Exploring the Impact of Model Fidelity Through Interactive Visualizations for System of Systems

A system-of-systems (SoS) approach is often used for simulating disruptions to business and infrastructure system networks allowing for integration of several models into one simulation. However, the integration is frequently challenging as each system is designed individually with different characteristics, such as time granularity. Understanding the impact of time granularity on propagation of disruptions between businesses and infrastructure systems and finding the appropriate granularity for the SoS simulation remain as major challenges. To tackle these, we explore how time granularity, recovery time, and disruption size affect the propagation of disruptions between constituent systems of an SoS simulation. To address this issue, we developed a high level architecture (HLA) simulation of three networks and performed a series of simulation experiments. Our results revealed that time granularity and especially recovery time have huge impact on propagation of disruptions. Consequently, we developed a model for selecting an appropriate time granularity for an SoS simulation based on expected recovery time. Our simulation experiments show that time granularity should be less than 1.13 of expected recovery time. We identified some areas for future research centered around extending the experimental factors space.

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Using Indicators to Monitor Security Risk in Systems of Systems

IT Security Governance Innovations - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-4666-2083-4.ch010 ◽

2012 ◽

pp. 256-292 ◽

Cited By ~ 1

Author(s):

Olav Skjelkvåle Ligaarden ◽

Atle Refsdal ◽

Ketil Stølen

Keyword(s):

Power Supply ◽

System Of Systems ◽

Dynamic Monitoring ◽

Security Risk ◽

Dynamic Nature ◽

Systems Of Systems ◽

It Service ◽

The Impact

Systems of systems are collections of systems interconnected through the exchange of services. Their often complex service dependencies and very dynamic nature make them hard to analyze and predict with respect to quality in general, and security in particular. In this chapter, the authors put forward a method for the capture and monitoring of impact of service dependencies on the security of provided services. The method is divided into four main steps focusing on documenting the system of systems and IT service dependencies, establishing the impact of service dependencies on risk to security of provided services, identifying measureable indicators for dynamic monitoring, and specifying their design and deployment, respectively. The authors illustrate the method in an example-driven fashion based on a case within power supply.

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Effect of Model Element Fidelity Within a Complex Function-Based Behavioral Model

Volume 3: 28th Computers and Information in Engineering Conference, Parts A and B ◽

10.1115/detc2008-49355 ◽

2008 ◽

Cited By ~ 2

Author(s):

Ryan S. Hutcheson ◽

Daniel A. McAdams ◽

Robert B. Stone ◽

Irem Y. Tumer

Keyword(s):

Complex Function ◽

Behavioral Model ◽

Design Tool ◽

Prior Work ◽

Behavioral Models ◽

Early Design ◽

Formula Sae ◽

Model Fidelity ◽

Performance Predictions ◽

The Impact

The Function-based Behavioral Modeling (FBBM) design tool was introduced in prior work as a means of using formal functional modeling as the foundation for creating detailed mathematical models of system behavior. The overall objective of this work is to create a framework for partitioning modeling efforts into functional elements and promoting model storage and re-use through the use of functional models. In prior work, the FBBM method was introduced to model the complete vehicle dynamics of a Formula SAE racecar, highlighting the representation of functionality and the development of behavioral models. The objective of the work presented in the current paper is to demonstrate the ability to incorporate models of varying fidelity within a function-based behavioral model of a complex system. Additionally, the impact of model fidelity on the model’s predictions is addressed. A previously developed model is used as a foundation for developing the necessary new models and illustrating the impact of model fidelity on performance predictions when selecting a tire during early design. The results illustrate that the FBBM framework allows models of varying fidelity to be quickly made and their effect on predicted performance to be measured in order to assist critical early design choices.

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The Role of Model Fidelity in Model Predictive Control Based Hazard Avoidance in Unmanned Ground Vehicles Using LIDAR Sensors

Volume 3: Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing; System Identification (Estimation for Automotive Applications, Modeling, Therapeutic Control in Bio-Systems); Variable Structure/Sliding-Mode Control; Vehicles and Human Robotics; Vehicle Dynamics and Control; Vehicle Path Planning and Collision Avoidance; Vibrational and Mechanical Systems; Wind Energy Systems and Control ◽

10.1115/dscc2013-4021 ◽

2013 ◽

Cited By ~ 18

Author(s):

