466 research outputs found

    Evaluation of Constant Potential Method in Simulating Electric Double-Layer Capacitors

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    A major challenge in the molecular simulation of electric double layer capacitors (EDLCs) is the choice of an appropriate model for the electrode. Typically, in such simulations the electrode surface is modeled using a uniform fixed charge on each of the electrode atoms, which ignores the electrode response to local charge fluctuations induced by charge fluctuations in the electrolyte. In this work, we evaluate and compare this Fixed Charge Method (FCM) with the more realistic Constant Potential Method (CPM), [Reed, et al., J. Chem. Phys., 126, 084704 (2007)], in which the electrode charges fluctuate in order to maintain constant electric potential in each electrode. For this comparison, we utilize a simplified LiClO4_4-acetonitrile/graphite EDLC. At low potential difference (ΔΨ2V\Delta\Psi\le 2V), the two methods yield essentially identical results for ion and solvent density profiles; however, significant differences appear at higher ΔΨ\Delta\Psi. At ΔΨ4V\Delta\Psi\ge 4V, the CPM ion density profiles show significant enhancement (over FCM) of "partially electrode solvated" Li+^+ ions very close to the electrode surface. The ability of the CPM electrode to respond to local charge fluctuations in the electrolyte is seen to significantly lower the energy (and barrier) for the approach of Li+^+ ions to the electrode surface.Comment: Corrected typo

    Eolas: video retrieval application for helping tourists

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    In this paper, a video retrieval application for the Android mobile platform is described. The application utilises computer vision technologies that, given a photo of a landmark of interest, will automatically locate online videos about that landmark. Content-based video retrieval technologies are adopted to find the most relevant videos based on visual similarity of video content. The system has been evaluated us- ing a custom test collection with human annotated ground truth. We show that our system is effective, both in terms of speed and accuracy. This application is proposed for demonstration at MMM2014 and we are sure that this application would benefit tourists either planning travel or while travelling in real-time

    Analysis of Contact Characteristic of Overhead Line and Suspension Clamp

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    In this paper, a LGJ150/25 type ACSR transmission line and a CGU-3 type suspension clamp are taken as research objects. A contact model of the conductor and the clamp was established by using finite element method. The effects of sag angle of the conductor, holding force and tension force in section are analyzed. The results showed that the contact area in the middle of the clamp is of belt-like type. The extreme values of tress were observed on the edge of the contact area and near the edge of keeper. In clamp section, suspension angle had the greatest influence on contact stress, and then the clamp force. The tension force in section played a most important role in these affecting factors. In the exit section of clamp, the biggest impact factor was tension force in this section, then the suspension angle, the third was clamp force. The results provide theoretical basis on reducing corona loss, optimization the clamp. Doubtlessly, the conclusion has important theoretical significance and application value. DOI: http://dx.doi.org/10.11591/telkomnika.v11i3.222

    A New Algorithm for Bearings-Only Parametric Trajectory Tracking

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    AbstractFor the single sensor target tracking with bearings-only measurements, a novel trajectory invariable-information target tracking algorithm was proposed, and bearings-only target can be tracked by the parameter trajectory. For the measure frequency of sensor is high, the mathematic model of bearings-only tracking is analyzed by dividing the trajectory into many linearization parts. The tracking parameter of bearings-only target trajectory is deduced, so the bearings-only target can be tracked by the parameter trajectory. The simulation results show that the new algorithm has a favorable tracking precision

    Efficient Last-iterate Convergence Algorithms in Solving Games

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    No-regret algorithms are popular for learning Nash equilibrium (NE) in two-player zero-sum normal-form games (NFGs) and extensive-form games (EFGs). Many recent works consider the last-iterate convergence no-regret algorithms. Among them, the two most famous algorithms are Optimistic Gradient Descent Ascent (OGDA) and Optimistic Multiplicative Weight Update (OMWU). However, OGDA has high per-iteration complexity. OMWU exhibits a lower per-iteration complexity but poorer empirical performance, and its convergence holds only when NE is unique. Recent works propose a Reward Transformation (RT) framework for MWU, which removes the uniqueness condition and achieves competitive performance with OMWU. Unfortunately, RT-based algorithms perform worse than OGDA under the same number of iterations, and their convergence guarantee is based on the continuous-time feedback assumption, which does not hold in most scenarios. To address these issues, we provide a closer analysis of the RT framework, which holds for both continuous and discrete-time feedback. We demonstrate that the essence of the RT framework is to transform the problem of learning NE in the original game into a series of strongly convex-concave optimization problems (SCCPs). We show that the bottleneck of RT-based algorithms is the speed of solving SCCPs. To improve the their empirical performance, we design a novel transformation method to enable the SCCPs can be solved by Regret Matching+ (RM+), a no-regret algorithm with better empirical performance, resulting in Reward Transformation RM+ (RTRM+). RTRM+ enjoys last-iterate convergence under the discrete-time feedback setting. Using the counterfactual regret decomposition framework, we propose Reward Transformation CFR+ (RTCFR+) to extend RTRM+ to EFGs. Experimental results show that our algorithms significantly outperform existing last-iterate convergence algorithms and RM+ (CFR+)

    Adaptive Robust Actuator Fault Accommodation for a Class of Uncertain Nonlinear Systems with Unknown Control Gains

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    An adaptive robust fault tolerant control approach is proposed for a class of uncertain nonlinear systems with unknown signs of high-frequency gain and unmeasured states. In the recursive design, neural networks are employed to approximate the unknown nonlinear functions, K-filters are designed to estimate the unmeasured states, and a dynamical signal and Nussbaum gain functions are introduced to handle the unknown sign of the virtual control direction. By incorporating the switching function σ algorithm, the adaptive backstepping scheme developed in this paper does not require the real value of the actuator failure. It is mathematically proved that the proposed adaptive robust fault tolerant control approach can guarantee that all the signals of the closed-loop system are bounded, and the output converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated by the simulation examples
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