81 results about "Self-organizing map" patented technology

A method is provided for enhanced data routing in neural networks using Self-Organizing Maps (SOM) integrated with Autoencoder-GAN. The method comprises training an autoencoder to encode input data into a latent space representation; applying a Self-Organizing Map (SOM) to organize the latent space representation into a topological map; refining the latent space representation using a Generative Adversarial Network (GAN), wherein the generator generates enhanced latent space representations and the discriminator evaluates their quality; using the refined latent space representations to update the SOM topology dynamically; generating routing coefficients based on the updated SOM topology to guide data routing in a capsule network; and dynamically adjusting routing within the capsule network using the generated routing coefficients to enhance performance based on the refined latent representations.

The invention discloses an enterprise data asset intelligent analysis method and system based on AI recognition, and the method comprises the steps: receiving an enterprise multi-source heterogeneous data stream, carrying out the joint feature extraction and semantic alignment through a pre-trained multi-modal fusion recognition model, and generating a structured data asset recognition result; constructing a dynamic enterprise data asset atlas according to the structured data asset identification result in combination with the data access trajectory and authority metadata collected in real time; performing spatio-temporal evolution analysis on the dynamic enterprise data asset map, and extracting potential data value density features and risk exposure features; inputting the data value density features and the risk exposure features into a self-organizing mapping network to generate a data asset grading topological graph; and based on the data asset grading topological graph, through strategy constraint reinforcement learning, generating an executable data governance action sequence. According to the embodiment of the invention, the identification precision and real-time analysis capability of special assets of enterprises can be improved.

The invention discloses a dynamic DEM spatial interpolation method which comprises the following steps: performing depression filling, flow direction analysis and confluence cumulant calculation on DEM data, and extracting a natural sub-basin unit by adopting a minimum catchment area threshold method; constructing a topographic feature matrix, performing refined second-level classification on the first-level drainage basin through an improved self-organizing mapping network, generating a hydrological response unit through boundary processing, and establishing a hydrological attribute library; fusing multi-source data, supplementing attribute interpolation such as underlying surface and rainfall, and constructing an interpolation auxiliary parameter system; a drainage basin is divided into regular grids as neurons, a dynamic neural network containing dynamic states and static attributes is constructed, and nonlinear mapping of DEM correction parameters is achieved through optimization of a dynamic activation function and a loss function. The method overcomes the defects that a traditional interpolation algorithm does not consider hydrological boundary constraints, a neural network model topological structure is fixed and the like, and the DEM interpolation precision and the hydrological simulation effect of the complex terrain area are improved.

The invention provides a coastal erosion rate prediction method, and belongs to the technical field of coastal erosion, and the method comprises the steps: building a three-dimensional seabed grid model, solving a hydrodynamic field through depth average simplification and GPU parallel calculation, and calculating sediment flux distribution based on shear stress discrimination and a high-order windward format. Outputting an erosion mode category and a local erosion strength coefficient by using a coastline erosion feature recognition model comprising a Josephh ring screening layer and a self-organizing mapping projection layer, starting adaptive grid encryption when the local erosion strength exceeds a threshold value, and calling a corresponding parameter set according to an erosion mode to calculate a seabed elevation change and a coastline erosion rate; and a wave energy spectrum reconstruction algorithm is adopted to generate future wave sequence cycle prediction, so that the technical problem that calculation precision and calculation efficiency are difficult to consider in coast erosion rate prediction under a complex wave power condition is solved.

The invention provides a power grid constant value detection method and system, equipment and a storage medium, and belongs to the technical field of power grid detection, and the method comprises the steps: obtaining to-be-detected test constant value data; determining a target neuron matched with the test constant value data from the self-organizing mapping model, and determining a test error between the test constant value data and a weight vector of the target neuron; based on the test error and an error threshold value, determining whether the test constant value data is abnormal or not; wherein the error threshold value is determined by the following steps: acquiring each historical constant value data of the power grid; determining first neurons respectively matched with the historical constant value data from a self-organizing mapping model; and determining an error threshold value based on the weight vector corresponding to each first neuron and the historical constant value data corresponding to each first neuron. According to the power grid constant value detection method and system, the equipment and the storage medium provided by the invention, the recognition precision of the abnormal constant value can be improved.

