23 results about "Pulse computation" patented technology

The invention belongs to the technical field of machine state prediction, and particularly relates to a fault diagnosis method and system based on a coding and decoding attention mechanism enhanced pulse neural network, and the method comprises the steps: collecting a device vibration signal, and segmenting the device vibration signal into time sequence segments; converting each fragment into a two-dimensional time-frequency image by using S transformation to construct a data set; the image is input into a lightweight hybrid network model to extract deep features, the model is composed of a pulse convolution encoder and a pulse efficient additive attention mechanism encoder which are alternately cascaded, and the pulse convolution encoder extracts local features through pulse neurons and depth separable convolution; the global context dependency is modeled by adopting an efficient additive attention mechanism with low linear complexity; and finally, fault classification is completed based on deep features. Through fusion of pulse calculation and an efficient attention mechanism, the feature extraction capability and diagnosis precision are improved while the parameter quantity of the model is remarkably reduced, and the method is particularly suitable for being deployed on edge equipment with limited resources to realize fault diagnosis.

The invention discloses a time service synchronization method, a computer readable storage medium and electronic equipment, and the method comprises the steps: starting a high-frequency SPXO clock source in each preset period based on the timing of a low-frequency SPXO clock source, and enabling the high-frequency SPXO clock source to be aligned with a low-frequency clock moment after buffering; calculating the planned output time of the pulse time service signal by combining the high-frequency clock receiving window, the synchronous packet sending duration, the preset ToF correction window timing value and the latest ToF value; the synchronization packet sent by the host is received before the plan time, correction output time is determined according to the receiving moment, the ToF correction window timing value and the ToF value, and the host sends a pulse to the slave after sending the synchronization packet and passing through the ToF correction window; calculating correction and plan time deviation; performing clock taming on a low-frequency SPXO clock source and a high-frequency SPXO clock source through a multi-cycle deviation value to realize time service synchronization of the slave and the host; and then the high-frequency SPXO clock source is closed.

The application belongs to the technical field of photon imaging detection, and particularly relates to a photon counting imaging detection system based on a mixed anode. The system comprises a vacuum sealed tube detector composed of a window coated with a photocathode, a microchannel plate stack and a mixed anode, and a signal processing circuit. The photocathode is used for converting photons incident through the window into photoelectrons. The microchannel plate stack is used for converting the photoelectrons into electron clouds. The mixed anode comprises a strip anode and a delay line anode arranged orthogonally, and the mixed anode transmits the collected charge short pulses of each channel to the signal processing circuit. The signal processing circuit calculates the two-dimensional coordinates of the photons based on the collected charge short pulses of each channel, and then transmits the two-dimensional coordinates of the photons to a terminal for imaging display. The application provides a photon counting imaging detection system based on a cross strip-delay line mixed anode, and solves the problem that the existing position sensitive anode detector cannot simultaneously have high spatial resolution and high temporal resolution.

The application discloses a kind of based on dynamic pulse discrimination steam turbine speed anti-interference measurement method, it is related to industrial automation control field, the method includes: S1: by the speed sensor of installation in steam turbine rotor speed measuring gear flank alternately changed and generated sine wave signal of tooth valley conversion into digital pulse signal by conditioning circuit;S2: calculate the effective pulse frequency f of this sampling time, to calculate effective pulse cut-off period T;S3: for subsequent new pulse, calculate the time interval Δt between it and the last pulse being judged as effective;If Δt≥T, then determine that the new pulse is effective pulse;If Δt < T, then determine that the new pulse is interference pulse and is rejected;S4: update effective pulse frequency f, calculate the real-time speed of steam turbine, calculate the real-time speed of steam turbine solves the defect that present art relies on hardware filtering and is difficult to eliminate singular interference pulse completely, leading to unreliable speed measurement.

