Page 67 |
Save page Remove page | Previous | 67 of 171 | Next |
|
small (250x250 max)
medium (500x500 max)
Large (1000x1000 max)
Extra Large
large ( > 500x500)
Full Resolution
All (PDF)
|
This page
All
|
55
Although Methods 3 and 4 are not as accurate as Methods 1 and 2, they are more
direct to implement. In this research, Method 4 is chosen and modified to specify the
time intervals in the design process. The time intervals are nearly of the same lengths
but not exactly equal because the divisions occurred only after one finished his/her
conversations and there was no immediate following-ups. If another designer was
ready to talk while one was still talking, divisions of time intervals would wait until
both finished. Even in this way, the real preference of the team may change inside the
interval as well. For the sake of simplicity, it is assumed that designers do not change
their preferences for design alternatives within a time interval in this study. The
preferences are considered accumulatively for each interval and the preference
changes are only considered between the intervals. The precise granularity of the
changes inside the time interval could be studied in future research.
4.8 Case Study
4.8.1 Case Background
This case example is the same as the one used for the thematic analysis of the
discussion transcript in Section 3.2. In that project, designers attempted to solve two
component selection problems for the large-scale space system re-design, with three
alternative candidates respectively in a time range of 3 sessions. The case study in this
section is focused on the first session of the second component selection problem.
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 67 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 55 Although Methods 3 and 4 are not as accurate as Methods 1 and 2, they are more direct to implement. In this research, Method 4 is chosen and modified to specify the time intervals in the design process. The time intervals are nearly of the same lengths but not exactly equal because the divisions occurred only after one finished his/her conversations and there was no immediate following-ups. If another designer was ready to talk while one was still talking, divisions of time intervals would wait until both finished. Even in this way, the real preference of the team may change inside the interval as well. For the sake of simplicity, it is assumed that designers do not change their preferences for design alternatives within a time interval in this study. The preferences are considered accumulatively for each interval and the preference changes are only considered between the intervals. The precise granularity of the changes inside the time interval could be studied in future research. 4.8 Case Study 4.8.1 Case Background This case example is the same as the one used for the thematic analysis of the discussion transcript in Section 3.2. In that project, designers attempted to solve two component selection problems for the large-scale space system re-design, with three alternative candidates respectively in a time range of 3 sessions. The case study in this section is focused on the first session of the second component selection problem. |
