Extraction of SPICE BJT model parameters in BIPOLE3 using optimization methods

1996 ◽  
Vol 15 (11) ◽  
pp. 1332-1339 ◽  
Author(s):  
A.D. Sadovnikov ◽  
D.J. Roulston ◽  
D. Celi
Keyword(s):  
Optimization Methods ◽  
Model Parameters

scholarly journals Influence of Selected Model Parameters on the Electromagnetic Levitation Melting Efficiency

2021 ◽  
Vol 11 (9) ◽  
pp. 3827
Author(s):  
Blazej Nycz ◽  
Lukasz Malinski ◽  
Roman Przylucki
Keyword(s):  
Electromagnetic Levitation ◽  
Optimization Methods ◽  
Calculation Model ◽  
Model Parameters ◽  
Starting Point ◽  
Low Efficiency ◽  
The Relationship ◽  
Reactive Metals ◽  
Electromagnetic Levitation Melting ◽  
Metal Melting

The article presents the results of multivariate calculations for the levitation metal melting system. The research had two main goals. The first goal of the multivariate calculations was to find the relationship between the basic electrical and geometric parameters of the selected calculation model and the maximum electromagnetic buoyancy force and the maximum power dissipated in the charge. The second goal was to find quasi-optimal conditions for levitation. The choice of the model with the highest melting efficiency is very important because electromagnetic levitation is essentially a low-efficiency process. Despite the low efficiency of this method, it is worth dealing with it because is one of the few methods that allow melting and obtaining alloys of refractory reactive metals. The research was limited to the analysis of the electromagnetic field modeled three-dimensionally. From among of 245 variants considered in the article, the most promising one was selected characterized by the highest efficiency. This variant will be a starting point for further work with the use of optimization methods.

scholarly journals Adaptive Control of Fishing Systems

1976 ◽  
Vol 33 (1) ◽  
pp. 145-159 ◽  
Author(s):  
Carl J. Walters ◽  
Ray Hilborn
Keyword(s):  
Sockeye Salmon ◽  
Optimization Methods ◽  
Fishing Effort ◽  
Model Parameters ◽  
Fraser River ◽  
The Face ◽  
Stock Recruitment ◽  
Best Fitting ◽  
Uncertainty Parameter ◽  
Recruitment Model

This paper discusses some formal techniques for deciding how harvesting policies should be modified in the face of uncertainty. Parameter estimation and dynamic optimization methods are combined for the Ricker stock-recruitment model to show how exploitation rates should be manipulated to give more information about the model parameters; in general, harvesting rates should be lower than would be predicted by the best fitting recruitment curve unless this curve predicts that the stock is very productive. A decision procedure is developed for comparing alternative stock-recruitment models; when applied to the Fraser River sockeye salmon (Oncorhynchus nerka), the procedure indicates that an experimental increase in escapements would be quite worthwhile. It appears that there is considerable promise for extending these methods and procedures to cases where the stock size is unknown and where fishing effort is poorly controlled.

Exploring More Representative States of Hidden Markov Model in Optical Character Recognition: A Clustering-Based Model Pre-Training Approach

2015 ◽  
Vol 29 (03) ◽  
pp. 1550014 ◽  
Author(s):  
Zhiwei Jiang ◽  
Xiaoqing Ding ◽  
Liangrui Peng ◽  
Changsong Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Hidden Markov ◽  
Recognition Performance ◽  
Optimization Methods ◽  
Model Parameters ◽  
Model Structure ◽  
Training Approach

Hidden Markov Model (HMM) is an effective method to describe sequential signals in many applications. As to model estimation issue, common training algorithm only focuses on the optimization of model parameters. However, model structure influences system performance as well. Although some structure optimization methods are proposed, they are usually implemented as an independent module before parameter optimization. In this paper, the clustering feature of states in HMM is discussed through comparing the mechanism of Quadratic Discriminant Function (QDF) classifier and HMM. Then, through the clustering effect of Viterbi training and Baum–Welch training, a novel clustering-based model pre-training approach is proposed. It can optimize model parameters and model structure by turns, until the representative states of all models are explored. Finally, the proposed approach is evaluated on two typical OCR applications, printed and handwritten Arabic text line recognition. And it is compared with some other optimization methods. The improvement of character recognition performance proves the proposed approach can make more precise state allocation. And the representative states are benefit to HMM decoding.

scholarly journals Mathematical Multiobjective Optimization for Metal Hip Arthroplasty with Medical Physics Applications

2021 ◽  
Keyword(s):  
Experimental Testing ◽  
Medical Physics ◽  
Optimization Methods ◽  
Model Parameters ◽  
Model Parameter ◽  
Erosion Prediction ◽  
Model Match ◽  
Acceptable Model ◽  
Parameter Values

Total hip metal arthroplasty (THA) model-parameters for a group of commonly used ones is optimized and numerically studied. Based on previous ceramic THA optimization software contributions, an improved multiobjective programming method/algorithm is implemented in wear modeling for THA. This computational nonlinear multifunctional optimization is performed with a number of THA metals with different hardnesses and erosion in vitro experimental rates. The new software was created/designed with two types of Sytems, Matlab and GNU Octave. Numerical results show be improved/acceptable for in vitro simulations. These findings are verified with 2D Graphical Optimization and 3D Interior Optimization methods, giving low residual-norms. The solutions for the model match mostly the literature in vitro standards for experimental simulations. Numerical figures for multifunctional optimization give acceptable model-parameter values with low residual-norms. Useful mathematical consequences/calculations are obtained for wear predictions, model advancements and simulation methodology. The wear magnitude for in vitro determinations with these model parameter data constitutes the advance of the method. In consequence, the erosion prediction for laboratory experimental testing in THA add up to the literature an efficacious usage-improvement. Results, additionally, are extrapolated to efficient Medical Physics applications and metal-THA Bioengineering designs.

