GPR-based novel approach for non-linear aerodynamic modelling from flight data

2018 ◽  
Vol 123 (1259) ◽  
pp. 79-92
Author(s):  
A. Kumar ◽  
A. K. Ghosh
Keyword(s):  
Aerodynamic Force ◽  
Equations Of Motion ◽  
Gaussian Process Regression ◽  
Likelihood Estimation ◽  
Flight Data ◽  
Novel Approach ◽  
Non Linear ◽  
Research Aircraft ◽  
Novel Method ◽  
Aerodynamic Modelling

ABSTRACTIn this paper, a Gaussian process regression (GPR)-based novel method is proposed for non-linear aerodynamic modelling of the aircraft using flight data. This data-driven regression approach uses the kernel-based probabilistic model to predict the non-linearity. The efficacy of this method is examined and validated by estimating force and moment coefficients using research aircraft flight data. Estimated coefficients of aerodynamic force and moment using GPR method are compared with the estimated coefficients using maximum-likelihood estimation (MLE) method. Estimated coefficients from the GPR method are statistically analysed and found to be at par with estimated coefficients from MLE, which is popularly used as a conventional method. GPR approach does not require to solve the complex equations of motion. GPR further can be directed for the generalised applications in the area of aeroelasticity, load estimation, and optimisation.

scholarly journals A novel approach to the computation of one-loop three- and four-point functions: I. The real mass case

2019 ◽  
Vol 2019 (11) ◽  
Author(s):  
J Ph Guillet ◽  
E Pilon ◽  
Y Shimizu ◽  
M S Zidi
Keyword(s):  
Building Blocks ◽  
The Real ◽  
Alternative Method ◽  
Novel Approach ◽  
Reduction Methods ◽  
Real Mass ◽  
Novel Method ◽  
Algebraic Reduction ◽  
The One ◽  
Mass Case

Abstract This article is the first of a series of three presenting an alternative method of computing the one-loop scalar integrals. This novel method enjoys a couple of interesting features as compared with the method closely following ’t Hooft and Veltman adopted previously. It directly proceeds in terms of the quantities driving algebraic reduction methods. It applies to the three-point functions and, in a similar way, to the four-point functions. It also extends to complex masses without much complication. Lastly, it extends to kinematics more general than that of the physical, e.g., collider processes relevant at one loop. This last feature may be useful when considering the application of this method beyond one loop using generalized one-loop integrals as building blocks.

scholarly journals Towards a contemporary player learning in development framework for sports practitioners

2021 ◽  
pp. 174795412110023
Author(s):  
Mark O Sullivan ◽  
Carl T Woods ◽  
James Vaughan ◽  
Keith Davids
Keyword(s):  
Linear Process ◽  
Task Environment ◽  
Coach Education ◽  
Development Framework ◽  
Novel Approach ◽  
Non Linear ◽  
And Performance ◽  
Learning Development ◽  
High Level ◽  
User Friendly

As it is appreciated that learning is a non-linear process – implying that coaching methodologies in sport should be accommodative – it is reasonable to suggest that player development pathways should also account for this non-linearity. A constraints-led approach (CLA), predicated on the theory of ecological dynamics, has been suggested as a viable framework for capturing the non-linearity of learning, development and performance in sport. The CLA articulates how skills emerge through the interaction of different constraints (task-environment-performer). However, despite its well-established theoretical roots, there are challenges to implementing it in practice. Accordingly, to help practitioners navigate such challenges, this paper proposes a user-friendly framework that demonstrates the benefits of a CLA. Specifically, to conceptualize the non-linear and individualized nature of learning, and how it can inform player development, we apply Adolph’s notion of learning IN development to explain the fundamental ideas of a CLA. We then exemplify a learning IN development framework, based on a CLA, brought to life in a high-level youth football organization. We contend that this framework can provide a novel approach for presenting the key ideas of a CLA and its powerful pedagogic concepts to practitioners at all levels, informing coach education programs, player development frameworks and learning environment designs in sport.

scholarly journals On Randomized Trace Estimates for Indefinite Matrices with an Application to Determinants

2021 ◽  
Author(s):  
Alice Cortinovis ◽  
Daniel Kressner
Keyword(s):  
Large Scale ◽  
Gaussian Process Regression ◽  
Likelihood Estimation ◽  
Random Vectors ◽  
Symmetric Positive Definite ◽  
Matrix Logarithm ◽  
The Matrix ◽  
Trace Estimation ◽  
Scale Matrix ◽  
Existing Result

AbstractRandomized trace estimation is a popular and well-studied technique that approximates the trace of a large-scale matrix B by computing the average of $$x^T Bx$$ x T B x for many samples of a random vector X. Often, B is symmetric positive definite (SPD) but a number of applications give rise to indefinite B. Most notably, this is the case for log-determinant estimation, a task that features prominently in statistical learning, for instance in maximum likelihood estimation for Gaussian process regression. The analysis of randomized trace estimates, including tail bounds, has mostly focused on the SPD case. In this work, we derive new tail bounds for randomized trace estimates applied to indefinite B with Rademacher or Gaussian random vectors. These bounds significantly improve existing results for indefinite B, reducing the number of required samples by a factor n or even more, where n is the size of B. Even for an SPD matrix, our work improves an existing result by Roosta-Khorasani and Ascher (Found Comput Math, 15(5):1187–1212, 2015) for Rademacher vectors. This work also analyzes the combination of randomized trace estimates with the Lanczos method for approximating the trace of f(B). Particular attention is paid to the matrix logarithm, which is needed for log-determinant estimation. We improve and extend an existing result, to not only cover Rademacher but also Gaussian random vectors.

