6 results about "Characteristic response" patented technology

The application provides a PI controller parameter setting method and device, relates to the technical field of automation control, is applied to a PI control system, the PI control system comprises a PI controller and a controlled object, and the method comprises the following steps: acquiring a characteristic response curve corresponding to the controlled object; acquiring a first-order inertia delay model by using a square difference integral minimum processing method according to the characteristic response curve; and determining target parameters corresponding to the PI controller according to a first formula acquired in advance through the first-order inertia delay model, so that the dynamic performance of a closed-loop response curve reaches an optimal state. The application finds optimal parameters by using the distribution of root loci on the real axis, realizes the optimal dynamic performance of the closed-loop response curve, and endows controller parameter setting and design with clear physical meanings.

This invention relates to the field of industrial intelligent nondestructive testing, and more specifically, to an intelligent identification method for fatigue failure precursor signals of forgings under complex stress environments. The method includes: first, constructing a physical space mapping; extracting streamline vector fields based on finite element simulation data of the forging; calculating sensor path coupling factors; and quantifying the anisotropic constraints of complex stress structures on signal propagation. Next, dynamically setting the signal decomposition algorithm penalty factor according to the streamline geometric curvature; constructing a reference signal by combining the load spectrum and coupling factors; and extracting characteristic responses from stress environment monitoring data. Subsequently, calculating the real-time signal-to-noise ratio (SNR), constructing an adaptive index, and obtaining a blocking index by combining the coupling factor and directional transfer entropy. Finally, setting a dual statistical window based on the load period; and completing the identification and judgment of failure precursor signals according to their statistical characteristics. Through streamline field physical constraints and adaptive SNR modulation, the accuracy and timeliness of fatigue failure early warning are significantly improved.

The present application relates to touch human-computer interaction technical field, especially touch data processing method, system and touch screen. The method comprises the following steps: collecting the capacitance signal of touch surface, extracting the pressure value and contact area parameter of touch point, constructing the touch feature chain with time sequence association, and storing the touch feature chain as user operation data; identifying the pressing action sequence in the touch feature chain, extracting the force variation feature of pressing process, calculating the pressure change rate based on the force variation feature, identifying the force inflection point and the force inflection point, distinguishing physiological fluctuation and random noise, and generating pressure behavior feature; calculating the ratio of pressure change rate and area change rate to form pressure-area response function, determining key pressure interval, calculating function eigenvalue in each interval, and forming tool characteristic response data. The present application greatly improves the accuracy, naturalness and tool modeling ability of touch interaction through touch human-computer interaction technology.

This application discloses a method and system for detecting low-altitude unmanned aerial vehicles (UAVs) based on event stream frequency enhancement, relating to the fields of computer vision and UAV monitoring technology. The method includes acquiring raw event stream data of a scene to be detected; reconstructing brightness signals and performing frequency analysis on the raw event stream data to extract frequency features characterizing the rotor rotation characteristics of the UAV in the scene to be detected, and mapping the frequency features to a frequency embedding vector; generating dynamic gating parameters based on the frequency embedding vector, and using the dynamic gating parameters to modulate and fuse the event features to obtain frequency-enhanced features; and outputting the detection result of the UAV in the scene to be detected based on the frequency-enhanced features, the detection result including the UAV's category and location information. This application can effectively suppress background noise that does not conform to the rotor frequency characteristics, while enhancing the feature response of the UAV target, significantly improving the robustness and accuracy of UAV detection.

This invention relates to the field of low-voltage electrical control technology, and discloses a control method and system for a low-voltage intelligent control cabinet based on parameter adaptation. The method includes: performing deviation quantization mapping on the initial state parameter set of the low-voltage intelligent control cabinet to obtain a deviation vector; performing symbolic deconstruction on the deviation vector to obtain a sequence of symbolic changes; using a priori disturbance characteristic spectrum, weighting the symbolic change sequence based on characteristic responses to obtain disturbance decoupling weights for the deviation vector; performing disturbance recovery on the deviation vector based on the disturbance decoupling weights to obtain the true deviation amplitude of the deviation vector; performing transient compensation on the current control output value of the low-voltage intelligent control cabinet according to the true deviation amplitude to obtain a control correction amount for the current control output value; and performing fusion limiting encoding on the control correction amount and the current control output value to obtain the control command for the low-voltage intelligent control cabinet. This invention can improve the efficiency of a low-voltage intelligent control cabinet control based on parameter adaptation.

This invention discloses a low-power electronic nose system and control method based on a spiking neural network, belonging to the field of electronic noses and intelligent sensing systems. The system uses a spiking neural network as the algorithm module for the electronic nose. A low-power management module establishes bidirectional signal connections with both the sensor array module and the algorithm module. It reads the pulse sequence output by the algorithm module and the characteristic response signal of the sensor array module, generates a mode control signal, and feeds it back to the algorithm module to control the operating mode of the electronic nose system. Simultaneously, it outputs a rotation control signal to the sensor array module to control the sensors to work in rotation, forming a closed-loop control. This invention can effectively reduce the power consumption of the electronic nose and plays an important role in scenarios with inconvenient power supply, such as portable testing equipment and remote industrial sites.