The application provides a pulse remote signaling acquisition method and a pulse remote signaling acquisition terminal of an electric energy meter, and relates to the technical field of data acquisition. The method comprises the following steps: applying corresponding target storage resources for a pulse input port in a storage module; performing state scanning on the pulse input port to obtain current pulse output data of the electric energy meter; performing pulse calculation according to pulse configuration information of a pre-designed measurement point and the current pulse output data to obtain current pulse measurement data; acquiring current remote signaling input signals of the electric energy meter collected by a remote signaling port; determining current operating states of each controlled switch in the electric energy meter according to the current remote signaling input signals; and uploading the current pulse output data, the current pulse measurement data and the current operating states stored in the target storage resources to a master station system by using a remote communication module. The application can upload all kinds of data of the electric energy meter to the master station system through the pulse remote signaling acquisition terminal, and realizes real-time monitoring of the state of the electric energy meter.

This state change detection system comprises: a rotation detection unit that outputs a pulse signal; a pulse calculation unit that obtains a pulse calculation waveform representing a pulse period for each rotation angle of a rotating body; a reference generation unit that generates a reference waveform in which an average pulse calculation waveform, which is obtained by averaging values of the pulse calculation waveform with an accumulated value for each rotation angle, is represented by the distribution for one rotation of the rotating body; a rotational fluctuation waveform generation unit that uses the reference waveform to generate a rotational fluctuation waveform obtained by extracting, from the pulse calculation waveform used to generate the reference waveform or a pulse calculation waveform succeeding the pulse calculation waveform, a fluctuation component other than a steady component that steadily occurs with the rotation of the rotating body; and a state change detection unit that detects a change in rotation state on the basis of the rotational fluctuation waveform.

The invention provides an alternating-current dynamic traceability system and method for a novel electric power system, and aims to realize analog output of various types of wide dynamic characteristics and variable power signals and test and calibrate output signals. The system comprises a man-machine interaction unit used for carrying out parameter setting on a three-phase AC dynamic power output unit; the high-precision clock unit is used for providing a clock signal and a three-phase phase synchronization control signal for the three-phase alternating-current dynamic power output unit; the three-phase alternating-current dynamic power output unit is used for setting the parameters, the clock signal and the three-phase phase synchronization control signal, generating a standard pulse and a digital waveform signal, and generating a three-phase alternating-current dynamic power signal based on the digital waveform signal; the detected electric energy meter is used for receiving the three-phase alternating current dynamic power signal, measuring the three-phase alternating current dynamic power signal and generating a corresponding detected pulse; and the error calculation unit is used for receiving the standard pulse and the detected pulse and calculating the error of the detected pulse based on the standard pulse.

The application belongs to the technical field of integrated circuits, and discloses a hardware system for deploying a sparse pulse Transformer model, which comprises an event input module, a space-time pruning module, a scheduling module, a sparse pulse calculation module and a classification head output module. The event input module converts a time sequence pulse data stream into a pulse token sequence. The space-time pruning module judges the importance of each time step and each token through a pulse accumulation value, generates a space-time pruning mask, and the scheduling module screens the token sequence according to the mask to form a sparse token sequence. The sparse pulse calculation module performs sparse inference on the sparse token sequence, and finally the classification head output module outputs a classification result. The system can be used for deploying a sparse pulse Transformer model through a special hardware control and scheduling mechanism, and provides accurate execution paths and efficient scheduling support for dynamically changing sparse data streams.

The application belongs to the technical field of incremental learning, and discloses a pulse neural network model construction method for task incremental learning. A gated parameter neuron layer of the pulse neural network comprises: a synaptic current calculation unit for calculating synaptic current of an input neuron at time t based on screened pulses fired by each neuron of a previous layer; a screened mask generation unit for randomly generating a screened mask for each task; a screened membrane voltage calculation unit for multiplying the screened mask and the membrane voltage v t to obtain screened membrane voltage at time t; a pulse firing module for calculating a hidden state membrane voltage based on the obtained gating value and the screened membrane voltage to determine whether a neuron fires a pulse s t ; and a screened pulse calculation unit for calculating the fired screened pulse and transmitting the fired screened pulse to each neuron of a next layer. The application can alleviate the catastrophic forgetting problem of the pulse neural network in task incremental learning, thereby improving the classification accuracy of the model.

