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Universal algorithm for the analysis of charge distributions in segmented electrodes of gas detectors

EUROPEAN PHYSICAL JOURNAL A 2012年第5期48卷 60-60页

作者： Carbone, D. Cappuzzello, F. Cavallaro, M. Ist Nazl Fis Nucl Lab Nazl Sud Via S Sofia 62 I-95125 Catania Italy Univ Catania Dipartimento Fis & Astron I-95125 Catania Italy

A general algorithm to extract the center of gravity of charge distributions generated in a patterned electrode is presented. The results obtained analyzing the data produced by the gas focal plane detector of the MAGNEX spectrometer are discussed. The algorithm is characterized by an unprecedented efficiency and accuracy when dealing with data with small signal-to-noise ratio. A clear recovery of the smooth position distribution from the quantized one obtained with standard algorithms is observed. The optimized algorithm is also able to naturally mitigate the cross-talk phenomena that are quite strong in this kind of compact detectors.

关键词： plate detector optimization algorithms Center of gravity Gas detectors algorithms generalized algorithm Electrodes CHARGE DISTRIBUTION analysis of algorithm

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AN OPTIMAL SCALING FRAMEWORK FOR COLLABORATIVE FILTERING RECOMMENDATION SYSTEMS

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2012年第6期21卷 1250033-1250033页

作者： Vozalis, Manolis G. Markos, Angelos I. Margaritis, Konstantinos G. Univ Macedonia Dept Appl Informat Thessaloniki 54006 Greece Democritus Univ Thrace Dept Primary Educ Alexandroupolis Greece

Collaborative Filtering (CF) is a popular technique employed by Recommender Systems, a term used to describe intelligent methods that generate personalized recommendations. Some of the most efficient approaches to CF are based on latent factor models and nearest neighbor methods, and have received considerable attention in recent literature. Latent factor models can tackle some fundamental challenges of CF, such as data sparsity and scalability. In this work, we present an optimal scaling framework to address these problems using Categorical Principal Component Analysis (CatPCA) for the low-rank approximation of the user-item ratings matrix, followed by a neighborhood formation step. CatPCA is a versatile technique that utilizes an optimal scaling process where original data are transformed so that their overall variance is maximized. We considered both smooth and non-smooth transformations for the observed variables (items), such as numeric, (spline) ordinal, (spline) nominal and multiple nominal. The method was extended to handle missing data and incorporate differential weighting for items. Experiments were executed on three data sets of different sparsity and size, MovieLens 100k, 1M and Jester, aiming to evaluate the aforementioned options in terms of accuracy. A combined approach with a multiple nominal transformation and a "passive" missing data strategy clearly outperformed the other tested options for all three data sets. The results are comparable with those reported for single methods in the CF literature.

关键词： Collaborative filtering recommender systems low-rank approximation optimization algorithms categorical principal component analysis optimal scaling

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A Min-Max Control Synthesis for Uncertain Nonlinear Systems Based on Fuzzy T-S Model

A Min-Max Control Synthesis for Uncertain Nonlinear Systems ...

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IEEE International Conference Intelligent Systems

作者： Tatjana Kolemishevska-Gugulovska Mile Stankovski Imre J. Rudas Nan Jiang Juanwei Jing SS Cyril and Methodius University in Skopje Faculty of Electrical Eng. and Information Technologies Rugjer Boshkovik bb 1000 Skopje R. Macedonia School of Information Science & Robotics Obuda Univeristy H-1034 Budapest Hungary NEU College of Information Science & Engineering Liaoning Shenyang P.R. China

ISBN: (纸本)9781467322768

The min-max robust control synthesis for uncertain nonlinear systems is solved using Takagi-Sugeno fuzzy model and fuzzy state observer. Existence conditions are derived for the output feedback min-max control in the sense of Lyapunov asymptotic stability and formulated in terms of linear matrix inequalities. The convex optimization algorithm is used to obtain the minimum upper bound on performance and the optimum parameters of mini-max controller. The close-loop system is asymptotically stable under the worst case disturbances and uncertainty. Benchmark of inverted pendulum plant is used to demonstrate the robust performance within a much larger equilibrium region of attraction achieved by the proposed design.

关键词： Fuzzy T-S models fuzzy observer nonlinear systems optimum intelligent control robust control uncertainty Uncertain nonlinear system min-max control optimal parameter Robust control T-S fuzzy model Fuzzy optimization algorithms region of attraction

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Customer Sampling in a Smart Grid Pilot

Customer Sampling in a Smart Grid Pilot

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IEEE Power and Energy Society General Meeting

作者： Wouter Labeeuw Geert Deconinck Linear is an acronym for Local Intelligent Networks and Energy Active Regions more information available on A synthetic load profile defines the consumption of one type of consumer for each fifteen minutes during the year. The profiles are used by retailers to estimate the consumption during a certain period.

