251 results about "Smoothing filter" patented technology

The invention relates to the technical field of geothermal drilling monitoring management, in particular to a construction monitoring system and method for geothermal drilling. The method comprises the following steps: constructing a state variable set, quantifying structural disturbance intensity by adopting a disturbance derivative method, normalizing the structural disturbance intensity, and defining a state transition tensor mapping behavior influence coefficient; in combination with the high-order derivative of the disturbance trend function and the behavior collaborative change matrix, an offset risk factor is generated through fusion for early warning; dividing a well drilling area into three-dimensional grids, calculating local stress potential energy, adjusting weight through a dynamic sensitive belt, and generating a risk distribution diagram through smooth filtering; performing closed-loop dynamic regulation and control by using risk potential energy centroid coordinates and gradient field inversion intervention vectors for medium and high risk areas; and through multi-period risk potential energy tensor analysis, calculating the matching degree of a self-healing index and a stable cone region, evaluating the self-healing capability of the system and feeding back a regulation and control strategy. According to the invention, a closed-loop monitoring and active control system for nonlinear risks of geothermal drilling is formed.

The invention discloses a spectral gas concentration inversion method based on a CNN-Transform fusion model, and belongs to the technical field of trace gas detection and spectral signal processing. The method comprises the following steps: acquiring second harmonic spectrum data of target gas through a TDLAS (tunable diode laser absorption spectroscopy) system; carrying out abnormal value elimination and smooth filtering processing on the acquired signal, and constructing a standard input vector; a depth model fusing a one-dimensional convolutional neural network (CNN) and a Transform architecture is designed, the CNN is used for extracting local spectrogram features, and the Transform is used for modeling a global dependency relationship; and inputting the training set and the verification set into the model for training optimization, and finally realizing high-precision inversion of the sample gas concentration of the test set. Experimental results show that the method is superior to an existing model in the aspects of fitting precision, robustness and error control, and has good physical consistency and engineering popularization value.

The invention discloses a multi-base-station AOA cooperative low-altitude target rapid positioning system. The system comprises an AOA measurement base station network, an AOA data preprocessing module, an adaptive weighted intersection positioning module, an extended Kalman filtering state estimation module and a data fusion and system integration module. The system synchronously measures the arrival angle of a target signal through multiple base stations, removes noise in combination with smoothing filtering and an anomaly rejection algorithm, and then solves the initial position of a target by using a self-adaptive weighted intersection algorithm based on measurement quality and geometric distribution. And then, fusing the target motion model and the measurement model by adopting an extended Kalman filtering algorithm to realize dynamic estimation and prediction of the position, the speed and the course. The system can realize high-precision and real-time positioning and continuous tracking of targets such as low-altitude unmanned aerial vehicles, small aircrafts and the like in a complex electromagnetic environment and a sight distance limited scene, and has visual display and regional alarm functions.

The invention discloses an automatic control method and system for a bias point of a photoelectric modulator, and relates to the field of signal processing, and the method comprises the steps: generating a composite disturbance voltage signal through a digital signal processor; the bias electrode is injected into the photoelectric modulator and used for collecting the optical signal output by the photoelectric modulator and converting the optical signal into an electric signal; extracting a fundamental component amplitude and phase difference data of a second harmonic component, a third harmonic component and a fundamental wave; dynamic characteristic parameters are calculated through spectral analysis, and the mapping relation between the characteristic parameters and the offset of the offset points is established; outputting the offset direction and the offset level of the offset point through a deep learning model; a bias voltage compensation amount is generated through a fuzzy PID (Proportion Integration Differentiation) controller; and adjusting the bias electrode voltage, smoothing the filter, and eliminating high-frequency jitter. The method has the advantages that high-precision and stable bias point adjustment is realized through the composite disturbance signal, real-time spectrum analysis and deep learning optimization decision in combination with fuzzy PID control and a dynamic closed-loop mechanism, and the system performance and stability are remarkably improved.