This invention relates to the field of laser detection and navigation positioning technology, and discloses a real-time noise reduction and in-situ calibration system and method for laser ranging sensors. The system includes an acquisition module, an extraction unit, a mapping unit, a control unit, and a compensation unit. The system extracts the zero-position calibration pulse generated by reflection within the optical window, calculates the waveform asymmetry parameter to construct a timing compensation operator for the receiving link, and the control unit adjusts the measurement echo detection threshold according to the pulse energy distribution parameter. The compensation unit uses the operator to correct the target echo phase centroid and uses the real-time phase center of the pulse as the timing start anchor point. This invention converts the ranging reference from static setting to dynamic physical reconstruction, and synchronously eliminates clock temperature drift and circuit delay deviation caused by ambient temperature rise during the ranging cycle, thereby eliminating the cumulative error caused by spatiotemporal reference drift in long-distance navigation.

Techniques are disclosed for operating a time-of-flight (TOF) sensor. The TOF may be operated in a low power mode by repeatedly performing a low power mode sequence, which may include performing a depth frame by emitting light pulses, detecting reflected light pulses, and computing a depth map based on the detected reflected light pulses. Performing the low power mode sequence may also include performing an amplitude frame at least one time by emitting a light pulse, detecting a reflected light pulse, and computing an amplitude map based on the detected reflected light pulse. In response to determining that an activation condition is satisfied, the TOF may be switched to operate in a high accuracy mode by repeatedly performing a high accuracy mode sequence, which may include performing the depth frame multiple times.

The application relates to a vehicle speed calculation method, system, calculation device and medium, the method comprising collecting a plurality of pulses generated by tire rotation once per preset timestamp, and determining the start time and end time of each preset timestamp; when no pulse generated by tire rotation is collected for a continuous preset duration, recording the current time; determining a target timestamp according to the current time; determining a statistical start time and a statistical end time according to the target timestamp; determining the first number of pulses between the start time and the end time in the target timestamp, and determining the second number of pulses between the statistical start time and the statistical end time; and calculating the vehicle speed according to the average of the first number and the second number. The application solves the problem that a complete tooth pulse cannot be collected through periodic sampling at low speed, so that the effective number of pulses collected by periodic sampling is not necessarily accurate, and the accuracy of the vehicle speed calculated according to the pulses in the period is low.

The application generally relates to a computing system including at least one processor. The at least one processor is configured to execute instructions stored in at least one memory to: initiate emission of a light pulse by an illuminator, after a predetermined delay from emission of the light pulse, initiate capturing of a plurality of pixels in a scene using a plurality of sensors based on the plurality of captured pixels, for a point in the scene, compute a respective value for volumetric density, normal, reflectance and ambient light using a corresponding neural field. The processor further, based upon the emitted light pulse, computes a shadow component corresponding to an origin of the illuminator and a direction of the emitted light pulse and using the computed respective value for volumetric density, normal, reflectance and ambient light and the computed shadow component, constructs a gated image through a volume rendering formulation.

This invention belongs to the field of machine state prediction technology, specifically relating to a fault diagnosis method and system based on an encoder-decoder attention mechanism-enhanced spiking neural network. The method includes: acquiring equipment vibration signals and segmenting them into time-series segments; using S-transform to convert each segment into a two-dimensional time-frequency image to construct a dataset; inputting the images into a lightweight hybrid network model to extract deep features. This model consists of alternating cascaded spiking convolutional encoders and spiking efficient additive attention mechanism encoders. The former extracts local features through spiking neurons and depthwise separable convolutions, while the latter uses a low-linear-complexity, efficient additive attention mechanism to model global context dependencies; finally, fault classification is completed based on the deep features. This invention, through the fusion of spiking computation and efficient attention mechanisms, significantly reduces the number of model parameters while improving feature extraction capabilities and diagnostic accuracy, making it particularly suitable for deployment on resource-constrained edge devices for fault diagnosis.

