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International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： T.S. Trevisan V.S. Costa L. Whately C.L. Amorim COPPE Systems Engineering Program Federal University of Rio de Janeiro Brazil Brazil COPPE Systems Engineering Program Federal University of Rio de Janeiro Brazil

In this paper we introduce MOMEMTO (MOre MEMory Than Others) a new set of kernel mechanisms that allow users to have full control of the distributed shared memory on a cluster of personal computers. In contrast to many existing software DSM systems, MOMEMTO supports efficiently and flexibly global shared-memory allowing applications to address larger memory space than that available in a single node. MOMEMTO has been implemented in the Linux 2.4 kernel and preliminary performance results show that MOMEMTO has low memory management and communication overheads and that it can indeed perform very well for large memory configurations.

关键词： Kernel Memory management Application software Operating systems Microcomputers Web server Scalability Writing Database systems Power system management

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Validating an unsupervised weightless perceptron

Validating an unsupervised weightless perceptron

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International Conference on Neural Information Processing

作者： I. Wickert F.M.G. Franca Computer Systems Engineering Program COPPE-UFRJ Rio de Janeiro Brazil

The paper presents a comparison between two unsupervised neural network models: (i) the well-known fuzzy ART, and (ii) AUTOWISARD, a new unsupervised version of the classic WISARD weightless neural network model. It is shown that AUTOWISARD is simple, fast and stable, whilst keeping compatibility with the original WISARD architecture. Experimental test results over binary patterns benchmarks have shown that, although both unsupervised learning models are remarkably simple, AUTOWISARD consistently exhibits better classification skills than fuzzy ART. It is also shown that such superiority happens thanks to AU-TOWISARD's richer internal representation of the trained patterns and the training methods employed by the algorithm, such as the learning window and partial training strategies.

关键词： Subspace constraints Neurons Fuzzy control Pattern recognition Fuzzy sets Resonance Testing Fuzzy neural networks Neural networks Stability

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GloVE: a distributed environment for low cost scalable VoD systems

GloVE: a distributed environment for low cost scalable VoD s...

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International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： L.B. Pinho C.L. Amorim E. Ishikawa COPPE Systems Engineering Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Systems Engineering Department Military Institute of Engineering Rio de Janeiro Brazil

In this paper we introduce a scalable Video-on-Demand (VoD) system called GloVE (Global Video Environment) in which active clients cooperate to create a shareable video cache that is used as the primary source of video content for subsequent client requests. In this way, GloVE server's bandwidth does not limit the number of simultaneous clients that can watch a video since once its content is in the cooperative video cache (CVC) it can be directly transmitted from the cache rather than the VoD server Also, GloVE follows the peer-to-peer approach, allowing the use of low-cost PCs as video servers. In addition, GloVE supports video servers without multicast capability and videos in any stored format. We analyze preliminary performance results of GloVE implemented in a PC server using a Fast Ethernet interconnect and small video buffers at the clients. Our results confirm that while the GloVE-based server uses only a single video channel to deliver a highly popular video simultaneously to N clients, conventional VoD servers require as much as N times more channels.

关键词： Costs Network servers Video sharing Ethernet networks Streaming media Delay Bandwidth Bit rate systems engineering and theory Watches

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Fuzzy Markov predictor in electric load forecasting

Fuzzy Markov predictor in electric load forecasting

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International Joint Conference on Neural Networks (IJCNN)

作者： M.A. Teixeira G. Zaverucha Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Brazil Systems Engineering and Computer Science Program E28093 Graduate Programs in Engineering (COPPE) Federal University of Rio de Janeiro Brazil

We present the fuzzy Markov predictor (FMP), a hybrid system that is applied to the task of monthly electric load forecasting. The FMP is a modification we introduce in the hidden Markov model in order to enable it to predict numerical values. The FMP can be seen as an extension of the fuzzy Bayes predictor (FBP) that was modified from the naive Bayes classifier. For verifying the efficiency of the FMP's prediction, we compare it with the FBP, one fuzzy system and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.

关键词： Load forecasting Fuzzy systems Hidden Markov models Power engineering and energy Smoothing methods Niobium compounds systems engineering and theory Computer science Predictive models Electric variables measurement

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AUTO WIS ARD: Unsupervised modes for the WISARD

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6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001

作者： Wickert, Iuri Fraņca, Felipe M. G. Computer Systems Engineering Program - COPPE UFRJ Caixa Postal 68511 21945-970 Rio de Janeiro RJ Brazil

ISBN: (纸本)3540422358

This work in traducest wo new unsupervised learning algorithms based on the WISARD weightless neural classifier model. The first one, the standard AUTOWISARD model, is able to perform fast one-shot, learning of unsorted sets of input patterns. The second oik? is a recursive version of AUTOWISARD which not only keeps the good features of the basic model, agility, plasticit v and stability', but also produces hierarc hically structured tree-lik? classifications. Although the standard A UT(WISARD model exhibits good classification skills when exposed to symbolic patterns (by producing only few classes containing patterns of different meanings), its recursive version produces even less confusing classes. The stability of both learning algorithms is also demonstrated. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Learning algorithms

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Fuzzy Bayes predictor in electric load forecasting

Fuzzy Bayes predictor in electric load forecasting

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International Joint Conference on Neural Networks (IJCNN)

作者： M.A. Teixeira G. Zaverucha V.N.A.L. da Silva G.F. Ribeiro Systems Engineering and Computer Science Program Graduate Programs in Engineering COPPE Federal University of Rio de Janeiro Brazil Electric Power Research Center CEPEL CEPEL Brazil

ISBN: (纸本)0780370449

We present the fuzzy Bayes predictor (FBP), a hybrid system for the task of monthly electric load forecasting. The FBP is a modification we introduce in the naive Bayes classifier in order to enable it to predict numerical values. We consider three versions of the FBP, each one with a different dependence among the input data: independence, first-order and second-order dependence. For verifying the efficiency of the FBP's prediction, we compare it with two fuzzy systems and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.

