2results about How to "Alleviate scarcity" patented technology

This invention discloses a satellite fire detection method based on dynamic exponential features and deep learning. The method includes: acquiring multi-band spatiotemporal observation data from geostationary meteorological satellites; filtering candidate pixels from the full-disk satellite data using multiple threshold conditions; extracting spatiotemporal input features and channel input features of the candidate pixels based on the multi-band spatiotemporal observation data from the geostationary meteorological satellites; and inputting the spatiotemporal input features and channel input features of the candidate pixels into a pre-trained fire spatiotemporal network model to obtain the fire detection result. This invention effectively solves the problem of high false alarms and false negatives caused by cloud cover, high-temperature ground surfaces, and vegetation interference in traditional methods, overcomes the difficulties of weak fire signal extraction and spatial positioning in mixed pixel backgrounds, and significantly improves detection accuracy, stability, and computational efficiency. It can provide key technical support for forest fire monitoring and emergency decision-making.

The present application relates to the field of display technology and human-computer interaction, and provides a flat panel display visual comfort prediction method and system based on a multi-modal fusion model, wherein the method comprises the following steps: step one, collecting an original image set, for each original image, using a generative adversarial network model to convert an input random sequence into enhanced materials, and then fusing the enhanced materials and the original image at a preset ratio to obtain a plurality of new images; step two, collecting physiological features and physical features to obtain training samples; step three, training a multi-modal fusion model based on a stacked ensemble framework; and step four, inputting test data to obtain a visual comfort prediction result. The present application aims to solve the problems of a lack of high-quality labeled samples, a lack of coupling relationship in single-modal analysis, and poor scene adaptability in the field of flat panel display visual comfort prediction.