Adaptive network traffic control with an integrated model-based and data-driven approach and a decentralised solution method

2021 ◽  
Vol 128 ◽  
pp. 103154
Author(s):  
Z.C. Su ◽  
Andy H.F. Chow ◽  
R.X. Zhong
Keyword(s):  
Network Traffic ◽  
Traffic Control ◽  
Solution Method ◽  
Integrated Model ◽  
Data Driven ◽  
Adaptive Network ◽  
Model Based ◽  
Data Driven Approach

scholarly journals A DATA-DRIVEN APPROACH TO USER-EXPERIENCE-FOCUSED MODEL-BASED ROADMAPPING FOR NEW PRODUCT PLANNING

2021 ◽  
Vol 1 ◽  
pp. 61-70
Author(s):  
Ilia Iuskevich ◽  
Andreas-Makoto Hein ◽  
Kahina Amokrane-Ferka ◽  
Abdelkrim Doufene ◽  
Marija Jankovic
Keyword(s):  
New Product Development ◽  
User Experience ◽  
Planning Process ◽  
New Product ◽  
Data Driven ◽  
User Needs ◽  
User Testing ◽  
Model Based ◽  
Data Driven Approach ◽  
To Receive

AbstractUser experience (UX) focused business needs to survive and plan its new product development (NPD) activities in a highly turbulent environment. The latter is a function of volatile UX and technology trends, competition, unpredictable events, and user needs uncertainty. To address this problem, the concept of design roadmapping has been proposed in the literature. It was argued that tools built on the idea of design roadmapping have to be very flexible and data-driven (i.e., be able to receive feedback from users in an iterative manner). At the same time, a model-based approach to roadmapping has emerged, promising to achieve such flexibility. In this work, we propose to incorporate design roadmapping to model-based roadmapping and integrate it with various user testing approaches into a single tool to support a flexible data-driven NPD planning process.

A Comparison Between Model-Based and Data-Driven Design of an Active Stability Control System for Two-Wheeled Vehicles

2011 ◽  
Author(s):  
Pierpaolo De Filippi ◽  
Simone Formentin ◽  
Sergio M. Savaresi
Keyword(s):  
Control System ◽  
Strong Interaction ◽  
High Speed ◽  
Stability Control ◽  
Data Driven ◽  
Output Controller ◽  
Model Based ◽  
Data Driven Approach ◽  
Stability Control System ◽  
Wheeled Vehicles

The design of an active stability control system for two-wheeled vehicles is a fully open problem and it constitutes a challenging task due to the complexity of two-wheeled vehicles dynamics and the strong interaction between the vehicle and the driver. This paper describes and compares two different methods, a model-based and a data-driven approach, to tune a Multi-Input-Multi-Output controller which allows to enhance the safety while guaranteeing a good driving feeling. The two strategies are tested on a multibody motorcycle simulator on challenging maneuvers such as kick-back and strong braking while cornering at high speed.

Prognostics of Embedded Planar Capacitors Under Temperature and Voltage Aging

2010 ◽  
Author(s):  
Mohammed A. Alam ◽  
Michael H. Azarian ◽  
Michael Osterman ◽  
Michael Pecht
Keyword(s):  
Elevated Temperature ◽  
Mahalanobis Distance ◽  
Failure Modes ◽  
Gradual Decrease ◽  
Data Driven ◽  
Time To Failure ◽  
Insulation Resistance ◽  
Sharp Drop ◽  
Model Based ◽  
Data Driven Approach

This paper presents the application of model-based and data-driven approaches for prognostics and health management (PHM) of embedded planar capacitors under elevated temperature and voltage conditions. An embedded planar capacitor is a thin laminate that serves both as a power/ground plane and as a parallel plate capacitor in a multilayered printed wiring board (PWB). These capacitors are typically used for decoupling applications and are found to reduce the required number of surface mount capacitors. The capacitor laminate used in this study consisted of an epoxy-barium titanate (BaTiO3) composite dielectric sandwiched between Cu layers. Three electrical parameters, capacitance, dissipation factor, and insulation resistance, were monitored in-situ once every hour during testing under elevated temperature and voltage aging conditions. The failure modes observed were a sharp drop in insulation resistance and a gradual decrease in capacitance. An approach to model the time-to-failure associated with these failure modes as a function of the stress level is presented in this paper. Model-based PHM can be used to predict the time-to-failure associated with a single failure mode, consisting of a drop in either insulation resistance or capacitance. However, failure of an embedded capacitor could occur due to either of these two failure modes and was not captured using a single model. A combined model for both these failure modes can be developed but there was a large variance in the time-to-failure data of failures as a result of a sharp drop in insulation resistance. Therefore a data-driven approach, which utilizes the trend and correlation between the parameters to predict remaining life, was investigated to perform PHM. The data-driven approach used in this paper is the Mahalanobis distance (MD) method that reduces a multivariate data set to a single parameter by considering correlations among the parameters. The Mahalanobis distance method was successful in predicting the failures as a result of a gradual decrease in capacitance. However, prediction of failures as a result of a drop in insulation resistance was generally challenging due to their sudden onset. An experimental approach to address such sudden failures is discussed to facilitate identifying any trends in the parameters prior to failure.

scholarly journals A data-driven approach to modeling cancer cell mechanics during microcirculatory transport

