An Investigation of the Electrical Degradation of GaN High-Electron-Mobility Transistors by Numerical Simulations of DC Characteristics and Scattering Parameters

The effects of direct-current (DC) stress on GaN high-electron-mobility transistors (HEMTs) are investigated by means of numerical simulations, by which the creation of an acceptor trap in the AlGaN barrier layer was correlated to the observed experimental degradation. An increase in the trap concentration induces a worsening of the saturated current I _DSS, transconductance g _m, and output conductance g _O. An increase in the length of the trapping region induces a degradation of I _DSS and g _m, but can reduce g _O. Analysis of scattering parameters in the saturation region shows that the cutoff frequency f _T matches the trend of g _m.     

A metatheory of a mechanized object theory

In this paper we propose a metatheory, MT, which represents the computation which implements its object theory, OT, and, in particular, the computation which implements deduction in OT. To emphasize this fact we say that MT is a metatheory of a mechanized object theory. MT has some “unusual” properties, e.g. it explicitly represents failure in the application of inference rules, and the fact that large amounts of the code implementing OT are partial, i.e. they work only for a limited class of inputs. These properties allow us to use MT to express and prove tactics, i.e. expressions which specify how to compose possibly failing applications of inference rules, to interpret them procedurally to assert theorems in OT, to compile them into the system implementation code, and, finally, to generate MT automatically from the system code. The definition of MT is part of a larger project which aims at the implementation of self-reflective systems, i.e. systems which are able to introspect their own code, to reason about it and, possibly, to extend or modify it.     

Integration of polynomials over n-dimensional polyhedra

Explicit formulae and algorithms for computing integrals of polynomials over n-dimensional polyhedra are given. Two different approaches are discussed: one uses a decompositive representation, while the other one uses a boundary representation of the polyhedron. The algorithms are followed by a discussion of the complexity. In the appendix the pseudo-code and some examples of calculation are given.     

Electric load analysis using an artificial neural network

Load forecasting in the current, increasingly liberalized, electricity power market is of crucial importance as a means for producers to optimize and rationalize energy supply. A number of electric power companies are equipped to make forecasts with the aid of traditional statistical methods. This paper presents the use of an artificial neural net to an hourly based load forecasting application for a small electric grid on an Italian island (Lipari) not connected to the mainland. The aim was to examine the forecasting ability of a neural net in a situation where the electric load was subject to considerable seasonal variations over the year. The variations are affected by energy demand related to the tourism season as well as by climatic conditions, especially temperature. The network developed was a multi‐layer perceptron type built on three layers trained with a back‐propagation algorithm. The input layer receives all the most relevant information regarding: the class of day type, the hour in the daytime, the load and background temperature recorded at the indicated time for the months of March, August and October whilst the output layer provides the forecast value at the indicated time in December. The results obtained are encouraging; in the training phase the RMS error rate was around 2% and this rate settled at about 2.6% during testing. As both the margins of error recorded are acceptable, the use of a neural network for electric load forecasting applications can be considered an attractive option. Copyright © 2005 John Wiley & Sons, Ltd.     

A theoretical analysis of methanol synthesis from CO2 and H2 in a ceramic membrane reactor

In this theoretical work the CO₂ conversion into methanol in both a traditional reactor (TR) and a membrane reactor (MR) is considered. The purpose of this study was to investigate the possibility of increasing CO₂ conversion into methanol with respect to a TR. A zeolite MR, able to combine catalytic reactions with separation properties of zeolite membranes, which allows only vapours to permeate, is considered. A mathematical model is used to simulate a traditional chemical reactor: a comparison among the model results and literature experimental data confirmed the validity of the model. Afterwards, the model is used to predict the behaviour of a zeolite MR in terms of both CO₂ conversion and methanol selectivity. The results show that it is possible to obtain both higher CO₂ conversion and methanol selectivity with respect to a TR operating at the same experimental conditions.     

Programmable CNN Analogue Chip for RD-PDE Multi-Method Simulations

Cellular Non Linear Networks can be useful applied for the solution of several types of Partial Differential Equations (PDEs). This paper will describe an analogue circuit implementation for the simulation of one-dimensional Reaction-Diffusion PDE with the possibility to set different boundary conditions as well as to select different discretization methodologies. Fausto Sargeni was born in Riano (ROMA) in 1961. He received the Dipl. Eng. degree in Electronic Engineering at the University of Rome “La Sapienza" in 1987. In 1989 he jointed the Dept. of Electronic Engineering at the University of Rome “Tor Vergata" as Assistant Professor. In 1998 he became Associate Professor. His research interests include analog VLSI circuits for non linear circuits and high-speed interconnections. Vincenzo Bonaiuto was born in Rome, Italy, in 1962. He received the Dipl. Eng. degree in Electronic Engineering at the University of Rome “La Sapienza". In 1997 he received the the Ph.D. in Telecommunication and Microelectronics. In 1996 he jointed the Electronic Engineering Dept. as Assistant Professor at the University of Rome “Tor Vergata” and, in 2002, he became Associate Professor. His main research interests are in the area of non linear circuits, Artificial Neural Networks analogue/digital VLSI circuits implementation.     

Advanced analytics for detection and diagnosis of false alarms and faults: A real case study

Onshore and offshore wind farms require a high level of advanced maintenance. Supervisory control and data acquisition (SCADA) and condition monitoring systems are now being employed, generating large amounts of data. They require robust and flexible approaches to convert dataset into useful information. This paper presents a novel approach based on the correlations of SCADA variables to detect and identify faults and false alarms in wind turbines. A correlation matrix between all the SCADA variables is used for pattern recognition. A new method based on curve fittings is employed for detecting false alarms and abnormal behaviours or faults in the components. The study is done in a real case study, validated with false alarms.