849 resultados para hybrid dynamical system


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The low complexity of IIR adaptive filters (AFs) is specially appealing to realtime applications but some drawbacks have been preventing their widespread use so far. For gradient based IIR AFs, adverse operational conditions cause convergence problems in system identification scenarios: underdamped and clustered poles, undermodelling or non-white input signals lead to error surfaces where the adaptation nearly stops on large plateaus or get stuck at sub-optimal local minima that can not be identified as such a priori. Furthermore, the non-stationarity in the input regressor brought by the filter recursivity and the approximations made by the update rules of the stochastic gradient algorithms constrain the learning step size to small values, causing slow convergence. In this work, we propose IIR performance enhancement strategies based on hybrid combinations of AFs that achieve higher convergence rates than ordinary IIR AFs while keeping the stability.

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The Internet boom in recent years has increased the interest in the field of plagiarism detection. A lot of documents are published on the Net everyday and anyone can access and plagiarize them. Of course, checking all cases of plagiarism manually is an unfeasible task. Therefore, it is necessary to create new systems that are able to automatically detect cases of plagiarism produced. In this paper, we introduce a new hybrid system for plagiarism detection which combines the advantages of the two main plagiarism detection techniques. This system consists of two analysis phases: the first phase uses an intrinsic detection technique which dismisses much of the text, and the second phase employs an external detection technique to identify the plagiarized text sections. With this combination we achieve a detection system which obtains accurate results and is also faster thanks to the prefiltering of the text.

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The purpose of this paper is to derive the dynamical equations for the period vectors of a periodic system under constant external stress. The explicit starting point is Newton’s second law applied to halves of the system. Later statistics over indistinguishable translated states and forces associated with transport of momentum are applied to the resulting dynamical equations. In the final expressions, the period vectors are driven by the imbalance between internal and external stresses. The internal stress is shown to have both full interaction and kinetic-energy terms.

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Energy Department, Washington, D.C.

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Thesis (M.S.)--University of Illinois at Urbana-Champaign, 1965.

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This thesis is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variant of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here two new extended frameworks are derived and presented that are based on basis function expansions and local polynomial approximations of a recently proposed variational Bayesian algorithm. It is shown that the new extensions converge to the original variational algorithm and can be used for state estimation (smoothing). However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new methods are numerically validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein-Uhlenbeck process, for which the exact likelihood can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz '63 (3-dimensional model). The algorithms are also applied to the 40 dimensional stochastic Lorenz '96 system. In this investigation these new approaches are compared with a variety of other well known methods such as the ensemble Kalman filter / smoother, a hybrid Monte Carlo sampler, the dual unscented Kalman filter (for jointly estimating the systems states and model parameters) and full weak-constraint 4D-Var. Empirical analysis of their asymptotic behaviour as a function of observation density or length of time window increases is provided.

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Hydrogels containing carbon nanotubes (CNTs) are expected to be promising conjugates because they might show a synergic combination of properties from both materials. Most of the hybrid materials containing CNTs only entrap them physically, and the covalent attachment has not been properly addressed yet. In this study, single-walled carbon nanotubes (SWNTs) were successfully incorporated into a poly(ethylene glycol) (PEG) hydrogel by covalent bonds to form a hybrid material. For this purpose, SWNTs were functionalized with poly(ethylene glycol) methacrylate (PEGMA) to obtain water-soluble pegylated SWNTs (SWNT–PEGMA). These functionalized SWNTs were covalently bonded through their PEG moieties to a PEG hydrogel. The hybrid network was obtained from the crosslinking reaction of poly(ethylene glycol) diacrylate prepolymer and the SWNT–PEGMA by dual photo-UV and thermal initiations. The mechanical and swelling properties of the new hybrid material were studied. In addition, the material and lixiviates were analyzed to elucidate any kind of SWNT release and to evaluate a possible in vitro cytotoxic effect. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011.

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Even simple hybrid systems like the classic bouncing ball can exhibit Zeno behaviors. The existence of this type of behavior has so far forced simulators to either ignore some events or risk looping indefinitely. This in turn forces modelers to either insert ad hoc restrictions to circumvent Zeno behavior or to abandon hybrid modeling. To address this problem, we take a fresh look at event detection and localization. A key insight that emerges from this investigation is that an enclosure for a given time interval can be valid independently of the occurrence of a given event. Such an event can then even occur an unbounded number of times, thus making it possible to handle certain types of Zeno behavior.

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To fully utilize second-life batteries on the grid system, a hybrid battery scheme needs to be considered for several reasons: the uncertainty over using a single source supply chain for second-life batteries, the differences in evolving battery chemistry and battery configuration by different suppliers to strive for greater power levels, and the uncertainty of degradation within a second-life battery. Therefore, these hybrid battery systems could have widely different module voltage, capacity, and initial state of charge and state of health. In order to suitably integrate and control these widely different batteries, a suitable multimodular converter topology and an associated control structure are required. This paper addresses these issues proposing a modular boost-multilevel buck converter based topology to integrate these hybrid second-life batteries to a grid-tie inverter. Thereafter, a suitable module-based distributed control architecture is introduced to independently utilize each converter module according to its characteristics. The proposed converter and control architecture are found to be flexible enough to integrate widely different batteries to an inverter dc link. Modeling, analysis, and experimental validation are performed on a single-phase modular hybrid battery energy storage system prototype to understand the operation of the control strategy with different hybrid battery configurations.

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We report a refractive index (RI) and liquid level sensing system based on a hybrid grating structure comprising of a 45° and an 81° tilted fiber gratings (TFGs) that have been inscribed into a single mode fiber in series. In this structure, the 45°-TFG is used as a polarizer to filter out the transverse electric (TE) component and enable the 81°-TFG operating at single polarization for RI and level sensing. The experiment results show a lower temperature cross-sensitivity, only about 7.33 pm/°C, and a higher RI sensitivity, being around 180 nm/RIU at RI=1.345 and 926 nm/RIU at RI=1.412 region, which are significantly improved in comparison with long period fiber gratings. The hybrid grating structure has also been applied as a liquid level sensor, showing 3.06 dB/mm linear peak ratio sensitivity.

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Hybrid WDM/TDM enabled microstructure based optical fiber sensor network with large capacity is proposed. Assisted by Fabry-Perot filter, the demodulation system with high speed of 500Hz and high wavelength resolution less than 4.91pm is realized. © OSA 2015.

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In this paper we proposed a demodulation scheme based on tunable FP filter for the WDM/FDM sensing system of the microstructure mentioned in the previous work. Simulation is done to prove the feasibility of demodulating the microstructure with the tunable FP filter. The experiments result showed high consistence with the simulation. And with the help of the high speed FPGA module and a high resolution AD/DA card, the system has achieved a very high resolution, up to 2.5 pm, and wavelength ranges 1520nm to 1590 nm.