The invention relates to the technical field of metal spectrum analysis, in particular to a method for determining the element content of a metal sample by using a direct-reading spectrometer. The method comprises the following steps: acquiring a spectrogram of a to-be-analyzed metal sample; further carrying out iterative decomposition on each spectrum peak to obtain a decomposition peak; further inputting the spectrogram and all decomposition peaks of the spectrogram into a spectrum recognition model obtained through self-organizing mapping network training, and recognizing corresponding element categories; and finally, substituting the spectral intensity of each decomposition peak into a pre-established quantitative correction curve of the corresponding element, and calculating to obtain the content of each element in the metal sample without depending on a priori standard spectrogram or a preset overlapping peak number, so that accurate element quantitative determination under a complex overlapping spectrum peak is realized, and the determination precision and adaptability are improved.

The invention belongs to the field of control, relates to a resin production waste gas recovery control method and system, and aims to solve the technical problems that in the prior art, working condition division cannot be matched with the real state of a multivariable system, and PID parameters are balanced among a plurality of control targets. The neurons are divided into stable working condition neurons, transition working condition neurons and disturbance working condition neurons; s2, obtaining a Pareto optimal solution set composed of a PID parameter group, wherein the Pareto optimal solution set comprises an ITAE optimal solution, a control output change rate optimal solution and an equilibrium solution; s3, determining an optimal matching unit of the potential feature vector in the self-organizing mapping network; s4, when the optimal matching unit is a stable working condition neuron and the absolute value of the current control error is greater than a high-order threshold value, selecting an ITAE optimal solution; when the absolute value of the current control error is smaller than a low-level threshold value, the optimal solution of the control output change rate is selected; and a balance solution is selected between the two. Accurate identification and division of the operation state of the resin production waste gas recovery process are realized.

The invention discloses a harmonic current signal-based high-voltage cable defect identification method and system. The method comprises the steps of obtaining harmonic current monitoring data of a high-voltage cable metal sheath; analyzing the harmonic current monitoring data by using a principal component analysis method to obtain a principal component characteristic quantity; determining a defect identification initial model based on a self-organizing mapping algorithm, and training and optimizing the defect identification initial model based on the principal component characteristic quantity to obtain a defect identification optimal model; and inputting harmonic data of a to-be-detected high-voltage cable into the defect identification optimal model so as to identify the defect type of the to-be-detected high-voltage cable. Amplitude-frequency characteristics of harmonic current signals are extracted through principal component analysis, abnormal harmonic data generated by different defects are classified and distinguished through a self-organizing mapping algorithm, and therefore defect identification based on high-voltage cable line harmonic signal abnormity is achieved. And reliable technical support can be provided for cable defect identification in a power system in practical application.

The invention discloses an electric pile state identification and classification method, equipment and a storage medium, and relates to the field of battery data calibration. The method comprises the following steps: according to the similarity between each type of galvanic pile parameters and galvanic pile voltage, determining the correlation between each type of galvanic pile parameters and galvanic pile voltage; selecting galvanic pile parameters with large correlation as training data; training the training data through a self-organizing mapping neural network to obtain an optimal clustering model for clustering the pile parameters; clustering the training data through the optimal clustering model to obtain a plurality of clusters of clustering data; generating a pseudo pile state label for each type of clustering data and then forming a sample set; and training the sample set through a one-dimensional convolutional neural network to obtain a pile state classification model for pile state classification. According to the method, the workload of manual labeling is reduced, and the state of the galvanic pile can be effectively identified and classified.

This application belongs to the field of control and relates to a control method and system for recovering waste gas from resin production. It addresses the technical problems in existing technologies, such as the inability of operating condition division to match the true state of a multivariable system and the need for PID parameters to balance multiple control objectives. The control method includes the following steps: S1, dividing neurons into stable operating condition neurons, transitional operating condition neurons, and disturbance operating condition neurons; S2, obtaining a Pareto optimal solution set composed of PID parameter groups, including the ITAE optimal solution, the control output rate of change optimal solution, and the equilibrium solution; S3, determining the best matching unit for the latent eigenvectors in a self-organizing map network; S4, when the best matching unit is a stable operating condition neuron, selecting the ITAE optimal solution when the absolute value of the current control error is greater than a high threshold; selecting the control output rate of change optimal solution when the absolute value of the current control error is less than a low threshold; and selecting the equilibrium solution when it is between the two. This achieves accurate identification and division of the operating state of the resin production waste gas recovery process.

