23results about How to "Filter noise" patented technology

The invention relates to the technical field of bolt axial force measurement, and discloses a bolt pre-tightening force calculation system, method and device, computer equipment and a medium. An electromagnetic ultrasonic transmitting and receiving device in the system is used for transmitting a radio frequency pulse signal for an electromagnetic ultrasonic transducer; the electromagnetic ultrasonic transducer is used for receiving the radio-frequency pulse signal transmitted by the electromagnetic ultrasonic transceiver and acting the radio-frequency pulse signal on the tested bolt, so that ultrasonic waves are generated by excitation inside the tested bolt; the controller is used for obtaining ultrasonic wave corresponding sound time based on the ultrasonic wave and the synchronous extrusion wavelet transform algorithm and calculating the pretightening force of the measured bolt based on the corresponding sound time, accurate calculation of the pretightening force of the bolt is achieved, the method can be directly used for pretightening force calculation of bolts with poor surface quality and high temperature, and the method has high interference resistance to noise and is suitable for popularization and application. The problem that accurate measurement of the bolt pre-tightening force cannot be achieved when accurate ultrasonic sound of bolt ultrasonic waves cannot be obtained is solved.

The invention relates to the technical field of data processing, in particular to an intelligent classified storage system and method for enterprise management data based on big data, and the method comprises the steps: carrying out the data clustering processing of collected enterprise historical business information feature data, generating the clustering feature data of the enterprise historical business information, and storing the clustering feature data into an enterprise management data storage unit; setting corresponding enterprise business information category text feature data, and performing data preprocessing on the collected to-be-classified enterprise business information data to generate to-be-classified enterprise business information feature data; performing text feature data search processing according to the to-be-classified enterprise business information feature data and the enterprise business information category text feature data to generate to-be-classified enterprise business information category text feature data, and storing the to-be-classified enterprise business information feature data to an enterprise management data storage unit according to the to-be-classified enterprise business information category text feature data, thereby realizing accurate classification and intelligent storage of enterprise management data.

The present application discloses a method and management system for merging alarms of power distribution equipment, the method comprising: constructing an alarm relationship matrix according to the causal relationship between the alarm types of the power distribution equipment; when a new alarm occurs in the power distribution equipment management system, querying the alarm relationship matrix according to the alarm type of the new alarm, wherein all alarm types that can trigger the new alarm are the parent alarm types of the new alarm; querying whether the parent alarm type exists in the unprocessed alarm message in the power distribution equipment management system: if it exists, merging the alarms according to the alarm occurrence time of the parent alarm type and the relationship between the parent alarm occurrence time and the new alarm occurrence time, after the alarms are merged, the alarm notification of the new alarm is absorbed by the parent alarm type. The method has a fast parent alarm query process, and the merging logic is clear and simple, which greatly improves the processing speed, effect and system scalability of alarm merging, and can meet the requirements of the power distribution management system for alarm merging.

This application discloses a circuit board integrated within the rear cover of a motor, applicable to the field of electronic circuits. This circuit board integrates an electromagnetic compatibility (EMC) suppression circuit and a Hall sensor signal feedback circuit with memory function. The EMC suppression circuit, used to suppress electromagnetic interference from the motor, includes an EMC filter, a transient voltage suppression diode, a first filter capacitor, a second filter capacitor, and the motor itself. The Hall sensor signal feedback circuit includes a Hall sensor, a third filter capacitor, and a pull-up resistor. The Hall sensor is a bipolar latching type, used to output speed and position feedback signals based on the magnetic field of a bipolar magnetic ring mounted on the motor rotor. Thus, through the coordinated operation of the EMC filter, the transient voltage suppression diode, and multiple filter capacitors, the EMC of the motor is improved.

This invention discloses a semantically guided method and system for separating auditory evoked brainwave latent space, belonging to the field of brain-computer interface technology. The method includes: acquiring multi-channel EEG signals evoked by mixed audio from multiple sound sources and preprocessing them to obtain mixed spatiotemporal features; compressing the features into a low-dimensional latent space to generate an EEG latent code sequence; fusing semantic text prompts and time-span prompts to generate a semantic conditional embedding sequence; using conditional embedding as a guide, generating a separation latent code specific to the target sound source through diffusion in the latent space; and decoding the latent code to output the corresponding auditory evoked EEG components. The system adopts a five-layer architecture consisting of a hardware layer, a preprocessing layer, an interaction layer, a core model layer, and an output layer, and is equipped with a multimodal joint separation model to achieve end-to-end processing. This invention requires no original audio input and can complete source-level neural signal separation based on natural language, exhibiting high computational efficiency and excellent separation accuracy, making it suitable for auditory brain-computer interface and cognitive neuroscience research.