ISBN: (纸本)9781467327275

Smart grid pilot projects require a representative subset of the total population to draw relevant conclusions from test results. However, customers willing to participate in such projects are not always representative to the whole population. Standard random sampling gives some problems because not all results can be scaled. Defining sub-populations or strata to random samples from is theoretically sound, but the definition of sub-populations is quite expensive. The paper presents a customer sampling technique based on quota. The domains for the quota are defined by machine learning algorithms and the quota themselves are based on realistic data. Sampling is done by an optimization algorithm, which eliminates the common'human error'-factor in quota sampling. The approach is a cost efficient and convenient way of sampling that is able to balance the representativeness of the electricity consumption patterns for the population against sampling accuracy. The method has been applied and validated on a large customer data set.

关键词： Smart Grid Sampling Statistical distributions Data analysis Smart grids Quotas optimization algorithms Machine learning algorithms random sample STRATA Data sampling Pilots Sampling

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Parameter extraction from experimental PEFC data using an evolutionary optimization algorithm

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EUROPEAN PHYSICAL JOURNAL-APPLIED PHYSICS 2011年第2期54卷 23409-1-23409-10页

作者： Zaglio, M. Schuler, G. Wokaun, A. Mantzaras, J. Buechi, F. N. Paul Scherrer Inst Electrochem Lab CH-5232 Villigen Switzerland Paul Scherrer Inst Combust Res Lab CH-5232 Villigen Switzerland

The accurate characterization of the parameters related to the charge and water transport in the ionomer membrane of polymer electrolyte fuel cells (PEFC) is highly important for the understanding and interpretation of the overall cell behavior. Despite the big efforts to experimentally determine these parameters, a large scatter of data is reported in the literature, due to the inherent experimental difficulties. Likewise, the porosity and tortuosity of the gas diffusion layers affect the membrane water content and the local cell performance, but the published data are usually measured ex-situ, not accounting for the effect of clamping pressure. Using a quasi two-dimensional model and experimental current density data from a linear cell of technical size, a multiparameter optimization procedure based on an evolutionary algorithm has been applied to determine eight material properties highly influencing the cell performance. The optimization procedure converges towards a well defined solution and the resulting parameter values are compared to those available in the literature. The quality of the set of parameters extracted by the optimization procedure is assessed by a sensitivity analysis.

关键词： optimization Procedure moisture content Cells Diffusion layers Sensitivity analysis tortuosity optimization algorithms Parameter extraction evolutionary algorithm POLYMER ELECTROLYTE FUEL CELLS water absorption grip strength

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An Open Energy Consumption-relevant Factory Automation Dataset in the Cloud

An Open Energy Consumption-relevant Factory Automation Datas...

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IEEE 10th International Conference on Industrial Informatics(第十届IEEE工业信息学国际学术会议 INDIN2012)

作者： Bin Zhang Corina Postelnicu Jani Jokinen Jose L. Martinez Lastra Tampere University of Technology

ISBN: (纸本)9781467303125

In order to fully exploit the potential of optimization algorithms for energy savings in factory automation settings, it is needed to bring together engineers that have knowledge of signal processing algorithms with shop floor engineers that are experienced with real manufacturing processes and factory automation settings. This paper presents an open database storing energy-relevant data from a multi-robot flexible factory automation system. The database is currently populated with data regarding equipment CAMX state events, process/cell/device energy consumption and quality. The database is accessible via a URL and will be further populated with data related to production performance, machine failures and temporary stops etc. The intention is to provide algorithm developers with the needed datasets for design of optimization algorithms for energy efficient manufacturing settings.

关键词： factory automation optimization algorithms energy consumption Signal processing algorithms Manufacturing process Uniform Resource Locator energy saving Dataset automation system Database

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Partial Joint Processing with Efficient Backhauling in Coordinated MultiPoint Networks

Partial Joint Processing with Efficient Backhauling in Coord...

