Accurate Design of Array Coils for Transcranial Magnetic Stimulation by means of Continuous FSO

The closed forms of the Continuous Flock-of-Starling Optimization (CFSO) are applied to the optimization of the array coils usually used for TranscranialMagnetic Stimulation. The CFSO is the continuous equivalent model of the FSO algorithm, and it is expressed in terms of a state space representation. The trajectories of the CFSO particles, which explore the space solutions of the optimization problem, are obtained by solving a differential equations system within suitable Time-Windows (TWs). Thanks to the representation in terms of differential equations, it is possible to drive the trajectory by passing from convergence to divergence or vice-versa, and then from exploration to exploitation. Moreover, it is possible to refine the solution by reducing the amplitude of the TWs, during the optimization procedure, enhancing the performance of numerical FSO algorithm. The use of closed forms makes the CFSO easy to be implemented and accurate in quality of solution. Validation results are presented and the performances of different optimal array coils configurations have been compared

Neural Fem approach for the analysis of hysteretic materials in unbounded domain

"Purpose – This paper aims the application of a novel synergy between a neural network (NN) and the finite element method (FEM) in the solution of electromagnetic problem involving hysteretic material in unbounded domain.. . Design\/methodology\/approach – The hysteretic nature of the material is taken into account by an original NN able to perform the modelling of any kind of quasi-static loop (saturated and non-saturated, symmetric or asymmetric). An appositely developed iterative FEM procedure is presented for the solution of this kind of problems in unbounded domains.. . Findings – By starting from a small set of measured loops, the NN manages the values of the magnetic field, H, and the flux density, B, as inputs while the differential permeability is the output. In particular, the proposed NN is capable to perform the modelling of saturated and non-saturated, symmetric or asymmetric hysteresis loops.. . Practical implications – The development of an efficient method for the solution of a complicated electromagnetic problem in unbounded domain by using an iterative approach and NNs, which can be implemented also in existing FEM code.. . Originality\/value – The paper shows that the combination of FEM, iterative procedure and NNs allows us to produce effective solutions of electromagnetic problems in unbounded domains involving also nonlinear hysteretic magnetic materials with an acceptable computational cost.

A Neural Network-Based Low-Cost Solar Irradiance Sensor

Measuring solar irradiance allows for direct maximization of the efficiency in photovoltaic power plants. However, devices for solar irradiance sensing, such as pyranometers and pyrheliometers, are expensive and difficult to calibrate and thus seldom utilized in photovoltaic power plants. Indirect methods are instead implemented in order to maximize efficiency. This paper proposes a novel approach for solar irradiance measurement based on neural networks, which may, in turn, be used to maximize efficiency directly. An initial estimate suggests the cost of the sensor proposed herein may be price competitive with other inexpensive solutions available in the market, making the device a good candidate for large deployment in photovoltaic power plants. The proposed sensor is implemented through a photovoltaic cell, a temperature sensor, and a low– cost microcontroller. The use of a microcontroller allows for easy calibration, updates, and enhancement by simply adding code libraries. Furthermore, it can be interfaced via standard communication means with other control devices; integrated into control schemes; and remote–controlled through its embedded web server. The proposed approach is validated through experimental prototyping and compared against a commercial device

Reduced–Form of the Photovoltaic Five–parameter Model for Efficient Computation of Parameters

"This brief note presents a reduced–form of the five–parameter model introduced. by Desoto et al. (2006) and subsequently generalized by Tian et al.. (2012). The five–parameter model computes five reference parameters utilized. to synthesize performance curves in photovoltaic panels from information. available in datasheets. The improvement resulting from the reduced–. form is two–fold: (i ) the model is reduced from five to two parameters and. (ii ) as a result of (i ) a domain of attraction of the solution space is defined. analytically, which guarantees nonlinear solvers will provide the correct, that. is, physically feasible, solution for the parameters at first launch.

An Efficient Architecture for Floating Point based MISO Neural Neworks on FPGA

The present paper documents the research towards the development of an efficient algorithm to compute the result from a multiple-input-single-output Neural Network using floating-point arithmetic on FPGA. The proposed algorithm focus on optimizing pipeline delays by splitting the "Multiply and accumulate" algorithm into separate steps using partial products. It is a revisit of the classical algorithm for NN computation, able to overcome the main computation bottleneck in FPGA environment. The proposed algorithm can be implemented into an architecture that fully exploits the pipeline performance of the floating-point arithmetic blocks, thus allowing a very fast computation for the neural network. The performance of the proposed architecture is presented using as target a Cyclone II FPGA Device