27 results about "Feature estimation" patented technology

The invention belongs to the technical field of feature recognition of underwater complex targets, and relates to a boundary element-based multi-bright-spot cluster target interference feature estimation method, which comprises the steps of geometric model construction and introduction, numerical calculation based on multi-physics field coupling, echo intensity representation by adopting an angle-frequency spectrum, and analysis of resonance generated by multi-body interference. According to the method, a numerical model for solving a three-dimensional double-gas-cylinder omnidirectional scattering sound field is established based on a boundary element method, and omnidirectional and broadband scattering characteristics of a target in horizontal and vertical attitudes are calculated; a multi-bright-spot dual-target strong scattering coupling mechanism is mainly analyzed, and a multi-body configuration and single target scattering interference fringe generation mechanism is disclosed; according to the method, the inherent attribute characteristics of the single target can still be effectively identified in a cluster target scattering field; the mapping relation among the geometric configuration, the spatial arrangement mode and the frequency response is obtained, and a theoretical formula suitable for double-target sound scattering forecast is deduced.

An acoustic feature estimation method is an acoustic feature estimation method for estimating an acoustic feature of a space and includes acquiring data on the space, estimating situations in the space in accordance with the acquired data, correcting a provisional value of the acoustic feature in accordance with the estimated circumstances, and outputting the corrected provisional value.

Identify an object in various environments. 【Solution means】Communication terminal ( 2 ) is From the color image data using the first estimation model (30) 、 color image Colors that indicate the amount of facial features included a color feature estimation unit (40) that estimates a feature vector; From depth image data using the second estimation model (31) 、 depth image Depth of facial features included a depth feature estimation unit (41) that estimates a feature vector; From the color feature vector and depth feature vector using the third estimation model (32) 、 a three-dimensional image including a color image and a depth image Three-dimensional representation of facial features included in the image. a three-dimensional feature estimation unit (42) that estimates a feature vector; Color characteristics feature vector, depth degree characteristic feature vector, and three-dimensional Gentoku and an authentication unit (24) that performs face authentication using any of the feature vectors.

The application provides a multi-line structured light three-dimensional reconstruction frame point cloud real-time de-stacking and fusion method, and relates to the technical field of line structured light three-dimensional reconstruction. The distribution characteristics of the spatial position of the point cloud are utilized, a global grid structure is established through the position of the multi-view frame point cloud during three-dimensional reconstruction, the corresponding domain point set of each frame point cloud is solved, and the normal feature estimation is performed according to the distribution characteristics of the neighborhood points. The overlapping condition of the point cloud is detected according to the global grid structure, the cylindrical neighborhood structure of the overlapping frame point cloud is established, and the point cloud fusion is realized through the weighted sum of the point cloud vector direction of the target point and the fusion neighborhood point set and the normal vector feature. The algorithm can meet the system stability requirement in the three-dimensional reconstruction, improves the quality of the final point cloud model, greatly improves the measurement adaptability of the multi-line structured light three-dimensional reconstruction in the complex environment, and effectively improves the multi-line structured light three-dimensional reconstruction precision.

The present disclosure provides a point cloud smoothing method and device based on local feature estimation, electronic equipment and storage medium. By introducing the noise point elimination of local density features, the normal estimation based on weighted local statistical analysis, and the neighbor update weight construction mechanism of fusing spatial distance, normal difference and normal distance, adaptive and structure-preserving smoothing processing of point cloud is realized. The method can effectively eliminate outliers and noise points, reduce the thickness of the point cloud, improve the stability of the normal estimation, and at the same time, preserve the object edges and geometric details in the smoothing process, significantly improving the overall quality and applicability of the point cloud.

