Parameter design considerations in precision engineering

Parameter design is an experimental design and analysis methodology for developing robust processes and products. Robustness implies insensitivity to noise disturbances. Subtle experimental realities, such as the joint effect of process knowledge and analysis methodology, may affect the effectiveness of parameter design in precision engineering; where the objective is to detect minute variation in product and process performance. In this thesis, approaches to statistical forced-noise design and analysis methodologies were investigated with respect to detecting performance variations. Given a low degree of process knowledge, Taguchi's methodology of signal-to-noise ratio analysis was found to be more suitable in detecting minute performance variations than the classical approach based on polynomial decomposition. Comparison of inner-array noise (IAN) and outer-array noise (OAN) structuring approaches showed that OAN is a more efficient design for precision engineering. ^

Employer participation in Florida school -to -work initiatives

This study used a cross-sectional design and descriptive research methodology to assess the characteristics and practices of employers participating in Florida school-to-work (STW) initiatives, the factors influencing their participation, and their STW partnerships with schools. The study also examined the relationships between employer characteristics and participation. ^ A sampling frame of 15,202 employers from the 28 Florida STW regions was constructed. Data were collected via a mail survey with a random sample of employers, using a researcher-designed questionnaire. Data were analyzed using descriptive statistics, correlational analysis, and analysis of variance. ^ At the 95% confidence level, it was estimated that the mean establishment size in the population is between 25 and 51 employees and that employers have been involved in STW partnerships between five and seven years. ^ The study revealed broad-based employer participation in three of four areas: Working with Students, Working with Educators, and Internal Company Practices Supporting STW. A fourth area, Building a System, showed generally low participation. Data indicate that workforce needs are important incentives to employer STW participation beyond their desire to contribute to education or the community. Data also indicate that lack of information on STW is the greatest barrier to employer participation. ^ Sample employers have more positive perceptions of the value of their partnerships with schools than of the quality of the partnerships. Ninety-four percent agreed that students are better prepared for work and careers as a result of the partnership's activities. More than half agreed that a sense of trust and good communication exist between educators and employer partners. ^ Employer variables found to have a significant, positive relationship with participation include size (coefficient of determination r2 = .116), years in STW partnerships (r2 = .128), and perceptions of partnership quality (r2 = .092) and value (r2 = .112). ^ A major conclusion based on the findings of this study is that employer participation is optimized in initiatives that achieve important STW outcomes for students and build long-term relationships between employers and schools. Another that in Florida, the STW Opportunities Act of 1994 has not resulted in substantially greater employer involvement in building a STW system. ^

Quality of experience evaluation of voice communication: an affect-based approach

Voice communication systems such as Voice-over IP (VoIP), Public Switched Telephone Networks, and Mobile Telephone Networks, are an integral means of human tele-interaction. These systems pose distinctive challenges due to their unique characteristics such as low volume, burstiness and stringent delay/loss requirements across heterogeneous underlying network technologies. Effective quality evaluation methodologies are important for system development and refinement, particularly by adopting user feedback based measurement. Presently, most of the evaluation models are system-centric (Quality of Service or QoS-based), which questioned us to explore a user-centric (Quality of Experience or QoE-based) approach as a step towards the human-centric paradigm of system design. We research an affect-based QoE evaluation framework which attempts to capture users' perception while they are engaged in voice communication. Our modular approach consists of feature extraction from multiple information sources including various affective cues and different classification procedures such as Support Vector Machines (SVM) and k-Nearest Neighbor (kNN). The experimental study is illustrated in depth with detailed analysis of results. The evidences collected provide the potential feasibility of our approach for QoE evaluation and suggest the consideration of human affective attributes in modeling user experience.