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IEEE Vehicular Technology Conference

作者： Tilak Rajesh Lakshmana Carmen Botella Tommy Svensson Signals and Systems Chalmers University of Technology Gothenburg Sweden Institute of Robotics and Information & Communication Technologies (IRTIC) Universitat de València València Spain

ISBN: (纸本)9781467309899

Joint processing between base stations is a promising technique to improve the quality of service to users at the cell edge, but this technique poses tremendous requirements on the backhaul signaling capabilities. Partial joint processing is a technique aimed to reduce feedback load, in one approach the users feed back the channel state information of the best links based on a channel gain threshold mechanism. However, it has been shown in the literature that the reduction in the feedback load is not reflected in an equivalent backhaul reduction, unless additional scheduling or precoding techniques are applied. The reason is that reduced feedback from users yields sparse channel state information at the Central Coordination Node. Under these conditions, existing linear precoding techniques fail to remove the interference and reduce backhaul, simultaneously, unless constraints are imposed on scheduling. In this paper, a partial joint processing scheme with efficient backhauling is proposed, based on a stochastic optimization algorithm called particle swarm optimization. The use of particle swarm optimization in the design of the precoder promises efficient backhauling with improved sum rate.

关键词： Joint Processing Zero Forcing Backhaul load reduction Particle Swarm optimization Stochastic optimization Particle swarm optimization return stroke Channel state information Consumers Feedback Joints Joints optimization algorithms prediction coding BASE STATION

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Distributed Alternating Direction Method of Multipliers

Distributed Alternating Direction Method of Multipliers

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IEEE Annual Conference on Decision and Control

作者： Ermin Wei Asuman Ozdaglar Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology

ISBN: (纸本)9781467320658

We consider a network of agents that are cooperatively solving a global unconstrained optimization problem, where the objective function is the sum of privately known local objective functions of the agents. Recent literature on distributed optimization methods for solving this problem focused on subgradient based methods, which typically converge at the rate O(1/{the square root of}k) where k is the number of iterations. In this paper, we introduce a new distributed optimization algorithm based on Alternating Direction Method of Multipliers (ADMM), which is a classical method for sequentially decomposing optimization problems with coupled constraints. We show that this algorithm converges at the rate O (1/k).

关键词： Multiplying circuits Objective function Distribute optimization methods optimization algorithms iterative methods Agents optimization method NETWORKING AGENT UNCONSTRAINED OPTIMISATION

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A Novel State Estimation Formulation for Distribution Grids with Renewable Energy Sources

A Novel State Estimation Formulation for Distribution Grids ...

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IEEE Power and Energy Society General Meeting

作者： F. Shabaninia M. Vaziri S. Vadhva J. Vaziri Shiraz University California State University Sacramento CA 95819 USA UC Berkeley Berkeley CA 94720

ISBN: (纸本)9781467327275

Interconnection of Distributed Generation (DG) using renewable resources to the distribution networks in on the rise. Distribution grids having DG interconnections at higher penetration levels are challenged for more accurate monitoring and controls. In this paper a novel approach and a theoretical formulation of state estimation is proposed for distribution grids with Renewable Energy Sources (RES). The recommended formulation pertains to an optimization algorithm with variable weighting factor for a more precise estimation of the system states. The formulation can serve as as a general framework for a self-adapting dynamic estimator for improved states forecasting and thus preventive control of the distribution grid. Precisely estimated system states can also serve beneficial in;optimal scheduling, security assessment, and real time transactions.

关键词： Renewable Energy Sources RES Distributed Generation DG State Estimation Distribution Grid Power grids State estimation Renewable energy renewable energy sources optimization algorithms renewable resource

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An Efficient Differencing Algorithm Based on Suffix Array for Reprogramming Wireless Sensor Networks

An Efficient Differencing Algorithm Based on Suffix Array fo...

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IEEE International Conference on Communications

作者： Biyuan Mo Wei Dong Chun Chen Jiajun Bu Qiang Wang Zhejiang Provincial Key Laboratory of Service Robot CS College Zhejiang University

ISBN: (纸本)9781457720529

Wireless reprogramming is a crucial technique for managing large-scale wireless sensor networks (WSNs). It is, however, energy intensive to disseminate the code to enable reprogramming. Incremental reprogramming is a promising approach to reduce the dissemination cost. In incremental reprogramming, only the delta between the new code and the old code needs to be disseminated, resulting much less energy consumption. The differencing algorithm plays a key role in incremental reprogramming. It takes inputs of two successive versions of codes and generates a small delta script for dissemination. Existing incremental algorithms have several limitations. First, they do not ensure the smallest delta size for dissemination. Second, some of them may incur a large overhead in terms of execution time and memory consumption. To address these issues, we propose DASA, an efficient differencing algorithm based on suffix array. DASA performs byte-level comparison and ensure the optimal result in terms of the delta size. Moreover, DASA has a low execution overhead. The time complexity and space complexity of DASA are O(n log n) and O(n), respectively. To the best of our knowledge, DASA is the optimal algorithm with the lowest time and space complexity for reprogramming WSNs.

关键词： diazobenzenesulfonic acid reprogramming space complexities Codes Dissemination benzoylprop-ethyl Suffix optimization algorithms Wireless sensor networks

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