Polarized surface-enhanced Raman scattering was applied to low- and high- friction surfaces of tetrahedral amorphous carbon nitride (ta-CNx) to reveal the presence of the graphite structure and investigate the angular...
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For prime knots K1 and K2, we write K1 ≥ K2 if there is an epimorphism from the knot group of K1 to that of K2 which preserves the meridian. We construct a family of pairs of knots with K1 ≥ K2 such that an epimorph...
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Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have sinc...
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In this paper, the inverse Kalman filtering problem is addressed using a duality-based framework, where certain statistical properties of uncertainties in a dynamical model are recovered from observations of its poste...
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In this paper, the inverse Kalman filtering problem is addressed using a duality-based framework, where certain statistical properties of uncertainties in a dynamical model are recovered from observations of its posterior estimates. The duality relation in inverse filtering and inverse optimal control is established. It is shown that the inverse Kalman filtering problem can be solved using results from a well-posed inverse linear quadratic regulator. Identifiability of the considered inverse filtering model is proved and a unique covariance matrix is recovered by a least squares estimator, which is also shown to be statistically consistent. Effectiveness of the proposed methods is illustrated by numerical simulations.
Given 2D point correspondences between an image pair, inferring the camera motion is a fundamental issue in the computer vision community. The existing works generally set out from the epipolar constraint and estimate...
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In this paper, we propose a prediction system of the effect of electrical defibrillation. In order to develop the proposed system, we firstly analyze from pre-shock (immediately before defibrillation) ECGs (ElectroCar...
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ISBN:
(数字)9798331540319
ISBN:
(纸本)9798331540326
In this paper, we propose a prediction system of the effect of electrical defibrillation. In order to develop the proposed system, we firstly analyze from pre-shock (immediately before defibrillation) ECGs (ElectroCardioGrams) by using Gabor Wavelet Transform, Poincaré plot analysis, Spectral entropy, and feature parameters are extracted by those extracted analysis results, we focus on time-transition feature parameter. Next, effective feature parameters are selected based on $\chi^{2}$ -test. Finally, “Effective” and “Ineffective” for electrical defibrillation are classified by using SVM (Support Vector Machine) with three kernels (Linear, Gaussian, and Polynomial) and regularization. In this way, we shows the effectiveness of the proposed feature extraction methods and prediction system.
Carbon nanotube (CNT)-based thermoelectric (TE) materials have the potential to be used for the recovery of low-grade thermal energy. As TE devices are placed on heat sources, thermal stability of the materials-especi...
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ISBN:
(数字)9798350380200
ISBN:
(纸本)9798350380217
Carbon nanotube (CNT)-based thermoelectric (TE) materials have the potential to be used for the recovery of low-grade thermal energy. As TE devices are placed on heat sources, thermal stability of the materials-especially, retention ability of the doped states of CNTs- is one of the most important issues. Here, we show that the dopant selection via coordination chemistry allowed for establishing the stable p-doped CNTs lasting for more than 2 years at $100^{\circ} \mathrm{C}$ in air. Based on the less fluctuation of TE power factors of the doped CNTs, our results offer a promising avenue for creating CNT-based TE devices.
In this paper, we present a data-driven methodology to predict and control the behaviour of nonlinear and non-autonomous systems based on kernel functions. The technique computes the forecasting by means of a linear c...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
In this paper, we present a data-driven methodology to predict and control the behaviour of nonlinear and non-autonomous systems based on kernel functions. The technique computes the forecasting by means of a linear combination of past data. The weights used to compute the prediction are obtained by solving a convex optimization problem that stems from a novel kriging formulation. A control Lyapunov Function (CLF) based controller using the presented predictor is also built. Finally, numerical examples of both prediction and control are presented, showing the efficacy of the proposed approach.
Timed weighted marked graphs are a subclass of timed Petri nets that have wide applications in the control and performance analysis of flexible manufacturing *** to the existence of multiplicities(i.e.,weights)on edge...
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Timed weighted marked graphs are a subclass of timed Petri nets that have wide applications in the control and performance analysis of flexible manufacturing *** to the existence of multiplicities(i.e.,weights)on edges,the performance analysis and resource optimization of such graphs represent a challenging *** this paper,we develop an approach to transform a timed weighted marked graph whose initial marking is not given,into an equivalent parametric timed marked graph where the edges have unitary *** order to explore an optimal resource allocation policy for a system,an analytical method is developed for the resource optimization of timed weighted marked graphs by studying an equivalent ***,we apply the proposed method to a flexible manufacturing system and compare the results with a previous heuristic *** analysis shows that the developed approach is superior to the heuristic approach.
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