Intelligent Assembly Time Analysis Using a Digital Knowledge-Based Approach

The implementation of effective time analysis methods fast and accurately in the era of digital manufacturing has become a significant challenge for aerospace manufacturers hoping to build and maintain a competitive advantage. This paper proposes a structure oriented, knowledge-based approach for intelligent time analysis of aircraft assembly processes within a digital manufacturing framework. A knowledge system is developed so that the design knowledge can be intelligently retrieved for implementing assembly time analysis automatically. A time estimation method based on MOST, is reviewed and employed. Knowledge capture, transfer and storage within the digital manufacturing environment are extensively discussed. Configured plantypes, GUIs and functional modules are designed and developed for the automated time analysis. An exemplar study using an aircraft panel assembly from a regional jet is also presented. Although the method currently focuses on aircraft assembly, it can also be well utilized in other industry sectors, such as transportation, automobile and shipbuilding. The main contribution of the work is to present a methodology that facilitates the integration of time analysis with design and manufacturing using a digital manufacturing platform solution.

Analysis of Receiver Algorithms for LTE SC-FDMA Based Uplink MIMO Systems

This letter derives mathematical expressions for the received signal-to-interference-plus-noise ratio (SINR) of uplink Single Carrier (SC) Frequency Division Multiple Access (FDMA) multiuser MIMO systems. An improved frequency domain receiver algorithm is derived for the studied systems, and is shown to be significantly superior to the conventional linear MMSE based receiver in terms of SINR and bit error rate (BER) performance.

Country-wise differences in perception of health-related messages in cereal-based food products

The objective of the present study was to explore the impact of health-related messages on the perceived overall healthiness and consumers' likelihood to buy cereal-based products or non-cereal products containing beneficial compounds from grains, across four European countries. The data were collected from a sample of 2392 members of the public in Finland, Germany, Italy and the UK. The results from a conjoint task with a main effects additive model were reported. In general, the presence of a verbal health claim on foods had positive influence on respondents perception of healthiness and on likelihood to buy the products, whereas the pictorial health claims were found to have a weak influence on the two dependent variables. However, the findings showed that health-related information on food labels differently influenced the healthiness perception and the likelihood to buy the product across the four countries, suggesting that different cultures, traditions, and eating habits have to be taken into account before positioning cereal-based products containing beneficial compounds from grains on the market. (C) 2009 Elsevier Ltd. All rights reserved.

The matrix metalloproteinase-3 (MMP-3) 5A/6A promoter polymorphism is not associated with ischaemic heart disease: Analysis employing a family based approach

Matrix metalloproteinase-3 (MMP-3) has been proposed as an important mediator of the atherosclerotic process. The possible role of the functional -1612(.)5A/6A polymorphism of the MMP-3 gene in the susceptibility to ischaemic heart disease (IHD) was investigated in a well-defined Irish population using two recently described family based tests of association. One thousand and twelve individuals from 386 families with at least one member prematurely affected with IHD were genotyped. Using the combined transmission disequilibrium test (TDT)/sib-TDT and the pedigree disequilibrium test (PDT), no association between the MMP-3 -1612 5A/6A polymorphism and IHD was found. Our data demonstrate that, in an Irish population, the MMP-3 -1612 5A/6A polymorphism is not associated with IHD.

Palmprint Recognition Based on Subspace Analysis of Gabor Filter Bank

This paper introduces a new technique for palmprint recognition based on Fisher Linear Discriminant Analysis (FLDA) and Gabor filter bank. This method involves convolving a palmprint image with a bank of Gabor filters at different scales and rotations for robust palmprint features extraction. Once these features are extracted, FLDA is applied for dimensionality reduction and class separability. Since the palmprint features are derived from the principal lines, wrinkles and texture along the palm area. One should carefully consider this fact when selecting the appropriate palm region for the feature extraction process in order to enhance recognition accuracy. To address this problem, an improved region of interest (ROI) extraction algorithm is introduced. This algorithm allows for an efficient extraction of the whole palm area by ignoring all the undesirable parts, such as the fingers and background. Experiments have shown that the proposed method yields attractive performances as evidenced by an Equal Error Rate (EER) of 0.03%.