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J. G. Muga - One of the best experts on this subject based on the ideXlab platform.
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shortcuts to adiabaticity fast Forward Approach
Physical Review A, 2012Co-Authors: E. Torrontegui, J. G. Muga, Andreas Ruschhaupt, S MartinezgaraotAbstract:The ``fast-Forward''Approach by Masuda and Nakamura generates driving potentials to accelerate slow quantum adiabatic dynamics. First we present a streamlined version of the formalism that produces the main results in a few steps. Then we show the connection between this Approach and inverse engineering based on Lewis-Riesenfeld invariants. We identify in this manner applications in which the engineered potential does not depend on the initial state. Finally we discuss more general applications exemplified by wave splitting processes.
Hossein Hashemi - One of the best experts on this subject based on the ideXlab platform.
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Semiconductor laser phase-noise cancellation using an electrical feed-Forward scheme
Optics letters, 2009Co-Authors: M. Bagheri, Firooz Aflatouni, Alireza Imani, Ankush Goel, Hossein HashemiAbstract:We demonstrate the reduction of semiconductor laser phase noise by using an electrical feed-Forward scheme. We have carried out proof-of-concept experiments on a commercially available distributed-feedback laser emitting at the 1550 nm communication band. The preliminary results show more than 20 times reduction in the phase-noise power spectrum. The feed-Forward scheme does not have the limited bandwidth, stability, and speed issues that are common in feedback systems. Moreover, in the absence of electronic noise, feed-Forward can completely cancel the close-in phase noise. In this scheme, the ultimate achievable phase noise will be limited by the electronics noise. Using the proposed feed-Forward Approach, the linewidth of semiconductor lasers can be reduced by 3-4 orders of magnitude in a monolithic Approach using today's low-noise scaled transistors with terahertz gain-bandwidth product.
H J Van Zuylen - One of the best experts on this subject based on the ideXlab platform.
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freeway travel time prediction with state space neural networks modeling state space dynamics with recurrent neural networks
Transportation Research Record, 2002Co-Authors: J W C Van Lint, S P Hoogendoorn, H J Van ZuylenAbstract:An Approach to freeway travel time prediction based on recurrent neural networks is presented. Travel time prediction requires a modeling Approach that is capable of dealing with complex nonlinear spatio-temporal relationships among flows, speeds, and densities. Based on the literature, feedForward neural networks are a class of mathematical models well suited for solving this problem. A drawback of the feed-Forward Approach is that the size and composition of the input time series are inherently design choices and thus fixed for all input. This may lead to unnecessarily large models. Moreover, for different traffic conditions, different sizes and compositions of input time series may be required, a requirement not satisfied by any feedForward data-driven method. The recurrent neural network topology presented is capable of dealing with the spatiotemporal relationships implicitly. The topology of this neural net is derived from a state-space formulation of the travel time prediction problem, which is in l...
E. Torrontegui - One of the best experts on this subject based on the ideXlab platform.
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shortcuts to adiabaticity fast Forward Approach
Physical Review A, 2012Co-Authors: E. Torrontegui, J. G. Muga, Andreas Ruschhaupt, S MartinezgaraotAbstract:The ``fast-Forward''Approach by Masuda and Nakamura generates driving potentials to accelerate slow quantum adiabatic dynamics. First we present a streamlined version of the formalism that produces the main results in a few steps. Then we show the connection between this Approach and inverse engineering based on Lewis-Riesenfeld invariants. We identify in this manner applications in which the engineered potential does not depend on the initial state. Finally we discuss more general applications exemplified by wave splitting processes.
Amornchai Arpornwichanop - One of the best experts on this subject based on the ideXlab platform.
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neural network hybrid model of a direct internal reforming solid oxide fuel cell
International Journal of Hydrogen Energy, 2012Co-Authors: Kattiyapon Chaichana, Yaneeporn Patcharavorachot, Bhawasut Chutichai, Dang Saebea, Suttichai Assabumrungrat, Amornchai ArpornwichanopAbstract:Abstract A mathematical model is an important tool for analysis and design of fuel cell stacks and systems. In general, the complete description of fuel cells requires an electrochemical model to predict their electrical characteristics, i.e., cell voltage and current density. However, obtaining the electrochemical model is quite a difficult and complicated task as it involves various operational, structural and electrochemical reaction parameters. In this study, a neural network model was first proposed to predict the electrochemical characteristics of solid oxide fuel cell (SOFC). Various NN structures were trained based on the back-propagation feed-Forward Approach. The results showed that the NN with optimal structure reliably provides a good estimation of fuel cell electrical characteristics. Then, a neural network hybrid model of a direct internal reforming SOFC, combining mass conservation equations with the NN model, was developed to determine the distributions of gaseous components in fuel and air channels of SOFC as well as the performance of the SOFC in terms of power density and fuel cell efficiency. The effects of various key parameters, e.g., temperature, pressure, steam to carbon ratio, degree of pre-reforming, and inlet fuel flow rate on the SOFC performance under steady-state and isothermal conditions were also investigated. A combination of the first principle model and NN presents a significant advantage of predicting the SOFC performance with accuracy and less computational time.