190 results about "Smoothing filter" patented technology

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 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 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 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 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 application discloses a bearing-frequency-based target passive tracking trajectory division method, and belongs to the technical field of underwater multi-target tracking of passive sonar.The application aims to solve the problem of poor tracking effect of the existing method in the presence of target trajectory intersection scenes.The application introduces a frequency measurement value into a target state to obtain a bearing-frequency augmented state vector under the condition of passive tracking of a single observation platform in the presence of the underwater multi-target tracking trajectory intersection scenes.Bearing-frequency joint information can improve the discrimination degree of the solution of the posterior probability of the measurement value corresponding to the target, so that the correctness of batch division of the target in the trajectory intersection case is improved.Combining with an RB particle filter, a multi-hypothesis tracking technology and a Kalman smoothing filter technology, good target tracking effect is achieved, and the correctness of the tracking result is ensured.The method can be applied to the technical field of underwater multi-target tracking.

The application provides a kind of nonlinear filtering method of pulse eddy current detection signal, belong to nondestructive testing technical field, can at least partially solve the problem that magnetic field decay voltage signal is greatly influenced by noise in pulse eddy current detection, traditional smoothing filter is easy to introduce signal distortion.The method is used for the pulse eddy current detection signal receiving processing of metal structure with cladding layer, comprising: collecting magnetic field decay voltage signal in pulse eddy current detection process;Nonlinear compression transformation is carried out on the time axis corresponding to the magnetic field decay voltage signal;Smooth filtering time window of preset length is set on the time axis after nonlinear compression, and the corresponding conventional time interval is determined;The magnetic field decay voltage signal falling into the conventional time interval is carried out smoothing filter processing, and the filtered pulse eddy current detection signal is obtained.The application makes the filtering process match the time characteristics of the magnetic field decay signal, improves the stability of signal processing, and is beneficial to subsequent wall thickness or state evaluation.

The invention relates to a pollutant detection method and system based on machine learning, and relates to the technical field of pollutant detection.The method comprises the steps that original spectral data are collected and denoised through a Savitz-golay smoothing filter, parameters are dynamically adjusted, and stable spectral data with pollutant core characteristics reserved are obtained; the method can effectively solve the technical problem that low-concentration interval heterovariance noise masks weak features of left censored data, and avoids the defect of excessive smoothness or insufficient denoising of a traditional denoising method. Secondly, in combination with a blank spectrum noise optimization detection limit and a quantification limit, effective features are extracted by utilizing machine learning, and incomplete observation characteristics of left-censored data can be adapted, so that the problem of systematic bias caused by data distribution hypothesis distortion of an existing algorithm is solved, and the defect that a low-concentration risk early warning window is neglected in a traditional method is overcome; finally, filter parameters are analyzed and optimized through a loss function, a detection result is finally output, and detection limit heterogeneity caused by instrument drifting and working condition fluctuation can be dealt with.

The application provides a high-precision direction finding method and device combining time difference and phase difference of microwave and photon, and the method comprises the following steps: obtaining a target broadband microwave signal; performing smoothing filtering on each microwave signal respectively to obtain time difference estimation values corresponding to two microwave signals; obtaining phase difference estimation values corresponding to the two microwave signals according to the relationship between the time difference and the phase difference; performing consistency correction on each microwave signal respectively to obtain time-phase calibration coefficients; obtaining time difference measurement values and phase difference measurement values through the time-phase calibration coefficients respectively; performing combined direction finding processing operation according to the time difference measurement values and the phase difference measurement values to obtain a direction finding angle; the improved time difference and phase difference combined direction finding method is innovatively provided by using the advantages of time difference direction finding and phase difference direction finding, the phase difference ambiguity is solved through time difference surface fitting interpolation, high-precision direction finding of distributed single baseline two antennas can be realized, the requirements of equipment volume, power consumption and cost are reduced, and the method has good application value.

The invention relates to a restricted space accident risk dynamic assessment method and system based on the Internet of Things. The method comprises the following steps: acquiring multi-source heterogeneous time-space sequence data, constructing a time-space data cube based on the data, inputting the time-space data cube into a physical constraint generative adversarial network, creating a large amount of high-quality virtual risk scene data conforming to a physical rule, and combining the virtual risk scene data with real data to obtain a real risk scene. And training a risk assessment model, seamlessly connecting the trained model with a real-time data stream, and assisting with a smooth filtering technology to obtain a comprehensive accident risk dynamic assessment result. By adopting the method, small sample limitation can be overcome, a physically compliant virtual risk scene is generated, and accurate dynamic assessment of the accident risk in the limited space is realized.

The invention discloses a six-dimensional force sensor adaptive filtering method based on load type identification. The method comprises the following steps: acquiring six-channel original output signals of a sensor; the time domain, frequency domain and multi-channel collaborative features are extracted and quantized; recognizing the load state as a quasi-static state, a transition state or a dynamic state according to the comparison between the load characteristic value and quasi-static and dynamic thresholds; a strong smoothing filter is adopted in a quasi-static state, a weak smoothing filter is adopted in a dynamic state to retain details, and weighted fusion is carried out on output of the two filters according to a first dynamic weight in a transition state. The method overcomes the limitation of traditional fixed parameter filtering, can adapt to different working conditions, ensures smooth signal transition while effectively suppressing noise, and remarkably improves the signal quality and control stability of the six-dimensional force sensor in complex application.

A video signal decoding device is disclosed. The video signal decoding device comprises a processor. The processor acquires smoothing filtering information indicating whether or not smoothing filtering is disabled from a video signal, and predicts the current block by determining whether or not to apply smoothing filtering in intra prediction according to the smoothing filtering information at a predetermined level.

