Abstract-Indoor positioning systems based on Wireless LAN (WLAN) are being widely investigated in academia and industry. Meanwhile, the emerging low-cost MEMS sensors can also be used as another independent positioning source. In this paper, we propose a pedestrian tracking framework based on particle filters, which extends the typical WLAN-based indoor positioning systems by integrating low-cost MEMS accelerometer and map information. Our simulation and real world experiments indicate a remarkable performance improvement by using this fusion framework.
In this study, a model is developed to assess external corrosion in buried pipelines based on the unification of Bayesian inferential structure derived from Markov chain Monte Carlo techniques using clustered inspection data. This proposed stochastic model combines clustering algorithms that can ascertain the similarity of corrosion defects and Monte Carlo simulation that can give an accurate probability density function estimation of the corrosion rate. The metal loss rate is chosen as the indicator of corrosion damage propagation, obeying a generalized extreme value (GEV) distribution. Bayesian theory was employed to update the probability distribution of metal loss rate as well as the GEV parameters in order to account for the model uncertainty. The proposed model was validated with direct and indirect inspection data extracted from a 110‐km buried pipeline system.
An ultra-low operating voltage bipolar resistive switching is observed in Ag/TaOx/Pt devices. They show a typical bipolar resistive switching with both low operating voltages and high cycling endurance when the compliance current (ICC) is 0.3 mA. Moreover, the operating voltage is considerably influenced by the grain size of the film. The VForming increases dramatically when the grain size exceeds a critical value. Meanwhile, the bipolar resistive switching and threshold switching in Ag/TaOx/Pt devices can be converted to each other by changing the magnitude of the ICC. Finally, a model based on the migration of Ag+ is proposed to explain the ultra-low operating voltage and the critical effect of grain size. The model is proved by simulation. These findings may lead to ultra-low power memories and contribute to a further understanding of the resistive switching effect.
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