330 results about "Signal mapping" patented technology

The invention provides an automatic operation process management method and device based on multi-sensor fusion, and relates to the technical field of automatic processes, and the method comprises the steps: constructing an instrument pose flow and operation field geometric model under a unified coordinate system through unified timestamp alignment and spatial geometry solution of multi-source sensor data; semantic segmentation and three-dimensional reprojection are combined to generate a shielding probability graph, cross-channel compensation reconstruction is carried out, and the image integrity is improved. And forming a conflict risk set through voxel field discretization and short-time prediction, and performing combined solution with standardized operation process constraints to generate a scheduling instruction and a safety time window. And finally, mapping the image, the pose and the physiological signal to a knowledge graph, and outputting consistency judgment and deviation correction suggestions in combination with a time sequence attention recognition process node. According to the method, the problem that the operation process management is not accurate enough due to the problems of view overlapping and the like of multi-source sensor data can be solved.

The invention relates to the field of chip testing, in particular to a chip signal integrity test analysis method, device and equipment and a storage medium. The method comprises the following steps: extracting a chip performance index based on a chip design requirement document; performing saturation frequency excitation setting based on the chip performance index to generate an excitation test signal; performing a multi-port signal excitation test and parallel signal sampling based on the excitation test signal to generate a high signal-to-noise ratio response data set; obtaining a chip layout design file; performing chip communication link analysis on the chip layout design file, and constructing communication line layout data; performing signal mapping and waveform quality evaluation on the communication line layout data based on the high signal-to-noise ratio response data set to obtain a node signal distortion rate; and performing signal integrity quantization and theoretical signal integrity score comparison based on the node signal distortion rate, and marking a fault node. According to the invention, the chip testing efficiency is improved, and the signal abnormal part of the chip is accurately and efficiently detected.

The invention provides a multi-channel one-core multi-detection integrated detection method, which is applied to the technical field of in-vitro diagnosis and comprises the following steps: acquiring time sequence original signals of blood routine, liver function and kidney function channels; obtaining a baseline stable primary signal through cross-channel baseline trend coupling and adaptive correction; adopting a machine learning model to identify interference components and performing component offset to obtain a decoupling signal; mapping the decoupling signal into a physiological parameter value and carrying out rationality test; when the low credible parameter exists, a rechecking mechanism is triggered; performing weighted fusion on the basis of the rationality test result and the recheck signal to obtain a comprehensive parameter set; and finally generating a comprehensive detection report containing the credibility score. Cooperative processing and fusion analysis of multi-channel detection signals are realized, and the accuracy and reliability of a detection result are remarkably improved through interference identification and a dynamic recheck mechanism.

The invention provides a helicopter cable fault detection system and method, and belongs to the technical field of helicopter electrical system detection. The system comprises a positioning and matching device and a multi-modal signal analysis device, the positioning and matching device determines the relative position with a to-be-detected aviation plug through a dual-spectrum camera, generates a contact coordinate matrix and a contact and probe mapping table, injects a test signal to identify the contact material so as to obtain a topology result, and detects the contact pressure; and the multi-modal signal analysis device is used for matching parameters based on the topology result and the mapping table, setting a detection signal, positioning the contact, applying a signal in combination with pressure, acquiring a response signal, establishing a mapping table, and analyzing the response signal to generate a contact and fault result mapping table. According to the invention, the whole process automation from aviation plug positioning, signal detection to fault analysis is realized, the system can complete the core links of contact coordinate generation, signal mapping, fault determination and the like without manual intervention, and the detection efficiency is greatly improved.

The invention relates to the technical field of thunder and lightning signal sampling, and discloses a GAN-based thunder and lightning very-low-frequency electromagnetic wave signal up-sampling method, a terminal and a medium. The method comprises the following steps: preprocessing a lightning very-low-frequency low-sampling signal and a real high-sampling signal; constructing a generative adversarial network; the generator is used for extracting initial features from the low-sampling signals and performing residual enhancement, then mapping low-sampling features after residual enhancement to a high-sampling dimension through a feature-guided dynamic interpolation mechanism, and performing correction and signal mapping on intermediate features after interpolation to obtain reconstructed signals matched with a target high-sampling rate; the discriminator is used for reconstructing the authenticity probability of the signal; after a generator and a discriminator are jointly trained by adopting an alternative updating mode of discriminator loss and generator loss, a newly input low sampling signal is preprocessed, and a corresponding reconstruction signal is output through the generator. According to the invention, accurate reconstruction from a lightning very-low-frequency low-sampling signal to a high-sampling signal is realized.

