20 results about "Threshold estimation" patented technology

The invention is suitable for the field of pain assessment, and particularly provides a pain threshold estimation method and system based on multi-modal data fusion, and the method comprises the steps: constructing a cross-modal weak feature enhancement fusion network, carrying out the processing of collected multi-modal data, and extracting physiological weak features and behavior weak features; introducing a time-varying attention Transform model, dynamically allocating weights for the processed physiological weak features, behavior weak features and environment auxiliary data by using a pain dynamic tag, and outputting cross-modal fusion features; constructing a double-branch pain threshold estimation model, wherein the model comprises a supervised estimation branch and an unsupervised estimation branch; and introducing a pain feature element library, carrying out online calibration on parameters of the double-branch model in combination with a meta learning algorithm, and outputting a real-time pain threshold value and a short-term pain threshold value change trend of the target object. According to the embodiment of the invention, the clinical operability of pain assessment is remarkably improved, and ineffective intervention caused by inaccurate assessment is reduced.

An apparatus for data processing and related non-transitory computer-readable medium are provided. In the method, the apparatus computes a burst threshold for a data stream. The burst threshold is associated with a throughput of the data stream. The apparatus further identifies a set of bursts in the data stream based on the burst threshold, and detect a pattern in the data stream based on the set of bursts. The apparatus further estimates at least one subsequent burst in the data stream based on the pattern and the burst threshold, and outputs an indication of the at least one subsequent burst in the data stream. The method enables a device to predict one or more future data bursts based on characteristics of existing data. The predicted data bursts allow the transmission resource and power to be adaptively arranged to significantly reduce power consumption and improve transmission efficiency.

An electronic circuit includes a fast-settling threshold estimator for determining a fast-settling threshold voltage which includes a maximum peak detector that receives an analog signal containing digital data and produces an estimate of a maximum value of the analog signal; a differentiator that produces an output voltage responsive to a change in the estimate of a maximum value of the analog signal; a preselected voltage coupled to a comparator that outputs a pulse when the output voltage is greater than the preselected voltage; a minimum peak detector that produces an estimate of a minimum value of the analog signal; and an averaging circuit that produces a weighted average of the estimate of a maximum value of the analog signal and the estimate of a minimum value of the analog signal. In one embodiment, the fast-settling threshold estimator is coupled to a bit slicer and to a RF receiver.

The invention discloses an intelligent self-service pure tone threshold test method and device and a medium, and relates to the technical field of threshold test, and the method comprises the steps: executing a pure tone threshold test process based on an information gain selection mechanism according to the recognition difficulty level in noise, the matching degree score and a pure tone threshold test starting strategy, the key pressing behavior of the testee is judged in real time in combination with the personal operation rhythm template, the hearing threshold estimation result of each frequency band and the corresponding uncertainty index and behavior event mark are output, and fixed-point deviation correction complementary measurement and weight correction are carried out based on the hearing threshold estimation result of each frequency band, the uncertainty index and the behavior event mark; obtaining a final hearing threshold result and an overall credibility grade; by establishing the candidate test set and introducing the pure tone threshold measurement process based on the information gain selection mechanism, the dynamic adjustment of the test path according to the hearing threshold estimation result and the uncertainty index of each frequency band is realized, and the test efficiency and the result stability are improved.

The application discloses a kind of semi-supervised multi-label image classification method, system, equipment and storage medium.The method utilizes feature extraction network to obtain the spatial feature representation of input image, and respectively constructs class-independent global feature and class-specific global attention feature;Feature map is divided into local area, local average feature and local class-specific attention feature are generated, the fusion of global feature and local feature is realized by residual form, attention enhancement prediction of teacher network is constructed based on fusion feature, and the optimal threshold value of each class is searched on labeled data using class adaptive threshold estimation method, to generate high-quality pseudo label on unlabeled data;Semi-supervised collaborative optimization is realized by combining network training again.The application can effectively improve the accuracy and stability of multi-label image classification under limited annotation conditions, and is especially suitable for medical image recognition, retail commodity detection, remote sensing scene analysis and other multi-label complex scenes.

The invention relates to the field of material analysis, and relates to a DIC-based irregular crack self-adaption and multi-target synchronous width calculation method. The method comprises the following steps: step S101, data reading: obtaining a displacement component of each point of a DIC displacement field; reading images frame by frame to form a two-dimensional array; recording input metadata for each frame of image; s102, carrying out ROI (Region of Interest) self-adaption and masking; step S103, pixel-physical quantity outline calibration is carried out, and a displacement field is converted to millimeter in a unified manner; step S104, estimating a self-adaptive threshold value; step S105, carrying out discontinuous displacement detection; step S106, carrying out candidate segment aggregation and multi-target grouping; step S107, carrying out local trend regression fitting; and S108, geometric normal correction is carried out. According to the method, the crack boundary is identified by the displacement discontinuity of the DIC displacement field, the slope k of the center line is obtained by combining local trend regression, the row width is geometrically corrected into the normal real width, and the system error caused by orientation is eliminated.

