Magnetic Beamforming for Wireless Power Transfer

Magnetic resonant coupling (MRC) is an efficient method for realizing the near-field wireless power transfer (WPT). The use of multiple transmitters (TXs) each with one coil can be applied to enhance the WPT performance by focusing the magnetic fields from all TX coils in a beam toward the receiver (RX) coil, termed as "magnetic beamforming". In this paper, we study the optimal magnetic beamforming for an MRC-WPT system with multiple TXs and a single RX. We formulate an optimization problem to jointly design the currents flowing through different TXs so as to minimize the total power drawn from their voltage sources, subject to the minimum power required by the RX load as well as the TXs' constraints on the peak voltage and current. For the special case of identical TX resistances and neglecting all TXs' constraints on the peak voltage and current, we show that the optimal current magnitude of each TX is proportional to the mutual inductance between its TX coil and the RX coil. In general, the problem is a non-convex quadratically constrained quadratic programming (QCQP) problem, which is reformulated as a semidefinite programming (SDP) problem. We show that its semidefinite relaxation (SDR) is tight. Numerical results show that magnetic beamforming significantly enhances the deliverable power as well as the WPT efficiency over the uncoordinated benchmark scheme of equal current allocation.Comment: 13 Pages, 3 figures, submitted to IEEE ICASSP 201

DRAG FORCE RELATED TO BODY DIMENSIONS IN FRONT CRAWL SWIMMING

So far, a great deal of attention has been given to find out relationship between body dimensions (anthropometrical variables) and hydrodynamic resistance for actively swimming subjects. The development of a new indirect method for determining active drag (IMAD) warranted a reevaluation of this relationship, which was the aim of present study. Twenty one novice male swimmers with different body shape and experience ranging from 11 to 14 years and in mass from 35 to 70 kg have volunteered in this study. The variables were mass, height, upper limit length, arm, forearm, hand lengths, and torso, arm, and head circumferences. Very high and significant correlations were found between active drag and anthropometric variables. The drag force was ranging from 14.5 to 52.5 N. The results achieved from this study agreed well with the results obtained by other researchers using direct measurement systems

A Memtic genetic algorithm for a redundancy allocation problem

In general redundancy allocation problems the redundancy strategy for each subsystem is predetermined. Tavakkoli- Moghaddam presented a series-parallel redundancy allocation problem with mixing components (RAPMC) in which the redundancy strategy can be chosen for individual subsystems. In this paper, we present a bi-objective redundancy allocation when the redundancy strategies for subsystems are considered as a variable of the problem. As the problem belongs to the NP-hard class problems, we will present a new approach for the non-dominated sorting genetic algorithm (NSGAII) and Memtic algorithm (MA) with each one to solve the multi-objective model

Pipeline failure prediction in water distribution networks using evolutionary polynomial regression combined with Κ- means clustering

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this record.This paper presents a new approach for improving pipeline failure predictions by combining a data driven statistical model, i.e. Evolutionary Polynomial Regression (EPR), with K-means clustering. The EPR is used for prediction of pipe failures based on length, diameter and age of pipes as explanatory factors. Individual pipes are aggregated using their attributes of age, diameter and soil type to create homogenous groups of pipes. The created groups were divided into training and test datasets using the cross-validation technique for calibration and validation purposes respectively. The K-means clustering is employed to partition the training data into a number of clusters for individual EPR models. The proposed approach was demonstrated by application to the cast iron pipes of a water distribution network in the UK. Results show the proposed approach is able to significantly reduce the error of pipe failure predictions especially in the case of a large number of failures. The prediction models were used to calculate the failure rate of individual pipes for rehabilitation planning.The work reported is supported by the UK Engineering & Physical Sciences Research Council (EPSRC) project Safe &SuRe (EP/K006924/1)

Optimal Adaptive Output Regulation of Uncertain Nonlinear Discrete-Time Systems using Lifelong Concurrent Learning

This Paper Addresses Neural Network (NN) based Optimal Adaptive Regulation of Uncertain Nonlinear Discrete-Time Systems in Affine Form using Output Feedback Via Lifelong Concurrent Learning. First, an Adaptive NN Observer is Introduced to Estimate Both the State Vector and Control Coefficient Matrix, and its NN Weights Are Adjusted using Both Output Error and Concurrent Learning Term to Relax the Persistency Excitation (PE) Condition. Next, by Utilizing an Actor-Critic Framework for Estimating the Value Functional and Control Policy, the Critic Network Weights Are Tuned Via Both Temporal Different Error and Concurrent Learning Schemes through a Replay Buffer. the Actor NN Weights Are Tuned using Control Policy Errors. to Attain Lifelong Learning for Performing Effectively during Multiple Tasks, an Elastic Weight Consolidation Term is Added to the Critic NN Weight Tuning Law. the State Estimation, Regulation, and the Weight Estimation Errors of the Observer, Actor and Critic NNs Are Demonstrated to Be Bounded When Performing Tasks by using Lyapunov Analysis. Simulation Results Are Carried Out to Verify the Effectiveness of the Proposed Approach on a Vander Pol Oscillator. Finally, Extension to Optimal Tracking is Given Briefly