116results about How to "Guaranteed Computational Efficiency" patented technology

The invention discloses a power system false data injection attack detection method and system. Measurement data of a power distribution system are acquired; constructing an attack sequence, and injecting the constructed attack sequence into the obtained measurement data to simulate a false data attack; constructing a false data injection attack detection model based on the expansion causal convolution and the recurrent neural network to analyze the state change of the power distribution system; and judging an attack behavior according to the analyzed state of the power distribution system. According to the method, time sequence data can be processed, the long-term dependency relationship and the complex mode can be captured, and the response capability to the complex attack mode can be improved, so that the false data injection attack can be effectively detected and prevented, and the safety and reliability of the power distribution system can be maintained.

The invention, which relates to the technical field of offshore wave simulation, discloses a wave hydrodynamic numerical simulation method for a sea area with a breakwater, comprising the following steps: S1, determining a target sea area, and collecting sea chart data and water depth topographic data of the target sea area; s2, determining a shoreline position of the target sea area, and establishing a water depth terrain database of the target sea area; s3, setting grid precision, and constructing a wave hydrodynamic two-dimensional model of the target sea area; s4, setting boundary conditions for driving the wave hydrodynamic two-dimensional model, and setting initial parameters; and S5, based on the wave hydrodynamic two-dimensional model constructed in the S3, performing numerical simulation on the target sea area by using a sub-grid method to obtain the characteristic wave height and periodic data of the target sea area. According to the method provided by the invention, the calculation efficiency can be ensured, the wave transmission characteristics of the open type oyster reef are considered, and the influence of open type oyster reef construction on the wave hydrodynamic environment field can be accurately simulated.

The invention discloses a conductor instance detection method and system based on key point matching, and the method comprises the steps: obtaining original aerial image data, generating a four-dimensional feature matrix through dynamic feature mapping and enhancement processing, obtaining three-dimensional projection feature data through projection transformation, and carrying out the feature enhancement to obtain enhanced feature data. Generating enhanced feature map data through multi-scale feature fusion; performing feature recombination on the enhanced feature map data to obtain feature matrix data, generating response matrix data through recursive decomposition and mapping, and calculating adaptive threshold data to screen key points; generating thermodynamic diagram data for the screened key point set through a multi-layer thermodynamic diagram, obtaining a spatial incidence matrix by combining spatial feature enhancement, and performing endpoint matching based on multi-dimensional constraints; and finally, through boundary feature analysis and cross-block instance matching, a global traverse map is constructed, and accurate detection of traverse instances is realized. According to the invention, the accuracy and robustness of wire detection can be effectively improved.

The invention discloses a low-light image denoising method based on a local-global interactive Transform, and the method is characterized in that the method comprises the following steps: 1, carrying out the denoising of a low-light image; the method comprises the following steps: step 1, constructing an overall structure of LGIT for recovering a given degraded image; 2, a Top-N Patches selection module is used for selecting N patches most related to the target patch and providing feature input for subsequent modules; step 3, the Patch selects an attention Transform module, and the Transform module is used for carrying out global feature aggregation based on the information of Top-N Patches; and step 4, mixing the double-gate feed-forward network MDGFF for further processing the features output by the PSATB and generating a final de-noised image. According to the method, the self-attention inside the patch and between the patches is effectively calculated on different scales, and accurate removal and detail recovery of low-light image noise are realized.

The invention relates to a medical image target detection method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining a to-be-detected medical image and a medical image target detection model, according to the medical image target detection model, an expandable residual module is added in a backbone network, a multi-branch attention mechanism module is added to a head in a neck network, and at least part of conv modules in the backbone network and the neck network are replaced by YOLOv8 models of deep down-sampling modules. The medical image target detection model is obtained through joint regression loss function training; and inputting the to-be-detected medical image into the medical image target detection model to obtain a medical image target detection result. According to the medical image target detection method provided by the invention, the accuracy of medical image target detection is improved.

