108 results about "Neuron network" patented technology

The invention provides an intelligent material damage diagnosis method and system based on magnetic characteristic data fusion, and relates to the technical field of nondestructive testing, and the method comprises the following steps: collecting magnetic characteristic data by using a magnetic stress detection robot; performing multi-scale decomposition on the data to construct a magnetic domain evolution characteristic spectrum; inputting the characteristic spectrum into a composite neural network to extract a material damage characteristic matrix; constructing a fusion feature vector by using tensor dimensionality reduction and a multilayer belief transfer network; and realizing damage calculation through temperature field modulation and self-adaptive weight distribution to obtain the damage grade and the residual life. According to the invention, accurate diagnosis of the material damage in the welding seam area of the pressure vessel is realized.

The invention particularly relates to an underwater vehicle multi-energy hybrid energy storage system and a coordinated energy management strategy. The system comprises a lithium ion battery, a super capacitor and a fuel cell applying a proton exchange membrane which are respectively connected to a direct current bus. The method for coordinating the energy management strategy comprises the following steps: acquiring a current motion parameter and a current energy parameter of an underwater vehicle; inputting the current motion parameter, the current energy parameter and the expected navigational speed into a load prediction model based on a BP neural network, and estimating the expected total power at the next moment; and determining a corresponding action strategy according to the current operation mode of the underwater vehicle, and performing power distribution on the expected total power based on the action strategy. According to the scheme, the hybrid energy system can perform smooth working mode switching and optimal power distribution under different working conditions.

The invention relates to the technical field of unmanned vehicle management, in particular to an unmanned vehicle intelligent monitoring management system, which accurately detects electronic activities in a battery pack through a quantum tunneling effect electric quantity sensor, generates quantum signals, obtains multi-dimensional electric quantity data through processing, and provides a basis for energy management. Tensor analysis and a convolutional neural network are used to carry out deep mining on the multi-dimensional signal data, an energy flow state is monitored in real time, and an abnormal energy flow mode is identified; in combination with an energy flow analysis result, a charging scheduling scheme is generated and optimized by using chaotic mapping and a multi-objective optimization algorithm, and the charging efficiency, the vehicle waiting time and the power grid load are balanced; a charging adaptation model based on a biological neural network is constructed, intelligent charging adaptation decision making and charging equipment operation control are realized through reinforcement learning algorithm training, and the charging flexibility and efficiency are improved.

The invention discloses a novel power system primary frequency modulation optimization method based on deep learning, and relates to the technical field of power optimization. The method comprises the following steps: collecting state data of a hydroelectric generating set running in a power grid, constructing a three-dimensional feature tensor, and extracting spatio-temporal features through a TCN-GRU hybrid network; constructing a deep reinforcement learning decision layer, and defining an action space and a reward function; constructing a PID neural network with a time-varying forgetting factor, and outputting a dynamic weight; establishing a dual-time scale updating mechanism; a safety verification module is arranged, and when the system frequency deviation value is larger than a threshold value, the traditional PID mode is switched. Characteristics are extracted from state data of operation of a hydroelectric generating set in a power grid, the time sequence dependency relation of the characteristics is processed through deep learning, the frequency change trend of the power grid is captured, the optimal control strategy is explored by applying the DRL technology, PID controller parameters are adjusted in a dynamic environment, and the optimal control strategy is obtained. And the primary frequency modulation response speed and the control precision of the hydroelectric generating set during power grid frequency fluctuation are improved through self-adaptive control.

A system and method for real-time time series forecasting using a compound large codeword model with integrated supervisory neurons. The system processes diverse inputs through adaptive codebook generation and codeword allocation. A projection network fuses different data types for a latent transformer-based machine learning core. A hierarchical supervisory network, comprising low-level, mid-level, and high-level nodes, monitors local neural network regions, performing real-time statistical analysis and implementing structural modifications. The system efficiently handles multi-modal data, capturing complex relationships between input types. An adaptive codebook generation method, coupled with the supervisory architecture, ensures responsiveness to evolving data patterns and task requirements. This approach provides accurate and timely forecasts by leveraging diverse data types in a sophisticated, integrated manner, while continuously adapting its structure during operation to maintain optimal performance.