Modeling the Climber Fall Arrest Dynamics

2005 ◽  
Author(s):  
Lionel Manin ◽  
Jarir Mahfoudh ◽  
Matthieu Richard ◽  
David Jauffres
Keyword(s):  
Dynamic Characteristics ◽  
Equations Of Motion ◽  
Close Approximation ◽  
Non Linear ◽  
The Future ◽  
Safety Recommendations ◽  
Improved Design ◽  
Fall Arrest

Sports and mountaineering activities are becoming more and more popular. Equipment constructors seek to develop products and devices that are easy to use and that take into account all safety recommendations. PETZL and INSA have collaborated to develop a model for the simulation of displacements and efforts involved during the fall of a climber in the “safety chain”. The model is based on the classical equations of motion, in which climber and belayer are considered as rigid masses, while the rope is considered as a series of non-linear stiffness passing through several devices as brakes and runners. The main goal is to predict the forces in the rope and on the return anchor at the first rebound of the fall. Experiments were first performed in order to observe and determine the dynamic characteristics of the rope, and then to validate results stemming from simulations. Several fall configurations are simulated, and the model performs satisfactorily. It also provides a close approximation of the phenomena observed experimentally. The model enables the assessment of the existing equipments and the improved design of the future one.

A novel approach for the stability problem in non-linear dynamical systems

2003 ◽  
Vol 155 (1) ◽  
pp. 21-30 ◽  
Author(s):  
Tarcı́sio M. Rocha Filho ◽  
Iram M. Gléria ◽  
Annibal Figueiredo
Keyword(s):  
Dynamical Systems ◽  
Stability Problem ◽  
Linear Dynamical Systems ◽  
Novel Approach ◽  
Non Linear ◽  
The Stability

Novel method for bearing performance degradation assessment – A kernel locality preserving projection-based approach

2013 ◽  
Vol 228 (3) ◽  
pp. 548-560 ◽  
Author(s):  
Chuang Sun ◽  
Zhousuo Zhang ◽  
Zhengjia He ◽  
Zhongjie Shen ◽  
Binqiang Chen ◽  
...  
Keyword(s):  
Test Data ◽  
Performance Degradation ◽  
Inner Product ◽  
Data Sets ◽  
Degradation Index ◽  
Locality Preserving Projection ◽  
Locality Preserving ◽  
Non Linear ◽  
Linear Information ◽  
Novel Method

Bearing performance degradation assessment is meaningful for keeping mechanical reliability and safety. For this purpose, a novel method based on kernel locality preserving projection is proposed in this article. Kernel locality preserving projection extends the traditional locality preserving projection into the non-linear form by using a kernel function and it is more appropriate to explore the non-linear information hidden in the data sets. Considering this point, the kernel locality preserving projection is used to generate a non-linear subspace from the normal bearing data. The test data are then projected onto the subspace to obtain an index for assessing bearing degradation degrees. The degradation index that is expressed in the form of inner product indicates similarity of the normal data and the test data. Validations by using monitoring data from two experiments show the effectiveness of the proposed method.

Relational asymmetry, trust, and innovation in supply chain management: a non-linear approach

2019 ◽  
Vol 30 (1) ◽  
pp. 303-328 ◽  
Author(s):  
Marek Michalski ◽  
Jose Luis Montes ◽  
Ram Narasimhan
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Institutional Environment ◽  
Least Squares Method ◽  
Cross Sectional ◽  
Chain Management ◽  
Content Type ◽  
Relationship Analysis ◽  
Novel Approach ◽  
Non Linear

PurposeThe purpose of this paper is to examine the non-linear aspects of the asymmetry-performance relationship under varying conditions of trust and innovation. Its novel approach is useful for addressing the strategic elements of supply chain management (SCM) relationships based on trust and innovation decisions.Design/methodology/approachResults are based on a study of 90 managers from small- and medium-sized firms in Spain. Instead of a classical linear relationship analysis, the authors performed a non-linear analysis, using polynomial modeling and Warp 3 partial least squares method, which provides a more nuanced view of the data and constitutes an original approach to empirical research in SCM.FindingsThis study adds a new viewpoint on SC relationships by suggesting that not all trust and innovation development leads directly to performance improvement. The principal finding is, in varying trust and innovation contexts, that the influences of asymmetry on performance have uneven characteristics and follow non-linear paths.Research limitations/implicationsThis study focuses on only one particular institutional environment in one country. The data are also cross-sectional, which makes it difficult to empirically test causality.Practical implicationsThe findings provide rational insights to managers on when it is appropriate to reduce (or not) asymmetric relationships with partners.Originality/valueTrust and innovation are important and ones of the key requirements of supply chain relationships in any environment, this study argues that the interactions of key SCM elements that drive members to better performance are more complex and non-linear.

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