scholarly journals Constructing Scenarios’ Network-of-Flight Conflict in Approach of Intersecting Runway

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ming Cheng ◽  
Yixuan Li ◽  
Xiaolian Han
Keyword(s):  
Conflict Resolution ◽  
Complex Network ◽  
Network Model ◽  
Control Strategy ◽  
General Rule ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Operational Risks ◽  
New Research

For studying the mechanism of flight conflict in approach of the intersecting runway, this paper applies the case study, scenario construction, and complex network, analyzes the operational risks of the intersecting runway, and researches the general rule of flight conflict. We constructed a network model of scenario evolution of flight conflict with selecting Beijing Daxing International Airport as the research object, which included 169 nodes and 263 edges. It proposed path evolution and verified the effectiveness of this network. We analyzed the degree centrality, median centrality, and closeness centrality of the network, and the results showed that the comprehensive value of 5 nodes is high, including go-around (V2), conflict resolution (C22), the warning light of aft cargo dJor was extinguished (F12), suspend subsequent take-off operations (F5), and keeping visual flying (C26). The results show that this method provides a new research way for the control strategy of chain breakage and the mechanism of scenario evolution of flight conflict.

Measuring Influence and Impact by Complex Network

2014 ◽  
Vol 551 ◽  
pp. 359-364
Author(s):  
Zheng Chang Zhang
Keyword(s):  
Complex Network ◽  
Betweenness Centrality ◽  
Relative Weight ◽  
Measurement Model ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Weighted Sum ◽  
Tennis Players ◽  
Influence Measurement ◽  
Set Up

This paper built two kinds of networks:co-author network and competition network and set up a system of influence measurement to determine who is most influential in the network.To evaluate the influence of co-authors, this paper introduced three norms: degree centrality, closeness centrality and betweenness centrality. Then, entropy value method was applied to get the relative weight of norms and establish co-author influence measurement model by the weighted sum of the three norms as influence marks. Meanwhile, the number of times players competed with each other among 10 tennis players in nearly 20 years was chosen to build our network. Because same as the co-author network, the competition network is undirected, we employ same algorithm to rank tennis players and analyze the first three players' competition relationship.

Centrality-Based Connected Dominating Sets for Complex Network Graphs

2014 ◽  
Vol 6 (2) ◽  
pp. 1-24 ◽  
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Complex Network ◽  
Real World ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Dominating Sets ◽  
Eigenvector Centrality ◽  
Connected Dominating Sets ◽  
The Core ◽  
Centrality Metrics ◽  
Betweeness Centrality

The author proposes the use of centrality-metrics to determine connected dominating sets (CDS) for complex network graphs. The author hypothesizes that nodes that are highly ranked by any of these four well-known centrality metrics (such as the degree centrality, eigenvector centrality, betweeness centrality and closeness centrality) are likely to be located in the core of the network and could be good candidates to be part of the CDS of the network. Moreover, the author aims for a minimum-sized CDS (fewer number of nodes forming the CDS and the core edges connecting the CDS nodes) while using these centrality metrics. The author discusses our approach/algorithm to determine each of these four centrality metrics and run them on six real-world network graphs (ranging from 34 to 332 nodes) representing various domains. The author observes the betweeness centrality-based CDS to be of the smallest size in five of the six networks and the closeness centrality-based CDS to be of the smallest size in the smallest of the six networks and incur the largest size for the remaining networks.

Centrality-Based Assortativity Analysis of Complex Network Graphs

2018 ◽  
pp. 66-77
Keyword(s):  
Complex Network ◽  
Real World ◽  
Betweenness Centrality ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Centrality Metrics

In this chapter, the author analyzes the assortativity of real-world networks based on centrality metrics (such as eigenvector centrality, betweenness centrality, and closeness centrality) other than degree centrality. They seek to evaluate the levels of assortativity (assortative, dissortative, neutral) observed for real-world networks with respect to the different centrality metrics and assess the similarity in these levels. The author observes real-world networks are more likely to be neutral (neither assortative nor dissortative) with respect to both R-DEG and BWC, and more likely to be assortative with respect to EVC and CLC. They observe the chances of a real-world network to be dissortative with respect to these centrality metrics to be very minimal. The author also assesses the extent to which they can use the assortativity index (A.Index) values obtained with a computationally light centrality metric to rank the networks in lieu of the A.Index values obtained with a computationally heavy centrality metric.

