scholarly journals STRUCTURAL AND PARAMETRIC OPTIMIZATION OF GAS-HYDRODYNAMIC MEASURING TRANSDUCERS OF PHYSICAL AND MECHANICAL PARAMETERS OF FLUIDS

2021 ◽  
pp. 23-31
Author(s):  
Ye. P. Pistun ◽  
H. F. Matiko ◽  
H. B. Krykh
Keyword(s):  
Parametric Optimization ◽  
Measuring Transducer ◽  
Optimization Methods ◽  
Optimization Method ◽  
Mechanical Parameters ◽  
Structural Synthesis ◽  
Optimization Parameters ◽  
Design Characteristics ◽  
Supply Mode ◽  
The Mathematical Model

The article is devoted to improving the methods for building throttle diagrams of gas-hydrodynamic measuring transducers of physical and mechanical parameters of fluids. The authors reviewed modern throttle transducers of various parameters, built on different diagrams, with different numbers and types of throttle elements, with different output signals. We established that the goodness of the measuring transducer is determined both by the structural diagram and the design characteristics of the throttle elements of a specific measuring diagram. The article proposes using structural synthesis with parametric optimization to achieve the specified characteristics of the gas-hydrodynamic transducers. The aim is to develop an effective method for building throttle diagrams of gas-hydrodynamic measuring transducers of physical and mechanical parameters of fluids using structural optimization of diagrams and to evaluate each dia-gram using parametric optimization methods with the appropriate criterion that quantifies the goodness of the measur-ing transducer. To achieve this goal, the authors analyzed the criteria and resources of structural and parametric optimization of gas-hydrodynamic transducers. In particular, the following resources of structural synthesis of measuring transducers’ dia-grams are analyzed: diagram order and throttle arrangement, type of throttles, output signals, supply mode of the transducer. Approaches to parametric optimization of throttle diagrams are offered: based on the mathematical model, one defines the objective function, forms restrictions on variable and fixed values, substantiates optimization parameters, chooses the optimization method. As a result of the research, the authors developed a technique for structural and parametric optimization of gas-hydrodynamic measuring transducers, making it possible to synthesize throttle diagrams and build mathematical models of transducers of specific parameters of the fluid with optimal characteristics.

scholarly journals Resources for structural optimization of gas-hydrodynamic measuring transducers

2021 ◽  
Vol 7 (2) ◽  
pp. 136-143
Author(s):  
Yevhen Pistun ◽  
◽  
Halyna Matiko ◽  
Hanna Krykh
Keyword(s):  
Graph Theory ◽  
Set Theory ◽  
Parametric Optimization ◽  
Measuring Transducer ◽  
Mechanical Parameters ◽  
Mathematical Apparatus ◽  
Structural Synthesis ◽  
Specific Parameter ◽  
Supply Mode ◽  
Diagram Type

The paper presents the analysis of the resources of structural and parametric optimization of gas-hydrodynamic measuring transducers of physical and mechanical parameters of fluids. Resources such as the number of throttles and their arrangement in the diagram, type of throttle elements, measuring channels with a certain type of output signal, the supply mode of the measuring transducer can be integrated into the design process of the measuring transducer of a specific parameter. A mathematical apparatus based on set theory and combinatorial analysis is proposed for synthesizing the possible structures of throttle diagrams, graph theory – for forming a set of measuring channels. The authors have given examples demonstrating the possibilities of building different diagrams of measuring transducers using the resources for structural synthesis. The proposed resources are the means of structural and parametric optimization for synthesizing the gas-hydrodynamic measuring transducers with optimal characteristics.

