Mechanical bound states in the continuum (BICs) present an alternative avenue for developing high-frequency, high-Q mechanical resonators, distinct from the conventional band structure engineering method. While symmet...
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This paper introduces a plug-and-play descriptor that can be effectively adopted for image retrieval tasks without prior initialization or preparation. The description method utilizes the recently proposed Vision Tran...
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We introduce the *** package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imagi...
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Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving cont...
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Gauge field theory provides the mathematical and conceptual framework to describe and understand topological singularities such as Weyl points and magnetic monopoles. While singularities associated with vector electro...
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Gauge field theory provides the mathematical and conceptual framework to describe and understand topological singularities such as Weyl points and magnetic monopoles. While singularities associated with vector electromagnetic gauge fields have been well studied, those of higher-form tensor gauge fields, like the four-dimensional (4D) tensor monopoles predicted by string theory, have remained largely theoretical or limited to experimental demonstration in pure synthetic dimensions, thereby not allowing investigations of the associated boundary effects. Here, we present a 4D system with tensor monopoles using engineered acoustic metamaterials. Our momentum space combines three real momentum dimensions and a geometric parameter as the fourth. By varying this fourth momentum, we experimentally reveal two distinct topological surface states in 3D subsystems: Fermi-arc surface states in a gapless subsystem and Dirac-cone surface states in a gapped subsystem. Our work introduces a novel platform for exploring new topological structures associated with tensor gauge field and topological phenomena in higher dimensions.
An energy management strategy (EMS) has an essential role in ameliorating the efficiency and lifetime of the powertrain components in a hybrid fuel cell vehicle (HFCV). The EMS of intelligent HFCVs is equipped with ad...
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This paper introduces a “green” routing game between multiple logistic operators (players), each owning a mixed fleet of internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. Each player faces ...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
This paper introduces a “green” routing game between multiple logistic operators (players), each owning a mixed fleet of internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. Each player faces the cost of delayed delivery (due to charging requirements of EVs) and a pollution cost levied on the ICEVs. This cost structure models: 1) limited battery capacity of EVs and their charging requirement; 2) shared nature of charging facilities; 3) pollution cost levied by regulatory agency on the use of ICEVs. We characterize Nash equilibria of this game and derive a condition for its uniqueness. We also use the gradient projection method to compute this equilibrium in a distributed manner. Our equilibrium analysis is useful to analyze the trade-off faced by players in incurring higher delay due to congestion at charging locations when the share of EVs increases versus a higher pollution cost when the share of ICEVs increases. A numerical example suggests that to increase marginal pollution cost can reduce inefficiency of equilibria.
Integrated sensing and communication (ISAC) systems may face a heavy computation burden since the sensory data needs to be further processed. This paper studies a novel system that integrates sensing, communication, a...
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In order to strengthen their security issues,electrical companies devote particular efforts to developing and enhancing their fraud detection techniques that cope with the information and communication technologies in...
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In order to strengthen their security issues,electrical companies devote particular efforts to developing and enhancing their fraud detection techniques that cope with the information and communication technologies integration in smart grid *** been treated earlier by several researchers,various detection schemes adapted from attack models that benefit from the smart grid topologies weaknesses,aiming primarily to the identification of suspicious incoming *** meshes have been extensively used in smart grid communication architectures due to their facility,lightness of conception and low cost installation;however,the communicated packets are still exposed to be intercepted maliciously in order either to falsify pertinent information like the smart meter readings,or to inject false data instead,aiming at electricity theft during the communication *** this reason,this paper initiates a novel method based on RSA cryptographic algorithm to detect electricity fraud in smart *** new method consists of generating two different cryptograms of one electricity measurement before sending,after which the recipient is used to find the same value after decrypting the two cyphers in a normal ***,a fraudulent manipulation could occur during the transmission *** presented method allows us to kill two birds with one ***,satisfactory outcomes are shown:the algorithm accuracy reaches 100%,from one hand,and the privacy is protected thanks to the cryptology concept on the other hand.
Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize wat...
Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry.
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