scholarly journals Model-Based Assistance for Making Time/Fidelity Trade-Offs in Component Compositions

2014 ◽  
Author(s):  
Vishal Dwivedi ◽  
David Garlan ◽  
Jurgen Pfeffer ◽  
Bradley Schmerl
Keyword(s):  
Model Based ◽  
Trade Offs

scholarly journals VALIDATION OF A SURVIVABLE PUBLISH-SUBSCRIBE SYSTEM

2014 ◽  
pp. 77-94
Author(s):  
Sankalp Singh ◽  
Adnan Agbaria ◽  
Fabrice Stevens ◽  
Tod Courtney ◽  
John F. Meyer ◽  
...  
Keyword(s):  
Top Down ◽  
Model Based ◽  
Trade Offs ◽  
Validation Procedure ◽  
High Level

We describe, with respect to high-level survivability requirements, the validation of a survivable publish subscribe system that is under development. We use a top-down approach that methodically breaks the task of validation into manageable tasks, and for each task, applies techniques best suited to its accomplishment. These efforts can be largely independent and use a variety of validation techniques, and the results, which complement and supplement each other, are seamlessly integrated to provide a convincing assurance argument. We also demonstrate the use of model-based validation techniques, as a part of the overall validation procedure, to guide the system’s design by exploring different configurations and evaluating trade-offs.

scholarly journals A Model-Based Active Testing Approach to Sequential Diagnosis

2010 ◽  
Vol 39 ◽  
pp. 301-334 ◽  
Author(s):  
A. Feldman ◽  
G. Provan ◽  
A. Van Gemund
Keyword(s):  
Pattern Generation ◽  
Model Based ◽  
Trade Offs ◽  
Additional Input ◽  
Active Testing ◽  
Testing Algorithms ◽  
Input Variables ◽  
Testing Algorithm ◽  
Sequential Diagnosis ◽  
Test Vectors

Model-based diagnostic reasoning often leads to a large number of diagnostic hypotheses. The set of diagnoses can be reduced by taking into account extra observations (passive monitoring), measuring additional variables (probing) or executing additional tests (sequential diagnosis/test sequencing). In this paper we combine the above approaches with techniques from Automated Test Pattern Generation (ATPG) and Model-Based Diagnosis (MBD) into a framework called FRACTAL (FRamework for ACtive Testing ALgorithms). Apart from the inputs and outputs that connect a system to its environment, in active testing we consider additional input variables to which a sequence of test vectors can be supplied. We address the computationally hard problem of computing optimal control assignments (as defined in FRACTAL) in terms of a greedy approximation algorithm called FRACTAL-G. We compare the decrease in the number of remaining minimal cardinality diagnoses of FRACTAL-G to that of two more FRACTAL algorithms: FRACTAL-ATPG and FRACTAL-P. FRACTAL-ATPG is based on ATPG and sequential diagnosis while FRACTAL-P is based on probing and, although not an active testing algorithm, provides a baseline for comparing the lower bound on the number of reachable diagnoses for the FRACTAL algorithms. We empirically evaluate the trade-offs of the three FRACTAL algorithms by performing extensive experimentation on the ISCAS85/74XXX benchmark of combinational circuits.

Design and Performance Studies of Model-Based Variable-Structure Adaptive Controllers for Industrial Manipulators

1992 ◽  
Vol 206 (4) ◽  
pp. 245-262 ◽  
Author(s):  
S K Tso ◽  
P L Law
Keyword(s):  
Performance Studies ◽  
Variable Structure ◽  
Adaptive Controllers ◽  
Model Based ◽  
Trade Offs ◽  
Control Scheme ◽  
Industrial Manipulators ◽  
And Performance ◽  
Distinguishing Features ◽  
Adaptive Control Scheme

A model-based variable-structure adaptive control scheme recently proposed for the trajectory tracking of robot manipulators has shown significant promise. By actual implementation with a commercial manipulator, the practical means for improvement are examined, the design trade-offs are systematically explored, and the distinguishing features of the performance achievable in the improved version are brought out and compared with those of earlier schemes.

Reconstructing Sea Surface Dynamics Using a Linear Koopman Kalman Filter

2021 ◽  
Author(s):  
Said Ouala ◽  
Ronan Fablet ◽  
Ananda Pascual Pascual ◽  
Bertrand Chapron ◽  
Fabrice Collard ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Linear Representation ◽  
Sea Surface ◽  
Dynamical Model ◽  
Optimal Interpolation ◽  
Model Based ◽  
Trade Offs ◽  
Non Linear ◽  
Spatio Temporal

<p>Spatio-temporal interpolation applications are important in the context of ocean surface modeling. Current state-of-the-art techniques typically rely either on optimal interpolation or on model-based approaches which explicitly exploit a dynamical model. While the optimal interpolation suffers from smoothing issues making it unreliable in retrieving fine-scale variability, the selection and parametrization of a dynamical model, when considering model-based data assimilation strategies, remains a complex issue since several trade-offs between the model's complexity and its applicability in sea surface data assimilation need to be carefully addressed. For these reasons, deriving new data assimilation architectures that can perfectly exploit the observations and the current advances in signal processing, modeling and artificial intelligence is crucial.</p><p>In this work, we explore new advances in data-driven data assimilation to exploit the classical Kalman filter in the interpolation of spatio-temporal fields. The proposed algorithm is written in an end-to-end differentiable setting in order to allow for the learning of the linear dynamical model from a data assimilation cost. Furthermore, the linear model is formulated on a space of observables, rather than the space of observations, which allows for perfect replication of non-linear dynamics when considering periodic and quasi-periodic limit sets and providing a decent (short-term) forecast of chaotic ones. One of the main advantages of the proposed architecture is its simplicity since it utilises a linear representation coupled with a Kalman filter. Interestingly, our experiments show that exploiting such a linear representation leads to better data assimilation when compared to non-linear filtering techniques, on numerous applications, including the sea level anomaly reconstruction from satellite remote sensing observations.</p>

