It is not perfect in view of the fact that the information guidance system of parking spaces in large and medium-sized parking lots at present, it is difficult to find a empty parking spaces in parking lots. One of th...
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ISBN:
(纸本)9781665404457
It is not perfect in view of the fact that the information guidance system of parking spaces in large and medium-sized parking lots at present, it is difficult to find a empty parking spaces in parking lots. One of the problems is large amount of calculation in traditional Dijkstra algorithm. In this paper, the improved Dijkstra algorithm is presented and optimized to find the best parking path with the purpose of looking for the nearest free parking space based on the layout model in parking lot parking guidance. The experiments show that it can find the optimal parking space and the optimal parking path by the improved Dijkstra algorithm, and improve the parking efficiency.
A patent pool strategy was proposed for use in the electric vehicle cell industry to manage patent licensing disputes and litigation. How to promote EV cell innovation diffusion under a patent pool scenario is unclear...
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A patent pool strategy was proposed for use in the electric vehicle cell industry to manage patent licensing disputes and litigation. How to promote EV cell innovation diffusion under a patent pool scenario is unclear. We introduced an innovation diffusion channel model comprising different players with patent licensing relationships and market competition relationships following evolutionary game analysis and simulation. We found the interlinked factors that influenced evolutionary stable strategies with a sensitivity test on all factors to identify the important and unimportant factors. To achieve the maximum return for the players, an optimization algorithm was introduced to find the maximum weighted object function. The decision and policy makers could focus on important factors such as improving the technology's competitive advantages, delivering more profits to its licensees with reasonable licensing fees, and finding the best patent pool strategy with the support of the optimization algorithm
Electric vehicle cell industry is an emerging area with fierce competition on technical innovation, in which the patent holder can choose different innovation diffusion options to maximize the return;however, the stra...
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Electric vehicle cell industry is an emerging area with fierce competition on technical innovation, in which the patent holder can choose different innovation diffusion options to maximize the return;however, the strategy is unclear in certain scenarios. We tried to explain the question of how to maximize the patent holder's return by appropriate patent license strategy to promote EV cell innovation diffusion, when competition and patent licensing relationship exist in the supply chain. A multistage and multichannel diffusion model of EV cell comprising the patent holder, EV cell producer and EV producers is developed;the evolutionary game is analyzed considering the competition among same stage players and patent licensing relationship among different stage players;and an optimization algorithm is introduced to find the maximum weighted object function of the patent holder. We established the multistage and multichannel diffusion model and found a nonlinear complex relationship between patent holder object function and the key factors including patent royalty pricing and innovation advantage coefficient;in addition, an optimization algorithm is developed based on adopters' decision-making related with competition and patent licensing.
A wideband butterfly antenna based on deep learning parameter optimization algorithm is proposed in this paper. Two neural networks are designed to model the mapping relationship of structure parameters to frequency r...
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ISBN:
(纸本)9781728181813
A wideband butterfly antenna based on deep learning parameter optimization algorithm is proposed in this paper. Two neural networks are designed to model the mapping relationship of structure parameters to frequency response and frequency response to structure parameters respectively. The parameter optimization algorithm proposed consists of two stages: training NN1 and optimizing parameters using NN2. A wideband butterfly antenna is designed to verify the algorithm. The experiment shows that the structure parameter optimization algorithm proposed can quickly optimize the structure parameters of antenna with the best preformance and save a lot of manpower and time cost.
An efficient algorithm is proposed for the stochastic responses of vehicle-bridge systems. The pseudo-excitation method is combined with the self-adaptive Gauss integration to handle the stochastic responses of vehicl...
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An efficient algorithm is proposed for the stochastic responses of vehicle-bridge systems. The pseudo-excitation method is combined with the self-adaptive Gauss integration to handle the stochastic responses of vehicle-bridge systems, for which the statistic characteristics are calculated. The applicability and accuracy of the proposed hybrid numerical method are demonstrated by comparing the results with those from Monte Carlo simulation. In addition, nondimensional time is introduced to evaluate the stochastic responses of vehicles.
