scholarly journals MIXING-MODEL SENSITIVITY TO INPUT PARAMETER VARIATION

2017 ◽  
Author(s):  
JOSIAH BIGELOW ◽  
HUMBERTO SILVA III ◽  
C. RANDALL TRUMAN ◽  
PETER VOROBIEFF
Keyword(s):  
Input Parameter ◽  
Parameter Variation ◽  
Mixing Model ◽  
Model Sensitivity

scholarly journals Quantitative methods to direct exploration based on hydrogeologic information

2006 ◽  
Vol 8 (2) ◽  
pp. 77-90 ◽  
Author(s):  
Andrew J. Graettinger ◽  
Jejung Lee ◽  
Howard W. Reeves ◽  
Deepu Dethan
Keyword(s):  
Input Data ◽  
Quantitative Methods ◽  
Input Parameter ◽  
Model Output ◽  
Information Uncertainty ◽  
Analysis Model ◽  
Model Sensitivity ◽  
Piezometric Head ◽  
Spatial Continuity ◽  
Input Information

Quantitatively Directed Exploration (QDE) approaches based on information such as model sensitivity, input data covariance and model output covariance are presented. Seven approaches for directing exploration are developed, applied, and evaluated on a synthetic hydrogeologic site. The QDE approaches evaluate input information uncertainty, subsurface model sensitivity and, most importantly, output covariance to identify the next location to sample. Spatial input parameter values and covariances are calculated with the multivariate conditional probability calculation from a limited number of samples. A variogram structure is used during data extrapolation to describe the spatial continuity, or correlation, of subsurface information. Model sensitivity can be determined by perturbing input data and evaluating output response or, as in this work, sensitivities can be programmed directly into an analysis model. Output covariance is calculated by the First-Order Second Moment (FOSM) method, which combines the covariance of input information with model sensitivity. A groundwater flow example, modeled in MODFLOW-2000, is chosen to demonstrate the seven QDE approaches. MODFLOW-2000 is used to obtain the piezometric head and the model sensitivity simultaneously. The seven QDE approaches are evaluated based on the accuracy of the modeled piezometric head after information from a QDE sample is added. For the synthetic site used in this study, the QDE approach that identifies the location of hydraulic conductivity that contributes the most to the overall piezometric head variance proved to be the best method to quantitatively direct exploration.

Analysis of the sensitivity of the delay time in the hydrocarbon fuel ignition to the input parameter variation

1990 ◽  
Vol 26 (4) ◽  
pp. 428-432
Author(s):  
M. A. Gorokhovskii ◽  
A. M. Salamatin
Keyword(s):  
Delay Time ◽  
Input Parameter ◽  
Hydrocarbon Fuel ◽  
Parameter Variation

Determination of interface width using EELS fine structure changes

1991 ◽  
Vol 49 ◽  
pp. 730-731
Author(s):  
Vinayak P. Dravid ◽  
M.R. Notis ◽  
C.E. Lyman
Keyword(s):  
Fine Structure ◽  
Materials Science ◽  
Input Parameter ◽  
Core Loss ◽  
Directionally Solidified ◽  
Interface Width ◽  
Structure Changes ◽  
Important Input ◽  
Directionally Solidified Eutectics

The concept of interfacial width is often invoked in many materials science phenomena which relate to the structure and properties of internal interfaces. The numerical value of interface width is an important input parameter in diffusion equations, sintering theories as well as in many electronic devices/processes. Most often, however, this value is guessed rather than determined or even estimated. In this paper we present a method of determining the effective structural and electronic- structural width of interphase interfaces using low- and core loss fine structure effects in EELS spectra.The specimens used in the study were directionally solidified eutectics (DSEs) in the system; NiO-ZrO2(CaO), NiO-Y2O3 and MnO-ZrO2(ss). EELS experiments were carried out using a VG HB-501 FE STEM and a Hitachi HF-2000 FE TEM.

COMPARISON OF EMPIRICAL AND THEORETICAL LAWS OF PARAMETER VARIATION OF EXPLOSION WAVES IN THE SEA

1979 ◽  
Vol 40 (C8) ◽  
pp. C8-62-C8-67
Author(s):  
V. E. Fridman
Keyword(s):  
Parameter Variation

Design and Simulation of Smart Control System for Internet Traffic Distribution on Servers by Using Fuzzy Logic System

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Raid Daoud ◽  
Yaareb Al-Khashab
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Input Parameter ◽  
Traffic Density ◽  
The Internet ◽  
Linguistic Variables ◽  
Internet Service ◽  
Traffic Distribution ◽  
Smart Control ◽  
High Level

The internet service is provided by a given number of servers located in the main node of internet service provider (ISP). In some cases; the overload problem was occurred because a demand on a given website goes to very high level. In this paper, a fuzzy logic control (FLC) has proposed to distribute the load into the internet servers by a smart and flexible manner. Three effected parameters are tacked into account as input for FLC: link capacity which has three linguistic variables with Gaussian membership function (MF): (small, medium and big), traffic density with linguistic variables (low, normal and high) and channel latency with linguistic variables (empty, half and full); with one output which is the share server status (single, simple and share). The proposed work has been simulated by using MATLAB 2016a, by building a structure in the Fuzzy toolbox. The results were fixed by two manners: the graphical curves and the numerical tables, the surface response was smoothly changed and translates the well-fixed control system. The numerical results of the control system satisfy the idea of the smart rout for the incoming traffics from the users to internet servers. So, the response of the proposed system for the share of server ratio is 0.122, when the input parameter in the smallest levels; and the ratio is 0.879 when the input parameters are in highest level. The smart work and flexible use for the FLC is the main success solution for most of today systems control.

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

Decoupling Stochastic Systems under Plant and Controller Parameter Variation

1986 ◽  
Author(s):  
Gordon K. F. Lee ◽  
Ali H. Bamani
Keyword(s):  
Stochastic Systems ◽  
Parameter Variation ◽  
Controller Parameter

Mixture theory model sensitivity to effective viscosity in simulations of sandy bedform dynamics

OCEANS 2009 ◽  
2009 ◽  
Author(s):  
Allison M. Penko ◽  
Joseph Calantoni ◽  
Donald N. Slinn
Keyword(s):  
Effective Viscosity ◽  
Mixture Theory ◽  
Theory Model ◽  
Model Sensitivity
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