845 results about "Feedforward neural network" patented technology

Disclosed are a complex device fault diagnosis method and system based on multi-dimensional features, and the method comprises the following steps of: acquiring fault data of a target complex device, and carrying out input coding mapping and position coding; utilizing a multi-head attention mechanism and multi-head dilated convolution series connection, and acquiring fault features covering global and local deep information in combination with a feedforward neural network; fusing the extracted fault features to acquire a multi-dimensional feature vector; and acquiring a fault type probability score matrix by a classifier, and outputting a fault diagnosis result of the target complex device. The global modeling advantage of multi-head self-attention and the local multi-scale feature perception advantage of multi-head dilated convolution are fused, and a device fault diagnosis model in which a Transform model and the multi-head dilated convolution are mutually fused is established.

A fuel cell fault diagnosis method based on a neural network. The method comprises: for a PEMFC system, acquiring operating state data, wherein the PEMFC system comprises a humidifier and a fuel cell stack; inputting the operating state data into a trained fault pre-diagnosis model, so as to predict a fault diagnosis result of the fuel cell stack at a subsequent moment. The fault pre-diagnosis model is constructed on the basis of a Transformer neural network, and comprises an encoder and a decoder, wherein the encoder comprises a plurality of small encoders, and each small encoder is provided with a self-attention layer and a feedforward neural network layer; and the decoder comprises a plurality of small decoders, and each small decoder comprises a self-attention layer, an attention layer and a feedforward neural network layer. An early warning can be provided for a water management fault of a PEMFC in an operating state, and the accuracy of pre-diagnosis is effectively improved.

The invention discloses an electric power system time sequence prediction model training method, system and device based on large language model weight fine tuning and a storage medium. The method comprises the steps of obtaining time sequence data of different fields and performing data cleaning to obtain a data set; performing weight freezing on a feedforward neural network and multi-head attention of the large language model, and performing first-stage training on the large language model by using the data set; reconstructing an input layer of the large language model to enable the reconstructed input layer to generate a corresponding natural language instruction according to the time sequence data; and freezing weight parameters of the large language model, and performing second-stage training on the large language model by using source load data of the power system to adjust parameters of the reconstructed input layer so as to obtain a time sequence prediction pre-training model. According to the method, the prediction effect can be improved while the weight of the large language model backbone network adapts to the specific task of time sequence prediction.

The invention discloses a process parameter optimization control method and system for an automobile injection molding part, and belongs to the technical field of data processing, and the method comprises the steps: collecting sample data to train a feedforward neural network based on the historical processing record of a target injection molding part, and constructing a product quality prediction plug-in; performing parameter fluctuation influence analysis according to the historical processing record, and constructing a fluctuation influence analyzer; determining product demand characteristics of the target injection molded part in combination with the automobile use scene; and by taking the product demand characteristics as expectations, optimizing the process parameters by using the product quality prediction plug-in and the fluctuation influence analyzer, and outputting the optimal process parameters for processing control. The problems of large product quality fluctuation and low production efficiency caused by insufficient injection molding process parameter control precision in the prior art are solved. By combining historical records, product quality prediction plug-ins and fluctuation analysis, precise optimization of process parameters is realized, the product quality is stabilized, and the controllability and efficiency of the production process are improved.

The invention relates to the technical field of electric power engineering purchase demand prediction, in particular to an electric power engineering purchase demand prediction system based on machine learning, and the system comprises the steps: obtaining historical purchase data, construction progress information and electric power engineering design parameters, carrying out the standard stage division and time alignment, and constructing a stage sequence model reflecting the material use rhythm; and a coupling factor matrix is generated based on the material co-occurrence frequency and the stage position relationship, and the modeling capability of the model for the material cooperation relationship is enhanced. And the stage time sequence features, the coupling information and the structured engineering parameter vectors are fused and input into a regression prediction model, so that accurate mapping of material demands and multi-dimensional engineering features is realized, and the purchase prediction precision in a target period is improved. A deviation sequence is constructed based on historical prediction errors, and error correction is performed through a feedforward neural network, so that prediction accuracy and response capability are effectively improved, and resource waste and construction delay are reduced.

