Evolutionary Approach to Function Model Synthesis: Development of Parameterization and Synthesis Rules

2020 ◽  
Author(s):  
Amaninder Singh Gill ◽  
Chiradeep Sen
Keyword(s):  
Evolutionary Algorithm ◽  
A Priori ◽  
Automated Synthesis ◽  
Function Model ◽  
Model Synthesis ◽  
Parameterization Method ◽  
Electromechanical Products ◽  
Feature Based ◽  
Algorithm Framework ◽  
Function Models

Abstract The goal of this paper is to develop the groundwork for automated synthesis of function models. To this end, an evolutionary algorithm based framework has been developed. A parameterization method that can completely describe any given function models has been proposed. The parameterization makes the function models compatible for use within the evolutionary algorithm framework. Validation of the parameterization method is carried out by using an evolutionary algorithm to synthesize the function models for five different electromechanical products. The algorithm converged in each case, indicating that the method is satisfactory and that function models can actually be synthesized using an evolutionary framework. In addition, the adaptation of several a priori rules for use in this framework has been proposed. These rules are categorized as grammar, logical and feature based rules. An updated evolutionary framework that incorporates these rules is also presented.

Logic Rules for Automated Synthesis of Function Models Using Evolutionary Algorithms

2021 ◽  
Author(s):  
Amaninder Singh Gil ◽  
Chiradeep Sen
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Automated Synthesis ◽  
Function Model ◽  
Rule Based ◽  
Model Synthesis ◽  
And Topology ◽  
Software Code ◽  
Rule Based Approach ◽  
Function Models

Abstract This paper presents the development of logic rules for evaluating the fitness of function models synthesized by an evolutionary algorithm. A set of 65 rules for twelve different function verbs are developed. The rules are abstractions of the definitions of the verbs in their original vocabularies and are stated as constraints on the quantity, type, and topology of flows connected to the functions. The rules serve as an objective and unambiguous basis of evaluating the fitness of function models developed by a genetic algorithm. The said algorithm and the rules are implemented in software code, which is used to both demonstrate and validate the efficacy of the rule-based approach of converging function model synthesis using GAs.

A Statistical Adaptable Distribution Function Model for Low Probabilities of Failure

1993 ◽  
Author(s):  
Suryaji R. Bhonsle ◽  
Paul Thompson
Keyword(s):  
Experimental Data ◽  
Distribution Function ◽  
Experimental Results ◽  
Function Model ◽  
Log Normal ◽  
Function Models

Abstract Weibull, log normal, and some other Distribution function models (D.F.M.) have a tendency to deviate from experimental results. This deviation, either exceedingly conservative or nonconservative, is amplified at low probabilities of failure. To remedy such problems a new D.F.M. is derived. It is then used to predict low probabilities of failure. The predictions are consistent with experimental data and are not too conservative or too nonconservative.

scholarly journals Machine Learning for Turbulence Model Development Using a High-Fidelity HPT Cascade Simulation

2017 ◽  
Author(s):  
Jack Weatheritt ◽  
Richard Pichler ◽  
Richard D. Sandberg ◽  
Gregory Laskowski ◽  
Vittorio Michelassi
Keyword(s):  
High Pressure ◽  
Evolutionary Algorithm ◽  
Turbine Blade ◽  
A Priori ◽  
Model Development ◽  
Boussinesq Approximation ◽  
High Pressure Turbine ◽  
Stress Strain ◽  
Non Linear ◽  
Anisotropy Tensor

The validity of the Boussinesq approximation in the wake behind a high-pressure turbine blade is explored. We probe the mathematical assumptions of such a relationship by employing a least-squares technique. Next, we use an evolutionary algorithm to modify the anisotropy tensor a priori using highly resolved LES data. In the latter case we build a non-linear stress-strain relationship. Results show that the standard eddy-viscosity assumption underpredicts turbulent diffusion and is theoretically invalid. By increasing the coefficient of the linear term, the farwake prediction shows minor improvement. By using additional non-linear terms in the stress-strain coupling relationship, created by the evolutionary algorithm, the near-wake can also be improved upon. Terms created by the algorithm are scrutinized and the discussion is closed by suggesting a tentative non-linear expression for the Reynolds stress, suitable for the wake behind a high-pressure turbine blade.

scholarly journals Correction to: DNMT3A reads and connects histone H3K36me2 to DNA methylation

2019 ◽  
Vol 11 (3) ◽  
pp. 230-230
Author(s):  
Wenqi Xu ◽  
Jiahui Li ◽  
Bowen Rong ◽  
Bin Zhao ◽  
Mei Wang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Histone Mark ◽  
The Other ◽  
Therapeutic Implication ◽  
Loss Of Function ◽  
Function Model ◽  
Gain Of Function ◽  
Intergenic Dna ◽  
Preferential Recognition ◽  
Function Models

The author would like to add the below information in this correction. A similar study from Chao Lu group was published online on 5 September 2019 in Nature, entitled “The histone mark H3K36me2 recruits DNMT3A and shapes the intergenic DNA methylation landscape” (Weinberg et al., 2019). Although both the studies reported the preferential recognition of H3K36me2 by DNMT3A PWWP, ours in addition uncovered a stimulation function by such interaction on the activity of DNMT3A. On the disease connections, we used a NSD2 gain-of-function model which led to the discovery of potential therapeutic implication of DNA inhibitors in the related cancers, while the other study only used NSD1 and DNMT3A loss-of-function models.

scholarly journals Time Function Model of Surface Subsidence Based on Inversion Analysis in Deep Soil Strata

