Quadratic Stability of Switched a ne DAEs Via State-Dependent Switching Law

Marie-Louise Dossou-Yovo, Guy Degla — Sat, 13 May 2023 00:00:00 +0000

In this paper, we deal with quadratic stabilization for a particular class of impulse-free switched differential affine algebraic systems (switched affine DAEs) which consisted of a finite set of affine subsystems with the same algebraic constraint, where both subsystem matrices and affine vectors in the vector fields are switched independently and no single subsystem has desired quadratic stability. We show that if a convex combination of subsystem matrices such that the result LTI algebraic system is assymptotically stable, and another convex combination of affine vectors is zero, then we can design a state-dependent switching law such that the entire switched affine DAEs system is quadratically stable at the origin. But when the convex combination of affine vectors is not zero, we discuss the quadratic stabilization to a convergence set defined by the convex combination of subsystem matrices and the convex combination of affine vectors.

An Exploration of Prediction Accuracy of Developmental Degrees of Music Expression in Early Childhood Based on A Simultaneous Analysis of Both Eye Movement and Full-Body Movement

Mina Sano — Sun, 21 May 2023 00:00:00 +0000

This article explores validity to introduce eye movement of children in association with body movement as feature quantities of machine learning, in particular, to assess children’s maturity level of musical expression. There is a growing awareness of applying machine learning technique to yield practical benefit on predicting developmental degrees of music education by observing body movements with motion capture technology. From professional teaching perspective, it is believed only experienced teachers can evaluate children’s music achievement because coordinated functioning of body movements need carefully be observed in connection with various music factor such as rhythm, beat strength, tones, etc. If new teachers can take advantage of having objective results of evaluation of children’s expression level by machines, it will enhance educational efficiency to certain levels which experts could attain. The author aims to improve classification accuracy of machine learning by focusing on eye movements data simultaneously recorded with body movements. In this study, children (n=43) at two child facilities participated in the data capture of both eye movement and body movement during musical expression. Feature quantities were extracted from the results of a three-way of ANOVA, and applied to machine learning to improve the classification accuracy of developmental degree in musical expression. Specifically, when using several classifiers such as NN (Neural Network model), the classification accuracy of developmental degree of musical development was more precisely in the case of both body movement data and eye movement data included in feature quantities than in the case of only body movement data as feature quantities.

Transactions on Engineering and Computing Sciences

Quadratic Stability of Switched a ne DAEs Via State-Dependent Switching Law

An Exploration of Prediction Accuracy of Developmental Degrees of Music Expression in Early Childhood Based on A Simultaneous Analysis of Both Eye Movement and Full-Body Movement