Predicting the salience of one or more data entities to a particular (target) data entity from among a plurality of data entities may comprise generating a graph of the plurality of data entities and a machine-learned model architecture that predicts the salience of the one or more data entities output by the machine-learned model architecture using the graph. For example, the machine-learned model architecture may comprise a first machine-learned model for generating an embedding using the content of the target data entity, a second machine-learned model for generating a vector using the data type indicated by the target data entity, and a third machine-learned model (e.g., a graph neural network or other feed-forward neural network) for generating a contextual representation of the target data entity to which other contextual representations associated with the plurality of data entities may be compared (e.g., using Euclidean distance, cosine similarity, dot product).

The invention discloses a calculation and measurement fused high-fidelity digital twinning dynamic monitoring method for a cantilever beam structure. Firstly, a real-time updated database is constructed based on a physical entity system; secondly, constructing a three-dimensional digital model by using SolidWorks, and performing multi-physics field simulation analysis in combination with Ansys to obtain high-precision mechanism data of a structural key area; thirdly, performing reduced-order calculation on a simulation result by using MWorks, training and optimizing a reduced-order model through a feedforward neural network, and reducing an error between simulation data and reduced-order data; fusing the one-dimensional system data order reduction model and the three-dimensional field data order reduction model based on a Modelica language; and finally, integrating a mechanism-data hybrid model, constructing a dynamic response characteristic and spatial distribution characteristic integrated high-fidelity digital twinborn body, comparing twinborn body calculation data with sensor actual measurement data in real time, and accurately representing geometric morphology, mechanical properties and dynamic behaviors of a physical system. The method is suitable for high-precision monitoring, real-time state evaluation and performance optimization of the cantilever beam structure.

The invention discloses a large language model accelerator architecture based on a three-dimensional NAND flash memory, and belongs to the technical field of calculation, reckoning or counting. The architecture comprises a three-dimensional NAND flash memory used for executing feedforward neural network calculation; the auxiliary calculation unit is used for executing attention mechanism calculation; the DRAM chip is used for storing attention mechanism related weights and KV cache; and the interconnection resource is used for realizing data interaction among the components. Wherein the three-dimensional NAND flash memory comprises a logic chip and an NAND array chip, the logic chip is used for controlling and executing calculation, and the NAND array chip is used for storing weights and participating in calculation. The invention further provides a scheduling method based on KV cache awareness. The scheduling method comprises decomposition and dynamic allocation of calculation tasks. Through collaborative design of the hardware architecture and the scheduling method, the memory wall bottleneck in large language model calculation can be relieved, the energy consumption is reduced, and the overall calculation performance is improved.

The invention discloses a multi-modal large language model training-free acceleration method based on visual token efficient processing, and the method comprises the following steps: obtaining multi-modal question and answer data, and dividing the multi-modal question and answer data into a verification set and a test set; inputting the verification set into the target multi-modal large language model, and sorting the redundancy of each layer of the model through a search method; according to the redundancy ranking, inputting the test set into the target multi-modal large language model, and obtaining the redundancy of each layer of the target multi-modal large language model; and according to the redundancy, carrying out an acceleration operation about visual token processing on a part of layers of the target multi-modal large language model. According to the method, the characteristic that the computing power required by the visual token is less than that required by a text token is utilized, the redundancy layer in the large language model is positioned, and the self-attention operation and the feedforward neural network operation about the visual token are optimized, so that the model reasoning efficiency is greatly improved.

The invention relates to a hybrid network 4D flight path prediction method based on LSTM and Transform, and belongs to the technical field of flight path prediction. Comprising the following steps: inputting preprocessed flight path data into an embedding layer, mapping the preprocessed flight path data into high-dimensional feature representation, and forming an input sequence; inputting the input sequence into the LSTM network to generate an output representation with a time-dependent feature; performing global feature extraction on the output representation of the LSTM network by using a multi-head attention mechanism so as to capture a dependency relationship and relevance between different trajectory data points; performing normalization processing on global features output by the multi-head attention mechanism, and further enhancing feature expression ability through a feedforward neural network; a mask multi-head attention mechanism is adopted in a decoder, and prediction features are output; and mapping the prediction features output by the decoder to a target space through a linear layer, and generating a final result of flight path prediction. The method is excellent in performance in complex flight path prediction tasks.

