Neural Network Modeling of Arbitrary Hysteresis Processes: Application to GO Ferromagnetic Steel

A computationally efficient hysteresis model, based on a standalone deep neural network, with the capability of reproducing the evolution of the magnetization under arbitrary excitations, is here presented and applied in the simulation of a commercial grain-oriented electrical steel sheet. The main novelty of the proposed approach is to embed the past history dependence, typical of hysteretic materials, in the neural net, and to illustrate an optimized training procedure. Firstly, an experimental investigation was carried out on a sample of commercial GO steel by means of an Epstein equipment, in agreement with the international standard. Then, the traditional Preisach model, identified only using three measured symmetric hysteresis loops, was exploited to generate the training set. Once the network was trained, it was validated with the reproduction of the other measured hysteresis loops and further hysteresis processes obtained by the Preisach simulations. The model implementation at a low level of abstraction shows a very high computational speed and minimal memory allocation, allowing a possible coupling with finite-element analysis (FEA)

Search for heavy neutral lepton production in K+ decays

A search for heavy neutral lepton production in K + decays using a data sample collected with a minimum bias trigger by the NA62 experiment at CERN in 2015 is reported. Upper limits at the 10−7 to 10−6 level are established on the elements of the extended neutrino mixing matrix |Ue4| 2 and |Uμ4| 2 for heavy neutral lepton mass in the ranges 170–448 MeV/c2 and 250–373 MeV/c2, respectively. This improves on the previous limits from HNL production searches over the whole mass range considered for |Ue4|2 and above 300 MeV/c2 for |Uμ4|2

A Matlab Simulink model for the study of smart grid — Grid-integrated vehicles interactions

The market of electrical vehicles is in continuous expansion. When the number of the full electrical or hybrid vehicles will be significant, they could represent an interesting further resource of electrical storage in an intermittent renewable power sources scenario. The focus of this paper is to describe a MATLAB Simulink model well suited for the analysis of the behavior of a car park of electric vehicles used as energy storage in a smart grid environment

CNN cell for computing disparity map

The real-time estimation of the distance of objects from an observer is a critical issue in several application fields. A new cellular neural network circuit that uses a stereo vision algorithm to compute the disparity map is presented

Very efficient VLSI implementation of CNN with discrete templates

Hardware implementation of programmable neural networks requires multiplications of input analogue voltages and constant values. In the Letter a very efficient implementation of cellular neural networks with digital templates is presented

Multiplexed circuit for star-CNN architecture

Star-CNN is a particular architecture of Cellular Neural Networks that has been recently proposed. This dynamic nonlinear system is defined by connecting N identical dynamical sysstem called local cell with a Central System in the shape of a star. Each of the local cells communicates with others through the Central System. Because of the hardware requirements of such a system, its implementation comes out extremely expansive from the silicon area occupation point of view. This paper presents a hardwaare implementation of this new CNN architecture based on a time division approach that allows to significantly reduce the silicon area occupation by minimizing the number of the analogue multipliers. © 2008 IEEE

Prospects for K+→π+νν¯ at CERN in NA62

The NA62 experiment will begin taking data in 2015. Its primary purpose is a 10% measurement of the branching ratio of the ultrarare kaon decay $K^+ \to \pi^+ \nu \bar{ \nu }$, using the decay in flight of kaons in an unseparated beam with momentum 75 GeV/c.The detector and analysis technique are described here