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Sebastian Kelly
Sebastian Kelly

Theory And Performance Of Electrical Machines J B Gupta

Brazos is a third generation distributed shared memory (DSM) system designed for x86 machines running Microsoft Windows NT 4.0. Brazos is unique among existing systems in its use of selective multicast, a software-only implementation of scope consistency, and several adaptive runtime performance tuning mechanisms. The Brazos runtime system is multithreaded, allowing the overlap of computation with the long communication latencies typically associated with software DSM systems. Brazos also supports multithreaded user-code execution, allowing programs to take advantage of the local tightly-coupled shared memory available on multiprocessor PC servers, while transparently interacting with remote "virtual" shared memory. Brazos currently runs on a cluster of Compaq Proliant 1500 multiprocessor servers connected by a 100 Mbps FastEthernet. This paper describes the Brazos design and implementation, and compares its performance running five scientific applications to the performance of Solaris and Windows NT implementations of the TreadMarks DSM system running on the same hardware.

Theory And Performance Of Electrical Machines J B Gupta

Instructor: Dan Kersten ( Office: S212 Elliott For the third time in the past sixty years, there has been a surge of interest in the science and application of artificial neural networks. Spurred in part by the availability of large datasets for machine learning and advances in computer hardware, the field has seen unprecedented successes in pattern recognition, including algorithms that approach and on occasion surpass human performance. This seminar will review the history of neural network theory, and then focus on recent technical and theoretical advances. We will look at successes and failures in explaining aspects of human visual recognition and the functional architecture of the primate visual system. We will also compare discriminative vs. generative models of human visual perception and cognition. The class format will consist of short lectures to provide overviews of upcoming themes together with discussions of journal articles led by seminar participants.

Cossale, Marco, Krings, Andreas, Soulard, Juliette, Boglietti, Aldo and Cavagnino, Andrea (2015)Practical investigations on cobalt-iron laminations for electrical machines. IEEE Transactions on Industry Applications, 51 (4). pp. 2933-2939. doi:10.1109/TIA.2015.2394404 ISSN 0093-9994.

Zhu, Z. Q., Wu, Zhongze, Evans, David James and Chu, W. Q. (2015)Novel electrical machines having separate PM Excitation Stator. IEEE Transactions on Magnetics, 51 (4). pp. 1-9. 8104109. doi:10.1109/TMAG.2014.2358199 ISSN 0018-9464.

Abdi, Salman, Abdi, Ehsan and McMahon, Richard A. (2015)A study of rotor eccentricities effects on brushless doubly fed machines performance. In: IEEE International Electric Machines & Drives Conference (IEMDC) 2015, Coeur d'Alene, 10-15 May 2015. Published in: 2015 IEEE International Electric Machines & Drives Conference (IEMDC), 2015 pp. 66-71. doi:10.1109/IEMDC.2015.7409038

Krings, Andreas, Cossale, Marco, Tenconi, Alberto, Soulard, Juliette, Cavagnino, Andrea and Boglietti, Aldo (2015)Characteristics comparison and selection guide for magnetic materials used in electrical machines. In: IEEE International Electric Machines & Drives Conference (IEMDC) 2015, Coeur d'Alene, 10-13 May 2015. Published in: Electric Machines & Drives Conference (IEMDC), 2015 IEEE International, 2015 pp. 1152-1157. ISBN 9781479979417. doi:10.1109/IEMDC.2015.7409206

Measurements are presented for a large number of machines ranging from small workstations to supercomputers. The authors combine these measurements into groups of parameters which relate to specific aspects of the machine implementation, and use these groups to provide overall machine characterizations. The authors also define the concept of pershapes, which represent the level of performance of a machine for different types of computation. A metric based on pershapes is introduced that provides a quantitative way of measuring how similar two machines are in terms of their performance distributions. The metric is related to the extent to which pairs of machines have varying relative performance levels depending on which benchmark is used.

