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A data-driven approach to predict daily risk of Clostridium difficile infection at two large academic health centers

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Open Forum Infectious Diseases 2017年第SUPPL_1期4卷 S403-S404页

作者： Makar, Maggie Oh, Jeeheh Fusco, Christopher Marchesani, Joseph McCaffrey, Robert Rao, Krishna Ryan, Erin E Washer, Laraine West, Lauren R Young, Vincent B Guttag, John Hooper, David C Shenoy, Erica S Wiens, Jenna Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge Massachusetts Computer Science and Engineering University of Michigan Ann Arbor Michigan Information Systems Partners HealthCare Boston Massachusetts Department of Internal Medicine Division of Infectious Diseases University of Michigan Medical School Ann Arbor Michigan Division of Infectious Diseases Massachusetts General Hospital Boston Massachusetts Infection Control Unit Massachusetts General Hospital Boston Massachusetts Harvard Medical School Boston Massachusetts Department of Medicine Division of Infectious Diseases Massachusetts General Hospital Boston Massachusetts Medical Practice Evaluation Center Department of Medicine Massachusetts General Hospital Boston Massachusetts

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Real-time Pricing and Distributed Decision Makings Leading to Optimal Power Flow of Power Grids

Real-time Pricing and Distributed Decision Makings Leading t...

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American control Conference

作者： Kenji Hirata Joao P. Hespanha Kenko Uchida Department of Mechanical Engineering Nagaoka University of Technology Nagaoka 940 2188 Japan Center for Control Dynamical Systems and Computation and the Department of Electrical and Computer Engineering University of California Santa Barbara CA 93106 USA Department of Electrical Engineering and Bioscience Waseda University Tokyo 169 8555 Japan

ISBN: (纸本)9781479917730

Motivated by distributed control problems of power supply/demand networks, this paper investigates application case studies of the real-time pricing and distributed decision makings methodology. We consider the optimal power flow problem with the DC power flow model, and the New England 39-bus test system is used. Stability of the resulting price based control system is analyzed with consideration for specific structures of the power flow problem. The resulting simulation studies illustrate the efficiency of the proposed method and validate the stability analysis procedure.

关键词： optimal load flow Power grids Power flow distributed decision making Pricing decentralised control Test systems DC load flow

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μ-based Approaches to Determining Guaranteed Consistent and Inconsistent Parameter Sets

μ-based Approaches to Determining Guaranteed Consistent and...

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IEEE Annual Conference on Decision and control

作者： Masako Kishida Rolf Findeisen Electrical and Computer Engineering department at University of Canterbury New Zealand Laboratory for Systems Theory and Control at the Otto-von-Guericke Universitat Magdeburg Germany

ISBN: (纸本)9781479978878

This paper considers the problem of characterizing the sets of consistent and inconsistent parameters for given constraints for linear fractional models. Based on previous results on the single box volume maximization problem using structured singular value, algorithms that express the (in)consistent parameter set as a union of multiple boxes are developed. Since no relaxation is involved in the proposed algorithms, the obtained sets are guaranteed to be (in)consistent with the given constraints even when the real set of (in)consistent parameters is nonconvex. A numerical example is included to illustrate the difference of the sets obtained by the proposed algorithms.

关键词： Inconsistent Difference sets SETTING numerical example Consistent

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Localization of multiple nodes based on correlated measurements and shrinkage estimation 9

Localization of multiple nodes based on correlated measureme...

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9th Workshop on Sensor Data Fusion: Trends, Solutions, Applications, SDF 2014

作者： Salman, Naveed Mihaylova, Lyudmila Kemp, A.H. Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom School of Electronic and Electrical Engineering University of Leeds Leeds United Kingdom

ISBN: (纸本)9781479973873

Accurate covariance matrix estimation has applications in a wide range of disciplines. For many applications the estimated covariance matrix needs to be positive definite and hence invertible. When the number of data points is insufficient, the estimated sample covariance matrix has two fold disadvantages. Firstly, although it is unbiased, it consists of a large estimation error. Secondly, it is not positive definite. A shrinkage technique has been proposed in the fields of finance and life sciences to estimate the covariance matrix that is invertible and contains relatively a small estimation error variance. In this paper, we introduce the shrinkage covariance matrix concept in the area of multiple target localization in wireless networks with correlated measurements. For localization, we use the low cost received signal strength (RSS) measurements. Unlike most studies, where the links between sensor nodes (SNs) and targets nodes (TNs) are independent, we use a realistic model where these links are correlated. Optimization in location accuracy is achieved by weighting each link via the shrinkage covariance matrix. Simulation results show that using the estimated shrinkage covariance improves the location accuracy of the localization algorithm. © 2014 IEEE.

