1718 results about "Signal response" patented technology

The invention discloses an online course learning management method and system based on a knowledge graph, and the method comprises the steps: S1, analyzing course data, extracting knowledge points and an association relationship, and generating an initial knowledge graph signal containing knowledge point nodes and association edges; s2, collecting user interaction behavior data and extracting behavior features to generate behavior feature vector signals associated with knowledge point nodes; s3, detecting an abnormal learning mode, triggering a knowledge graph weight updating signal, and dynamically adjusting the associated edge weight; s4, updating the signal according to the initial knowledge graph signal and the knowledge graph weight; and S5, responding to the path optimization signal, and generating an interface sorting adjustment signal and a content push signal. According to the air quality intelligent monitoring system based on sensing data feedback, the problem that a traditional online course management system is single in data acquisition dimension and static and solidified in knowledge dependency relationship can be solved.

A localization system comprises: a device; a master unit which wirelessly transmits a first localization signal; a plurality of lateration units distributed about the area within which the device is being localized, wherein each lateration unit of the plurality independently starts its own timer upon its receipt of the first localization signal; and a localization unit. The device receives the first localization signal and responsively wirelessly transmits a second localization signal. Each of the lateration units: independently receives the second localization signal; stops its respective timer responsive to receipt of the second localization signal; and wirelessly transmits a timer count signal to a localization unit. The timer count signal identifies the transmitting lateration unit and a count of its respective timer. The localization unit utilizes the plurality of timer along with respective distances between the master unit and the lateration units to localize the first device via time-of-flight lateration.

The invention relates to the technical field of grinding wheel monitoring, in particular to a centerless grinding machine full-life-cycle grinding wheel state monitoring system and method.According to the centerless grinding machine full-life-cycle grinding wheel state monitoring system and method, through combined difference extraction of a main shaft current mean value fluctuation ratio and a power gradient continuous variation value and assisted by transverse comparison of periodic response intervals, effective recognition of time period boundaries of working condition states is achieved; according to the method, operation stage division does not depend on static threshold judgment any more, a track trend is established through linkage judgment of an amplitude kick point and a slope extreme value, a dynamic evolution path of signal response is clearer, the identification resolution of abnormal changes and local deviation trends is improved, response synchronous identification of parameter state changes is achieved, and the identification accuracy is improved. In multi-signal collaborative analysis, directivity comparison between a temperature rise mutation fragment and a current residual error response is carried out, so that the consistency analysis capability of response logic between different monitoring paths is enhanced, and the real-time performance, stability and anti-interference capability of state identification are improved.

A wearable device configured to monitor physiological parameters of a wearer can include: a physiological parameter measurement sensor configured to monitor a plurality of physiological parameters; a hardware processor; a display in communication with the processor; and a band configured to secure the physiological parameter measurement sensor on a wrist of the wearer. In some implementations, the hardware processor is configured to: obtain a first plurality of signals from the physiological parameter measurement sensor when the band is secured on the wrist at a first tightness; determine a signal quality responsive to the first plurality of signals; and output an indication on the display to adjust tightness of the band with respect to the wrist from the first tightness to a second tightness based on the determined signal quality.

The invention discloses an electrostatic discharge immunity automatic test management method and system. The method comprises the steps that real-time environment state data in an experimental environment are collected in real time, then redundancy mechanism and check algorithm processing is carried out, and discharge parameters of an electrostatic generator are calculated and adjusted in real time through a multi-dimensional nonlinear self-adaptive control algorithm; performing electrostatic discharge based on the adjusted discharge parameters of the electrostatic generator, generating electrostatic interference of specified parameters, simulating an actual scene of electrostatic discharge, further transmitting a generated electrostatic discharge interference signal to test equipment, and generating a corresponding response signal; the response signals and the interference signals are preprocessed and then subjected to deep spectrum analysis, key features are extracted, and the immunity of the test equipment is intelligently evaluated through a neural network model. The control precision of the electrostatic generator is improved, the electrostatic generator can dynamically adapt to the change of the experimental environment, and it is ensured that the discharging condition is always in the optimal state.