The present disclosure presents a Multi-Input, Multi-Output artificial intelligence system that jointly processes video with other physiological signals during training to uncover shared representations across modalities and prevent the mislearning observed in existing methods. Once trained, the system requires video input at inference, yet remains capable of reconstructing physiological signals learned during training. By leveraging these crossmodal patterns, the system can use video alone to estimate complex signals such as blood pressure. This integration of video with complementary vital features during training therefore provides a pathway toward accurate, contactless estimation of a broad range of vital signs. A method for estimating vital features of a patient is provided. The method includes receiving a video segment showing a skin-exposed region of the patient, cropping the video segment, and estimating vital features by providing the crops as input to a neural network.

The application discloses an information transmission method and device based on adaptive coding, and the method comprises the following steps: generating a probe information sequence; sending the probe information sequence from a sending end to a receiving end through a transmission channel; receiving a receiving information sequence by the receiving end; performing feature estimation processing on the probe information sequence and the receiving information sequence to obtain a channel estimation matrix, channel time-varying feature information and dimension information; and performing information transmission processing on the sending information by using the channel estimation matrix, the channel time-varying feature information and the dimension information. The application realizes high-reliable data communication in complex time-varying multi-type wireless communication scenes.

The invention discloses an angle steel tower defect identification method based on a graph neural network. The method comprises the following steps: step 1, constructing an initial structure graph; 2, constructing visual structure joint features of dominant nodes to form a first node feature set; step 3, constructing label missing perceptual coding features of the hidden nodes to form a second node feature set; 4, performing feature estimation on the second node feature set by using a feature probability migration module to form a third node feature set; 5, correcting the third node feature set to obtain final hidden node features, and forming a fourth node feature set; 6, fusing the first node feature set and the fourth node feature set to generate a graph embedding vector set; step 7, performing defect type prediction on each node; and 8, outputting a defect identification result. According to the invention, the graph neural network and the feature probability migration module are combined to realize structural level defect intelligent identification of the unobservable area of the angle steel tower.

A method of audio signal processing, a hearing system and a hearing device. A primary input audio signal is obtained using an audio input unit of a hearing device. A secondary input audio signal is obtained using a second device of the hearing system. The secondary input audio signal is transmitted from the second device to the hearing device. A level feature is determined based on the primary and secondary input audio signals using a feature estimation unit of the hearing device. An output audio signal is obtained by an audio processing unit, wherein the level feature is used for steering at least one audio processing routine of the audio processing unit. The output audio signal is outputted by an audio output unit of the hearing device.

The application belongs to the technical field of polarization imaging, and particularly relates to a water image despeckling model construction method based on polarization imaging, which comprises the following steps: using a feature extraction encoder to map four polarization angle images and calculated intensity images to a low-dimensional latent feature space; introducing a diffusion model network, a forward process of which is used to gradually add Gaussian noise until the noise data conform to a standard Gaussian distribution, starting from clear latent features extracted based on a clear water target intensity image and a turbid water target polarization image; a reverse process of the diffusion model network is used to denoise the noise data under the guidance of polarization features of the turbid water target, generate clear latent features of the underwater target, and obtain clear latent feature estimation results; using a reconstruction module network, based on the estimation results and the intensity image of the underwater target in the corresponding sample, a corresponding clear underwater target image is generated. The application can adapt to scattering environments of water bodies with different turbidity, and has high generalization and high robustness.

This invention relates to the field of random sound source localization and feature estimation technology, specifically to a method for random sound source localization and feature estimation in layered media, comprising the following steps: establishing a random sound source model in the layered media; at each observation point in the upper layer of the layered media, for multiple different frequencies corresponding to wavenumbers, using sensors to perform multiple independent sound wave field measurements to obtain sound wave field measurement data; dividing the region where the random sound source is located into a two-dimensional uniform grid, and determining the initial values ​​of the numerical solutions for the mean and variance of the random sound source based on the grid points; based on the sound wave field measurement data, and based on the kernel matrix singular value decomposition, solving the random sound source model in the layered media, iteratively updating the numerical solutions for the mean and variance of the random sound source to obtain the final numerical solutions for the mean and variance; this invention can achieve stable and high-precision reconstruction of the spatial distribution characteristics of random sound sources in layered media.