The invention discloses a method and device for automatic roasting and process intelligent control of a belt type roasting machine, and relates to the technical field of pellet roasting of the belt type roasting machine in the metallurgical iron and steel industry. The method comprises the four steps of historical data slice acquisition and modeling, target process parameter determination, feedback verification and online correction and rolling prediction control, a model historical library is established through service slices and time slices, modeling is performed based on a three-layer feed-forward neural network, flexible weighted deviation calculation combined with dynamic weight and rolling optimization of a softening coefficient are combined, and the rolling prediction control is performed. And accurate regulation and control of equipment parameters are realized. The automatic control system is mainly used for automatically controlling the pellet production process of the belt type roasting machine, the product quality stability is improved, the energy consumption is reduced, the labor intensity of workers is relieved, and support is provided for unmanned and intelligent production.

Disclosed is a method for determining a vehicle clutch temperature of a vehicle clutch by a neuron network. The method includes determining at least one input value representing a power supplied to the vehicle clutch, such that the at least one input value is determined on the basis of processing consecutive values of power supplied to the vehicle clutch. At least one input value and at least one value of an operating parameter are input as input data into the neuron network. A clutch temperature is determined by the neuron network on the basis of the input data and of a relationship learned by the neuron network between a time variation of the input data and the clutch temperature. A method is also disclosed for generating a training data set for a neuron network, and a control unit for determining a vehicle clutch temperature using a neuron network.

The invention discloses a wireless communication network intelligent optimization method and system based on neuron collaboration, and relates to the technical field of wireless communication. The method comprises the following steps: mapping a communication node into a bottom layer sensing neuron and constructing a neural state variable set; constructing a node neural situation function based on the variables; when the local threshold value is exceeded, excitation pulses are generated and uploaded to middle-layer convergence neurons; the middle layer carries out pulse space aggregation and extreme value search, and outputs a selection strategy and an adjustment strategy; and reporting to a top layer to execute whole network neural synchronization index analysis, and optimizing the wireless communication network. The technical problems of low spectrum resource utilization rate and unstable network performance caused by the fact that a traditional wireless communication network cannot realize high-efficiency spectrum allocation and dynamic topology reconstruction under user mobility change are solved, and the purposes of realizing local quick response and global collaborative optimization by constructing a layered neural network and improving the network performance are achieved. And the spectrum resource utilization rate is improved, and the network dynamic adaptive capability is enhanced, so that the user service quality is guaranteed.

The invention discloses a multi-parameter micro-nano sensing chip for detecting a neural network. The multi-parameter micro-nano sensing chip comprises an ECIS interdigital electrode, an MEA multi-channel planar electrode, an electrochemical SWNT film array and a microfluidic structure. Three-dimensional heterogeneous integration is achieved through vertical stacking and partition cooperation, an ECIS interdigital electrode and an MEA chip share a plane base, an electrode layer is isolated through a nanoscale insulating layer, electrical signal crosstalk is avoided, meanwhile, the space of the chip is utilized to the maximum extent, meanwhile, a micro-fluidic cavity is divided into three corresponding detection areas, and the detection accuracy of the micro-fluidic cavity is improved. Therefore, signal detection of the in-vitro neural network in different states can be completed. According to the invention, multi-mode signal detection can be carried out on the neural network cultured by the same iPSCs neuron, and the ECIS interdigital electrode, the MEA multi-channel planar electrode and the electrochemical SWNT thin film array are combined together by using the micro-fluidic chip, so that the problems of single function, data splitting, insufficient sensitivity, difficulty in long-term stability and the like of the traditional neural chip are solved.

The embodiment of the invention discloses a business scene digital transformation method and device, equipment and a storage medium, and relates to the technical field of business scene digital transformation information. According to the scheme, digital plans of business scenes with different granularities are aggregated, features and resources of the business scenes are fused, multi-modal data are associated, key features are extracted, and the semantic comprehension ability of the digital plans and a large language model is intelligently matched; the neural network of the service scene is calculated according to the service scene attribution, the associated data and the positioning information, and the disposal scheme corresponding to the service scene is generated, so that subjective deviation of a traditional manual compiling solution is avoided, sudden working conditions and various service scenes are flexibly adapted, and the adaptability of complex scenes is enhanced.

