Prediction of Ultimate Capacity of Laterally Loaded Piles in Clay: A Relevance Vector Machine Approach

This study investigates the potential of Relevance Vector Machine (RVM)-based approach to predict the ultimate capacity of laterally loaded pile in clay. RVM is a sparse approximate Bayesian kernel method. It can be seen as a probabilistic version of support vector machine. It provides much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. RVM model outperforms the two other models based on root-mean-square-error (RMSE) and mean-absolute-error (MAE) performance criteria. It also stimates the prediction variance. The results presented in this paper clearly highlight that the RVM is a robust tool for prediction Of ultimate capacity of laterally loaded piles in clay.

Magneto acoustical emission in nanocrystalline Mn-Zn ferrites

Mn0.4Zn0.6Fe2O4 powders were prepared by microwave hydrothermal method. The powders were characterized by X-ray diffraction, transmission electron microscope. The powders were sintered at different temperatures 400, 500, 600, 700, 800 and 900 degrees C/30 min using microwave sintering method. The grain size was estimated by scanning electron microscope. The room temperature dielectric and magnetic properties were studied in the frequency range (100 kHz-1.8 GHz). The magnetization properties were measured upto 1.5 T. The acoustic emission has been measured along the hysteresis loops from 80 K to Curie temperature. It is found that the magneto-acoustic emission (MAE) activity along hysteresis loop is proportional to the hysteresis losses during the same loop. This law has been verified on series of polycrystalline ferrites and found that the law is valid whatever the composition, the grain size and temperature. It is also found that the domain wall creation/or annihilation processes are the origin of the MAE. (C) 2013 Published by Elsevier Ltd.

Energy Harvesting WSNs for Accurately Estimating the Maximum Sensor Reading: Trade-Offs and Optimal Design

Computing the maximum of sensor readings arises in several environmental, health, and industrial monitoring applications of wireless sensor networks (WSNs). We characterize the several novel design trade-offs that arise when green energy harvesting (EH) WSNs, which promise perpetual lifetimes, are deployed for this purpose. The nodes harvest renewable energy from the environment for communicating their readings to a fusion node, which then periodically estimates the maximum. For a randomized transmission schedule in which a pre-specified number of randomly selected nodes transmit in a sensor data collection round, we analyze the mean absolute error (MAE), which is defined as the mean of the absolute difference between the maximum and that estimated by the fusion node in each round. We optimize the transmit power and the number of scheduled nodes to minimize the MAE, both when the nodes have channel state information (CSI) and when they do not. Our results highlight how the optimal system operation depends on the EH rate, availability and cost of acquiring CSI, quantization, and size of the scheduled subset. Our analysis applies to a general class of sensor reading and EH random processes.