scholarly journals Is Machine Learning Ready for Traffic Engineering Optimization?

2021 ◽  
Author(s):  
Guillermo Bernardez ◽  
Jose Suarez-Varela ◽  
Albert Lopez ◽  
Bo Wu ◽  
Shihan Xiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traffic Engineering ◽  
Engineering Optimization

scholarly journals Active learning of deep surrogates for PDEs: application to metasurface design

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Raphaël Pestourie ◽  
Youssef Mroueh ◽  
Thanh V. Nguyen ◽  
Payel Das ◽  
Steven G. Johnson
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Engineering Optimization ◽  
Optical Surface ◽  
Optical Wavelength ◽  
Random Samples ◽  
Training Cost ◽  
Order Of Magnitude

Abstract Surrogate models for partial differential equations are widely used in the design of metamaterials to rapidly evaluate the behavior of composable components. However, the training cost of accurate surrogates by machine learning can rapidly increase with the number of variables. For photonic-device models, we find that this training becomes especially challenging as design regions grow larger than the optical wavelength. We present an active-learning algorithm that reduces the number of simulations required by more than an order of magnitude for an NN surrogate model of optical-surface components compared to uniform random samples. Results show that the surrogate evaluation is over two orders of magnitude faster than a direct solve, and we demonstrate how this can be exploited to accelerate large-scale engineering optimization.

Logistics engineering optimization based on machine learning and artificial intelligence technology

2020 ◽  
pp. 1-12
Author(s):  
Suhua Bu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Optimal Solution ◽  
Engineering Optimization ◽  
Artificial Intelligence Technology ◽  
Important Means ◽  
Practical Effect ◽  
Combined Classifier ◽  
The Internet Of Things

In the era of the Internet of Things, smart logistics has become an important means to improve people’s life rhythm and quality of life. At present, some problems in logistics engineering have caused logistics efficiency to fail to meet people’s expected goals. Based on this, this paper proposes a logistics engineering optimization system based on machine learning and artificial intelligence technology. Moreover, based on the classifier chain and the combined classifier chain, this paper proposes an improved multi-label chain learning method for high-dimensional data. In addition, this study combines the actual needs of logistics transportation and the constraints of the logistics transportation process to use multi-objective optimization to optimize logistics engineering and output the optimal solution through an artificial intelligence model. In order to verify the effectiveness of the model, the performance of the method proposed in this paper is verified by designing a control experiment. The research results show that the logistics engineering optimization based on machine learning and artificial intelligence technology proposed in this paper has a certain practical effect.

scholarly journals Design Strategy of New and Reconstructed Interchanges on Expressways

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Jing Zhou ◽  
Heng Zhang ◽  
Xuguang Zheng
Keyword(s):  
Urban Development ◽  
Continuous Improvement ◽  
Traffic Engineering ◽  
Road Traffic ◽  
Design Strategy ◽  
Engineering Optimization ◽  
Specific Design ◽  
Economic Level ◽  
National Economic ◽  
Design Type

China's urban development is very fast, and the continuous improvement of the national economic level in recent years has also prompted most families to have their own scooters. In this situation, the increase and transformation of road traffic has been a necessary work for the development of various regions. This paper starts with the basic reasons for the design of new and improved interchanges, and expounds the specific design type division and main influencing factors. Finally, the paper puts forward the design strategy of newly added and reconstructed interactive interchanges with high feasibility, hoping to provide reasonable reference for relevant road traffic engineering optimization.

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