Various embodiments provide for performing a read voltage level calibration process with selective skipping collection of calibration-related information on a memory device of a memory system, such as a memory sub-system. For example, some embodiments provide for performing a coarse threshold estimate (CTE) calibration process, with selective skipping of failed bit count (CFBit) information collection, on a memory system.

Various embodiments provide for performing a read voltage level calibration process with selective skipping collection of calibration-related information on a memory device of a memory system, such as a memory sub-system. For example, some embodiments provide for performing a coarse threshold estimate (CTE) calibration process, with selective skipping of failed bit count (CFBit) information collection, on a memory system.

The invention discloses a multi-target detection method based on a millimeter-wave radar, and the method comprises the steps: collecting the echo data of multiple targets in a complex scene through the millimeter-wave radar, and carrying out the preprocessing of the echo data, and obtaining the distance-Doppler two-dimensional data to be processed; based on the obtained two-dimensional data to be processed, adopting a CFAR detection algorithm to obtain a coarse estimation CFAR mark detection result; the maximum module value square of each column of pulses on the two-dimensional data to be processed is stored, and a false target interference mark is obtained through dynamic adaptive threshold estimation; and based on the obtained CFAR mark and the false target interference mark, accurate detection of the target is realized through a comprehensive mark. According to the method, a CFAR-based comprehensive marking algorithm is adopted, target detection is realized by dynamically estimating background noise and a self-adaptive threshold value, multi-dimensional information such as distance, speed and time-frequency characteristics is fused, a real target and clutter are effectively distinguished, and the target can be accurately identified and related information of the target can be extracted in a complex scene.

The application discloses a low signal-to-noise ratio condition modulation identification method based on wavelet transform and channel attention, comprising: acquiring a received signal; inputting the received signal into a pre-trained neural network identification model to output a classification identification result and a classification accuracy rate. Based on the signal reconstruction and the identification model of the neural network, the wavelet threshold estimation module and the wavelet reconstruction module constitute a signal reconstruction path, the multi-scale feature extraction module and the prediction classification module constitute an identification path, the two paths are mutually enhanced, the advantages of the digital signal processing technology and the neural network are combined, the wavelet threshold estimation module is introduced to predict a denoising threshold through the neural network, and the network parameters of the wavelet threshold estimation module are updated according to the back propagation; the signal reconstruction path and the identification path are combined, the network interpretability is enhanced, the multi-scale modulation features are utilized, and the modulation identification accuracy rate in the low signal-to-noise ratio scene is significantly improved.

The application relates to a three-component seismic data random noise suppression method, device, medium and equipment based on a quaternion wavelet transform, which comprises the following steps: representing X, Y and Z three-component seismic data as a quaternion signal in the form of a quaternion array in a time-space domain; implementing quaternion wavelet transform on the quaternion array; using the quaternion wavelet transform to perform multi-layer decomposition on the quaternion signal in the form of the quaternion array, so as to obtain corresponding quaternion wavelet coefficients of each layer; calculating a threshold value by using a new threshold value estimation expression, and performing threshold value operation on the quaternion wavelet coefficients of each layer by using a semi-soft threshold value function, wherein the wavelet coefficients greater than the threshold value are reserved, and the wavelet coefficients less than the threshold value are set to zero; performing quaternion wavelet inverse transform on the threshold value processed quaternion wavelet coefficients, reconstructing a denoised quaternion signal, taking out X, Y and Z components from three virtual parts of the denoised quaternion signal, and completing vector denoising.

The present application relates to the field of high-voltage engineering and insulation technology, and particularly relates to a method for artificial contamination test under high-voltage environment, which comprises the following steps: firstly, constructing a target deposition distribution function and a target response set on a parameterized grid on the surface of an insulator, and merging an environmental boundary condition time sequence set to form a target deposition and response data set; then, implementing layered deposition of soluble contaminants and insoluble particles under the electrode system time-varying electric field and particle charging control in the on-power state, collecting surface state field to generate an in-situ deposition state data set; then, obtaining electrical and surface responses and estimating a median flashover voltage according to segmented loading and stepwise flashover test to form an electrical and physical response data set; finally, based on the kinetic model, closed-loop optimization is performed on the electrode system time-varying trajectory, the environmental program and the deposition spectrum within the leakage current constraint. The present application realizes reproducible configuration, stable threshold estimation and fast test convergence.

Various embodiments provide for performing a read voltage level calibration process with selective skipping collection of calibration-related information on a memory device of a memory system, such as a memory sub-system. For example, some embodiments provide for performing a coarse threshold estimate (CTE) calibration process, with selective skipping of failed bit count (CFBit) information collection, on a memory system.

The invention discloses a multi-parameter satellite component health assessment and prediction method based on joint distribution evolution. The method comprises the following steps: S1, acquiring initial reference data of normal work of a satellite component and current operation data of a latest complete period; s2, carrying out joint distribution difference analysis on the current data and the reference data, and calculating the distribution difference by adopting a slice Wasserstein distance; s3, generating a health state index (HSI), and smoothing the HSI through index moving average; s4, constructing a monotonic constraint Gaussian process regression model; and S5, estimating the residual life of the satellite component in combination with the early warning and failure threshold values. According to the method, the multi-parameter coupling degradation trend is captured, non-physical rising of a prediction result is avoided, a large number of samples and complex mechanism models are not needed, calculation is efficient, prediction is stable, in-orbit deployment is easy, and the reliability of health assessment and life prediction of satellite components is remarkably improved.