The invention provides a bedding structure rock finite element model reconstruction method based on a rock core digital image, and belongs to the technical field of digital rock cores, and the method comprises the following steps: S1, obtaining a gray level image of a rock sample; s2, calculating a transverse mean value of the analysis area to obtain a longitudinal gray level distribution curve; s3, applying moving average filtering and smooth filtering to obtain a smooth gray curve; s4, first-order difference is carried out on the smooth gray level curve, the position with the absolute difference value larger than a threshold value is taken as an initial stripe boundary candidate, and starting and stopping pixels, the width and the average gray level of each stripe are determined; s5, mapping each clustering label interval into a physical interval based on FEM grid partition; and S6, constructing a sample finite element model, and generating a reconstructed finite element model with real partition information. According to the method, the macrostructure features of the rock can be efficiently recognized and extracted by directly utilizing the pictures and combining gray profile analysis, and efficient and low-cost reconstruction of the finite element model of the bedding structure rock is achieved.

The invention discloses a 4D face reconstruction method and device, and relates to the field of face reconstruction, and the method comprises the steps: obtaining a plurality of face image groups in a preset time period; reconstructing a face triangular mesh model for each face image group; performing fitting optimization on each face triangular mesh model by adopting a three-dimensional face deformation model to obtain a corresponding registered face mesh model; for any face image group, performing flicker removal processing, extracting an initial texture image, and mapping the initial texture image to the corresponding registered face mesh model to obtain a mesh with texture; and performing time sequence smooth filtering processing on the grids with textures corresponding to all the face image groups to obtain a 4D dynamic face grid sequence. According to the method, the 4D dynamic face grid sequence with high fidelity and coherent time sequence can be reconstructed end to end.

The invention discloses a PCB semantic segmentation method fusing ResNet and an attention mechanism. The method comprises the following steps of performing defect segmentation area cutting and data enhancement on a PCB image; carrying out image smoothing filtering on the enhanced PCB defect image data set by adopting a generalization edge preserving and structure preserving image smoothing model, and inputting a UNet model encoder to extract defect area features; performing layer-by-layer down-sampling on the defect area features to obtain initial feature maps of different scales; inputting the initial feature maps of different scales into a CBAM module according to corresponding levels, and adding the feature maps processed by the CBAM attention mechanism with the initial feature maps of different scales by the CBAM module; and the UNet model decoder obtains the feature map of each layer of the CBAM module, recovers the image resolution layer by layer, obtains an image boundary, and outputs a defect segmentation result. According to the invention, the segmentation precision of PCB image detection can be improved.

Acoustic resonators with piston mode patches are disclosed. In one aspect, an interdigitated acoustic resonator (300) having plural fingers (308(1)-308(N)) or digits includes a modified piston mode rail. In particular, the piston mode rail is replaced with individual piston mode patches (320(1)-320(N); 322(1)-322(N)) that may be uniform or varied in size. Selection of patch size allows spurious modes to be suppressed to have a smooth filter passband with low overall insertion loss and minimal ripple. Further, the patches may be made through a monolithic process, which reduces overall production cost, complexity and cycle time.

The invention discloses a mechanical arm teleoperation control method and system based on incremental mapping, and the method comprises the steps: carrying out the noise reduction processing of the position and posture of a handle through adaptive low-pass filtering, and achieving the position and posture consistency mapping from a handle space to a mechanical arm working space after coordinate system axis rearrangement and similar transformation mapping; the system provides two kinds of control output of a pose tracking mode and a speed tracking mode, real-time inverse kinematics solution is carried out through a Jacobian matrix and a damping least square method in the speed mode, and resolution scaling and exponential moving average / spherical linear interpolation smooth filtering are carried out on a target pose in the pose mode. And then the handle motion instruction is mapped into a Cartesian instruction or a joint space instruction at the tail end of the mechanical arm, closed-loop execution is conducted through a real-time state feedback link, and the method can be used for scenes such as robot service, teaching, remote operation and data collection.

The invention relates to a computed tomography reconstruction method and system for a sparse angle of a cylindrical lithium battery, and the method comprises the steps: obtaining a cylindrical lithium battery projection image sequence as original projection data, and carrying out the diagonal interpolation processing of the original projection data, and generating complete sinogram data; performing Radon inverse transformation on the sinogram data to generate an initial iteration reconstruction image and taking the initial iteration reconstruction image as an iteration initial value, and performing iteration optimization by adopting a simultaneous algebraic reconstruction technology as a data fidelity term to obtain an optimized reconstruction image; designing a tangential smoothing filter, and filtering the optimized reconstructed image to suppress sparse artifacts; a radial weighting template is designed, and differential smoothing processing is carried out on the reconstructed image after sparse artifact suppression according to the distance between pixels and the center of the image; and performing reconstruction image optimization, sparse artifact suppression and differential smoothing processing circularly until a preset number of iterations is reached, thereby obtaining a final reconstruction image. According to the method disclosed by the invention, detail information of the image is effectively recovered while sparse artifacts are suppressed.