The invention discloses a relay state prediction and fault early warning method and system based on deep learning, and the method comprises the steps: S1, building a constraint condition of a generative adversarial network based on a relay physical model, and forming an enhanced fault waveform signal according with a physical rule through adversarial training; s2, receiving a real-time current and voltage signal and a mechanical vibration signal, and extracting an electric signal feature vector by using a time sequence convolutional network; s3, inputting the combined feature tensor into the lightweight assessment model, and outputting a health degree scoring signal; s4, responding to the meta-learning activation instruction, loading historical data of equipment to construct a parameter optimization set, performing online fine tuning on the early warning model based on a meta-learning framework, and generating a fault determination parameter; and S5, analyzing real-time signal characteristics according to the fine-tuned judgment parameters, and outputting graded early warning signals to a monitoring terminal. According to the method, the problems of early state prediction and accurate early warning of the relay under small sample fault data can be solved.

An audio apparatus comprising a receiver (501) receiving audio data and metadata including data for reverberation parameters for the environment. A modifier (503) generates a modified first parameter value for a first reverberation parameter which is a reverberation delay parameter or a reverberation decay rate parameter. A compensator (505) generates a modified second parameter value for a second reverberation parameter in response to the modification of the first reverberation parameter / The second reverberation parameter is indicative of an energy of reverberation in the acoustic environment. A renderer (400) generates audio output signals by rendering the audio data using the metadata and specifically a reverberation renderer (407) generates at least one reverberation signal component for at least one audio output signal from at least one of the audio signals and in response to the first modified parameter value and the second modified parameter value. The compensation may provide improved perceived reverberation while allowing flexible adaptation.

The invention provides a marine engine lubricating oil system fault diagnosis method and system based on transfer learning from a simulation domain to a real domain, and the method comprises the steps: carrying out the fault simulation of different working conditions based on a built marine engine lubricating oil system simulation model, and obtaining a simulation fault signal response; acquiring real domain fault data of different working conditions of an engine lubricating oil system based on an engine experiment platform; a feature fusion structure based on an encoder-decoder is adopted to obtain simulation domain fault data; an engine lubricating oil system fault diagnosis model based on data migration and algorithm migration dual migration learning is obtained; and based on the engine lubricating oil fault diagnosis model, performing real-time fault diagnosis on the marine engine lubricating oil system to obtain a diagnosis result. According to the method, transfer learning from simulation domain fault features to real domain fault features is realized, so that the problems of high construction cost and insufficient generalization performance of a diagnosis model are solved.

The invention relates to the technical field of motor control, in particular to motor equipment and an operation control method thereof. The method comprises the following steps of collecting operation data of motor equipment in the numerical control machine tool under various working conditions in real time to obtain an initial operation data set; training a neural network model by using the initial operation data set to identify harmonic characteristics under each working condition, and generating a harmonic spectrum identification model; acquiring current and voltage signals of the motor equipment in real time, inputting the current and voltage signals to the harmonic spectrum identification model for identification, and generating dynamic harmonic characteristic monitoring data; and comparing the dynamic harmonic characteristic monitoring data with a preset threshold value, generating an isolation instruction, and dynamically adjusting the frequency response range of the motor equipment to obtain optimized signal response data. According to the invention, through real-time data acquisition, intelligent model training, dynamic adjustment and a feedback mechanism, the adaptability, efficiency and stability of motor equipment are improved.

The invention relates to the technical field of water quality pollution detection, in particular to a water quality pollution rapid detection method driven by a quantum sensing technology. A quantum sensor array is deployed in a water area to be detected, and quantum signal response data of the water area is collected; constructing a pollutant quantum recognition model, and inputting quantum signal response data into the model to obtain an initial predicted value of pollutant concentration; generating an initial detection parameter of the quantum detection instrument based on the initial predicted value, executing primary pollution detection by the instrument according to the parameter, and collecting primary deviation data of an actual detection value and an expected detection value; determining a detection error according to the deviation data, further correcting the initial detection parameter, and generating an optimized detection parameter; and finally, the instrument performs secondary pollution detection based on the optimized detection parameters. According to the method, complex water sample pretreatment is not needed, water quality pollution detection can be rapidly and accurately completed, detection requirements of different water areas are met, and the detection efficiency and accuracy are improved.

