Wang, H.; Liu, G.; Wu, L., and Zhang, T., 2015. Modeling and parameter revising method of rigid-flexible coupling dynamics model.Virtual prototype technology has been widely applied to simulate the performance of various complex mechanical systems. However, errors existing in analysis models usually cannot be ignored, particularly rigid-flexible coupling dynamics model, so many parameters factors have to be considered and to be improved. In this paper, taking a multi-body impact system as the subject investigated, a rigid-flexible coupling virtual prototype model of the multi-body impact system is built by using the multi-body dynamics software Virtual Lab. The simulation and analysis of the impact dynamics model is presented. The accuracy of the simulation results is evaluated by combining with test data. Some key system parameters are analyzed and revised base on the sensitivity method and the perturbation method. The scatter degree of the simulation results of the revised impact dynamics model has better consistency with the test results, and the validity of the virtual prototype model is improved.
This letter proposes a novel convolutional neural network (CNN) method for dual-polarized synthetic aperture radar (SAR) ship grained classification. The network employs hybrid channel feature loss that jointly utilizes the information contained in the polarized channels (VV and VH). It is demonstrated that, by adopting the proposed CNN framework and the novel loss function, the classification performance can be efficiently improved. First, instead of the prevalently used threefold or fourfold division (container ship, oil tanker, bulk carrier, and so on), the proposed method can further divide vessels into eight accurate categories. Second, this method can not only effectively classify targets into eight categories but also its accuracy in terms of fewer category classifications surpasses existing methods. Third, the method can achieve good performance on a small training data set. Experiments conducted on the OpenSARShip data sets indicate that the proposed classification method achieves state-of-the-art results.
Long-term analysis of climate trends and patterns relies on continuous and high-frequency observation data sets. Still, due to limitations in historical meteorological observation techniques and national policies, most weather stations worldwide can only provide three, four, or eight observations per day, hindering climate change research progress. To solve the problem of low-frequency daily observation in part of global meteorological stations, we propose a time-downscaling model of observation series based on deep learning, Land Surface Observation Simulator-Time Series Version (LOS-T), taking 2m air temperature as an example. LOS-T, combined with multimodal technology and Transformer architecture, effectively merges multiple types of data, including low-frequency observations, ERA5-land, and geographic information, to convert low-frequency observations into hourly high-frequency observations. The model showed significant accuracy improvements by training on millions of meteorological observations worldwide, especially on downscaling the data, which only has three observations per day. The results showed that LOS-T substantially improved over baseline models such as Bilinear and vanilla Transformer on several metrics such as MAE, RMSE, COR, and R2. In addition, case studies have confirmed that LOS-T can effectively utilize ERA5-land's high-frequency temperature change information to improve the accuracy and robustness of predictions, even when there is a significant deviation between ERA5-land data and Ground Truth. In short, LOS-T provides new ways to refine global meteorological observation data and helps advance climate science.
The aim of the gravity-measuring satellite, like GOCE, is to provide accurate model of the Earth's gravity field and of the geoid with high accuracy and spatial resolution. To achieve this goal, it is necessary to provide the classification of the error sources affecting the gravity field measurements and allocate performance requirements to the identified errors, which is an important task during all design phases and even beyond. The detailed study of the errors affecting the gravity-measuring satellite is complex, however a complete scheme of error analysis and allocation has been developed in this paper. Moreover, a simulation platform is designed based on simulink, which is served as the test bed for the performance analysis and error budget method of gradiometric satellites, providing good interaction and extensibility. Overall, the numerical simulation results, based on the well-established platform, clearly establish that error budget on the GGT trace is compliant with the specified requirements. It is illustrated sufficiently that the established platform is helpful for performance analysis and error budget of gravity-measuring satellite.
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