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George W. Woodruff School of Mechanical Engineering

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https://hdl.handle.net/1853/70728

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College of Engineering

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Advanced Mechanical Bipedal Experimental Robotics Lab

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Computational Reactor and Medical Physics Laboratory

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Cryo Lab

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Environmentally Conscious Design and Manufacturing

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Fusion Research Center

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https://finding-aids.library.gatech.edu/agents/corporate_entities/120

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Publication Search Results

Now showing 1 - 10 of 37

Validating Behavioral Models For Reuse

(Georgia Institute of Technology, 2007) Paredis, Christiaan J. J. ; Malak, Richard J., Jr.

When using a model to predict the behavior of a physical system of interest, engineers must be confident that, under the conditions of interest, the model is an adequate representation of the system. The process of building this confidence is called model validation. It requires that engineers have knowledge about the system and conditions of interest, properties of the model and their own tolerance for uncertainty in the predictions. To reduce time and costs, engineers often reuse preexisting models that other engineers have developed. However, if the user lacks critical parts of this knowledge, model validation can be as time consuming and costly as developing a similar model from scratch. In this article, we describe a general process for performing model validation for reused behavioral models that overcomes this problem by relying on the formalization and exchange of knowledge. We identify the critical elements of this knowledge, discuss how to represent it and demonstrate the overall process on a simple engineering example.
Multi-Attribute Utility Analysis in Set-Based Conceptual Design

(Georgia Institute of Technology, 2007) Paredis, Christiaan J. J. ; Malak, Richard J., Jr. ; Aughenbaugh, Jason Matthew

During conceptual design, engineers deal with incomplete product descriptions called design concepts. Engineers must compare these concepts in order to move towards the more desirable designs. However, comparisons are difficult because a single concept associates with numerous possible final design specifications, and any meaningful comparison of concepts must consider this range of possibilities. Consequently, the performance of a concept can only be characterized imprecisely. While standard multi-attribute utility theory is an accepted framework for making preference-based decisions between precisely characterized alternatives, it does not directly accommodate the analysis of imprecisely characterized alternatives. By extending uncertainty representations to model imprecision explicitly, it is possible to apply the principles of utility theory to such problems. However, this can lead to situations of indeterminacy, meaning that the decision maker is unable to identify a single concept as the most preferred. Under a set-based perspective and approach to design, a designer can work towards a single solution systematically despite indecision arising from imprecise characterizations of design concepts. Existing work in set-based design primarily focuses on feasibility conditions and single-attribute objectives, which are insufficient for most design problems. In this article, we combine the framework of multi-attribute utility theory, the perspective of set-based design, and the explicit mathematical representation of imprecision into a single approach to conceptual design. Each of the component theories are discussed, and their combined application developed. The approach is illustrated using the conceptual design of a fixed-ratio power transmission as an example. Additionally, important directions for future research are identified, with a particular focus on the process of modeling abstract design concepts.
Why Are Intervals and Imprecision Important In Engineering Design?

(Georgia Institute of Technology, 2006-02) Aughenbaugh, Jason Matthew ; Paredis, Christiaan J. J.

It is valuable in engineering design to distinguish between two different types of uncertainty: inherent variability and imprecision. While variability is naturally random behavior in a physical process or property, imprecision is uncertainty that is due to a lack of knowledge or information. There are many sources of imprecision in design. Sequential decision making introduces imprecision because the results of future decisions are unknown. Statistical data from finite samples of environmental factors are inherently imprecise. Bounded rationality leads to imprecise subjective probabilities. Expert opinions and judgments often are imprecise due to a lack of information or conflict. Behavioral simulations and analysis models are imprecise abstractions of reality. Knowledge of a decision maker's preferences may be imprecise due to bounded rationality or other constraints. Consequently, the engineering design community needs efficient computational methods for interval data and imprecise probabilities in order to support decision making in the design process. This paper introduces these sources and needs, with the aim of forming a foundation for future collaboration with the reliable engineering computing community.
Computational Methods for Decision Making

(Georgia Institute of Technology, 2006-02) Bruns, Morgan Chase ; Paredis, Christiaan J. J. ; Ferson, Scott

In this paper, we investigate computational methods for decision making based on imprecise information in the context of engineering design. The goal is to identify the subtleties of engineering design problems that impact the choice of computational solution methods, and to evaluate some existing solution methods to determine their suitability and limitations. Although several approaches for propagating imprecise probabilities have been published in the literature, these methods are insufficient for practical engineering analysis. The dependency bounds convolution approach of Williamson and Downs and the distribution envelope determination approach of Berleant work sufficiently well only for open models (that is, models with known mathematical operations). Both of these approaches rely on interval arithmetic and are therefore limited to problems with few repeated variables. In an attempt to overcome the difficulties faced by these deterministic methods, we propose an alternative approach that utilizes both Monte Carlo simulation and optimization. The Monte Carlo/optimization hybrid approach has its own drawbacks in that it assumes that the uncertain inputs can be parameterized, that it requires the solution of a global optimization problem, and that it assumes independence between the uncertain inputs.
Applying Information-Gap Decision Theory to a Design Problem having Severe Uncertainty

(Georgia Institute of Technology, 2006-01) Duncan, Scott Joseph ; Paredis, Christiaan J. J. ; Bras, Berdinus A.

