43 results about "Neuron network" patented technology

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 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 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.

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 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.

The invention discloses a high-precision temperature and humidity control method for a clean room. The high-precision temperature and humidity control method comprises the steps that the flow of chilled water introduced into a primary cooling section, the flow of pure water introduced into a humidifying section, the flow of medium-temperature water introduced into a heating section and the flow of chilled water introduced into a secondary cooling section are controlled through regulating valves V1-V4 correspondingly; constructing a PID (Proportion Integration Differentiation) neuron network controller, connecting the regulating valve with the PID neuron network controller, and setting temperature and humidity control target values T0 and H0 of the clean room and an initial network weight; acquiring measured actual temperature and humidity values T and H of the clean room through a temperature and humidity sensor; the PID neural network controller calculates and obtains valve position control values of the regulating valves V1-V4; comparing difference values between T0 and H0 and the adjusted T and H, and if the difference values meet requirements, continuously monitoring the temperature and humidity of the clean room; otherwise, correcting the network weight. The PID neural network control technology is adopted, the control quantity can be automatically adjusted, the temperature and the humidity serve as independent output quantities respectively for decoupling control, and therefore high-precision control over the temperature and the humidity of the clean room is achieved, and meanwhile system energy consumption is reduced.

The invention provides a capacitor abnormal discharge detection method and device, a storage medium and computer equipment, and relates to the technical field of capacitor state monitoring, and the method comprises the steps: collecting multiple paths of voltages and multiple paths of currents of a capacitor to be detected, and carrying out the pulse coding of each path of voltage and each path of current, and obtaining each path of pulse sequence; a spiking neural network is determined, the number of input channels of the spiking neural network is the same as the number of the pulse sequences, and the spiking neural network establishes a relation with the characteristic response of abnormal discharge by adopting a specific neuron group; and inputting each pulse sequence into the pulse neuron network to obtain the abnormal discharge type of the capacitor to be detected. Therefore, the adaptive learning capability of the complex pulse sequence can be realized, so that the health state of the capacitor can be accurately and comprehensively evaluated.

The application discloses a small-scale Hopfield neural network circuit based on a memristor, which comprises an activation function module circuit, a memristor model circuit and a Hopfield neural network circuit; the Hopfield neural network circuit comprises three neuron networks X1, X2 and X3; and a connection weight in the Hopfeild neural network is replaced by the memristor module circuit to obtain a new neural network. By replacing the synaptic weight of the Hopfield neural network system, it is found that the originally stable system exhibits rich dynamic behaviors, including chaos, hyperchaos, quasi-periodicity, double-vortex attractor and the like. The circuit is simple in design, and can adjust the weight parameters Rr and the parameter w32 to realize various dynamic behaviors. Therefore, the circuit can be used in the field of secret communication and is helpful for the research on the nervous system.

The invention discloses a low-delay pulse neural network conversion method and system based on threshold value correction and BPTT fine tuning, and the method comprises the steps: introducing a nonlinear threshold value correction function in a training stage of a source ANN, forcibly mapping a trainable threshold value parameter into a strict positive value, and carrying out the calculation of the positive value; the risks of excessive activity and function collapse of the neural network caused by a negative threshold value are thoroughly eliminated from a physical mechanism; and then, constructing a fine tuning framework based on BPTT, and carrying out time sequence dynamics optimization on the converted SNN by using a teacher-student architecture and a dynamically weighted composite loss function. The method not only ensures the stability of the conversion process, but also guides the network to learn a high-efficiency coding strategy of'front-end sparse and rear-end focusing ', and realizes high-precision and high-energy efficiency reasoning under ultra-low delay.

The invention discloses a low-cost in-vitro brain-computer interface chip batch preparation method and a low-cost in-vitro brain-computer interface chip batch preparation device, and belongs to the technical field of biosensor and micro-nano manufacturing. The method comprises the following steps: cleaning, photoetching, sputtering a metal layer, stripping, depositing an insulating layer, overlay, etching and exposing an electrode site and a bonding pad on a glass or silicon insulating substrate in sequence, preparing a 3D electrode by combining secondary overlay with electrochemical deposition, and finally separating a plurality of independent microelectrode arrays through scribing or laser cutting. The microelectrode bonding pad and the adapter plate are connected by adopting an aluminum wire pressure welding technology, and an aluminum wire is embedded by high-temperature-resistant insulating glue and adheres to the cell culture ring. According to the method, the microelectrode arrays of various specifications are prepared on a single substrate in batches, the process compatibility is high, the cost is low, the efficiency is high, microscopic observation and 3D electrode integration are facilitated, and the method is suitable for in-vitro neuron network detection and organoid electrophysiology and electrochemical research.