The present disclosure relates to a flash memory system including a non-volatile memory and a circuit for performing operations on the non-volatile memory. The circuit can determine, by a machine learning model at a first point in time, a plurality of voltage thresholds for a read operation on the non-volatile memory based on one or more stress conditions at the first point in time. The plurality of voltage thresholds can correspond to respective durations since the first point in time. The circuit can obtain, from the plurality of voltage thresholds, one or more voltage thresholds associated with a particular duration of the respective durations. The circuit can perform the read operation on the non-volatile memory with the one or more voltage thresholds associated with the particular duration at a second point in time later than the first point in time by the particular duration.

The application is suitable for the technical field of geological disaster early warning and risk assessment, and provides a landslide threshold estimation method based on an adaptive hybrid Copula model and an OSFOA algorithm. The method comprises the following steps: through dynamic detection of the optimal lag relationship between landslide displacement and influencing factors and the adaptive hybrid Copula model, the model can automatically adapt to the complex time-lag response mechanism and variable tail dependence characteristics of the landslide system, without prior assumptions, and has strong generalization ability. The OSFOA algorithm is introduced to optimize the hybrid weight, solving the high-dimensional and nonlinear optimization problem, and improving the model precision and stability. In combination with VaR, CoVaR, dynamic threshold adjustment, Bootstrap uncertainty analysis and joint return period calculation, a complete, rigorous and operable risk early warning system from threshold calculation, risk measurement to uncertainty evaluation is constructed. In the actual landslide case, the multi-level early warning threshold with clear physical meaning and statistical confidence is successfully output, and the effectiveness and practicability of the method are verified.

The present invention relates to threshold estimation and calibration for anomaly detection. Herein are machine learning (ML) and extreme value theory (EVT) techniques for normalizing and thresholding anomaly scores without presuming a values distribution. In an embodiment, a computer receives many unnormalized anomaly scores and, according to peak over threshold (POT), selects a highest subset of the unnormalized anomaly scores that exceed a tail threshold. Based on the highest subset of the unnormalized anomaly scores, parameters of a probability density function are trained according to EVT. After training and in a production environment, a normalized anomaly score is generated based on an unnormalized anomaly score and the trained parameters of the probability density function. Anomaly detection compares the normalized anomaly score to an optimized anomaly threshold.

The invention discloses an electromagnetic sensitive threshold estimation method based on sensitive response difference modeling, and the method comprises the steps: determining a first frequency range based on the working frequency range of a device having potential interference with a to-be-tested device and the working frequency range of the to-be-tested device; determining discrete frequency sampling points in a first frequency range based on an ideal electromagnetic sensitive threshold function of the to-be-tested equipment; determining an electromagnetic sensitivity threshold estimation function of the to-be-tested equipment in the first frequency range based on a difference value sequence between an actually measured electromagnetic sensitivity threshold and an ideal electromagnetic sensitivity threshold of the to-be-tested equipment under the discrete frequency sampling points; the electromagnetic sensitive threshold estimation function is used for determining an estimated value of an electromagnetic sensitive threshold of the to-be-tested equipment at a target frequency, and the target frequency is within the first frequency range. The method gives consideration to pertinence, efficiency and reliability of threshold estimation, and can effectively adapt to electromagnetic sensitive threshold evaluation requirements in a complex electromagnetic environment.

A memory system and an operating method thereof are disclosed. The memory system includes a memory device and a controller. The controller determines, for each read operation, a mathematical model by using a) a function of a set of read threshold voltage among a plurality of read threshold voltages and b) a set of checksum values, determines a polynomial regression model based on the mathematical model, determines a set of parameters using a plurality of calculations between an input matrix and an output matrix based on the polynomial regression model, and estimates a next read threshold voltage for a next read operation based on the set of parameters. The controller calculates a mathematical operation algorithm to replace a normal multiplication operation, a normal division operation, and a normal multiplication-division operation.

The invention discloses a signal bandwidth and pulse width detection method, and relates to the field of communication signal reconnaissance. The method comprises the following steps of: segmenting baseband data and calculating a power spectrum of each segment; smooth filtering is carried out on the power spectrum; thirdly, processing the smoothed data through a first-stage self-adaptive threshold, and preliminarily estimating the bandwidth and the pulse width of the signal; and finally, frequency domain filtering is carried out based on the roughly estimated bandwidth, fine detection is carried out by using a second-stage adaptive threshold, and the accurate bandwidth and pulse width are finally obtained. According to the method, the detection process is converted from the time domain to the frequency domain, and a multistage threshold estimation mechanism is adopted, so that the defects that a traditional time domain method is easily interfered by noise and multiple signals are difficult to detect at the same time are effectively overcome, and the accuracy and reliability of pulse signal parameter measurement are remarkably improved.