The invention discloses a human body behavior recognition method and system based on space-time decoupling multi-scale feature fusion. The system comprises a local space-time multi-scale feature fusion network MDCNN and a space-time decoupling network DTST. Extracting different scale features of multichannel sensor data by using a multi-scale convolutional neural network, extracting shallow layer features and deep layer features of signals at the same time, and extracting features representing action information by fully utilizing complementary information among different level features; the method comprises the following steps of: decoupling a network into a time decoupling structure and a space decoupling structure through a time-space decoupling network based on Transform, so as to accurately extract time-space characteristics in an inertial signal; and finally, using a Softmax classifier to carry out action classification and recognition. According to the method, consistency and complementarity among multi-sensor data can be efficiently utilized, multi-scale spatial-temporal characteristics are fused, changes in time dimensions and relevant information in spatial positions are fully considered, different attention degrees are kept for signals of different sensing channels, and the recognition precision in human behavior recognition is improved.

The invention relates to the technical field of computational fluid mechanics, in particular to a method for calculating the blocking effect of an underwater vehicle towing tank, and the method comprises the steps: building an underwater vehicle physical model; defining each boundary condition of the CFD numerical calculation domain, and solving the total resistance and each resistance component of the underwater vehicle based on the calculation domain grid and the configured numerical calculation condition; and post-processing the total resistance and each resistance component to obtain a calculation numerical result of the underwater vehicle under different blocking ratio working conditions. According to the method, by verifying the independence of the grids, the appropriate number of the grids is determined, and the calculation precision and efficiency are guaranteed.

The invention relates to the fields of wind engineering, weather forecast, environmental protection and aviation, and provides a complex terrain wind field refined simulation method based on a high-resolution grid. The objective of the invention is to solve the problem of simulation errors caused by different topographic data formats and coordinate systems in complex topographic wind field simulation and to improve simulation precision and efficiency. According to the main scheme, the method comprises the following steps: generating a three-dimensional computational grid of wind mine software according to satellite image elevation data, and obtaining a three-dimensional unstructured grid for wind field CFD simulation; selecting a proper entrance boundary condition type for four side boundary surfaces and a top surface of the target area; and wind mine software is driven to carry out numerical calculation to obtain wind field information data of the refined simulation of the complex terrain. The method is used for wind field characteristic evaluation under complex terrains, and is beneficial to efficient utilization of wind energy, weather forecast, environmental protection and aviation safety.

The invention belongs to the technical field of climate prediction, particularly relates to a Markov chain Monte Carlo-based paleoclimate inversion method, system and equipment, and aims to solve the problems of accuracy and high efficiency of tracking climate change details in global inversion. The method comprises the following steps: determining a to-be-inverted paleoclimate index; inputting the to-be-inverted paleoclimate index into a paleoclimate inversion model to obtain a paleoclimate inversion result output by the paleoclimate inversion model; wherein the paleoclimate inversion model is obtained by performing iterative matching based on a target curve of a paleoclimate index and an initial parameter set determined based on the target curve of the paleoclimate index so as to continuously update a to-be-inverted parameter set. According to the method, in global inversion, climate change details are tracked more accurately, calculation is more efficient, and the response capability and adaptability to the time sequence are improved.

The invention discloses a wave hydrodynamic numerical simulation method for a sea area provided with a hedge shrub dam, and relates to the technical field of offshore wave simulation, and the method comprises the following steps: S1, determining a target sea area, and collecting actually measured water depth topographic data and chart water depth data of the target sea area, the target sea area being a salt marsh beach sea area where the hedge shrub dam is planned to be arranged; s2, setting grid precision, and constructing a wave hydrodynamic two-dimensional model of the target sea area by using a structured grid; s3, setting boundary conditions for driving the wave hydrodynamic two-dimensional model, and setting initial parameters; and S4, based on the wave hydrodynamic two-dimensional model constructed in the S2, performing numerical simulation on the target sea area by using a sub-grid method to obtain the characteristic wave height and periodic space-time distribution of the target sea area. According to the method, the calculation efficiency can be guaranteed, the wave transmission characteristics of the hedge shrub dam are considered, and the influence of construction of the hedge shrub dam on the wave hydrodynamic environment field can be accurately simulated.