The invention discloses a remote sensing image segmentation method fusing frequency modulation and spatial perception, and the method comprises the steps: obtaining and preprocessing an original remote sensing image, and generating a standardized input image; the image is input into a multi-scale frequency domain enhanced feature extraction network, features are extracted step by step according to a plurality of feature levels, each level realizes frequency adaptive semantic enhancement through frequency domain modulation transformation and spatial feature fusion, and deep feature expression is enhanced through feedforward neural network modeling and residual connection output and cross-level residual fusion introduction; the final multi-scale features are decoded through a decoding module, the spatial resolution is recovered, and a pixel-level segmentation result is generated; and constructing a composite loss function containing classification errors, boundary perception and frequency consistency items, and carrying out optimization training on the network. According to the method, semantic complementarity of a remote sensing image in a frequency domain and a space domain is fully mined, so that segmentation precision and robustness of a ground object target in a complex scene are improved, and the method has good generalization ability and engineering practicability.

The invention provides a prediction method and device for operation parameters of a virtual power plant, a storage medium and a computer program product. Comprising the steps of performing data preprocessing on acquired influence factor data; using a time sequence decomposition algorithm to decompose the influence factor data into three-dimensional sequences including a trend component, a season component and a residual component, and segmenting each sequence into sequence blocks and mapping the sequence blocks into time sequence feature vectors; performing K-means clustering on word embedding used for pre-training the large language model, and selecting K clustering centers as semantic anchor points to be spliced with the time sequence feature vectors; finely adjusting the position embedding parameters of the large language model, the weight of the feedforward neural network in residual connection and the parameters of the normalization layer; and according to the semantic enhancement time sequence feature vector, predicting to obtain a trend component, a season component and a residual component of each dimension, and carrying out splicing and reverse normalization processing on the components to obtain a prediction result. According to the prediction method, the accuracy and real-time performance of virtual power plant load and electricity price prediction are improved.

The invention discloses an H-bridge key equipment service life and system reliability evaluation method and system for a cascade network construction type energy storage system, and belongs to the technical field of power system automation. The method comprises the following steps: firstly, extracting task profile parameters under multiple time scales, and constructing a time sequence feature model; secondly, estimating a hot spot temperature sequence of the IGBT device and the capacitor based on a multilayer feedforward neural network; then, in combination with a continuous extreme point paired temperature cycle extraction method and a Miner linear cumulative damage criterion, the damage factor and the residual life of the device are evaluated; then, task profile samples are expanded based on a generative adversarial network with gradient penalty, and life distribution and reliability indexes of key devices under different profiles are calculated; and finally, based on H-bridge series structure mapping device level information, constructing a system level reliability model, obtaining system failure rate, average fault-free operation time and a reliability function, and realizing health state perception and reliability quantitative evaluation of the energy storage system.

The invention belongs to the technical field of biological feature recognition, and relates to a Transform-based gene data mining method, which comprises the following steps of: loading gene expression data from a file, and preprocessing; inputting the data into a designed model for training and evaluation; the designed model comprises a CNN (Convolutional Neural Network) and an improved Transform model; the improved Transform model comprises an encoder and a classifier; the encoder adopts a multi-head self-attention mechanism, a convolutional self-attention mechanism and a feedforward neural network; processing the data by using the trained model, and storing the fusion attention weight obtained by the coding layer in the data processing process; and finally, averaging the fusion attention weights of all batches, calculating the comprehensive attention weight of each gene at different time points, and identifying the most important gene by calculating and analyzing the attention score of each gene. According to the method, the problems of scarcity and imbalance of gene data can be solved, the risk of overfitting is reduced, and the precision of gene data analysis is improved.

The embodiment of the invention discloses an inference method and device for a large language model, which are used for reducing bandwidth consumption of inference of the large language model. The method comprises the steps that a parallel reasoning system receives reasoning tasks of a large language model, wherein the reasoning tasks comprise a calculation-intensive task and a self-attention calculation task; the processor executes calculation-intensive tasks, generates intermediate data and sends the intermediate data to the near-memory calculation module, and the calculation-intensive tasks comprise one or more of the following tasks of the large language model: a feedforward neural network calculation task, a projection task and a layer normalization task. And the near memory calculation module executes a self-attention calculation task according to the intermediate data to generate a near memory calculation result. And the processor generates a reasoning result corresponding to the reasoning task based on the proximity calculation result.

