scholarly journals Predictive Filter Flow Network for Universal Demosaicking

2021 ◽  
Vol 25 (6) ◽  
pp. 257-261
Author(s):  
Daiki Arai ◽  
Taishi Iriyama ◽  
Masatoshi Sato ◽  
Hisashi Aomori ◽  
Tsuyoshi Otake
Keyword(s):  
Flow Network

The impact of China's import ban on global copper scrap flow network and the domestic copper sustainability

2021 ◽  
Vol 169 ◽  
pp. 105525
Author(s):  
Sen Liu ◽  
Wei Liu ◽  
Quanyin Tan ◽  
Jinhui Li ◽  
Wenqing Qin ◽  
...  
Keyword(s):  
Flow Network ◽  
The Impact

Military Human Resource Planning through Flow Network Modeling

2021 ◽  
pp. 1-12
Author(s):  
Oussama Mazari Abdessameud ◽  
Filip Van Utterbeeck ◽  
Marie-Anne Guerry
Keyword(s):  
Human Resource ◽  
Network Modeling ◽  
Resource Planning ◽  
Flow Network ◽  
Human Resource Planning ◽  
Through Flow

scholarly journals Hidden Topological Structure of Flow Network Functionality

2021 ◽  
Vol 126 (2) ◽  
Author(s):  
Jason W. Rocks ◽  
Andrea J. Liu ◽  
Eleni Katifori
Keyword(s):  
Topological Structure ◽  
Flow Network ◽  
Structure Of Flow ◽  
Network Functionality

scholarly journals Study on Tourism Flow Network Patterns on May Day Holiday

2021 ◽  
Vol 13 (2) ◽  
pp. 947
Author(s):  
Shanshan Wu ◽  
Lucang Wang ◽  
Haiyang Liu
Keyword(s):  
Transportation Network ◽  
Edge Structure ◽  
Flow Network ◽  
Factors Affecting ◽  
Spatial Distribution Characteristics ◽  
Transportation Modes ◽  
May Day ◽  
High Level ◽  
Network Patterns ◽  
Main Factor

The development of tourism is based on tourism flow and studying a tourism flow network can help to elucidate its mechanism of operation. Transportation network is the path to realize the spatial displacement of tourism flow. This study used “Tencent migration” big data to explore the spatial distribution characteristics and rules of tourism flow in China, providing suggestions for the development of tourism. The results demonstrate that the 361 cities studied can be divided into three types: destination-oriented, tourist-origin-oriented, and destination-oriented and tourist-origin-oriented. There are significant differences in the quantity of flow, the area of concentration, and the factors affecting the flow in the three types of cities. The larger the flow of tourism between cities, the higher the network level, and the wider the network range. The high-level nodes are closely related, while the peripheral nodes are more widely distributed, with weak attractiveness and inconvenient traffic, forming a “core-edge” structure. Different network patterns are established for different modes of transportation. The degree of response of different types of transportation to distance is the main factor influencing the network patterns of diverse paths. These findings have practical implications for the choice of appropriate travel destinations and transportation modes for tourists.

scholarly journals Maximum Entropy Analysis of Flow Networks: Theoretical Foundation and Applications

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 776 ◽  
Author(s):  
Robert K. Niven ◽  
Markus Abel ◽  
Michael Schlegel ◽  
Steven H. Waldrip
Keyword(s):  
Maximum Entropy ◽  
Joint Probability ◽  
Financial Economic ◽  
Flow Networks ◽  
Flow Network ◽  
Physical Constraints ◽  
Generalized Maximum Entropy ◽  
Deterministic Solution ◽  
Network Properties ◽  
The Cost

The concept of a “flow network”—a set of nodes and links which carries one or more flows—unites many different disciplines, including pipe flow, fluid flow, electrical, chemical reaction, ecological, epidemiological, neurological, communications, transportation, financial, economic and human social networks. This Feature Paper presents a generalized maximum entropy framework to infer the state of a flow network, including its flow rates and other properties, in probabilistic form. In this method, the network uncertainty is represented by a joint probability function over its unknowns, subject to all that is known. This gives a relative entropy function which is maximized, subject to the constraints, to determine the most probable or most representative state of the network. The constraints can include “observable” constraints on various parameters, “physical” constraints such as conservation laws and frictional properties, and “graphical” constraints arising from uncertainty in the network structure itself. Since the method is probabilistic, it enables the prediction of network properties when there is insufficient information to obtain a deterministic solution. The derived framework can incorporate nonlinear constraints or nonlinear interdependencies between variables, at the cost of requiring numerical solution. The theoretical foundations of the method are first presented, followed by its application to a variety of flow networks.

scholarly journals Bridging inter-flow and intra-flow network coding in wireless mesh networks: From theory to implementation

2018 ◽  
Vol 145 ◽  
pp. 1-12 ◽  
Author(s):  
Jonas Hansen ◽  
Jeppe Krigslund ◽  
Daniel E. Lucani ◽  
Peyman Pahlevani ◽  
Frank H.P. Fitzek
Keyword(s):  
Network Coding ◽  
Wireless Mesh Networks ◽  
Mesh Networks ◽  
Flow Network ◽  
Wireless Mesh
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