scholarly journals Weak cosmic censorship, superradiance, and quantum particle creation

2015 ◽  
Vol 92 (10) ◽  
Author(s):  
İbrahim Semiz ◽  
Koray Düztaş
Keyword(s):  
Quantum Particle ◽  
Particle Creation ◽  
Cosmic Censorship

Quantum particle creation (Hawking effect) in nonstationary black holes

1976 ◽  
Vol 29 (2) ◽  
pp. 1012-1021 ◽  
Author(s):  
I. V. Volovich ◽  
V. A. Zagrebnov ◽  
V. P. Frolov
Keyword(s):  
Black Holes ◽  
Quantum Particle ◽  
Particle Creation ◽  
Hawking Effect

Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

2019 ◽  
Vol 12 (3) ◽  
pp. 277-283
Author(s):  
Jiatang Cheng ◽  
Yan Xiong
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Wind Turbine Gearbox ◽  
Bp Network ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

scholarly journals Entanglement content of quantum particle excitations. Part II. Disconnected regions and logarithmic negativity

2019 ◽  
Vol 2019 (11) ◽  
Author(s):  
Olalla A. Castro-Alvaredo ◽  
Cecilia De Fazio ◽  
Benjamin Doyon ◽  
István M. Szécsényi
Keyword(s):  
Quantum Particle ◽  
Logarithmic Negativity

scholarly journals Addendum to “Strong cosmic censorship: The nonlinear story”

2021 ◽  
Vol 103 (10) ◽  
Author(s):  
Raimon Luna ◽  
Miguel Zilhão ◽  
Vitor Cardoso ◽  
João L. Costa ◽  
José Natário
Keyword(s):  
Cosmic Censorship ◽  
Strong Cosmic Censorship

Research on disruption management of urgent arrival in job shop with deteriorating effect

2021 ◽  
pp. 1-13
Author(s):  
Ning Tao ◽  
Duan Xiaodong ◽  
An Lu ◽  
Gou Tao
Keyword(s):  
Prospect Theory ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Cumulative Prospect Theory ◽  
Disruption Management ◽  
Particle Swarm Algorithm ◽  
Shop Scheduling ◽  
Management Method

A disruption management method based on cumulative prospect theory is proposed for the urgent with deteriorating effect arrival in flexible job shop scheduling problem (FJSP). First, the mathematical model of problem is established with minimizing the completion time of urgent order, minimizing the total process time of the system and minimizing the total cost as the target. Then, the cumulative prospect theory equation of the urgent arrival in job shop scheduling process is induced designed. Based on the selected model, an optimized multi-phase quantum particle swarm algorithm (MQPSO) is proposed for selecting processing route. Finally, using Solomon example simulation and company Z riveting shop example as the study object, the performance of the proposed method is analyzed. It is compared with the current common rescheduling methods, and the results verify that the method proposed in this paper not only meets the goal of the optimized objects, but improves the practical requirements for the stability of production and processing system during urgent arrival. Lastly, the optimized multiphase quantum particle swarm algorithm is used to solve disruption management of urgent arrival problem. Through instance analysis and comparison, the effectiveness and efficiency of urgent arrival disruption management method with deteriorating effect are verified.

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