Generalized linear models for empirical performance modeling in circuit design

Discussion of Dr Green's Paper

In this paper it is proposed the implementation in Matlab & Tools environment, by using Simulink and Simscape plug-ins, of empirical models of the Carbon Nanotube Field Effect Transistor (CNTFET) in order to evaluate the capability of this very interesting design and simulation environment to easily design the most advanced generation of digital electronic circuits. Empirical models of electronic devices are particularly suitable for the Computer Aided Design (CAD) of digital electronic circuits. They are generally very compact, fast and accurate, without any physical approximation, and then they can be easily implemented in circuit simulators. The paper aims at describing the core of a possible CAD tool for digital circuits using CNTFETs, designed by the author, and at evaluating its suitability and usefulness by design examples. Therefore, they have been implemented two models of CNTFETs in Matlab & Tools environment and it has been performed the design of basic digital logic gates and memory elements. One of the two models has been developed by the author and already validated. The other model has been developed by valuable authors at Standford University and is used for comparisons. Results show the great usefulness of the empirical approach and of the Matlab & Tools environment for the quick and easy design of digital electronic circuits using CNTFETs.

In this paper, an orthogonal local search genetic algorithm (OLSGA) is proposed for the design and optimization of power electronic circuits. The genetic algorithm is accelerated with a fast local search operator that automatically adjusts the search direction and the step size. An experimental design method called orthogonal design is used to determine the most promising direction of the potential region in the local search. In each generation, the step size is adaptively expanded or shrunk according to whether there is a newly improvement in the given local region. As a result, with proper direction and step size, the local search operator is able to stride forward and provide better exploitation ability to speed up the convergence rate of the genetic algorithm. The proposed method is applied to design and optimize a buck regulator. The results in comparison with other published results indicate that our proposed algorithm is effective and efficient.

Taguchi method is a method that purposes to improve the quality of products and processes at the same time with the purpose of reducing costs and resources to a minimum. Taguchi method is one example of a fractional factorial design that uses orthogonal arrays to reduce the number of experiments. The analytical tool used was ANOVA and Signal to Noise Ratio. ANOVA was used to determine the factors that affect the response and Signal to Noise Ratio are used to determine the combination of factors that affect the performance of the product so that the resulting optimal response. Based on the results of tests performed to determine the factors that influence the design of electronic circuits that will produce the center frequency of 35.75 megahertz at a temperature of -10 ℃, the significant factor is the factor A, B, C, D, F, and H. The best combination is obtained A 2 , B 2 , C 2 , D 3 , F 2 , dan H 3 . Factor F has the greatest percent contribution is 42.57%, the next factor D, H, C, A, and B, respectively 8.83%, 7.37%, 5.93%, 3.90% and 3.84%.

A general linear regression model for the usual least squares estimated rate of change (slope) on censoring time is described as an approximation to account for informative right censoring in estimating and comparing changes of a continuous variable in two groups. Two noniterative estimators for the group slope means, the linear minimum variance unbiased (LMVUB) estimator and the linear minimum mean squared error (LMMSE) estimator, are proposed under this conditional model. In realistic situations, we illustrate that the LMVUB and LMMSE estimators, derived under a simple linear regression model, are quite competitive compared to the pseudo maximum likelihood estimator (PMLE) derived by modeling the censoring probabilities. Generalizations to polynomial response curves and general linear models are also described.

In this chapter, we discuss constrained ordination and related approaches: stepwise selection of a set of explanatory variables, Monte Carlo permutation tests and variance partitioning procedure.

Here I propose a fundamentally new flexible model to reveal the association between a trait and a set of genetic variants in a genomic region/gene. This model was developed for the situation when original individual-level phenotype and genotype data are not available, but the researcher possesses the results of statistical analyses conducted on these data (namely, SNP-level summary Z score statistics and SNP-by-SNP correlations). The new model was analytically derived from the classical multiple linear regression model applied for the region-based association analysis of individual-level phenotype and genotype data by using the linear compression of data, where the SNP-by-SNP correlations are among the explanatory variables, and the summary Z score statistics are categorized as the response variables. I analytically show that the regional association analysis methods developed within the framework of the classical multiple linear regression model with additive effects of genetic variants can be reformulated in terms of the new model without the loss of information. The results obtained from the regional association analysis utilizing the classical model and those derived using the proposed model are identical when SNP-by-SNP correlations and SNP-level statistics are estimated from the same genetic data.

The classical model only provides a correct analysis if all the effects are fixed. For experiments that include fixed and random effects, the general linear mixed model is appropriate for handling the non-normal distributed response variables. The aim of this study is to perform the genotype selection through a generalized linear mixed model and identify the impact of treatment and the related traits on grain yield. The data were collected using a lattice square design and measured the phenotype traits of sorghum. The result of PCA was used as an input variable for the general linear mixed model. The data analysis was performed using a general linear mixed model with maximum likelihood methods to estimate the parameters of the model. The result showed that the grain yield had a gamma distribution and a treatment effect on grain yield. The first principal component was significant for grain yield. The variability of grain yield due to the random effects of replication within treatment, genotype, and the interaction of genotype by treatment were significant. The best genotypes effective for the mass production of sorghum were G137, G66 and G156 under stress conditions and G55, G41 and G78 under irrigated conditions. Overall, genotype selection using a general linear mixed model for grain yield is recommended for genotype selection of plant breeding.

