Extracting of silicon carbide schottky diode model parameters using lateral optimization method including the parallel conductance

2005 ◽  
Author(s):  
Z. Ouennoughi ◽  
R. Weiss ◽  
S. Benlala ◽  
H. Ryssel
Keyword(s):  
Silicon Carbide ◽  
Schottky Diode ◽  
Optimization Method ◽  
Model Parameters ◽  
Parallel Conductance

Extraction of Schottky diode parameters including parallel conductance using a vertical optimization method

2002 ◽  
Vol 46 (5) ◽  
pp. 615-619 ◽  
Author(s):  
A. Ferhat-Hamida ◽  
Z. Ouennoughi ◽  
A. Hoffmann ◽  
R. Weiss
Keyword(s):  
Schottky Diode ◽  
Optimization Method ◽  
Parallel Conductance

Closed Loop Identification by Optimization Method

2015 ◽  
Vol 1084 ◽  
pp. 636-641
Author(s):  
Valeriy F. Dyadik ◽  
Nikolay S. Krinitsyn ◽  
Vyacheslav A. Rudnev
Keyword(s):  
Closed Loop ◽  
Uranium Hexafluoride ◽  
Optimization Method ◽  
Model Parameters ◽  
Control Loop ◽  
Loop Control ◽  
Identification Method ◽  
Closed Loop Control System ◽  
Closed Loop Identification ◽  
Loop Control System

The article is devoted to the adaptation of the controller parameters during its operation as a part of a control loop. The possibility to identify the parameters of the controlled plant model in the closed control loop has been proved by a computer simulation. The described active identification method is based on the response processing of the closed loop control system to standard actions. The developed algorithm has been applied to determine the model parameters of the flaming fluorination reactor used for the production of uranium hexafluoride. Designed identification method improves the quality of the product and the efficiency of the entire production.

scholarly journals LWD DATA INVERSION FOR STRUCTURE WITH ANGULAR UNCONFORMITY

2021 ◽  
Vol 2 (1) ◽  
pp. 336-344
Author(s):  
Anna S. Astrakova ◽  
Elena V. Konobriy ◽  
Dmitry Yu. Kushnir ◽  
Nikolay N. Velker ◽  
Gleb V. Dyatlov
Keyword(s):  
Real Time ◽  
Optimization Method ◽  
Parametric Model ◽  
Model Parameters ◽  
Time Inversion ◽  
Water Contact ◽  
2D Inversion ◽  
Angular Unconformity ◽  
Oil Water ◽  
2D Resistivity

Non-structural traps and reservoir flanks are characterized by angular unconformities. Angular unconformity between dipping formation and sub-horizontal oil-water contact is common in the North Sea fields. This paper presents an approach to real-time inversion of LWD resistivity data for the scenario with angular unconformity. The approach utilizes artificial neural networks (ANNs) for calculating the tool responses in parametric surface-based 2D resistivity models. We propose a parametric model with two non-parallel boundaries suitable for scenarios with angular unconformity and pinch-out. Training of ANNs for this parametric model is performed using a database containing samples with the model parameters and corresponding tool responses. ANNs are the kernel of 2D inversion based on the Levenberg-Marquardt optimization method. To demonstrate applicability of our approach and compare with the results of 1D inversion, we analyze Extra Deep Azimuthal Resistivity tool responses in a 2D synthetic model. It is shown that 1D inversion determines either the position of the oil-water contact or dipping layers structure. At the same time, 2D inversion makes it possible to correctly reconstruct the positions of non-parallel boundaries. Performance of 2D inversion based on ANNs is suitable for real-time applications.

Experimental Non-Linear Model Updating Applied in Cylindrical Journal Bearings

2001 ◽  
Author(s):  
Katia Lucchesi Cavalca ◽  
Sérgio Junichi Idehara ◽  
Franco Giuseppe Dedini ◽  
Robson Pederiva
Keyword(s):  
Linear Model ◽  
Model Updating ◽  
Optimization Method ◽  
Model Parameters ◽  
Numeric Model ◽  
Rotating Systems ◽  
Non Linear ◽  
Symmetric Rotor ◽  
Set Up ◽  
Experimental Values

Abstract The present paper proposes the use of non linear model updating applying unrestricted optimization method, in order to obtain a methodology, which allows the calibration of mathematical models in rotating systems. An experimental set up for this purpose consists of a symmetric rotor, on a rigid foundation supported by two hidrodynamic cylindrical bearings and with a central disk of considerable mass, working as na unbalancing excitation force. Once the numeric and experimental values are obtained, error vectors are defined, which are the minimization parameters, through the variation of the numeric model parameters. The method presented satisfactory results, as it was able to calibrate the mathematical model, and then to obtain reliable responses for the physical system studied. The research also presents a contribution for the rotating machine desing area as it presents a relatively simple methodology on the updating and revalidation of computacional models for machines and structures.

scholarly journals Learning Compact Model for Large-Scale Multi-Label Data

2019 ◽  
Vol 33 ◽  
pp. 5385-5392 ◽  
Author(s):  
Tong Wei ◽  
Yu-Feng Li
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Performance Metrics ◽  
State Of The Art ◽  
Optimization Method ◽  
Model Parameters ◽  
Generalization Capability ◽  
Constrained Optimization Problem ◽  
Unseen Data ◽  
Model Size

Large-scale multi-label learning (LMLL) aims to annotate relevant labels from a large number of candidates for unseen data. Due to the high dimensionality in both feature and label spaces in LMLL, the storage overheads of LMLL models are often costly. This paper proposes a POP (joint label and feature Parameter OPtimization) method. It tries to filter out redundant model parameters to facilitate compact models. Our key insights are as follows. First, we investigate labels that have little impact on the commonly used LMLL performance metrics and only preserve a small number of dominant parameters for these labels. Second, for the remaining influential labels, we reduce spurious feature parameters that have little contribution to the generalization capability of models, and preserve parameters for only discriminative features. The overall problem is formulated as a constrained optimization problem pursuing minimal model size. In order to solve the resultant difficult optimization, we show that a relaxation of the optimization can be efficiently solved using binary search and greedy strategies. Experiments verify that the proposed method clearly reduces the model size compared to state-of-the-art LMLL approaches, in addition, achieves highly competitive performance.

ON-state characteristics of proton irradiated 4H–SiC Schottky diode: The calibration of model parameters for device simulation

2014 ◽  
Vol 94 ◽  
pp. 32-38 ◽  
Author(s):  
J. Vobecký ◽  
P. Hazdra ◽  
V. Záhlava ◽  
A. Mihaila ◽  
M. Berthou
Keyword(s):  
Schottky Diode ◽  
Device Simulation ◽  
Model Parameters
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