Jiechao Liu ◽

Paramsothy Jayakumar ◽

James L. Overholt ◽

Jeffrey L. Stein ◽

Tulga Ersal

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Vehicle Dynamics ◽

High Fidelity ◽

Unmanned Ground Vehicles ◽

Ground Vehicles ◽

Model Fidelity ◽

Hazard Avoidance ◽

The Impact

Unmanned ground vehicles (UGVs) are gaining importance and finding increased utility in both military and commercial applications. Although earlier UGV platforms were typically exclusively small ground robots, recent efforts started targeting passenger vehicle and larger size platforms. Due to their size and speed, these platforms have significantly different dynamics than small robots, and therefore the existing hazard avoidance algorithms, which were developed for small robots, may not deliver the desired performance. The goal of this paper is to present the first steps towards a model predictive control (MPC) based hazard avoidance algorithm for large UGVs that accounts for the vehicle dynamics through high fidelity models and uses only local information about the environment as provided by the onboard sensors. Specifically, the paper presents the MPC formulation for hazard avoidance using a light detection and ranging (LIDAR) sensor and applies it to a case study to investigate the impact of model fidelity on the performance of the algorithm, where performance is measured mainly by the time to reach the target point. Towards this end, the case study compares a 2 degrees-of-freedom (DoF) vehicle dynamics representation to a 14 DoF representation as the model used in MPC. The results show that the 2 DoF model can perform comparable to the 14 DoF model if the safe steering range is established using the 14 DoF model rather than the 2 DoF model itself. The conclusion is that high fidelity models are needed to push autonomous vehicles to their limits to increase their performance, but simulating the high fidelity models online within the MPC may not be as critical as using them to establish the safe control input limits.

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Feature Variation and its Impact on Structural Acoustic Response Predictions

Journal of Vibration and Acoustics ◽

10.1115/1.1523072 ◽

2003 ◽

Vol 125 (1) ◽

pp. 31-38

Author(s):

Kenneth A. Cunefare

Keyword(s):

Acoustic Power ◽

Reference State ◽

Acoustic Response ◽

Model Fidelity ◽

Quantity Of Interest ◽

Resonance Frequencies ◽

Quadratic Response ◽

Constant Bandwidth ◽

Bandwidth Analysis ◽

The Impact

This paper presents a screening technique to assess the impact on model fidelity introduced by variations in the properties or positions of features in harmonically forced fluid-loaded structural acoustic models. The perspective taken is one of knowledge of a reference state, with a desire to determine the impact on the total radiated acoustic power due to perturbations in the reference state. Such perturbations change the predicted resonance frequencies of a structure under consideration, and hence, change the predicted response amplitudes. The method uses a single degree of freedom response model in the local region of each fluid-loaded resonance, coupled with eigenvalue sensitivities or variations, to estimate the perturbation impact. The perturbation is scaled by the degree to which each given mode participates in the response quantity of interest. The SDOF model yields results that indicate that proportional bandwidth analysis will be less sensitive to perturbation than constant bandwidth analysis. This is demonstrated through comparison of a constant bandwidth analysis and a 1/3 octave analysis applied to the same system. Elements of the analysis method are not necessarily restricted to model perturbations nor acoustic power, rather they may be used to assess the perturbation of any quadratic response quantity of interest due to changes in resonance frequency.

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Vermont: a multi-perspective visual interactive platform for mutational analysis

10.1101/166348 ◽

2017 ◽

Author(s):

Alexandre V Fassio ◽

Pedro M Martins ◽

Samuel da S Guimarães ◽

Sócrates S A Junior ◽

Vagner S Ribeiro ◽

...

Keyword(s):

Protein Function ◽

Visual Analytics ◽

Large Scale ◽

Mutational Analysis ◽

Structural Parameters ◽

Point Mutations ◽

Huge Amount ◽

Interactive Visualizations ◽

And Function ◽

The Impact

AbstractBackgroundA huge amount of data about genomes and sequence variation is available and continues to grow on a large scale, which makes experimentally characterizing these mutations infeasible regarding disease association and effects on protein structure and function. Therefore, reliable computational approaches are needed to support the understanding of mutations and their impacts. Here, we present VERMONT 2.0, a visual interactive platform that combines sequence and structural parameters with interactive visualizations to make the impact of protein point mutations more understandable.ResultsWe aimed to contribute a novel visual analytics oriented method to analyze and gain insight on the impact of protein point mutations. To assess the ability of VERMONT to do this, we visually examined a set of mutations that were experimentally characterized to determine if VERMONT could identify damaging mutations and why they can be considered so.ConclusionsVERMONT allowed us to understand mutations by interpreting position-specific structural and physicochemical properties. Additionally, we note some specific positions we believe have an impact on protein function/structure in the case of mutation.

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