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73
W = (w1, w2,..., wn )
where n is the total number of preferential probabilities attained either by PPT or PPS,
the product of the number of intervals and the number of alternatives. vi (1 ≤ i ≤ n) is
the ith preferential probability extracted from the transcript with PPT, wi (1 ≤ i ≤ n) is
the ith preferential probability transferred from the rating values on surveys.
There are several ways to measure distances derived from coordinate geometry,
including L1 norm (Manhattan distance), L2 norm (Euclidean distance), Lp norm,
and L∞ norm (Chebyshev distance). In research, Lp norm is rarely used for the values
of p other than 1, 2 and ∞. In a two-dimensional space, L1 norm distance is like is
the distance a car would drive in a city laid out in square blocks (if there are no one-way
streets), L2 norm distance is the length of the line between the two points, and L∞
is the greatest distance along one of the coordinates. In this study, the samples of the
preferential probabilities in vectors andL1 , L2 and L∞ norms are employed to measure
the distances of two groups of preferential probabilities.
1
1
( , ) | | | |
n
i i
i
L VW V W v w
=
= − = Σ −
(5.4)
Object Description
| Title | Extraction of preferential probabilities from early stage engineering design team discussion |
| Author | Ji, Haifeng |
| Author email | haifengj@usc.edu; haifeng.ji@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Industrial & Systems Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2008-08-19 |
| Date submitted | 2008 |
| Restricted until | Unrestricted |
| Date published | 2008-10-07 |
| Advisor (committee chair) | Yang, Maria C. |
| Advisor (committee member) |
Lu, Stephen Jin, Yan |
| Abstract | Activities in the early stage of engineering design typically include the generation of design choices and selection among these design choices. A key notion in design alternative selection is that of preference in which a designer or design team assigns priorities to a set of design choices. However, preferences become more challenging to assign on both a practical and theoretical level when done by a group of individuals. Preferences may also be explicitly obtained via surveys or questionnaires in which designers are asked to rank the choices, rate choice with values, or select a "most-preferred" choice. However, these methods are typically employed at a single point of time; therefore, it may not be practical to use surveys to elicit a team’s preference change and evolution throughout the process.; This research explores the text analysis on the design discussion transcripts and presents a probabilistic approach for implicitly extracting a projection of aggregated preference-related information from the transcripts. The approach in this research graphically represents how likely a choice is to be "most preferred" by a design team over time. For evaluation purpose, two approaches are established for approximating a team's "most preferred" choice in a probabilistic way from surveys of individual team members. A design selection experiment was conducted to determine possible correlations between the preferential probabilities estimated from the team's discussion and survey ratings explicitly stated by team members. Results suggest that there are strong correlations between extracted preferential probabilities and team intents that are stated explicitly, and that the proposed methods can provide a quantitative way to understand and represent qualitative design information using a low overhead information extraction method. |
| Keyword | preferences; probabilities; concept selection; design process; design decision-making |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m1635 |
| Contributing entity | University of Southern California |
| Rights | Ji, Haifeng |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Ji-2413 |
| Archival file | uscthesesreloadpub_Volume14/etd-Ji-2413.pdf |
Description
| Title | Page 85 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 73 W = (w1, w2,..., wn ) where n is the total number of preferential probabilities attained either by PPT or PPS, the product of the number of intervals and the number of alternatives. vi (1 ≤ i ≤ n) is the ith preferential probability extracted from the transcript with PPT, wi (1 ≤ i ≤ n) is the ith preferential probability transferred from the rating values on surveys. There are several ways to measure distances derived from coordinate geometry, including L1 norm (Manhattan distance), L2 norm (Euclidean distance), Lp norm, and L∞ norm (Chebyshev distance). In research, Lp norm is rarely used for the values of p other than 1, 2 and ∞. In a two-dimensional space, L1 norm distance is like is the distance a car would drive in a city laid out in square blocks (if there are no one-way streets), L2 norm distance is the length of the line between the two points, and L∞ is the greatest distance along one of the coordinates. In this study, the samples of the preferential probabilities in vectors andL1 , L2 and L∞ norms are employed to measure the distances of two groups of preferential probabilities. 1 1 ( , ) | | | | n i i i L VW V W v w = = − = Σ − (5.4) |