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jihuan Han ◽  
Chenchen Hu ◽  
Jiuqun Zou
Keyword(s):  
Soil Surface ◽  
Measured Data ◽  
Surface Subsidence ◽  
Time Function ◽  
Model Parameters ◽  
Deep Soil ◽  
Function Model ◽  
Composite Function ◽  
Inversion Analysis ◽  
Function Models

As a common geological disaster, surface subsidence caused by mining underground resources has always been a hot and difficult topic in the civil engineering field. Aimed at the shortcomings of existing time function models in predicting mining subsidence in deep soil strata, a more accurate and reasonable time function model, called the composite function model, was established based on an inverted analysis of measured data. The results showed that the composite function model could describe the whole subsidence process of a deep soil surface and agreed well with the measured data. The model parameters were calculated by specific formulas, which improved the reliability of the subsidence prediction results under different mining conditions. The new model provided important guiding significance for preventing subsidence geological disasters and determining the coal mining time under the buildings, the railways, and the water bodies in deep soil strata.

Statistical and Mathematical Approach for Distribution Function Model for Low Probability of Failures

1991 ◽  
Author(s):  
S. R. Bhonsle ◽  
C. V. VanKarsen ◽  
J. R. Michler
Keyword(s):  
Distribution Function ◽  
Statistical Models ◽  
Mathematical Approach ◽  
Probabilistic Design ◽  
Function Model ◽  
Lower Tail ◽  
Low Probability ◽  
Statistical Distribution Function ◽  
Function Models ◽  
Design Factors

In probabilistic design it is common practice to use statistical models such as normal, lognormal, and Weibull to describe random design factors. However these distribution function models deviate in the lower tail, i.e. percentiles below 1%. The deviation is nonconservative in that since it predicts life longer than observed. A Statistical Distribution Function called Adaptive Distribution Function Model similar to Abelkis model was developed. It is compatible with the collected data, and it produces conservative designs at low tail ends. It is also relatively easy to use.

scholarly journals A Closed-Form Error Model of Straight Lines for Improved Data Association and Sensor Fusing

2018 ◽  
Author(s):  
Volker Sommer
Keyword(s):  
Closed Form ◽  
Covariance Matrix ◽  
Mobile Robotics ◽  
A Priori ◽  
Data Association ◽  
Error Model ◽  
Accurate Estimation ◽  
Feature Based ◽  
Single Data ◽  
Straight Lines

Linear regression is a basic tool in mobile robotics, since it enables accurate estimation of straight lines from range-bearing scans or in digital images, which is a prerequisite for reliable data association and sensor fusing in the context of feature-based SLAM. This paper discusses, extends and compares existing algorithms for line fitting applicable also in case of strong covariances between the coordinates at each single data point, which must not be neglected if range-bearing sensors are used. Besides, particularly the determination of the covariance matrix is considered, which is required for stochastic modeling. The main contribution is a new error model of straight lines in closed form for calculating fast and reliably the covariance matrix dependent on just a few comprehensible and easily obtainable parameters. The model can be applied widely in any case when a line is fitted from a number of distinct points also without a-priori knowledge of the specific measurement noise. By means of extensive simulations the performance and robustness of the new model in comparison to existing approaches is shown.

scholarly journals Peramalan Kebutuhan Daya Listrik Menggunakan Model ARIMA dan Fungsi Transfer (Studi Kasus: PT. PLN (Persero) Area Sumbawa)

2018 ◽  
Vol 4 (2) ◽  
pp. 122-127
Author(s):  
Mikhratunnisa Mikhratunnisa ◽  
Tri Susilawati
Keyword(s):  
Economic Development ◽  
Transfer Function ◽  
Arima Model ◽  
Transfer Function Model ◽  
Customer Demand ◽  
Function Model ◽  
Electrical Capacity ◽  
The Right ◽  
Function Models ◽  
Transfer Function Models

Energy is one of the basic need of human being. One of the vital energy is electricity. The need of electricity in NTB is increase along with the citizen economic development in NTB especially in Sumbawa regency. Therefore, there is a need for the right way in adjusting the amount of electrical capacity to match customer demand. One way that can be done is to forecast/ predict the need for electricity. The forecast can be used by using the ARIMA and Transfer Function models. The results of the study show that using the ARIMA model is estimated to require electricity in 2018 experienced an increase of 18,21% from the previous year, while using the transfer function model is estimated to increase by 18,18% from the previous year.

Composite Evolutionary Algorithm for Constrained Optimization

2012 ◽  
Vol 220-223 ◽  
pp. 2846-2851
Author(s):  
Si Lian Xie ◽  
Tie Bin Wu ◽  
Shui Ping Wu ◽  
Yun Lian Liu
Keyword(s):  
Evolutionary Algorithms ◽  
Constrained Optimization ◽  
Evolutionary Algorithm ◽  
Selection Criteria ◽  
Optimization Problems ◽  
Experimental Results ◽  
Constrained Optimization Problems ◽  
Mutation Operators ◽  
Single Operator ◽  
Algorithm Framework

Evolutionary algorithms are amongst the best known methods of solving difficult constrained optimization problems, for which traditional methods are not applicable. Due to the variability of characteristics in different constrained optimization problems, no single evolutionary with single operator performs consistently over a range of problems. We introduce an algorithm framework that uses multiple search operators in each generation. A composite evolutionary algorithm is proposed in this paper and combined feasibility rule to solve constrained optimization problems. The proposed evolutionary algorithm combines three crossover operators with two mutation operators. The selection criteria based on feasibility of individual is used to deal with the constraints. The proposed method is tested on five well-known benchmark constrained optimization problems, and the experimental results show that it is effective and robust

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