Modeling throttle bridge measuring transducers of physical-mechanical parameters of Newtonian fluids

2021 ◽  
Vol 8 (3) ◽  
pp. 515-525
Author(s):  
Ye. P. Pistun ◽  
◽  
H. F. Matiko ◽  
H. B. Krykh ◽  
F. D. Matiko ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Newtonian Fluid ◽  
Kinematic Viscosity ◽  
Parametric Optimization ◽  
Measuring Transducer ◽  
Jet Fuel ◽  
Newtonian Fluids ◽  
Mechanical Parameters

The paper proposes a measuring transducer of the physical-mechanical parameters of a Newtonian fluid based on a throttle bridge measuring diagram with identical turbulent and laminar throttles in opposite arms. A mathematical model is built for the throttle bridge transducer of the combined parameter, which depends on the kinematic viscosity and density of the fluid. The problem of parametric optimization of the proposed measuring transducer is formulated and analytically solved in the paper. The authors calculated the transform function of the measuring transducer of the combined parameter of jet fuel.

scholarly journals PARAMETRIC OPTIMIZATION METHOD FOR WIDE NEURAL NETWORKS USING GENETIC ALGORITHMS

2021 ◽  
Vol 23 (2) ◽  
pp. 51-56
Author(s):  
D.A. Trokoz ◽  
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Parametric Optimization ◽  
Optimization Methods ◽  
Optimization Method ◽  
Data Representation ◽  
Subject Area ◽  
Target Problem ◽  
Technical Problems ◽  
The Subject

In many complex technical problems, the number of parameters available for analysis is in the thousands. At the same time, depending on the specifics of the problem being solved, usually the number of key parameters, that is, parameters that have a significant impact on the processes associated with the target problem, does not exceed several tens. However, determining a subset of these parameters from those available in itself is a difficult task, which in most cases is solved with the assistance of experts in the relevant subject area. This paper proposes a parametric optimization method that can be used for wide neural networks, that is, neural networks with a large number of neurons in a layer. This method uses evolutionary optimization methods, namely, genetic algorithms, together with the method of invariant data representation in wide neural networks, using the algebra of hyperdimensional binary vectors, due to which, when the number of parameters of a neural network model changes during optimization, its topology does not change. At the same time, the more parameters are included in the model, the less accurately their values are transmitted, thus, in the course of optimization, a balance is achieved between the composition, number and accuracy of the parameters of the target problem. The proposed method does not require the participation of an expert corresponding to the subject area, allowing the process of parametric optimization to be fully automated.

scholarly journals Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System

2021 ◽  
Vol 13 (4) ◽  
pp. 707
Author(s):  
Yu’e Shao ◽  
Hui Ma ◽  
Shenghua Zhou ◽  
Xue Wang ◽  
Michail Antoniou ◽  
...  
Keyword(s):  
Optimization Methods ◽  
Optimization Method ◽  
Satellite System ◽  
Radar System ◽  
Target Localization ◽  
Computational Load ◽  
Matrix Fusion ◽  
Global Navigation Satellite ◽  
Multistatic Radar ◽  
Combined Algorithm

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time.

scholarly journals Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

2021 ◽  
Vol 10 (6) ◽  
pp. 420
Author(s):  
Jun Wang ◽  
Lili Jiang ◽  
Qingwen Qi ◽  
Yongji Wang
Keyword(s):  
Parameter Optimization ◽  
Scale Parameter ◽  
Human Perception ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initiation Point ◽  
Scale Parameters ◽  
Object Based ◽  
Geographic Object ◽  
Multiresolution Segmentation

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

Comparison between Optimization Method and Experience-Based Method in Soil Conservation

2013 ◽  
Vol 726-731 ◽  
pp. 3811-3817
Author(s):  
Yuan Feng ◽  
Ji Xian Wang
Keyword(s):  
Slope Stability ◽  
Soil Conservation ◽  
Computer Aided Design ◽  
Optimization Methods ◽  
Optimization Method ◽  
Interaction Function ◽  
The Standard Model ◽  
Computer Aided ◽  
Slice Method ◽  
Aided Design

The analysis of the slope stability is important in soil conservation. To analyze the slope stability, optimization methods were coded and compared with the traditional experience-based methods. Furthermore, the results were visualized in the program, so that the user can easily check the results and can designate an area, in which the program seeks the center and radius of the most hazardous slide arc. Moreover, the graphic interaction function was implemented in the program. In addition, the Standard Model One, recommended by ACAD (The Association for Computer Aided Design), was calculated by the program, of which the results (safety factor Ks=0.95~0.96) were smaller than the official recommend value (Ks=1). It is because that the traditional slice method, which neglects the normal stress and shear stress between the slices, was applied for calculation of Ks.

