151 research outputs found
Effect of cerium salt-activated ceria on the UV degradation resistance of waterborne epoxy coatings
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Accelerated /Push-Pull Methods for Distributed Optimization over Time-Varying Directed Networks
This paper investigates a novel approach for solving the distributed
optimization problem in which multiple agents collaborate to find the global
decision that minimizes the sum of their individual cost functions. First, the
/Push-Pull gradient-based algorithm is considered, which employs row- and
column-stochastic weights simultaneously to track the optimal decision and the
gradient of the global cost function, ensuring consensus on the optimal
decision. Building on this algorithm, we then develop a general algorithm that
incorporates acceleration techniques, such as heavy-ball momentum and Nesterov
momentum, as well as their combination with non-identical momentum parameters.
Previous literature has established the effectiveness of acceleration methods
for various gradient-based distributed algorithms and demonstrated linear
convergence for static directed communication networks. In contrast, we focus
on time-varying directed communication networks and establish linear
convergence of the methods to the optimal solution, when the agents' cost
functions are smooth and strongly convex. Additionally, we provide explicit
bounds for the step-size value and momentum parameters, based on the properties
of the cost functions, the mixing matrices, and the graph connectivity
structures. Our numerical results illustrate the benefits of the proposed
acceleration techniques on the /Push-Pull algorithm
Geometric Convergence of Distributed Heavy-Ball Nash Equilibrium Algorithm over Time-Varying Digraphs with Unconstrained Actions
We propose a new distributed algorithm that combines heavy-ball momentum and
a consensus-based gradient method to find a Nash equilibrium (NE) in a class of
non-cooperative convex games with unconstrained action sets. In this approach,
each agent in the game has access to its own smooth local cost function and can
exchange information with its neighbors over a communication network. The
proposed method is designed to work on a general sequence of time-varying
directed graphs and allows for non-identical step-sizes and momentum
parameters. Our work is the first to incorporate heavy-ball momentum in the
context of non-cooperative games, and we provide a rigorous proof of its
geometric convergence to the NE under the common assumptions of strong
convexity and Lipschitz continuity of the agents' cost functions. Moreover, we
establish explicit bounds for the step-size values and momentum parameters
based on the characteristics of the cost functions, mixing matrices, and graph
connectivity structures. To showcase the efficacy of our proposed method, we
perform numerical simulations on a Nash-Cournot game to demonstrate its
accelerated convergence compared to existing methods
Distributed Stochastic Optimization with Gradient Tracking over Time-Varying Directed Networks
We study a distributed method called SAB-TV, which employs gradient tracking
to collaboratively minimize the sum of smooth and strongly-convex local cost
functions for networked agents communicating over a time-varying directed
graph. Each agent, assumed to have access to a stochastic first-order oracle
for obtaining an unbiased estimate of the gradient of its local cost function,
maintains an auxiliary variable to asymptotically track the stochastic gradient
of the global cost. The optimal decision and gradient tracking are updated over
time through limited information exchange with local neighbors using row- and
column-stochastic weights, guaranteeing both consensus and optimality. With a
sufficiently small constant step-size, we demonstrate that, in expectation,
SAB-TV converges linearly to a neighborhood of the optimal solution. Numerical
simulations illustrate the effectiveness of the proposed algorithm
Optimal Workload Allocation for Distributed Edge Clouds With Renewable Energy and Battery Storage
This paper studies an optimal workload allocation problem for a network of
renewable energy-powered edge clouds that serve users located across various
geographical areas. Specifically, each edge cloud is furnished with both an
on-site renewable energy generation unit and a battery storage unit. Due to the
discrepancy in electricity pricing and the diverse temporal-spatial
characteristics of renewable energy generation, how to optimally allocate
workload to different edge clouds to minimize the total operating cost while
maximizing renewable energy utilization is a crucial and challenging problem.