The invention provides a large 3D scene texture dynamic loading and unloading method, medium and system, and belongs to the technical field of 3D scene loading.The large 3D scene texture dynamic loading and unloading method, medium and system comprise the steps that three-dimensional scene parameters are collected, texture frequency parameters are decomposed to obtain basic and fluctuation components, a texture loading model is constructed, and a coefficient matrix is generated; packaging the texture file into a fixed-size block, building a double-layer resource optimization model based on a coefficient matrix, setting global and local optimization targets, and calculating a comprehensive contribution coefficient; an initial resource allocation scheme is obtained through an evolutionary algorithm, and a final scheduling scheme is obtained through dynamic programming; according to the texture blocks, loading the texture blocks, determining a recovery strategy in combination with life cycle, grouping association and state transition prediction, and executing resource recovery; the process comprises texture analysis, model construction, resource optimization, scheduling and recovery, and efficient management of three-dimensional scene textures is realized; the resource scheduling decision-making mechanism can be optimized, and the user experience is improved.

The invention belongs to the field of meta-universe object pose estimation technology and robots, and discloses a meta-universe environment-oriented moving object self and multi-target pose multi-sensor fusion simultaneous estimation method. A more robust object-level data association is established using a multi-probability fusion tracker based on a Kalman filter and points in the optical flow. On the basis, a dynamic point cloud elimination method fusing the speed prior of a 3D Kalman filter and camera optical flow decoupling is provided so as to improve the precision of self pose estimation of the speedometer. And finally, combining multiple constraint factors in the proposed framework to refine the poses of the object and the object. Evaluation is carried out in a KITTI original data set, and compared with a reference and most advanced method, the method obtains better attitude precision of the KITTI and a tracking target. In addition, the proposed system shows the feasibility of real-time performance so as to ensure the practical application value of the system.

The invention relates to the field of intelligent individual soldiers, in particular to an intelligent individual soldier system based on AI multiple modes. The method comprises the following steps: acquiring and preprocessing multi-modal original data, extracting initial features from the multi-modal preprocessed data, and carrying out standardization processing to obtain multi-modal standard features; the multi-modal standard features are optimized, and a comprehensive feature matrix is obtained; analyzing the comprehensive feature matrix, and extracting spatial features; inputting the spatial features into a gating circulation unit to obtain a time sequence state, and predicting the time sequence state to generate a prediction result; and the prediction result is transmitted to inspection personnel. The problem that although a traditional convolutional neural network and a long-short-term memory network can effectively process spatio-temporal data, long-time stable operation in a resource-limited single-soldier system is difficult due to high calculation complexity and resource consumption is solved. The problems that in the feature fusion process, fixed weights cannot dynamically adapt to environment changes, and a traditional feature processing method is low in robustness to noise and abnormal values are solved.

The invention discloses a personalized federal learning method and system, and the method comprises the steps: introducing an scSE attention mechanism at a client, adding the scSE attention mechanism into a generator and a discriminator of a GAN, obtaining important features of data in channel and spatial dimensions, and carrying out the enhancement of the features; by adaptively adjusting network parameters, optimizing a generative adversarial network structure, constructing a client local model, and uploading the client local model to a central server; and at the central server side, constructing a personalized federal learning model, receiving the local model uploaded by the client side, carrying out aggregation training on the local model, obtaining an optimized global model, and sending parameters of the optimized global model to each client side. Key features in the data are adaptively concerned, the training efficiency and the data generation quality of the GAN in personalized federated learning are improved, the performance of the model on various tasks is improved, the method adapts to the differences of data sets on distribution and characteristics, and the problem of heterogeneous data is solved.

The embodiment of the invention discloses a power grid optimal power flow calculation method based on a convex relaxation technology and related equipment, first active power flow, first reactive power flow, second active power flow and second reactive power flow can simulate operation of alternating current and direct current power grids including high-voltage direct current power transmission, and through the convex relaxation technology, the optimal power flow of the power grid can be calculated. And a non-convex optimization problem in the power flow is converted into a convex optimization problem which is easy to solve, so that the accuracy and the calculation efficiency of the optimal power flow are ensured by calculating the optimal power flow by using the branch power flow model optimized by the convex relaxation technology, and the analysis requirement of an alternating current and direct current hybrid power grid is met.

The invention belongs to the technical field of vehicle suspension system control, and discloses a semi-active suspension control system based on Transform architecture, which comprises the following steps: arranging a sensor network, and collecting vehicle dynamic data; using a preset algorithm to align asynchronous data from different sensors, and keeping time sequence consistency of heterogeneous data; the measured value of the sensor i at the time t is xi (t), in the invention, the weight distribution of the state quantity is reduced through a Transform mechanism, the error control quantity caused by the sensor error is avoided, the robustness of the suspension control system to the data exception is improved, and the reliability of the suspension control system is improved. According to the method, stable control can be kept under sensor faults, external attacks and false sensor data injection or extreme road conditions, through a DDPG reinforcement learning control algorithm, the adaptive capacity of the system in a complex and changeable environment is enhanced, different road conditions, loads and driving styles can be rapidly adapted, and the control effect is improved while the calculation efficiency is guaranteed.

