Due to the constrained energy and computational resources available to sensor nodes, the number of nodes deployed to cover the whole monitored area completely is often higher than if a deterministic procedure were use...
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Due to the constrained energy and computational resources available to sensor nodes, the number of nodes deployed to cover the whole monitored area completely is often higher than if a deterministic procedure were used. Activating only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy of the system. A novel coverage control scheme based on multi-objective genetic algorithm is proposed in this paper. The minimum number of sensors is selected in a densely deployed environment while preserving full coverage. As opposed to the binary detection sensor model in the previous work, a more precise detection model is applied in combination with the coverage control scheme. Simulation results show that our algorithm can achieve balanced performance on different types of detection sensor models while maintaining high coverage rate. With the same number of deployed sensors, our scheme compares favorably with the existing schemes. (C) 2008 Elsevier Ltd. All rights reserved.
High-brightness electron beams are required to drive LINAC-based free-electron lasers(FELs)and storage-ring-based synchrotron radiation light *** bunch charge and RMS bunch length at the exit of the LINAC play a cruci...
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High-brightness electron beams are required to drive LINAC-based free-electron lasers(FELs)and storage-ring-based synchrotron radiation light *** bunch charge and RMS bunch length at the exit of the LINAC play a crucial role in the peak current;the minimum transverse emittance is mainly determined by the injector of the ***,a photoin-jector with a high bunch charge and low emittance that can simultaneously provide high-quality beams for 4th generation synchrotron radiation sources and FELs is *** design of a 1.6-cell S-band 2998-MHz RF gun and beam dynamics optimization of a relevant beamline are presented in this *** dynamics simulations were performed by combining ASTRA and the multi-objective genetic algorithm NSGA *** effects of the laser pulse shape,half-cell length of the RF gun,and RF parameters on the output beam quality were analyzed and *** normalized transverse emittance was optimized to be as low as 0.65 and 0.92 mm·mrad when the bunch charge was as high as 1 and 2 nC,***,the beam stability properties of the photoinjector,considering misalignment and RF jitter,were simulated and analyzed.
The multi-objective genetic algorithm(MOGA) is proposed to calibrate the non-linear camera model of a space manipulator to improve its locational accuracy. This algorithm can optimize the camera model by dynamic balan...
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The multi-objective genetic algorithm(MOGA) is proposed to calibrate the non-linear camera model of a space manipulator to improve its locational accuracy. This algorithm can optimize the camera model by dynamic balancing its model weight and multi-parametric distributions to the required accuracy. A novel measuring instrument of space manipulator is designed to orbital simulative motion and locational accuracy test. The camera system of space manipulator, calibrated by MOGA algorithm, is used to locational accuracy test in this measuring instrument. The experimental result shows that the absolute errors are [0.07, 1.75] mm for MOGA calibrating model, [2.88, 5.95] mm for MN method, and [1.19, 4.83] mm for LM method. Besides, the composite errors both of LM method and MN method are approximately seven times higher that of MOGA calibrating model. It is suggested that the MOGA calibrating model is superior both to LM method and MN method.
A simple but reasonably accurate battery model is required for simulating the performance of electrical systems that employ a battery for example an electric vehicle, as well as for investigating their potential as an...
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A simple but reasonably accurate battery model is required for simulating the performance of electrical systems that employ a battery for example an electric vehicle, as well as for investigating their potential as an energy storage device. In this paper, a relatively simple equivalent circuit based model is employed for modeling the performance of a battery. A computer code utilizing a multi-objective genetic algorithm is developed for the purpose of extracting the battery performance parameters. The code is applied to several existing industrial batteries as well as to two recently proposed high performance batteries which are currently in early research and development stage. The results demonstrate that with the optimally extracted performance parameters, the equivalent circuit based battery model can accurately predict the performance of various batteries of different sizes, capacities, and materials. Several test cases demonstrate that the multi-objective genetic algorithm can serve as a robust and reliable tool for extracting the battery performance parameters. (C) 2013 Elsevier B.V. All rights reserved.
A kind of shell-and-tube heat exchangers with fold baffles was proposed to eliminate the triangular leakage zones betifeen adjacent baffles. An effective algorithm combing second-order polynomial response surface meth...
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A kind of shell-and-tube heat exchangers with fold baffles was proposed to eliminate the triangular leakage zones betifeen adjacent baffles. An effective algorithm combing second-order polynomial response surface method and multi-objective genetic algorithm was adopted to study the effect of fold baffle configuration parameters on the performance of flow and heat transfer. The helical angle, overlapped degree and shell-side inlet velocity were chosen as design parameters, and the Nusselt number and shell-side pressure drop were considered as objective functions. The results show that both the Nusselt number and shell-side pressure drop increase with the decrease of helical angle and shell-side inlet velocity, and increase with the increasing overlapped degree. A set of Pareto-optimal points were obtained, and the optimization results illustrate a good agreement with CFD simulation data with the relative deviation less than 3%. And the empirical correlations of Nusselt number and friction coefficient were obtained based on response surface method, the helical angle and overlapped degree were fitted into empirical correlations as correction factors for the first time. It is found that the adjusted coefficient of determination of the Nusselt number and friction coefficient is 0.943 and 0.999, respectively, which illustrate the fitting is correct and reliable. (C) 2017 Elsevier Ltd. All rights reserved.