Using Networks to Measure Influence and Impact

2014 ◽  
Vol 556-562 ◽  
pp. 2668-2671
Author(s):  
Li Xian Zhang ◽  
Yu Jia Liu ◽  
Xin Zhong Lu
Keyword(s):  
Social Network ◽  
Complex Network ◽  
Calculation Result ◽  
Betweenness Centrality ◽  
Small World ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Scale Free ◽  
Important Type ◽  
Ranking Order

The co-author networks are important type of social network. In this paper, we establishes the Erdös co-author network and proves that the Erdös co-author network is a complex network which has three main properties, including small world, scale-free and clustering properties. Besides, this article gives the calculation formulas for degree centrality, closeness centrality and betweenness centrality of a network. According to the calculation result we give a ranking order for authors within Erdös co-author network.

Systemic Unsustainability as a Threat to Democracy

2020 ◽  
Author(s):  
Andrea Felicetti
Keyword(s):  
Research Agenda ◽  
Ecological Crisis ◽  
Illustrative Case ◽  
Sustainability Transition ◽  
Ways Of Thinking ◽  
Illustrative Case Study ◽  
New Research

Resilient socioeconomic unsustainability poses a threat to democracy whose importance has yet to be fully acknowledged. As the prospect of sustainability transition wanes, so does perceived legitimacy of institutions. This further limits representative institutions’ ability to take action, making democratic deepening all the more urgent. I investigate this argument through an illustrative case study, the 2017 People’s Climate March. In a context of resilient unsustainability, protesters have little expectation that institutions might address the ecological crisis and this view is likely to spread. New ways of thinking about this problem and a new research agenda are needed.

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.

scholarly journals Optimization of Low Head Axial-Flow Turbines for an Overtopping BReakwater for Energy Conversion: A Case Study

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4618
Author(s):  
Antonio Mariani ◽  
Gaetano Crispino ◽  
Pasquale Contestabile ◽  
Furio Cascetta ◽  
Corrado Gisonni ◽  
...  
Keyword(s):  
Energy Conversion ◽  
Control Strategy ◽  
Energy Production ◽  
San Antonio ◽  
Axial Flow ◽  
Complex Process ◽  
Low Head ◽  
Wave Power Conversion ◽  
And Control

Overtopping-type wave power conversion devices represent one of the most promising technology to combine reliability and competitively priced electricity supplies from waves. While satisfactory hydraulic and structural performance have been achieved, the selection of the hydraulic turbines and their regulation is a complex process due to the very low head and a variable flow rate in the overtopping breakwater set-ups. Based on the experience acquired on the first Overtopping BReakwater for Energy Conversion (OBREC) prototype, operating since 2016, an activity has been carried out to select the most appropriate turbine dimension and control strategy for such applications. An example of this multivariable approach is provided and illustrated through a case study in the San Antonio Port, along the central coast of Chile. In this site the deployment of a breakwater equipped with OBREC modules is specifically investigated. Axial-flow turbines of different runner diameter are compared, proposing the optimal ramp height and turbine control strategy for maximizing system energy production. The energy production ranges from 20.5 MWh/y for the smallest runner diameter to a maximum of 34.8 MWh/y for the largest runner diameter.

scholarly journals TO SUPPORT IOT COLLABORATIVE EXPRESSIVENESS ON THE SHOP FLOOR

2021 ◽  
Vol 1 ◽  
pp. 3149-3158
Author(s):  
Álvaro Aranda Muñoz ◽  
Yvonne Eriksson ◽  
Yuji Yamamoto ◽  
Ulrika Florin ◽  
Kristian Sandström
Keyword(s):  
Industry 4.0 ◽  
Shop Floor ◽  
Manufacturing Companies ◽  
Technical Aspects ◽  
Industrial Landscape ◽  
The Future ◽  
New Research ◽  
Empirical Material

AbstractThe availability of new research for IoT support and the human-centric perspective of industry 4.0 opens a gap to support operators in unleashing their creativity so they can provide improvements opportunities with IoT technology. This paper presents a case-study carried out in four Swedish manufacturing companies, where four different workshops were facilitated to support operators in the conceptualization of manufacturing improvements with IoT technologies. The empirical material gathered during these workshops has been analyzed in five different reflective sessions and discussed in light of previous research from industry 4.0, operators, and IoT support. Results indicate that operators can collaboratively create conceptual IoT solutions and that expressiveness in communicating their ideas and needs using IoT technology is more relevant than technical aspects and details of their proposed IoT solutions. This technological expressiveness is identified as a necessary skill to be cultivated on the shop floor and can potentially contribute to making a more effective and socially sustainable industrial landscape in the future.

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