The invention relates to the technical field of radar signal processing, and discloses a complex ground reflection environment-oriented dual-path target detection method, which comprises the following steps of: performing quantile truncation and discretization processing on original range profile data, mapping a processed signal to a D-channel discrete feature space and generating a D-dimensional discrete feature matrix; the D-dimensional discrete feature matrix is input into a dual-channel parallel processing architecture, a CFAR adaptive detection module is adopted in a first path to suppress strong clutters to generate a first response matrix, and a false attention mechanism is adopted in a second path to enhance weak targets to generate a second response matrix; performing spatial alignment and weighting processing on the first response matrix and the second response matrix, and performing cross-domain confidence fusion to output a comprehensive response matrix; and carrying out fixed threshold binarization processing on the comprehensive response matrix based on a global threshold to obtain a target detection result. The method has both local gain compensation and global adaptive suppression, and can realize more robust detection of a weak target under a complex background.

A wearable electroencephalography device includes an ear cuff around an auricle of an ear comprising a curved structure conforming to contours of the ear, an electroencephalography electrode positioned on the ear cuff and contacting skin for detecting brain activity signals, a wireless controller integrated with the ear cuff and processing the brain activity signals from the electroencephalography electrode, and a wireless communication module transmitting the processed brain activity signals to an external device. The wearable electroencephalography device includes a mobile application receiving the transmitted brain activity signals from the wireless communication module, analyzing the brain activity signals using machine learning algorithms to determine emotional states of a user, and providing real-time feedback regarding the emotional states through a user interface. The mobile application comprises a self-report interface allowing the user to input subjective state data and a live-feed interface displaying real-time subjective state data derived from the brain activity signals.

The invention relates to an analog quantity selection method and system based on a Boolean switch, in particular to the field of industrial automation, and aims to directly realize signal mapping by adopting a pure hardware combinational logic circuit and thoroughly eliminate instruction execution time, so that the state change of a Boolean switch signal can be instantaneously reflected on analog quantity output; according to the method, real zero-delay switching is achieved, meanwhile, program adjusting algorithms in any form are avoided, disturbance phenomena such as overshoot or undershoot are completely eradicated from the principle, and high stability and accuracy of output signals are ensured. Finally, according to the technology, the control instruction can be accurately and synchronously issued without delay, and the response speed and the synchronization precision of the whole control system in a high dynamic response industrial scene are remarkably improved.

The invention provides a radar signal anti-interference processing method and system based on a space-time attention mechanism. The method comprises the following steps: mapping an original horizontal polarization channel signal and an original vertical polarization channel signal into an interference suppression space, and generating a three-dimensional feature tensor comprising an amplitude component, a polarization phase difference component and an interference suppression residual component; according to the amplitude component, dividing a radar observation area into multiple layers of target blocks in a spatial dimension, and calculating a space-time association weight between adjacent layers of target blocks through a space-time attention mechanism; and according to the space-time correlation weight, screening out a plurality of target blocks meeting a preset continuous motion condition, and decoding a motion mode of the plurality of target blocks by using a long short-term memory network to generate an anti-interference target trajectory coordinate and a velocity vector. According to the method, the polarization characteristics and the space-time attention mechanism are fused, the anti-interference effect of radar signal processing in a strong interference environment is improved, and then the detection precision of the multi-target motion trail is improved.

