Source signals often contain various hidden waveforms, which further provide precious information. Therefore, detecting and capturing these waveforms is very important. For signal decomposition (SD), discrete Fourier ...
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Source signals often contain various hidden waveforms, which further provide precious information. Therefore, detecting and capturing these waveforms is very important. For signal decomposition (SD), discrete Fourier transform (DFT) and empirical mode decomposition (EMD) are two main tools. They both can easily decompose any source signal into different components. DFT is based on Cosine functions;EMD is based on a collection of intrinsic mode functions (IMFs). With the help of Cosine functions and IMFs respectively, DFT and EMD can extract additional information from sensed signals. However, due to a considerably finite frequency resolution, EMD easily causes frequency mixing. Although DFT has a larger frequency resolution than EMD, its resolution is also finite. To effectively detect and capture hidden waveforms, we use an optimization algorithm, differential evolution (DE), to decompose. The technique is called SD by DE (SDDE). In contrast, SDDE has an infinite frequency resolution, and hence it has the opportunity to exactly decompose. Our proposed SDDE approach is the first tool of directly applying an optimization algorithm to signal decomposition in which the main components of source signals can be determined. For source signals from four combinations of three periodic waves, our experimental results in the absence of noise show that the proposed SDDE approach can exactly or almost exactly determine their corresponding separate components. Even in the presence of white noise, our proposed SDDE approach is still able to determine the main components. However, DFT usually generates spurious main components;EMD cannot decompose well and is easily affected by white noise. According to the superior experimental performance, our proposed SDDE approach can be widely used in the future to explore various signals for more valuable information.
Carbon exchange between the atmosphere and terrestrial ecosystem is a key component affecting climate changes. Because the in situ measurements are not dense enough to resolve CO2 exchange spatial variation on various...
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Carbon exchange between the atmosphere and terrestrial ecosystem is a key component affecting climate changes. Because the in situ measurements are not dense enough to resolve CO2 exchange spatial variation on various scales, the variation has been mainly simulated by numerical ecosystem models. These models contain large uncertainties in estimating CO2 exchange owing to incorporating a number of empirical parameters on different scales. This study applied a global optimization algorithm and ensemble approach to a surface CO2 flux scheme to (1) identify sensitive photosynthetic and respirational parameters, and (2) optimize the sensitive parameters in the modeling sense and improve the model skills. The photosynthetic and respirational parameters of corn (C4 species) and soybean (C3 species) in NCAR land surface model (LSM) are calibrated against observations from AmeriFlux site at Bondville, IL during 1999 and 2000 growing seasons. Results showed that the most sensitive parameters are maximum carboxylation rate at 25 degrees C and its temperature sensitivity parameter (V-cmax25 and a(vc)), quantum efficiency at 25 degrees C (Q(e25)), temperature sensitivity parameter for maintenance respiration (a(rm)), and temperature sensitivity parameter for microbial respiration (a(mr)). After adopting calibrated parameter values, simulated seasonal averaged CO2 fluxes were improved for both the C4 and the C3 crops (relative bias reduced from 0.09 to -0.02 for the C4 case and from 0.28 to -0.01 for the C3 case). An updated scheme incorporating new parameters and a revised flux-integration treatment is also proposed.
A novel heuristic method GA-PSO (genetic algorithm-particle swarm optimization) is proposed. Its genetic principles and application technologies are discussed on emphasis. The algorithm combines the advantages of GA&#...
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A novel heuristic method GA-PSO (genetic algorithm-particle swarm optimization) is proposed. Its genetic principles and application technologies are discussed on emphasis. The algorithm combines the advantages of GA's global search and diverse population and PSOs strong evolutionary direction and convergence. On the basis of PSO, it evolves by updating its velocity and location of the particles. Its new population is produced by a mother particle which is the elite particle of the past generation. The genetic operations of GA are implemented to the new population, such as elite reservation and roulette wheel selection, arithmetic crossover, gaussian mutation and micro rotation. The particles are coded by real numbers and the inertia weight is adjusted by the self adaptation mechanism. The examples show that the GA-PSO algorithm is correct and effective.
In recent years, different optimization methods have been developed for optimization problem. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new algorithm for optimization inspired b...
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In recent years, different optimization methods have been developed for optimization problem. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new algorithm for optimization inspired by the gases brownian motion and turbulent rotational motion is introduced, which is called Gases Brownian Motion optimization (GBMO). The proposed algorithm is created using the features of gas molecules. The proposed algorithm is an efficient approach to search and find an optimum solution in search space. The efficiency of the proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various functions. (C) 2012 Elsevier B. V. All rights reserved.
This paper proposed an optimization algorithm for exit choice for the *** algorithm aims at minimizing the overall evacuation time from the global *** basic concept of the algorithm is balancing the number of pedestri...