A voice generation method and apparatus, a product, a device, and a medium, which are applied to the technical field of voice generation. The method comprises: using a quantizer to discretize a voice feature vector of an original voice signal, obtaining a discrete symbol representation corresponding to the original voice signal (S11); extracting a text feature corresponding to a target text (S12); and inputting the text feature and the discrete symbol representation into a voice generation model, so that the voice generation model uses the text feature as a condition, and generates a target voice on the basis of the discrete symbol representation (S13). The quantizer is obtained by training a self-organizing map network by means of voice signal training samples. The method may restore an original voice feature more accurately, and improve the quality of the generated voice.

The invention relates to a commercial vehicle accident high-risk scene portraying method and system. The method comprises the following steps: acquiring original data; constructing a high-risk scene index system, and defining a risk level; preprocessing the original data based on the high-risk scene index system to obtain preprocessed data; training the self-organizing mapping neural network by utilizing the preprocessed data, and adjusting network node weights by adopting a forgetting suboptimal rule to obtain an initial clustering center; and taking the initial clustering center as a starting point, taking the risk level as an initial K value, and carrying out K-means clustering analysis on the preprocessed data to obtain a commercial vehicle accident high-risk scene portrait result. According to the method, the SOM neural network and the weight updating mechanism with the forgetting factor are introduced to optimize the clustering effect, and the pendulum effect generated in the later period is avoided; through methods of clustering analysis, multiple correspondence analysis and the like, accurate description of an accident high-incidence scene of the commercial vehicle is realized, and a support is provided for comprehensively improving the safety of the road transport vehicle.

A system and method for training relational networks. A method includes applying a self-organizing map (SOM) to training data in order to create a visualization. The SOM is a neural network configured to transform relationships between data items. The visualization has a lower dimensionality than the training data. The method also includes training machine learning models of a generative relational network (GRN) based on the visualization, where the GRN includes sets of nodes having respective machine learning models among the machine learning models of the GRN and the sets of nodes include a set of dominance factor nodes and a set of evolution of internal component nodes. The set of dominance factor nodes defines a dominance factor based on change intensity and change frequency, and the set of evolution of internal component nodes defines evolution with respect to changes determined based on values of the dominance factor over time.

The invention discloses a supply chain multi-modal interactive question and answer method based on a large language model, and the method specifically comprises the steps: S1, collecting text, voice, image and table data in a supply chain scene, and extracting semantic features to form a multi-modal semantic vector; s2, extracting inventory, transportation, production and order time sequence data, and inputting the data into the improved self-organizing mapping neural network to generate a semantic state topological structure; s3, executing concept drift detection and locally reconstructing nodes, and outputting a stable supply chain semantic state vector; s4, fusing the supply chain semantic state vector and the user context information to generate context state enhanced representation; s5, calculating a multi-modal correlation weight to realize semantic alignment and unified coding; and S6, inputting the large language model and combining with knowledge graph reasoning to generate text, voice or chart answers. According to the method, supply chain multi-modal information intelligent fusion and semantic question and answer accurate generation are realized, and the decision-making efficiency and the intelligent interaction level are remarkably improved.

The invention discloses a 5G communication modulation signal identification method and system, and belongs to the technical field of signal identification, and the 5G communication modulation signal identification method comprises the steps of signal receiving, spatial feature separation, multi-domain feature extraction and feature fusion and identification. According to the invention, spatial spectrum estimation is carried out on the multipath beam superposition signals received by the MassiveMIMO array by using the MUSIC algorithm, efficient separation of N paths of independent beam signals is realized, and the technical problem that a traditional single-antenna model cannot adapt to a multi-antenna scene is thoroughly solved; according to the method, the sparse Transform model and the stacked self-organizing mapping are combined, accurate classification and proportion calculation of QPSK, 16QAM, 64QAM and 256QAM hybrid modulation signals are achieved, the recognition accuracy is larger than or equal to 92% when SNR = 0dB, the recognition accuracy is improved by more than 18% compared with a traditional algorithm, and the problems that high-order modulation constellation points are fuzzy and hybrid modulation classification is difficult are effectively solved.