The invention discloses an electrical stimulation type nerve regeneration method based on calcium signal regulation and control, and relates to the technical field of electrical stimulation type nerve regeneration, and the method comprises the following steps: based on a Schwann cell population calcium signal response feature set, identifying whether the calcium ion concentration is in a microliter stage which breaks through a lowest activation threshold and does not reach a stable state; judging whether the target nervous tissue is in a calcium signal activation critical state or not based on the behavior mode change of the Schwann cell population; based on the judgment result of the calcium signal activation critical state, an electrical stimulation adjustment control instruction is generated in real time in combination with the calcium ion concentration fluctuation trend and Schwann cell population response characteristics, and the calcium ion concentration stability is optimized and the activation state is maintained by adjusting the electrical stimulation intensity, the electrical stimulation pulse width, the electrical stimulation waveform type and the electrical stimulation application time sequence. According to the method, the problem that a calcium signal is difficult to recognize and dynamically optimize in a microliter stage is solved, and precise maintenance of Schwann cell activation and improvement of nerve regeneration efficiency are realized.

The invention discloses a brain deep nerve stimulation method and system based on adaptive adjustment, and relates to the technical field of brain deep nerve regulation, and the method comprises the steps: collecting a local field potential signal of a brain deep target region of a target patient, and extracting a beta frequency band power spectrum density and a gamma frequency band phase synchronization index as neural activity characteristic parameters; determining an individual baseline value and a preset threshold value based on historical data, and outputting a stimulation adjustment trigger signal when the beta frequency band power spectral density exceeds the individual baseline value and the gamma frequency band phase synchronization index is lower than the preset threshold value; in response to the trigger signal, calculating an optimal stimulation parameter combination through a gradient descent optimization algorithm and executing nerve regulation; and monitoring the signal change after regulation and control, calculating a relative change rate and updating a threshold value. Through a two-parameter joint judgment mechanism and a threshold updating strategy, individualized adaptive adjustment of stimulation parameters is realized, and the stimulation accuracy and the treatment effect are improved.

Provided is a method for ambient IoT based communication. The method is applicable to an ambient IoT device. The ambient IoT device supports at least two reflection modes. The method includes: receiving an input signal; and determining, in response to a trigger of the input signal, a reflection mode of the ambient IoT device from the at least two reflection modes.

The invention relates to the technical field of intelligent illumination, and discloses an intelligent illumination explosion-proof single lamp controller optimization scheduling method and system, and the method comprises the steps: firstly obtaining the deployment position of a controller, determining a target illumination region, collecting ambient light data to construct an illumination distribution diagram, and recognizing a distribution control link; monitoring real-time energy consumption of a link, analyzing load characteristics and calculating an optimal dimming coefficient; acquiring a signal response frequency band based on the dimming coefficient, fitting to generate a dimming response curve, identifying a defect response section and calculating a harmonic distortion rate; illumination compensation is carried out according to the harmonic distortion rate, scheduling grouping is carried out after a compensation threshold value is determined, high-load nodes are identified, and power is corrected; and finally, acquiring the temperature change gradient of the lamp according to the load correction parameter, generating a regulation and control instruction, and formulating an optimal scheduling strategy. According to the invention, the scheduling optimization of the explosion-proof single lamp can be improved.

The invention relates to the technical field of operation and maintenance management, in particular to a medical equipment operation and maintenance management system based on information mining analysis. According to the method, accurate screening of data abnormal features is introduced, so that the evaluation of the equipment operation state is more refined. According to the method, abnormal data can be effectively identified based on the mean offset of the data measured by the blood oxygen sensor, and the accuracy of equipment state evaluation is ensured in combination with the distribution trend of measurement errors. The influence of environmental factors on the equipment performance is deeply excavated, the dynamic change trend of the equipment performance under different environmental conditions can be identified by utilizing the correlation calculation of the environmental temperature change and the power consumption offset, and the sensitivity to the influence of external factors is improved. The evaluation mode of the operation state is more comprehensive, the subtle change trend of the equipment operation state can be accurately described through the collaborative analysis of the measured frequency fluctuation amplitude and the signal response time, and the accuracy of fault prediction is improved.

