Conceptual of Type-2 Fuzzy Geometric Modelling

Geometric modelling is a method of data representation illustrated through the formation of curves and surfaces in various forms. The construction of curves and surfaces is complicated when it comes to data that has complex uncertainty characteristics. Type-1 Fuzzy Set Theory (T1FST) is unable to define this complex uncertainty problem. To overcome this problem, Type-2 Fuzzy Set Theory (T2FST) is used due to its ability to define a higher level of uncertainty problem. In certain cases, both uncertainty and complex uncertainty data occur when there is a combination of degrees of ambiguity in a collection of data sets that would be modelled through the representation of curves and surfaces. Therefore, this paper will review some significant reason for implementation of T1FST and T2FST in geometric modelling. A review on type-1 and type-2 in fuzzy geometric modelling is also presented

Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty

It is well known that the accuracy of a calibration depends strongly on the choice of camera poses from which images of a calibration object are acquired. We present a system -- Calibration Wizard -- that interactively guides a user towards taking optimal calibration images. For each new image to be taken, the system computes, from all previously acquired images, the pose that leads to the globally maximum reduction of expected uncertainty on intrinsic parameters and then guides the user towards that pose. We also show how to incorporate uncertainty in corner point position in a novel principled manner, for both, calibration and computation of the next best pose. Synthetic and real-world experiments are performed to demonstrate the effectiveness of Calibration Wizard.Comment: Oral presentation at ICCV 201

Intelligent computational sketching support for conceptual design

Sketches, with their flexibility and suggestiveness, are in many ways ideal for expressing emerging design concepts. This can be seen from the fact that the process of representing early designs by free-hand drawings was used as far back as in the early 15th century [1]. On the other hand, CAD systems have become widely accepted as an essential design tool in recent years, not least because they provide a base on which design analysis can be carried out. Efficient transfer of sketches into a CAD representation, therefore, is a powerful addition to the designers' armoury.It has been pointed out by many that a pen-on-paper system is the best tool for sketching. One of the crucial requirements of a computer aided sketching system is its ability to recognise and interpret the elements of sketches. 'Sketch recognition', as it has come to be known, has been widely studied by people working in such fields: as artificial intelligence to human-computer interaction and robotic vision. Despite the continuing efforts to solve the problem of appropriate conceptual design modelling, it is difficult to achieve completely accurate recognition of sketches because usually sketches implicate vague information, and the idiosyncratic expression and understanding differ from each designer

A non-linear optimal estimation inverse method for radio occultation measurements of temperature, humidity and surface pressure

An optimal estimation inverse method is presented which can be used to retrieve simultaneously vertical profiles of temperature and specific humidity, in addition to surface pressure, from satellite-to-satellite radio occultation observations of the Earth's atmosphere. The method is a non-linear, maximum {\it a posteriori} technique which can accommodate most aspects of the real radio occultation problem and is found to be stable and to converge rapidly in most cases. The optimal estimation inverse method has two distinct advantages over the analytic inverse method in that it accounts for some of the effects of horizontal gradients and is able to retrieve optimally temperature and humidity simultaneously from the observations. It is also able to account for observation noise and other sources of error. Combined, these advantages ensure a realistic retrieval of atmospheric quantities. A complete error analysis emerges naturally from the optimal estimation theory, allowing a full characterisation of the solution. Using this analysis a quality control scheme is implemented which allows anomalous retrieval conditions to be recognised and removed, thus preventing gross retrieval errors. The inverse method presented in this paper has been implemented for bending angle measurements derived from GPS/MET radio occultation observations of the Earth. Preliminary results from simulated data suggest that these observations have the potential to improve NWP model analyses significantly throughout their vertical range.Comment: 18 (jgr journal) pages, 7 figure

High speed rail transport valuation

The present paper investigates the optimal timing of investment for a high speed rail (HSR) project, in an uncertain environment, using a real options analysis (ROA) framework. It develops a continuous time framework with stochastic demand that allows for the determination of the optimal timing of investment and the value of the option to defer in the overall valuation of the project. The modelling approach used is based on the differential utility provided to railway users by the HSR service.info:eu-repo/semantics/publishedVersio

High speed rail transport valuation

The present paper investigates the optimal timing of investment for a high speed rail (HSR) project, in an uncertain environment, using a real options analysis (ROA) framework. It develops a continuous time framework with stochastic demand that allows for the determination of the optimal timing of investment and the value of the option to defer in the overall valuation of the project. The modelling approach used is based on the differential utility provided to railway users by the HSR service.info:eu-repo/semantics/publishedVersio

aFold – using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data

Data normalization and identification of significant differential expression represent crucial steps in RNA-Seq analysis. Many available tools rely on assumptions that are often not met by real data, including the common assumption of symmetrical distribution of up- and down-regulated genes, the presence of only few differentially expressed genes and/or few outliers. Moreover, the cut-off for selecting significantly differentially expressed genes for further downstream analysis often depend on arbitrary choices

Impact of Stratigraphic and Sedimentological Heterogeneity on Hydrocarbon Recovery in Carbonate Reservoirs

Imperial Users onl