Data Driven Model Identification for a Chaotic Pendulum With Variable Interaction Potential

2020 ◽  
Author(s):  
Melih C. Yesilli ◽  
Firas A. Khasawneh
Keyword(s):  
Numerical Models ◽  
Model Identification ◽  
Potential Interaction ◽  
Data Driven ◽  
Estimation Methods ◽  
Total Variation Regularization ◽  
Data Sets ◽  
Derivative Estimation ◽  
Long Time ◽  
And Performance

Abstract Data driven model identification methods have grown increasingly popular due to enhancements in measuring devices and data mining. They provide a useful approach for comparing the performance of a device to the simplified model that was used in the design phase. One of the modern, popular methods for model identification is Sparse Identification of Nonlinear Dynamics (SINDy). Although this approach has been widely investigated in the literature using mostly numerical models, its applicability and performance with physical systems is still a topic of current research. In this paper we extend SINDy to identify the mathematical model of a complicated physical experiment of a chaotic pendulum with a varying potential interaction. We also test the approach using a simulated model of a nonlinear, simple pendulum. The input to the approach is a time series, and estimates of its derivatives. While the standard approach in SINDy is to use the Total Variation Regularization (TVR) for derivative estimates, we show some caveats for using this route, and we benchmark the performance of TVR against other methods for derivative estimation. Our results show that the estimated model coefficients and their resulting fit are sensitive to the selection of the TVR parameters, and that most of the available derivative estimation methods are easier to tune than TVR. We also highlight other guidelines for utilizing SINDy to avoid overfitting, and we point out that the fitted model may not yield accurate results over long time scales. We test the performance of each method for noisy data sets and provide both experimental and simulation results. We also post the files needed to build and reproduce our experiment in a public repository.

scholarly journals Knotty protostellar jets as a signature of episodic protostellar accretion?

2018 ◽  
Vol 613 ◽  
pp. A18 ◽  
Author(s):  
Eduard I. Vorobyov ◽  
Vardan G. Elbakyan ◽  
Adele L. Plunkett ◽  
Michael M. Dunham ◽  
Marc Audard ◽  
...  
Keyword(s):  
Observational Data ◽  
Numerical Models ◽  
Causal Link ◽  
Data Sets ◽  
Solar Mass ◽  
Numerical Hydrodynamics ◽  
Protostellar Jets ◽  
Long Time ◽  
Inclination Angles ◽  
Kolmogorov Smirnov

Aims. We aim to study the causal link between the knotty jet structure in CARMA 7, a young Class 0 protostar in the Serpens South cluster, and episodic accretion in young protostellar disks. Methods. We used numerical hydrodynamics simulations to derive the protostellar accretion history in gravitationally unstable disks around solar-mass protostars. We compared the time spacing between luminosity bursts Δτmod, caused by dense clumps spiralling on the protostar, with the differences of dynamical timescales between the knots Δτobs in CARMA 7. Results. We found that the time spacing between the bursts have a bi-modal distribution caused by isolated and clustered luminosity bursts. The former are characterized by long quiescent periods between the bursts with Δτmod = a few × (103–104) yr, whereas the latter occur in small groups with time spacing between the bursts Δτmod = a few × (10–102) yr. For the clustered bursts, the distribution of Δτmod in our models can be fit reasonably well to the distribution of Δτobs in the protostellar jet of CARMA 7, if a certain correction for the (yet unknown) inclination angle with respect to the line of sight is applied. The Kolmogorov–Smirnov test on the model and observational data sets suggests the best-fit values for the inclination angles of 55–80°, which become narrower (75–80°) if only strong luminosity bursts are considered. The dynamical timescales of the knots in the jet of CARMA 7 are too short for a meaningful comparison with the long time spacings between isolated bursts in our models. Moreover, the exact sequences of time spacings between the luminosity bursts in our models and knots in the jet of CARMA 7 were found difficult to match. Conclusions. Given the short time that has passed since the presumed luminosity bursts (tens to hundreds years), a possible overabundance of the gas-phase CO in the envelope of CARMA 7 compared to what could be expected from the current luminosity may be used to confirm the burst nature of this object. More sophisticated numerical models and observational data on jets with longer dynamical timescales are needed to further explore the possible causal link between luminosity bursts and knotty jets.