A scan driver includes a plurality of stages. A n-th stage among the plurality of stages includes a first input unit controlling a voltage of a first node in response to a previous carry signal, a scan output unit outputting a current scan signal corresponding to a scan clock signal in response to the voltage of the first node, a first switching unit controlling a voltage of a second node in response to the previous carry signal, a sensing output unit outputting a current sensing signal corresponding to a sensing clock signal in response to the voltage of the second node, a carry output unit outputting a current carry signal corresponding to a carry clock signal in response to the voltage of the second node, and a second switching unit controlling the voltage of the second node in response to the sensing clock signal or the carry clock signal.

The invention relates to an unmanned aerial vehicle inspection method and system based on an overhead transmission line, and the method comprises the steps: S1, generating an unmanned aerial vehicle three-dimensional space coordinate signal, and outputting a position correction signal based on a tower topology database; s2, in response to the position correction signal, controlling a multispectral sensing module to synchronously acquire a visible light image signal, an infrared temperature signal and a laser radar point cloud signal of the line; s3, inputting the visible light image signal into a defect identification model to generate a first defect feature signal; s4, fusing the first defect characteristic signal and the second defect characteristic signal to form a cross-modal diagnosis signal, and triggering a defect classification decision signal; and S5, classifying decision signals and cross-modal diagnosis signals according to defects. According to the air quality intelligent monitoring method and system based on sensing data feedback, the problems of inaccuracy in routing inspection positioning, defect missing detection and insufficient endurance in a complex environment can be solved.

The invention belongs to the technical field of pressure sensors, and particularly relates to a wireless passive pressure sensor for detecting bolt pretightening force, which comprises a pressure-sensitive capacitor and an outer ring encapsulation ring, the voltage-sensitive capacitor comprises an inner ring basin cavity, and an upper insulation pad, an upper polar plate, an elastic cushion layer, a lower polar plate and a lower insulation pad which are sequentially arranged in the inner ring basin cavity from top to bottom, the bottom of the inner ring pelvic cavity, the upper insulating pad, the upper polar plate, the elastic cushion layer, the lower polar plate and the lower insulating pad are provided with concentric holes to form a central through hole of the voltage-sensitive capacitor; the outer ring potting ring wraps the outer ring of the inner ring basin cavity, and a circuit and an antenna are arranged in the outer ring potting ring; the circuit is a frequency conversion circuit which is connected with the upper polar plate and the lower polar plate; and the circuit is also connected with the antenna. The sensor realizes wireless passive real-time monitoring, is convenient to install, does not need direct current power supply, does not have a signal processing unit inside, only transmits frequency signals, and is high in response speed.

The invention relates to a preparation method and application of an electrochemical-colorimetric dual-mode sensor based on nanoflowers and a bifunctional NiFe-PBA nano enzyme. The NiFe-PBA loaded gold-platinum bimetallic nanoparticles have excellent oxidation-reduction activity and peroxidase activity, and are used as a bifunctional probe to construct an electrochemical colorimetric dual-mode sensor. More signal probes are introduced into the branched DNA structure to participate in the reaction, the specificity of the sensor is enhanced through specific recognition of the aptamer on OTA, and finally, an electrochemical and colorimetric dual-mode channel is ingeniously constructed by utilizing a magnetic separation technology, so that environmental interference is effectively reduced, and false positive signals are avoided. NiFe-PBA loaded gold-platinum bimetallic nanoparticles participate in the dual-mode channels, so that the experimental operation is simplified while the signal response caused by the target is realized. The dual-mode sensor constructed by the invention has the characteristics of sensitivity, reliability, rapidness and stability, and is beneficial to popularization and application. And the biosensor has good anti-interference capability and practical application capability, and provides a novel biosensing platform for practical sample analysis.

The invention discloses a cross-channel marketing automation method and system based on real-time behavior triggering. The method comprises the following steps: capturing a user behavior event flow in real time through a front-end acquisition unit; performing real-time mode matching on the event stream by utilizing a complex event processing engine, and generating a marketing trigger signal when a preset rule is matched; dynamically selecting an optimal channel for the user based on a real-time scoring model in response to the signal; the marketing content is sent to the selected channel through the unified execution module; and finally, tracking user feedback and updating model parameters and user preferences in real time to form closed-loop optimization. The corresponding system comprises a front-end data acquisition module, a streaming event processing module, a real-time intelligent decision-making module, a multi-channel execution module and a closed-loop optimization module which are used for implementing the steps. According to the method, millisecond-level intention perception, cost and experience optimal channel intelligent decision and data-driven adaptive optimization closed loop are realized, and the problems of delayed response, extensive channel and lack of self-adaption of traditional marketing are solved.