The invention relates to the field of computational mechanics, in particular to a method for calibrating bonding strength parameters during simulation of a brittle solid material, and failure strength data of the brittle solid material is obtained at a preset dynamic strain rate through a split Hopkinson pressure bar (SHPB) iterative test. The failure strength under a certain strain rate is used as an initial bonding strength parameter, and then the bonding strength parameter is updated according to the failure strength of the previous time and the current time in each iteration. And then, constructing a target function, performing parameter optimization by utilizing a simulated annealing algorithm, and judging whether updated bonding strength parameters are accepted or not by adjusting the temperature and the search step size and calculating the increment of the target function. If accepting, continuing iteration until the temperature is lower than the set value, and finally reserving the bonding strength parameter as the calibration value. Based on the calibration method provided by the invention, the real bonding strength of the material can be effectively approached, and the precision and reliability of parameter calibration are improved.

The invention discloses a pavement disease detection network model which comprises an encoder, an APM module, a DSConv module, an HFEM module, a GAM module and a decoder. The encoder is used for enhancing the feature extraction capability of the model so as to process image features under different scales; the APM module is used for enhancing feature representation by using an attention mechanism; the DSConv module is used for providing multi-scale feature representation for subsequent processing steps; the HFEM module is used for aggregating multi-level features of different deep levels, and redistributing attention weights based on an attention mechanism to highlight target features and reduce interference of redundant features on model performance; the GAM module is used for modeling crack characteristics under a long distance; and the decoder is used for recovering the low-resolution feature map subjected to multi-layer feature extraction and enhancement to a high resolution so as to generate a prediction result which is the same as or similar to the input image in size. The model can effectively capture subtle and complex pavement disease characteristics, and can improve the detection accuracy while maintaining the calculation efficiency.

The invention discloses a blade aerodynamic robustness evaluation method based on a dual-fidelity collaborative neural network, and relates to the technical field of turbomachinery blade aerodynamic analysis. Acquiring a plurality of groups of uncertainty parameters influencing the aerodynamic characteristics of the blade; inputting the uncertainty parameters into the dual-fidelity collaborative neural network model, extracting feature vectors of the uncertainty parameters through a basic trend prediction module, and predicting low-fidelity aerodynamic performance; weighting the feature vector through a cross-fidelity attention fusion module, fusing the uncertainty parameter and the weighted feature vector, and inputting the fused vector into a residual error correction module to obtain a residual error prediction value; adding the residual prediction value and the prediction value of the low-fidelity aerodynamic performance to obtain a prediction value of the high-fidelity aerodynamic performance; and determining the robustness index of the blade according to the high-fidelity aerodynamic performance predicted values corresponding to the multiple groups of uncertainty parameters. According to the method, the calculation efficiency is ensured while the aerodynamic performance precision of the blade is improved.

The invention discloses a Reformer-based toxic gas detection method, which comprises the following steps: acquiring gas data, storing the gas data, and preprocessing by using weighted recursive average filtering to obtain first gas data; performing forward calculation on the first gas data to obtain a loss function of the neural network model, and performing backward calculation by using a reversible network layer; feature extraction is carried out, different feature vectors are sorted according to the hash values, the same feature vectors are stacked, and a first gas data sequence is obtained; the first gas data sequence is partitioned through segmented full connection, the length of each block of sequence is calculated, and a first neural network model is obtained; and inputting the first gas data into the first neural network model, outputting the gas information, predicting gas classification for the gas information by using the first activation function to obtain a gas prediction result, performing regression prediction on the gas prediction result by using the second activation function to obtain an actual gas detection result, and improving the performance of the whole system.