To this end, we introduce and formulate an optimization-based framework
designed for Edge Service Providers (ESPs) with the overarching goal of
simultaneously reducing energy costs and environmental impacts through the
integration of renewable energy sources and battery storage systems, all while
maintaining essential quality-of-service standards. Numerical results
demonstrate the effectiveness of the proposed model and solution in maintaining
service quality as well as reducing operational costs and emissions.
Furthermore, the impacts of renewable energy generation and battery storage on
optimal system operations are rigorously analyzed
CrowdCache: A Decentralized Game-Theoretic Framework for Mobile Edge Content Sharing
Mobile edge computing (MEC) is a promising solution for enhancing the user
experience, minimizing content delivery expenses, and reducing backhaul
traffic. In this paper, we propose a novel privacy-preserving decentralized
game-theoretic framework for resource crowdsourcing in MEC. Our framework
models the interactions between a content provider (CP) and multiple mobile
edge device users (MEDs) as a non-cooperative game, in which MEDs offer idle
storage resources for content caching in exchange for rewards. We introduce
efficient decentralized gradient play algorithms for Nash equilibrium (NE)
computation by exchanging local information among neighboring MEDs only, thus
preventing attackers from learning users' private information. The key
challenge in designing such algorithms is that communication among MEDs is not
fixed and is facilitated by a sequence of undirected time-varying graphs. Our
approach achieves linear convergence to the NE without imposing any assumptions
on the values of parameters in the local objective functions, such as requiring
strong monotonicity to be stronger than its dependence on other MEDs' actions,
which is commonly required in existing literature when the graph is directed
time-varying. Extensive simulations demonstrate the effectiveness of our
approach in achieving efficient resource outsourcing decisions while preserving
the privacy of the edge devices
Nash equilibrium seeking over digraphs with row-stochastic matrices and network-independent step-sizes
In this paper, we address the challenge of Nash equilibrium (NE) seeking in
non-cooperative convex games with partial-decision information. We propose a
distributed algorithm, where each agent refines its strategy through
projected-gradient steps and an averaging procedure. Each agent uses estimates
of competitors' actions obtained solely from local neighbor interactions, in a
directed communication network. Unlike previous approaches that rely on
(strong) monotonicity assumptions, this work establishes the convergence
towards a NE under a diagonal dominance property of the pseudo-gradient
mapping, that can be checked locally by the agents. Further, this condition is
physically interpretable and of relevance for many applications, as it suggests
that an agent's objective function is primarily influenced by its individual
strategic decisions, rather than by the actions of its competitors. In virtue
of a novel block-infinity norm convergence argument, we provide explicit bounds
for constant step-size that are independent of the communication structure, and
can be computed in a totally decentralized way. Numerical simulations on an
optical network's power control problem validate the algorithm's effectiveness
A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation
Edge Computing (EC) offers a superior user experience by positioning cloud
resources in close proximity to end users. The challenge of allocating edge
resources efficiently while maximizing profit for the EC platform remains a
sophisticated problem, especially with the added complexity of the online
arrival of resource requests. To address this challenge, we propose to cast the
problem as a multi-armed bandit problem and develop two novel online pricing
mechanisms, the Kullback-Leibler Upper Confidence Bound (KL-UCB) algorithm and
the Min-Max Optimal algorithm, for heterogeneous edge resource allocation.