The invention relates to the technical field of motor fault detection, discloses a motor turn-to-turn fault feature observation method based on an improved sliding-mode observer, and aims to solve the technical problem that the fault feature estimation precision is reduced due to the buffeting problem of a traditional sliding-mode observer (SMO). An improved fault characteristic observer is designed by taking a back electromotive force residual error under a two-phase static coordinate system as a fault characteristic quantity, the observer is optimized by adopting a linear superhelix algorithm (STA) and a hyperbolic tangent function (tanh), high-frequency buffeting is effectively inhibited, and the convergence of the observer is proved through a Lyapunov stability theory. According to the method, the counter electromotive force estimation precision can be remarkably improved, reliable input is provided for motor fault detection, and the method is suitable for online monitoring and diagnosis of PMSM stator turn-to-turn short circuit faults.

The present application belongs to the technical field of anti-radiation target sorting, and particularly relates to an anti-radiation target sorting method based on a data stream clustering algorithm. The anti-radiation target sorting method comprises: direction finding, direction finding is performed on full pulses, and a target line-of-sight angle calculated by an anti-radiation seeker is taken as real-time angle data stream; signal feature estimation, the real-time angle data stream is clustered to obtain pulse clusters with similar spatial relationships, signal feature estimation and threat determination of electromagnetic targets are performed on the pulse clusters after clustering, and target sorting is realized. The target sorting method provided by the present application can obtain high-quality and stable target sorting results, so that the anti-radiation seeker has stronger adaptability to interference and deception in complex environments, thereby improving the combat effectiveness of anti-radiation weapons.

To provide a feature amount estimation method, an estimation program, and a feature amount estimation apparatus for improving the estimation accuracy of a shape feature amount on a surface of a substrate.SOLUTION: The method for estimating a shape feature amount on a surface of a substrate after applying processing for semiconductor manufacturing, includes: generating a plurality of regression models representing a relation between a target parameter and the shape feature amount according to each of a plurality of pre-processing conditions having a plurality of parameters, based on actually measured data of the shape feature amount on the surface of the substrate that has been pre-processed; calculating, for each of the plurality of regression models, an index value representing the reliability of estimated results of the shape feature amount; evaluating the suitability of a setting method of the plurality of pre-processing conditions; selecting a model to be selected as a selection model from among the plurality of regression models based on calculated results of the index value and evaluated results of the suitability of the setting method; and calculating an estimated value of the shape feature amount corresponding to any value of the target parameter by using the selection model.SELECTED DRAWING: Figure 11

A product life cycle (PLM-AI) intelligent decision-making system based on artificial intelligence relates to the technical field of industrial big data processing, and comprises the following steps: collecting production data of each PLM sub-stage, encrypting and uploading the production data to an intelligent decision-making platform, constructing an industrial knowledge graph, fault influence weights are added to connection relations among the characterization design features, the production features and the operation and maintenance features in the industrial knowledge graph; carrying out prospective matching on the process characteristics of each PLM sub-stage of the target product and carrying out fault influence weight matching on the scheduling evaluation index of each design characteristic to obtain the process characteristics of each PLM sub-stage; the production data of each PLM sub-stage is monitored in real time, the monitoring index stability coefficient of each PLM sub-stage is obtained, an online feature estimation model is constructed, decision making is performed on the monitoring index stability coefficient based on the online feature estimation model, and the efficiency and value of product life cycle decision making are remarkably improved.

The invention provides a point cloud smoothing method and device based on local feature estimation, electronic equipment and a storage medium, and the method comprises the steps: introducing noise point elimination of local density features, normal estimation based on weighted local statistical analysis, and a neighborhood updating weight construction mechanism fusing a spatial distance, a normal difference and a normal distance. And self-adaptive and structure-preserving smoothing processing of the point cloud is realized. According to the method, outliers and noise points can be effectively eliminated, the thickness of the point cloud is reduced, the stability of normal estimation is improved, meanwhile, the edge and geometric details of an object are reserved in the smoothing process, and the overall quality and applicability of the point cloud are remarkably improved.

