Aircraft Cost Modelling using the Genetic Causal Technique within a Systems Engineering Approach

The paper is primarily concerned with the modelling of aircraft manufacturing cost. The aim is to establish an integrated life cycle balanced design process through a systems engineering approach to interdisciplinary analysis and control. The cost modelling is achieved using the genetic causal approach that enforces product family categorisation and the subsequent generation of causal relationships between deterministic cost components and their design source. This utilises causal parametric cost drivers and the definition of the physical architecture from the Work Breakdown Structure (WBS) to identify product families. The paper presents applications to the overall aircraft design with a particular focus on the fuselage as a subsystem of the aircraft, including fuselage panels and localised detail, as well as engine nacelles. The higher level application to aircraft requirements and functional analysis is investigated and verified relative to life cycle design issues for the relationship between acquisition cost and Direct Operational Cost (DOC), for a range of both metal and composite subsystems. Maintenance is considered in some detail as an important contributor to DOC and life cycle cost. The lower level application to aircraft physical architecture is investigated and verified for the WBS of an engine nacelle, including a sequential build stage investigation of the materials, fabrication and assembly costs. The studies are then extended by investigating the acquisition cost of aircraft fuselages, including the recurring unit cost and the non-recurring design cost of the airframe sub-system. The systems costing methodology is facilitated by the genetic causal cost modeling technique as the latter is highly generic, interdisciplinary, flexible, multilevel and recursive in nature, and can be applied at the various analysis levels required of systems engineering. Therefore, the main contribution of paper is a methodology for applying systems engineering costing, supported by the genetic causal cost modeling approach, whether at a requirements, functional or physical level.

Demonstration of glycated insulin in human diabetic plasma and decreased biological activity assessed by euglycemic-hyperinsulinemic clamp technique in humans

The presence and biological significance of circulating glycated insulin has been evaluated by high-pressure liquid chromatography (HPLC), electrospray ionization mass spectrometry (ESI-MS), radioimmunoassay (RIA), receptor binding, and hyperinsulinemic-euglycemic clamp techniques. ESI-MS analysis of an HPLC-purified plasma pool from four male type 2 diabetic subjects (HbA(1e) 8.1 +/- 0.2%, plasma glucose 8.7 +/- 1.3 mmol/l [means +/- SE]) revealed two major insulin-like peaks with retention times of 14-16 min. After spectral averaging, the peak with retention time of 14.32 min exhibited a prominent triply charged (M+3H)(3+) species at 1,991.1 m/z, representing monoglycated insulin with an intact M-r of 5,970.3 Da. The second peak (retention time 15.70 min) corresponded to native insulin (M-r 5,807.6 Da), with the difference between the two peptides (162.7 Da) representing a single glucitol adduct (theoretical 164 Da). Measurement of glycated insulin in plasma of type 2 diabetic subjects by specific RIA gave circulating levels of 10.1 +/- 2.3 pmol/l, corresponding to -9% total insulin. Biological activity of pure synthetic monoglycated insulin (insulin B-chain Phe(1)-glucitol adduct) was evaluated in seven overnight-fasted healthy nonobese male volunteers using two-step euglycemic-hyperinsulinemic clamps (2 h at 16.6 mug (.) kg(-1) (.) min(-1), followed by 2 h at 83.0 mug (.) kg(-1) (.) min(-1); corresponding to 0.4 and 2.0 mU (.) kg(-1) (.) min(-1)). At the lower dose, the exogenons glucose infusion rates required to maintain euglycemia during steady state were significantly lower with glycated insulin (P

Fast model based feature matching technique applied to airport lighting

The use of image processing techniques to assess the performance of airport landing lighting using images of it collected from an aircraft-mounted camera is documented. In order to assess the performance of the lighting, it is necessary to uniquely identify each luminaire within an image and then track the luminaires through the entire sequence and store the relevant information for each luminaire, that is, the total number of pixels that each luminaire covers and the total grey level of these pixels. This pixel grey level can then be used for performance assessment. The authors propose a robust model-based (MB) featurematching technique by which the performance is assessed. The development of this matching technique is the key to the automated performance assessment of airport lighting. The MB matching technique utilises projective geometry in addition to accurate template of the 3D model of a landing-lighting system. The template is projected onto the image data and an optimum match found, using nonlinear least-squares optimisation. The MB matching software is compared with standard feature extraction and tracking techniques known within the community, these being the Kanade–Lucus–Tomasi (KLT) and scaleinvariant feature transform (SIFT) techniques. The new MB matching technique compares favourably with the SIFT and KLT feature-tracking alternatives. As such, it provides a solid foundation to achieve the central aim of this research which is to automatically assess the performance of airport lighting.

A novel shrinkage technique based on the normal inverse Gaussian density model

This paper proposes a novel image denoising technique based on the normal inverse Gaussian (NIG) density model using an extended non-negative sparse coding (NNSC) algorithm proposed by us. This algorithm can converge to feature basis vectors, which behave in the locality and orientation in spatial and frequency domain. Here, we demonstrate that the NIG density provides a very good fitness to the non-negative sparse data. In the denoising process, by exploiting a NIG-based maximum a posteriori estimator (MAP) of an image corrupted by additive Gaussian noise, the noise can be reduced successfully. This shrinkage technique, also referred to as the NNSC shrinkage technique, is self-adaptive to the statistical properties of image data. This denoising method is evaluated by values of the normalized signal to noise rate (SNR). Experimental results show that the NNSC shrinkage approach is indeed efficient and effective in denoising. Otherwise, we also compare the effectiveness of the NNSC shrinkage method with methods of standard sparse coding shrinkage, wavelet-based shrinkage and the Wiener filter. The simulation results show that our method outperforms the three kinds of denoising approaches mentioned above.