Mobile crowd sensing (MCS) is a new paradigm for urban-scale monitoring. This article concentrates on the Cost-Fair Task Allocation (CFA) problem for the MCS scenario where the collaboration of multiple probabilistic ...
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Mobile crowd sensing (MCS) is a new paradigm for urban-scale monitoring. This article concentrates on the Cost-Fair Task Allocation (CFA) problem for the MCS scenario where the collaboration of multiple probabilistic mobilephone users is needed to yield more reliable observation. CFA aims to allocate sensing tasks to users so that the sensing costs undertaken by all users are as balancing as possible, while the requirement of the requester for data reliability can be satisfied. CFA is greatly important to MCS campaigns in terms of reliability and sustainability. We design two algorithms to solve the CFA problem in the offline and online cases, respectively. Specifically, we propose a novel penalty-based model to reformulate the offline CFA problem, and based on this model, we design an offline algorithm, which can yield a computation-efficient e-solution with any small epsilon > 0. For the online case, we design a polynomial-time approximation algorithm, which struggles to allocate each of the sequentially arriving tasks to users as fairly as possible, and can achieve an upper-bounded competitiveness relative to the optimal CFA solution. Finally, we conduct extensive numeric analyses to validate the performance of our algorithms under diverse experimental setups.
Multi-service networks are broadly used in different service industries. Effective design of these networks has a considerable impact on customer satisfaction. Controlling congestion created at service facilities (SFs...
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Multi-service networks are broadly used in different service industries. Effective design of these networks has a considerable impact on customer satisfaction. Controlling congestion created at service facilities (SFs) is an important function for improving service quality. There are different studies on modeling multi-service network design, but they mostly neglect incorporating service-quality aspects such as congestion into their models. This paper considers the problem of simultaneous service portfolio selection, SF location, and capacity planning in a multi-service network with M/M/1 servers where the total congestion at established facilities should be controlled. The problem is formulated as a fractional 0-1 programming model, which is linearized in several different ways. The linear reformulations are enhanced by exploiting the special structure of the original model. A comprehensive numerical study is carried out to compare the linear models. Finally, a real case on a community healthcare network is studied to provide a set of managerial insights.
For the problem of free⁃floating space robot(FFSR)that the motion of manipulator will cause a large disturbance to the attitude of satellite,a path planning method based on hp⁃adaptive Gauss pseudospectral method(hp⁃A...
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For the problem of free⁃floating space robot(FFSR)that the motion of manipulator will cause a large disturbance to the attitude of satellite,a path planning method based on hp⁃adaptive Gauss pseudospectral method(hp⁃AGPM)is proposed in this *** this method,the minimum reaction torque acting on satellite is taken as the objective function,and the number of segments and the order of polynomial in each segment are determined adaptively to improve the accuracy and the efficiency of the *** the same time,the theoretical convergence of the designed method is innovatively proved to ensure that the solution of the discretized nonlinear programming(NLP)problem is the optimal solution to the original optimal *** simulation results of a planar two degree⁃of⁃freedom(2⁃DOF)space manipulator show that the proposed path planning method is more effective than the resolved acceleration control(RAC)method and the control variable parameterization(CVP)method,and is better than other pseudospectral methods both in computation speed and the number of collocation points.
The last years witnessed a steep rise in data generation worldwide and, consequently, the widespread adoption of software solutions able to support data-intensive application. Competitiveness and innovation have stron...
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The last years witnessed a steep rise in data generation worldwide and, consequently, the widespread adoption of software solutions able to support data-intensive application. Competitiveness and innovation have strongly benefited from these new platforms and methodologies, and there is a great deal of interest around the new possibilities that Big Data analytics promise to make reality. Many companies currently engage in data-intensive processes as part of their core businesses;however, fully embracing the data-driven paradigm is still cumbersome, and establishing a production-ready, fine-tuned deployment is time-consuming, expensive, and resource-intensive. This situation calls for innovative models and techniques to streamline the process of deployment configuration for Big Data applications. In particular, the focus in this paper is on the rightsizing of Cloud deployed clusters, which represent a cost-effective alternative to installation on premises. This paper proposes a novel tool, integrated in a wider DevOps-inspired approach, implementing a parallel and distributed simulation-optimization technique that efficiently and effectively explores the space of alternative Cloud configurations, seeking the minimum cost deployment that satisfies quality of service constraints. The soundness of the proposed solution has been thoroughly validated in a vast experimental campaign encompassing different applications and Big Data platforms.
Electromagnetic formation flight (EMFF) uses the electromagnetic force to control the relative positions of multiple satellites without using conventional fuel-based propulsion. To compensate for the electromagnetic t...
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Electromagnetic formation flight (EMFF) uses the electromagnetic force to control the relative positions of multiple satellites without using conventional fuel-based propulsion. To compensate for the electromagnetic torque generated alongside the electromagnetic force, in most previous studies, all satellites were assumed to have reaction wheels (RWs) besides electromagnetic coils. However, the RW-loaded angular momentum becomes nonuniformly distributed among the satellites, because the electromagnetic torque usually differs between satellites. Without a proper control scheme, this deviation increases over time, and the RWs become saturated quickly, preventing the attitudes of the satellites from being controlled. In this study, a new controller is proposed that enables the electromagnetic force and torque to be controlled simultaneously. The EMFF kinematics derived from the conservation of angular momentum are used for the controller design. This controller can control n satellites without saturating the RWs, and only one set of RWs is required among all satellites. The combination of the proposed controller with a simple unloading control exclusive to the chief satellite results in the elimination of the accumulation of angular momentum in the entire system. The effectiveness of the proposed controller is demonstrated through numerical simulations of the formation maintenance and formation reconfiguration of a five-satellite system.