A method in an illustrative embodiment comprises configuring a software development process to include a plurality of features in respective distinct feature domains, and for each of the features, decomposing the feature into a plurality of epics, determining a number of sprints associated with each of the epics of the feature, determining a total number of sprints across all of the epics of the feature, adjusting the total number of sprints based at least in part on historical data, and automatically generating an estimate for completion of the feature based at least in part on the adjusted total number of sprints. One or more characteristics of the software development process are controlled based at least in part on the estimates generated for respective ones of the features. The software development process in some embodiments is illustratively an agile software development process implemented in a continuous integration / continuous deployment (CI / CD) system.

The application provides an adaptive noise reduction method and device for a head-mounted computer, and relates to the technical field of voice noise reduction.The method comprises the following steps: acquiring a multi-channel signal in a rail transit operation environment, performing enhanced noise estimation on the multi-channel signal to obtain noise spectrum feature estimation, and collecting the multi-channel signal through a head-mounted computer; modeling background noise according to the noise spectrum feature estimation to obtain a noise source model; designing an adaptive spectrum filter based on the noise source model, filtering the multi-channel signal through the adaptive spectrum filter to obtain a preliminary noise reduction signal; and performing voice enhancement on the preliminary noise reduction signal based on a voice enhancement model to obtain a voice signal.The application solves the problem that adaptive noise reduction in a rail transit operation environment cannot simultaneously consider accuracy, robustness and real-time performance.

This application discloses a method, system, and storage medium for detecting slowly varying faults in inertial measurement units (IMUs) under dynamic environments. Addressing the technical challenges of difficult identification, high false alarm rates, and missed alarm rates in IMUs, this application introduces a Gaussian process regression model and a particle swarm optimization (PSO) algorithm for multidimensional feature estimation of the IMU. A dynamic envelope model of the normal signal is constructed using the Gaussian process regression model, and the PSO algorithm is used to optimize the multidimensional statistical feature thresholds within the envelope. This achieves high-precision detection of slowly varying faults without the need for fault data, making it suitable for scenarios with small data volumes and high noise levels. Experiments demonstrate that this method can meet the diagnostic needs of various IMU faults, can be applied to different types of IMUs, exhibits better generalization ability, and has higher fault diagnosis accuracy.

The invention relates to the technical field of chemical engineering intelligent calculation optimization, in particular to a feature calculation method, system and device for silica gel chemical synthesis and a medium, and the method comprises the steps that the optimal value interval of silica gel chemical synthesis task features is recognized, and the optimal value interval comprises the maximum production threshold value Xmax and the minimum production threshold value Xmin; obtaining quality characteristic values corresponding to the maximum production threshold value Xmax and the minimum production threshold value Xmin according to the optimal value interval; setting a computing node according to a preset rule; establishing a feature slope set ki within the maximum production threshold Xmax and the minimum production threshold Xmin based on the computational nodes; and performing production index estimation based on the computational nodes and the feature slope set, and outputting a quality feature estimation value. According to the method, the problems of prediction precision attenuation and insufficient adaptability caused by the equipment scale effect when a silica gel industrial chemical synthesis complex task is processed due to strong process parameter coupling can be solved, and the current situation of system prediction misalignment caused by laboratory-industrial parameter drift is improved.

This invention discloses a method for screening echo signals in sea clutter regions, comprising the following steps: Step 1, beam illumination area data extraction; Step 2, smoothing filtering and clutter-to-noise ratio estimation; Step 3, multi-domain feature estimation; Step 4, feature label vector generation; Step 5, comprehensive screening and region filling expansion. The screening method disclosed in this invention addresses the problem of screening clutter region signals from large amounts of sea clutter data under various parameter conditions. When the amplitude fluctuations between range gates are large and various local strong values ​​may be present, it can retain strong clutter signals such as sea spikes as much as possible for further in-depth research on sea clutter characteristics while eliminating non-clutter signals.