关键词： Covariance matrix

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Inversion-free feedforward dynamic compensation of hysteresis nonlinearities in piezoelectric micro/nano-positioning actuators

Inversion-free feedforward dynamic compensation of hysteresi...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Omar Aljanaideh Mohammad Al Janaideh Micky Rakotondrabe Department of Automatic Control and Micro-Mechatronic Systems. (AS2M) CNRS - University of Franche-Comt at Besanon (UFC) Besanon France Department of Mechatronics Engineering University of Jordan Amman Jordan

ISBN: (纸本)9781479969241

A new methodology that employs the rate-dependent Prandtl-Ishlinskii model (RDPI) as a model and a compensator is suggested in this study for modeling and compensation of rate-dependent hysteresis nonlinearities of a piezoelectric actuator. The technique employs a restructuration of the model that ignores the need to derive an inverse model, which avoids the additional calculations required to formulate a compensator. The simulation results are presented to demonstrate the effectiveness of the strategy on modeling and compensation of hysteresis nonlinearities at various frequencies. The simulation results were followed by experimental study on a piezoelectric actuator that exhibits rate-dependent hysteresis nonlinearities. The results demonstrate that the proposed methodology can be employed effectively for compensation of rate-dependent hysteresis nonlinearities without developing an inverse model.

关键词： Hysteresis Mathematical model Piezoelectric actuators Feedforward neural networks Time-frequency analysis Predictive models

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Constructive state space model induced kernels for regularized system identification 19

Constructive state space model induced kernels for regulariz...

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19th IFAC World Congress on International Federation of automatic control, IFAC 2014

作者： Chen, Tianshi Ljung, Lennart Division of Automatic Control Department of Electrical Engineering Linköping University Sweden

ISBN: (纸本)9783902823625

There are two key issues for the kernel-based regularization method: the kernel structure design and the hyper-parameter estimation. In this contribution, we introduce a new family of kernel structures based on state space models. It has more flexible and more general structure, and includes some of stable spline kernels and diagonal correlated kernels as special cases. We also tested a different method for the hyper-parameter estimation by maximizing a profile marginal likelihood and examined three methods dealing with the initialization. Monte Carlo simulations show that the tested kernels are on the average a bit better than the tuned correlated kernel and the profile marginal likelihood maximization and the pre-windowing method work well for hyper-parameter estimation and initialization. © IFAC.

关键词： Parameter estimation

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Bayesian Identification of LPV Box-Jenkins Models

Bayesian Identification of LPV Box-Jenkins Models

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IEEE Annual Conference on Decision and control

作者： Mohamed Darwish Pepijn Cox Gianluigi Pillonetto Roland Toth Control Systems Group Department of Electrical Engineering Eindhoven University of Technology P.O. Box 513 5600 MB Eindhoven The Netherlands Information Engineering Department University of Padova Padova 35131 Italy

ISBN: (纸本)9781479978878

In this paper, we introduce a nonparametric approach in a Bayesian setting to efficiently estimate, both in the stochastic and computational sense, linear parameter-varying (LPV) input-output models under general noise conditions of Box-Jenkins (BJ) type. The approach is based on the estimation of the one-step-ahead predictor model of general LPV-BJ structures, where the sub-predictors associated with the input and output signals are captured as asymptotically stable infinite impulse response models (IIRs). These IIR sub-predictors are identified in a completely nonparametric sense, where not only the coefficients are estimated as functions, but also the whole time evolution of the impulse response is estimated as a function. In this Bayesian setting, the one-step-ahead predictor is modelled as a zero-mean Gaussian random field, where the covariance function is a multidimensional Gaussian kernel that encodes both the possible structural dependencies and the stability of the predictor. The unknown hyperparameters that parameterize the kernel are tuned using the empirical Bayes approach, i.e., optimization of the marginal likelihood with respect to available data. It is also shown that, in case the predictor has a finite order, i.e., the true system has an ARX noise structure, our approach is able to recover the underlying structural dependencies. The performance of the identification method is demonstrated on LPV-ARX and LPV-BJ simulation examples by means of a Monte Carlo study.

关键词： Bayes methods Kernel Predictive models Computational modeling Optimization Estimation Asymptotic stability

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Self-Organising Fuzzy Logic control with a new on-line Particle Swarm Optimisation-based supervisory layer 6

Self-Organising Fuzzy Logic Control with a new on-line Parti...