The invention relates to the technical field of debugging systems, in particular to an adaptive debugging system and method for touch screen FW parameters, and the system comprises a signal acquisition module, a trend triggering module, a temperature compensation calculation module, a window generation module and a parameter output module. According to the invention, the edge of the driving voltage is carefully collected, the sliding difference algorithm is introduced to filter out high-frequency noise, meanwhile, real-time sampling of environment temperature and baseline capacitance adjustment are combined, the influence of external variables on signals can be dynamically corrected, and accurate judgment of capacitance fluctuation is improved by utilizing periodic delay deviation and regression trend analysis. By means of main frequency feature extraction and zero crossing point sudden change detection, threshold setting can be refined, self-adaptive window expansion can be achieved, touch signal response is kept sensitive and consistent all the time, adjustment and optimization can still be automatically completed even under the condition that the temperature changes frequently or signal interference is complex, parameter drift and debugging failure are effectively prevented, and user experience is improved. And the parameter automatic matching efficiency and the finished product consistency in batch production are improved.

The invention relates to the technical field of medical instruments, and discloses an implantable neural electrode for long-term electrophysiological signal monitoring and a preparation method and application thereof. The neural electrode comprises an upper hydrogel insulation packaging layer, a lower hydrogel insulation packaging layer, an electrode site, a connecting line and a bonding pad, wherein the electrode site, the connecting line and the bonding pad are arranged between the upper hydrogel insulation packaging layer and the lower hydrogel insulation packaging layer, and the electrode site and the bonding pad are connected through the connecting line. The electrode sites, the connecting lines and the bonding pads are made of conductive hydrogel; the upper hydrogel insulation packaging layer and the lower hydrogel insulation packaging layer are made of insulation polymers and hydrogel. The insulating polymer is a plasma surface-modified insulating polymer. The preparation method of the neural electrode is simple and efficient, biotoxic materials are not introduced in the preparation process, good biocompatibility is achieved, continuous monitoring in a living body can be achieved for one year, important application value is achieved for studying signal response and neural circuits of all brain regions, and a beneficial tool is provided for long-term in-vivo monitoring.

A method for validating operation of a driver integrated circuit includes providing a signal using an output node. The signal is provided using multiple set points in response to a change in state of an input signal. Each set point corresponds to a different phase of a multi-phase transition of the signal. The method includes providing a timer value at an end of a phase of the multi-phase transition and determining whether the signal is in a target signal range of the phase based on the timer value at the end of the phase, a predetermined value defining the target signal range of the phase, and a predetermined time limit for the phase. A current through the output node may be provided using the multiple set points, and a voltage on the output node may have the multi-phase transition.

The invention discloses an aptamer sensor based on a bimetallic organic framework nano-enzyme as well as a preparation method and application of the aptamer sensor. According to the aptamer sensor, the catalytic activity of the bimetallic organic framework nano-enzyme is simply and conveniently regulated through an aptamer, so that colorimetric and photo-thermal dual-mode detection on kanamycin is realized; the aptamer can regulate and control the activity of the bimetal organic framework nano-enzyme through co-incubation without immobilization, the aptamer falls off from the surface of the nano-enzyme to generate signal response by utilizing the high selective affinity of kanamycin to the aptamer, and a temperature signal is generated by further utilizing the photothermal effect of an oxidation product. Therefore, the comparison of color and photo-thermal double-signal detection results is realized, and the credibility of the detection results is improved.

