This work develops a novel approach based on Machine Learning (ML)-assisted Quality Function Deployment (QFD) to sift the gold from the stone. It includes Time-Varying Filter-based Empirical Mode Decomposition (TVF-EMD), Deep Ensemble Random Vector Functional Link (DE-RVFL), and a Bayesian optimization algorithm for optimizing the shaped DE-RVFLTVF-EMD hyperparameters. This approach makes it possible for the proposed methods to be dynamic enough to deal with the data's volatility, complexity, uncertainty, and ambiguity. It is demonstrated that incorporating TVF-EMD to provide time-frequency analysis along DE-RVFL, and goal-oriented social media analytics boosts the performance of out-of-sample predictions statistically and compensates for the “warranty data maturation” effect. The proposed algorithm's Root Mean Square Error (RMSE) decreases by 23.37%-88.76% relative to other benchmark cutting-edge models. This study contributes significantly to the services management com
This study examines the frequency, type and severity of interference events at specific locations in the country, providing valuable insights into the use of detectors, the nature of interference encountered, their potential sources and the impact on GNSS-derived positions. NURAG RAGHUVANSHI, SUNIL
Vertical Flight Society Announces Winner of 2022 Alfred Gessow Best Paper Award prweb.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from prweb.com Daily Mail and Mail on Sunday newspapers.
Abstract
Self-mixing interferometry (SMI) is a promising non-contact sensing technology, which has attracted much research attention in the last few decades. The SMI effect takes place when a small portion of laser is back-scattered from an external target and re-enters the laser diode (LD) internal cavity. A typical sensing system using an SMI configuration consists of an LD as the laser source, a target to reflect the laser, a photo diode (PD) to capture the optical signal. This configuration indicates the SMI-based sensing technology’s merits of minimum part-count scheme, low cost in implementation and ease in optical alignment. Various SMI-based sensing applications have been reported, including the measurement of displacement, velocity, vibration, laser related parameters, thickness, mechanical resonance, imaging, material parameter measurement, near-field microscopy, chaotic radar, acoustic detection, biomedical applications, etc.