The invention is suitable for the technical field of energy consumption control, and provides an energy consumption prediction and energy-saving control method adopting a deep neural network. According to the method, virtual training data is generated by constructing a machine room digital twinborn model including a three-dimensional geometric model and a CFD simulation model, and the electric energy use efficiency and the overheating condition are predicted in combination with a deep neural network optimized by an attention mechanism. And the operation strategy system generates an optimal control suggestion by predicting the energy-saving effect and the safety risk after the air conditioner is turned off or the temperature is increased. The visual interface integrates real-time parameters, historical data and three-dimensional airflow organization dynamic display, and assists an administrator in decision making. The method breaks through the limitation of real data, improves the prediction precision, reduces the PUE on the premise of ensuring that the operation number of air conditioners is greater than or equal to 3, and achieves the efficient and energy-saving management of a machine room. Experiments show that the PUE can be reduced by about 4.3%, and the cabinet overheating probability can be reduced by 60%.

The invention relates to the technical field of power electronics, and discloses a dynamic regulation and control method and system for an active clamping three-level converter adapting to working condition changes, and the method comprises the steps: calculating the loss sample data of each power device in different modulation modes through obtaining the historical data of characteristic parameters under different working conditions; calculating the junction temperature of the power device based on the loss in the hybrid modulation mode; with the minimum junction temperature distribution imbalance degree as a target, calculating a hybrid modulation coefficient, and constructing a neural network model; after operation parameters of a power device are collected in real time, the optimal modulation coefficient is obtained based on the model, the modulation mode of the converter is dynamically adjusted, and loss distribution is optimized. According to the method, the loss distribution characteristic and efficiency of the converter under all working conditions can be improved, and the loss balance performance is optimized.

The invention discloses an intelligent detection navigation system and method for an inland ship. The method comprises the following steps: step 1, acquiring multi-modal sensing data in the navigation process of the inland ship; 2, constructing an initial cognitive map; step 3, carrying out dynamic modeling on each semantic state node by adopting an improved liquid neural network, embedding symbolic logic constraints in state transition, and generating a navigation control bias steering quantity; step 4, calculating a tension value of a causal directed edge and executing a structure evolution operation to generate a self-evolution cognitive map; 5, constructing a candidate path set; step 6, calculating an atlas winding degree index of each candidate path; 7, constructing a path decision vector, executing a path backstepping subduction operation, and screening to obtain a target optimal path; and 8, converting the target optimal path into a navigation control instruction, and outputting the navigation control instruction to a ship control system. According to the invention, cognitive map modeling and neural network reasoning are fused, and intelligent navigation control of inland ships is realized.

The present invention discloses a microfluidic bio-interconnected neural network chip and a preparation method thereof. The chip consists of a microelectrode array, a microfluidic structure, and a cell culture ring structure. The microelectrode array can detect and stimulate the activity of neurons; the microfluidic structure limits the growth direction of axons between cell culture partitions by setting cell culture partitions, microfluidic channel dimensions, microfluidic channel shapes, and fillet distribution, thereby achieving controllable low-power transmission of neuronal electrical information; the cell culture ring structure provides nutrients for cells in the cell culture chamber. The present invention provides a method for manufacturing a microfluidic bio-interconnected neural network chip, using fully exposed positive photoresist as a sacrificial layer for microfluidic structure peeling, thereby solving the problem of difficult demolding of thin-film microfluidic structures. The chip can be used to explore the response characteristics of neuronal networks to external stimuli, study the internal information transmission mode of neuronal networks, and other fields.

The present invention provides a method and system for intelligent diagnosis of material damage by magnetic characteristic data fusion, which relates to the field of nondestructive testing technology. The method includes using a magnetic stress detection robot to collect magnetic characteristic data; performing multi-scale decomposition on the data to construct a magnetic domain evolution feature spectrum; inputting the feature spectrum into a composite neuron network to extract a material damage feature matrix; constructing a fusion feature vector by using tensor dimensionality reduction and a multi-layer belief transfer network; and realizing damage calculation through temperature field modulation and adaptive weight allocation to obtain the damage level and remaining life. The present invention realizes the accurate diagnosis of material damage in the weld area of a pressure vessel.