In our approach, the machine mind is the applicative dynamic system represented by its algorithmically evolvable internal language. By other words, the mind and the language of mind are synonyms. Coming out from Shaumyan's semiotic theory of languages, we present the representation of language concepts in the machine mind as a result of our experiment, to show non-redundancy of the language of mind. To provide useful restriction for further research, we also introduce the hypothesis of semantic saturation in Computer-Computer communication, which indicates that a set of machines is not self-evolvable. The goal of our research is to increase the abstraction of Human-Computer and Computer-Computer communication. If we want humans and machines comunicate as a parent with the child, using different symbols and media, we must find the language of mind commonly usable by both machines and humans. In our opinion, there exist a kind of calm language of thinking, which we try to propose for machines in this paper. We separate the layers of a machine mind, we present the structure of the evolved mind and we discuss the selected properties. We are concentrating on the representation of symbolized concepts in the mind, that are languages, not just grammars, since they have meaning.

Prevention of the global warming has called for a great necessity for energy saving. This applies to the improvement of the COP of absorption chiller-heaters. We started the development of the high efficiency gas-fired double-effect absorption chiller-heater using LiBr-H2O to achieve target performance in short or middle term. To maintain marketability, the volume of the high efficiency machine has been set below the equal to the conventional machine. The absorption cycle technology for improving the COP and the element technology for downsizing the machine is necessary in this development. In this study, the former is investigated. In this report, first of all the target performance has been set at cooling COP of 1.35(on HHV), which is 0.35 higher than the COP of 1.0 for conventional machines in the market. This COP of 1.35 is practically close to the maximum limit achievable by double-effect absorption chiller-heater. Next, the design condition of each element to achieve the target performance and the effect of each mean to improve the COP are investigated. Moreover, as a result of comparing the various flows(series, parallel, reverse)to which the each mean is applied, it has been found the optimum cycle is the parallel flow.

Restricted Boltzmann machines (RBMs), which apply graphical models to learning probability distribution over a set of inputs, have attracted much attention recently since being proposed as building blocks of multi-layer learning systems called deep belief networks (DBNs). Note that temperature is a key factor of the Boltzmann distribution that RBMs originate from. However, none of existing schemes have considered the impact of temperature in the graphical model of DBNs. In this work, we propose temperature based restricted Boltzmann machines (TRBMs) which reveals that temperature is an essential parameter controlling the selectivity of the firing neurons in the hidden layers. We theoretically prove that the effect of temperature can be adjusted by setting the parameter of the sharpness of the logistic function in the proposed TRBMs. The performance of RBMs can be improved by adjusting the temperature parameter of TRBMs. This work provides a comprehensive insights into the deep belief networks and deep learning architectures from a physical point of view.

Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

Quality assurance of providing a work ready machine in multiple theatre operating rooms in a tertiary modern medical center in Riyadh. The aim of the following study is to keep high quality environment for workers and patients in surgical operating rooms. Technicians based audit by using key performance indicators to assure inspection, passing test of machine worthiness for use daily and in between cases and in case of unexpected failure to provide quick replacement by ready to use another anesthetic machine. The anesthetic machines in all operating rooms are daily and continuously inspected and passed as ready by technicians and verified by anesthesiologist consultant or assistant consultant. The daily records of each machines were collected then inspected for data analysis by quality improvement committee department for descriptive analysis and report the degree of staff compliance to daily inspection as "met" items. Replaced machine during use and overall compliance. Distractive statistic using Microsoft Excel 2003 tables and graphs of sums and percentages of item studied in this audit. Audit obtained highest compliance percentage and low rate of replacement of machine which indicate unexpected machine state of use and quick machine switch. The authors are able to conclude that following regular inspection and running self-check recommended by the manufacturers can contribute to abort any possibility of hazard of anesthesia machine failure during operation. Furthermore in case of unexpected reason to replace the anesthesia machine in quick maneuver contributes to high assured operative utilization of man machine inter-phase in modern surgical operating rooms.


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