On finite complete rewriting systems and large subsemigroups

Let $S$ be a semigroup and $T$ be a subsemigroup of finite index in $S$ (that is, the set $S\setminus T$ is finite). The subsemigroup $T$ is also called a large subsemigroup of $S$. It is well known that if $T$ has a finite complete rewriting system then so does $S$. In this paper, we will prove the converse, that is, if $S$ has a finite complete rewriting system then so does $T$. Our proof is purely combinatorial and also constructive.Comment: We have made major changes to the paper and simplified most of the proof

Non-Langevin behaviour of the uncompensated magnetisation in nanoparticles of artificial ferritin

The magnetic behaviour of nanoparticles of antiferromagnetic ferritin has been investigated by 57Fe Mossbauer absorption spectroscopy and magnetisation measurements, in the temperature range 2.5K-250K and with magnetic fields up to 7T. Samples containing nanoparticles with an average number of Fe atoms ranging from 400 to 2500 were studied. The value of the anisotropy energy per unit volume was determined and found to be in the range 3-6 10**5 ergs/cm3, which is a value typical for ferric oxides. By comparing the results of the two experimental methods at large field, we show that, contratry to what is currently assumed, the uncompensated magnetisation of the feritin cores in the superparamagnetic regime does not follow a Langevin law. For magnetic fields below the spin-flop field, we propose an approximate law for the field and temperature variation of the uncompensated magnetisation which has so far never been applied in antiferromagnetic systems. This approach should more generally hold for randomly oriented antiferro- magnetic nanoparticles systems with weak uncompensated moments.Comment: 11 pages, 11 figure

A unified model of the electrical power network

Traditionally, the different infrastructure layers, technologies and management activities associated with the design, control and protection operation of the Electrical Power Systems have been supported by numerous independent models of the real world network. As a result of increasing competition in this sector, however, the integration of technologies in the network and the coordination of complex management processes have become of vital importance for all electrical power companies. The aim of the research outlined in this paper is to develop a single network model which will unify the generation, transmission and distribution infrastructure layers and the various alternative implementation technologies. This 'unified model' approach can support ,for example, network fault, reliability and performance analysis. This paper introduces the basic network structures, describes an object-oriented modelling approach and outlines possible applications of the unified model

Update of an early warning fault detection method using artificial intelligence techniques

This presentation describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. In an earlier paper [11], a computer simulated medium length transmission line has been tested by the detector and the results clearly demonstrate the capability of the detector. Today’s presentation considers a case study illustrating the suitability of this AI Technique when applied to a distribution transformer. Furthermore, an evolutionary optimisation strategy to train ANNs is also briefly discussed in this presentation, together with a ‘crystal ball’ view of future developments in the operation and monitoring of transmission systems in the next millennium

Power system fault prediction using artificial neural networks

The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system . Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port circuit ) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher

Early warning fault detection using artificial intelligent methods

This paper describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. A simulated medium length transmission line has been tested by the detector and the results demonstrate the capability of the detector. Furthermore, comments on an evolutionary technique as the optimisation strategy for ANNs are included in this paper

Today's problems with the evaluation methods of full lightning impulse parameters as described in IEC 60060-1

In this paper the present problems with the evaluation methods for lightning impulse parameters, as defined in IEC 60060-1, are described. Also the current practice of evaluation in many laboratories world-wide, that is obtained by a questionnaire, is presented. Some of the work performed up the present time and the initial conclusions are reported, then some recommendations are made for future work

Dynamics of Neural Networks with Continuous Attractors

We investigate the dynamics of continuous attractor neural networks (CANNs). Due to the translational invariance of their neuronal interactions, CANNs can hold a continuous family of stationary states. We systematically explore how their neutral stability facilitates the tracking performance of a CANN, which is believed to have wide applications in brain functions. We develop a perturbative approach that utilizes the dominant movement of the network stationary states in the state space. We quantify the distortions of the bump shape during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable, and the reaction time to catch up an abrupt change in stimulus.Comment: 6 pages, 7 figures with 4 caption

Pion Interferometry for Hydrodynamical Expanding Source with a Finite Baryon Density

We calculate the two-pion correlation function for an expanding hadron source with a finite baryon density. The space-time evolution of the source is described by relativistic hydrodynamics and the Hanbury-Brown-Twiss (HBT) radius is extracted after effects of collective expansion and multiple scattering on the HBT interferometry have been taken into account, using quantum probability amplitudes in a path-integral formalism. We find that this radius is substantially smaller than the HBT radius extracted from the freeze-out configuration.Comment: 4 pages, 2 figure