Short-term time series wind power predictions are extremely essential for accurate and
efficient offshore wind energy evaluation and, in turn, benefit large wind farm operation and …

A wind power short-term forecasting method based on discrete wavelet transform and long
short-term memory networks (DWT_LSTM) is proposed. The LSTM network is designed to …

Adaptive methods such as Adam and RMSProp are widely used in deep learning but are not
well understood. In this paper, we seek a crisp, clean and precise characterization of their …

Symmetric data play an effective role in the risk assessment process, and, therefore,
integrating symmetrical information using Failure Mode and Effects Analysis (FMEA) is …

Abstract We propose ACProp (Asynchronous-centering-Prop), an adaptive optimizer which
combines centering of second momentum and asynchronous update (eg for $ t $-th update …

In this paper, we propose a novel fault diagnosis (FD) approach for micro-electromechanical
systems (MEMS) inertial sensors that recognize the fault patterns of MEMS inertial sensors …

Vehicle detection and classification is an important task for street surveillance and scene
perception for robot navigation or autonomous vehicles. This research work focuses on …

Characterization of microembolic behavior, as solid or gaseous, guides to an efficient
treatment protocol. In this study a new methodology to classify microembolic signals by …

This study shows the application of the deep learning (DL) algorithm in the inversion of the
crucial constitutive model parameters directly referring to the surface settlement monitoring …

Optimization is often cast as a deterministic problem, where the solution is found through
some iterative procedure such as gradient descent. However, when training neural networks …