The invention discloses a high-voltage ultralow-ripple DC-DC power supply which comprises a Royer resonance circuit, a voltage doubling rectifying circuit, a voltage feedback control circuit and a filter circuit. The Royer resonance circuit comprises a transformer U1, a coil L1, a capacitor C2, a triode Q1 and a triode Q2; a secondary side T6 of the transformer U1 and a secondary side T10 of the transformer U1 are both connected with a first end of the voltage doubling rectifying circuit; the second end of the voltage doubling rectifying circuit is connected with the filter circuit; the third end of the voltage doubling rectifying circuit is connected with one end of the voltage feedback control circuit; and the other end of the voltage feedback control circuit is connected between the emitter of the triode Q1 and the emitter of the triode Q2. The problems that in the prior art, a power supply cannot meet the requirement for high-voltage ultra-low ripples, and a driving circuit of an existing power supply is complex and high in cost are solved. The technical effects that the power supply has no switching noise and the driving circuit is simpler are achieved.

The present application relates to the technical field of medical film, and particularly relates to a medical film quality detection system based on multi-parameter fusion, which comprises an acquisition module, a discrete determination module, an identification module, a risk determination module, an adjustment module and an early warning module. The present application takes a dynamic discrete coefficient as a trigger condition, analyzes the contribution of each process parameter to the current thickness fluctuation to identify the primary root cause, forms a quantitative process risk index by comprehensively considering the out-of-control degree of the root cause parameter and the thickness deviation, and according to the process risk index, an adjustment instruction with clear direction and strength suggestion can be sent, and the adjustment efficiency is evaluated after adjustment, early warning is given when the control loop fails or the problem escalates, and the problems of lagging quality defect discovery and blind and lagging process adjustment caused by the failure to quickly and accurately locate the process abnormal root cause in the complex multi-process coupling production process are effectively solved.

An integrated circuit device includes: a substrate; and a metal oxide semiconductor capacitor (MOSCAP) on the substrate. The MOSCAP includes: a lower semiconductor device on a substrate, the lower semiconductor device including a pair of lower source / drain regions and a lower gate structure between the pair of lower source / drain regions; and an upper semiconductor device on the lower semiconductor device, the upper semiconductor device including a pair of upper source / drain regions and an upper gate structure between the pair of upper source / drain regions. A lower gate structure is electrically connected to both of the pair of upper source / drain regions.

This invention discloses a robot grasping pose estimation method and system based on multi-frame fusion and depth calibration. The method includes: acquiring multiple frames of RGB-D data using a depth camera, extracting a target mask using a lightweight instance segmentation network; using a random sample consensus algorithm to detect ground planes based on the depth map and calculate the relative height of the camera; extracting point clouds based on the mask and depth data, and calculating the initial 6D pose using principal component analysis; performing nonlinear calibration of the pose based on radial basis functions, combined with camera height and observation distance; and robustly fusing the multi-frame poses using a Z-score-based outlier removal algorithm to output the optimal target pose and convert it into a robot arm base coordinate system command.

The invention provides a multi-frequency-hopping signal sorting method, system and device used in a complex electromagnetic environment and a storage medium, and aims to realize multi-frequency-hopping signal sorting in the complex electromagnetic environment without any prior information. The method comprises the steps that multi-frequency-hopping signal data in a complex electromagnetic environment are acquired, and the multi-frequency-hopping signal data comprise effective signal data and noise signal data; performing time-frequency analysis processing on the multi-frequency-hopping signal data to obtain time-frequency data corresponding to the multi-frequency-hopping signal data; predicting an optimal radius parameter in a DBSCAN clustering algorithm by using a neural network learning method; based on the optimal radius parameter, performing DBSCAN clustering algorithm processing on the time-frequency data to obtain a cluster of effective signals; and extracting characteristic parameters of the effective signals based on the clusters of the effective signals.

The utility model relates to the technical field of direct-current power supply cabinets, and discloses a direct-current power supply cabinet based on a leakage current detection sensor, which comprises a power supply cabinet body, and a direct-current power supply module, an electric leakage detection module and an alarm module are arranged on the inner side of the power supply cabinet body. The direct-current power supply module comprises a direct-current transformer, one end of the direct-current transformer is provided with a direct-current voltage stabilization source, the direct-current power supply cabinet based on the leakage current detection sensor inputs an alternating-current electric signal into the direct-current transformer, and the direct-current transformer is responsible for converting the input alternating-current electric signal into a direct-current electric signal; through accurate control and adjustment, it is ensured that the output direct current meets the working requirements of subsequent equipment, then a direct current signal is output to the direct current voltage stabilization source, the direct current voltage stabilization source further stabilizes the direct current output from the direct current transformer, and it is ensured that stability is provided for rear-end equipment; therefore, the operation of converting the alternating current into the direct current for stable processing is completed.