MACHINE RECOGNITION AS REPRESENTATION AND SEARCH — A SURVEY

1991 ◽  
Vol 05 (05) ◽  
pp. 715-747 ◽  
Author(s):  
FENG ZHAO
Keyword(s):  
Recognition System ◽  
Image Features ◽  
Future Research ◽  
Recognition Model ◽  
Model Based ◽  
Comparative Framework ◽  
Trade Offs ◽  
And Control ◽  
Made In

Generality, representation, and control have been the central issues in machine recognition. Model-based recognition is the search for consistent matches of model and image features. We present a comparative framework for the evaluation of different approaches, particularly those of ACRONYM, RAF, and Ikeuchi et al. The strengths and weaknesses of these approaches are discussed and compared, and remedies are suggested. Various trade-offs made in the implementations are analyzed with respect to the systems' intended task domains. The requirements for a versatile recognition system are motivated. Several directions for future research are pointed out.

scholarly journals Short-term versus long-term decision trade-offs: Evidence from a model-based observational experiment with African small-scale farmers

2019 ◽  
Vol 36 (2) ◽  
pp. 215-228
Author(s):  
Birgit Kopainsky ◽  
Andreas Gerber ◽  
David Lara-Arango ◽  
Progress H. Nyanga
Keyword(s):  
Small Scale ◽  
Short Term ◽  
Model Based ◽  
Trade Offs ◽  
Small Scale Farmers

Matching method with theory in person-oriented developmental psychopathology research

2010 ◽  
Vol 22 (2) ◽  
pp. 239-254 ◽  
Author(s):  
Sonya K. Sterba ◽  
Daniel J. Bauer
Keyword(s):  
Developmental Psychopathology ◽  
Matching Method ◽  
The Past ◽  
Model Based ◽  
Trade Offs ◽  
Oriented Approach ◽  
Do So

AbstractThe person-oriented approach seeks to match theories and methods that portray development as a holistic, highly interactional, and individualized process. Over the past decade, this approach has gained popularity in developmental psychopathology research, particularly as model-based varieties of person-oriented methods have emerged. Although these methods allow some principles of person-oriented theory to be tested, little attention has been paid to the fact that these methods cannot test other principles, and may actually be inconsistent with certain principles. Lacking clarification regarding which aspects of person-oriented theory are testable under which person-oriented methods, assumptions of the methods have sometimes been presented as testable hypotheses or interpreted as affirming the theory. This general blurring of the line between person-oriented theory and method has even led to the occasional perception that the method is the theory and vice versa. We review assumptions, strengths, and limitations of model-based person-oriented methods, clarifying which theoretical principles they can test and the compromises and trade-offs required to do so.

Steps substantive researchers can take to build a scientifically strong case for the existence of trajectory groups

2010 ◽  
Vol 22 (2) ◽  
pp. 273-275 ◽  
Author(s):  
Nicholas Ialongo
Keyword(s):  
Strong Case ◽  
Model Based ◽  
Trade Offs ◽  
Do So

AbstractSterba and Bauer's Keynote Article does a superb job of reviewing the “… assumptions, strengths, and limitations of model-based person-oriented methods—clarifying which theoretical principles [researchers] can test and the compromises and trade-offs required to do so.” Their writing is exceptionally clear, and the examples given highly instructive. At the same time, their arguments may be so convincing that the reader may be reluctant to pursue person-oriented analyses in a longitudinal context. The purpose of this Commentary is not to contradict Sterba and Bauer's arguments but to briefly review the steps that substantive researchers can take in building a scientifically strong case for either assuming continuously varied growth “… or that [trajectory groups] actually exist” according to Raudenbush. These steps have been elaborated in a series of papers by Muthén and colleagues, but it is useful to briefly review them here.

scholarly journals A review of model-based scenario analysis of poverty for informing sustainability

2021 ◽  
Author(s):  
QI LIU ◽  
Zhaoxia Guo ◽  
Lei Gao ◽  
Yucheng Dong ◽  
Enayat A. Moallemi ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Scenario Analysis ◽  
Future Research ◽  
Model Based ◽  
Complex Interactions ◽  
Trade Offs ◽  
Pros And Cons ◽  
Development Goals ◽  
Effective Models

Ending poverty in all its forms everywhere is the first goal being targeted by the United Nations 2030 Agenda for Sustainable Development. Poverty eradication is a long-term process that faces the challenges of many uncertainties and complex interactions with other Sustainable Development Goals (SDGs). In order to better understand poverty and contribute to addressing poverty in a sustainable manner, this paper aims to conduct a systematic review of model-based analysis for poverty scenario in the context of SDGs. We first review 144 studies from the perspectives of bibliometric information (i.e., publication types, research topics for poverty, research objects, research scales and geographic locations) and models information for poverty scenario analysis (i.e., model types, purposes, states, temporal and spatial range, sectors considered, poverty and other SDGs indicators). Second, we discuss the pros and cons of different types of models and identify seven representative models. We also discuss the synergies and trade-offs between poverty and other SDGs. Finally, we identify four potential research gaps in model-based poverty scenario analysis and provide suggestions for future research. The review shows that poverty scenario analysis was carried out mainly from a single perspective, such as economic, ecological, and agricultural. Few studies used effective models to analyze poverty under an integrated interactions analysis of multiple sectors. Comprehensive multi-sector models are needed for global and regional poverty scenario analysis over the medium- or long-term to enhance the ability of analyzing the combined effects, synergies, and trade-offs between poverty and a variety of other SDGs.

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