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This paper proposed an optimization algorithm for exit choice for the *** algorithm aims at minimizing the overall evacuation time from the global *** basic concept of the algorithm is balancing the number of pedestrians selecting each exit to make all the evacuees leave the building simultaneously as far as *** a theater with six exits as an example,the T'DS+EVAC software is used to simulate the evacuation *** results of pedestrians' exit choice and the evacuation time with and without optimization are compared and *** shows that adopting the optimized choice strategy can greatly improves the utilization of the exits during the evacuation,and all of the evacuees leave the building almost simultaneously.
The pressure on marine renewable resources has rapidly increased over past *** resulting scarcity has led to a variety of different control and surveillance *** dynamics of fish stocks are an important consideration i...
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The pressure on marine renewable resources has rapidly increased over past *** resulting scarcity has led to a variety of different control and surveillance *** dynamics of fish stocks are an important consideration in determining appropriate fishery management *** crucial are the dynamics of fishing effort.A variety of growth curves have been applied to model both unpredated,intraspecific population dynamics and more general biological *** effort dynamics are determined by the difference in profits and opportunity *** management alternatives are evaluated at *** variables such as equilibrium catch,social profits,consumer surplus,social benefits,direct fishery employment and income of individual crew are used in the evaluation.
Since it is difficult to find the optimal solution directly by the traditional CFD optimization method due to its strong dependence on the designer's experience, an automatic aerodynamic optimization design platfo...
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Since it is difficult to find the optimal solution directly by the traditional CFD optimization method due to its strong dependence on the designer's experience, an automatic aerodynamic optimization design platform for automotive shape was built based on mesh deformation technology, surrogate model and optimization algorithm in this paper. A parameterized model of an automotive was established. Latin hypercube method was adopted to select sample points. The drag coefficients corresponding to sample points were calculated by CFD simulation, whereby the influence of each parameter on drag coefficient was obtained. By comparing the calculation time, optimization effect and optimization accuracy of 9 combinations of surrogate models and optimization algorithms, the combination of RBF model and NLPQL algorithm was selected as the optimal one which is the most appropriate for the aerodynamic optimization design for automotive shape.
Based on Kirchhoff Law about arbitrary sinusoidal steady-state circuit network, optimization principle of dynamic design variables is adopted. Making real parts and imaginary parts of sub-circuit current and node pote...
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ISBN:
(纸本)9781467374439
Based on Kirchhoff Law about arbitrary sinusoidal steady-state circuit network, optimization principle of dynamic design variables is adopted. Making real parts and imaginary parts of sub-circuit current and node potential as design variables, and equilibrium relation between node potential and sub-circuit current as frame-objective function, dynamic design variables optimization algorithm analysis of arbitrary complicated sinusoidal steady-state circuit network is proposed. Universal program computing sub-circuit current and node potential is completed. Practical examples are computed. Effectiveness and feasibility is verified. A new clue is set up for computing complicated alternating-current circuit network rapidly and precisely.
The chaos optimization algorithm and immune algorithm are combination of their respective characteristics, which is a chaos immune algorithm. The test results show that the algorithm not only keep s population diversi...
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ISBN:
(纸本)9781510830028
The chaos optimization algorithm and immune algorithm are combination of their respective characteristics, which is a chaos immune algorithm. The test results show that the algorithm not only keep s population diversity, also better and faster convergence speed and search ability. The influence cause of boron steel sheet on size precision is studied, which is contained with additional drawing stress, emergency ratio, board deep, plastic modulus, subdued point, sclerotic index, crushing force, curvature radius and so *** order to lay the further foundations of optimizing design, it is analyzed that the latent relation of influence cause of boron steel sheet on size precision by orthogonal experiment. It is pointed orderly out that the influences with rebound result of certain automobile back wheel casing are that sheet crushing force occupies the first place, sheet thick comes second, and sclerotic index comes third, emergency ratio and curvature radius in order.
For the earth observation satellite mission planning problem, objectives such as observed target quantity, observation profit, energy consumption, image quality should be considered simultaneously, which is a many-obj...
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For the earth observation satellite mission planning problem, objectives such as observed target quantity, observation profit, energy consumption, image quality should be considered simultaneously, which is a many-objective optimization problem. Classical optimization-based mission planning algorithms obtain a set of non-dominated solutions in the entire search space, while only a single satisfy final plan is desired by decision maker. In this paper, a five-objective optimization model for satellite mission planning problem is constructed, then a region preference-based evolutionary algorithm, HMOEA-T, is applied to obtain the desired solutions. The decision makers describe the preference on each objective in target region form, then the algorithm guides a more detailed search within the preference region rather than the entire Pareto front. Comparative studies with preference-based methods(T-NSGA-III) and classical methods(NSGA-III) are conducted. We have exemplified the proposed method manage to obtain the solutions satisfying the mission planning preference and achieve better performance in convergence and diversity.
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