scholarly journals Study of SEIRD Adaptive-Compartmental Model of COVID-19 Epidemic Spread in Russian Federation Using Optimization Methods

2021 ◽  
Vol 16 (1) ◽  
pp. 136-151
Author(s):  
S.P. Levashkin ◽  
S.N. Agapov ◽  
O.I. Zakharova ◽  
K.N. Ivanov ◽  
E.S. Kuzmina ◽  
...  
Keyword(s):  
Russian Federation ◽  
Compartmental Model ◽  
Intelligent System ◽  
Optimization Methods ◽  
Daily Basis ◽  
Automated System ◽  
Model Parameters ◽  
Epidemic Spread ◽  
The Russian Federation ◽  
The Government

A systemic approach to the study of a new multi-parameter model of the COVID-19 pandemic spread is proposed, which has the ultimate goal of optimizing the manage parameters of the model. The approach consists of two main parts: 1) an adaptive-compartmental model of the epidemic spread, which is a generalization of the classical SEIR model, and 2) a module for adjusting the parameters of this model from the epidemic data using intelligent optimization methods. Data for testing the proposed approach using the pandemic spread in some regions of the Russian Federation were collected on a daily basis from open sources during the first 130 days of the epidemic, starting in March 2020. For this, a so-called data farm was developed and implemented on a local server (an automated system for collecting, storing and preprocessing data from heterogeneous sources, which, in combination with optimization methods, allows most accurately tune the parameters of the model, thus turning it into an intelligent system to support management decisions). Among all model parameters used, the most important are the rate of infection transmission, the government actions and the population reaction.

Side Impact Crash Modelling: Development of a Numerical Identification Tool Using Optimization Methods

1999 ◽  
Author(s):  
Jean-Yves Jaskulski ◽  
Mounib Mekhilef
Keyword(s):  
Objective Function ◽  
Automotive Industry ◽  
Evaluation Criteria ◽  
Optimization Methods ◽  
Side Impact ◽  
Model Parameters ◽  
Side Impacts ◽  
Crash Model ◽  
Crash Modelling ◽  
Identification Tool

Abstract Currently, in automotive industry, identification of vehicle crash model parameters on test measurements is a key point. This paper outlines an approach based on optimization methods for this problem in the context of side impacts. It presents the problematic of crash model parameter identification. The engineer’s evaluation criteria of correlation are translated into an optimization objective function. Several optimization strategies are applied to identification of side impact crash model parameters. The comportment on our problem of these strategies are characterized, and numerical results show that the method of tabou search provides a good solution.

scholarly journals Transfer Learning by Reusing Structured Knowledge

AI Magazine ◽  
2011 ◽  
Vol 32 (2) ◽  
pp. 95 ◽  
Author(s):  
Qiang Yang ◽  
Vincent W. Zheng ◽  
Bin Li ◽  
Hankz Hankui Zhuo
Keyword(s):  
Transfer Learning ◽  
Common Knowledge ◽  
Optimization Methods ◽  
Data Reuse ◽  
Learning Problems ◽  
Model Parameters ◽  
New Learning ◽  
Data Clusters ◽  
Structured Knowledge ◽  
The Common

Transfer learning aims to solve new learning problems by extracting and making use of the common knowledge found in related domains. A key element of transfer learning is to identify structured knowledge to enable the knowledge transfer. Structured knowledge comes in different forms, depending on the nature of the learning problem and characteristics of the domains. In this article, we describe three of our recent works on transfer learning in a progressively more sophisticated order of the structured knowledge being transferred. We show that optimization methods, and techniques inspired by the concerns of data reuse can be applied to extract and transfer deep structural knowledge between a variety of source and target problems. In our examples, this knowledge spans explicit data labels, model parameters, relations between data clusters and relational action descriptions. 

scholarly journals Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1638
Author(s):  
Md. Abdul Awal ◽  
Sheikh Shanawaz Mostafa ◽  
Mohiuddin Ahmad ◽  
Mohammad Ashik Alahe ◽  
Mohd Abdur Rashid ◽  
...  
Keyword(s):  
Gaussian Function ◽  
Model Performance ◽  
Clinical Importance ◽  
Optimization Methods ◽  
Model Parameters ◽  
Search Technique ◽  
Time Frequency ◽  
Model Based ◽  
Cardiac Dysrhythmias ◽  
Ecg Morphology

The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (>0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used.

scholarly journals Mini-batch optimization enables training of ODE models on large-scale datasets

2019 ◽  
Author(s):  
Paul Stapor ◽  
Leonard Schmiester ◽  
Christoph Wierling ◽  
Bodo M.H. Lange ◽  
Daniel Weindl ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimization Methods ◽  
Scale Model ◽  
Model Parameters ◽  
Dynamical Models ◽  
Scaling Properties ◽  
Ode Models ◽  
Large Scale Model ◽  
Cellular Signal ◽  
Order Of Magnitude

AbstractQuantitative dynamical models are widely used to study cellular signal processing. A critical step in modeling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. However, mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models thereby establishing a direct link between dynamic modeling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modeling of even larger and more complex systems than what is currently possible.

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