关键词： Load forecasting Fuzzy systems Power engineering and energy Smoothing methods Testing Niobium compounds systems engineering and theory Computer science Predictive models Electric variables measurement

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Elastic neural net algorithm for cluster analysis

Elastic neural net algorithm for cluster analysis

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Brazilian Symposium on Neural Networks

作者： R.L. Salvini L.A. V. de Carvalho Program of Systems Engineering and Computer Science COPPE/UFRJ Rio de Janeiro Brazil

Proposes a method for data clustering in a n-dimensional space using the elastic net algorithm which is a variant of the Kohonen topographic map learning algorithm. The elastic net algorithm is a mechanical metaphor in which an elastic ring is attracted by points in a bi-dimensional space while their internal elastic forces try to shun the elastic expansion. The different weights associated with these two kinds of forces lead the elastic to a gradual expansion in the direction of the bi-dimensional points. In this method, the elastic net algorithm is employed with the help of a heuristic framework that improves its performance for application in the n-dimensional space of cluster analysis. Tests were made with two types of data sets: (1) simulated data sets with up to 1000 points randomly generated in groups linearly separable with up to dimension 10 and (2) the Fisher Iris Plant database, a well-known database referred to in the pattern recognition literature. The advantages of the method presented are its simplicity, its fast and stable convergence, beyond efficiency in cluster analysis.

关键词： Neural networks Clustering algorithms Algorithm design and analysis Databases Performance analysis Testing Test pattern generators Iris Pattern recognition Convergence

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Industry Transformations for High Service Provisioning with Lower Energy and Material Demand: A Review of Models and Scenarios

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Environment and Resources 1000年第1期49卷 249-279页

作者： Dominik Wiedenhofer Jan Streeck Frauke Wiese Elena Verdolini Alessio Mastrucci Yiyi Ju Benigna Boza-Kiss Jihoon Min Jonathan Norman Hanspeter Wieland Nuno Bento María Fernanda Godoy León Leticia Magalar Andreas Mayer Simone Gingrich Ayami Hayashi Joni Jupesta Gamze Ünlü Leila Niamir Tao Cao Marianne Zanon-Zotin Barbara Plank Johan Vélez-Henao Eric Masanet Volker Krey Keigo Akimoto Arnulf Grubler Bas van Ruijven Stefan Pauliuk 1Institute of Social Ecology University of Natural Resources and Life Sciences Vienna Austria email: Dominik.Wiedenhofer@boku.ac.at 2Department for Sustainable Energy Transition Europa-Universität Flensburg Flensburg Germany 3Law Department University of Brescia Brescia Italy 4Euro-Mediterranean Center on Climate Change (CMCC Foundation) Lecce Italy 5RFF-CMCC European Institute on Economics and the Environment Milan Italy 6International Institute for Applied Systems Analysis Laxenburg Austria 7Waseda Institute for Advanced Study Tokyo Japan 8Institute for Future Initiatives University of Tokyo Tokyo Japan 9School of Earth and Environment University of Leeds Leeds United Kingdom 10University Institute of Lisbon (ISCTE) Lisbon Portugal 11Faculty of Bioscience Engineering Ghent University Ghent Belgium 12Centre for Energy and Environmental Economics (Cenergia) Energy Planning Program (PPE) COPPE Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil 13Department of Geography University of Innsbruck Innsbruck Austria 14Research Institute of Innovative Technology for the Earth Kizugawa Japan 15Institute for the Advanced Study of Sustainability United Nations University Shibuya Japan 16Copernicus Institute of Sustainable Development Utrecht University Utrecht Netherlands 17Faculty of Environment and Natural Resources University of Freiburg Freiburg Germany 18Bren School of Environmental Science and Management University of California Santa Barbara California USA 19Department of Energy and Process Engineering Norwegian University of Science and Technology Trondheim Norway

Developing transformative pathways for industry's compliance with international climate targets requires model-based insights into how supply- and demand-side measures affect industry, material cycles, global supply chains, socioeconomic activities, and service provisioning that support societal well-being. We review the recent literature modeling the industrial system in low energy and material demand futures, which mitigates environmental impacts without relying on risky future negative emissions and technological fixes. We identify 77 innovative studies drawing on nine distinct industry modeling traditions. We critically assess system definitions and scopes, biophysical and thermodynamic consistency, granularity and heterogeneity, and operationalization of demand and service provisioning. We find that combined supply- and demand-side measures could reduce current economy-wide material use by 56%, energy use by 40% to 60%, and greenhouse gas emissions by 70% to net zero. We call for strengthened interdisciplinary collaborations between industry modeling traditions and demand-side research to produce more insightful scenarios, and we discuss challenges and recommendations for this emerging field.

关键词： climate change mitigation sustainable resource use integrated assessment modeling IAM ecological economics industrial ecology

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