and artificial report event data, and extracting traffic flow parameters and event features after preprocessing; s2, fuzzy parameters are introduced to improve a traditional traffic wave model, the traffic wave velocity is calculated through the fuzzy parameters, wave front position transmission based on a state transition model is researched, and a fuzzy traffic wave model is constructed; s3, a VMD-GA-ConvLSTM network is constructed to carry out traffic state estimation; s4, determining a traffic state membership degree by adopting a GWO-FCM clustering algorithm, and judging a vehicle queuing state through an SVM classifier; and S5, improving the fuzzy traffic wave model in the network connection environment, and dynamically correcting the model from two dimensions of road section macroscopic characteristics and vehicle microscopic behaviors. The method provided by the invention can provide theoretical and technical support for accurate perception and dynamic regulation and control of the highway traffic situation.

The invention discloses a short-time traffic jam prediction method fusing FCM clustering and GA-Bi-GRU, relates to the technical field of traffic safety, and aims to solve the problem of low traffic jam prediction accuracy due to the fact that time sequence characteristics of traffic data are not fully considered in the prior art. And then, establishing a traffic flow prediction model, and inputting predicted traffic flow parameters into an FCM model to realize traffic congestion level prediction. According to the prediction model, forward and backward GRU time recurrent neural networks are designed on the basis of a gating recurrent unit (GRU) so as to fully mine data features, and in order to further capture the influence of different time steps, a time attention mechanism is introduced into the model, the time features of data are deeply mined, and optimization is carried out in combination with a genetic algorithm (GA).

The invention relates to the technical field of video content analysis, in particular to a mine surveillance video key frame extraction method, which comprises the following steps: extracting multi-dimensional features of each frame of a candidate frame set, obtaining a fusion value, and obtaining a candidate key frame set; based on an Euclidean distance method, calculating an Euclidean distance between continuous frames of the candidate key frame set, and determining a total clustering number according to a relationship between the Euclidean distance and a preset clustering threshold value; and clustering the frames in the candidate key frame set by using a preset mixed GWO-FCM clustering algorithm and the total clustering number to obtain a key frame set. According to the method, a hybrid clustering algorithm GWO-FCM combining grey wolf optimization and fuzzy C-means is adopted, global search and local optimization capabilities are considered, and the accuracy and representativeness of key frame clustering are ensured. The high-quality extraction of the key frames is realized, the number of redundant frames is obviously reduced, and the compactness and readability of the video abstract are improved.

The invention discloses a frequency agility radar signal sorting and individual clustering method combining inter-pulse and intra-pulse characteristics. The method comprises the following steps: screening an original pulse stream to obtain a screened pulse stream; performing frequency domain filtering on the intermediate frequency data corresponding to each pulse after screening to obtain corresponding intermediate frequency filtering data, and performing down-conversion on the intermediate frequency filtering data of all the pulses after screening to the same frequency to obtain preprocessed intermediate frequency data of each pulse after screening; sorting all pulses corresponding to the preprocessed intermediate frequency data based on the pulse description word of each type of radar to obtain a cluster of the corresponding type of radar; based on an intra-pulse fingerprint feature extraction mode, extracting a fingerprint feature vector corresponding to each cluster after sorting; and clustering the fingerprint feature vectors based on an FCM clustering algorithm of a joint parameter optimization K value to obtain an individual clustering result of the radar of the corresponding model. According to the invention, more accurate radar individual clustering can be realized.

The invention discloses a product recommendation method based on personality features and FCM clustering optimization. The method comprises the steps that 1, a user and article set and a user big five personality feature score set are obtained; 2, clustering is carried out based on big five personality data of the user, and an optimal clustering center and an optimal membership matrix are obtained; 3, complementing the missing value of the user-article scoring matrix based on user personality feature clustering, so as to calculate the user similarity; and 4, calculating the preference similarity between the user and the target user according to the user-article scoring matrix, calculating the personality similarity between the user and the target user according to the personality characteristics of the user, and carrying out weighted operation to obtain the user similarity so as to calculate the predicted score of the user. According to the method, the cold start problem, the data sparsity problem and the interest drifting problem of user interests along with time change in a recommendation system can be effectively solved, so that the individuation, accuracy and reliability of product recommendation can be improved, and higher conversion rate and user stickiness are brought to a sales platform.

