167 results about "Current sample" patented technology

The invention discloses a buck-boost circuit-based mppt charging current backflow prevention control method, and relates to the technical field of switching power supplies, and the method comprises the steps: collecting a photovoltaic input voltage, a battery output voltage, and an inductive current flowing through an inductor; the current input power is calculated based on the photovoltaic input voltage and the inductive current, and the reference voltage is dynamically adjusted through an MPPT loop algorithm; pI voltage loop operation is carried out on the output voltage and the adjusted reference voltage, and expected charging current is generated; pI current loop operation is carried out on the expected charging current and the inductive current sampling value after proportionality coefficient adjustment, and a PWM duty ratio control signal is output to adjust the driving states of the four switching tubes; when the judgment circuit is in an intermittent conduction mode and meets critical conditions, a driving signal of the fourth switching tube is shielded, and only a body diode realizes a current path to prevent reverse flow of current; the battery current is effectively prevented from flowing backwards, and the system safety and the energy utilization efficiency are improved.

The invention relates to the technical field of power distribution network power supply, and discloses a power distribution network power supply system based on time domain control, comprising a unified time service synchronization module used for establishing a unified time service link and keeping node time synchronization; the time domain acquisition and judgment module is used for acquiring a zero-crossover timestamp and voltage and current samples and determining candidate target moments and execution modes; the instruction generation and calculation module is used for calculating a zero-crossing time difference and an instantaneous voltage difference and generating a time domain action instruction set; the starting advance locking module is used for determining and locking starting advance; the instruction rechecking judgment module is used for rechecking the effectiveness of the instruction set; the action execution checking module is used for completing loop closing or loop opening at a candidate target moment and judging a border crossing state; and the time difference updating and correcting module is used for calculating a time deviation value and updating control parameters. According to the invention, high-precision time synchronization, dynamic self-calibration and action safety coordination control of the loop closing and opening process of the power distribution network are realized.

An example method of video coding includes receiving a video bitstream comprising a plurality of frames, including a current frame comprising a current block. The method also includes determining an edge direction by performing edge detection for the current block, and determining a difference between a current sample of the current block and a neighboring sample based on the edge direction. The method further includes applying a filter to the current block based on the determined difference.

The invention discloses a residual current detection system and method based on wireless synchronization and software confluence, and relates to the technical field of residual current detection. A host central processing device receives current sampling data with timestamps from a slave central processing device through a signal receiver to complete centralized receiving of multiple paths of current signals; after the host central processing device receives synchronous current sampling data with timestamps transmitted by the slave central processing device, aligning and synthesizing multiple paths of time-synchronized current signals through a software confluence algorithm, carrying out vector summation on the multiple paths of current signals based on a vector operation principle, and calculating the vector sum of residual current; and judging whether a grounding fault or an insulation defect exists according to whether the vector sum is zero, if the vector sum deviates from the zero value, judging that residual current abnormity exists, and giving an alarm or recording fault information. The detection system effectively improves the accuracy, reliability and applicability of residual current detection.

The invention discloses a manganin current sampling on-line self-correction method and system of an intelligent fusion terminal. The method and system are applied to the intelligent fusion terminal comprising a manganin diverter assembly. The assembly comprises a working resistor connected in series in a main current loop and a reference resistor for monitoring, the working resistor and the reference resistor are thermally coupled, and the reference resistor does not bear working current during normal sampling. The method comprises the following steps: respectively measuring real-time resistance values of the two resistors in a silent working window period; calculating a current actually measured resistance value ratio; obtaining an ideal resistance value ratio of the two in a stress relaxation-free ideal state; obtaining a correction resistance value ratio variable quantity according to a difference value between an actual measurement ratio and an ideal ratio; and finally, calculating a correction coefficient and updating a sampling channel parameter so as to compensate resistance drift caused by long-term electric thermal stress and realize automatic correction of subsequent current sampling. According to the method, on-line and high-precision self-correction independent of an external reference is realized, and the problem of long-term stress relaxation of a manganese-copper material is effectively solved.

A method comprising emulating (401) an implementation of a post-processing process of an SL-HDRx system before applying a pre-processing process of the SL-HDRx system using input color components of a current sample of a current picture up to a computation of at least one intermediate chroma component; determining (402, 404) that a value of the at least one intermediate chroma component is higher than a maximum chroma component value depending on a calculation precision set in the implementation of the post-processing process; applying (403, 405) a correction process on the input color components to obtain corrected color components for the current sample respecting at least a clipping prevention condition responsive to the value of the at least one intermediate chroma component is higher than the maximum chroma component value.