The invention relates to a machine tool spindle thermal deformation prediction method based on a GCN-Transform model. The method comprises the following steps: constructing a sensor network for a machine tool; a space-time diagram data set is generated in the three-dimensional model of the machine tool, and the spatial characteristics of the sensor network are represented through an adjacent matrix; constructing a GCN-Transform model, wherein the model comprises an input layer, a one-dimensional convolution component, a multi-head self-attention mechanism, a feedforward neural network layer, an addition and normalization layer and an output layer; performing prediction, extracting spatial features through GCN, extracting time features through Transform, an adjacent matrix and sensor data as input, and performing thermal error; according to the method, the time characteristics are captured by using the Transform model, and then the spatial characteristics in the topological space of the sensor are captured by using the GCN, so that the time characteristics of the thermal error can be accurately captured, and the spatial information of the sensor can be fully utilized, so that the prediction result is more accurate and robust.

The invention discloses an antibacterial peptide recognition method and system based on a sequence-structure dual-channel neural network, and the method comprises the steps: splicing amino acid features and amino acid-level manual features extracted by ProtT5 to obtain peptide embedding, and transmitting the peptide embedding to a sequence channel composed of a plurality of Transform blocks to extract the sequence features of the peptide; predicting a three-dimensional structure of the peptide by using ESM-Fold to construct an adjacency graph, taking amino acid features obtained by ESM-2 as node features of the adjacency graph, and performing layer-by-layer extraction and enhancement by fusing structural channels of multi-head graph attention, a residual network, layer normalization and a feedforward neural network; and carrying out maximum pooling and splicing on the sequence features and the structural features, and then, carrying out antibacterial peptide prediction. According to the method, a multi-feature fusion strategy is adopted, meanwhile, the three-dimensional structure information of the antibacterial peptide is introduced, and the sequence and the structural features are fused through a two-channel architecture, so that the recognition accuracy of the antibacterial peptide is effectively improved.

Aspects of the disclosure include methods for leveraging retrieval augmented generation (RAG) over a graph neural network (GNN) for edge building and the generation of reason-aware graph recommendations. A method can include constructing a graph neural network from an input graph having a plurality of nodes and one or more edges. The graph neural network includes one or more internal layers, each internal layer having one or more node vectors encoding a K-hop neighborhood for a target node of the plurality of nodes. RAG data including non-graph contextual data is retrieved for each of the plurality of nodes and transformed into embeddings using a large language model encoder. The RAG embeddings are encoded into node vectors of the graph neural network. The graph neural network generates a representation for the target node that is transformed by a feed forward neural network tower into an output vector.

The invention discloses a method for correcting a laser gas concentration sensor, which is used for measuring gas concentration by using laser with different wavelengths and correcting by combining environmental parameters. The method comprises the following steps: extracting laser absorption characteristics by using a first feedforward neural network, extracting environmental matrix characteristics by using a convolutional neural network, fusing the characteristics through an attention mechanism, and predicting the gas concentration through a second feedforward neural network. The loss function not only considers the difference between the predicted value and the true value, but also comprises the physical constraint between the light intensity value and the theoretical light intensity value. According to the method, the measurement accuracy and the model robustness under different environmental conditions are improved, and the influence of measurement errors and environmental fluctuation is reduced through the combination of multi-wavelength measurement and physical knowledge.

The invention discloses a fumigation treatment multi-parameter comprehensive monitoring system which comprises a multi-parameter sensor monitoring module, a multi-modal data processing and analyzing module, a system intelligent monitoring and adjusting module, a fumigation environment control module, a remote maintenance and management module and a system interaction and feedback module. The multi-parameter sensor monitoring module is used for collecting parameter data. The multi-modal data processing and analyzing module is used for fusing and analyzing data. The system intelligent monitoring and adjusting module is used for intelligently monitoring and adjusting fumigation strategies. The fumigation environment control module is used for adjusting environment parameters. The remote maintenance and management module is used for system maintenance and management. And the system interaction and feedback module is used for display interaction of data and a control state. According to the fumigation treatment multi-parameter comprehensive monitoring system, a multi-modal data fusion algorithm based on information channel decomposition is provided for multi-parameter data fusion and analysis; and a multi-parameter intelligent monitoring algorithm based on a multi-layer feedforward neural network is provided to intelligently monitor the fumigation environment.