The invention discloses a lithium ion battery safety state estimation method based on working condition fragment features and unsupervised clustering, and the method comprises the steps: achieving the segmentation of different working condition data based on K-means based on an MIT-Stanford mixed working condition cycle data set; for the segmentation data, segment safety features and global safety features of the constant current charging stage, the constant voltage charging stage and the constant current discharging stage are extracted respectively; performing normalization, direction unification, dimension reduction and correlation screening processing on a data set formed by feature extraction in sequence, and combining to generate a plurality of feature data sets; aiming at a plurality of security feature sets, adopting a PSO algorithm to optimize FCM clustering center selection, introducing GG distance measurement to replace Euclidean distance, and establishing an SOS estimation method based on particle swarm optimization-fuzzy clustering; and independently clustering each feature set, giving a 0 / 1 score according to different clustering center results, and finally aggregating a good reputation rate as an SOS estimated value. According to the method, the dependence of SOS estimation on the parameter threshold is overcome by combining the working condition characteristics and the unsupervised clustering scoring mechanism, and a new method is provided for the actual use of SOS estimation.

The invention discloses an SAR (Synthetic Aperture Radar) image change detection method combining convolution and mixed attention. The SAR image change detection method comprises the following implementation steps of: firstly, generating a difference chart for two SAR images by using a composite neighborhood intensity difference method; then, a hierarchical FCM clustering algorithm is used for carrying out pre-classification processing on the difference image, a pseudo label matrix is generated, variable and invariable high-probability sample pixels in pseudo label pixels are selected, spatial positions of the pixels are extracted, and on the pixels of the corresponding spatial positions of the two original SAR images, a pseudo label matrix is generated; pixel blocks with the pixel points as the centers are taken as a training set, and pixel blocks with all the pixel points as the centers are extracted from the two original SAR images to serve as a test set; and then training a neural network combining convolution and mixed attention by using the training sample set, and then carrying out change detection analysis on a test set by using the trained network to generate a final change detection result graph. The method has clear advantages in the aspects of SAR speckle noise suppression and change detection precision.

This invention discloses a system and method for elastically aggregating distributed electric heating load characteristics in the field of power system analysis. The system comprises the following steps: receiving electric heating load data from distributed electric heating users; preprocessing the electric heating load data; clustering distributed electric heating users using the FCM clustering algorithm, using seven load characteristic indicators as clustering indicators based on the preprocessed electric heating load data; and evaluating clustering quality and determining the optimal number of clusters by establishing three clustering effectiveness indicators. This invention solves the problem of identifying and aggregating the characteristics of distributed electric heating users, thereby facilitating the management and regulation of electric heating load groups and enhancing the controllability of electric heating load groups.

The present invention proposes a deep TSK fuzzy classifier based on multi-level feature fusion, including a feature learning module and a knowledge reasoning module; the feature learning module is a feature learning module based on a convolutional neural network, which takes the original data as the input of the deep TSK fuzzy classifier based on multi-level feature fusion, and the feature learning module extracts local information in a local connection manner (i.e., convolution kernel), and obtains the deep features of the hidden layer through layer-by-layer neural expression to automatically extract deep features from the original data; the knowledge reasoning module is a knowledge reasoning module based on the TSK fuzzy classifier, which takes the deep features as the training parameters of the fuzzy rules, adopts the FCM clustering algorithm to generate the antecedent parameters of the fuzzy rules, and uses the original data to train the consequent parameters of the fuzzy rules. By fusing the feature extraction capability of the convolutional neural network and the uncertainty processing capability of the fuzzy representation, a deep TSK fuzzy classifier that is easier to understand is formed.

The application discloses a Fl-engine system in a joint learning platform, which comprises a joint learning platform, a local server and an internet of things access end, wherein the joint learning platform is internally provided with an intelligent ecological circle module, a joint learning plan module, a joint learning engine module and a platform support module; the joint learning plan module and the joint learning engine module are electrically connected; and the joint learning engine module and the platform support module are electrically connected. The Fl-engine system in the joint learning platform provided by the application is provided with the joint learning engine module, and incorporates a joint machine learning algorithm, a joint deep learning algorithm, a horizontal joint algorithm and a vertical joint algorithm in the joint learning engine module; through a self-adaptive mechanism, a k-means clustering strategy, a hierarchical clustering strategy, a SOM clustering strategy or a FCM clustering strategy matched with the joint learning engine module is selected from an aggregation strategy module, so that aggregation calculation is realized.