The invention provides a renewable resource intelligent recovery data management system and method based on the Internet of Things, and relates to the technical field of renewable resource intelligent recovery. The method comprises the following steps: collecting original putting data of resident recycled materials in real time; performing preliminary cleaning and structured processing on the original delivery data by using an edge computing device to obtain cleaned delivery data; uploading the cleaning delivery data to a cloud data platform through a communication protocol; constructing a neural ODE network model, and predicting the resource recovery amount of each region in n days in the future; the cloud data platform performs tagging classification on user recovery behaviors based on self-organizing mapping analysis; and encrypting and storing the cleaning and putting data in the transmission and analysis process by adopting an encryption algorithm. The neural ODE network model and the self-organizing mapping analysis expand the application scene of the data, improve the environmental protection atmosphere and the resource recovery efficiency in the region, deeply mine the data value, and create more development opportunities for the resource recovery service.

The invention discloses a method and a system for monitoring internal discharge abnormity of a switch cabinet, and aims to realize accurate monitoring and visual display of internal discharge of an all-insulated closed inflatable high-voltage switch cabinet by combining an ultraviolet image transmission fiber bundle and a big data analysis technology, so that the online monitoring capability of an equipment operation state is improved, and the operation safety of the switch cabinet is improved. And safe operation of power equipment is ensured. According to the system, ultraviolet light signals in a switch cabinet are acquired by using ultraviolet image transmission fiber bundles, photoelectric conversion and signal amplification are performed through an APD avalanche photoelectric detection module, and historical data modeling, feature extraction and clustering, real-time data monitoring and anomaly detection and classification are performed in combination with an autoregression model, a self-organizing mapping neural network and a density clustering algorithm. The system can realize real-time monitoring and abnormity feedback of the equipment state in severe environments such as strong electromagnetic interference, high temperature and high pressure, provides high sensitivity and anti-interference capability, and ensures monitoring accuracy and timeliness.

Systems, methods, and apparatuses for performing real-time cytometry data analysis. One apparatus includes at least one electronic processor and at least one memory storing instructions executable by the at least one electronic processor. The at least one electronic processor is configured, through execution of the instructions, to obtain flow cytometry data generated by a cytometry instrument representing cells of multiple categories, generate a feature vector representation based on the flow cytometry data using a plurality of self-organizing maps (SOMs), wherein each SOM corresponds to a different category of multiple categories, and predict each of one or more target labels of the cells by applying each of one or more regression models to the feature vector representation.

The embodiment of the application provides an image clustering method and an electronic device, the method comprises the following steps: determining a plurality of images, and determining the structural similarity between the images to generate a structural similarity matrix. The structural similarity matrix is input into a self-organizing mapping network, and the self-organizing mapping network finds the best matching neuron node for a first image included in each image pair based on a competitive learning rule according to the structural similarity matrix, and maps a second image included in each image pair and having structural similarity with the first image to a topologically adjacent neuron node to generate a mapping result of the plurality of image pairs. Based on the mapping result, a multi-level clustering process is performed to obtain a fine clustering result. In this way, based on the combination of structural similarity and the self-organizing mapping network, visual-sensitive similarity measurement and hierarchical clustering division are realized, the clustering fineness is improved, and the operation and maintenance cost is reduced.

The invention relates to the technical field of new energy power prediction of an electric power system, and discloses a mountain area power distribution network distributed photovoltaic prediction method, system and device considering prediction errors and a medium, and the method comprises the steps: firstly collecting meteorological data, historical output data and topographic data of a distributed photovoltaic field station with a meteorological observation capability; based on multi-source data, clustering stations by adopting a dynamic time warping and dynamic self-organizing mapping algorithm, and dividing mountainous climate similar regions; constructing a photovoltaic output prediction model fusing a long short-term memory network and a time convolution network for each region, and generating a preliminary prediction value; on the basis, establishing a conditional error probability distribution model by utilizing a prediction error sequence; and finally, all stations are corrected and predicted by combining the prediction model and the error model, and a cluster output result is output. The method effectively improves the prediction precision and reliability in a mountainous area high-proportion distributed photovoltaic access scene, and is especially suitable for a station lacking local meteorological data.