Data-Driven Vs. Traditional Reservoir Numerical Models: A Case Study Comparison of Applicability, Practicality and Performance

2019 ◽  
Author(s):  
Yara Alatrach ◽  
Luigi Saputelli ◽  
Ram Narayanan ◽  
Richard Mohan ◽  
Mohamad Yousef Alklih ◽  
...  
Keyword(s):  
Numerical Models ◽  
Data Driven ◽  
And Performance

Agricultural Robotics

2019 ◽  
Vol 2 (1) ◽  
pp. 365-392 ◽  
Author(s):  
Stavros G. Vougioukas
Keyword(s):  
Data Driven ◽  
Farm Labor ◽  
Data Sets ◽  
Future Directions ◽  
Agricultural Robots ◽  
Benchmark Data ◽  
Labor Inputs ◽  
Sustainable Fashion ◽  
And Performance ◽  
Physical And Chemical

A key goal of contemporary agriculture is to dramatically increase production of food, feed, fiber, and biofuel products in a sustainable fashion, facing the pressure of diminishing farm labor supply. Agricultural robots can accelerate plant breeding and advance data-driven precision farming with significantly reduced labor inputs by providing task-appropriate sensing and actuation at fine spatiotemporal resolutions. This article highlights the distinctive challenges imposed on ground robots by agricultural environments, which are characterized by wide variations in environmental conditions, diversity and complexity of plant canopy structures, and intraspecies biological variation of physical and chemical characteristics and responses to the environment. Existing approaches to address these challenges are presented, along with their limitations; possible future directions are also discussed. Two key observations are that biology (breeding) and horticultural practices can reduce variabilities at the source and that publicly available benchmark data sets are needed to increase perception robustness and performance despite variability.

scholarly journals Data-driven components in a model of inner-shelf sorted bedforms: a new hybrid model

2014 ◽  
Vol 2 (1) ◽  
pp. 67-82 ◽  
Author(s):  
E. B. Goldstein ◽  
G. Coco ◽  
A. B. Murray ◽  
M. O. Green
Keyword(s):  
Machine Learning ◽  
Numerical Model ◽  
Hybrid Model ◽  
Learning Algorithm ◽  
Numerical Models ◽  
Data Driven ◽  
Data Sets ◽  
Inner Shelf ◽  
Reference Concentration ◽  
Deterministic Description

Abstract. Numerical models rely on the parameterization of processes that often lack a deterministic description. In this contribution we demonstrate the applicability of using machine learning, a class of optimization tools from the discipline of computer science, to develop parameterizations when extensive data sets exist. We develop a new predictor for near-bed suspended sediment reference concentration under unbroken waves using genetic programming, a machine learning technique. We demonstrate that this newly developed parameterization performs as well or better than existing empirical predictors, depending on the chosen error metric. We add this new predictor into an established model for inner-shelf sorted bedforms. Additionally we incorporate a previously reported machine-learning-derived predictor for oscillatory flow ripples into the sorted bedform model. This new "hybrid" sorted bedform model, whereby machine learning components are integrated into a numerical model, demonstrates a method of incorporating observational data (filtered through a machine learning algorithm) directly into a numerical model. Results suggest that the new hybrid model is able to capture dynamics previously absent from the model – specifically, two observed pattern modes of sorted bedforms. Lastly we discuss the challenge of integrating data-driven components into morphodynamic models and the future of hybrid modeling.

