Self-aligned molding technology (SAMT) for fabrication of 3D structures with a foldable imprint mold

Smolarczyk, Marek; Jablonka, Lukas; Reuter, Sabrina; Hillmer, Hartmut

doi:10.1007/s13204-019-01050-0

Cited by 1 publication

(1 citation statement)

References 35 publications

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“…Wireless sensor network technology integrates network technology, 7 signal processing technology, 8 application technology, 9 modern network technology, wireless communication technology, 10,11 and so forth and has a good application prospect in environmental monitoring, military, remote sensing information, and other fields. [12][13][14] In order to perceive and disseminate information in a variety of complex environments, researchers proposed wireless sensor networks applied to a variety of different applications.…”

Section: Introductionmentioning

confidence: 99%

Theory and algorithm of fire alarm coverage control in network

Liu

2020

Int J Communication

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Fire is a serious threat to human life and property, and effective prediction of fire can reduce the harm of fire to human. In the research of fire alarm automation, the fire alarm system based on wireless sensor network is a common method. When using wireless sensor to monitor the fire alarm information, it is necessary to ensure the safety coverage of the network in the monitoring area. Therefore, it is very necessary to study the network coverage in the fire alarm monitoring, which can ensure the effective work of the monitoring network, and good coverage control can reduce the energy consumption of the network and extend the service life of the network. Based on this, this paper designs a network coverage control algorithm based on multiobjective genetic algorithm optimization considering the coverage and energy consumption of wireless network. In this algorithm, the probability perception model is used to design the network coverage control model for the monitoring area with different coverage requirements, taking the maximum area coverage and the minimum network energy consumption rate as the optimization goal. Then the genetic algorithm is used to solve the model. In addition, because the multiobjective genetic algorithm is easy to fall into the local optimal solution when solving the optimization problem, this paper introduces the cross and variation coefficients of self-adaptive adjustment to improve the genetic algorithm. Through the simulation analysis, it is shown that when solving the multiobjective optimization problem, the introduction of self-adaptive adjustment of cross and variation coefficients can well overcome the problem that genetic algorithm falls into the local optimal solution, whereas the network coverage control algorithm designed in this paper based on the multiobjective genetic algorithm optimization can achieve greater coverage and lower energy consumption. Compared with the network coverage control algorithm before optimization, the optimized algorithm improves greatly in coverage, network residual energy, and number of nodes, which can well extend the service life of wireless sensor network, and is conducive to the good application of wireless sensor in fire prediction.

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Section: Introductionmentioning

confidence: 99%

Theory and algorithm of fire alarm coverage control in network

Liu

2020

Int J Communication

View full text Add to dashboard Cite

show abstract

Self-aligned molding technology (SAMT) for fabrication of 3D structures with a foldable imprint mold

Cited by 1 publication

References 35 publications

Theory and algorithm of fire alarm coverage control in network

Theory and algorithm of fire alarm coverage control in network

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