Roadside Unit (RSU) planning and management is not a straightforward task. Usually, the problem is modeled as an NP-hard mixed-integer combinatorial optimization problem especially when the planner ought to incorporat...
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ISBN:
(纸本)9781728127880
Roadside Unit (RSU) planning and management is not a straightforward task. Usually, the problem is modeled as an NP-hard mixed-integer combinatorial optimization problem especially when the planner ought to incorporate various problem components. For large-scale problems, heuristic approaches that achieve a trade-off between execution time and planning performance, can be good enough for making planning decisions. In this paper, we design an iterative reduction heuristic algorithm to maximize the coverage efficiency of a network of RSUs in an urban setting, given a daily amortized budget and other planning constraints. The framework also incorporates capture-and-use solar panels to offset operational electricity costs. We perform a sensitivity analysis, to study the model response to variations. The heuristic shows that variations in both financial as well as communication-related parameters have expected model solution response. We find that in 10% to 50% of the convergence time of the optimal solution, the heuristic found solutions that had a coverage efficiency around 10% of the optimal solution.
Although the community of nature-inspired computing has witnessed a wide variety of metaheuristics, it often requires considerable effort to adapt them to different combinatorial optimization problems (COPs), and few ...
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Although the community of nature-inspired computing has witnessed a wide variety of metaheuristics, it often requires considerable effort to adapt them to different combinatorial optimization problems (COPs), and few studies have been devoted to reducing this burden. This paper proposes a systematic approach that consists of a set of basic steps and strategies for adapting water wave optimization (WWO), a simple and generic metaheuristic, to concrete heuristic algorithms for different COPs. Taking advantages of the generic algorithmic framework, designers can only focus on adapting the prorogation operator and the wavelength calculation method according to the combinatorial properties of the given problem, and thus easily derive efficient problem-solving algorithms. We illustrate and test our approach on the flow-shop scheduling problem (FSP), the single-objective multidimensional knapsack problem (MKP), and the multi-objective MKP, and then present an application to a machine utilization optimization problem for a large manufacturing enterprise. The results demonstrate that our approach can derive concrete algorithms that are competitive to the state-of-the-arts. Our approach also provides insights into the adaptation of other metaheuristics and the development of new metaheuristics for COPs. (C) 2019 The Author(s). Published by Elsevier B.V.
We present a provably-good distributed algorithm for generalized task assignment problem in the context of multirobot systems, where robots cooperate to complete a set of given tasks. In multi-robot generalized assign...
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ISBN:
(纸本)9781467363563
We present a provably-good distributed algorithm for generalized task assignment problem in the context of multirobot systems, where robots cooperate to complete a set of given tasks. In multi-robot generalized assignment problem (MR-GAP), each robot has its own resource constraint (e.g., energy constraint), and needs to consume a certain amount of resource to obtain a payoff for each task. The objective is to find a maximum payoff assignment of tasks to robots such that each task is assigned to at most one robot while respecting robots' resource constraints. MR-GAP is a NP-hard problem. It is an extension of multi-robot linear assignment problem since different robots can use different amount of resource for doing a task (due to the heterogeneity of robots and tasks). We first present an auction-based iterative algorithm for MR-GAP assuming the presence of a shared memory (or centralized auctioneer), where each robot uses a knapsack algorithm as a subroutine to iteratively maximize its own objective (using a modified payoff function based on an auxiliary variable, called price of a task). Our iterative algorithm can be viewed as (an approximation of) best response assignment update rule of each robot to the assignment of other robots at that iteration. We prove that our algorithm converges to an assignment (approximately) at equilibrium under the assignment update rule, with an approximation ratio of 1 + α (where α is the approximation ratio for the Knapsack problem). We also combine our algorithm with a message passing mechanism to remove the requirement of a shared memory and make our algorithm totally distributed assuming the robots' communication network is connected. Finally, we present simulation results to depict our algorithm's performance.
Under the background of mobile Internet era, the application requirements of indoor positioning technology is increasingly urgent,and accurate positioning information has become an important prerequis
Under the background of mobile Internet era, the application requirements of indoor positioning technology is increasingly urgent,and accurate positioning information has become an important prerequis
We study the problem of computing personalized reserve prices in eager second price auctions without having any assumption on valuation distributions. Here, the input is a dataset that contains the submitted bids of n...
