Guiding Complex Design Optimisation Using Bayesian Networks

Urgency of the research. Mechatronics products become more sophisticated and complicated. Mechatronic engineers should be prepared for this complex design process. Practical experimental model helps improve educational process as preparing for practice. Target setting. Miniaturized model of the lift suitable for practical training on subjects focused to microcontrollers, sen-sors, actuators etc. Students have possibility to make practice on laboratory exercises, where they can verify theoretical knowledge obtained on lectures. The arrangement of the model has modular character, because of possibility to rearrange or adding of new function into model. The aim was to create minimized model of real lift with all functions and systems. Actual scientific researches and issues analysis. Many universities are oriented only to finished robotic kits and do not support creativity of students. Open access and open structure model missing in this field. There is a need for fast prototyping model, which allows the creation of new design of product. Uninvestigated parts of general matters defining. The question of the design of printed circuit board are uninvestigated, because they need more time than allows normal exercises. The research objective. The main aim of educational process is to educate engineers with basic knowledge, skills and handicraft. Practical models help as support devices for fulfil of this aim. All mechatronic students can practice a training on these practical models. They become as more skilled and well-oriented engineers.. The statement of basic materials. Construction consist of upper and lower base plate connected with four pillars used as linear guide for moving of lift cage. Lower base plate includes base microcontrollers boards, resistor network, power transis-tor array board, power supply terminals, relay modules, PWM module and signals terminals. Upper base plate consist of DC motor with gearing and screw mechanism for moving the lift cage. Conclusions. The model enables supports the creativity of the students. The starting point of the using of the model can be without any wired connections. Students should connect every part and try functionality of every function. The students receive the defined several problems and they have to analyze it and make any proposal for solution of defined problems.

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Are More Profiles Better Than Fewer?: Searching for Parsimony and Relevance in Stated Choice Experiments

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1719-22 ◽

2000 ◽

Vol 1719 (1) ◽

pp. 165-174 ◽

Cited By ~ 13

Author(s):

Peter R. Stopher ◽

David A. Hensher

Keyword(s):

New Zealand ◽

Degrees Of Freedom ◽

Choice Model ◽

Fractional Factorial ◽

Sources Of Information ◽

Stated Choice ◽

Complex Design ◽

Data Collection Instrument ◽

Main Effects ◽

Better Than

Transportation planners increasingly include a stated choice (SC) experiment as part of the armory of empirical sources of information on how individuals respond to current and potential travel contexts. The accumulated experience with SC data has been heavily conditioned on analyst prejudices about the acceptable complexity of the data collection instrument, especially the number of profiles (or treatments) given to each sampled individual (and the number of attributes and alternatives to be processed). It is not uncommon for transport demand modelers to impose stringent limitations on the complexity of an SC experiment. A review of the marketing and transport literature suggests that little is known about the basis for rejecting complex designs or accepting simple designs. Although more complex designs provide the analyst with increasing degrees of freedom in the estimation of models, facilitating nonlinearity in main effects and independent two-way interactions, it is not clear what the overall behavioral gains are in increasing the number of treatments. A complex design is developed as the basis for a stated choice study, producing a fractional factorial of 32 rows. The fraction is then truncated by administering 4, 8, 16, 24, and 32 profiles to a sample of 166 individuals (producing 1, 016 treatments) in Australia and New Zealand faced with the decision to fly (or not to fly) between Australia and New Zealand by either Qantas or Ansett under alternative fare regimes. Statistical comparisons of elasticities (an appropriate behavioral basis for comparisons) suggest that the empirical gains within the context of a linear specification of the utility expression associated with each alternative in a discrete choice model may be quite marginal.

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