Research on High-Quality Soft Shadow of Building Based Shadow Map

2012 ◽  
Vol 174-177 ◽  
pp. 1927-1930 ◽  
Author(s):  
Tao Shang ◽  
Shui Peng Zhang
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Data Sets ◽  
Depth Data ◽  
Soft Shadow ◽  
Shadow Map ◽  
Long Time ◽  
Depth Buffer ◽  
And Performance ◽  
The One

Image rendering of shadow faces a problem existed for a long time,that is the contradiction of quality and performance. Variant algorithms are presented to ameliorate this problem,shadow map is the one which is representative for that. Even though shadow maps have been widely used for the shadow of Three-dimensional scene,some imperfection still exist in this method like aliasing problem.So,the focus of the paper is introduce an algorithm which layering the data sets of the large scale building's shadow rapidly and intelligently based shadow map. First, we ascertain the fragment which create the shadow by shadow mapping's two scan. Second, we process the float data in the depth buffer by using uniformization and render the two depth data in the texture.Then use Gauss Filter to blur.Finally,use the algorithm of BIRCH cluster the uniformization data to improve the obscure and tweened effect.This method brings reduction of aliasing problem with low overhead as well as performance to a certain extent .

scholarly journals Data driven components in a model of inner shelf sorted bedforms: a new hybrid model

2013 ◽  
Vol 1 (1) ◽  
pp. 531-569 ◽  
Author(s):  
E. B. Goldstein ◽  
G. Coco ◽  
A. B. Murray ◽  
M. O. Green
Keyword(s):  
Machine Learning ◽  
Numerical Model ◽  
Hybrid Model ◽  
Learning Algorithm ◽  
Numerical Models ◽  
Data Driven ◽  
Data Sets ◽  
Inner Shelf ◽  
Reference Concentration ◽  
Deterministic Description

Abstract. Numerical models rely on the parameterization of processes that often lack a deterministic description. In this contribution we demonstrate the applicability of using machine learning, optimization tools from the discipline of computer science, to develop parameterizations when extensive data sets exist. We develop a new predictor for near bed suspended sediment reference concentration under unbroken waves using genetic programming, a machine learning technique. This newly developed parameterization performs better than existing empirical predictors. We add this new predictor into an established model for inner shelf sorted bedforms. Additionally we incorporate a previously reported machine learning derived predictor for oscillatory flow ripples into the sorted bedform model. This new "hybrid" sorted bedform model, whereby machine learning components are integrated into a numerical model, demonstrates a method of incorporating observational data (filtered through a machine learning algorithm) directly into a numerical model. Results suggest that the new hybrid model is able to capture dynamics previously absent from the model, specifically, the two observed pattern modes of sorted bedforms. However, caveats exist when data driven components do not have parity with traditional theoretical components of morphodynamic models, and we discuss the challenges of integrating these disparate pieces and the future of this type of modeling.

The 14th Annual AMSE Conference

1988 ◽  
Vol 5 (2) ◽  
pp. 311
Author(s):  
Mozaffar Partowmah
Keyword(s):  
North America ◽  
Nuclear Physics ◽  
Agricultural Research ◽  
Social Scientists ◽  
Electronic Engineering ◽  
Student Paper ◽  
Long Time ◽  
Continuous Support ◽  
And Performance ◽  
Sizable Number

The 14th Annual Conferknce of the Association of Muslim Scientistsand Engineers (ASME) was held during the weekend of qufur 2628,1409/0ctober 7-9, 1988, at the Islamic Center of North America in Plainfield,Indiana. Papers presented at the Conference dealt with a variety of subjectsranging from agriculture and health sciences to car manufacturing tips,computers, industrial, civil and electronic engineering, as well as resourcemanagement and organizational behavior.Members of the AMSS (Association of Muslim Social Scientists) whoattended the AMSE Conference, participated in the sessions with undividedattention. Dr. AbdulHamid AbuSulayman, the AMSS President, in his banquetspeech, stressed the need for an active AMSE that will eventually attracta more sizable number of Muslims in North America and coordinate theirscientific efforts for their common benefit.In a session entitled “Technology Transfer,” the Japanese and Koreanapproaches were contrasted with the Muslim world approach. A highlightof the Conference was the announcement of the A1 Khwarazmi Award thatthe AMSE will award annually to a distinguished Muslim scientist or engineer.The first Al Khwarazmi Award went to Dr. M.A.K. Lodhi of Texas A&MUniversity in appreciation of his continuous support for Muslim studentsand his long-time involvement in the AMSE in addition to his scientific interestand achievements in nuclear physics and field theory.The Best Student Paper Award went to the following: 1) Abdullah M.Elramsisi of Rochester Hill, Michigan for his paper “On Model-based ImageRestoration and Performance Evaluation;” and 2) Khatib Rajab of Morgantown,West Virginia for his paper on “Agricultural Research Needs and Prioritiesin Zanzibar as perceived by Administrators and Extension Workers.”Copies of all of the presented papers were distributed at the Conferenceand will be ppblished in the conference proceedings. Preprints and reprintsmay be obtained by writing to the AMSE office at P.O. Box 38, Plainfield,Indianna, 46168 ...