The invention discloses a substation monitoring and early warning method based on a neural network and a related device. The method comprises the following steps: predicting predicted values of key parameters of a substation in a future period of time based on the neural network; comparing the predicted value with an actual measured value, and when the deviation between the predicted value and the actual measured value is greater than or equal to a preset deviation, triggering and sending out a preliminary abnormal prompt; when the preliminary abnormal prompt is received, the preset value is compared with a preset safe operation domain of the preset value, when the preset safe operation domain is exceeded, an early warning signal is generated, and the method and the related device can improve the reliability, the economical efficiency and the intelligent level of operation of the transformer substation.

The invention discloses a BIM (Building Information Modeling) and Internet of Things integrated multi-parameter collaborative intelligent dosing system and device, and relates to the technical field of sewage treatment intellectualization, and the system comprises a BIM modeling unit, a data acquisition unit, a multi-parameter collaborative analysis unit, a dosing execution unit and a water quality analysis unit; according to the method, a 3D building information model is exported through a Revit interface to obtain a water treatment BIM model, a unique identifier is allocated to each component in the water treatment BIM model, a water quality prediction model is constructed based on a neural network algorithm, and the water quality prediction model is constructed by combining pipeline layout and equipment position information in the water treatment BIM model, dynamically calculating the dosage demand and adopting a fuzzy PID control strategy. The frequency of a dosing pump is automatically adjusted according to the deviation between real-time water quality parameters and target values, the dosing pump, a disinfectant preparation device and a proportioning system are driven through a PLC, a dosing adjustment instruction is executed, component state labels are updated, historical component state labels are obtained, water quality state differences are evaluated according to component state difference values, and a water quality evaluation report is generated.

To solve the problem that the defect information of the weld in the weld digital image generated by traditional DR ray detection is manually confirmed, resulting in low weld evaluation efficiency and high quality risk, an embodiment of the present invention provides a method and system for defect annotation of weld digital images based on deep learning, including: training a convolutional neural network with a data set to obtain a weld defect annotation model; wherein, the data set includes a training set; the training set includes a number of data units; each data unit includes a weld digital image and a defect annotation file generated by defect annotation of the weld digital image; using the weld defect annotation model to perform defect annotation on the weld digital image to be measured.

The invention relates to a method for evaluating the filling state of crystallizer casting powder liquid slag, and belongs to the technical field of continuous casting methods in the metallurgical industry. According to the technical scheme, the method comprises the steps that thermocouple temperatures at all positions of a crystallizer copper plate, vibration strokes on the two sides of a crystallizer vibration unit and hydraulic cylinder pressure are collected on line, and data are marked; and respectively establishing and training an MASKRCNN neural network and an LSTM neural network, and carrying out online evaluation on whether filling of the casting powder liquid slag is normal or not and an abnormal position by combining prediction results of the MASKRCNN neural network and the LSTM neural network. According to the method, heat and force related data of the crystallizer are collected online, an artificial intelligence model method is adopted, online evaluation of the filling state of the casting powder liquid slag is achieved in real time, dynamic tracking evaluation can be conducted on the whole using process of the casting powder, and the result better conforms to actual production; the position of a liquid slag filling abnormal state area can be accurately provided, and operators can conveniently check and confirm abnormal conditions.

The invention relates to an unmanned aerial vehicle intelligent path planning method and system based on visual signals, and belongs to the field of path planning. The method comprises the following steps: optimizing a neural network through progressive pruning; fuzzy coordinates and scene types are provided for the unmanned aerial vehicle, the unmanned aerial vehicle flies to a fuzzy coordinate area, and when the unmanned aerial vehicle is close to the fuzzy coordinate area, the unmanned aerial vehicle is switched to a pure vision mode to perform environment perception through a neural network, and a three-dimensional semantic map is constructed; performing real-time path planning based on the three-dimensional semantic map to obtain a flight path; the method comprises the following steps: performing autonomous flight along a flight path, when approaching an operation target, performing autonomous operation to obtain real-time monitoring data, storing the real-time monitoring data in airborne storage equipment, and returning the real-time monitoring data after returning. And an unmanned aerial vehicle intelligent path planning system with high robustness, high autonomy and high task adaptability is constructed.