In this paper, we propose a new approach to solving the backward reachability problem for nonlinear dynamical systems. Previously, this class of problems has been studied within frameworks of optimal control and zero-...
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In this paper, we propose a new approach to solving the backward reachability problem for nonlinear dynamical systems. Previously, this class of problems has been studied within frameworks of optimal control and zero-sum differential games, where a backward reachable set can be expressed as the zero sublevel set of the value function that can be characterized by solving the Hamilton-Jacobi-Bellman (HJB) partial differential equation (PDE). In many cases, however, a high computational cost is incurred in numerically solving such HJB PDEs due to the curse of dimensionality. We use the pseudospectral method to convert the associated optimal control problem into nonlinear programs (NLPs). We then show that the zero sublevel set obtained by the optimal cost of the NLP is the corresponding backward reachable set. Note that our approach does not require solving complex HJB PDEs. Therefore, it can reduce computation time and handle high-dimensional dynamical systems, compared with the numerical software package developed by I. Mitchell, which has been used widely in the literature to obtain backward reachable sets by solving HJB equations. We provide several examples to validate the effectiveness of the proposed approach.
This article presents an efficient numerical method for solving fractional optimal control problems (FOCPs) by utilizing the Hermite scaling function operational matrix of fractional-order integration. The proposed te...
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This article presents an efficient numerical method for solving fractional optimal control problems (FOCPs) by utilizing the Hermite scaling function operational matrix of fractional-order integration. The proposed technique is applied to transform the state and control variables into nonlinear programming (NLP) parameters at collocation points. The NLP solver is then used to solve FOCP. Furthermore, theL(2)-error estimates in the approximation of unknown variables and the approximation of block pulse operational matrix of fractional-order integration are derived and illustrative examples are included to demonstrate the applicability of the proposed method. Moreover, the results are compared with the Haar wavelet collocation method, hybrid of block-pulse and Taylor polynomials method, Bernstein polynomials method, and the Boubaker hybrid function method to show the superiority of the proposed method.
This paper presents a framework for the topology optimization of electro-mechanical design problems. While the design is parametrized by means of a level set function defined on a fixed mesh of the design domain, mesh...
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This paper presents a framework for the topology optimization of electro-mechanical design problems. While the design is parametrized by means of a level set function defined on a fixed mesh of the design domain, mesh adaptation is used to generate a second mesh that conforms to the domain delineated by the iso-zero of the level set function. This body-fitted mesh is used in the finite element simulation of the physical problem in order to accurately represent the electromagnetic interface phenomena. An appropriate combination of the two geometry representations is obtained through the velocity field to ensure a consistent design space as the topology optimization process unfolds. The method is applied to the joint electro-mechanical optimization of a synchronous reluctance machine (SynRM).
The steam power system (SPS) is one of the most significant contributors to the energy consumption and pollutant emissions in integrated iron and steel works. In this study, a mixed-integer nonlinear programming (MINL...
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The steam power system (SPS) is one of the most significant contributors to the energy consumption and pollutant emissions in integrated iron and steel works. In this study, a mixed-integer nonlinear programming (MINLP) model is developed to solve the problem of surplus byproduct gas utilization and reduce the economic operation cost (EOC) of the system. Fluctuations of surplus byproduct gas, dynamic demand of steam and electricity, time-of-use power price, and production safety constraints are also considered. The models have been verified by actual production data of the iron and steel works. Based on the MINLP, the results show that the reasonable use of heat value constraint will improve the utilization rate of the BFG and reduce the EOC, and the effect of equipment maintenance is discussed. The reasonable use of the heat value constraint is also discussed in the MINLP, which is helpful to improve the utilization rate of BFG and reduce the cost of economic operation.
The mutual inductance parameters change from time to time. When conducting dynamic wireless power transfer using an inductive power transfer system, it results in larger fluctuation of output power. Therefore, a param...
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The mutual inductance parameters change from time to time. When conducting dynamic wireless power transfer using an inductive power transfer system, it results in larger fluctuation of output power. Therefore, a parameter optimization method is necessary to improve the stability of inductive power transfer system during dynamic misalignment. In this study, a nonlinear programing model with objective function of minimum voltage gain difference was established by taking S-LCC topology as an example. Genetic algorithm and nonlinear programming were combined to optimize the compensating parameters of the system and to realize minimum fluctuation of output voltage gain of the system within any given range of mutual inductance parameters. Optimization results show that output stability can be realized by adjusting the compensation capacitance in the primary side. The feasibility of the theory was verified through stimulation and test prototype. Test results show that when the mutual induction range is from 29.3 mu H to 84.3 mu H, the voltage gain of the system varies from 0.67 to 0.77. The fluctuation ratio of voltage gain is 6.7%, and the fluctuation ratio of voltage gain under the circumstance of resonance parameters is 40.2%.
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