Utility operational experience on the NASA/DOE MOD-0A 200-kW wind turbine

The Mod-0A 200 wind turbine was designed and fabricated as part of the Federal Wind Energy Program. Early wind turbine operation and performance data were obtained while gaining initial experience in the operation of large, horizontal axis wind turbines in typical utility environments. The Mod-0A wind turbine was turned over to the Town of Clayton Light and Water Plant, Clayton, NM, for utility operation and on December 31, 1978, the machine had completed ten months of utility operation. The machine is described and the recent operational experience at Clayton, NMis documented

Identifying the machine translation error types with the greatest impact on post-editing effort

Translation Environment Tools make translators' work easier by providing them with term lists, translation memories and machine translation output. Ideally, such tools automatically predict whether it is more effortful to post-edit than to translate from scratch, and determine whether or not to provide translators with machine translation output. Current machine translation quality estimation systems heavily rely on automatic metrics, even though they do not accurately capture actual post-editing effort. In addition, these systems do not take translator experience into account, even though novices' translation processes are different from those of professional translators. In this paper, we report on the impact of machine translation errors on various types of post-editing effort indicators, for professional translators as well as student translators. We compare the impact of MT quality on a product effort indicator (HTER) with that on various process effort indicators. The translation and post-editing process of student translators and professional translators was logged with a combination of keystroke logging and eye-tracking, and the MT output was analyzed with a fine-grained translation quality assessment approach. We find that most post-editing effort indicators (product as well as process) are influenced by machine translation quality, but that different error types affect different post-editing effort indicators, confirming that a more fine-grained MT quality analysis is needed to correctly estimate actual post-editing effort. Coherence, meaning shifts, and structural issues are shown to be good indicators of post-editing effort. The additional impact of experience on these interactions between MT quality and post-editing effort is smaller than expected

Against the Virtual: Kleinherenbrink’s Externality Thesis and Deleuze’s Machine Ontology

Drawing from Arjen Kleinherenbrink's recent book, Against Continuity: Gilles Deleuze's Speculative Realism (2019), this paper undertakes a detailed review of Kleinherenbrink's fourfold "externality thesis" vis-à-vis Deleuze's machine ontology. Reading Deleuze as a philosopher of the actual, this paper renders Deleuzean syntheses as passive contemplations, pulling other (passive) entities into an (active) experience and designating relations as expressed through contraction. In addition to reviewing Kleinherenbrink's book (which argues that the machine ontology is a guiding current that emerges in Deleuze's work after Difference and Repetition) alongside much of Deleuze's oeuvre, we relate and juxtapose Deleuze's machine ontology to positions concerning externality held by a host of speculative realists. Arguing that the machine ontology has its own account of interaction, change, and novelty, we ultimately set to prove that positing an ontological "cut" on behalf of the virtual realm is unwarranted because, unlike the realm of actualities, it is extraneous to the structure of becoming-that is, because it cannot be homogenous, any theory of change vis-à-vis the virtual makes it impossible to explain how and why qualitatively different actualities are produced

Understanding How Image Quality Affects Deep Neural Networks

Image quality is an important practical challenge that is often overlooked in the design of machine vision systems. Commonly, machine vision systems are trained and tested on high quality image datasets, yet in practical applications the input images can not be assumed to be of high quality. Recently, deep neural networks have obtained state-of-the-art performance on many machine vision tasks. In this paper we provide an evaluation of 4 state-of-the-art deep neural network models for image classification under quality distortions. We consider five types of quality distortions: blur, noise, contrast, JPEG, and JPEG2000 compression. We show that the existing networks are susceptible to these quality distortions, particularly to blur and noise. These results enable future work in developing deep neural networks that are more invariant to quality distortions.Comment: Final version will appear in IEEE Xplore in the Proceedings of the Conference on the Quality of Multimedia Experience (QoMEX), June 6-8, 201

How to Commission, Operate and Maintain a Large Future Accelerator Complex from Far Remote

A study on future large accelerators [1] has considered a facility, which is designed, built and operated by a worldwide collaboration of equal partner institutions, and which is remote from most of these institutions. The full range of operation was considered including commi-ssioning, machine development, maintenance, trouble shooting and repair. Experience from existing accele-rators confirms that most of these activities are already performed 'remotely'. The large high-energy physics ex-periments and astronomy projects, already involve inter-national collaborations of distant institutions. Based on this experience, the prospects for a machine operated remotely from far sites are encouraging. Experts from each laboratory would remain at their home institution but continue to participate in the operation of the machine after construction. Experts are required to be on site only during initial commissioning and for par-ticularly difficult problems. Repairs require an on-site non-expert maintenance crew. Most of the interventions can be made without an expert and many of the rest resolved with remote assistance. There appears to be no technical obstacle to controlling an accelerator from a distance. The major challenge is to solve the complex management and communication problems.Comment: ICALEPCS 2001 abstract ID No. FRBI001 invited talk submitting author F. Willeke 5 pages, 1 figur

Minimalist's Linux Cluster

Using barebone PC components and NIC's, we construct a linux cluster which has 2-dimensional mesh structure. This cluster has smaller footprint, is less expensive, and use less power compared to conventional linux cluster. Here, we report our experience in building such a machine and discuss our current lattice project on the machine.Comment: 3 pages, 2 figures, Proceedings of the Lattice 03 Conference (Tsukuba, Japan