According to an embodiment, a wireless power receiving device includes a demodulator that generates carrier cycle data from a received frequency-shift keying modulated signal with dithering. A high-pass filtering circuit extracts offset values from the carrier cycle data. A polyphase filtering circuit includes multiple smoothing filters and distributes the extracted offset values among these filters based on a dithering pattern length. A compensation circuit stores smoothed offset values from the polyphase filtering circuit in a dithering table during an evaluation window and applies these stored values to compensate carrier cycle data during subsequent demodulation.

An image acquisition device (1) is provided with: a relationship calculation unit (202) that calculates relationship data between pixel values in an image and statistical values of the pixel values; and a parameter setting unit (204) and a filter processing unit (205) that process an image to be processed, the parameter setting unit (204) and the filter processing unit (205) calculating the statistical values of the pixel values on the basis of the pixel values of each pixel in the image and the relationship data. A statistical value is specified for each pixel of an image, a parameter of an edge-saving smoothing filter is set for each pixel of the image on the basis of the specified statistical value, and the image is processed using the edge-saving smoothing filter in which the parameter is set for each pixel of the image.

A kind of time domain calculation method for processing steam turbine blade vibration signal trigger time, belong to steam turbine detection technical field.To solve the problem of inaccuracy of trigger time calculation of non-uniform steam turbine blade, the field blade vibration monitoring system collects data, including time series data and signal column data;Using two hampel filters to remove outliers of the collected signal column data, using a fixed data interval to resample the signal column data after removing outliers, performing multiple smoothing filter processing on the obtained resampled signal column data, performing ascending and descending sequence calibration on the obtained smoothed signal column data after filtering, finding the maximum and minimum values, and identifying the upper envelope and lower envelope;The obtained envelope is multiplied by a coefficient as the trigger line of the signal, and the intersection point of the obtained smoothed signal column data after filtering and the trigger line is taken as the steam turbine blade vibration signal trigger time.The present application has good applicability.

The invention provides a temperature measurement method of a hollow ball screw and a flexible temperature measurement device in contact with the inner wall of the hollow ball screw, and the method comprises the steps: setting a logic channel identifier of each thin film temperature sensor according to the axial position of each thin film temperature sensor of a flexible thin film sensor array on the inner wall of the hollow ball screw; periodically measuring and acquiring temperature data of the hollow ball screw until the set acquisition times are reached; after the temperature data of all the channels are obtained, an axial smooth filtering algorithm is executed, the temperature data are corrected, and corrected temperature data are generated; after the corrected temperature data, the timestamp corresponding to the corrected temperature data and the logic channel identifier corresponding to the corrected temperature data are packaged, a temperature field data frame is sent and stored in a storage device of the computer terminal; and after sending of the temperature field data frame is completed, the temperature measuring device is controlled to enter a low-power-consumption sleep mode. According to the temperature measurement method provided by the embodiment of the invention, the temperature of the hollow ball screw can be simply and effectively measured.

The invention discloses a divergence discrimination method for a deformable aircraft attitude control system based on multi-modal feature extraction, and belongs to the technical field of aircraft safety boundary evaluation. The method comprises the following steps: firstly, performing Gaussian smoothing filtering preprocessing on attitude angle tracking error signals of each channel to remove high-frequency noise; then, direct divergence judgment is realized based on sliding window linear regression and fluctuation intensity detection, and oscillation divergence judgment is completed by combining high-frequency and high-amplitude detection; and finally, correcting the divergence time, determining the earliest divergence channel, combining the divergence type and natural frequency of the error signal, analyzing the influence of nonlinear factors on the frequency domain characteristics of the system, and determining the relationship between divergence and cut-off frequency, thereby accurately identifying the divergence time and type. The method can effectively deal with non-linear interference such as atmospheric perturbation and execution mechanism saturation, realizes early divergence detection and mode recognition under multi-channel conjoint analysis, and has the characteristics of high accuracy, low calculation complexity and high engineering applicability.

The invention relates to the technical field of industrial control, in particular to a control method and system for an insulating material production line. The method comprises the steps of obtaining initial operation data of an insulating material production line in real time; calculating a transient specific energy index in combination with a standard plasticizing reference absolute temperature; performing smooth filtering on the machine head melt pressure to extract a time differential item to obtain a pressure change rate; deducing a dynamic thinning coefficient by combining the transient specific energy index, the previous sampling moment value, the pressure change rate and the machine head melt pressure; constructing a state matrix mismatch correction factor based on the dynamic thinning coefficient, a sliding data window historical maximum thinning limit value and a standard steady-state thinning reference value; and correcting the discrete state space model system matrix in a targeted manner by using the correction factor, and executing adaptive model prediction control to output an optimal control instruction. The method can effectively solve the mismatch problem of the static model under the nonlinear sudden change condition, improves the control precision and reliability, and guarantees the stable and efficient operation of a production line.

Methods, systems, and apparatus, for enhanced clock frequency control. In some implementations, a clock system tracks time using a clock signal having a clock frequency. An interface receives a time reference from a reference clock, and a feedback loop synchronizes the clock system with the reference clock. The feedback loop includes a feedback loop controller configured to determine a clock frequency adjustment for the clock system based on an offset between the time reference and a time indicated by the clock system. The feedback loop also includes a smoothing filter configured to alter the clock frequency adjustment determined using the feedback loop controller. The feedback loop updates the clock frequency of the clock system based on the altered clock frequency adjustment.