A Hybrid Method to Design Wire Antennas: Design and optimization of antennas using artificial intelligence.

2015 ◽  
Vol 57 (4) ◽  
pp. 23-31 ◽  
Author(s):  
Sergio Ledesma ◽  
Jose Ruiz-Pinales ◽  
Gustavo Cerda-Villafana ◽  
M.G. Garcia-Hernandez
Keyword(s):  
Artificial Intelligence ◽  
Hybrid Method ◽  
Design And Optimization ◽  
Wire Antennas

scholarly journals Research on Optimization and Design of Sustainable Urban Underground Logistics Network Framework

2020 ◽  
Vol 12 (21) ◽  
pp. 9147
Author(s):  
Hairui Wei ◽  
Anlin Li ◽  
Nana Jia
Keyword(s):  
Hybrid Method ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Programming Model ◽  
Mixed Integer ◽  
Set Cover ◽  
Exhaust Emission ◽  
Logistics Network ◽  
Design And Optimization ◽  
Fuzzy Clustering Method

As a new mode of transportation, the underground logistics system (ULS) has become one of the solutions to the problems of environmental pollution and traffic congestion. Considering the environmental and economic factors in urban logistics, this paper conducts comprehensive design and optimization research on the network nodes and passages of urban underground logistics and proposes a relatively complete framework for a sustainable underground logistics network. A hybrid method is proposed, which includes the set cover model used to perform the first location of urban underground logistics nodes, the fuzzy clustering method applied to classify the located logistics nodes into the first-level and second-level nodes considering the congestion in different urban areas of the city and a mixed integer programming model proposed to optimize and design the underground logistics passage to find optimal passage parameters at every underground logistics node. Based on the above hybrid method, a sustainable underground logistics network framework including all-levels logistics nodes and passages is formed, with a subdistrict of Nanjing as a case study. The discussion of results shows that this underground logistics network framework proposal is very effective in reducing logistics time cost, exhaust emission and congestion cost. It provides support for decisions in the design and development of urban sustainable underground logistics networks.

scholarly journals Design and Optimization of a Low-Resonant-Frequency Piezoelectric MEMS Energy Harvester Based on Artificial Intelligence

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 930 ◽  
Author(s):  
Seyedfakhreddin Nabavi ◽  
Lihong Zhang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Resonant Frequency ◽  
Deep Neural Network ◽  
Energy Harvester ◽  
Vibration Energy Harvester ◽  
Design And Optimization ◽  
Speed Up ◽  
Operational Frequency ◽  
Piezoelectric Mems

In this study we propose a piezoelectric MEMS vibration energy harvester with the capability of oscillating at low (i.e., less than 200 Hz) resonant frequency. The mechanical structure of the proposed harvester is comprised of a doubly clamped cantilever with a serpentine pattern associated with several discrete masses. In order to obtain the optimal physical aspects of the harvester and speed up the design process, we have utilized a deep neural network, as an artificial intelligence (AI) method. Firstly, the deep neural network was trained with 108 data samples gained from finite element modeling (FEM). Then this trained network was integrated with the genetic algorithm (GA) to optimize geometry of the harvester to enhance its performance in terms of resonant frequency and generated voltage. Our numerical results confirm that the accuracy of the network in prediction is above 90%. Consequently, by taking advantage of this efficient AI-based performance estimator, the GA is able to reduce the device operational frequency from 169 Hz to 110.5 Hz and increase its efficiency on harvested voltage from 2.5 V to 3.4 V under 0.25 g excitation.

A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms

2018 ◽  
Vol 220 ◽  
pp. 480-495 ◽  
Author(s):  
Minggang Wang ◽  
Longfeng Zhao ◽  
Ruijin Du ◽  
Chao Wang ◽  
Lin Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Crude Oil ◽  
Complex Network ◽  
Hybrid Method ◽  
Network Science ◽  
Oil Prices ◽  
Crude Oil Prices

Semantic Enrichment for Geospatial Information in a Tourism Recommender System

2013 ◽  
pp. 2208-2229
Author(s):  
Joan de la Flor ◽  
Joan Borràs ◽  
David Isern ◽  
Aida Valls ◽  
Antonio Moreno ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Recommender System ◽  
Hybrid Method ◽  
Spatial Data ◽  
Recommendation System ◽  
Geographic Information ◽  
Computational Time ◽  
Limited Information ◽  
Geospatial Information ◽  
Visitor Satisfaction

Geospatial information is commonly used in tourism to facilitate activity planning, especially in a context of limited information on the territory, as it is common in the case of complex and heterogeneous tourism destination regions where the constrained spatial activity of visitor is likely to generate inefficiencies in the use of assets and resources, and hinder visitor satisfaction. Because of the large amount of spatial and non-spatial data associated with different resources and activities, it is a logical choice to use geographic information systems (GIS) for storing, managing, analyzing, and visualizing the data. Nevertheless, in order to facilitate personalized recommendations to visitors, interaction with Artificial Intelligence is needed. This chapter presents SigTur/E-Destination, a tourism recommender system based on a semantically-enriched GIS that provides regional tourist organizations and the industry with a new powerful tool for the sustainable management of their destinations. The recommendation system uses innovative Artificial Intelligence techniques, such as a hybrid method that integrates content-based and collaborative filtering and clustering methodologies that improve computational time.

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