In this paper the case study of a smart home powered by solar and wind energy is presented. The benefits of having a smart home that can control the amount of power needed, according to the context of the usage, are a...
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ISBN:
(纸本)9781457710001
In this paper the case study of a smart home powered by solar and wind energy is presented. The benefits of having a smart home that can control the amount of power needed, according to the context of the usage, are also shown. Simulation shows that with a good control of the load it might be possible to reduce the installation costs of the Green Energy System. Furthermore, to support the results, a load balancing algorithm is created based on the Knapsack problem. An economic analysis of the approach is also shown to demonstrate the viability of the project, and how can the intelligence in the home lower the cost.
Design is a decision-making process that depends on multiple attributes. Analysis of alternatives with respect to a single metric representing the "goodness" of the concept is difficult without resorting to ...
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Design is a decision-making process that depends on multiple attributes. Analysis of alternatives with respect to a single metric representing the "goodness" of the concept is difficult without resorting to subjective weightings on an overall evaluation criterion. The use of subjective factors in a decision-making process is often met with criticism, as the selection of a design may be traced to preferential decisions made on a certain day by a single individual. A methodology is needed that reduces the impact of uncertainty in the subjective weighting factors while retaining the traceability, defensibility, and rigor provided by traditional multiple attribute decision-making techniques. In this work, a standard process for systems engineering using the quality function deployment approach with a multiple attribute decision-making technique for concept selection is supplemented through the use of parametric slide bars to play "what-if" games and a probabilistic environment that plays all possible "what-if" games and summarizes the results. Using the modified process, families of concepts can be rapidly examined based on varying levels of subjective preferences. Decision makers, armed with a rapid parametric sensitivity analysis tool, can make more informed decisions about future concepts, policies, and acquisition decisions. An interactive graphical environment can be used to visualize the diverse sets of trades and understand non-intuitive answers by tracing customer needs to proposed solutions in real-time. In practice, the proposed process facilitates iteration between needs and concepts and fosters and increased understanding of the concept space between designer and decision maker.
Real-world operational use of parallel multi-objectiveevolutionaryalgorithms requires successful searches in constrained wall-clock periods, limited trial-and-error algorithmic analysis, and scalable use of heteroge...
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Real-world operational use of parallel multi-objectiveevolutionaryalgorithms requires successful searches in constrained wall-clock periods, limited trial-and-error algorithmic analysis, and scalable use of heterogeneous computing hardware. This study provides a cross-disciplinary collaborative effort to assess and adapt parallel multi-objectiveevolutionaryalgorithms for operational use in satellite constellation design using large dedicated clusters with heterogeneous processor speeds/architectures. A statistical, metric-based evaluation framework is used to demonstrate how time-continuation, asynchronous evolution, dynamic population sizing, and epsilon dominance archiving can be used to enhance both simple master-slave parallelization strategies and more complex multiple-population schemes. Results for a benchmark constellation design coverage problem show that simple master-slave schemes that exploit time-continuation are often sufficient and potentially superior to complex multiple-population schemes.
Recently, it has been shown that taking the total velocity characteristic, the time of flight, and the trajectory safety into consideration and constructing a multi-objective optimization problem is an attractive and ...
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Recently, it has been shown that taking the total velocity characteristic, the time of flight, and the trajectory safety into consideration and constructing a multi-objective optimization problem is an attractive and realistic proposition for rendezvous trajectory design. Luo et al. [1] formulated the multi-objective linearized rendezvous optimization problem and solved it through the multi-objective genetic algorithm NSGA-II. It was shown that the tradeoffs between time of flight, propellant cost, and trajectory safety are quickly established using NSGA-II. In recognition of the drawbacks associated with linearized rendezvous equations, this study was expanded to a nonlinear two-body rendezvous by using NSGA-II and a Lambert algorithm. The nonlinear two-body multi-objective model is more accurate and suitable for more problems in comparison with linearized rendezvous models, which are limited to circular and near-rendezvous. However, the two-body model still does not take into account trajectory perturbations, such as nonspherical perturbations and atmospheric drag, which exist in real operational missions. Thus, it is desirable to be able to obtain Pareto-optimal solutions for perturbed rendezvous trajectories.
In this study, a new optimization approach for robust design, design for multi-objective six sigma, has been developed and applied to three robust optimization problems. The design for multi-objective six sigma builds...
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In this study, a new optimization approach for robust design, design for multi-objective six sigma, has been developed and applied to three robust optimization problems. The design for multi-objective six sigma builds on the ideas of design for six sigma, coupled with multiobjectiveevolutionary algorithm, for an enhanced capability to reveal tradeoff information considering both optimality and robustness of design. While design for six sigma requires careful input parameter setting, design for multi-objective six sigma needs no such prior tuning, plus it can reveal the tradeoff information in a single optimization run. Three robust optimization problems were taken as to demonstrate the capabilities of design for multiobjective six sigma. Results indicate that design for multi-objective six sigma has a more practical and more efficient capability than the design for six sigma to reveal tradeoff design information considering both optimality and robustness of design.
multi-objectiveevolutionaryalgorithms have been shown to be effective optimization tools to search the complex tradeoff spaces of satellite constellation design. Often, the metrics that make up the design tradeoff r...
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multi-objectiveevolutionaryalgorithms have been shown to be effective optimization tools to search the complex tradeoff spaces of satellite constellation design. Often, the metrics that make up the design tradeoff require lengthy function evaluation time, resulting in a decreased utility of serial multi-objectiveevolutionaryalgorithms. In this research, the authors implement two parallel processing multi-objectiveevolutionary algorithm paradigms, the master-slave and island models, on a heterogeneous system of processors and operating systems. The efficiency and effectiveness of each approach is studied in the context of a regional coverage design problem. The island scheme outperforms the master-slave model with respect to efficiency. A study of the search dynamics for each paradigm demonstrates that both reliably meet the goals of multi-objective optimization (progressing toward the Pareto-optimal front while maintaining a diverse set of solutions). A key conclusion of this research is that both paradigms provide excellent approximations of the true Pareto frontier using a single seed, and when combined across multiple trial runs, they find nearly the entire set of Pareto-optimal solutions.
A method for transonic compressor multi-objective design optimization was developed and applied to the NASA rotor 37, a test case representative of complex three-dimensional viscous flow structures in transonic bladin...
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A method for transonic compressor multi-objective design optimization was developed and applied to the NASA rotor 37, a test case representative of complex three-dimensional viscous flow structures in transonic bladings. The optimization problem considered was to maximize the isentropic efficiency of the rotor and to maximize its pressure ratio at the design point, using a constraint on the mass flow rate. The three-dimensional Navier-Stokes code CFX-TASCflow(R) was used for the aerodynamic analysis of blade designs. The capability of the code was validated by comparing the computed results to experimental data available in the open literature from probe traverses up-and downstream or the rotor. A multi-objectiveevolutionary algorithm was used for handling the optimization problem that makes use of Pareto optimality concepts and implements a novel genetic diversity evaluation method to establish a criterion for fitness assignment. The optimal rotor configurations, which correspond to the maximum pressure ratio and maximum efficiency, were obtained and compared to the original design.
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