The invention discloses a thermal power generating unit boiler wall temperature early warning method and device, and the method comprises the steps: determining a steady-state working condition according to boiler operation parameters; the boiler operation parameters comprise coal feeding quantity, water feeding quantity, primary air quantity and secondary air quantity; calculating the wall temperature change rate of each monitoring point in a preset time window; the time window is set according to a data smooth filtering requirement and an early warning response timeliness requirement, and the monitoring points comprise a superheater, a reheater and a water cooling wall; and if the wall temperature change rate of at least one monitoring point is greater than or equal to a preset early warning threshold value and the duration is greater than or equal to a corresponding time threshold value, executing a corresponding early warning strategy. Whether the boiler is in the steady-state operation state or not is recognized before early warning is triggered, so that wall temperature fluctuation interference in the normal dynamic process such as unit load adjustment and combustion working condition fine adjustment is eliminated, it is avoided that the normal working condition is misjudged to be abnormal, the early warning timeliness of the wall temperature of the boiler is improved, meanwhile, the misjudgment rate is reduced, and the early warning accuracy is improved.

The invention discloses an AUV (Autonomous Underwater Vehicle) pose detection method and system based on underwater light vision, a medium and a program product, and belongs to the field of underwater robots. The method comprises the following steps: judging the turbidity of a water body by analyzing the blue channel characteristics of an image, dynamically adjusting the graying weight, and generating a preprocessed image by adopting blue channel enhancement; constructing triple constraint conditions of contour number, spacing and area, and combining a variable step size strategy to realize threshold adaptive optimization; wick pixel coordinates are extracted through contour circularity screening, a leak detection grading mechanism is established, and feature point compensation is performed based on exponential smoothing filtering prediction and historical frame data; and finally, performing P3P pose calculation according to confidence levels. The method can be suitable for various water areas, blue light source feature extraction and feature point matching tasks are well achieved, and the stability and accuracy of AUV pose detection are improved.

A method for designing a spectacle lens, said method comprising: an approximate curvature map acquisition step for acquiring a distribution map on xy plane coordinates of an approximate curvature (Cp) for each point on a surface (alpha), which is a design surface; a smoothing processing step of performing smoothing filtering processing on the distribution map; and a design step for designing a new surface (alpha ') on the basis of the distribution diagram of the new approximation curvature (Cp') obtained by the smoothing step.

The invention discloses a soil heavy metal content prediction method and device, a storage medium and electronic equipment, and relates to the technical field of soil heavy metal prediction.The method comprises the steps that soil samples with different heavy metal concentrations and different particle size grades are prepared; acquiring spectral reflectivity data of the soil sample corresponding to multiple observation angles, and performing smooth filtering processing on the spectral reflectivity data by adopting a preset smooth filtering algorithm; on the basis of the spectral reflectivity data of different observation zenith angles under the same observation azimuth angle after smooth filtering processing, same-direction difference spectral features are constructed; and performing cross-particle size robustness verification on the same-direction difference spectral features to screen out target spectral features, and inputting the target spectral features into the trained soil heavy metal content prediction model to obtain a soil heavy metal content prediction result. According to the method, the soil heavy metal content can be stably and accurately predicted.

The invention discloses a water surface target edge detection algorithm based on evidence fusion driving, and the algorithm comprises the following steps: collecting a water surface target sample image through a visual sensor, carrying out the graying of the water surface target sample image, and obtaining a gray image Iray (x, y); suppressing high-frequency noise in the grayscale image by using Gaussian smoothing filtering; constructing an edge feature extraction operator to extract a gradient magnitude M (x, y) and a direction theta (x, y) of the image; a probabilistic non-maximum suppression mechanism is constructed, so that edge judgment is credible; performing bilateral threshold on the gradient amplitude, and dividing edges into a strong edge, a weak edge and a non-edge; determining the probability of dividing weak edges into edges under different evidence sources from three aspects of gradient magnitude, gradient direction and neighborhood connectivity; s2, calculating a joint confidence coefficient m'e (x, y), and determining a credible weak edge as an edge according to the joint confidence coefficient, thereby obtaining the final edge information of the image; according to the invention, the detection precision of the water surface target contour in a complex environment can be effectively improved.