The application provides an output voltage estimation system and method of a dielectric barrier discharge pulse resonant converter, which comprises a pulse resonant conversion module, a data processing module, a dielectric barrier discharge load output voltage prediction neuron network model and a host computer; the pulse resonant conversion module is used for converting a DC bus voltage into a high-frequency high-voltage pulse voltage and providing the high-frequency high-voltage pulse voltage to a dielectric barrier discharge load; the data processing module is used for collecting an input voltage of the pulse resonant conversion module and transmitting the input voltage to the dielectric barrier discharge load output voltage prediction neuron network model; the dielectric barrier discharge load output voltage prediction neuron network model takes the collected input voltage and a current control switch frequency as input features and estimates a peak output voltage of the dielectric barrier discharge load; and the host computer is used for parameter setting and receiving and displaying the peak output voltage estimation value.

The application provides a protein molecular machine motion mode simulation method, comprising the following steps: S10, obtaining a protein molecular machine structure data file; S20, simulating the molecular dynamics of the protein molecular machine to obtain a complex and its receptor, ligand motion topological file and coordinate file, and calculating the binding free energy thereof; S30, predicting the motion trajectory of the protein molecular machine based on the built NRI model and the coordinate file, wherein the NRI model comprises an encoder for predicting the interaction of a given dynamic system trajectory, and a decoder for predicting the given dynamic system trajectory in the interaction graph; and S40, observing and recording the three-dimensional structure and motion trajectory of the protein molecular machine based on a VR device. The application realizes accurate simulation and prediction of the motion mode of the protein molecular machine by combining molecular dynamics simulation and deep neural network.

The application particularly relates to a multi-energy hybrid energy storage system and a coordinated energy management strategy for an underwater vehicle. The system comprises a lithium ion battery, a super capacitor and a fuel cell using a proton exchange membrane, which are connected to a DC bus respectively. The method of the coordinated energy management strategy comprises: obtaining current motion parameters and current energy parameters of the underwater vehicle; inputting the current motion parameters, the current energy parameters and a desired speed into a load prediction model based on a BP neuron network to estimate a desired total power at a next time; determining a corresponding action strategy according to a current operation mode of the underwater vehicle, and performing power distribution on the desired total power based on the action strategy. The scheme can enable the hybrid energy system to perform smooth operation mode switching and optimal power distribution under different working conditions.

The application discloses a low-cost ex vivo brain-machine interface chip batch preparation method and device, and belongs to the field of biosensors and micro-nano manufacturing technology.The method comprises sequentially performing cleaning, photolithography, sputtering of a metal layer, stripping, deposition of an insulating layer, overlaying, etching to expose electrode sites and pads, and preparing 3D electrodes through second overlaying and electrochemical deposition, and finally separating multiple independent microelectrode arrays through scribing or laser cutting.The aluminum wire pressure welding technology is adopted to connect the microelectrode pads and the adapter plate, the aluminum wire is embedded with high-temperature-resistant insulating glue, and cell culture rings are adhered.The application realizes batch preparation of multiple specifications of microelectrode arrays on a single substrate, has strong process compatibility, low cost and high efficiency, is convenient for microscopic observation and 3D electrode integration, and is suitable for ex vivo neuron network detection and organoid electrophysiology and electrochemical research.

The application discloses a kind of automatic roasting and process intelligent control method and device of belt roaster, it is related to the technical field of pellet roasting of belt roaster in metallurgical iron and steel industry.The method includes four steps of historical data slice acquisition and modeling, target process parameter determination, feedback check and online correction, rolling prediction control, establishes model history library through business slice and time slice, is modeled based on three-layer feedforward neuron network, is combined with the flexible weighted deviation calculation of dynamic weight and the rolling optimization of softening coefficient, realizes equipment parameter accurate regulation and control.It is mainly used for the automatic control of belt roaster pellet production process, improves product quality stability, reduces energy consumption, reduces manual labor intensity, provides support for production unmanned, intelligent.