The invention discloses a large-scale pattern sorting method applied to curved surface laser processing, and relates to the technical field of laser processing. The method comprises the following steps: firstly, importing a to-be-processed curved surface graph igs file into modeling software, extracting a to-be-processed point set by utilizing a macro tool, and storing the to-be-processed point set as a file; then, the file is input into a compiler, a self-organizing mapping neural network SOM algorithm is written for sorting, and the optimal point sorting is output with the shortest total path as the target; then modifying a G code field according to the optimal point order; importing the new G code into processing software to carry out a hand wheel trial cutting test; and finally, importing the error-free G code into software for processing. According to the large-scale pattern sorting method applied to curved surface laser processing, through a unique process and an SOM algorithm, the defect of complex curved surface pattern sorting in the prior art is overcome, the method has the advantages of being not limited by a curved surface topological structure, simple in data interaction and high in solving speed, and the processing efficiency and quality are effectively improved.

The application provides a navigation radar oil spill detection method and system based on BiLSTM and SOM, and relates to the technical field of target detection. The method comprises the following steps: performing pretreatment on original radar image data; extracting the multi-modal texture features of the pretreated image, and generating pseudo labels of the image by using a K-means clustering algorithm; extracting the time sequence features of the pretreated image by using a BiLSTM algorithm; inputting the time sequence features into a trained self-organizing mapping network, and finding a winning neuron for each feature vector; assigning the pseudo labels of the pixels corresponding to each feature vector to the corresponding winning neurons, and then obtaining the classification results of each pixel in the pretreated image; based on the categories of other pixels in the neighborhood window of each pixel in the classification results, iteratively updating the category of each pixel to determine the final classification result of each pixel, and then performing morphological processing to obtain an oil spill image. The application provides a new solution for accurate radar oil spill detection in a complex scene.

The invention discloses a protection system and method for managing field equipment and maintaining a full life cycle. The method comprises the following steps: S1, constructing an equipment digital twin model; s2, generating a DNA fingerprint spectrum of the equipment; s3, evaluating a health state; s4, maintaining a decision; the invention relates to the technical field of equipment management. According to the protection system and method for managing the field equipment and maintaining the full life cycle, through deep coupling of a vibration characteristic spectrum and an energy consumption base line, genetic representation of the health state of the equipment is achieved, a three-dimensional tensor architecture of physical parameters, environmental data and operation logs is adopted, and through second-level time granularity alignment and similarity screening, the health state of the equipment is maintained. Space-time fusion of multi-source heterogeneous data is achieved, dynamic and multi-dimensional health assessment is achieved by calculating the statistical deviation degree of a current DNA vector and a health benchmark, a self-organizing mapping neural network is introduced into equipment health mode classification, more explanatory support is provided for maintenance decision making of equipment, and the health assessment efficiency is improved. And a reliable guarantee is provided for early failure judgment and active maintenance of equipment.

The invention provides a drought multi-dimensional risk zoning method and system based on a clustering algorithm, and the method comprises the steps: obtaining drought index data with a continuous time sequence in a research region, and obtaining a drought index spatio-temporal data set through data quality control; based on the drought index spatio-temporal data set, calculating three drought characteristic indexes of drought duration, drought intensity and drought frequency, and constructing drought multi-dimensional characteristics; inputting the drought multidimensional features into a self-organizing mapping network for preliminary clustering to obtain a neuron weight vector, performing secondary clustering by taking the neuron weight vector as input, and determining an optimal clustering number in combination with a clustering performance evaluation index; and according to a clustering result, sorting, dividing and visually displaying the drought risk levels. According to the method, automatic division of drought risk levels is realized, a visual spatial distribution diagram is generated, and technical support is provided for identifying high-risk areas and formulating differentiated drought control and resource management strategies.

