Decentralized power systems are commonly used in high-speed trains. However, many parameters in decentralized power systems are uncertain and inevitably have errors. We present a reasoning method based on the interval numbers for decentralized power systems in high-speed trains. Uncertain parameters and their unavoidable errors are quantitatively described by interval numbers. We also define generalized linear equations with interval numbers (LAIs), which can be used to describe the movement of the train. Furthermore, it is proven that the zero sets of LAIs are convex. Therefore, the inside of the fault-tolerance area can be formed by their vertexes and edges and represented by linear inequalities. Consequently, we can judge whether the system is working properly by verifying that the current system state is in the fault-tolerance area. Finally, a fault-tolerance area is obtained, which can be determined by linear equations with an interval number, and we test the correctness of the fault-tolerance area through large-scale random test cases.
Abstract-Semantic overlay could improve query performance in peer-to-peer (P2P) systems. When peers join or leave frequently, it will lead to network traffic surge, since most semantic search methods maintaining a large number of routing tables. In this paper, we address these problems by proposing Interest Attenuation Search(INS), a novel efficient peer-to-peer semantic search approach based on interest attenuation policy. In INS, the interest attenuation policy is introduced to help peers decide whether to forward messages. Before peer floods the request in semantic overlay network(SON), it will check the history information about the request message, then uses INS to make forwarding decision. Simulation results show that INS significantly improves query performance and reduces the traffic overhead generated by unstable network environment.
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