2 Optimum Sampling for the Identification Of Compartmental Systems

2015 ◽  
pp. 23-35
Author(s):  
Fumihiko Kajiya ◽  
Mitsuyasu Kagiyama ◽  
Masatsugu Hori ◽  
Katsuhiko Tsujioka ◽  
Go Tomonaga
Keyword(s):  
Compartmental Systems ◽  
Optimum Sampling

scholarly journals Measuring Hydrometeors Using a Precipitation Microphysical Characteristics Sensor: Sampling Effect of Different Bin Sizes on Drop Size Distribution Parameters

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xichuan Liu ◽  
Taichang Gao ◽  
Yuntao Hu ◽  
Xiaojian Shu
Keyword(s):  
Drop Size ◽  
Rain Rate ◽  
Monte Carlo Model ◽  
Rain Gauge ◽  
Sampling Schemes ◽  
Distribution Parameters ◽  
Optimum Sampling ◽  
Simulation Results ◽  
The Impact ◽  
Good Agreement

In order to improve the measurement of precipitation microphysical characteristics sensor (PMCS), the sampling process of raindrops by PMCS based on a particle-by-particle Monte-Carlo model was simulated to discuss the effect of different bin sizes on DSD measurement, and the optimum sampling bin sizes for PMCS were proposed based on the simulation results. The simulation results of five sampling schemes of bin sizes in four rain-rate categories show that the raw capture DSD has a significant fluctuation variation influenced by the capture probability, whereas the appropriate sampling bin size and width can reduce the impact of variation of raindrop number on DSD shape. A field measurement of a PMCS, an OTT PARSIVEL disdrometer, and a tipping bucket rain Gauge shows that the rain-rate and rainfall accumulations have good consistencies between PMCS, OTT, and Gauge; the DSD obtained by PMCS and OTT has a good agreement; the probability of N0, μ, and Λ shows that there is a good agreement between the Gamma parameters of PMCS and OTT; the fitted μ-Λ and Z-R relationship measured by PMCS is close to that measured by OTT, which validates the performance of PMCS on rain-rate, rainfall accumulation, and DSD related parameters.

Using dynamic simulation models and the ‘Dot-to-Dot’ method to determine the optimum sampling times in field trials

2006 ◽  
Vol 18 (4) ◽  
pp. 370-375
Author(s):  
J.U. Smith ◽  
P. Smith ◽  
K. Coleman ◽  
P.R. Hargreaves ◽  
A.J. Macdonald
Keyword(s):  
Dynamic Simulation ◽  
Simulation Models ◽  
Field Trials ◽  
Optimum Sampling ◽  
Sampling Times

Contribution to the identification of linear compartmental systems

Kybernetes ◽  
2004 ◽  
Vol 33 (8) ◽  
pp. 1277-1291 ◽  
Author(s):  
B. Hebri ◽  
Y. Cherruault
Keyword(s):  
Compartmental Systems

scholarly journals Basic approaches to simulation of resist mask formation in computational lithography

2020 ◽  
Vol 6 (1) ◽  
pp. 37-45
Author(s):  
Nikita N. Balan ◽  
Vladidmir V. Ivanov ◽  
Alexey V. Kuzovkov ◽  
Evgenia V. Sokolova ◽  
Evgeniy S. Shamin
Keyword(s):  
Model Calibration ◽  
Physical Simulation ◽  
Water Immersion ◽  
Compact Model ◽  
Process Noise ◽  
Variable Threshold ◽  
Immersion Lithography ◽  
Optimum Sampling ◽  
Rms Error ◽  
Basic Approaches

Main currently used resist mask formation models and problems solved have been overviewed. Stages of "full physical simulation" have been briefly analyzed based on physicochemical principles for conventional diazonapthoquinone (DNQ) photoresists and chemically enhanced ones. We have considered the concepts of the main currently used compact models predicting resist mask contours for full-scale product topologies, i.e., VT5 (Variable Threshold 5) and CM1 (Compact Model 1). Computation examples have been provided for full and compact resist mask formation models. Full resist mask formation simulation has allowed us to optimize the lithographic stack for a new process. Optimal thickness ratios have been found for the binary anti-reflecting layers used in water immersion lithography. VT5 compact model calibration has allowed us to solve the problem of optimal calibration structure sampling for maximal coverage of optical image parameters space while employing the minimal number of structures. This problem has been solved using cluster analysis. Clustering has been implemented using the k-means method. The optimum sampling is 300 to 350 structures, the rms error being 1.4 nm which is slightly greater than the process noise for 100 nm structures. The use of SEM contours for VT5 model calibration allows us to reduce the rms error to 1.18 nm for 40 structures.

scholarly journals MATHEMATICAL TECHNIQUES FOR SOLVING ANALYTICALLY LARGE COMPARTMENTAL SYSTEMS

2003 ◽  
Vol 85 (2) ◽  
pp. 184-193 ◽  
Author(s):  
Guillermo Sanchez ◽  
Jesus Lopez-Fidalgo
Keyword(s):  
Compartmental Systems ◽  
Mathematical Techniques

Optimum sampling time for detection of dental bacteraemia in children

1992 ◽  
Vol 35 (3) ◽  
pp. 311-315 ◽  
Author(s):  
Graham J. Roberts ◽  
Paul Gardner ◽  
Norman A. Simmons
Keyword(s):  
Sampling Time ◽  
Optimum Sampling
Sign in / Sign up

Export Citation Format

Share Document