Distributed Sounding Art – Practices in Distributing Sound (eds. Barry Truax, Marcel Cobussen and Vincent Meelberg)

This chapter examines distributed sounding art by focusing on three key aspects that we consider essentially tied to the notion of distribution: assignment, transport and sharing. These aspects aid us in navigating through a number of nodes in a history of sounding art practices where sound becomes assigned, transported and shared between places and people. Sound or data become distributed, and in the process of distribution, meanings become assigned and altered through differing socio-cultural contexts of places and people. We have selected several works, commencing in the 1960’s as we consider this period as having produced some of the seminal works that address distribution.
We draw on works by composers, performers and sound artists and thus present a history of sounding art, which is amongst the many histories of sounding art in the 20th and 21st century.

Michael Allen: Close Readings: Essays on Irish Poetry

The late Michael Allen was a member of the famous Belfast Group, and one of the most authoritative critical voices on poetry from Northern Ireland, intimately part of the North’s poetic movement since the early 1960s. He taught at Queen’s University, where he was a colleague of Seamus Heaney and tutor to poets such as Paul Muldoon and Medbh McGuckian. Seamus Heaney called him ‘the reader over my shoulder’. Close Readings brings together interlinked critical writings which have crucially influenced approaches to Irish poetry during the last forty years. The book ends with an extended essay, hitherto unpublished: ‘Doubles, Twins and the Feminine: Development in the Poetry of Michael Longley’.

Close Readings contains a Foreword by Fran Brearton, which relates Michael Allen’s essays to continuing critical and cultural debates. Edna Longley’s Afterword offers a personal view of Allen’s involvement with poetry in Belfast.

CSI 2264: Simultaneous Optical and Infrared Light Curves of Young Disk-bearing Stars in NGC 2264 with CoRoT and Spitzer—Evidence for Multiple Origins of Variability

We present the Coordinated Synoptic Investigation of NGC 2264, a continuous 30 day multi-wavelength photometric monitoring campaign on more than 1000 young cluster members using 16 telescopes. The unprecedented combination of multi-wavelength, high-precision, high-cadence, and long-duration data opens a new window into the time domain behavior of young stellar objects. Here we provide an overview of the observations, focusing on results from Spitzer and CoRoT. The highlight of this work is detailed analysis of 162 classical T Tauri stars for which we can probe optical and mid-infrared flux variations to 1% amplitudes and sub-hour timescales. We present a morphological variability census and then use metrics of periodicity, stochasticity, and symmetry to statistically separate the light curves into seven distinct classes, which we suggest represent different physical processes and geometric effects. We provide distributions of the characteristic timescales and amplitudes and assess the fractional representation within each class. The largest category (>20%) are optical "dippers" with discrete fading events lasting ~1-5 days. The degree of correlation between the optical and infrared light curves is positive but weak; notably, the independently assigned optical and infrared morphology classes tend to be different for the same object. Assessment of flux variation behavior with respect to (circum)stellar properties reveals correlations of variability parameters with Hα emission and with effective temperature. Overall, our results point to multiple origins of young star variability, including circumstellar obscuration events, hot spots on the star and/or disk, accretion bursts, and rapid structural changes in the inner disk. Based on data from the Spitzer and CoRoT missions. The CoRoT space mission was developed and is operated by the French space agency CNES, with participation of ESA's RSSD and Science Programmes, Austria, Belgium, Brazil, Germany, and Spain.

Barry Lieberman & Friends present a Recital, February 26, 2012

Concert program for Barry Lieberman & Friends present a Recital, February 26, 2012

Barry Lieberman & Friends, Maria Larionoff and Robin McCabe, May 6, 2012

Concert program for Barry Lieberman & Friends present Guest Artists, May 6, 2012

Margaret Ann Nessel, Soprano, January 11, 1963

Concert program for Margaret Ann Nessel, Soprano, January 11, 1963

ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

ANN based support for distributed energy resources scheduling in smart grids

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.

Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.