Page 75 |
Save page Remove page | Previous | 75 of 171 | Next |
|
small (250x250 max)
medium (500x500 max)
Large (1000x1000 max)
Extra Large
large ( > 500x500)
Full Resolution
All (PDF)
|
This page
All
|
63
4.9 Comparisons between Preference Evolution and Frequency Evolution
Above gives the implementation on how to extract the preferences from the
utterance data in the first session of the design process. The implementation can be
extended on the whole process. Figures 4.5-4.7 give the preference evolutions of three
alternatives (b1, b2, b3) of the second component selection problem (Problem B) over
the whole 3-session process. Figures 3.4-3.6 regarding the frequency evolution over
the whole 3-session process are also given as comparisons. The overall tendencies of
the figures are almost consistent, although the preference evolutions seem less
oscillating. The information entropy of the most-preferred alternative can be
calculated as in Equation (4.18):
1 1 2 1 1
1
( )
( | , ,..., )log ( | , ,..., )
i
n
i k i i i k i i
j
entropy K
P π a σ σ − σ P π a σ σ − σ
=
= −Σ = =
(4.18)
where Ki is the most-preferred alternative variable in the ith time interval, whose
value can be chosen from the alternative range {1, 2, …, n} for a component selection
problem with n alternatives. The preferential probability of each alternative is known
from Equations (4.6) and (4.7). Figure 4.8 gives the information entropy evolution of
the most-preferred alternative of Problem B. The information entropy of the
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 75 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 63 4.9 Comparisons between Preference Evolution and Frequency Evolution Above gives the implementation on how to extract the preferences from the utterance data in the first session of the design process. The implementation can be extended on the whole process. Figures 4.5-4.7 give the preference evolutions of three alternatives (b1, b2, b3) of the second component selection problem (Problem B) over the whole 3-session process. Figures 3.4-3.6 regarding the frequency evolution over the whole 3-session process are also given as comparisons. The overall tendencies of the figures are almost consistent, although the preference evolutions seem less oscillating. The information entropy of the most-preferred alternative can be calculated as in Equation (4.18): 1 1 2 1 1 1 ( ) ( | , ,..., )log ( | , ,..., ) i n i k i i i k i i j entropy K P π a σ σ − σ P π a σ σ − σ = = −Σ = = (4.18) where Ki is the most-preferred alternative variable in the ith time interval, whose value can be chosen from the alternative range {1, 2, …, n} for a component selection problem with n alternatives. The preferential probability of each alternative is known from Equations (4.6) and (4.7). Figure 4.8 gives the information entropy evolution of the most-preferred alternative of Problem B. The information entropy of the |
