scholarly journals Circulation Characteristic Analysis of Implied Water Flow Based on a Complex Network: A Case Study for Beijing, China

Water ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 834
Author(s):  
Siyang Hong ◽  
Hongrui Wang ◽  
Tao Cheng
Keyword(s):  
Complex Network ◽  
Water Flow ◽  
Characteristic Analysis ◽  
Circulation Characteristic ◽  
Beijing China

IMPACT OF THE SOUTH-TO-NORTH WATER DIVERSION PROJECT ON GROUNDWATER RESOURCES: A CASE STUDY IN PINGGU BASIN, BEIJING, CHINA

2013 ◽  
Vol 12 (11) ◽  
pp. 2239-2247 ◽  
Author(s):  
Guomin Li ◽  
Haizhen Xu ◽  
Ming Li ◽  
Shouquan Zhang ◽  
Yanhui Dong ◽  
...  
Keyword(s):  
Groundwater Resources ◽  
Water Diversion ◽  
The South ◽  
Water Diversion Project ◽  
North Water ◽  
Beijing China

Deep learning and complex network theory based analysis on socialized manufacturing resources utilisations and an application case study

2021 ◽  
pp. 1063293X2110031
Author(s):  
Maolin Yang ◽  
Auwal H Abubakar ◽  
Pingyu Jiang
Keyword(s):  
Deep Learning ◽  
Complex Network ◽  
Network Models ◽  
Neural Network Models ◽  
Complex Network Theory ◽  
New Type ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Resource Characteristics

Social manufacturing is characterized by its capability of utilizing socialized manufacturing resources to achieve value adding. Recently, a new type of social manufacturing pattern emerges and shows potential for core factories to improve their limited manufacturing capabilities by utilizing the resources from outside socialized manufacturing resource communities. However, the core factories need to analyze the resource characteristics of the socialized resource communities before making operation plans, and this is challenging due to the unaffiliated and self-driven characteristics of the resource providers in socialized resource communities. In this paper, a deep learning and complex network based approach is established to address this challenge by using socialized designer community for demonstration. Firstly, convolutional neural network models are trained to identify the design resource characteristics of each socialized designer in designer community according to the interaction texts posted by the socialized designer on internet platforms. During the process, an iterative dataset labelling method is established to reduce the time cost for training set labelling. Secondly, complex networks are used to model the design resource characteristics of the community according to the resource characteristics of all the socialized designers in the community. Two real communities from RepRap 3D printer project are used as case study.

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