The present application provides a noise suppression circuit, an isolation decoder, a chip and an electronic device, wherein the noise suppression circuit includes: a differential operational amplifier circuit for gaining an input differential signal; a first filter circuit, whose input end is connected to the output end of the operational amplifier circuit, for filtering the signal after gaining the operational amplifier circuit, and outputting a first frequency signal; a second filter circuit, whose input end is connected to the output end of the operational amplifier circuit, for filtering the signal after gaining the operational amplifier circuit, and outputting a second frequency signal; a third filter circuit, whose input end is respectively connected to the output end of the first filter circuit and the output end of the second filter circuit, for further filtering the first frequency signal and the second frequency signal to remove excess noise and unnecessary frequency components, and output a pulse width modulation signal. The present application can improve signal quality and anti-interference ability.

The invention relates to the technical field of industrial control systems with image edge detection, and in particular to an industrial control system with image edge detection, comprising a camera image acquisition module, an image processing module and a single-chip embedded module arranged on a plastic welding machine, wherein the single-chip embedded module comprises a motor drive module, a temperature heating module, a human-machine interaction detection protection module and a single-chip main control module, the image processing module is electrically connected to the camera image acquisition module and the single-chip main control module respectively, and the single-chip main control module is electrically connected to the motor drive module, the temperature heating module and the human-machine interaction detection protection module respectively, so that the edge of the image can be accurately identified, and when there is interference or image jitter in the system during the image acquisition process, this part of the noise can be filtered out, so that the edge acquired by the system is more consistent with the actual edge trajectory.

According to the relaxation oscillator circuit, an average voltage feedback circuit is adopted to improve the frequency precision of the oscillator circuit, the stability of the oscillator circuit is improved, MOS transistors are adopted to form a reference voltage generation circuit, the device area is saved, the cost is reduced, meanwhile, circuit design parameters are easier to adjust, and a second-order or high-order filter is adopted to form an integrator. Noise of the oscillator can be filtered to the greatest extent, and the influence of temperature is avoided while the device area is saved.

The utility model discloses a reinforcing steel bar detector with a probe for identifying distribution of a reinforcing steel bar mesh, which belongs to the technical field of reinforcing steel bar detection and comprises a sensor module and a host controller connected with the sensor module through a signal line. The sensor module comprises an electromagnetic induction module, an ultra-wideband radar module, an ultrasonic transducer, a frequency-mixing medium-frequency amplifying circuit, an orthogonal frequency-mixing filter circuit, an analog signal conditioning circuit, an A / D converter, a measurement module and a laser module; the electromagnetic induction module comprises a signal generation circuit, an induction coil and a signal acquisition circuit; the host controller comprises a microcontroller module, a data transmission module, an interface module, a clock module, a memory module, a USB port module and a power supply module. According to the utility model, the detection of the steel bar diameter and the steel bar burial depth in the building component is effectively realized, and the requirements in actual engineering detection are met.

The invention provides a finished bridge linear smoothness judgment method and system based on a main beam splicing process, and relates to the technical field of linear smoothness judgment, and the method comprises the following steps: obtaining a design curve of a main beam finished bridge target elevation, an elevation curve of an actual main beam splicing section and a to-be-judged splicing section length; and if the actual elevation curve exceeds the preset threshold, determining the elevation curve of the first bridge section according to the design curve. And fitting the actual elevation curve with the elevation curve of the first bridge section by adopting a preset spline curve fitting method to generate an elevation curve of the second bridge section. And combining the elevation curves of the actual splicing section, the first bridge section and the second bridge section into a virtual bridge forming line shape, performing high-pass filtering on the line shape, extracting an irregularity maximum absolute amplitude, comparing the irregularity maximum absolute amplitude with a preset irregularity allowable limit value, and judging whether the smoothness of the splicing section meets the requirement or not. According to the method, the smoothness judgment precision of the finished bridge line shape can be effectively improved.

The present invention relates to the technical field of parking management, and in particular to a method for automatically filling abnormal parking orders based on Lagrange interpolation. The method uses vehicle structured data and camera-captured images to simulate driving trajectories through wavelet transform, Lagrange interpolation and Kalman filtering, detects and records license plate information through a license plate correction and recognition system, analyzes parking locations, parking duration, entry and exit times and image evidence, so as to fill in or update missing parking data in an existing database. The method has low calculation cost and high speed, and improves the accuracy and stability of trajectory simulation. Complete parking data is obtained through trajectory simulation and license plate detection technology, and is synchronously updated to a database in real time, so as to realize automatic filling and ensure the integrity and accuracy of parking records.