The application discloses a multifunctional radar signal sorting method and system based on a hypergraph, and the method steps are as follows: S1, for RIPS obtained through radar reconnaissance receiver interception, interference pulses in the RIPS are removed; S2, the remaining pulse sequence is subjected to screening rules and FCM clustering, and clustering labels of partial pulse sequences are obtained; S3, feature parameters and data potential energy values of the remaining pulse sequence are used to construct a hypergraph; and S4, the clustering labels of the partial pulse sequences are used to learn the constructed hypergraph, and a final radar signal sorting result is obtained. The application can sort multifunctional radar signals without a marked sample, can effectively alleviate the 'batch increase' problem, and has good sorting performance.

The invention provides a target identification method and system based on a K-MEANS and FCM fusion algorithm, electronic equipment and a storage medium. The method comprises the following steps: data preprocessing: preprocessing input data; the preprocessing comprises normalization, noise reduction and feature extraction; k-MEANS initial clustering: clustering the preprocessed data by using a K-MEANS algorithm to obtain a first initial target category; fCM clustering: applying an FCM algorithm to the preprocessed data, processing target points with fuzzy boundaries, obtaining a fuzzy membership degree, and obtaining a second preliminary target category; target consistency screening: comparing the first preliminary target category with the second preliminary target category to obtain a final screened target; and target marking: marking the finally screened target through an image processing or visualization means to complete target identification. According to the scheme provided by the invention, more accurate and more stable target identification can be realized in a multi-source complex data environment.

The application discloses an image classification method based on pseudo-label semi-supervised learning. The method firstly improves the label assignment strategy of the FCM clustering algorithm, can generate the membership of each data with a label, and combines the self-training semi-supervised method to propose a new semi-supervised training algorithm SST, and improves the training result of the semi-supervised training. The method is applied to medical image classification processing, and improves the diagnosis efficiency of medical treatment.

The application provides a traffic cell division method based on resident travel characteristics. Administrative division boundaries and hierarchical road network vector data are acquired and data preprocessing is performed; basic clustering units are divided based on the road network and the administrative division; a clustering index system is established, and resident travel characteristics in the units are acquired through mobile phone signaling in combination with the clustering units; according to the location of the clustering units in the administrative division, a k-means clustering algorithm with spatial characteristics is adopted for preliminary clustering, and clustering results and clustering centers corresponding to each clustering number are obtained; clustering effectiveness indexes of the clustering results of each clustering number are calculated; the clustering effectiveness indexes of each clustering number are compared to determine a proper clustering number; final clustering division is performed through a FCM clustering algorithm with comprehensive spatial characteristics; the clustering results are modified in combination with main hierarchical roads, and traffic cell division is completed. The application improves the robustness of the traffic cell division results and can support the construction of subsequent traffic planning models.

This invention claims protection for a task offloading method for selecting and optimizing energy consumption of trusted edge servers, comprising the following main steps: S1, constructing a system model based on server and device related information data; S2, standardizing task information and MEC cache task classes and performing FCM clustering, followed by encoding matching to obtain the MEC server to be preferentially selected for offloading for each task class; S3, using set pair analysis theory to measure and analyze the reliability relationship in S2; S4, based on the results of S2, dividing the task into subtasks and offloading them in a multi-access point network while calculating energy consumption. Using the WOA algorithm, energy consumption is optimized within the tolerable latency range to obtain the offloading decision for each subtask; S5, based on S4, offloading the subtasks to the corresponding edge nodes or processing them locally. This invention considers issues such as trusted edge server selection and energy consumption optimization during task offloading, meeting user needs and reducing system energy consumption within the tolerable latency range.

The invention discloses a palm fruit and leaf image segmentation method. The method comprises the following steps: acquiring RGB color space images of palm fruits and leaves; converting the image into an HSV color space image; extracting an H component, and drawing an H component histogram; analyzing H component thresholds of the fruits and the leaves under different illumination; on the basis of a traditional FCM clustering segmentation algorithm, weight coefficients are added, calculation of each pixel point is converted into calculation of H component frequency, an improved FCM clustering algorithm is obtained, the H component of the image is segmented, and an interested area is obtained and serves as a result; morphological processing is carried out on the result, the influence of interference factors such as noise is reduced, the obtained mask is applied to an original image, and segmented fruit and leaf images are obtained. According to the method, on the premise that the adverse effect of uneven illumination is reduced, the images of the palm fruits and the leaves can be quickly and accurately segmented, and a basis is provided for work such as three-dimensional positioning and fruit quality detection.