The invention relates to the technical field of organic material detection, in particular to an imine molecular sensor with a five-detection function, two-component gel as well as a preparation method and application of the imine molecular sensor. The imine molecular sensor has three signal responses of fluorescence on-off, ultraviolet absorption and color change to Fe < 3 + >, Co < 2 + > and Ni < 2 + > in a solution state, and has ultraviolet absorption and fluorescence on-off dual-detection performance to acid and alkali changes; the imine molecular sensor and cholesteryl half ester can form two-component gel through a heating-low-temperature ultrasonic cooling method; after Fe < 3 + >, Co < 2 + >, Ni < 2 + >, NaOH and HCl are respectively added, the orange of the gel becomes milky white, reddish brown, transparent red, milky white and milky white under sunlight, and the fluorescence of the orange of the gel disappears under an ultraviolet lamp, so that the rapid and sensitive detection of Fe < 3 + >, Co < 2 + >, Ni < 2 + >, acid and alkali under two states of the imine molecular solution and the gel is realized. The imine molecular sensor provided by the invention has the advantages of mild synthesis conditions, simple preparation and the like, and creates favorable conditions for popularization and application of the imine molecular sensor.

The invention discloses a transformer area phase identification method and system based on non-contact voltage measurement. The method comprises the following steps: acquiring voltage data of each monitoring node on a power line of target power distribution network transformer area power transmission equipment; performing intelligent judgment on the voltage data, and generating a corresponding high-frequency noise scene signal; in response to the high-frequency noise scene signal, a monitoring result of the target power distribution network area power transmission equipment is obtained in the process of processing the voltage data based on a Kalman filtering algorithm, the processing process is configured to obtain a first prediction result corresponding to each monitoring node based on a state prediction equation, and the first prediction result is configured to obtain a second prediction result corresponding to each monitoring node; inputting a fusion analysis result of a measurement noise matrix determined by a Bayesian fusion algorithm for each first prediction result and a feature extraction result of the corresponding voltage data into an observation update equation; and carrying out transformer area phase identification on the target power distribution network transformer area power transmission equipment based on a monitoring result so as to realize accurate detection of weak signals.

The invention provides a power failure keeping method and system for a control system. The method comprises the steps that the power supply state of the control system is monitored, and when the voltage of a power supply of the control system is lower than a preset voltage threshold value, a power failure event signal is generated; in response to the power failure event signal, switching a power supply of the control system into a power failure protection circuit, and temporarily supplying power to the control system by using a super capacitor in the power failure protection circuit; and in the power-down protection time of the temporary power supply, storing the power-down retention data and the running log file of the control system into the nonvolatile memory. According to the invention, the storage capacity of the power-down holding data is improved, the cost of power-down holding is reduced, and the manufacturing requirements of a control system can be greatly met.

The invention relates to the technical field of circuit breaker switch monitoring, and provides a method for monitoring the position state of a circuit breaker switch based on a video recognition technology, and the method comprises the steps: collecting a real-time image of a circuit breaker through a camera device, judging the physical state of the circuit breaker, and determining a first judgment result; acquiring real-time data of the relay protection device, recording the operation state of the circuit breaker, and determining a second judgment result; inputting the first judgment result and the second judgment result into a preset signal response device to generate a corresponding response action; wherein the response action comprises an opening action, a fault action and a closing action.

A control device controls an authentication process related to device authentication together with at least one communication device on a basis of information included in a signal received through wireless communication with the communication device. The control device performs the control in such a manner that authentication information is changed for each process group in which the authentication process is repeated more than once. Signals transmitted or received in the authentication process include a trigger signal, a request signal, and a response signal. The control device executes the process group in which the authentication process is repeated more than once for the trigger signal, the request signal, and the response signal, and changes the authentication information to be used for each of the trigger signal, the request signal, and the response signal each time the process group is executed.

The invention discloses an electronic component quality control method and system, and relates to the technical field of transistor performance quality control. The quality control method for the electronic component comprises the following steps: evaluating transistor switch abnormity; switching speed delay regulation and control; and evaluating the input signal response control accuracy. Transistor switching abnormality evaluation is carried out in the transistor switching process, then whether switching speed delay optimization is carried out or not is judged based on a quantization result, if switching speed delay optimization is carried out, switching speed delay regulation and control are carried out, and if switching speed delay optimization is not carried out, input signal response control accuracy evaluation is carried out. And finally, judging whether to carry out input signal response control optimization or not based on a quantification result of the input signal response control accuracy evaluation, thereby achieving the effect of improving the response control sufficiency of the transistor on the input signal in the switching process. The problem that the switch response difference is not fully considered in the response control process of a transistor to an input signal in the prior art is solved.