The parameters of foaming and nano-clay percentage on the density of polymer foam and cell size with the PVC field is studied. Cell size and density have a significant impact on the strength of foam and its insulation...
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The parameters of foaming and nano-clay percentage on the density of polymer foam and cell size with the PVC field is studied. Cell size and density have a significant impact on the strength of foam and its insulation (including sounds and thermal insulation). By optimizing cell size and density, foam can be produced with the best mechanical properties. In foaming process of the nanocomposite samples by mass method, the design variables (input parameters) are foaming time and temperature and MMT content. The controlled elitist multi-objective GA is applied to minimize both the foam density and the cell size. To that end, the population size and the Pareto fraction are selected as 100 and 0.5, respectively. The noninferior solution obtained by the controlled elitist multi-objective GA is illustrated. When both the MMT and the temperature are high, the resulting foam does not have ideal characteristics.
Railway system is a reliable and efficiency major public transportation. It is supported by many countries since it has a less environmental effect compared to another type of transportation. As the railway networks h...
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Railway system is a reliable and efficiency major public transportation. It is supported by many countries since it has a less environmental effect compared to another type of transportation. As the railway networks have become larger and more complex with increasing passenger demand, both aspects from the passenger satisfaction and operational cost need to be satisfied. This paper proposes a Parallel multi-objective Evolutionary algorithm with Hybrid Sampling Strategy and learning-based mutation to solve the railway train scheduling problem. Learning techniques have been coupled with a multi-objective genetic algorithm to guide the search for better solutions. In this paper, we incorporate a learning-based algorithm into a mutation process. The evaluation process is divided into sub-process and calculated by a parallel computational unit using GPU CUDA framework. Two sets of numerical experiments based on a small-scale case of Thailand ARL transit line and a larger case of BTS transit network are implemented to verify the effectiveness of the proposed approaches. The experimental results show the effectiveness of the proposed algorithm comparing to sequential CPU computational and two classical multi-objective evolutionary algorithms. With the same number of operating trains, the proposed algorithm can obtain schedule with less average waiting time and the time used for computational is significantly reduced.
This paper proposes a coupling between Risk-Based Inspection (RBI) methodology and multi-objective genetic algorithm (MOGA) for defining efficient inspection programs in terms of inspection costs and risk level, which...
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This paper proposes a coupling between Risk-Based Inspection (RBI) methodology and multi-objective genetic algorithm (MOGA) for defining efficient inspection programs in terms of inspection costs and risk level, which also comply with restrictions imposed by international standards and/or local government regulations. The proposed RBI+MOGA approach has the following advantages: (i) a user-defined risk target is not required;(ii) it is not necessary to estimate the consequences of failures;(iii) the inspection expenditures become more manageable, which allows assessing the impact of prevention investments on the risk level;(iv) the proposed framework directly provides, as part of the solution, the information on how the inspection budget should be efficiently spent. Then, genetic operators are tailored for solving this problem given the huge size of the search space. The ability of the proposed RBI+MOGA in providing efficient solutions is evaluated by means of two examples, one of them involving an oil and gas separator vessel subject to internal and external corrosion that cause thinning. The obtained results indicate that the proposed genetic operators significantly reduce the search space to be explored and RBI+MOGA is a valuable method to support decisions concerning the mechanical integrity of plant equipment. (C) 2014 Elsevier Ltd. All rights reserved.
To avoid the hazardous material (Hazmat) transportation accidents, it is necessary to design the Hazmat transportation network in advance. Due to the uncertainty of risks and time during the Hazmat transportation, the...
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To avoid the hazardous material (Hazmat) transportation accidents, it is necessary to design the Hazmat transportation network in advance. Due to the uncertainty of risks and time during the Hazmat transportation, the paper studies the optimal network design method under the uncertain environment. The transportation scenario is divided into two types including single-vehicle centralised service and multi-vehicle coordinated service. The opportunity constrained programming model for the optimal design of Hazmat transportation network is constructed and the improved multi-objective genetic algorithm is used to solve the model. The case study shows the opportunity constrained programming model can better describe the optimal design of Hazmat transportation network than the traditional method under the uncertain environment. The repeating computer simulation tests show the proposed improved multi-objective genetic algorithm is feasible.
Three types of model for forecasting inundation levels during typhoons were optimized: the linear autoregressive model with exogenous inputs (LARX), the nonlinear autoregressive model with exogenous inputs with wavele...
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Three types of model for forecasting inundation levels during typhoons were optimized: the linear autoregressive model with exogenous inputs (LARX), the nonlinear autoregressive model with exogenous inputs with wavelet function (NLARX-W) and the nonlinear autoregressive model with exogenous inputs with sigmoid function (NLARX-S). The forecast performance was evaluated by three indices: coefficient of efficiency, error in peak water level and relative time shift. Historical typhoon data were used to establish water-level forecasting models that satisfy all three objectives. A multi-objective genetic algorithm was employed to search for the Pareto-optimal model set that satisfies all three objectives and select the ideal models for the three indices. Findings showed that the optimized nonlinear models (NLARX-W and NLARX-S) outperformed the linear model (LARX). Among the nonlinear models, the optimized NLARX-W model achieved a more balanced performance on the three indices than the NLARX-S models and is recommended for inundation forecasting during typhoons.
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