The intelligent apple slice fine sorting and drying equipment comprises a conveying mechanism, a height limiting mechanism is mounted at the feeding end of the conveying mechanism, the conveying mechanism comprises a mounting side plate, and a conveying belt is mounted on the inner side of the mounting side plate; a plurality of protruding strip-shaped structures are arranged on the outer surface of the conveying belt at equal intervals. The intelligent selection area comprises a multispectral imaging mechanism, a color grading mechanism and a shape screening mechanism. Multi-spectral fusion detection: RGB + NIR + HDR imaging is combined to improve a YOLOv7 model, the defect detection rate is improved, the error rejection rate is reduced, dynamic color grading, HSV and spectral feature splicing + FCM clustering are adopted, incremental learning is supported, variety differences are adapted, the color sorting accuracy is improved, and the color sorting efficiency is improved. And meanwhile, 3D shape screening, laser triangulation and Poisson curved surface reconstruction are adopted, so that the warping degree detection precision is improved, and the rejection rate of deformed pieces is increased.

The invention belongs to the technical field of new energy power generation grid connection, and particularly discloses a photovoltaic station classification aggregation equivalent modeling method, which comprises the following steps: acquiring control parameters and sensitivity of a photovoltaic power generation unit, and calculating a clustering index according to the control parameters and the sensitivity; dividing the clustering indexes into a plurality of clusters, inputting the plurality of clusters into a pre-trained improved FCM clustering algorithm model, and obtaining corresponding cluster centers under different cluster numbers by adopting a spore propagation search mode; obtaining a plurality of division schemes for clustering indexes; and calculating division coefficients and classification entropies under corresponding cluster numbers in different division schemes, determining a classification aggregation result of the photovoltaic power generation units, and performing equivalent modeling on the photovoltaic power generation units according to the classification aggregation result. According to the method, the global search capability and convergence efficiency of a traditional clustering algorithm are improved, and accurate clustering of the dynamic characteristics of the photovoltaic power generation units is realized.

This invention relates to the field of agricultural fertilization, and more particularly to a method for optimizing the path coordination scheduling of corn straw return to the field and fertilization operations. The method includes: obtaining standardized feature vectors for field units by standardizing multi-source raw data of farmland; obtaining a feature contribution balancing factor by analyzing the imbalance of distance components in the feature dimensions; obtaining a spatial membership smoothing factor by evaluating the activity of the neighborhood membership distribution of field units; obtaining an adaptive distance metric by combining the feature contribution balancing factor and the spatial membership smoothing factor; and obtaining a set of agricultural machinery path coordination scheduling instructions by hardening the final membership matrix. This method solves the problems of inaccurate partitioning results, fragmented management boundaries, and unreasonable agricultural machinery path scheduling in existing standard FCM clustering algorithms based on abnormal straw accumulation areas.

The invention discloses a combined model photovoltaic output short-term prediction method and model based on self-adaptive FCM clustering, and aims to solve the problems that in a traditional prediction model, the clustering number is difficult to determine autonomously, feature correlation mining is insufficient, parameter optimization is insufficient, prediction precision is limited and the like, and the method comprises the steps that photovoltaic related data such as solar irradiance, temperature and relative humidity are collected; performing z-score standardized preprocessing to remove abnormal values and missing values; high-correlation feature vectors are screened through Pearson's correlation coefficient analysis, and data dimension reduction is achieved; a self-adaptive FCM clustering algorithm is adopted, Markov-included angle cosine comprehensive distance optimization membership calculation is introduced, the optimal clustering number is autonomously determined through a self-adaptive function L (c), and precise clustering is carried out on the data after dimension reduction; and inputting the divided clusters into a CNN-BiLSTM-SA combination model constructed based on a CNN, BiLSTM, an improved Kepler optimization algorithm and a self-attention mechanism, and finally outputting a photovoltaic output short-term prediction result by using key parameters of an IKOA optimization model and important information of an SA dynamic focusing sequence.

This invention discloses a method and apparatus for fault location of a metering master station based on accelerated fuzzy c-means clustering. The method includes: calling the accelerated fuzzy c-means clustering algorithm to process the abnormal dataset, iterating continuously until the cluster set of the abnormal dataset is less than a preset threshold or the number of iterations reaches a set upper limit, obtaining the cluster set and membership set after clustering; obtaining the classification result corresponding to the fault type and fault characteristics based on the cluster set and the membership set; comparing the classification result with the normal data in the log to locate the fault area. This invention uses a KCN network to accelerate the iteration speed of the FCM clustering network, ensuring both accuracy and speed in data classification and fault location.

