scholarly journals Direct and Inverse Results for Kantorovich Type Exponential Sampling Series

2020 ◽  
Vol 75 (3) ◽  
Author(s):  
Sathish Kumar Angamuthu ◽  
Shivam Bajpeyi
Keyword(s):  
Inverse Results ◽  
Sampling Series

Approaching to the ideal condition of plasma confinement by applying external resonant fields in IR-T1 tokamak

2015 ◽  
Vol 81 (3) ◽  
Author(s):  
Sakineh Meshkani ◽  
Mahmood Ghoranneviss ◽  
Mansoureh Lafouti
Keyword(s):  
Magnetic Field ◽  
Turbulent Transport ◽  
Edge Plasma ◽  
Ideal Condition ◽  
Plasma Confinement ◽  
Applied Bias ◽  
Inverse Results ◽  
Electric And Magnetic Field ◽  
Helical Magnetic Field ◽  
The Ideal

For understanding the effect of resonant helical magnetic field (RHF) and bias on the edge plasma turbulent transport, the radial and poloidal electric field (Er, EP), poloidal and toroidal magnetic field (BP, Br) were detected by the Langmuir probe, magnetic probe and diamagnetic loop. The poloidal, toroidal and radial velocity (VP, Vr, Vt) can be determined from the electric and magnetic field. In the present work, we have investigated the effect of the magnitude of bias (Vbias = 200v, Vbias = 320v) on Er, EP, BP, Bt, VP, Vr, Vt. Moreover, we applied RHF with L = 2, L = 3 and L = 2 and 3 and investigate the effect of the helical windings radius on above parameters. Also, the experiment was repeated by applying the positive biasing potentials and RHF's simultaneously. The results show that by applying bias to the plasma at t = 15 msec at r/a = 0.9, Er, BP and Bt increase while EP decreases. The best modification occurs at Vbias = 200v. By applying RHF to the plasma, both the electric and magnetic field vary. Er reaches the highest in the presence of RHF with L = 3. The same results are obtained for BP, Bt, VP and Vt. While the inverse results are obtained for EP and Vr. Finally, RHF and bias are applied simultaneously to the plasma. With applied bias with Vbias = 200v and RHF with L = 2 and 3, we reach to the ideal circumstance. The same results obtain in the situation with Vbias = 320v and RHF with L = 2 and 3.

scholarly journals Inversion of particle size distribution by spectral extinction technique using the attractive and repulsive particle swarm optimization algorithm

2015 ◽  
Vol 19 (6) ◽  
pp. 2151-2160 ◽  
Author(s):  
Hong Qi ◽  
Zhen-Zong He ◽  
Shuai Gong ◽  
Li-Ming Ruan
Keyword(s):  
Particle Size ◽  
Particle Swarm Optimization ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Particle Swarm ◽  
Incident Laser ◽  
Swarm Optimization ◽  
N Distribution ◽  
Repulsive Particle Swarm Optimization ◽  
Inverse Results

The particle size distribution (PSD) plays an important role in environmental pollution detection and human health protection, such as fog, haze and soot. In this study, the Attractive and Repulsive Particle Swarm Optimization (ARPSO) algorithm and the basic PSO were applied to retrieve the PSD. The spectral extinction technique coupled with the Anomalous Diffraction Approximation (ADA) and the Lambert-Beer Law were employed to investigate the retrieval of the PSD. Three commonly used monomodal PSDs, i.e. the Rosin-Rammer (R-R) distribution, the normal (N-N) distribution, the logarithmic normal (L-N) distribution were studied in the dependent model. Then, an optimal wavelengths selection algorithm was proposed. To study the accuracy and robustness of the inverse results, some characteristic parameters were employed. The research revealed that the ARPSO showed more accurate and faster convergence rate than the basic PSO, even with random measurement error. Moreover, the investigation also demonstrated that the inverse results of four incident laser wavelengths showed more accurate and robust than those of two wavelengths. The research also found that if increasing the interval of the selected incident laser wavelengths, inverse results would show more accurate, even in the presence of random error.

scholarly journals Translating surveillance data into incidence estimates

2018 ◽  
Author(s):  
Yoann Bourhis ◽  
Timothy R. Gottwald ◽  
Frank van den Bosch
Keyword(s):  
Disease Incidence ◽  
Surveillance Data ◽  
Estimation Methods ◽  
Full Model ◽  
Estimation Model ◽  
Sampling Series ◽  
Wide Range ◽  
Incidence Estimation

AbstractMonitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which to estimate the disease incidence,i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence is not changing between monitoring rounds, resulting in underestimation of the disease incidence. In this paper we develop an incidence estimation model accounting for epidemic growth with monitoring rounds sampling varying incidence. We also show how to accommodate the asymptomatic period characteristic to most diseases. For practical use, we produce an approximation of the model, which is subsequently shown accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations.

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