The invention belongs to the technical field of target detection, and discloses an unmanned aerial vehicle image target detection method based on an improved RTDETR model, and the method introduces an FAPPA module in a backbone network, and effectively alleviates the information loss problem of small target features in a deep network. According to the method, a feedforward neural network of an AIFI module in an encoder is replaced by an EDFFN module, and high-frequency features such as edges and textures are selectively enhanced by using a patchwise FFT strategy and learnable frequency domain weights. Besides, according to the method disclosed by the invention, adaptive alignment of multi-scale features is realized by utilizing a Zoomcat module, and local details and global semantics are fused by utilizing a convolution attention double-path architecture of a CAFMFusion module, so that complementary information of different levels is fully utilized. According to the method, the unmanned aerial vehicle image target detection capability can be remarkably improved, and small target features in a complex background can be identified.

The invention discloses an improved tissue ultrasonic image segmentation system, and the system comprises an improved image segmentation network model FSwinNet which achieves the efficient segmentation of an image in a resource-constrained environment. The image segmentation network model comprises one or more encoder modules, one or more decoder modules and one or more bottleneck blocks; the Swin Transformer of the encoder module uses an SFFN (Sandwich Forward Neural Network) sand clock feedforward neural network module, and the decoder module uses an MBDW (Multimedia Broadcast Downlink Weight) lightweight operator. The invention further discloses a method for carrying out tissue ultrasound image lightweight segmentation by using the image segmentation system, and the method has a wide application prospect.

The invention discloses an energy storage rapid compensation method and system based on track overhead line system voltage fluctuation feature recognition, and relates to the technical field of track traffic traction power supply. The method comprises the following steps: synchronously acquiring voltage and current signals of the overhead line system, and extracting a composite feature vector containing disturbance root attributes and transient change rate; on the basis of the vector, a transient energy vacancy sequence covering the ultra-short-term future is output in real time through a prediction model fusing dynamic phasor analysis and a feedforward neural network; according to the sequence, a cooperative control strategy fusing overshoot and active damping is generated in combination with a proximity compensation principle and a voltage recovery state so as to drive an along-line energy storage unit; the strategy is executed in advance, compensation energy is injected, and finally double closed-loop correction is conducted based on the voltage residual error. According to the method, the problems of response lag, unclear disturbance identification, poor collaboration and lack of adaptive ability in the prior art are solved, and advanced, accurate, collaborative and self-optimized rapid suppression of the voltage fluctuation of the overhead line system is realized.

The invention discloses a knowledge graph retrieval intelligent power storage system based on a feedforward neural network, and relates to the field of power equipment accessory management. The method comprises the following steps: constructing a power equipment resource classification model and a meteorological disaster risk periodic evaluation model in a coverage storage range; obtaining static data and dynamic data of different power devices; predicting the replacement demand level of the equipment through the feedforward neural network model; a model prediction evaluation result is used as a data source of the knowledge graph warehousing system, and terminal real-time fault prediction evaluation query, periodic equipment purchase guidance and backlog warning are realized. According to the invention, the storage demand can be intelligently predicted according to the actual state of the power equipment, the accuracy and response speed of storage management are improved, and an efficient spare part support scheme is provided for power network maintenance and equipment storage.

The invention discloses a multi-context semantic recognition and understanding method based on a large language model. The method comprises the following steps: S1, generating a semantic unit sequence; s2, constructing a context nested vector sequence; s3, constructing context priori representation, and generating a semantic representation sequence; s4, outputting a state vector of the semantic path by adopting a gating loop unit, obtaining a matching degree score according to a feedforward neural network, and determining a deliberate map tag and an alternative intention tag; s5, slot field extraction and semantic filling are completed, and a structured semantic task unit is generated; and S6, completing semantic recognition and service response closed loop. According to the method, by introducing a prefix regulation and control mechanism and a multi-context semantic modeling structure, the accuracy of intention recognition in multiple rounds of dialogues and the consistency of the context generated in response are remarkably improved, and the method is suitable for natural language understanding scenes of multi-language intelligent customer service, cross-context man-machine interaction and complex task driving.