The present invention discloses an underwater planar structure sonar image crack quantification method, which is used to calculate the actual width and length of cracks in sonar images and includes: inputting the original crack sonar image; denoising the original image by combining logarithmic transformation and BM3D algorithm; performing image enhancement processing on the denoised image based on piecewise gray transformation; performing crack segmentation on the processed image based on FCM clustering and connecting the segmented fractured cracks based on KD tree; extracting the crack skeleton curve to calculate crack parameters; converting the obtained crack pixel parameters into real parameters; and outputting crack parameters. The present invention constructs a complete process from inputting the crack sonar image to outputting crack parameters, can extract the required target cracks in sonar images with noise interference and low contrast, and calculate the actual length and width of the cracks, greatly reducing the required time compared with manual measurement and improving the efficiency of sonar crack detection.

The invention discloses an image denoising method and device based on fuzzy C-means clustering, and relates to the technical field of image processing, and the method comprises the following steps: S1, obtaining an original noisy image, and carrying out the FCM clustering segmentation of the original noisy image; s2, on the basis of the clustering result obtained in the S1, identifying the noise type contained in the original noise-containing image; s3, adopting a corresponding denoising strategy according to the noise type identification result obtained in the S2, and carrying out denoising processing on the original noise-containing image; the technical problems that a traditional denoising method in the prior art is poor in adaptability to mixed noise, parameter setting depends on experience, and the detail keeping capacity is insufficient are solved, self-adaptive processing on different types of noise is achieved, and meanwhile the detail features of the image are effectively kept.

The invention discloses an EMD-BiGRU photovoltaic power generation power prediction method based on FCM clustering and SSA optimization. Photovoltaic related data is obtained and preprocessed, the photovoltaic related data is divided into three types of weather data sets through FCM clustering, EMD decomposition parameters and BiGRU hyper-parameters are optimized through SSA, decomposed subsequences and meteorological characteristics are input into optimized BiGRU prediction, and a time sequence is superposed to obtain a result. According to the method, hyper-parameter empirical configuration is replaced, the data complexity is reduced, the photovoltaic power frequency characteristics are accurately captured, the prediction decision coefficient is higher than 0.96, and the method is suitable for short-term photovoltaic power generation power prediction.

The method for identifying abnormal water level data of the main stream of the Yangtze River based on meta learning comprises the following steps: step 1: identifying abnormal water level data based on an FCM abnormal value detection model; step 2: based on a meta learning MAML model, the meta learning task is divided into a meta training task and a meta testing task; step 3: in the meta training task stage, a plurality of task training FCM abnormal value detection models are designed to obtain FCM abnormal value detection model initialization parameters θ; and step 4: in the meta testing task stage, the FCM clustering model initialization parameters θ are fine-tuned through support set data to predict query set node categories and detect whether the nodes are abnormal. The method for identifying abnormal water level data of the main stream of the Yangtze River based on meta learning can train an abnormal identification model applicable to water level data of other stations based on water level data of a few stations, and can achieve good identification effect and strong generalization ability of the model.

The invention discloses a power transmission line icing thickness prediction method based on similar day selection and CNN-BiLSTM. The method comprises the following steps: obtaining the icing thickness and meteorological data of a power transmission line; meteorological data with high correlation with the icing thickness is screened out to serve as input data of a data clustering and prediction model; fCM clustering processing is carried out on the meteorological data according to the characteristics of the meteorological data, and a historical data set is divided into different clusters with respective similar characteristics; calculating membership degrees of the to-be-tested day under different clusters by using a grey correlation analysis method, and taking the cluster with the highest membership degree as a selected similar day set for training and prediction; a CNN is added on the basis of the BiLSTM model, and a CNN-BiLSTM model is obtained; inputting key meteorological data and icing thickness data of a similar day set to train a CNN-BiLSTM model; using the trained CNN-BiLSTM model to predict the icing thickness of the to-be-measured day, and outputting an icing thickness prediction result. According to the method, the prediction performance is more stable, the prediction precision is higher, and the problems that the model is easily interfered by irrelevant modes and the prediction precision of the icing thickness of a single model is low are effectively solved.