With the advances in network technology and development of an e-learning population, using the Internet to realize a distance laboratory has become prevalent and the distance laboratory can be accessed anytime from anywhere. However, it is difficult to present a seamless environment from the traditional physical laboratory into a distance laboratory. Practical devices in the traditional laboratory rarely supported distance access functions, and thus, constructing distance laboratories is difficult and time consuming. Hence, this work presents a distance laboratory framework with transparent and ubiquitous access platforms for various console-based experiments, especially for networking and embedded system laboratories. The proposed system can support multiple users to access a device at the same time. Thus, students can easily share their experiences with each other to increase cooperative learning chances. Moreover, the proposed framework can be integrated to various distance laboratories and the user can access multiple platforms in one or several distance laboratories at the same time. The experimental results demonstrate that the proposed framework can effectively improve the learning effect and the access platform can be used to increase the utilization of system resources. It can also reduce the cost of conducting laboratories and enhance student learning interest. ß
Innovations in network and information technology have transformed traditional classroom lectures into new approaches that have given universities the opportunity to create a virtual laboratory. However, there is no systematic framework in existing approaches for the development of virtual laboratories. Further, developing a virtual laboratory from scratch is time consuming and costly. This article proposes a systematic framework to classify the activities between learners and instructors in the laboratory and to design the mobile agent-based virtual laboratory by wrapping the existing CAI tools without knowing the source code. Using the existing CAI tools can reduce the time and cost in constructing a virtual laboratory. The framework consists of three parts: mobile agent execution environment, mobile agent and learning platform. Moreover, various mobile agent design patterns are provided for users to design and implement virtual laboratories. This framework of patterns could make mobile agent based virtual
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.
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