Ryerson University

About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Computer science & Population. The organization has 7671 authors who have published 20164 publications receiving 394976 citations. The organization is also known as: Ryerson Polytechnical Institute & Ryerson Institute of Technology.

Does the tourist care? A comparison of tourists in Koh Phi Phi, Thailand and Gili Trawangan, Indonesia

Abstract: Koh Phi Phi, Thailand, and Gili Trawangan, Indonesia, are two islands in Southeast Asia that face several developmental challenges associated with the rapid growth of tourism. Both islands are part of a marine park, are small in size and have several natural resources that need protection. They both face burgeoning tourist numbers that have increased tourism infrastructure development and are leading to environmental degradation. These islands have discussed ways to incorporate sustainability into their management and marketing practices, and they have looked to tourists as a support for change. This study examines the motivations, profiles and perceptions of tourists in island destinations, their level of awareness of environmental issues there, the extent to which they feel responsible about preserving or protecting their natural resources and the role they may be willing to take in their management, including their willingness to pay for environmental protection. The findings showed that the majority o...

Estimation of Battery State of Charge With $H_{\infty}$ Observer: Applied to a Robot for Inspecting Power Transmission Lines

Abstract: Battery state-of-charge (SOC) estimation is essential for a mobile robot, such as inspection of power transmission lines. It is often estimated using a Kalman filter (KF) under the assumption that the statistical properties of the system and measurement errors are known. Otherwise, the SOC estimation error may be large or even divergent. In this paper, without the requirement of the known statistical properties, a SOC estimation method is proposed using an H∞ observer, which can still guarantee the SOC estimation accuracy in the worst statistical error case. Under the conditions of different currents and temperatures, the effectiveness of the proposed method is verified in the laboratory and field environments. With the comparison of the proposed method and the KF-based one, the experimental results show that the proposed method can still provide accurate SOC estimation when there exist inexact or unknown statistical properties of the errors. The proposed method has been applied successfully to the robot for inspecting the running 500-kV extra high voltage power transmission lines.