The invention provides a charging pile verification method based on wireless communication, and the method comprises the steps: carrying out the data preprocessing of obtained multi-source operation data of a target charging pile, obtaining synchronous data with a timestamp, and enabling the synchronous data to comprise a voltage sampling data set, a current sampling data set, and an electric energy pulse data set; performing data encapsulation based on the synchronous data to obtain a transmission data packet, and sending the transmission data packet to a verification terminal through a wireless network; in response to the transmission data packet received by the verification terminal, carrying out data buffering and reordering on the transmission data packet based on the timestamp, and generating ordered time sequence data; based on the ordered time sequence data, time axis reconstruction and interpolation alignment processing are carried out, and an alignment data matrix is generated; and performing electric energy metering error analysis based on the alignment data matrix to obtain a charging pile verification result. By adopting the method, the accuracy and reliability of remote verification of the charging pile can be improved.

The invention provides a current sampling zero offset correction method and device. The current sampling zero-bias correction method comprises the following steps: determining a correction trigger opportunity, wherein the correction trigger opportunity is a moment when an output voltage vector of a power converter is in a zero vector state and a theoretical value of current flowing through a current sampling resistor is zero; sampling a current sampling channel through an analog-to-digital converter at a correction triggering time, and obtaining original sampling data under a zero current condition; processing the original sampling data to obtain a current zero current bias value and updating the stored current bias data variable; a corrected current value is determined based on the updated current bias data variable. By setting the specific correction triggering time and correction mechanism, the current sampling zero bias can be dynamically tracked and corrected online, automatically and in real time in the whole operation process of the driver, adverse effects caused by static deviation and dynamic temperature excursion are overcome, and the control precision and robustness of the system under all working conditions are improved.

The invention discloses a three-phase inverter few-sensor model prediction control method and device. The method comprises the following steps: acquiring voltage and current sample data based on a three-phase inverter model prediction control system; aiming at the sample data, constructing and training a neural network; and voltage and current signals are calculated by adopting a neural network, and model prediction control is implemented. According to the designed method, the number of sensors of the three-phase inverter model predictive control system is reduced, and the anti-interference performance of the system is improved.

The application relates to a method and device for monitoring over-error of a capacitive voltage transformer, electronic equipment and a computer readable storage medium. The method comprises: performing kernel principal component analysis based on historical sample data to determine an SPE statistical quantity threshold; calculating the SPE statistical quantity of current sample data collected in real time; if the SPE statistical quantity is less than or equal to the SPE statistical quantity threshold, determining that the operation state of the transformer group is normal; and if the SPE statistical quantity is greater than the SPE statistical quantity threshold, calculating the contribution rate of each element in the current sample data to the square prediction error statistical quantity, determining a fault element, and locating an over-error transformer in the transformer group. The application effectively processes the nonlinear problem of data by using KPCA, more accurately extracts data features, realizes fault element separation, accurately locates the over-error CVT, performs online real-time monitoring on the CVT error, and improves the stability and reliability of the power system.

A method for implementing an adaptive loop filter (ALF) in a video system is provided. A video coder receives data for a block of pixels to be encoded or decoded as a current block of a current picture of a video. The video coder receives a current sample of the current block. The video coder applies a filter to the current sample to generate a correction value. Neighboring samples from two or more different sources are used as inputs to the filter. When a first neighboring sample is within a virtual boundary, the first neighboring sample is used as an input to the filter. When the first neighboring sample is beyond the virtual boundary, the first neighboring sample is precluded as an input to the filter. The video coder adds the correction value to the current sample as a filtered sample of the current block.

The application discloses a lightning impulse grounding grid frequency characteristic analysis method, system, device and medium, including: obtaining initial sample data; using the sample and a preset test frequency set to construct at least two different structure rational function interpolation models, and calculating the output residual thereof in the test frequency band; determining the target frequency point with the maximum modeling uncertainty according to the residual, calling the moment method to supplement high-precision response samples at the point; based on the updated sample set, constructing a diagonal or near-diagonal form rational interpolation model, and judging whether the prediction error of the model at the target frequency point is lower than a preset convergence threshold; if the convergence condition is not met, returning to the multiple model construction step, reiterating using the current sample set until the error meets the standard, and finally outputting the grounding grid frequency response characteristics covering the lightning impulse frequency band. The application realizes efficient analysis of the grounding grid frequency characteristics under lightning impulse, and effectively reduces the time used for analyzing the grounding grid frequency characteristics under lightning impulse.

