4 results about "Spectral energy distribution" patented technology

The application provides a far-infrared-based polyester filament spectral reflectance detection method, and belongs to the technical field of polyester filament spectral reflectance detection. The method comprises the following steps: obtaining enhanced spectral data of a polyester filament sample; performing feature band extraction, calculating a correlation matrix, and obtaining a high-dimensional spectral feature vector; constructing a polyester filament spectral reflectance three-dimensional simulation model, and obtaining a spectral energy distribution diagram; performing matching degree verification, and obtaining a pre-judgment matching index; analyzing a spectral shift type, and obtaining a reflectance defect classification standard; constructing a reflection mode parameter data set, extracting a feature coincidence degree parameter, inputting the feature coincidence degree parameter into a random forest algorithm, and determining a reflectance anomaly grade; and generating a spectral reflectance detection report. The application positions an abnormal interval through three-dimensional simulation simulation, establishes a quantitative correlation between spectral anomalies and process parameters to realize tracing, generates a standardized defect classification and process optimization suggestion to form a closed-loop management and control, can be adapted to detection of multiple types of synthetic fibers, and effectively improves production quality management and control level.

This invention provides a method and system for real-time identification and classification of partial discharge acoustic signatures based on FPGA, relating to the field of power equipment monitoring technology. The method includes: receiving raw acoustic signature signals from a high-frequency ultrasonic acoustic signature sensor array installed on the sidewalls of each air chamber in a GIS (Gas Grid Integrated Circuit); pre-conditioning the raw acoustic signature signals within the FPGA to obtain conditioned acoustic signature signals; performing adaptive noise reduction processing on the conditioned acoustic signature signals to obtain denoised acoustic signature signals; extracting time-domain envelope, spectral energy distribution, and pulse waveform parameters from the denoised acoustic signature signals to obtain an initial acoustic signature feature vector of partial discharge characteristics; and constructing a virtual spatial reference configuration of the GIS air chamber geometry and sound wave propagation path using a parametric modeling method based on spatial pose gradient projection, using the initial feature vector and combining the spatial coordinates of each sensor and the corresponding air chamber volume parameters. This invention improves the accuracy and real-time performance of GIS partial discharge monitoring.

This invention provides a flight trajectory prediction method bridging the time and frequency domains. It constructs and preprocesses a flight trajectory dataset, dividing it into training, validation, and test sets. It captures local time-domain fluctuation features of the training set in the time domain to generate time-domain prediction results. Simultaneously, it generates frequency-domain prediction results by periodically learning the global dependencies of the flight trajectory in the training set under frequency-domain representation. Based on the spectral energy distribution, it calculates the energy proportion of dominant harmonic sequences, dynamically allocates the fusion weights of the time-domain and frequency-domain prediction results, and generates the final flight trajectory prediction result, constructing a preliminary flight trajectory prediction model. It constructs a loss function for the preliminary flight trajectory prediction model based on the training set and trains it. The model is then validated using a validation set, and parameters are fine-tuned according to evaluation metrics until the validation effect meets the requirements when using the test set, resulting in the final flight trajectory prediction model. This method not only reduces model complexity but also improves prediction accuracy.

This application provides a method for networked observation of lightning early warning, comprising: collecting trigger signal data from each observation station, extracting the trigger time and pulse waveform record of each station, identifying the combination of stations that trigger simultaneously within the theoretical arrival time difference tolerance window, and obtaining a preliminary set; based on the geometric landing point of each event in the preliminary set, and verifying whether the landing point is located within the effective coverage area of ​​the station network, eliminating invalid solutions, and obtaining a set of lightning strike candidate events; extracting the rise time and spectral energy distribution of each candidate event in the lightning strike candidate event set, and obtaining a subset of suspected interference events after pattern matching of the candidate events; querying the facility density value of the area where each suspected interference event is located from a pre-established industrial facility density map, identifying events with facility density values ​​higher than a preset threshold as high-risk interference events, and separating a preliminary set of real lightning strike events.