The application discloses a kind of isomorphic neuron system for generating hidden coexisting attractor, it is related to neuromorphic computing and nonlinear circuit field, the system includes: memristor, first neuron network and second neuron network;The structure of the first neuron network and the second neuron network is identical;Synaptic connection is carried out between the first neuron network and the second neuron network by the memristor, to simulate the chaotic dynamic behavior of biological neuron, to generate hidden coexisting attractor.The application can accurately simulate the synaptic transmission characteristics of biological neuron, improve the authenticity of bionics, successfully reproduce the chaotic dynamic behavior of biological neuron, efficiently generate hidden coexisting attractor, and enrich the dynamic performance of neuron system.

The invention relates to a gamma photon positioning method and device for an orthogonal strip-shaped tellurium-zinc-cadmium detector. The method comprises the following steps: acquiring current signals respectively generated by interaction of a plurality of gamma rays and the orthogonal strip-shaped tellurium-zinc-cadmium detector; vectorizing the plurality of current signals to obtain an input vector set; an initialized neural network is constructed based on the positions of a plurality of electrode strips in the orthogonal strip-shaped cadmium zinc telluride detector, and the number of initialized neurons is the same as that of the plurality of electrode strips; performing weight vectorization processing on the plurality of initialized neurons to construct an initialized weight vector set; and based on the input vector set and the initialized weight vector set, iteratively reducing the range of the initialized weight vector set through a self-organizing mapping algorithm to obtain a target weight vector, and determining the position of the gamma photon. The purpose of determining the position of the gamma photon based on the neural network and the self-organizing mapping algorithm is achieved, and therefore the technical effects of improving the accuracy of the position of the gamma photon and improving the position obtaining efficiency are achieved.

A method is provided for correcting a reconstruction artefact of a three-dimensional tomographic image. The method includes the steps of providing an acquired three-dimensional tomographic image from a cell group, and applying the image to a neural network trained in advance to determine a corrected tomographic image.

The present application relates to a kind of bottle neck edge shrink model, neuron network and its construction method, bottle neck edge shrink model is arranged in bottle neck edge shrink module, the bottle neck edge shrink model at least includes two 1*1 convolution layer and a layer m*m convolution layer, wherein m*m convolution layer is arranged between two 1*1 convolution layer, the edge shrink method of the bottle neck edge shrink module includes: G (x) = c_2 (sigma (conv (sigma (c_1 (x))))) ) + r (x) ;Wherein, conv (X) indicates that m*m convolution transformation is carried out to image matrix x without padding, and m>1;C_1 (x) and c_2 (x) indicate that 1*1 convolution transformation is carried out to image matrix x, sigma (x) indicates that nonlinear transformation is carried out to image matrix x;R (x) indicates that edge is shrunk to image matrix x.The bottle neck edge shrink model of the present application reduces the channel number of image matrix by 1×1 convolution layer c_1, so that the parameter number and the amount of calculation of 3×3 convolution kernel are greatly reduced, so that the parameter number of bottle neck edge shrink module is less than the corresponding basic edge shrink module, while the corresponding network depth is increased by 50%, by reducing the parameter number and improving the stability of entire neuron network.

The invention discloses a self-supervised medical image segmentation method and system based on quantum computing. Accurate segmentation of a focus in a medical image is realized by designing a three-layer qutr-it neural network structure. According to the method, a quantum tri-state system is adopted as a basic calculation unit, feature transfer and aggregation are realized by using a dual quantum gate operation scheme, and the dual quantum gate operation scheme comprises a T transformation gate for feature mapping and a phase Hadamard gate for weight mapping. According to the method, a quantum fuzzy level concept and a qutr-it-based adaptive multi-class quantum Sigmoi d activation function are innovatively introduced, and a quantum state judgment mechanism based on imaginary part measurement is designed. According to the method, the dependence on the annotated data is remarkably reduced, the feature extraction capability and the segmentation precision are improved, the problem of data scarcity in medical image segmentation can be effectively solved, and a new technical scheme is provided for computer-aided diagnosis.