Embodiments of the present invention provide a short-circuit fault detection system and method. The short-circuit fault detection system includes a power supply module for supplying power to the detection system; a drive module coupled to the power supply module and a motor for converting the system voltage provided by the power supply module into a voltage for the motor to operate; a current sampling module coupled to the drive module for sampling the phase current of the motor; and a fault detection module coupled to the power supply module and the current sampling module, configured to acquire the phase voltage corresponding to the phase current of the motor and to perform an initialization self-test of the detection system based on the phase voltage.

The application relates to the technical field of lithium batteries, in particular to a circuit based on MOS tube conduction resistance temperature detection and protection. The circuit comprises a battery and main power circuit, a linear voltage stabilizing power supply circuit, a MOS drain-source voltage detection circuit, a current sampling detection circuit, an over-temperature comparison circuit, a proportional mapping circuit and a protection execution circuit. The B+ port of the battery and main power circuit is connected with the VIN port of the linear voltage stabilizing power supply circuit; the B- port of the battery and main power circuit is respectively connected with the GND port of the linear voltage stabilizing power supply circuit, the GND port of the MOS drain-source voltage detection circuit and the GND port of the current sampling detection circuit. Compared with the prior art, the circuit reduces the complexity of peripheral devices and wiring, reduces the system cost, effectively avoids the problem of protection failure caused by program abnormality, introduces a fuse or latch structure, avoids the risk caused by repeated triggering of traditional recoverable protection, and significantly improves the system safety level.

The application discloses a kind of control circuit and method of server standby power supply, including main power drive unit, analog PI regulator, current sampling conditioning circuit and OringFET tube;Main power MOS tube realizes main loop on-off control, and triode is used to change the gate voltage of main power MOS tube to control its conduction, cut-off;Analog PI regulator is composed of reference source and operational amplifier, reference source provides accurate reference voltage, and operational amplifier compares and adjusts current detection signal with reference voltage, dynamically corrects the control level signal of output, and then controls the on-off of main power MOS tube through driving circuit;Current sampling conditioning circuit includes sampling resistance and differential amplifier, and real-time acquisition main loop current is converted into identifiable voltage signal, to provide accurate data support for analog PI regulator.The application is applied when server is in shutdown state, and can be remotely executed by network to start, restart, install operating system or monitor hardware health state and the like operation.

A neural network system is provided, implementing a generative model for autoregressively generating a distribution for a plurality of current filter-bank samples of an audio signal, wherein the current samples correspond to a current time slot, and each current sample corresponds to a channel of the filter-bank. The system includes a hierarchy of a plurality of neural network processing tiers ordered from a top to a bottom tier, each tier trained to generate conditioning information based on previous filter-bank samples and, for at least each tier but the top tier, also on the conditioning information from a tier higher up in the hierarchy, and an output stage trained to generate the probability distribution based on previous samples for one or more previous time slots and the conditioning information from the lowest processing tier.

The application discloses a high-precision power frequency phase angle synchronous measurement method under an undefined frequency sampling condition, and the method comprises the following steps: cyclically recording a current sampling completion time t clk_k and a current sampling code value x k ; recording a pulse trigger time t clk_pulse when a synchronous pulse is triggered, and recording sampling values and sampling completion times of a certain number of cycles from the current time backward to form a sampling time-sampling code value record table; and performing fixed frequency correction on the sampling code values in the record table through high-order interpolation according to the current electrical quantity frequency. Since the application does not require very accurate sampling intervals and is not affected by context switching, interruption preemption and other problems, it is unnecessary to use a chip with higher performance of a bare core to process, chip cost is reduced, and therefore the application can be applied to low-voltage secondary devices which are more sensitive to cost.

The invention discloses a silicon carbide power module parallel current sharing device and an electric vertical take-off and landing aircraft, which are applied to the technical field of aviation power electronics and are used for solving the problems of poor real-time performance and reliability of the silicon carbide power module parallel current sharing device in the prior art. The method specifically comprises the following steps: a current sampling module collects branch current of each silicon carbide power module and correspondingly outputs a sampling voltage signal of each silicon carbide power module; the simulation error processing module correspondingly generates error adjusting voltage related to the current deviation of each branch through an internal hardware simulation circuit; and the gate driving adjustment module adjusts the reference ground potential of the corresponding gate driving chip according to the received error adjustment voltage. Through full-hardware and software-intervention-free simulation negative feedback closed-loop control, top-speed, automatic and self-adaptive current-sharing adjustment of the silicon carbide power modules connected in parallel is realized, and the device is insensitive to noise, so that the reliability of long-term operation is improved.