Systems Actuated by Shape Memory Alloys: Identification and Modeling

2021 ◽  
Vol 1 (2) ◽  
pp. 12-20
Author(s):  
Najmeh Keshtkar ◽  
Johannes Mersch ◽  
Konrad Katzer ◽  
Felix Lohse ◽  
Lars Natkowski ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Mathematical Model ◽  
Constitutive Model ◽  
Shape Memory Alloys ◽  
Numerical Simulations ◽  
Shape Memory ◽  
Transfer Equation ◽  
Mechanical Parameters ◽  
Heat Transfer Equation ◽  
The Mathematical Model

This paper presents the identification of thermal and mechanical parameters of shape memory alloys by using the heat transfer equation and a constitutive model. The identified parameters are then used to describe the mathematical model of a fiber-elastomer composite embedded with shape memory alloys. To verify the validity of the obtained equations, numerical simulations of the SMA temperature and composite bending are carried out and compared with the experimental results.

Nonsmooth Optimization Method for Image Restoration

1995 ◽  
Author(s):  
Kazufumi Ito ◽  
Karl Kunisch
Keyword(s):  
Image Restoration ◽  
Nonsmooth Optimization ◽  
Numerical Optimization ◽  
Augmented Lagrangian ◽  
Energy Criterion ◽  
Optimization Methods ◽  
Optimization Method ◽  
Augmented Lagrangian Methods ◽  
Active Set ◽  
First Order

Abstract In this paper we discuss applications of the numerical optimization methods for nonsmooth optimization, developed in [IK1] for the variational formulation of image restoration problems involving bounded variation type energy criterion. The Uzawa’s algorithm, first order augmented Lagrangian methods and Newton-like update using the active set strategy are described.

Design Optimization Method for MR-Brake Actuators

2011 ◽  
Author(s):  
Ozan G. Erol ◽  
Hakan Gurocak ◽  
Berk Gonenc
Keyword(s):  
Optimal Solution ◽  
Volume Ratio ◽  
Optimization Methods ◽  
Search Space ◽  
Optimization Method ◽  
Friction Torque ◽  
Element Analysis ◽  
Taguchi Design Of Experiments ◽  
Variable Friction ◽  
Electronically Controllable

MR-brakes work by varying viscosity of a magnetorheological (MR) fluid inside the brake. This electronically controllable viscosity leads to variable friction torque generated by the actuator. A properly designed MR-brake can have a high torque-to-volume ratio which is quite desirable for an actuator. However, designing an MR-brake is a complex process as there are many parameters involved in the design which can affect the size and torque output significantly. The contribution of this study is a new design approach that combines the Taguchi design of experiments method with parameterized finite element analysis for optimization. Unlike the typical multivariate optimization methods, this approach can identify the dominant parameters of the design and allows the designer to only explore their interactions during the optimization process. This unique feature reduces the size of the search space and the time it takes to find an optimal solution. It normally takes about a week to design an MR-brake manually. Our interactive method allows the designer to finish the design in about two minutes. In this paper, we first present the details of the MR-brake design problem. This is followed by the details of our new approach. Next, we show how to design an MR-brake using this method. Prototype of a new brake was fabricated. Results of experiments with the prototype brake are very encouraging and are in close agreement with the theoretical performance predictions.

scholarly journals Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

2021 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Sadr ◽  
Amir-Homayoun Javadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Evolutionary Algorithms ◽  
Resting State ◽  
Optimization Methods ◽  
Resting State Fmri ◽  
Optimization Method ◽  
Effective Parameters

AbstractsIdentifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer’s disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n=36) and EMCI (n=34) extracted from the publicly available database of the Alzheimer’s disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.

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