The invention relates to the cross technical field of elevator fault diagnosis and signal processing, in particular to an elevator shaft breakage early fault detection signal denoising method. The method comprises the following steps: acquiring an original low and medium frequency discrete signal of elevator shaft body vibration, and filtering by adopting a four-order Butterworth band-pass filter to obtain a filtered signal; performing complementary set empirical mode decomposition on the filtering signal to obtain a plurality of IMF components and a residual component, and screening effective IMF components from all the IMF components; the center frequency of each effective IMF component is calculated, and Gaussian weight distribution is carried out on each effective IMF component based on the center frequency of each effective IMF component; performing signal reconstruction on all the effective IMF components and the residual components according to the weight of each effective IMF component to obtain a reconstructed signal; and smoothing residual noise in the reconstructed signal by adopting self-adaptive moving smoothing filtering to obtain a de-noised signal. According to the method, the 30-60Hz low and medium frequency characteristic signals generated in the elevator spindle microcrack propagation stage can be accurately extracted, so that fault judgment is facilitated.

Certain aspects of the disclosure provide techniques for predictive autoscaling. A method includes determining resource utilization metrics for a plurality of instances of a service running in a container-based cluster for a plurality of timestamps over a period of time; applying a smoothing filter to the resource utilization metrics to obtain smoothed resource utilization metrics; adjusting each of the smoothed resource utilization metrics by a nominal value; calculating a plurality of ratio metrics for the smoothed resource utilization metrics; processing, with a machine learning (ML) model trained to perform resource utilization forecasting, the plurality of ratio metrics and to predict a future ratio metric for the service after a prediction time window; determining a future resource utilization for the service after the prediction time window based on the future ratio metric; and automatically adjusting configuration parameter(s) to modify a state of the container-based cluster based on the future resource utilization.

The invention discloses an epileptic seizure detection system based on a multi-dimensional hypergraph fusion network, which comprises a multi-modal data collector, a signal preprocessor, a feature extractor, a multi-dimensional hypergraph builder, a hypergraph feature extractor, a feature fusion device, a classifier and a time sequence prediction corrector, the multi-dimensional hypergraph constructor constructs three hypergraph structures, namely an intra-modal hypergraph, an inter-modal hypergraph and a time sequence hypergraph; the feature fusion device integrates the features extracted by the three hypergraphs to generate a comprehensive feature vector; the classifier classifies the fusion features through a two-layer feedforward neural network, and outputs probability prediction of epileptic seizure; the time sequence prediction corrector applies a majority voting smoothing filter to carry out post-processing on a classification result, isolated misclassification is eliminated, and the time sequence consistency of prediction is enhanced. The invention provides an innovative multi-dimensional hypergraph fusion network, and the robustness and accuracy of epileptic seizure detection are remarkably improved by simultaneously modeling high-order relationships in modals, between modals and time sequence dimensions.

The invention discloses a method for realizing laser origin offset self-calibration based on sliding window point cloud overlapping. The method comprises the steps of segmented scanning and data acquisition, sliding window data management, overlapping region joint optimization modeling, joint optimization solution and initial value estimation, physical offset time smooth filtering processing and point cloud coordinate correction and compensation updating. According to the method, a laser module is driven by a steering engine to perform segmented scanning, and an overlapping region with rich geometrical characteristics is automatically selected to obtain data; according to the method, an error model taking a physical offset vector d and a pose (R, T) as optimization variables is constructed, joint optimization is carried out by using a sparse Gaussian-Newton algorithm, and a stable compensation value is obtained through time smoothing filtering and is used for correcting point cloud coordinates; according to the method, an external target is not needed, the modeling precision and consistency of long-distance segmented scanning can be remarkably improved, and the method is suitable for real-time or offline processing.