These mechanisms operate in real-time and do not require prior knowledge of
demand distribution, which can be difficult to obtain in practice. The proposed
posted pricing schemes allow users to select and pay for their preferred
resources, with the platform dynamically adjusting resource prices based on
observed historical data. Numerical results show the advantages of the proposed
mechanisms compared to several benchmark schemes derived from traditional
bandit algorithms, including the Epsilon-Greedy, basic UCB, and Thompson
Sampling algorithms
Methods for constructing VS30 maps from geology, borehole and topography data
The average shear wave velocity in the upper 30 meters of a soil profile (VS30) is used in building codes to separate sites into classes for the purpose of earthquake engineering design. In this paper, a method of Thompson E.N. and Wald D.J. (2012) was applied to build a VS30 map of Ho Chi Minh city using, many different data such as geologic, topographic and shear wave velocities derived from the Standard Penetration Test (SPT) data of 104 boreholes. The VS30 values are mainly within two ranges of 0 -180 m/s and 180 - 240 m/s corresponding to two site classes of type D and C, respectively, according to the soil classification in Vietnam Construction Code TCVN 9386:2012. The site class of type C is mainly distributed in the districts located in the northern area of Ho Chi Minh city, while the soft soil with VS30 180 m/s is concentrated in the south of the city. This result shows that this method can reduce the uncertainty of VS30 map by up to 11.4% relative to the topography slope-only approach. Moreover, the average topographic slope of Ho Chi Minh city area is about 0.0024. It implies that Ho Chi Minh city area is in a stable continental region, which is suitable to apply ground motion prediction equations developed for seismic stable regions for the purpose of seismic hazard assessment. Our study is a prerequisite for the wide application of this method to build VS30 maps for other areas in Vietnam.ReferencesAbrahamson, N., Atkinson, G., Boore, D.M., Bozorgnia, Y, Campbell, K., Chiou, B., Idriss, I.M., Silva, W., and Youngs, R., 2008. Comparisons of the NGA ground-motion relations. Earthquake Spectra 24(1), 45-66.Allen, T. I. and Wald D. J., 2009. On the Use of High-Resolution Topographic Data as a Proxy for Seismic Site Conditions (VS30). Bull. Seism. Soc. Am., 99(2A), 935-943.Borcherdt, R. D., 1994. 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Final report of R-RD scientific research funding program supported by Ministry of Natural Resources and Environment (in Vietnamese).Wald, D. J. and Trevor I. Allen, 2007. Topographic Slope as a Proxy for Seismic Site Conditions and Amplification. Bull. Seism. Soc. Am., 97(5), 1379-1395.Wills, C.J. and Clahan, K.B., 2006. Developing a Map of Geologically Defined Site-Condition Categories for California. Bull. Seism. Soc. Am., 96(4A), 1483-1501.Yong, A., Hough, S.E., Abrams, M.J., Cox, H.M., Wills, C.J., and Simila, G.W., 2008. Site Characterization Using Integrated Imaging Analysis Methods on Satellite Data of the Islamabad, Pakistan, Region. Bull. Seism. Soc. Am., 98(6), 2679-2693.Yong, A., Hough, S.E., Iwahashi, J., and Braverman, A., 2012. A Terrain-Based Site-Conditions Map of California with Implications for the Contiguous United States. Bull. Seism. Soc. Am., 102(1), 114-128
Effect of Polypyrrole on the Electrical, Dielectric and Mechanical Properties of Waterborne Epoxy Coatings
In this context, conducting composite based on waterborne epoxy system and polypyrrole (PPy) was investigated. The polypyrrole was synthesized by chemical oxidation polymerization. Its morphology and chemical structure were confirmed by using field emission scanning electron microscopy (FESEM) and Fourier transform infrared spectroscopy (FTIR). Then, PPy was well-dispersed in the epoxy coating and had a good compatibility with the matrix. The effects of PPy on dielectric, electrical and mechanical properties of epoxy/PPy composites was examined. The dielectric constant and electrical conductivity of the coatings increased with addition of PPy fillers. Over to 15 wt. % of PPy loading, the volume resistivity of samples slightly decreased from 6.7 × 1010 to 1.5 × 1010 Ω cm. In contrast, the presence of PPy diminished both impact and abrasion resistance of the epoxy/PPy composites, down to 160 kg cm and 10.2 L/mil, respectively, but they stayed acceptable for the coatings. The results reveal that the epoxy containing polypyrrole is suitable for various electrical and dielectric applications
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