The invention relates to a method for encoding a signal comprising at least two zones and comprising the following steps for one zone (Zoj) comprising a plurality of samples (Pn) to be encoded, constructing (E22) at least one feature map representative of the zone; for at least one sample, referred to as the current sample (Pn), of the zone (Zoj) and associated with a point in the at least one feature map, encoding a data item, referred to as the current value (Vn), associated with the point, comprising evaluating (F221) a proximity criterion of the point with respect to a boundary of the zone, constructing (F221) a vicinity vector (Cn) of the point according to the proximity criterion, and entropy-encoding (F223, F224) the current value according to the vicinity vector (Cn); updating (E23, E26, E27) at least one value of the feature map according to an encoding performance measurement, and encoding the feature map (FMci j) representative of the zone.

The application provides an oscilloscope synchronization method, system and medium for a multi-machine converter, which comprises the following steps: synchronizing a local second counter with a pulse signal as a synchronization source; collecting a local timestamp; determining whether a product can be divided by a common multiple to obtain a determination result; if the determination result is no, performing frame processing on the sampling cluster data in the current sampling cluster period after the current sampling cluster period ends to obtain a data frame with the local timestamp; if the determination result is yes, resetting and restarting counting of the local microsecond counter and the sampling cluster timer, and performing frame processing on the sampling cluster data in the current sampling cluster period after the current sampling cluster period ends to obtain a data frame with the local timestamp; and uploading the data frame to a remote monitoring module to restore the data frame into waveform data according to the local timestamp. The application solves the synchronization problem of different period signals by using a common multiple, and realizes periodic synchronization of a multi-machine converter.

The invention discloses a resource scheduling method and device, terminal equipment and a storage medium, and relates to the technical field of power systems, and the method comprises the steps: converting collected resource data into a resource sensing state value; identifying a feature parameter data set of each task, analyzing a matching relationship between the feature parameter data set and a task demand, screening out a parameter combination matched with the current resource sensing state value from the feature data set according to the matching relationship, and generating a demand modeling parameter set according to the parameter combination; collecting sample data related to the demand modeling parameter set, performing time sequence analysis on the sample data to obtain sample analysis data, comparing difference conditions of task completion rates under different sample analysis data, and adjusting parameters of the modeling parameter set according to the difference conditions to obtain a parameter docking result; and screening out a parameter combination matched with the current sample condition from a parameter docking result, and converting the parameter combination into a resource scheduling strategy. According to the invention, the resource scheduling effect can be effectively improved.

The invention discloses a current calibration method for a motor drive controller. The method comprises the following steps: S1, checking a zero point sampling value under the condition that a three-phase copper bar is not electrified; s2, the three-phase copper bar outputs the maximum current, the signal collected by the sampling system at the moment is the maximum value of the sampling signal, and if the signal collected by the sampling system is not the maximum value of the sampling signal, the sampling signal at the moment needs to be calibrated; s3, calibration: sequentially sampling points and checking the output current, and if the linearity error exceeds + / -1%, checking the design of a current sampling module or the quality state of a product, and then carrying out maximum current sampling calibration again; s4, collecting a temperature compensation coefficient function call set, and feeding back a temperature value by a controller according to a temperature sampling system; and S5, writing the standard linearity and the temperature compensation coefficient into a controller for calculating the sampling current. According to the invention, current calibration is carried out on an internal current sampling module of the electric drive controller after the controller is assembled.

The present application relates to a kind of multi-loop cable group electromagnetic loss intelligent calculation method and device, belong to power cable operation technical field, method includes: with finite element method to establish the cable group electromagnetic field model including multi-loop, current sample is randomly generated and corresponding cable core and metal sleeve loss are calculated, form current-loss sample set;GWO-RBF-Adaboost Intelligent Prediction Model is constructed, the center point of radial basis function neural network RBF, width and output weight are globally optimized by grey wolf optimization algorithm GWO, and the RBF neural network is used as the base learner of Adaboost, and current-loss sample set is used to train to obtain current-loss mapping relationship model;The current data of multi-loop cable group to be evaluated is input into the GWO-RBF-Adaboost model trained, and the cable core loss and metal sleeve loss of each loop are predicted.The present application realizes the convenient calculation of multi-loop cable group electromagnetic loss by current, and significantly improves the calculation precision of electromagnetic loss.

The present application provides a method for determining a distance to a fault in a power systems network. The method may involve determining, based on a set of measured voltage samples, a set of processed voltage samples; determining, based on a set of measured current samples, a set of processed reactive current samples, a set of processed resistive current samples, a set of processed negative sequence current samples, and a set of processed zero sequence current samples; selecting, based on an indication from a faulty phase indicator, a selected processed voltage sample, a selected processed reactive current sample, a selected processed resistive current sample, and a selected processed negative or zero sequence current sample; determining, based on the selected processed reactive current sample, that no distortion has occurred due to current transformer (CT) saturation; and calculating, based on the determination that no distortion has occurred, the distance to the fault.

