Debris flow disasters have increased in Taiwan due to various environmental factors. These disasters often bring a lot of rock and mud, causing a threat to the lives and properties of residents in the affected areas. The weather is changeable due to more and more extreme rainfall events. A monitoring system is needed to provide early-warning of debris flow disasters to reduce the loss of life and property. The number of installed precipitation stations is not adequate for the current early-warning system. Rainfall patterns are greatly affected by the variation in the topography. Therefore, the current system cannot fully integrate basin-wide rainfall data and lacks information on spatial dependency between rainfall stations. This paper proposes a watershed-based debris flow early-warning system that applies the OGC SWE standards to design its architecture. The standardized data exchange mechanism is used to integrate and share heterogeneous monitoring resources. A hierarchical architecture is proposed to build a wide range of precipitation stations. The system presents high density debris-flow-prone area monitoring. We propose dependency aggregation and SWE integration schemes that enable the system to collect data from upriver under dependency relationship of debrisflow-prone streams and achieve automated early-warning of debris flows. We use the SWE open source provided by 52North to implement the proposed watershed-based debris flow early-warning system. We develop a simulator using real rainfall data in Taiwan to compare to the current system. The experimental results demonstrate that our system can improve the sensing data problem and efficiently advance the warnings issue time.
SUMMARYIn a WSAN (Wireless Sensor and Actuator Network), most resources, including sensors and actuators, are designed for certain applications in a dedicated environment. Many researchers have proposed to use of gateways to infer and annotate heterogeneous data; however, such centralized methods produce a bottlenecking network and computation overhead on the gateways that causes longer response time in activity processing, worsening performance. This work proposes two distribution inference mechanisms: regionalized and sequential inference mechanisms to reduce the response time in activity processing. Finally, experimental results for the proposed inference mechanisms are presented, and it shows that our mechanisms outperform the traditional centralized inference mechanism. key words: wireless sensor and actuator network, distributed system, heterogeneous environment, inference mechanisms
Growing Book refers to an electronic textbook that is co-developed, and has the ability to be constantly maintained, by groups of independent authors, thus creating a rich and ever-growing learning environment that can be conveniently accessible from anywhere. This work designs and implements a Web Service-based Growing Book that has the merits of single sign-in, multilevel usage, multilingual access, and multimodal multimedia for content sharing. XML Web service techniques and authentication mechanisms are used to provide single sign-in service across distributed Web sites, allowing easy teaching material reusing. Also, a multilingual page switching mechanism for Growing Books is developed and implemented. Challenges during the development of Growing Books are evaluated and analyzed. In addition to the importance of the single sign-in function, our analysis results demonstrate that the content-based features of Growing Book appeal more to students than other features and are often used for immediate judgment of course materials. Furthermore, a multilingual Growing Book is good for worldwide usage and greater accessibility.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.