An important stage in the design of electronic circuits of various classes and purposes is the calculation of the zero-level instability of a developed device under the influence exerted on the parameters of its components by external disturbances (variations in temperature, humidity, pressure, radiation, etc.), as well as by technological variations in these parameters in the course of serial manufacturing. A mathematical description of the problem of calculating the zero-level instability of an electronic circuit is constructed for the common case when the coordinates of operating points of all circuit components are known and the task is to find their deviations under variations in the parameters of circuit components caused by external disturbances, as well as under technological variations in these parameters in the course of manufacturing. An advantage of the proposed technique is that it avoids multiple calculations of a nonlinear circuit and its sensitivity to parameter variations, instead dealing with a mathematical description of the linearized circuit, which is used to determine the deviations of variables from their initial stationary values. As a result, the amount of computational operations is dramatically reduced and the computation time is reduced as well, which is especially important for solving optimization problems in electronic circuit design.

Chaos science is also used in the fields of medicine, biology and finance besides in the engineering fields such as communications, electronics and computer. Up till today, many different chaotic systems have been introduced in the literature in order to be used in these fields. The majority of these systems have usually been examined numerically and only some of them have electronic circuit design. The chaotic systems must be able to be done electronically especially in the fields such as secure communication, encryption, etc. In 1994 were found 19 attractor systems that were simple-structured but producing complex chaotic signals and named A to S by Sprott. In this study electronic circuit design of Sprott H chaotic system with the positive (non-inverting) second generation current conveyor with high gain and high bandwidth were performed in Orcad-PSPICE program. Numerical analysis results of Sprott H chaotic system and the designed electronic circuit outputs were found to be the same. The system can be used secure communication and encryption applications of which electronic circuit were designed.

A method of multi-objective reliability tolerance design for electronic circuits

The profitability of insurance companies has been the focus of several studies within the rise of economies across the countries worldwide. This study aimed to identify determinants of profitability of insurance companies in Ethiopian. The study used nine insurance companies selected by purposive sampling technique among the total 17 insurance companies in Ethiopia from period of 2011 to 2020 based on their establishment. Descriptive, causal research design and quantitative research approach were adopted in carrying out this study. Classical linear regression model under estimation of ordinary least square was employed to identify the determinants of profitability of insurance companies in Ethiopia at 5% level of significance. The classical linear regression model revealed that variable age of the company, tangibility of an asset, size of the company, managerial efficiency, leverage ratio, premium growth and GDP have a positive coefficient relationship with return on asset while loss ratio and inflation have a negative coefficient relationship with return on asset. On the other hand, age of the company size, managerial efficiency, leverage ratio, liquidity ration inflation and premium growth have statistically significant at 5% confidence interval level, whereas the other variables such tangibility of asset and GDP have no statistical significance at 5% confidence interval level. The insurance companies’ previous profit, age of the company, company size, managerial efficiency, leverage ratio, liquidity ratio, loss ratio, premium growth and inflation rate variables are significant key drivers of profitability of Ethiopian insurance companies. Giving due attention to the sector in line with key factors affecting the profitability will improve the overall performance of the insurance companies in Ethiopia.

The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: -11.6%-23.8%) and 14.6% (range: -7.3%-27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: -6.8%-40.3%) and 13.1% (range: -1.5%-52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: -11.1%-20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography images and facilitate improved treatment management.

This paper presents pseudo-coevolutionary genetic algorithms (GA's) for power electronic circuit (PEC) optimization. Circuit parameters are optimized through two parallel co-adapted GA-based optimization processes for power conversion stage and feedback network, respectively. Each process has tunable and untunable parametric vectors. The best candidate of the tunable vector in one process is migrated into the other process as untunable vector through a migration controller, in which the migration strategy is adaptively controlled by a first-order projection of the maximum and minimum bounds of the fitness value in each generation. Implementation of this method is suitable for systems with parallel computation capacity, resulting in considerable improvement of the training speed. Optimization of a buck regulator for meeting requirements under large-signal changes and at steady state is illustrated. Simulation predictions are verified with experimental results.

In this study, the performance analysis of a 5.5 kW, 460 V, 60 Hz, 4-pole, and 3-phase squirrel- cage induction motor (IM) has been carried out by developing a portable electronic circuit, design, and the software, independent of any commercial software. The mathematical equations used in the design steps and the preliminary design of the main dimensions of the squirrel cage IM are realized with a developed electronic circuit. To perform preliminary design calculations Nextion 7-inch touch screen, Arduino mega 2560 microcontroller is used in electronic circuit design. Briefly, IM basic data are entered from the Nextion 7 inches touch screen in the electronic circuit created and the data are sent to the Arduino Mega 2560 microcontroller card via serial communication. On the Arduino Mega 2560 microcontroller board, IM mathematical calculations are accomplished, and the calculation results are presented on the Nextion screen. It has been observed that the analytical calculations and the calculation results in the developed calculation tool have been obtained in good agreement.