The application relates to a low-voltage distribution network topology identification method based on weighted Mahalanobis distance and granularity clustering, which comprises the following steps: collecting the voltage sequence of each user's electric meter box under each block as a sample point; using an improved weighted Mahalanobis distance to calculate the weighted Mahalanobis distance between each voltage meter sequence; calculating the density between the sample points according to the weighted Mahalanobis distance between two voltage meter sequences; for each sample point, constantly obtaining the sample point closest to the current sample point and having increased density, clustering, determining the clustering center, and discriminating the block to which the user belongs according to the clustering result. Compared with the prior art, the application is simple to realize, only needs to measure the voltage data of the smart electric meter, does not need to increase additional measuring equipment in the system, and the calculation result of the weighted Mahalanobis distance is more relevant and can better reflect the similarity relationship between the data.

The application discloses an intelligent Internet of Things axial flow regulating system and a regulating valve based on the Internet of Things and relates to the technical field of axial flow valve control. The system directly connects a pure passive analog AND gate through a displacement sampling branch and a differential pressure sampling branch, wakes up a high-frequency recording module and an edge deduction chip when a micro-opening dangerous area and a cavitation critical pressure drop occur at the same time, captures cavitation micro-shock waves through a multilayer composite sensing skin layer on the inner wall of a throttling sleeve, calculates a current sampling window transient energy extreme value based on the sampling results of each axial segment of the piezoelectric sensing layer, bypasses a steady-state following instruction of a wide-area scheduling network when the current sampling window transient energy extreme value reaches a takeover condition, injects an asymmetric pseudo-random spread spectrum jitter pulse sequence into a built-in actuator, and sends a local self-healing suspension state frame to freeze the integral accumulation of the wide-area scheduling network side, and restores the flow regulating authority after the shock wave falls back. The scheme can reduce sampling power consumption and improve cavitation symptom identification and local disposal capacity.

Industrial equipment and its progressive distillation unsupervised anomaly detection method, device and medium, relate to the technical field of equipment anomaly detection. The method comprises: acquiring monitoring time series data and preprocessing. The time neighbor set of each sample is calculated in the time dimension. The spatial similarity is measured in the spatial dimension, and the BallTree index structure is constructed through the hierarchical spatial division strategy, and the spatial neighbor set is searched. The current sample is normalized by the spatiotemporal weighted fusion strategy. After normalization, the adaptive threshold unsupervised anomaly detection teacher model is input for training. The student network is trained in stages by using the progressive distillation strategy, the teacher model is used to guide the student model learning, and the distillation student model is obtained. The data to be detected is input into the anomaly detection model, the reconstruction error between the input sample and the reconstructed sample is calculated, and the adaptive threshold judgment mechanism is combined to obtain the anomaly detection result.

A method and apparatus for in-loop filtering of reconstructed video are disclosed. According to the method, input data for a current blockis received, wherein the input data comprises reconstructed samples of the current block. An ALF (Adaptive Loop Filter) for a current sample in the current block is determined, wherein the ALF comprises one or more filter taps associated with one or more reference samples of the current sample. The ALF is applied to the current sample to generate a filtered output sample. Filtered output comprising the filtered output sample is provided.

This invention provides a method and system for reliable detection of the prediction confidence of a brain-computer interface (BCI) device. The method includes: treating each intention-based visualization task as a sample; having the BCI device to be tested perform intention recognition on the current sample; obtaining the intention recognition result and prediction confidence output by the BCI device corresponding to the current sample; obtaining the actual error corresponding to the current sample based on the intention recognition result; obtaining the prediction error corresponding to the current sample based on the prediction confidence; obtaining the individual calibration deviation corresponding to the current sample based on the actual error and the prediction error; and determining the reliability of the prediction confidence of the BCI device to be tested by summing the individual calibration deviations of all samples. This invention can detect the reliability of the prediction confidence of a BCI device.

This disclosure relates generally to video coding / decoding and particularly prediction using multiple scaling factors and multiple samples. One method includes receiving a video bitstream comprising a current block and a reference block, the reference block being used for predicting the current block by using a prediction function; receiving, from the video bitstream, a first syntax element indicating a manner for determining parameters of the prediction function; determining the parameters of the prediction function based on the manner; predicting a current sample in the current block based on the reference block using the prediction function; and reconstructing the current block based on the predicted current sample.