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ISBN:
(纸本)9781450367929
We study the problem of computing personalized reserve prices in eager second price auctions without having any assumption on valuation distributions. Here, the input is a dataset that contains the submitted bids of n buyers in a set of auctions and the goal is to return personalized reserve prices r that maximize the revenue earned on these auctions by running eager second price auctions with reserve r. We present a novel LP formulation to this problem and a rounding procedure which achieves a (1+2(√2-1)e√2-2)-1≅0.684-approximation. This improves over the 1/2-approximation algorithm due to Roughgarden and Wang. We show that our analysis is tight for this rounding procedure. We also bound the integrality gap of the LP, which bounds the performance of any algorithm based on this LP.
Conventional optimal power flow (OPF) solvers assume full observability of the involved system states. However in practice, there is a lack of reliable system monitoring devices in the distribution networks. To close ...
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ISBN:
(数字)9781538682661
ISBN:
(纸本)9781538682678
Conventional optimal power flow (OPF) solvers assume full observability of the involved system states. However in practice, there is a lack of reliable system monitoring devices in the distribution networks. To close the gap between the theoretic algorithm design and practical implementation, this work proposes to solve the OPF problems based on the state estimation (SE) feedback for the distribution networks where only a part of the involved system states are physically measured. The SE feedback increases the observability of the under-measured system and provides more accurate system states monitoring when the measurements are noisy. We analytically investigate the convergence of the proposed algorithm. The numerical results demonstrate that the proposed approach is more robust to large pseudo measurement variability and inherent sensor noise in comparison to the other frameworks without SE feedback.
The dense granular flow spallation target is a new target concept chosen for the Accelerator-Driven Subcritical (ADS) project in China. For the R&D of this kind of target concept, a dedicated Monte Carlo (MC) prog...
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The dense granular flow spallation target is a new target concept chosen for the Accelerator-Driven Subcritical (ADS) project in China. For the R&D of this kind of target concept, a dedicated Monte Carlo (MC) program named GMT was developed to perform the simulation study of the beam-target interaction. Owing to the complexities of the target geometry, the computational cost of the MC simulation of particle tracks is highly expensive. Thus, improvement of computational efficiency will be essential for the detailed MC simulation studies of the dense granular target. Here we present the special design of the GMT program and its high efficiency performance. In addition, the speedup potential of the GPU-accelerated spallation models is discussed. (C) 2017 Elsevier B.V. All rights reserved.
Neurofeedback has long been proposed as a promising form of adjunctive non-pharmaceutical treatment for a variety of neuropsychological disorders. However, there is much debate over its efficacy and specificity. Many ...
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Neurofeedback has long been proposed as a promising form of adjunctive non-pharmaceutical treatment for a variety of neuropsychological disorders. However, there is much debate over its efficacy and specificity. Many suggest that specificity can only be achieved when a specially trained clinician manually updates reward thresholds that indicate to the trainee when they are modulating their brain activity correctly, during training. We present a novel fully automated reward thresholding algorithm called progressive thresholding and test it with a frontal alpha asymmetry neurofeedback protocol. Progressive thresholding uses dynamic difficulty tuning and individual-specific progress models to simulate the shaping a clinician might perform when setting reward thresholds manually. We demonstrate in a double-blind comparison that progressive thresholding leads to significantly better learning outcomes compared with current automatic reward thresholding algorithms.
In November 2014 the CanX-4 and CanX-5 spacecraft became the first nanosatellites to demonstrate autonomous formation control with error less than 1 m. This feat was accomplished both in along-track formations at 1000...
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In November 2014 the CanX-4 and CanX-5 spacecraft became the first nanosatellites to demonstrate autonomous formation control with error less than 1 m. This feat was accomplished both in along-track formations at 1000 and 500 m range and projected circular orbit formations at 100 and 50 m. This control performance was enabled through carrier-phase differential GPS navigation techniques, providing online relative state estimates typically accurate to better than 10 cm. It was an important milestone on the road to regular and fully operational formation-flying missions. This paper provides an overview of the relative positioning algorithm design, presents an independent assessment of the receiver performance, and assesses the absolute and relative navigation results. The mission's on-orbit results are compared with an independently determined orbit solution computed using the GPS High Precision Orbit Determination Software Tools at the German Aerospace Centre.
Influence Maximization (IM), which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social...
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Influence Maximization (IM), which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technical challenges, IM has been extensively studied in the past decade. In this paper, we survey and synthesize a wide spectrum of existing studies on IM from an algorithmic perspective, with a special focus on the following key aspects: (1) a review of well-accepted diffusion models that capture the information diffusion process and build the foundation of the IM problem, (2) a fine-grained taxonomy to classify existing IM algorithms based on their design objectives, (3) a rigorous theoretical comparison of existing IM algorithms, and (4) a comprehensive study on the applications of IM techniques in combining with novel context features of social networks such as topic, location, and time. Based on this analysis, we then outline the key challenges and research directions to expand the boundary of IM research.
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