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.
First-order gradient-based optimization algorithms have been of core practical importance in the field of deep learning. In this paper, we propose a new weighting mechanism-based first-order gradient descent optimizat...
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First-order gradient-based optimization algorithms have been of core practical importance in the field of deep learning. In this paper, we propose a new weighting mechanism-based first-order gradient descent optimization algorithm, namely NWM-Adam, to resolve the undesirable convergence behavior of some optimization algorithms which employ fixed sized window of past gradients to scale the gradient updates and improve the performance of Adam and AMSGrad. The NWM-Adam is developed on the basis of the idea, i.e., placing more memory of the past gradients than the recent gradients. Furthermore, it can easily adjust the degree to which how much the past gradients weigh in the estimation. In order to empirically test the performance of our proposed NWM-Adam optimization algorithm, we compare it with other popular optimization algorithms in three well-known machine learning models, i.e., logistic regression, multi-layer fully connected neural networks, and deep convolutional neural networks. The experimental results show that the NWM-Adam can outperform other optimization algorithms.
This paper proposes a new optimization algorithm, namely HGAB3C, and presents its performance on the CEC-2014 test suite. In HGAB3C, simple genetic algorithms (GAs) and big bang-big crunch (BB-BC) are hybridized. The ...
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This paper proposes a new optimization algorithm, namely HGAB3C, and presents its performance on the CEC-2014 test suite. In HGAB3C, simple genetic algorithms (GAs) and big bang-big crunch (BB-BC) are hybridized. The algorithm carries out global searches using a simple GA. In every generation the BB-BC algorithm is used to carry out local searches. The addition of local search has improved the capability of simple GAs significantly. The performance of the proposed algorithm is compared with 17 other optimization algorithms on all 30 functions of the CEC-2014 benchmark suite. It is observed that HGAB3C outperforms all other algorithms on 4 benchmark functions. For the 3 other functions, its performance equaled the best of the competing algorithms, which makes HGAB3C's performance best in a total of 7 benchmark functions. Out of the 18 competing algorithms, the proposed algorithm ranked second for the unmatched best mean error measure. For the best performance measure (number of functions giving unmatched best and equaled best mean error), the proposed algorithm was the third best. As far as the speed of convergence is concerned, the algorithm gave an unmatched best performance for the shifted Schwefel function (function 10 of CEC-2014 test bench). It obtained a mean error value of 0.00E+00, outperforming the previous best of 1.23E-03, converging to the target result in an average of 346.44 generations, which no other algorithm could achieve.
Compensated pulsed alternator (compulsator) plays a significant role in the field of pulsed power supply. Different kinds of compulsators have been used to drive high-energy weapons. In this paper, the mathematical mo...
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Compensated pulsed alternator (compulsator) plays a significant role in the field of pulsed power supply. Different kinds of compulsators have been used to drive high-energy weapons. In this paper, the mathematical model of a two-phase four-pole air-core compulsator is established, taking the current coupling, the change of rotor speed and changing load characteristics into account. Both the self-excitation process and the discharge process are modeled. Simulation results indicate that our model has high accuracy in comparison with the results of the co-simulation method using finite-element method and circuit principle. Moreover, the usage of the mathematical model can improve the simulation efficiency and flexibility. The current pulse requirements of electromagnetic rail gun (EMRG), flash lamp, and electro-thermal-chemical gun (ETCG) are analyzed, respectively, and the pulse shape optimization problem is studied based on the intelligent optimization algorithm. For EMRG and flash lamp, the term "acceleration ratio" is introduced to identify whether a pulse is flat or spiked. For ETCG, the conception of "shape variance" is proposed to evaluate the fitness of its pulse shape. With the help of suitable objection function and intelligent optimization algorithm, the optimal discharge pulse for specific load can be obtained easily.
Although the bicycle-sharing system plays an important role in easing urban traffic congestion and reducing carbon emissions, it still suffers from the problem of unbalanced bicycle distribution between different stat...
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