The invention discloses a bridge pier damage identification method based on dual-area flight and a dual-trunk optimization network, and relates to the technical field of bridge vibration analysis, the bridge pier damage identification method comprises the following steps: firstly, adopting a dual-area flight control strategy combining PID cascade flight control and RTK positioning automatic flight control to identify the bridge pier damage; stable image acquisition of the upper area of the bridge pier without GNSS signals and automatic rapid image acquisition of the lower area of the bridge pier are realized; secondly, standardizing flight paths of various bridge pier section shapes through a standard elliptical layered flight path; secondly, a motion recovery structure algorithm and RTK position constraint-based dual-region image set fusion three-dimensional reconstruction framework is provided to generate a detail model of the bridge pier; and finally, improving the YOLOv11 network through double trunks and multiple layers of detection heads, so as to improve the crack and defect detection precision of the neural network in the visual image.

The application discloses a partial discharge on-line detection device based on a pulse current method, belongs to the technical field of partial discharge on-line detection of power distribution network equipment, and aims to solve the problems of low routine inspection sensitivity, insufficient operation efficiency, high online monitoring cost and poor environmental adaptability in the existing power distribution network intelligent operation and maintenance technology. The core is to expand the pulse current method (direct method) from offline test to on-line detection. With the help of the capacitive coupling sensor of the high-voltage on-line display device, the high-frequency pulse current is extracted through the direct connection type or the switching type test tool. The signal processing and digital conversion are completed through the data acquisition device. Then, through the multi-parameter comprehensive early warning model and the BP neuron network model, the data denoising, health early warning and defect automatic identification are realized. The device takes into account low cost and high efficiency, improves the detection accuracy and the field operation efficiency, and maximizes the performance of the power distribution equipment operation and maintenance.

A method for classifying lithology facies includes receiving a processed and interpreted borehole image. The method also includes receiving an openhole log. The method also includes pre-processing the borehole image to produce a pre-processed borehole image. The method also includes pre-processing the openhole log to produce a pre-processed openhole log. The method also includes modeling the pre-processed borehole image and the pre-processed openhole log to produce a first modelled output and a second modelled output, respectively. The method also includes concatenating the first modelled output and the second modelled output from first and second heads of the convolutional neuron network to produce a concatenated output. The method also includes passing the concatenated output through a softmax layer. The method also includes classifying lithology facies in the subsurface formation based at least partially upon an output of the softmax layer.

Systems and techniques for producing genetically engineered ion channels (ICs) with bioluminescence resonant energy transfer (BRET) complexes and using such ion channels for efficient readout of neural activity and outputs of networks of biological neurons are described. In one embodiment, the disclosed techniques include identifying a target location in an IC, to host a target protein including a donor tag protein and an acceptor tag protein and modifying a genome of the neuron cell in a part associated with the target location in the IC. The techniques further include causing the neuron cell to express the target protein into the IC according to the modified genome. In a first (second) state of the IC, the donor tag protein is at a first (second) distance from the acceptor tag protein associated with absence (presence) of energy transfer between the donor tag protein and the acceptor tag protein.

The application discloses a kind of cold-rolled thin strip steel plastic strain ratio on-line detection method and system, the cold-rolled thin strip steel plastic strain ratio on-line detection method includes the following steps: establishing the artificial neuron network algorithm model of cold-rolled thin strip steel;The plastic strain ratio of cold-rolled thin strip steel is obtained by artificial method;The artificial neuron network algorithm model of cold-rolled thin strip steel is modelled training;The artificial neuron network algorithm model of cold-rolled thin strip steel is used for on-line detection.The cold-rolled thin strip steel plastic strain ratio on-line detection method is applied to the comprehensive electromagnetic detection to running strip steel, and multiple electromagnetic signals are acquired in real time, and the electromagnetic signal is expanded, the cold-rolled thin strip steel plastic strain ratio on-line detection method is established by artificial neuron network algorithm model, realizes the purpose of on-line accurate measurement strip steel plastic strain ratio, scientific performance is strong, practical performance is high, reduces the waste of raw material and manpower cost.

This invention provides an intelligent optimization method and device for an atmospheric distillation unit. The method includes: collecting correlation parameters associated with key parameters of the atmospheric distillation unit, wherein the correlation parameters originate from on-site instrument measurement and analysis data of the atmospheric distillation unit and a process flow simulation model of the atmospheric distillation unit; inputting the correlation parameters into a pre-established artificial neural network mathematical model of the atmospheric distillation unit to obtain the key parameters of the atmospheric distillation unit; and providing a real-time optimization scheme design based on the predicted key parameters and the real-time operating status of the atmospheric distillation unit. This invention utilizes an artificial neural network model of the atmospheric distillation unit to predict and optimize key parameters that are difficult to measure and analyze in real time, thereby controlling the overall tower operation state to near-optimal levels, balancing product quality and energy conservation.