The utility model belongs to the technical field of power supplies, and discloses a low-ripple and high-integration power supply for a precise instrument. The power supply device comprises a positive voltage circuit and a negative voltage circuit which are arranged on the same circuit board in parallel, and the positive voltage circuit and the negative voltage circuit respectively comprise a voltage transformation circuit, a rectifier bridge circuit, a first filter circuit, a first voltage stabilizing circuit, a second filter circuit and a second voltage stabilizing circuit which are electrically connected in sequence. And meanwhile, a virtual ground end is arranged at a corresponding position. On the basis, compared with the existing power supply suitable for the precise instrument, the power supply provided by the utility model is high in integration level and simple in circuit structure; and the output voltage is more stable.

This invention relates to the technical field of behavioral analysis of children with autism, and provides a method and system for collecting daily behavioral data of children with autism. The method includes: real-time acquisition of triaxial acceleration and triaxial angular velocity from an inertial measurement unit as raw data; extraction of feature data from the raw data; judgment of the physical consistency of the raw data based on the feature data, distinguishing between real limb movement signals and sensor relative motion signals; decomposition of the raw data into a first information stream and a second information stream; identification of behavioral patterns of limb movements in children with autism based on the first information stream; extraction of features from the second information stream to obtain second information features; the second information features characterizing the intensity of sensor relative motion corresponding to the sensor relative motion signal; calculation of a behavior intensity index based on the second information features; and output of the behavior pattern and behavior intensity index. This invention improves the accuracy of collecting daily behavioral data of children with autism.

The embodiment of the invention discloses an image background similarity determination method and device, equipment and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: segmenting video frames through employing a preset panoramic segmentation network, and obtaining a semantic region node set corresponding to each video frame in a one-to-one manner; constructing an initial space-time diagram according to all semantic region node sets, and updating the initial space-time diagram by using a preset graph neural network to obtain a target space-time diagram; determining a global background descriptor according to a target background region node corresponding to the video frame; according to the global background descriptor corresponding to the first video frame and the global background descriptor corresponding to the second video frame in the to-be-processed video frame sequence, the image background similarity between the first video frame and the second video frame is accurately determined, so that the background matching accuracy can be remarkably improved; and the reliability of medical environment safety assessment or financial safety event analysis can be enhanced.

The invention relates to the technical field of video processing, and discloses an edge computing and AI fused real-time video stream processing optimization method. The edge calculation and AI fused real-time video stream processing optimization method comprises the following steps: preprocessing a real-time video; performing video optimization processing; obtaining an optimization result; according to the invention, the internal noise of the video is filtered by using a block and low-rank tensor recovery video denoising algorithm, so that the video definition is improved, and the video edge information is more significant; the edge of a denoised video is detected through a PSVM, the video edge detection effect is improved, the noise of an original video image can be effectively removed through an algorithm, the definition of the video image is improved, edge information is made to be more remarkable, when the noise combination level is higher, the entropy value, the AG value and the PSNR value of the two video sequences which are expanded and denoised through the method are all in a descending trend, and the noise combination level is higher. Therefore, the method provided by the invention has a better denoising effect when the video sequence with a simple and complex structure is processed.

The application relates to the technical field of voltage quality monitoring, in particular to a power distribution line voltage quality real-time monitoring method based on a low-voltage transformer area topology, which comprises the following steps: based on the historical output power of each distributed photovoltaic in the power grid topology structure of the low-voltage transformer area, a probability density distribution model is established, the distributed photovoltaic is classified, and the weight coefficients of each distributed photovoltaic are quantified, and the typical probability distribution model corresponding to each category is extracted; the fluctuation range of the output power is divided into multiple focal element intervals, and a basic trust degree is allocated to the focal element intervals to establish a focal element model of photovoltaic output; a probability interval power flow model is constructed; the probability interval power flow model is solved to obtain an interval solution of the node voltage amplitude, an accumulated trust function curve is constructed through evidence theory, and the quality confidence of the node voltage is obtained, so that the voltage quality of the power distribution line is evaluated. The application effectively improves the identification accuracy and early warning response capability of the voltage out-of-limit risk of the power distribution line.

The invention discloses a large language model integrity verification method and system based on output feature distribution statistical test, and belongs to the field of artificial intelligence safety. In order to solve the problem of unreliable verification caused by output randomness of a large language model, the method comprises the following steps of: obtaining model finger with expected output and actual output of a to-be-verified model; performing multi-level feature extraction on expected and actual outputs to obtain two groups of feature vector sets; and performing double-sample statistical test on the two groups of feature vector sets to judge whether the feature distribution has significant difference or not. Preferably, the check is a maximum mean difference (MMD) check in conjunction with a permutation check to obtain reliable p values in small samples. And finally, judging the integrity of the model according to a comparison result of the p value and a preset significance level. According to the method, random interference can be overcome, model tampering can be detected with high sensitivity by using a small amount of API calling, and the robustness and reliability of verification are remarkably improved.