Symphonic Band, February 23, 2022

Center for the Performing Arts February 23, 2022 Wednesday Evening 8:00 p.m

Medium-Term Prospects for Ireland: An Update. Quarterly Economic Commentary Special Article, April 1990

The Institute publishes its Medium-Term Review approximately every eighteen months. The Review attempts to project likely prospects for the Irish economy, predicated on trends in the world economy and the stance of domestic policy. Since we published our last Review in June 1989, some important and unexpected political events have taken place in Eastern Europe which could have substantial political and economic implications for developments within the EC and Ireland over the next few years. These events were not foreseen when we wrote the Review last June, although many of the other forces determining our future growth have developed as we expected ( e.g., the impending slow-down of the UK economy, relatively buoyant growth in the rest of the EC and the implementation of the EC Structural Fund projects, albeit at a lower level and different timing than envisaged in the National Development Plan 1989-1993)

Postharvest Handling of Fresh Vegetables: Proceedings of a workshop held in Beijing, People’s Republic of China, 9–11 May 2001

This proceedings covers papers presented at end of project workshop in Beijing in 2001. The workshop introduced new research challenges that have been proposed in the fields of postharvest handling of melons and in improvements to microbial safety of fresh vegetables. (ISSN 1447-0837

Making Economic Time-Series Available to Users of Micro-Computers in Ireland. Quarterly Economic Commentary Special Article, October 1987

The major raw material used by economists who engage in applied research or analysis is the vast range of economic time-series which are published by many different bodies in all developed countries. The very range of sources and definitions gives rise to many problems before any consideration is given to matters of economic theory. "Datagrubbing" has traditionally taken a major part of the time devoted to any individual research project. With many economists engaging in overlapping areas of research and analysis there has been considerable duplication of effort in the past in developing suitable sets of data. Even if the producers of the raw material, economic time-series, do not see it as their duty to produce consistent data in suitable machine readable formats ( can be read by a computer directly without retyping) covering a reasonable span of years, there is clearly an advantage to economists in cooperating in this onerous task. This article examines the range of economic time-series which are available in computer databases or databanks in Ireland and considers how best these data can be made available to users of micro-computers. Section 2 of the paper sets out the background to the development of these databases and Section 3 describes their current scope and contents. Section 4 discusses the future development of databases. Section 5 examines some technical considerations on how best to access these data and Section 6 presents proposals as to how these data, currently only available on one mainframe computer, could best be made available to users of micro-computers in Ireland

The use of neural networks to characterise problematic arc sounds

Automation of electric arc welding has been at the centre of considerable debate and the subject of much research for several decades. One conclusion drawn from all this effort is that there seems to be no single system that can monitor all of the variables and subsequently, fully control any welding process. To date there has been considerable success in the development of seam tracking systems employing various sensing techniques, good progress has been made in the area of penetration measurement and worthwhile use has been made of the integration of expert systems and modelling software within these control domains. Skilled welders develop their own monitoring and control systems and it has been observed that part of this expertise is the ability to listen subconsciously to the sound of the arc and to alter the electrode position in response to an adverse change in arc noise. Attempts have been made to analyse these sounds using both conventional techniques and more recently expert systems, neither have delivered any usable information. This paper describes a new approach involving the use of neural networks in the identification of sounds which indicate that the welding system is drifting out of control

The management of industrial arc welding by neural networks

New methods of monitoring industrial process variables are constantly being sought with the aim to improve control efficiency. It has been observed that skilled welders subconsciously adapt their manual arc welding technique in response to a variation in the sound produced from the process. This paper proposes an approach to the control of an automated submerged arc welding process using:- 1. Real time monitoring of acoustic emissions 2. The application of neural networks to predict the point of instability of the process variables

The real time analysis of acoustic weld emissions using neural networks

Artificial Neural Networks (ANNs) are becoming an increasingly viable computing tool in control scenarios where human expertise is so often required. The development of software emulations and dedicated VLSI devices is proving successful in real world applications where complex signal analysis, pattern recognition and discrimination are important factors. An established observation is that a skilled welder is able to monitor a manual arc welding process by subconsciously changing the position of the electrode in response to an adverse change in audible process noise. Expert systems applied to the analysis of chaotic acoustic emissions have failed to establish any salient information due to the inabilities of conventional architectures in processing vast quantities of erratic data at real time speeds. This paper describes the application of a hybrid ANN system, utilising a combination of multiple ANN architectures and conventional techniques, to establish system parameter acoustic signatures for subsequent on line control

The application of neural networks for the control of industrial arc welding

The use of automatic closed loop control is well established in all areas of manufacturing industry. New methods for measuring system variables, data processing and process control are being sought to improve system efficiency. Skilled welders are able to subconsciously monitor a manual arc welding process by listening to the sound and repositioning the electrode in response to a change in arc noise. This paper describes the real time monitoring of acoustic emissions from an automated submerged arc welding process and the application of Neural Networks to predict the point of instability of the process variables

The analysis of airborne acoustics of S.A.W. using neural networks

The analysis of acoustic emissions for machine health monitoring has made rapid advances in the last five years due to a revival of interest in the application of Artificial Neural Networks (ANNs). Complex signal analysis, which has often thwarted conventional statistical methods and expert systems, is now more possible with the introduction of 'neural' based computing methods. Acoustic emissions from welding processes are well documented. In particular, it has been established that a manual welder is capable of making intrinsic decisions concerning electrode position based on process noise. The analysis of time / amplitude signals and Fast Fourier Transforms (I-I-1s), within salient frequency bandwidths of the weld acoustic, has yielded erratic, unpredictable and noise polluted data. Extracting a meaningful interpretation from this data is computationally intensive when utilising standard statistical methods and leads to data explosions, especially when an 'on-line' corrective control signal is required. An Artificial Neural Network is 'trained' on examples from acquired data and performs a robust signal recognition task rather than relying on a programmed set of data samples as in the case of an expert system. This technique enables the network to generalise and, as a consequence, allows the input data to be erratic, erroneous and even incomplete. This research defines the development of a hybrid system, utilising high speed date capture and 141-1' computation for the signal pre-processing and a 'self organising' network paradigm to establish weld stability and real time corrective control of the process parameters. The paper describes a successful application of a Neural Network hybrid system to determine weld stability in submerged arc welding (S.A.W) through the interpretation of airborne acoustics

Extragalactic Sources for Ultra High Energy Cosmic Ray Nuclei

In this article we examine the hypothesis that the highest energy cosmic rays are complex nuclei from extragalactic sources. Under reasonable physical assumptions, we show that the nearby metally rich starburst galaxies (M82 and NGC 253) can produce all the events observed above the ankle. This requires diffusion of particles below $10^{20}$ eV in extragalactic magnetic fields $B \approx 15$ nG. Above $10^{19}$ eV, the model predicts the presence of significant fluxes of medium mass and heavy nuclei with small rate of change of composition. Notwithstanding, the most salient feature of the starburst-hypothesis is a slight anisotropy induced by iron debris just before the spectrum-cutoff.Comment: To appear in Phys. Rev. D, reference adde