This invention provides a single-row scribing simulation method and a multi-row scanning simulation method based on pulse-by-pulse ablation. These methods address the technical problem of existing pulsed laser processing simulations, which require consideration of the historical dependencies between pulses during global pulse-by-pulse calculations. This results in computation time increasing linearly or even non-linearly with the number of pulses, making them unsuitable for simulating ablation involving hundreds of thousands or even millions of pulses in actual processing. The single-row scribing simulation method and multi-row scanning simulation method provided by this invention decouple global pulse-by-pulse calculations into discrete, locally refined calculations. While ensuring computational accuracy, this significantly reduces the iterative solution range and the number of iterations, substantially lowering the computational dimensionality and time, achieving an order-of-magnitude improvement in computational efficiency. This enables rapid prediction and optimization of large-scale pulsed processing, providing key technical support for real-time process control in industrial applications.

The invention relates to the field of nonlinear system optimal control based on artificial intelligence, in particular to a nonlinear pulse control method, device and equipment based on hybrid reinforcement learning, and the method comprises the steps: obtaining the state data of a controlled system in real time; the continuous identifier learns nonlinear dynamics of the system online, and judges whether a pulse control moment is reached or not; a CNN calculation value function is pulsed, and the quality of a current control strategy is judged; the pulse ANN outputs an optimal pulse control signal to drive the system to execute; on the basis of a gradient approximation mechanism, a key basis is provided for weight updating of the pulse ANN; and loop iteration is carried out until the system state converges, and a stable control target is achieved. The problem that a nonlinear controlled system lacks an accurate mathematical model is solved; the problem of high energy consumption caused by continuous output of control signals in traditional continuous control is avoided; and meanwhile, the pulse ANN weight is updated by using a gradient approximate value, so that the problem that the gradient between control input and state transition in a nonlinear system is difficult to analyze and calculate is solved.

The invention relates to the technical field of edge AI chips, in particular to a distributed random pulse chip control method for a resource-constrained network. Comprising a heterogeneous neural computing core which is composed of a random computing neural convolution feature extractor and a miniature binary calibration coprocessor and is configured in an edge AI chip. The statistical confidence coefficient of random pulse calculation is monitored in real time through the random pulse flow analyzer, when the confidence coefficient reaches a preset threshold value, an early decision termination mechanism is triggered, calculation of the random calculation neural convolution feature extractor is stopped in advance, and a calculation result is transferred to the miniature binary calibration coprocessor for decision calibration. According to the method, the inherent contradiction between the random calculation precision and the delay is effectively relieved, the energy efficiency ratio and the response speed of the edge AI chip are remarkably improved, and the service life of a battery of resource-limited network equipment is prolonged.

The invention discloses a method and a device for distinguishing a discharge pulse and an interference pulse in a switch cabinet, and belongs to the technical field of pulse identification and monitoring of switch cabinets of underground power distribution stations, and the method comprises the steps: generating a simulation pulse of a target switch cabinet through a pre-constructed simulation circuit model; performing discharge detection on the target switch cabinet to obtain an actually measured pulse in the target switch cabinet; calculating an error value of the simulation pulse and the actually measured pulse, and reducing the error value to be not more than a preset error threshold value by adjusting configuration parameters of the simulation circuit model to obtain a discharge characteristic template and an interference characteristic template; and judging whether the actually measured pulse is a partial discharge pulse or a ground screen interference pulse according to the discharge characteristic template and the interference characteristic template. The method can greatly improve the identification accuracy of the discharge pulse in a strong interference environment, and provides reliable technical support for the evaluation of the insulation state of underground power distribution station equipment.

The invention discloses a non-stationary instantaneous impact monitoring method and system, and belongs to the technical field of vibration signal analysis and fault diagnosis. The method comprises the steps of firstly obtaining a vibration signal and framing; calculating an envelope signal of each passband and an envelope spectrum of a frequency domain of each passband through multi-passband filtering and Hilbert transform; calculating a relative envelope spectrum probability curve, and identifying a local maximum pulse by calculating a second-order difference of the relative envelope spectrum probability curve within an analysis frequency band of 0-200Hz; and calculating the ratio of each pulse to the average value in the minimum pulse interval on the left side and the right side of the pulse to carry out significance judgment, and finally judging whether non-stationary instantaneous impact damage exists or not in combination with multi-level threshold statistical test. According to the method, high-precision and high-reliability extraction and identification of non-stationary instantaneous impact characteristics can be realized under a strong noise background.