The invention discloses a distributed building heating parameter intelligent optimization method and system, and the method comprises the steps: carrying out the multi-scale feature extraction of a heating region through an improved SSD algorithm, building a population distribution dynamic data sequence through a heating time sequence and a population optimization model, collecting heating system parameters, and carrying out the normalization processing, and the data are input into an optimization decision model, an optimization combination scheme is calculated and output through a multi-layer neural network, heating parameters are adjusted in real time according to the optimization combination scheme, and operation data are fed back to an iterative optimization model in a set period. The system comprises a data acquisition unit, a feature extraction unit, a time sequence and population analysis unit, an optimization decision unit, a parameter adjustment unit and a feedback updating unit which work cooperatively. According to the method and system, environment and population dynamic changes can be fully considered, parameter association is deeply excavated, intelligent optimization of heating parameters is achieved, and the heating effect and the energy utilization efficiency are improved.

The invention discloses an isomorphic neuron system for generating hidden coexisting attractors, and relates to the field of neuromorphic calculation and nonlinear circuits, and the system comprises a memristor, a first neuron network and a second neuron network. The structures of the first neural network and the second neural network are the same; the first neuron network and the second neuron network are in synaptic connection through the memristor so as to simulate chaotic dynamic behaviors of biological neurons and generate hidden coexisting attractors. According to the invention, synaptic transfer characteristics of the biological neurons can be accurately simulated, bionic authenticity is improved, chaotic dynamic behaviors of the biological neurons are successfully reproduced, hidden coexisting attractors are efficiently generated, and dynamic performance of a neuron system is enriched.

The invention provides an output voltage estimation system and method for a dielectric barrier discharge pulse resonant converter. The system comprises a pulse resonant conversion module, a data processing module, a dielectric barrier discharge load output voltage prediction neural network model and an upper computer. Wherein the pulse resonant conversion module is used for converting a direct-current bus voltage into a high-frequency high-voltage pulse voltage and providing the high-frequency high-voltage pulse voltage to the dielectric barrier discharge load; the data processing module is used for collecting the input voltage of the pulse resonant transformation module and transmitting the input voltage to the dielectric barrier discharge load output voltage prediction neural network model; the output voltage prediction neural network model of the dielectric barrier discharge load estimates the peak output voltage of the dielectric barrier discharge load by taking the acquired input voltage and the current control switching frequency as input characteristics; and the upper computer is used for setting parameters and receiving and displaying the estimated value of the peak output voltage.

The invention provides a mouse neuron network construction method and system based on two-photon imaging, and the method comprises the steps: collecting neuron imaging data through a two-photon calcium imaging system, and extracting a fluorescence change time sequence of each neuron from the neuron imaging data; constructing a function connection matrix according to the fluorescence change time sequence, and then calculating a small world coefficient and a network core degree of the neural network based on the function connection matrix; and based on the small-world coefficient and the network core degree, using a liquid state machine to establish a brain-like neuron network. The problems that in the prior art, the network connection mode between single neurons cannot be researched at the cellular level, and effective simulation of a spatial-temporal information processing mechanism of a biological nervous system is lacked are solved.

This invention discloses a method and system for intelligent optimization of heating parameters in distributed buildings. The method uses an improved SSD algorithm to extract multi-scale features from the heating area, establishes a dynamic population distribution data sequence using a heating time-series and population optimization model, collects and normalizes heating system parameters, inputs the data into an optimization decision model, and outputs an optimized combination scheme through a multi-layer neural network. Heating parameters are adjusted in real time based on this, and the operating data is fed back to the optimization model at a set period. The system includes data acquisition, feature extraction, time-series and population analysis, optimization decision-making, parameter adjustment, and feedback update units, all of which work collaboratively. This method and system can fully consider dynamic changes in the environment and population, deeply explore parameter correlations, achieve intelligent optimization of heating parameters, and improve heating performance and energy efficiency.