Supervised learning for NN originally comes from certain parameters, such as self-revised learning, input learning datasets, and multiple second learning processes. In general, the investigation of NN (neural network) computing systems requires the management of a significant number of simultaneous distinct algorithms, such as parallel computing, fault tolerance, classification, and data optimization. The proposed method can be applied in damage diagnosis and disaster warning of bridges. The analysis indicated that the proposed method was effective in predicting structural response, with the prediction error less than 50% of the LSTM-based framework. The effectiveness of the proposed method was validated with the use of six-month strain response data of a concrete bridge, and the results are also compared with those of the most commonly used Long Short-Term Memory (LSTM)-based structural response prediction framework. The framework contains multi-layer encoder modules and attention modules that can precisely capture the history-dependent features in time-series data. To address this issue, a Transformer-based bridge structural response prediction framework was proposed in this paper. However, it appears to be difficult in accurately extracting structural response features on account of complex on-site environment and noise disturbance, resulting in poor prediction accuracy of the response values. Structural response prediction with desirable accuracy is considerably essential for the health monitoring of bridges.
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To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them. PDF is the official format for papers published in both, html and pdf forms.You may sign up for e-mail alerts to receive table of contents of newly released issues.Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.This article describes the sensor development cycle starting from the scientific objectives and continuing with the different design solutions adopted, the qualification, the calibration, and the validation of the retrieval methods with two campaigns performed in an Earth Martian analog. The Radiation and Dust Sensor within MEDA will provide unprecedented insights on it. Its optical properties and vertical distribution affect the absorption and reflection of solar radiation, modulating the planet’s energy balance.
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Suspended dust in the Martian atmosphere is one of the main drivers of the planet's climate. The MEDA meteorological station is onboard of the Perseverance rover with this aim. In situ weather observations are essential for understanding the past, present, and future climate of Mars and for the preparation of human exploration.