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Peter Balogh ◽  
John Gounley ◽  
Sayan Roychowdhury ◽  
Amanda Randles
Keyword(s):  
Experimental Data ◽  
Cancer Cells ◽  
Large Deformation ◽  
Cell Lines ◽  
Cancer Cell ◽  
Membrane Model ◽  
Data Driven ◽  
Specific Cell ◽  
Model Based ◽  
Data Driven Approach

AbstractIn order to understand the effect of cellular level features on the transport of circulating cancer cells in the microcirculation, there has been an increasing reliance on high-resolution in silico models. Accurate simulation of cancer cells flowing with blood cells requires resolving cellular-scale interactions in 3D, which is a significant computational undertaking warranting a cancer cell model that is both computationally efficient yet sufficiently complex to capture relevant behavior. Given that the characteristics of metastatic spread are known to depend on cancer type, it is crucial to account for mechanistic behavior representative of a specific cancer’s cells. To address this gap, in the present work we develop and validate a means by which an efficient and popular membrane model-based approach can be used to simulate deformable cancer cells and reproduce experimental data from specific cell lines. Here, cells are modeled using the immersed boundary method (IBM) within a lattice Boltzmann method (LBM) fluid solver, and the finite element method (FEM) is used to model cell membrane resistance to deformation. Through detailed comparisons with experiments, we (i) validate this model to represent cancer cells undergoing large deformation, (ii) outline a systematic approach to parameterize different cell lines to optimally fit experimental data over a range of deformations, and (iii) provide new insight into nucleated vs. non-nucleated cell models and their ability to match experiments. While many works have used the membrane-model based method employed here to model generic cancer cells, no quantitative comparisons with experiments exist in the literature for specific cell lines undergoing large deformation. Here, we describe a phenomenological, data-driven approach that can not only yield good agreement for large deformations, but explicitly detail how it can be used to represent different cancer cell lines. This model is readily incorporated into cell-resolved hemodynamic transport simulations, and thus offers significant potential to complement experiments towards providing new insights into various aspects of cancer progression.

scholarly journals A Hybrid Model-Based and Data-Driven Approach to Spectrum Sharing in mmWave Cellular Networks

2020 ◽  
Vol 6 (4) ◽  
pp. 1269-1282 ◽  
Author(s):  
Hossein S. Ghadikolaei ◽  
Hadi Ghauch ◽  
Gabor Fodor ◽  
Mikael Skoglund ◽  
Carlo Fischione
Keyword(s):  
Cellular Networks ◽  
Hybrid Model ◽  
Spectrum Sharing ◽  
Data Driven ◽  
Model Based ◽  
Data Driven Approach

Mechanism model-based and data-driven approach for the diagnosis of solid oxide fuel cell stack leakage

2021 ◽  
Vol 286 ◽  
pp. 116508
Author(s):  
Yuan-wu Xu ◽  
Xiao-long Wu ◽  
Xiao-bo Zhong ◽  
Dong-qi Zhao ◽  
Marco Sorrentino ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Solid Oxide ◽  
Data Driven ◽  
Oxide Fuel ◽  
Fuel Cell Stack ◽  
Mechanism Model ◽  
Model Based ◽  
Data Driven Approach

scholarly journals Shearlets as feature extractor for semantic edge detection: the model-based and data-driven realm

2020 ◽  
Vol 476 (2243) ◽  
pp. 20190841
Author(s):  
Héctor Andrade-Loarca ◽  
Gitta Kutyniok ◽  
Ozan Öktem
Keyword(s):  
Edge Detection ◽  
High Performance ◽  
Hybrid Methods ◽  
Tomographic Reconstruction ◽  
Data Driven ◽  
Model Based ◽  
Edge Classification ◽  
Wide Range ◽  
Data Driven Approach ◽  
Sparse Approximations

Semantic edge detection has recently gained a lot of attention as an image-processing task, mainly because of its wide range of real-world applications. This is based on the fact that edges in images contain most of the semantic information. Semantic edge detection involves two tasks, namely pure edge detection and edge classification. Those are in fact fundamentally distinct in terms of the level of abstraction that each task requires. This fact is known as the distracted supervision paradox and limits the possible performance of a supervised model in semantic edge detection. In this work, we will present a novel hybrid method that is based on a combination of the model-based concept of shearlets, which provides probably optimally sparse approximations of a model class of images, and the data-driven method of a suitably designed convolutional neural network. We show that it avoids the distracted supervision paradox and achieves high performance in semantic edge detection. In addition, our approach requires significantly fewer parameters than a pure data-driven approach. Finally, we present several applications such as tomographic reconstruction and show that our approach significantly outperforms former methods, thereby also indicating the value of such hybrid methods for biomedical imaging.

scholarly journals Leak localization in water distribution networks using a mixed model-based/data-driven approach

2016 ◽  
Vol 55 ◽  
pp. 162-173 ◽  
Author(s):  
Adrià Soldevila ◽  
Joaquim Blesa ◽  
Sebastian Tornil-Sin ◽  
Eric Duviella ◽  
Rosa M. Fernandez-Canti ◽  
...  
Keyword(s):  
Water Distribution ◽  
Mixed Model ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Data Driven ◽  
Model Based ◽  
Data Driven Approach ◽  
Leak Localization
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