A kind of urban street view information retrieval method, comprising the following steps: S100, street view data acquisition, by Google map API interface, obtain a city full city street view image information and its corresponding geographic latitude and longitude information;S200, street view image visual feature extraction and quantization, the visual feature of street view image is extracted and quantitatively analyzed multidimensionally, constructs comprehensive visual representation system, wherein including: visual entity representation, visual impression representation and visual field structure representation three big categories;S300, the formation of street view data comprehensive training and intelligent search system, the street view visual representation system data set is comprehensively trained using self-organizing mapping neural network algorithm, constructs structured, visual street view feature distribution diagram, forms the core system of intelligent search system;S400, the use of intelligent search system, including street view visual information retrieval, visual display and interactive exploration.Provide intuitive, efficient city street view data query and analysis service.

of the invention SYSTEM AND METHOD FOR PERFORMING UNCERTAINTY ANALYSIS A computer system (200) and method of performing uncertainty analysis for a product design. The method comprises receiving, by a processor (204), one or more design variables associated with an objective function from an input device (212). Further, an unconstrained design space corresponding to the design variables is sampled to generate a set of primary samples. Based on the primary samples, an interpretable Self-Organizing Map (iSOM) is initialized. For each of the primary samples, a plurality of secondary samples is generated. The initialized iSOM is trained based on each of the secondary samples and the objective function value corresponding to the secondary sample. A final iSOM is trained based on variations in weight vectors associated with the trained duplicated iSOMs. Further, a color-coded visualization of uncertainties in each of the design variables and the objective function based on the final iSOM are generated on a graphical user interface (216).

The invention provides a marine radar oil spill detection method and system based on BiLSTM and SOM, and relates to the technical field of target detection. The method comprises the following steps: preprocessing original radar image data; extracting multi-modal texture features of the preprocessed image, and generating a pseudo tag of the image by using a K-means clustering algorithm; using a BiLSTM algorithm to extract time sequence features of the preprocessed image; inputting the time sequence features into a trained self-organizing mapping network, and finding out a winning neuron for each feature vector; allocating a pseudo label of a pixel corresponding to each feature vector to a corresponding winning neuron so as to obtain a classification result of each pixel in the preprocessed image; and on the basis of the categories of other pixels in the neighborhood window of each pixel in the classification result, carrying out iterative updating on the category of each pixel to determine a final classification result of each pixel, and then carrying out morphological processing to obtain an oil spill image. According to the invention, a new solution is provided for accurate detection of radar oil spill in a complex scene.

A system and method for training relational networks. A method includes applying a self-organizing map (SOM) to training data in order to create a visualization. The SOM is a neural network configured to transform relationships between data items. The visualization has a lower dimensionality than the training data. The method also includes training machine learning models of a generative relational network (GRN) based on the visualization, where the GRN includes sets of nodes having respective machine learning models among the machine learning models of the GRN and the sets of nodes include a set of dominance factor nodes and a set of evolution of internal component nodes. The set of dominance factor nodes defines a dominance factor based on change intensity and change frequency, and the set of evolution of internal component nodes defines evolution with respect to changes determined based on values of the dominance factor over time.

A software analysis system collects a first set of time series counter values from an original version of software instrumented with software telemetry counters, and collects a second set of time series counter values from a modified version of the software that also is instrumented with the software telemetry counters. The first time series become dimensions input to train a weight matrix of a first self-organizing map, to cause the first self-organizing map to cluster the time series into a group of first clusters describing execution of the original software. The second time series become dimensions input to train a weight matrix of a second self-organizing map, to cause the second self-organizing map to cluster the time series into a group of second clusters describing execution of the modified software. Deviation analysis between the first and second groups of clusters is used to identify execution differences between the software versions.

The invention relates to the technical field of sewage management, in particular to an artificial intelligence-based industrial sewage treatment management system, which comprises a sewage classification unit, a scene generation unit, a sewage early warning unit, a sewage decision unit and a control optimization unit, the sewage classification unit is used for carrying out water quality monitoring on a sewage treatment process according to an industrial Internet of Things sensor so as to obtain a dynamic monitoring data set; and performing water quality parameter clustering analysis through a self-organizing mapping network according to the dynamic monitoring data set to obtain a water quality state partition map. Water quality data can be monitored in real time based on an industrial Internet of Things sensor, clustering analysis of water quality parameters is carried out in combination with a self-organizing mapping network, a water quality state partition map is constructed, different water quality states of sewage can be accurately classified in the process, a reliable basis is provided for follow-up processing and decision making, and the method is suitable for large-scale popularization and application. Therefore, the precision of water quality monitoring is improved, and the change of the sewage can be timely responded.