The invention provides an electric power insulator defect detection method and system based on deep learning, and belongs to the technical field of electric power equipment insulation control detection. The method comprises the following steps: synchronously acquiring an original visible light image, an original infrared thermal imaging image and original ultrasonic data of a target electric power insulator; after preprocessing, data enhancement is carried out, then standard data is transmitted to a feature extraction module, and visible light features, infrared features and ultrasonic features are extracted; performing feature splicing, mapping to a shared space, inputting into a multi-head attention mechanism, generating a combined parallel result, and based on the combined parallel result, generating a fusion feature vector from the visible light feature, the infrared feature and the ultrasonic feature through a feedforward neural network; and outputting the defect type of the target power insulator through an output layer of the cross-modal image matching network. The method provided by the invention has higher precision and stronger robustness.

The invention relates to the technical field of multi-modal sentiment analysis, in particular to a multi-modal sentiment analysis method and system based on pre-training and online learning. The method comprises the following steps: acquiring sentiment analysis multi-modal data, constructing a sentiment analysis model, namely constructing a multi-head attention mechanism to respectively encode text, image and audio modals, and splicing and fusing each encoded modal feature; mining an interaction relationship among all modes in the fusion features by using a feedforward neural network and a multi-head attention mechanism; evaluating the contribution degree of each mode in the joint features to the sentiment analysis task by constructing a full-connection neural network; according to the sentiment analysis method based on the multi-head attention mechanism, the multi-head attention mechanism is constructed to carry out coding processing on each mode, the coded mode features are spliced and fused, and therefore the sentiment analysis accuracy is remarkably improved. The multi-head attention mechanism based sentiment analysis method based on the multi-head attention mechanism has the advantages that the multi-head attention mechanism is constructed to carry out coding processing on each mode, and the coded mode features are spliced and fused.

The invention discloses a medical science popularization question and answer model fine tuning method, device, equipment, medium and product, and relates to the field of large language model fine tuning, the method comprises the following steps: cleaning data in a medical text question and answer data set, determining a cleaned instruction-input-output normal form data set, and using the data set as a fine tuning training data set; in a pre-trained large language model, positioning a QKV projection matrix in a multi-head self-attention mechanism and a weight matrix in a feedforward neural network; two low-rank matrixes are introduced into each matrix, the pre-trained large language model is finely tuned, and the finely tuned large language model is determined; the fine-tuned large language model is used for generating an answer according to question data in the process of using interaction between the user and the fine-tuned large language model, the large language model is finely tuned, the performance of processing a specific task is improved, the calculation cost is reduced, the grasp ability of the model for specific details is enhanced, and the user experience is improved. And the requirements of specific user groups or application scenes are met.

The invention provides an electro-hydraulic servo pump control method and system for controlling hydrogen compression based on fuzzy nerve, and relates to the technical field of hydrogen compression, and the method comprises the steps that the current state of a hydraulic cylinder controlled by an electro-hydraulic servo pump is determined, and an input control variable is obtained; determining an output control variable, establishing a fuzzy control rule, and dividing a basic discourse domain; performing composite defuzzification, and obtaining a corrected weight coefficient through a feedforward neural network; the discrete rule amplitude correction factor and the weight coefficient correction are dynamically optimized and corrected; neural network training is established, a loss function evaluation model is utilized, and hydrogen compression control is achieved through a double-buffering mechanism. Fuzzy PID control is adopted, composite defuzzification is carried out according to the real-time state change of a system in combination with fuzzy logic, a membership function output by the fuzzy PID is divided according to regions, a control signal of a driver is synthesized, and hydrogen compression control is achieved; system output is adjusted according to different working conditions, power loss is reduced, and system control precision is improved.

The invention relates to a method for generating relational data by means of a knowledge graph, and belongs to the technical field of artificial intelligence. According to the method, semantic association and importance among nodes can be effectively captured by using a Graph Transform and utilizing a multi-head attention mechanism and neighborhood aggregation of a feedforward neural network, so that the characteristics of an original structure are reserved in a newly generated map, and the structure of the newly generated map and the structure of the original map are kept consistent; data enhancement is carried out by using a large language model (LLM) in combination with a prompt project, an enhancement strategy is applied in context and entity levels, and it is ensured that the content of an enhanced text is consistent with reality logic through a replacement strategy while an original triple structure is kept. And the pre-training model is finely adjusted to adapt to entity recognition and relation extraction tasks, so that the reliability and the practical application value of the generated content are further improved.