scholarly journals Optimum Control for Nonlinear Dynamic Radial Deformation of Turbine Casing with Time-Varying LSSVM

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cheng-Wei Fei ◽  
Guang-Chen Bai ◽  
Wen-Zhong Tang ◽  
Yatsze Choy
Keyword(s):  
Nonlinear Dynamic ◽  
High Performance ◽  
High Reliability ◽  
Nonlinear Material ◽  
Support Vector ◽  
Radial Deformation ◽  
Time Varying ◽  
Optimum Control ◽  
Probabilistic Optimization ◽  
Turbine Casing

With the development of the high performance and high reliability of aeroengine, the blade-tip radial running clearance (BTRRC) of high pressure turbine seriously influences the reliability and performance of aeroengine, wherein the radial deformation control of turbine casing has to be concerned in BTRRC design. To improve BTRRC design, the optimum control-based probabilistic optimization of turbine casing radial deformation was implemented using time-varying least square support vector machine (T-LSSVM) by considering nonlinear material properties and dynamic thermal load. First the T-LSSVM method was proposed and its mathematical model was established. And then the nonlinear dynamic optimal control model of casing radial deformation was constructed with T-LSSVM. Thirdly, through the numerical experiments, the T-LSSVM method is demonstrated to be a promising approach in reducing additional design samples and improving computational efficiency with acceptable computational precision. Through the optimum control-based probabilistic optimization for nonlinear dynamic radial turbine casing deformation, the optimum radial deformation is 7.865 × 10−4 m with acceptable reliability degree 0.995 6, which is reduced by 7.86 × 10−5 m relative to that before optimization. These results validate the effectiveness and feasibility of the proposed T-LSSVM method, which provides a useful insight into casing radial deformation, BTRRC control, and the development of gas turbine with high performance and high reliability.

Nonlinear Dynamic Probabilistic Analysis for Turbine Casing Radial Deformation Using Extremum Response Surface Method Based on Support Vector Machine

2013 ◽  
Vol 8 (4) ◽  
Author(s):  
Chengwei Fei ◽  
Guangchen Bai
Keyword(s):  
Support Vector Machine ◽  
Response Surface ◽  
Response Surface Method ◽  
Probabilistic Analysis ◽  
Nonlinear Dynamic ◽  
Nonlinear Material ◽  
Support Vector ◽  
Radial Deformation ◽  
Surface Method ◽  
Turbine Casing

To improve the computational efficiency of nonlinear dynamic probabilistic analysis for aeroengine typical components, an extremum response surface method based on the support vector machine (SVM ERSM) was proposed in this paper. The basic principle was introduced and the mathematical model was established for the SVM ERSM. The probabilistic analysis of turbine casing radial deformation was taken as an example to validate the SVM ERSM considering the influences of nonlinear material property and dynamic heat loads. The results of probabilistic analysis imply that the distribution features of random parameters and the major factors are gained for more accurate the design of casing radial deformation. The SVM ERSM offers a feasible and valid method, which possesses high efficiency and high precision in the nonlinear dynamic probabilistic analysis. Moreover, the SVM ERSM is promising to provide an useful insight for casing dynamic optimal design and the blade-tip clearance control of aeroengine high pressure turbine.

scholarly journals Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 574
Author(s):  
Gennaro Tartarisco ◽  
Giovanni Cicceri ◽  
Davide Di Pietro ◽  
Elisa Leonardi ◽  
Stefania Aiello ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Behavioral Science ◽  
Autistic Traits ◽  
Classification Performance ◽  
Recursive Feature Elimination ◽  
Diagnostic Tools ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Autism Screening

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

scholarly journals Application of Artificial Intelligence (AI) for Sustainable Highway and Road System

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Md Arifuzzaman ◽  
Muhammad Aniq Gul ◽  
Kaffayatullah Khan ◽  
S. M. Zakir Hossain
Keyword(s):  
Experimental Data ◽  
High Performance ◽  
Adhesion Force ◽  
Model Development ◽  
Real Life ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Adhesive Properties ◽  
Bayesian Optimization Algorithm ◽  
The Mean

There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.