The application discloses a kind of based on task partition's shipborne unmanned aerial vehicle navigation beacon group path planning method, belong to offshore navigation beacon inspection technical field.The method first obtains navigation beacon, mother ship, obstacle and unmanned aerial vehicle constraint parameter, is divided into navigation beacon by recursion K-means and is checked by threefold verification of operation radius, load, endurance, and is recursively subdivided if unqualified;Second generation mother ship candidate residence point and correct to safe water area, use A* algorithm to plan the global transfer path of mother ship, model as closed loop traveling salesman problem in cluster inspection, to improve self-organizing mapping neural network is solved unmanned aerial vehicle path in combination with tabu search.The system contains data input, task partition and the like module.The scheme makes mother ship only stay near task cluster, greatly shortens voyage, reduces risk, improves inspection efficiency and engineering feasibility.

The application provides a method and device for simulating a water delivery system of an impulse water turbine and electronic equipment, and the method comprises the following steps: establishing a mathematical model of the water delivery system of the impulse water turbine with variable step grids; dividing the water delivery pipe upstream of the distribution ring and the side pipe of the distribution ring into grids with the variable step grids; determining the boundary conditions of the inlet and outlet of the pipe based on the calculation working condition, and determining the initial flow and pressure values of each grid node according to the constant flow theory; combining the initial flow and pressure values of the grid nodes, iteratively calculating the corresponding flow and pressure values of each grid node at the current time, and determining the unit side parameters at the current time until the termination condition is met. Through the application, certain theoretical basis and technical support are provided for the safe and stable operation of the impulse unit, and the problem that the existing related technologies are difficult to balance the pipe calculation efficiency and the calculation accuracy is solved.

The invention relates to a soil organic matter prediction method based on multi-feature fusion, and belongs to the technical field of soil organic matter prediction. The method comprises the following steps: preprocessing acquired soil data to obtain spectral features, and performing time domain reconstruction of frequency domain signals to obtain time domain data; obtaining an optimal delay time and an optimal embedding dimension based on the time domain data, and performing phase space reconstruction to obtain a phase space trajectory; chaos features are extracted based on the phase space trajectory, and the extracted chaos features, the optimal delay time and the optimal embedding dimension serve as final chaos features; obtaining a vegetation index based on the spectral feature and taking the vegetation index as an index feature; and inputting the spectral features, the final chaotic features and the index features into a constructed double-flow low-rank interaction network model to obtain a soil organic matter prediction result. The objective of the invention is to solve the technical problem of low prediction precision caused by the fact that spectral features extracted in the prior art cannot comprehensively represent complex nonlinear characteristics of soil.

The present application relates to the field of image processing technology, and in particular to an image compression method, device, electronic device and storage medium. The present application first cuts the acquired original image to obtain multiple regional images, and respectively determines the number of channels of the effective channels required for each regional image in the image compression process, and then, by inputting the number of channels corresponding to the original image and each regional image of the original image into the trained spatial domain adaptive compression autoencoder, the target potential representation and target reconstructed image of the original image after transformation by the neural network are obtained. The present application uses a spatial domain adaptive compression autoencoder to compress each regional image of the original image according to their respective adapted feature channel numbers, which can significantly improve the computational efficiency of the image compression process while ensuring that the target reconstructed image after decompression meets a certain quality, and can simultaneously take into account the joint optimization of distortion and coding overhead.

The invention discloses a cable insulation layer thickness automatic identification method and device, and belongs to the technical field of cable sheath detection. The method comprises the following steps: acquiring an image file by using an imread function, extracting a predetermined channel of an image as a grey-scale map, carrying out binarization processing on the predetermined channel through an imbinarize function to obtain a black and white binarized graph, calling hole filling of the binarized graph of which the imfill function is greater than the black and white binarized graph, and establishing a filling circular graph of which the contour is continuous and smooth and the internal hole is filled; measuring a circle center and a cross section in the filling circular ring graph by utilizing a circular Hough transform principle, and calculating the thickness of a circular ring in the filling circular ring graph; and extracting the diameter and thickness of the ring, introducing an ellipse fitting algorithm, calculating the shortest and longest outer diameters of the ring, obtaining an ellipse fitting result, and adjusting the measurement angle to measure the ring thickness of the corresponding angle. According to the method, the standard circular ring detection model is established, the ellipse fitting equation is fitted, irregular shapes occurring in actual production are processed, the detection accuracy is improved, and the adaptability to variation is enhanced.