A DC-DC converter is disclosed. The DC-DC converter includes a first switching element between an external input terminal and an inductor of a smoothing filter, a second switching element provided between the inductor and a reference voltage terminal, a control circuit for on-off controlling the first and second switching elements according to the upper and lower limit values of the output voltage, and a period determination circuit configured to determine the predetermined period based on the voltage corresponding to the upper limit value of the output voltage.

The invention provides a tire cornering stiffness estimation method and device based on double-extended Kalman filtering and a vehicle, and relates to the technical field of chassis control, and the method comprises the steps: building a state estimator and a parameter estimator which run in parallel, and achieving the combined online estimation of a state variable and a tire cornering stiffness parameter; according to the method, high-precision estimation can be completed only by using a conventional vehicle sensor signal without depending on a side slip angle or tire lateral force information which is difficult to obtain, an estimation result is continuously optimized through a closed-loop iteration mechanism, and stable and reliable cornering stiffness parameters are output for a vehicle stability controller to use after smooth filtering, so that the stability of the vehicle is improved. Therefore, the adaptability, robustness and safety of the control system under the non-linear working condition are remarkably improved.

Described herein are apparatuses and methods to detect tachycardias and selectively reject false tachycardia detections due to T-wave oversensing or noise. An apparatus includes electrodes, a sensing circuit coupled to at least two of the electrodes and configured to sense a signal indicative of cardiac electrical activity, and a smoothing filter configured to filter to the sensed signal indicative of cardiac electrical activity to thereby produce a filtered signal indicative of cardiac electrical activity. The apparatus produces a difference signal indicative of cardiac electrical activity by determining a difference between the sensed and filtered signals indicative of cardiac electrical activity. The apparatus also includes at least one processor configured to detect a tachycardia, or to determine whether or not to reject a tachycardia detection, based on the difference signal. The smoothing filter and / or difference circuitry can be implemented by the at least one processor, and / or other circuitry.

This application discloses an adaptive control method and electronic device for PVD and micro-arc oxidation equipment. The method includes: constructing a parameter inversion model characterized by a forward mapping relationship between prior distributions of process parameters and physical constraints; establishing a causal topology based on the mapping relationship, and identifying anomaly types by calculating the joint probability density of the two hypotheses; executing different strategies according to the anomaly type: shielding faulty sensors and reconstructing signals when measurement anomalies occur, and calculating parameter corrections through online optimization when mechanistic anomalies occur; performing safety verification and smoothing filtering on the corrections based on safety constraint envelopes; and generating an interpretable report containing root causes, contribution levels, and maintenance guidelines. This application achieves accurate anomaly type differentiation and robust adaptive control through physical-data decoupling modeling and causal reasoning, significantly improving the stability, safety, and interpretability of processes in complex industrial environments.

The invention discloses an aperture error and non-linear cooperative calibration circuit and method for a pipeline ADC, and relates to the technical field of mixed signal integrated circuits, the circuit comprises a pipeline ADC basic circuit and a calibration circuit integrated with the pipeline ADC basic circuit, and the calibration circuit comprises an aperture error calibration module and a non-linear error calibration module. And the aperture error calibration module dynamically adjusts the sampling clock phase of the first-stage Sub-ADC by counting a post-stage output code value histogram and combining an SAR logic feedback mechanism so as to compensate the sampling moment deviation between the first-stage Sub-ADC and the MDAC. The nonlinear error calibration module extracts errors by injecting pseudo-random jitter and utilizing double-threshold window crossing statistics, and after EWMA smooth filtering is carried out, a polynomial compensation coefficient is updated in a self-adaptive mode through an LMS algorithm, and harmonic distortion is restrained. According to the method, the linearity under high-speed and high-bandwidth application is remarkably improved, and the method has good robustness for PVT fluctuation.

The application provides a battery system SOC estimation method, comprising the following steps: S1, obtaining historical data for the same type of battery system, and pre-processing the data set; S2, dividing into different data subsets according to different states of the battery system, and respectively constructing features, and dividing training and test data; S3, constructing an SOC estimation model based on an XGBoost algorithm for the data subsets under different states of the battery system; and S4, collecting real-time monitoring data of the battery system, and pre-processing the data; the SOC estimation method based on data driving multi-algorithm fusion under different states of the battery system can accurately estimate the SOC value of the battery system, frequent calibration is not needed, there is no error accumulation effect, a smoothing filter strategy fusing a recursive average filter and a first-order lag filter is proposed, the SOC can be efficiently and smoothly controlled, and SOC jump can be avoided.