Often in the early stages of the engineering design process, a decision maker lacks the information needed to represent uncertainty in the input parameters of a performance model. In one particular form of severely deficient information, a nominal estimate is available for an input parameter, but the amount of discrepancy between that estimate and the parameter’s true value, as well as the implications of that discrepancy on system performance, are not known. In this paper, the concepts and techniques of information-gap decision theory (IGDT), an established method for making decisions robust to severely deficient information, are examined more closely through application to a design problem with continuous design variables. The uncertain variables in the chosen example problem are parameters of a probability distribution, so the relationship between IGDT and design approaches considering precise and/or imprecise probabilities is explained. Insight gained from a walkthrough of the design example is used to suggest the types of problems an IGDT approach will or will not effectively solve as well as potential limitations that could be encountered when solving more complex problems.
Managing Uncertainty in Environmentally Benign Design and Manufacture

(Georgia Institute of Technology, 2006) Bras, Berdinus A. ; Paredis, Christiaan J. J.

When making design decisions in environmentally benign design and manufacture, the decision maker is often faced with extreme uncertainty. Due to a lack of understanding of the complex dynamics of environmental and societal systems, it is very difficult to judge the impact different design alternatives have on the environment, the economy and the society, especially in the distant future. In this paper, two formalisms are illustrated for making design decisions under extreme uncertainty. The formalisms are probability bounds analysis and info-gap decision theory. We introduce the basic concepts for both formalisms, discuss the advantages and limitations, and identify under which circumstances they are useful in the context of design decision making. One can think of both decision methods as having a built-in sensitivity analysis allowing the decision maker to judge whether a decision can be made confidently based on the current information, or whether additional information needs to be gathered.
Eliminating Design Alternatives Based on Imprecise Information

(Georgia Institute of Technology, 2006) Recuk, Stephen Joseph ; Aughenbaugh, Jason Matthew ; Bruns, Morgan Chase ; Paredis, Christiaan J. J.

In this paper, the relationship between uncertainty and sets of alternatives in engineering design is investigated. In sequential decision making, each decision alternative actually consists of a set of design alternatives. Consequently, the decision-maker can express his or her preferences only imprecisely as a range of expected utilities for each decision alternative. In addition, the performance of each design alternative can be characterized only imprecisely due to uncertainty from limited data, modeling assumptions, and numerical methods. The approach presented in this paper recognizes the presence of both imprecision and sets in the design process by focusing on incrementally eliminating decision alternatives until a small set of solutions remains. This is a fundamental shift from the current paradigm where the focus is on selecting a single decision alternative in each design decision. To make this approach economically feasible, one needs efficient methods for eliminating alternatives—that is, methods that eliminate as many alternatives as possible given the available imprecise information. Efficient elimination requires that one account for dependencies between uncertain quantities, such as shared uncertain variables. In this paper, criteria for elimination with and without shared uncertainty are presented and compared. The set-based nature of design and the presence of imprecision are introduced, elimination criteria are discussed, and the overall set-based approach and elimination criteria are demonstrated with the design of a gearbox as an example problem.
Information Economics in Simulation-Based Design

(Georgia Institute of Technology, 2005-06-28) Paredis, Christiaan J. J. ; Aughenbaugh, Jason Matthew ; Malak, Richard J., Jr. ; Ling, Jay ; Bruns, Morgan Chase ; Rekuc, Steven Joseph
Information Economics in Design

(Georgia Institute of Technology, 2005-02-15) Paredis, Christiaan J. J.
Interactive Multi-Modal Robot Programming

(Georgia Institute of Technology, 2005) Paredis, Christiaan J. J. ; Khosla, Pradeep K. ; Iba, Soshi

As robots enter the human environment and come in contact with inexperienced users, they need to be able to interact with users in a multi-modal fashion—keyboard and mouse are no longer acceptable as the only input modalities. This paper introduces a novel approach for programming robots interactively through a multi-modal interface. The key characteristic of this approach is that the user can provide feedback interactively at any time—during both the programming and the execution phase. The framework takes a three-step approach to the problem: multi-modal recognition, intention interpretation, and prioritized task execution. The multi-modal recognition module translates hand gestures and spontaneous speech into a structured symbolic data stream without abstracting away the user's intent. The intention interpretation module selects the appropriate primitives to generate a task based on the user's input, the system's current state, and robot sensor data. Finally, the prioritized task execution module selects and executes skill primitives based on the system's current state, sensor inputs, and prior tasks. The framework is demonstrated by interactively controlling and programming a vacuum-cleaning robot. The demonstrations are used to exemplify the interactive programming and the plan recognition aspect of the research.