The application discloses a spacecraft inter-satellite closest time moment calculation method based on a neural network, and comprises the following steps: acquiring different initial state data of a spacecraft, calculating corresponding inter-satellite closest time moments, and jointly forming an inter-satellite closest time moment data set; constructing a spacecraft inter-satellite closest time moment calculation neural network model according to the inter-satellite closest time moment data set and performing neural network training; substituting target initial state data of the spacecraft into the trained spacecraft inter-satellite closest time moment calculation neural network model to perform calculation, and obtaining a spacecraft inter-satellite closest time moment calculation result. The spacecraft inter-satellite closest time moment calculation model constructed by the application can quickly calculate the spacecraft inter-satellite closest time moment with a small calculation error through the network trained in advance after acquiring the initial state parameters of the target spacecraft.

This invention discloses a dynamic inspection system for weld seams of a robotic arm on a flexible production line, integrating a laser tracker and dual photoelectric frequency combs. Based on numerical simulation, it establishes the discrete optimal intervals of the repetition frequency difference and carrier envelope offset frequency, constructing a precise time-domain measurement benchmark. Furthermore, it integrates fault saliency ranking and parallel convolutional neural networks, extracting signal morphology indicators of interference fringes and rearranging the sampling sequence to eliminate servo lag errors in high-speed tracking by the laser tracker. The inspection system provided by this invention can achieve real-time precise reconstruction of the weld seam trajectory of a robotic arm in complex environments on flexible production lines, effectively solving the problems of nonlinear interference and dynamic asynchrony in cross-scale measurements, significantly improving the inspection efficiency and positioning accuracy of automated welding, and possessing broad application prospects.

The application relates to a kind of low-carbon park multi-agent energy carbon collaborative decision-making methods based on data driving, constructs multi-agent dynamic Stackelberg game model, and introduces time-varying grid side carbon intensity and green electricity carbon offset benefit into multi-agent game decision-making process;Energy supplier is as leader, user is as follower, and with the sum of minimum energy economic cost and carbon cost based on dynamic carbon factor calculation as target, optimal energy strategy is solved;Hybrid intelligent solving framework is used to solve multi-agent dynamic game model;At the level of game strategy learning, improved multi-agent deep reinforcement learning algorithm is used to approximate game equilibrium;At the level of individual optimization solution, for follower agent, improved multi-objective particle swarm optimization algorithm is embedded, and the optimal response of follower under current price strategy is solved.Compared with prior art, the application can coordinate multi-agent interests and dynamic carbon target, promote source and load collaboration and efficient renewable energy consumption.

The invention relates to an unmanned vehicle obstacle identification method and system, and the method comprises the steps: obtaining surrounding point cloud data in real time through employing a laser radar sensor equipped on an unmanned vehicle, carrying out the noise filtering and down-sampling of the point cloud data, and calculating the normal vector and local point cloud density of each point in the point cloud data; for each target point, calculating the included angle between the target point and the normal of the neighborhood point, calculating the normal consistency weight of the neighborhood point through the normal included angle, calculating the statistical filtering weight of the neighborhood point according to the local point cloud density, and taking the product of the normal consistency weight and the statistical filtering weight as the weight of the neighborhood point; calculating a fast point feature histogram of the target point by using the weight of the neighborhood point, the simple point feature histogram of the neighborhood point and the simple point feature histogram of the target point; and segmenting the point cloud through the positions of the points in the point cloud and the fast point feature histograms, classifying the segmented regions by using a classifier according to the fast point feature histogram of each segmented region, and determining the type and the distance of the obstacle.

The application relates to a variational correlation evaluation method and device, which comprises the following steps: acquiring magnetic resonance image data to obtain to-be-processed data; determining an iteration position sequence of the to-be-processed data and extracting iteration features to obtain a training set and a test set; inputting the training set into a variational correlation evaluation classification model for iteration training and testing, judging whether the trained variational correlation evaluation classification model converges or not, and obtaining the variational correlation evaluation classification model when the model converges, and performing effective feature extraction based on the variational correlation evaluation classification model; determining each effective feature position sequence, converting the effective feature position sequence into a three-dimensional brain structure matrix, covering the three-dimensional brain structure matrix to a preset standard human brain template, and identifying effective features related to each stimulation condition. The application quantifies the contribution of a single voxel in the process of executing a specific cognitive function, and finally identifies and extracts the least amount of features that can best represent the target stimulation condition.