The invention discloses a robot motion control method, device and equipment and a storage medium. The method comprises the following steps: acquiring a current stacking observation value of a target robot in a movement process; based on a target encoder network obtained by pre-training, according to the historical observation value, determining speed estimation and environment feature estimation corresponding to the target robot; determining a target joint position of the target robot at the next moment according to the current observation value, the speed estimation and the environment feature estimation based on a target actor strategy network obtained by pre-training; and on the basis of the target joint position, joints of the target robot are controlled through a control device of the target robot, and motion control over the target robot is achieved. According to the method, the robustness and stability of robot motion are remarkably improved, the generalization ability of a strategy and the decision continuity are greatly enhanced, and efficient and stable motion control of the robot is achieved.

The invention discloses an inland reservoir depth remote sensing quantitative inversion method based on remote sensing data and machine learning, and relates to the technical field of water depth estimation, and the technical key points are that the method comprises the following steps: S1, obtaining a high spatial resolution remote sensing image of a reservoir to be measured, and carrying out the preprocessing; s2, performing feature screening extraction on the preprocessed remote sensing image, and generating a feature map; s3, combining the generated feature maps, and inputting the feature maps into a feature estimation model; s4, outputting reservoir depth data, and performing visualization to generate a reservoir depth inversion graph; according to the method, the water depth prediction model is constructed in a mode of combining remote sensing data and machine learning, so that the intelligent degree of inland reservoir depth prediction is enhanced, and the cost required by manual detection can be remarkably reduced.

A kind of regional scale large water delivery channel leakage feature estimation method, including selecting target large water delivery channel, collecting the spatial structure characteristics of target large water delivery channel, hydraulic characteristics and the spatial geographic data of study area, selecting the typical branch channel of target large water delivery channel to carry out hydrostatic test and measure channel bed leakage coefficient, obtain the channel system network after target large water delivery channel is dispersed, generate channel segment spatial topological relation, obtain the spatial geographic two-dimensional array of study area, generate channel water dynamic calculation input file, according to the spatial geographic two-dimensional array of study area and channel water dynamic calculation input file, construct surface water-groundwater coupling model, calculate surface ditch channel network water dynamic according to finite difference method, channel system leakage amount result statistics, the channel bed leakage coefficient of the present application is measured by the hydrostatic test of typical branch channel in local position and the spatial characteristics of large water delivery channel, to accurately calculate the leakage difference of large channel in different positions and overall leakage characteristics.

A brain network activity estimation system includes means for obtaining brain wave measurement data and functional magnetic resonance imaging measurement data simultaneously measured from a subject, means for constructing a feature estimation model which receives the brain wave measurement data as input data and determining a parameter which defines the feature estimation model, means for calculating a feature value for each module based on an output value from each module that is calculated when the brain wave measurement data is provided as the input data, means for calculating an image feature value for each brain network based on the functional magnetic resonance imaging measurement data, and means for determining one or more modules which express activities of a specific brain network among a plurality of modules by evaluating correlation between the feature value for each module and the image feature value for each brain network.

The application discloses a Taijiquan full-body posture estimation method and device based on full convolution, and a medium. Video data is acquired, and a single-frame image feature is extracted by preprocessing the video. Full-body posture estimation is performed based on the extracted feature to obtain a human skeleton key point heat map containing a hand and a head, and a human posture skeleton map is determined. A multi-frame time feature extraction and human posture estimation model is constructed, multi-feature posture estimation is performed according to the human posture skeleton map, multi-feature estimation results are fused, and a human posture estimation result is obtained. The human posture details of the hand and the head are fused to obtain a more accurate full-body posture estimation result. The multi-features of human key points are extracted and fused, and posture prediction is combined with time features, so that the precision and stability of the algorithm are enhanced, the multi-scale feature extraction of the video signal and the time domain feature of the signal itself are taken into account, the time and space features of the signal are considered, and the Taijiquan action posture estimation is more accurate.