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Tsai Y.-B.,Pacific Gas and Electric Company | Cheng C.-T.,Sinotech Engineering Consultants | Liu K.-S.,Kao Yuan University | Chang W.-Y.,No. 106
Bulletin of the Seismological Society of America | Year: 2010

In this study we recreated peak ground accelerations (PGA) and peak ground velocity (PGV) distributions for Taiwan by applying the attenuation relations of Liu and Tsai (2005) to calculate the PGA and PGV values for 1989 Mw ≥5.0 earthquakes in a catalog of earthquakes from 1900 to 2008 with homogenized magnitude (Mw) (Chen and Tsai, 2008). We further combined the PGA and PGV values to obtain corresponding modified Mercalli intensity (MMI) values (Wald, Quitoriano, Heaton, Kanamori, et al., 1999) and their spatial distributions and recurrence intervals. We adopted a logarithmic functional form analogous to the Gutenberg-Richter relation for seismicity to represent the annual frequency of seismic intensity parameters: log10(N)=a-b log10(PGA), log10(N)=a-b log10(PGV), and log10(N)=a-bI. The regions with high PGA and PGV values are often associated with low b values in these equations. As it is well known that the Mw 7.45 Chi-Chi earthquake of 21 September 1999 had produced high PGA values (in excess of 0:9g) and PGV values (in excess of 300 cm=s), we used these relations to estimate the Poisson probability distributions in Taiwan for MMI ≥ VIII (i.e., PGA ≥ 485g) for recurrence intervals of 30, 50, and 100 years. The results show a wide range of differences in the Poisson probability of MMI ≥ VIII among different areas of Taiwan. For example, for a 50-year interval, this probability at 10 major cities in Taiwan is as follows: Taipei 0.67%, Hsinchu 2.15%, Taichung 5.24%, Chiayi 24.35%, Tainan 1.61%, Kaohsiung 0.04%, Hengchun 4.94%, Ilan 17.67%, Hualien 37.04%, and Taitung 9.82%. These estimates should be of interest to city planners, especially for earthquake preparedness planning. Source


Liu H.,No. 106 | Liu H.,Tsinghua University | Yang H.,No. 106 | Wang Y.,No. 106 | And 2 more authors.
Journal of Network and Systems Management | Year: 2015

An intermediate node in an inter-flow network coding scheme needs to know exactly which are the previous hop and next hop of a packet before coding. It is difficult to incorporate inter-flow network coding into opportunistic routing (OR) because the next hop of a packet in OR can’t be determined in advance. Coding-aware opportunistic routing (CAR) is proposed in this paper to address this problem on fixed wireless mesh networks (WMNs). Meanwhile, it aims to maximize the number of native packets coded in each single transmission. It dynamically selects a route for a given flow according to the real-time coding opportunities. There are no control packets in CAR, which greatly reduces the overhead costs. CAR gives the coded packet that consists of a larger number of native packets with a smaller forwarding delay. The forwarder with the largest number of native packets coded together is ultimately selected to send data. Simulations demonstrate that CAR achieves significantly better throughput gains and derives a reasonable end-to-end delay in both cross topology and mesh topology under both transmission control protocol (TCP) and user datagram protocol (UDP) traffic, as explained below. CAR achieves more than 35 % throughput improvement under TCP traffic and more than 15 % throughput improvement under UDP traffic, compared to other state-of-art protocols in cross topology, respectively. CAR also provides a several-fold increase in throughput in a large scale network (mesh topology). In a word, CAR significantly improves network performance of a WMN. © 2014, Springer Science+Business Media New York. Source

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