A Modular State-Vector Based Modeling Architecture for Diesel Exhaust System Design, Analysis and Optimization

Driving is a complex process influenced by a wide range of factors, especially complex interactions between the driver, the vehicle and the environment. This paper aims at the representation of complex situations in smart car domain and the middleware support. A general architecture of the smart car is presented from the point of view of context-awareness. A hierarchical context model is proposed for description of the complex driving environment. A software platform is built to provide the running environment for the context model and applications. Performance evaluation shows that our approach is effective and applicable in a smart car.

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“…Christopher [14] presents a modular state-vector-based modeling. The exhaust system model includes an input file and output file.…”

Section: Related Workmentioning

confidence: 99%

Providing Context-awareness in the Smart Car Environment

Sun

Zhang

2010

2010 10th IEEE International Conference on Computer and Information Technology

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“…The AIDE (Adaptive Integrated Drivervehicle InterfacE) (Broström, Engström, Agnvall, & Markkula, 2006) project uses DVE state vector obtained from the Drivervehicle-environment monitor to model context. Christopher (Graff & Weck, 2006) presents a modular state-vector-based modeling. The exhaust system model includes an input file and output file.…”

Section: Related Workmentioning

confidence: 99%

A Petri-Net Based Context Representation in Smart Car Environment

Sun

Zhang

Fan

2011

International Journal of Handheld Computing Research

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Driving is a complex process influenced by a wide range of factors, especially complex interactions between the driver, the vehicle, and the environment. This paper represents the complex situations in smart car domain. Unlike existing context-aware systems which isolate one context situation from another, such as road congestion and car deceleration, this paper proposes a context model which considers the driver, vehicle and environment as a whole. The paper tries to discover the inherent relationship between the situations in the smart car environment, and proposes a context model to support the representation of situations and their correlation. The detailed example scenarios are given to illustrate our idea.

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“…Previous work in applying optimization to system architecture selection and design includes a state-vector approach to model the effect of different component technologies in diesel exhaust after-treatment systems 6 . However, in this approach, only three architectures were considered.…”

Section: Introductionmentioning

confidence: 99%

A Modeling Framework for Applying Discrete Optimization to System Architecture Selection and Application to In-Situ Resource Utilization

Chepko

Weck

Crossley

et al. 2008

12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

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This paper presents an approach to apply optimization to the selection of a system architecture. Early-stage design decisions that define the subsystem and component technologies that comprise a system architecture are often made with information generated from a limited number of trade studies and a historical background. These decisions can lock in a design too early, resulting in a sub-optimal or even infeasible design. Decisions for systems that have little or no historical background, such as lunar in-situ resource utilization (ISRU) systems, have only analyses and tests to be based on. To enable a wider search of the design space, a modeling framework is discussed that decomposes a system in a hierarchical fashion to describe primary subsystem functions and their technology implementation alternatives. This framework allows a system model to be built that captures all of the subsystem technology alternatives in one reconfigurable model for which the categorical, discrete technology decisions can be treated as design variables in an optimization routine. The NASA ISRU System Model follows this framework and is used as an application of system architecture optimization. A genetic algorithm is used to explore both the discrete and continuous design space of the model. Because the current ISRU System Model has a small set of discrete variables, the performance and computational cost of the genetic algorithm search are compared to a previously developed ISRU optimization scheme that involves full enumeration of the discrete variables followed by gradient-based optimization with the continuous variables. The initial tests of the genetic algorithm approach provide comparable results to the previously used approach, requiring 5100 function evaluations compared to a range of 4800-10,000 function evaluations with the enumeration methods.

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A Modular State-Vector Based Modeling Architecture for Diesel Exhaust System Design, Analysis and Optimization

Cited by 8 publications

References 3 publications

Providing Context-awareness in the Smart Car Environment

Providing Context-awareness in the Smart Car Environment

A Petri-Net Based Context Representation in Smart Car Environment

A Modeling Framework for Applying Discrete Optimization to System Architecture Selection and Application to In-Situ Resource Utilization

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