The invention discloses a blade flutter monitoring method and device, a monitoring system, a storage medium and a computer program product, and the method comprises the steps: carrying out the modal feature analysis of a blade to determine a flutter monitoring region, laying a flexible sensing element in the flutter monitoring region, and enabling the flexible sensing element to be used for collecting the strain signal of the blade; in the running process of the blade, real-time running parameters of the blade and real-time strain signals collected by the flexible sensing element are obtained; determining a reference strain signal corresponding to the inertial centrifugal force of the blade according to the real-time operation parameters and a preset mapping relation; the mapping relation is the mapping relation between the operation parameters of the blade and the reference strain signal; deducting the reference strain signal from the real-time strain signal to obtain a target strain signal corresponding to the aerodynamic force; and according to the target strain signal and a preset formula, calculating pneumatic work of the blade in a set vibration period, and according to the pneumatic work, judging whether the blade flutters or not. According to the scheme, the accuracy and the real-time performance of flutter judgment are improved.

A method may obtain a failure propagation model, wherein the failure propagation model comprises: a model representation of a plurality of hardware components, a set of hardware failure probabilities; and a logic that maps signals to the hardware components. A method may receive an alert signal from the aircraft. A method may map the alert signal to the plurality of hardware components. A method may perform a root-cause diagnosis of the alert signal that has been mapped to the plurality of hardware components comprising: determining via the failure propagation model, one or more combinations of hardware failures associated with the alert signal; and determining a probability of occurrence associated with the combinations of hardware failures. A method may display a report that includes combinations of hardware failures associated with the alert signal and the probability of occurrence associated with the combinations of hardware failures.

The invention relates to the field of virtual debugging, and particularly discloses a control program and a virtual debugging method of a cold roll forming machine shearing system, and the method comprises the steps: S1, building a virtual model comprising a feeding mechanism, a shearing mechanism, a servo driver and a photoelectric sensor in an NX MCD environment, outputting an accumulated feeding length and a sensor signal by the model, and receiving the position setting of a cutter; s2, a trimming procedure is written for an S7-1200 PLC (programmable logic controller) in a TIA Portal (TIA Portal); s3, two-way communication between the NX MCD and the PLC is established through OPC, the accumulated feeding length and a sensor signal are mapped into PLC input, and the cutter position set output by the PLC is mapped into NX MCD servo input; and S4, the two are simulated synchronously, control logic and parameters are optimized based on model feedback, and the target is to minimize fixed length error and motion instability. According to the invention, the problems of long field debugging period, high cost, inaccurate shearing triggering and unstable return in the prior art are solved, and the shearing precision and the operation reliability are improved.

The invention provides a reference signal transmission method and device, relates to the technical field of communication, and is used for improving the reliability and efficiency of information transmission. The method comprises: for a channel measurement reference signal to be sent on a time domain symbol of a wireless frame in a time domain, mapping the channel measurement reference signal to X subcarriers of a corresponding carrier in a frequency domain, the carrier being composed of continuous 157 subcarriers, and the X being less than or equal to 157; and sending the channel measurement reference signal on the X subcarriers through a target antenna port.

Disclosed are methods, systems, and computer-readable medium to perform operations including: generating a mapping pattern that comprises a length-4 frequency division orthogonal cover code (FD-OCC4), the mapping pattern for mapping demodulation reference signals (DMRS) in frequency to a plurality of resource elements in at least one orthogonal frequency-division multiplexing (OFDM) symbol, wherein the DMRS are associated with a plurality of DMRS ports; mapping, using the FD-OCC4, the DMRS to the plurality of resource elements in the at least one OFDM symbol; and transmitting a transmission comprising the at least one OFDM symbol.

The invention provides a digital human question-answering system, and the system comprises a voice receiving and processing module which is used for receiving the voice input of a user in real time, splitting the input voice, extracting the acoustic features of each frame of voice in real time, and caching the acoustic features of continuous frames to form a streaming data queue; the voice feature reasoning module is used for inputting the streaming data queue into a pre-training question and answer model and outputting a question and answer text result and a voice synthesis instruction in real time; and the digital human synchronous driving module is used for generating a synchronous voice signal based on the question and answer text result and the voice synthesis instruction, mapping the synchronous voice signal to a mouth shape mapping library and an action template library, generating a mouth shape sequence and a limb action sequence matched with the voice time sequence, and completing digital human broadcasting. According to the method, user text or voice input is received, the question and answer result is returned after model reasoning, and the digital person is driven to complete voice broadcasting.