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6th International Conference on Fuzzy Computation Theory and Applications, FCTA 2014, Part of the 6th International Joint Conference on Computational Intelligence, IJCCI 2014

作者： Ehtiawesh, M. Mahfouf, M. Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9789897580536

The Self-Organising Fuzzy Logic control (SOFLC) which is an extended version of the Fuzzy logic controller was designed to make Fuzzy controllers work with less dependency on previous knowledge. Since the introduction of the SOFLC, only a few attempts have been made to create a performance index table that is responsible for the corrections of the low-level control 'adaptable' according to the dynamics of the process under control. In this paper a new dynamic supervisory layer is proposed which enables the controller to adapt its structure on-line to any given certain performance criteria. In this mechanism, the controller starts from an empty rule-base and uses an on-line Particle Swarm Optimisation (PSO) algorithm to adapt the cells of the performance index (PI) table while issuing control actions to the low-level fuzzy rule-base. The Simulation results achieved when the proposed scheme was tested on a non-linear muscle relation process showed that it is superior to the standard SOFLC scheme in terms of accurate tracking and efficient fuzzy rule-base elicitation (a conservative number of fuzzy rules).

关键词： controllers

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Optimisation of operational reliability of large-scale industrial wind turbines 1st

Optimisation of operational reliability of large-scale indus...

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1st International Conference on Renewable Energies Offshore, RENEW 2014

作者： Camacho Questa, J.E. Requena Montejano, V. Polo, F. Moreno, L. Vanhnonacker, T. Stalaart, B. Karyotakis, A. Panagoiliopoulos, O. Karakassidis, V. Lang, Z.-Q. Roldán De La Cuadra, C. Murillo Calleja, M. Oses, I.E. Auer, G. Zalacain, I. Errea Mugica, J. Garcia Marquez, F.P. Pedegral, D. Lee, D. Hillmansen, S. Tricoli, P. Hajiabady, S. Fernando, G. Papaelias, Mayorkinos Ingeteam Service S.A Albacete Spain D2S International N.V Leuven Belgium TERNA Energy S.A Athens Greece Department of Automatic Control and Systems Engineering The University of Sheffield United Kingdom Indra Sistemas S.A Madrid Spain ACCIONA ENERGIA S.A Pamplona Spain ETSII Universidad De Castilla-La Mancha Ciudad Real Spain School of Electronics Engineering Kyungpook National University Daegu Korea Republic of School of Electronic Electrical and Computer Engineering The University of Birmingham United Kingdom School of Metallurgy and Materials The University of Birmingham United Kingdom

ISBN: (纸本)9781138028715

Condition monitoring (CM) and fault detection of the drive-train and power electronics are two very important tasks which are necessary in order to maintain the reliability of industrial wind turbines. Unexpected failures, expensive repair and downtime costs can be avoided or limited through effective remote CM technology. The consortium of the FP7 OPTIMUS project is involved in the development and demonstration of an integrated CM system based on a modular design, which enables reliable diagnosis and prognosis of the power electronic faults and drive-trains. CM systems have been installed onACCIONAAW-1500 wind turbines to continuously evaluate the condition of power electronics and drive-trains. Some of the key results and analysis methodologies employed are presented in this paper for the power converters. © 2015 Taylor & Francis Group, London.

关键词： Wind turbines

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Fuzzy inference system as decision-maker to automate cryo-EM data acquisition on a transmission electron microscope

Fuzzy inference system as decision-maker to automate cryo-EM...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement systems and Applications

作者： David Gil-Carton Mikel Valle Aitor J. Garrido Izaskun Garrido Iñigo Miguel Structural Biology Unit Cooperative Center for Research in Biosciences CIC bioGUNE Derio Spain Department of Automatic Control and Systems Engineering University of the Basque Country-UPV/EHU Bilbao Spain

Cryo-electron microscopy (Cryo-EM) data acquisition on modern transmission electron microscopes (TEM) is the first step during the single-particle analysis workflow. Importantly, the demand for large number of two dimensional images requires reliable and efficient automation of image data collection. We present a novel control scheme for automated cryo-EM data collection that monitors the quality of the data in real time and greatly improves the final efficiency of the acquisition. We propose the use of a fuzzy inference system (FIS) model to take decisions during the automated and sequential selection of hole areas in prefabricated EM grids. A new method based on adaptive neuro-fuzzy inference system (ANFIS) models was successfully trained to classify previously detected single particles from acquired images. In the methodology FIS and ANFIS are used to model expert behavior. The method is validated in real-time cryo-EM data acquisition for single-particle approach of bacterial ribosomes.

关键词： Fuzzy logic Data acquisition Image segmentation Ice Transmission electron microscopy Data collection

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