Integrated Approach for HP-LNG Pump Diagnostics and Prognostics Based on Health State Probability Estimation

Real-time, automatic digi-tailor mannequin robot adjustment based on human body classification through supervised learning

“…It is worth mentioning that in case new robots imitating shapes belonging the aforementioned extreme classes of sizes are developed, the proposed classification and activation algorithms are still supposed to be flexible and general enough to assimilate their properties appropriately. Last but not least, even though the proposed solution leads to significantly more reliable performance, compared to the classical methodology, especially if accompanied by the real-time, automatic robot adjustment procedure suggested in the study by Kim et al, 38 the time consumption of the proposed solution could be further reduced via making use of other devices or approaches leading to quicker responses. For instance, as per the current practice, scanning takes 29 s, being followed by around Algorithm 1.…”

“…Various forms of SVM have been introduced and implemented in previous studies. [33][34][35][36][37][38] For the purpose of this article, l 1 classification SVM (C-SVM), being described in the study by Duan and Keerthi, 33 is utilized, which is reviewed in what follows. With the assumption that the training data is represented as y i ¼ +1, ðx i ; y i Þ, the task of SVM is to calculate the specifications of the hyperplane w T FðxÞ þ b ¼ 0 that best separates the two classes involved, that is, to maximize the distance between the hyperplane and the closest point from each class to it and minimize the within-class distances through considering a cost function, which is tantamount to the following optimization problem:…”