The embodiment of the invention discloses a plasma operation electrode precise control method and system based on multi-dimensional signal mapping. The method comprises the steps that multi-dimensional signals in the working process of a plasma operation electrode are obtained; performing phase-space reconstruction on the multi-dimensional signal to obtain a state vector sequence representing the working state of the electrode; constructing a joint recursion plot based on the state vector sequence; joint recursion quantitative analysis is carried out on the joint recursion plot, joint recursion quantitative indexes used for representing multi-dimensional signal collaborative dynamic characteristics are extracted, and the joint recursion quantitative indexes at least comprise joint certainty, joint laminar flow, joint recursion rate and joint entropy; according to the corresponding relation between the joint recursion quantitative index and the plasma energy output stability, control parameters of a plasma generator are generated according to a preset mapping rule, and energy output of the plasma operation electrode is adjusted in real time according to the control parameters. The control precision is remarkably improved, thermal damage is reduced, and the operation safety is improved.

The present invention discloses a low-complex, reliable multidimensional orthogonal time-frequency space (ND-OTFS) modulation-based communication system for uplink and downlink wireless communication between resource-constrained Internet of Things (IoT) and machine-to-machine (M2M) devices comprising at least one transmitter configured to transmit bit stream involving N-Dimensional signal mapper and OTFS frame and at least one receiver configured to be integrated into said IoT or M2M devices, featuring a low-complexity detector for receiving the transmitted bit stream.

The invention provides a hanger CAN signal processing method and device, equipment and an upper system. The hanger CAN signal processing method comprises the following steps: acquiring an original CAN message of a hanger CAN bus; according to a preset data frame structure, packaging the original CAN message to obtain a standardized data frame; analyzing the standardized data frame according to a preset frame description template and the frame identifier of the standardized data frame to obtain a CAN signal mapping relation; verifying the CAN signal mapping relation to obtain a verification result; when the verification result represents that the CAN signal mapping relation meets the preset verification requirement, the CAN signal mapping relation is stored in the data cache pool, processing of the CAN signals of the lifting appliance is completed, multi-protocol compatibility of CAN messages is achieved, redundant transmission of data is reduced, and the CAN signal transmission efficiency of the lifting appliance communication system is improved.

Described is a Neuromorphic Adaptive Core (NeurACore) cognitive signal processor (CSP). The NeurACore CSP includes a NeurACore local learning layer block that is operable for receiving as an input a mixture of in-phase and quadrature (I / Q) signals and mapping the I / Q signals onto a neural network to determine complex-valued output weights of neural states of the neural network. A global learning layer is included that is operable for adapting the complex-valued output weights to predict a most likely next value of the input I / Q signal. Further, a neural combiner is included that operable for combining a set of delayed neural state vectors with weights of the global learning layer to compute an output signal, the output signal being separate in-phase and quadrature signals.

A system and method of tracking vessel movers within a liquid handler system using sparse sensor assemblies. The method includes mapping received sensor signals to pre-characterized functions and determining the positions of the vessel movers with respect to a single sensor from the sensor assembly accordingly. The sensors in the sparse sensor assembly can be positioned to have no or minimal overlap between their sensing ranges.

The invention provides a magnetotelluric signal denoising method and system based on a multi-path fusion network. The method comprises the following steps: constructing and training a noise identification model and a signal mapping model based on the multi-path fusion network; inputting a noisy signal segment identified based on the noise identification model into the signal mapping model to obtain a pure signal segment or a noise contour; and finally, splicing the pure signal segments in sequence to obtain the magnetotelluric signal after noise reduction. According to the multi-path fusion network provided by the invention, three convolution layers with convolution kernels of different sizes and a convolution path of an activation function are arranged in a multi-path convolution module, and the multi-path fusion network is used for multi-scale signal feature extraction; and in an attention-enhanced multi-path feature fusion module, outputs of different paths are respectively input into channel attention, space attention and a convolutional layer, and the corresponding outputs are spliced in a channel dimension, so that fusion of multi-path features is realized. In addition, physical constraints are further introduced, and deep optimization is carried out on the two models.