The present invention provides a system for detecting changes in the state of a rotating device, and a method for detecting changes in the state of a rotating device, which can reliably and quickly detect such changes. [Solution] The state change detection system comprises a rotation detection unit that outputs a pulse signal, a pulse calculation unit 49 that calculates a pulse calculation waveform representing the pulse period for each rotation angle of the rotating body, a reference generation unit 51 that generates a reference waveform in which the average pulse calculation waveform obtained by averaging the integrated values ​​of the pulse calculation waveforms for each rotation angle is represented as a distribution for one rotation of the rotating body, a rotation fluctuation waveform generation unit 53 that generates a rotation fluctuation waveform by extracting fluctuations other than the steady component that steadily occurs with the rotation of the rotating body using the reference waveform from the pulse calculation waveform used to generate the reference waveform or pulse calculation waveforms continuous with the pulse calculation waveform, and a state change detection unit 55 that detects a change in the rotation state based on the rotation fluctuation waveform.

The invention provides a single-row scribing simulation method and a multi-row scanning simulation method based on pulse-by-pulse ablation, which are used for solving the problems that the calculation time is linearly or nonlinearly increased along with the number of pulses due to the need of considering the historical dependence among the pulses during global pulse-by-pulse calculation in the existing pulse laser processing simulation method; and the method is difficult to apply to ablation simulation of hundreds of thousands of or even millions of pulses during actual processing. According to the single-row scribing simulation method and the multi-row scanning simulation method based on pulse-by-pulse ablation, global pulse-by-pulse calculation is decoupled into discrete local refined calculation, on the premise that calculation accuracy is guaranteed, the iteration solving range and the iteration frequency are remarkably reduced, the calculation dimension and time are greatly reduced, and the calculation efficiency is improved. The magnitude order of calculation efficiency is improved, rapid prediction and optimization of large-scale pulse processing become possible, and key technical support is provided for real-time process control in industrial application.

This invention discloses a data acquisition device for an energy meter based on pulse acquisition, comprising a pulse signal conditioning module, a photoelectric sensor, a multi-channel parallel acquisition unit, and a hybrid communication gateway. The output of the pulse signal conditioning module is connected to the pulse output interface of the energy meter. The photoelectric sensor is used to output pulse signals to the parallel acquisition unit, and the multi-channel parallel acquisition unit is used to send pulse calculation data to the hybrid communication gateway. This invention eliminates the need to replace the energy meter, reducing costs and construction time. Electromagnetic interference is eliminated through photoelectric isolation and RC filtering circuits. An HC32L196K8TA microcontroller is used for pulse counting and power-off protection. It supports RS-485 / 4G-CAT.1 dual-mode transmission and is compatible with photoelectric retrofitting of mechanical meters, direct acquisition from electronic meters, and 485 communication acquisition. A single concentrator supports pulse acquisition management of 48 meters and can be expanded through the 485 interface, reducing the pulse measurement error rate.

The invention discloses a gas classification method and device based on a learnable sensor attention network. The gas classification method comprises a learnable parameter coding module, a dual-scale residual module and a pulse attention module. The learnable parameter coding module introduces a current change rate as an explicit dynamic feature, and endows neurons with the ability to acutely capture gas adsorption and desorption gradients. And the dual-scale residual module realizes multi-level feature extraction of the complex odor manifold through parallel multi-cavity convolution branch. The pulse attention module adaptively searches and enhances cross-channel causal association under the condition of tolerating inherent response delay of a heterogeneous sensor, so that the problem of feature dislocation caused by asynchronous response is effectively solved. Finally, the pulse neural network can deeply explore and fully utilize the spatial-temporal dynamic and potential coupling relation in the sensor array under a low-power-consumption pulse calculation normal form, the robustness of the features is remarkably enhanced, and the recognition performance in a complex scene is improved.