Security Assessment Using Neural Computing

The advantage of fast computation capability of an artificial neural network (ANN) is used to introduce an iterative scheme for security assessment of power systems. Two related approaches are shown which demonstratedly work satisfactorily. The idea of feedback in a single-layer feedforward neural network is experimented yielding higher accuracy. The ANN is trained by using a set of data obtained from off-line analysis of the power network. After training, an approximate solution for a given condition may be found almost immediately. The approximate solution obtained is judged adequate for assessing the security of the power system. A case study is also presented for demonstrating the applicability of the approach

The δ18O and δD isoscapes of recent groundwater in Poland

Considering the country’s development and quality of life, recognition of the water cycle mechanism is of great importance. A significant contribution to this comes from the isotopic composition of particular elements of the water cycle. However, a weak point is that in Poland only one element of the water cycle, precipitation, is sampled and measured over more than 312 thousands km2 at a single station. It is therefore necessary to seek extension of or alternatives for these rare data. Such an alternative is the sampling of groundwater containing tritium in the national monitoring network of groundwater bodies that is maintained by the Polish Geological Institute. Based on such data we have constructed δ18O and δD isoscapes (i.e., maps of δ18O and δD values) of recent groundwater. These data provide spatial distribution of δ18O and δD values which can be used as input to hydrogeological models

Fast Power Flow with Capability of Corrective Control Using a Neural Network

The authors present a number of different configurations of a neural network and identify a particular case which is most suitable for power flow analysis in real-time applications. The advantage of fast computation of the artificial neural network (ANN) is used for obtaining power flow solutions in real time. The inputs to the ANN are the real and reactive power generating and demand in the system, and the output data are the complex bus voltages. A few configurations of the neural network were experimented with, and the best results were achieved with a single-layer feedforward neural network with nonlinear feedback. By using the trained neural network, an approximate solution of power flow can be obtained almost immediately. One particular configuration of the ANN can be used for determining corrective strategies during abnormal conditions of the power syste

Collective character of spin excitations in a system of Mn2+^{2+} spins coupled to a two-dimensional electron gas

We have studied the low energy spin excitations in n-type CdMnTe based dilute magnetic semiconductor quantum wells. For magnetic fields for which the energies for the excitation of free carriers and Mn spins are almost identical an anomalously large Knight shift is observed. Our findings suggests the existence of a magnetic field induced ferromagnetic order in these structures, which is in agreement with recent theoretical predictions [J. K{\"o}nig and A. H. MacDonald, submitted Phys. Rev. Lett. (2002)]Comment: 4 figure

SOM neural network design – a new Simulink library based approach targeting FPGA implementation

The paper presents a method for FPGA implementation of Self-Organizing Map (SOM) artificial neural networks with on-chip learning algorithm. The method aims to build up a specific neural network using generic blocks designed in the MathWorks Simulink environment. The main characteristics of this original solution are: on-chip learning algorithm implementation, high reconfiguration capability and operation under real time constraints. An extended analysis has been carried out on the hardware resources used to implement the whole SOM network, as well as each individual component block

g-Factor Tuning and Manipulation of Spins by an Electric Current

We investigate the Zeeman splitting of two-dimensional electrons in an asymmetric silicon quantum well, by electron-spin-resonance (ESR) experiments. Applying a small dc current we observe a shift in the resonance field due to the additional current-induced Bychkov-Rashba (BR) type of spin-orbit (SO) field. This finding demonstrates SO coupling in the most straightforward way: in the presence of a transverse electric field the drift velocity of the carriers imposes an effective SO magnetic field. This effect allows selective tuning of the g-factor by an applied dc current. In addition, we show that an ac current may be used to induce spin resonance very efficiently.Comment: 4 pages, 4 figure

Monolithically integrated gas distribution chamber for silicon MEMS fuel cells

This paper presents a gas distribution chamber for a silicon polymer-electrolyte-membrane fuel cell. The silicon structure contains the mechanical support, gas distribution channels, and hydrogen diffusion layer, all built from the same substrate. An identical structure can be used for the oxygen or air side, in case of forced circulation. The novel fabrication process has been designed for integrability of the different parts and low cost and is based on standard microfabrication techniques. The main advantages of the present design are the monolithic structure, the inclusion of patterned paths for the hydrogen flow, and the creation of solid pillars for the support of the membrane. Experimental results of the usage of the chamber as part of a fuel cell are shown, comparing different designs for the hydrogen path inside the distribution chamber. For the design with a patterned path for hydrogen, a power density of 30 mW/cm2 for 4 mL/min of hydrogen flow was measured, whereas in the design without the patterned path, the measured power density was only 15 mW/cm2