scholarly journals The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2085
Author(s):  
Xue-Bo Jin ◽  
Ruben Jonhson Robert RobertJeremiah ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Future Development ◽  
Data Driven ◽  
Estimation Methods ◽  
Multiple Model ◽  
Development Trends ◽  
Research Results ◽  
Model Driven ◽  
Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

scholarly journals Comparative Analysis of Kinect-Based and Oculus-Based Gaze Region Estimation Methods in a Driving Simulator

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 26
Author(s):  
David González-Ortega ◽  
Francisco Javier Díaz-Pernas ◽  
Mario Martínez-Zarzuela ◽  
Míriam Antón-Rodríguez
Keyword(s):  
Virtual Reality ◽  
Comparative Analysis ◽  
Driving Simulator ◽  
Estimation Method ◽  
Estimation Methods ◽  
Gaze Estimation ◽  
Driving Simulators ◽  
Oculus Rift ◽  
Gaze Patterns ◽  
And Performance

Driver’s gaze information can be crucial in driving research because of its relation to driver attention. Particularly, the inclusion of gaze data in driving simulators broadens the scope of research studies as they can relate drivers’ gaze patterns to their features and performance. In this paper, we present two gaze region estimation modules integrated in a driving simulator. One uses the 3D Kinect device and another uses the virtual reality Oculus Rift device. The modules are able to detect the region, out of seven in which the driving scene was divided, where a driver is gazing at in every route processed frame. Four methods were implemented and compared for gaze estimation, which learn the relation between gaze displacement and head movement. Two are simpler and based on points that try to capture this relation and two are based on classifiers such as MLP and SVM. Experiments were carried out with 12 users that drove on the same scenario twice, each one with a different visualization display, first with a big screen and later with Oculus Rift. On the whole, Oculus Rift outperformed Kinect as the best hardware for gaze estimation. The Oculus-based gaze region estimation method with the highest performance achieved an accuracy of 97.94%. The information provided by the Oculus Rift module enriches the driving simulator data and makes it possible a multimodal driving performance analysis apart from the immersion and realism obtained with the virtual reality experience provided by Oculus.

scholarly journals On Performance of Sparse Fast Fourier Transform Algorithms Using the Aliasing Filter

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1117
Author(s):  
Bin Li ◽  
Zhikang Jiang ◽  
Jie Chen
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
High Efficiency ◽  
Theory And Practice ◽  
Long Time ◽  
And Performance ◽  
Binary Tree Search ◽  
The One ◽  
Sparse Fast Fourier Transform ◽  
L1 And L2

Computing the sparse fast Fourier transform (sFFT) has emerged as a critical topic for a long time because of its high efficiency and wide practicability. More than twenty different sFFT algorithms compute discrete Fourier transform (DFT) by their unique methods so far. In order to use them properly, the urgent topic of great concern is how to analyze and evaluate the performance of these algorithms in theory and practice. This paper mainly discusses the technology and performance of sFFT algorithms using the aliasing filter. In the first part, the paper introduces the three frameworks: the one-shot framework based on the compressed sensing (CS) solver, the peeling framework based on the bipartite graph and the iterative framework based on the binary tree search. Then, we obtain the conclusion of the performance of six corresponding algorithms: the sFFT-DT1.0, sFFT-DT2.0, sFFT-DT3.0, FFAST, R-FFAST, and DSFFT algorithms in theory. In the second part, we make two categories of experiments for computing the signals of different SNRs, different lengths, and different sparsities by a standard testing platform and record the run time, the percentage of the signal sampled, and the L0, L1, and L2 errors both in the exactly sparse case and the general sparse case. The results of these performance analyses are our guide to optimize these algorithms and use them selectively.

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