Electrostatic bellow muscle actuators and energy harvesters that stack up

2021 ◽  
Vol 6 (51) ◽  
pp. eaaz5796
Author(s):  
I. D. Sîrbu ◽  
G. Moretti ◽  
G. Bortolotti ◽  
M. Bolignari ◽  
S. Diré ◽  
...  
Keyword(s):  
High Performance ◽  
High Reliability ◽  
Low Cost ◽  
Artificial Muscle ◽  
Pressure Head ◽  
Robotic Systems ◽  
Maximum Pressure ◽  
Term Operation ◽  
Energy Harvesters ◽  
Out Of Plane

Future robotic systems will be pervasive technologies operating autonomously in unknown spaces that are shared with humans. Such complex interactions make it compulsory for them to be lightweight, soft, and efficient in a way to guarantee safety, robustness, and long-term operation. Such a set of qualities can be achieved using soft multipurpose systems that combine, integrate, and commute between conventional electromechanical and fluidic drives, as well as harvest energy during inactive actuation phases for increased energy efficiency. Here, we present an electrostatic actuator made of thin films and liquid dielectrics combined with rigid polymeric stiffening elements to form a circular electrostatic bellow muscle (EBM) unit capable of out-of-plane contraction. These units are easy to manufacture and can be arranged in arrays and stacks, which can be used as a contractile artificial muscle, as a pump for fluid-driven soft robots, or as an energy harvester. As an artificial muscle, EBMs of 20 to 40 millimeters in diameter can exert forces of up to 6 newtons, lift loads over a hundred times their own weight, and reach contractions of over 40% with strain rates over 1200% per second, with a bandwidth over 10 hertz. As a pump driver, these EBMs produce flow rates of up to 0.63 liters per minute and maximum pressure head of 6 kilopascals, whereas as generator, they reach a conversion efficiency close to 20%. The compact shape, low cost, simple assembling procedure, high reliability, and large contractions make the EBM a promising technology for high-performance robotic systems.

A New Concept of High-Performance Marine Reversing Reduction Gears

1988 ◽  
Vol 110 (4) ◽  
pp. 572-577
Author(s):  
D. J. Folenta
Keyword(s):  
Gas Turbine ◽  
High Performance ◽  
Power Transmission ◽  
High Reliability ◽  
Mechanical Efficiency ◽  
Gas Turbine Engine ◽  
Turbine Engines ◽  
Turbine Engine ◽  
Epicyclic Gear ◽  
Gear Technology

This paper presents a brief description and several illustrations of a new concept of marine reversing gears that utilize high-performance differentially driven epicyclic gear arrangements. This new marine power transmission has the potential to offer high reliability, simplicity, light weight, high mechanical efficiency, compactness, and technological compatibility with aircraft derivative marine gas turbine engines. Further, this new reversing gear minimizes the danger of driving the free turbine in reverse as might be the case with conventional parallel shaft reversing gear arrangements. To illustrate the weight reduction potential, a modern naval ship propulsion system utilizing an aircraft derivative gas turbine engine as the prime mover in conjunction with a conventional parallel shaft reversing gear can be compared to the subject reversing gear differential. A typical 18,642 kW (25,000 hp) marine gas turbine engine might weigh approximately 5000 kg (11,000 lb) and a conventional marine technology parallel shaft reversing gear might weigh on the order of 90,000 to 136,000 kg (200,000 to 300,000 lb). Using gear technology derived from the aircraft industry, a functionally similar differentially driven marine reversing gear might weigh approximately 13,600 kg (30,000 lb).

Multiple CMOS Intersection Measuring System Modeling and Analysis

2012 ◽  
Vol 614-615 ◽  
pp. 1299-1302
Author(s):  
Ming Jing Li ◽  
Yu Bing Dong ◽  
Guang Liang Cheng
Keyword(s):  
High Speed ◽  
High Performance ◽  
High Reliability ◽  
System Modeling ◽  
Position Effect ◽  
Field Of View ◽  
Measuring System ◽  
Price Ratio ◽  
Matlab Simulation ◽  
Modeling And Analysis

Multiple high speed CMOS cameras composing intersection system to splice large effect field of view(EFV). The key problem of system is how to locate multiple CMOS cameras in suitable position. Effect field of view was determined according to size, quantity and dispersion area of objects, so to determine camera position located on below, both sides and ahead to moving targets. This paper analyzes effect splicing field of view, operating range etc through establishing mathematical model and MATLAB simulation. Location method of system has advantage of flexibility splicing, convenient adjustment, high reliability and high performance-price ratio.

Modeling Link Events in High Reliability Networks With Support Vector Machines

2010 ◽  
Vol 59 (1) ◽  
pp. 191-202 ◽  
Author(s):  
J. Feijoo ◽  
J.L. Rojo-Alvarez ◽  
J.C. Sueiro ◽  
P. Conde-Pardo ◽  
J.L. Mata-Vigil-Escalera
Keyword(s):  
Support Vector Machines ◽  
High Reliability ◽  
Support Vector ◽  
Vector Machines
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