This invention relates to the field of thermal management for new energy vehicles, specifically to an environmental temperature estimation system and method for range-extended electric commercial vehicles. The system acquires the current temperature values ​​of multiple available temperature sources for the entire vehicle in real time. Based on the current operating status of the vehicle, it dynamically assesses and filters the availability of each available temperature source. From the available temperature sources, it selects one or more temperature sources with high thermal inertia, no active heat source, or minimal thermal impact to form a candidate temperature set, and then performs a minimum value selection process, using the minimum value as the preliminary environmental temperature estimate. The preliminary environmental temperature estimate undergoes a rate of change rationality check and smoothing filter, outputting the final environmental temperature estimate. From the vehicle's existing temperature sensor network, it dynamically selects the signal closest to the actual environmental temperature as the estimation source, performs validity checks and data fusion, and finally outputs a reliable environmental temperature estimate, thereby completely replacing dedicated environmental temperature sensors and achieving hardware cost reduction.

The invention discloses a multi-index rapid detection method for Tangyou food, and belongs to the technical field of food quality detection. The method comprises the following steps: collecting spectral data and temperature data of a to-be-detected sample through a near infrared spectrum technology, sequentially carrying out standard normal variable transformation and smooth filtering pretreatment on the spectral data, and screening characteristic wavelengths related to the pickling depth, the oil content and the water activity by adopting a competitive self-adaptive reweighting sampling algorithm; and inputting the characteristic spectrum data into a joint partial least squares regression model to carry out multi-index collaborative prediction, carrying out temperature compensation correction on a predicted value according to the temperature data, and finally comparing the corrected predicted value with a quality standard threshold value and outputting a detection result. According to the method, the model complexity is reduced through characteristic wavelength screening, the prediction precision and efficiency are improved through joint modeling, the detection stability is ensured through temperature compensation, and rapid nondestructive detection of the quality index of the Tangyang food is realized.

The invention discloses a residual voltage detection method and a residual voltage detection circuit, relates to the technical field of residual voltage detection, and solves the technical problem that the residual voltage detection method is easy to cause misjudgment or missing detection under complex working conditions. The detection method comprises the following steps: carrying out continuous sampling through an analog-to-digital converter to obtain N voltage sampling points of an alternating current period; based on the N voltage sampling points, square calculation, calculation of a total average value Aversion after square calculation and calculation of an arithmetic square root S of the total average value Aversion are carried out, and the arithmetic square root S is multiplied by a voltage conversion coefficient Vr to obtain a voltage effective value RMS; performing null drift dynamic calibration and real-time smooth filtering processing on the voltage effective value RMS to obtain an output voltage value Vrms; and detecting whether the output voltage value Vrms is a residual voltage based on the output voltage value Vrms, obtaining a residual voltage detection result and storing the residual voltage detection result. The DC bias error introduced by the hardware operational amplifier is eliminated, the output stability is improved, and the stability and high efficiency of residual voltage detection are ensured.

The present invention belongs to the technical field of gas detection data processing, and specifically relates to a gas detection model training method, device, medium and equipment based on an electronic nose. The training method includes: acquiring a gas data set, performing smoothing filtering preprocessing on the sensor response data to obtain preprocessed data; converting the preprocessed data to generate two-dimensional sensor training images to form a two-dimensional sensor image set; using data enhancement technology on the two-dimensional sensor images to obtain a two-dimensional sensor image expansion set; analyzing the image to be analyzed in the two-dimensional sensor image expansion set using a pre-trained detection model, and determining a loss value based on the difference between the category label and the image analysis result; and training the pre-trained detection model based on the loss value, wherein the trained detection model is used to identify and detect the sensor image converted from the gas sensor response data.

A method for decoding an encoded video bit stream in a video decoder is provided that includes decoding an intra prediction mode for a block of pixels encoded in the video bit stream, determining, based on the intra prediction mode, whether or not a smoothing filter is to be applied to reference pixel values used for intra prediction of the block, applying a smoothing filter to the reference pixel values when a result of the determining is that a smoothing filter is to be applied, and performing intra prediction according to the intra prediction mode using the smoothed reference pixel values to generate a predicted block of pixels.