The invention relates to the technical field of power equipment, in particular to a high-voltage cable fault diagnosis method and system. The method comprises the following steps: collecting discharge data in a discharge process of a to-be-detected high-voltage cable, and analyzing fault trend data of the high-voltage cable in a target interval to obtain a trend prediction waveform; the method comprises the following steps: constructing a reference fault-sample waveform signal mapping library through historical discharge data of a to-be-detected high-voltage cable, selecting at least one reference fault from the reference fault-sample waveform signal mapping library to form a fault combination, and superposing sample waveform signals corresponding to the reference faults in the fault combination to obtain a sample waveform signal; obtaining a sample comprehensive signal corresponding to the fault combination; and predicting a potential fault and a possible occurrence time point of the high-voltage cable based on the sample comprehensive signal and the corresponding fault combination. According to the invention, before the fault occurs, the possible fault can be checked in time, and targeted advanced processing is carried out.

The invention discloses an emotion recognition method based on multi-band Riemannian manifold space learning, and belongs to the technical field of biological signal recognition, and the method comprises the steps: carrying out the self-adaptive frequency band division of an original signal through variational mode decomposition, restraining the mode aliasing, and extracting a multi-band sub-signal; further mapping the sub-signals to a Riemannian manifold space by using a multivariate phase space reconstruction + Riemannian manifold regularization Fisher discriminant analysis module, representing the geometrical characteristics of the signals through a covariance matrix, enhancing the characteristic separability by combining tangent space projection and discriminant analysis, and fusing the characteristic vectors by using an attention mechanism to obtain a multi-element phase space reconstruction and Riemannian manifold regularization Fisher discriminant analysis model; and finally, inputting the fused feature vectors into a classification model constructed by a graph convolutional neural network, and carrying out classification processing. According to the method, the problems of insufficient utilization of frequency band information and weak manifold feature discrimination in traditional emotion recognition are effectively solved, and the recognition precision and robustness are remarkably improved in scenes such as physiological signals.

The invention discloses a motor imagery classification algorithm for perturbing and sensing a double-flow attention mechanism in a Riemannian space, and relates to the technical field of brain-computer interfaces, and the algorithm comprises the steps: mapping an EEG signal to a Riemannian manifold space, modeling an inter-electrode nonlinear relation through the geometric characteristics of the Riemannian manifold space, and combining tangent space dynamic perturbations and a double-flow differential attention mechanism, high-precision classification in a noise environment is achieved, EEG signals are mapped to Riemannian manifold through block covariance matrix calculation, the nonlinear geometrical relationship between electrodes is effectively reconstructed, meanwhile, a double-flow differential attention mechanism based on disturbance flow and reference flow is designed, collaborative optimization of noise suppression and key feature enhancement is achieved, and the noise suppression and key feature enhancement efficiency is improved. And a reliable algorithm support is provided for the practical application of the brain-computer interface system.

The invention relates to the technical field of data mining analysis, in particular to a petrochemical operation state evaluation method and system based on big data, and the method comprises the following steps: collecting a sensor signal to generate standard time sequence monitoring data, constructing a high-dimensional space-time correlation map based on physical topology, extracting a global state feature vector through a map attention network, and carrying out the analysis of the global state feature vector. And calculating a feature distance and mapping to generate a petrochemical operation state evaluation result. According to the method, a high-dimensional space-time correlation graph based on physical topology and data correlation is constructed, discretely distributed sensor signals are mapped into graph structure nodes with logic connection, and hidden nonlinear coupling characteristics among multi-source parameters are deeply mined by using a graph attention network model; compared with the prior art, the method overcomes the defect that a single-point monitoring mode splits process continuity, achieves the precise quantification of the performance degradation trend of equipment under a complex working condition in combination with the dynamic evaluation logic based on the feature space distance, and effectively improves the global consistency and accuracy of petrochemical operation state evaluation.

