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Journal of Mechanical and Electrical Intelligent System (JMEIS, J. Mech. Elect. Intel. Syst.)

An international open-access peer-reviewed journal

ISSN 2433-8273

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Vol.3, No.3

TABLE OF CONTENTS

Articles

A Helmet Type Mask “Distancing-Free Mask” An Engineering Solution that Eliminates the Lockdown

Yusaku Fujii, Akihiro Takita, Seiji Hashimoto

Journal of Mechanical and Electrical Intelligent System, Vol.3, No.3, pp.1-7, 2020.

Abstract: As a solution to eliminate the lockdown and to guarantee the implementation of the Tokyo Olympic Games, we developed "Distancing-Free Mask" prototype and propose new social infrastructure and lifestyles. This mask simultaneously achieves the four items, (1) full virus shield, (2) lightweight body, (3) easy breathing, and (4) inexpensive manufacturing costs, at a high level by precise control of pressure and flow rate inside the helmet. The wearer of this mask, just like the antibody carrier, cannot be infected itself with the virus, nor infect others with the virus. Currently, a combination of "new normal" and "Lockdown (= outing control, behavior control, business control, economic regulation)" is being taken around the world. Since there is no prospect of developing a vaccine or treatment method, there is no prospect of leaving the current situation. Distributing the "Distancing-Free Mask" to each citizen means having a simple and reliable means of converging the infection. Even under the worst infection spread situation, people can go out as long as they wear "Distancing-Free Mask". Organizers of events such as the Tokyo Olympics need only take measures in anticipation of situations where masks are mandatory.

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Proposal of modified Kalman filter incorporating stochastic dynamic analysis in prediction procedure

Soichiro Takata, Susumu Tarao

Journal of Mechanical and Electrical Intelligent System, Vol.3, No.3, pp.8-20, 2020.

Abstract: This paper proposes the modified Kalman filter in order systematically to deal with a nonlinear system and a parametric uncertainty excitation system. Conventional Kalman type filtering problem for linear system with parametric uncertainties is based on a linear-Gaussian process. Therefore, this type state estimator cannot be applied to the nonlinear system. Hence, the Kalman filter methodology that can systematically account for the nonlinear and the parametric uncertainty excitation systems is desirable. The present study proposes a modified Kalman filter incorporating stochastic dynamic analysis (KF-SDA) in the prediction step to deal with the abovementioned systems systematically. The proposed KF-SDA algorithm will be derived using the solution of moment equation. Moreover, a fundamental verification will be performed for a single degree-of-freedom (DOF) system and two-DOF system subjected to white noise excitation. The state estimation of the single-DOF system, which is simultaneously subjected to white noise and stochastic parametric excitations, will be considered. Furthermore, the state estimation of the nonlinear single-DOF system subjected to the white noise excitation will be performed.

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