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Небесная энциклопедия

Космические корабли и станции, автоматические КА и методы их проектирования, бортовые комплексы управления, системы и средства жизнеобеспечения, особенности технологии производства ракетно-космических систем

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Мониторинг СМИ и социальных сетей. Сканирование интернета, новостных сайтов, специализированных контентных площадок на базе мессенджеров. Гибкие настройки фильтров и первоначальных источников.

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Применить Всего найдено 2. Отображено 2.
23-06-2023 дата публикации

InSAR atmospheric correction method based on sparse convolutional neural network

Номер: CN116299247A
Автор: JIANG LIMING, SHAO YI, YANG BO
Принадлежит:

The invention discloses an InSAR atmospheric correction method based on a sparse convolutional neural network, and the method comprises the steps: obtaining an original interference image, generating a training sample and a label according to the original interference image, and further generating a data sample set; normalizing the data sample set and dividing the data sample set into a training set and a verification set; an sARU-Net model is constructed; and training the sARU-Net model to obtain an optimal weight parameter. According to the method, the atmospheric noise in the InSAR data is accurately predicted and removed based on the internal information of the SAR image from the real SAR image, and the precision is high; interpolation processing does not need to be carried out on any data, a large amount of time can be saved in the processing process, and the process is accelerated.

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Номер записи: 1
16-05-2023 дата публикации

TLS multi-objective optimization site selection method for landslide mass deformation field time sequence monitoring

Номер: CN116125490A
Автор: HUANG RONGGANG, ZHANG WENDIAN, CHEN ZHIPING, CHANG RUIJIE, JIANG LIMING, WANG HANSHENG
Принадлежит:

The invention discloses a TLS multi-objective optimization site selection method for landslide mass deformation field time sequence monitoring, and the method comprises the steps: generating a three-dimensional point cloud of an observation region, converting the three-dimensional point cloud into voxels, and building an initial three-dimensional model of the observation region; determining candidate sites in the initial three-dimensional model; determining parameters of a TLS scanner, performing scanning simulation based on the parameters of the scanner and the initial three-dimensional model as constraints, screening the landslide voxels of which the sight lines are not shielded as observed voxels, and screening the landslide voxels of which the sight lines are shielded as unobserved voxels; calculating a station cardinal number of the observation area; a constrained NSGA-II algorithm is adopted to generate a Pareto optimal solution set; and a final individual is decided from the Pareto ...

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Номер записи: 2