The invention relates to the technical field of detection control, in particular to a steering gear detection system based on an embedded digital circuit, which comprises a control linked list module, a signal mapping module, a state acquisition module, a deviation calculation module and a regulation and control correction module. According to the invention, the torque value and the vehicle speed value are written into the embedded digital register according to the control period sequence, and the stepping sequence is constructed, so that the timing sequence precise sorting of the control instruction can be realized, the digital sampling packet is generated by using the feedback current, voltage and angle signals, and the synchronous frame is marked, and the unified analysis mechanism of the periodic feedback behavior is realized. The error percentage value and the tolerance threshold value are compared to count the deviation times, the abnormal condition of the response stability is quickly identified, the deviation trend is tracked and controlled by means of a continuous voltage recursion correction mode, output parameters are dynamically adjusted, and a periodic closed-loop correction capability and a node-level real-time regulation and control mechanism are provided. And the data accuracy, the feedback real-time performance and the control stability in the detection process of the steering gear are effectively improved.

The invention discloses a human body posture stability intelligent early warning system for running. The system comprises a sensor module used for collecting a motion signal and a pressure sensing signal of a runner; the information preprocessing module is used for preprocessing the motion signals and the pressure sensing signals through calibration and filtering; the edge processing module is used for mapping the preprocessed pressure sensing signal into runner gravity center position information and mapping the preprocessed motion signal into runner posture angle information to form a state vector measurement value of the runner; the Kalman filtering module is used for predicting the state vector of the runner according to the state vector measurement value of the runner; and the posture stability evaluation module is used for constructing a posture stability evaluation model according to the predicted state vector of the runner, calculating the posture stability of the runner and carrying out early warning if the posture stability of the runner is smaller than a posture stability threshold value. The posture stability of the runner can be accurately monitored, and early warning can be carried out in advance.

The embodiment of the present application relates to the technical field of vehicle intelligent diagnosis, and discloses a CAN signal intelligent analysis method and system, comprising the following steps: reading CAN messages and CAN protocols, and analyzing the CAN messages according to the CAN protocols, wherein the CAN protocols are used to define signal mapping and analysis basis; checking the analyzed CAN signals, including verifying whether the CAN signals meet the requirements of the CAN protocols and evaluating health degree indexes, recording various data in the signal checking process, and obtaining signal test results; reading a preset test report template, automatically matching, filling and abnormity marking the signal test results according to the test report template, and generating a test report; and generating a checking result report based on the health degree indexes and the test report and displaying the checking result report. The present application can realize automatic CAN signal analysis and multi-dimensional checking.

This invention discloses a sleep stage identification method, comprising: a multimodal data acquisition step; a preprocessing step, which normalizes the acquired physiological signals and segments them according to a set duration to obtain several original signal segments; a Williams RGB compression step, which maps the original signals to an RGB image to obtain a Williams RGB energy map; a feature extraction step, which inputs the Williams RGB energy map into a neural network model for feature extraction; and a comparison and classification step, which compares the feature values ​​with RGB standard thresholds to determine the corresponding sleep stage. This sleep stage identification method avoids directly processing the complex frequency domain features of physiological signals, thus reducing computational complexity. Three physiological signals within a certain time period only need to be represented by a single pixel in the image, achieving physiological signal compression and dimensionality reduction, further reducing computational load. This invention also effectively utilizes medical knowledge graphs to provide interpretable evidence for the sleep stage identification decision-making process.

The invention discloses an acoustic processing method for directionally generating, grouping and managing sound frequency bands and eliminating and counteracting the sound frequency bands, which comprises the following steps of: S1, carrying out AD (Analog to Digital) sampling on analog sound signals to be processed to obtain digital sound signals to be processed; s2, performing dynamic frequency band division on the to-be-processed digital sound signal to obtain a plurality of frequency band signals; s3, mapping the plurality of frequency band signals into a plurality of sound source groups; s4, each sound source group generates a directive sound signal pointing to the target area by using the sound radiation unit of the sound source group, and after the directive sound signal is synchronously transmitted to the target area, an actual mixed sound signal is formed; and S5, detecting, eliminating and counteracting a sound wave counteracting effect generated by the actual mixed sound signal in the target area. According to the method, high-quality accurate directional propagation can be realized, and the adaptability to a complex environment is enhanced.