The application discloses an intelligent modeling and optimization method for a double-carbon energy-saving ecological water conservancy drainage system, and relates to the technical field of intelligent water affairs.The method first constructs a digital twin basic platform of the drainage system, which contains physical mechanism models of water power, water quality, energy consumption and carbon accounting;then, a digital mirror neuron network is deployed and run, which continuously compares dynamic operation data with simulation data through deviation perception, parameter inversion, structure identification and sensor health diagnosis, dynamically self-calibrates the parameters and structure of the digital twin model, and forms a high-fidelity self-calibration digital twin system.Based on the system, combined with energy fingerprints and carbon footprint flow models, model predictive control is used for multi-objective real-time optimization scheduling to realize double-carbon energy saving in the operation stage;at the same time, a multi-scale collaborative evolution optimization framework is used to assist in the planning and design optimization of ecological drainage facilities.The application solves the pain points of low precision of traditional models and inability to adapt to system changes.

The invention provides a new energy power assembly control method and system based on a multi-intelligence algorithm, and the method comprises the steps: obtaining the historical speed of a new energy power assembly, and constructing a historical data set; performing off-line training on the constructed power assembly model prediction controller based on a historical data set; the speed and acceleration of the new energy power assembly at the current moment are obtained, the trained power assembly model prediction controller is used for predicting the state in the future time domain, the required power of the new energy power assembly is calculated, and the optimal power distribution in the prediction time domain is controlled according to the required power; according to the power assembly model prediction controller, hyper-parameter optimization is carried out on the long-short-term memory neural network by adopting an umbrella-sky-grey wolf combinatorial algorithm, the future moment state is predicted by utilizing the optimized long-short-term memory neural network, and a finite time domain open-loop optimization problem is solved by utilizing a dynamic programming method according to a prediction result. And obtaining an optimal control sequence in a prediction time domain. According to the invention, the power distribution of the power assembly can be optimized.

A method for classifying lithology facies includes receiving a processed and interpreted borehole image. The method also includes receiving an openhole log. The method also includes pre-processing the borehole image to produce a pre-processed borehole image. The method also includes pre-processing the openhole log to produce a pre-processed openhole log. The method also includes modeling the pre-processed borehole image and the pre-processed openhole log to produce a first modelled output and a second modelled output, respectively. The method also includes concatenating the first modelled output and the second modelled output from first and second heads of the convolutional neuron network to produce a concatenated output. The method also includes passing the concatenated output through a softmax layer. The method also includes classifying lithology facies in the subsurface formation based at least partially upon an output of the softmax layer.

The application relates to a self-learning method and device for quantitatively representing welding parameters, equipment and a storage medium, and comprises the following steps: obtaining welding process related parameters; inputting the welding related parameters into a pre-constructed neuron network model to obtain optimal welding parameters; and inputting the optimal parameters into a pre-constructed welding parameter expert system to obtain a welding seam quality prediction scheme of different welding parameters. The application helps to realize skill-free manual arc welding, and the welding seam quality prediction scheme of different welding parameters is obtained by obtaining detailed welding parameters and reasonably using the parameters, so as to provide a process solution for robot high-quality automatic welding under complex working conditions.

The application discloses an intelligent detection and navigation system and method for inland river ships, which comprises the following steps: step one, collecting multi-modal perception data during the navigation of the inland river ship; step two, constructing an initial cognitive graph; step three, dynamically modeling each semantic state node by using an improved liquid-state neuron network, embedding a symbolic logic constraint in state transition, and generating a navigation control partial derivative vector; step four, calculating the tension value of a causal directed edge and performing a structure evolution operation to generate a self-evolution cognitive graph; step five, constructing a candidate path set; step six, calculating the graph winding degree index of each candidate path; step seven, constructing a path decision vector and performing a path backstepping reduction operation to screen a target optimal path; and step eight, converting the target optimal path into a navigation control instruction and outputting the navigation control instruction to a ship control system. The application combines cognitive graph modeling and neural network reasoning to realize intelligent navigation control of the inland river ship.