An advanced multi-configuration stator well cooling test facility

Optimisation of cooling systems within gas turbine engines is of great interest to engine manufacturers seeking gains in performance, efficiency and component life. The effectiveness of coolant delivery is governed by complex flows within the stator wells and the interaction of main annulus and cooling air in the vicinity of the rim seals. This paper reports the development of a test facility which allows the interaction of cooling air and main gas paths to be measured at conditions representative of those found in modern gas turbine engines. The test facility features a two stage turbine with an overall pressure ratio of approximately 2.6:1. Hot air is supplied to the main annulus using a Rolls-Royce Dart compressor driven by an aero-derivative engine plant. Cooling air can be delivered to the stator wells at multiple locations and at a range of flow rates which cover bulk ingestion through to bulk egress. The facility has been designed with adaptable geometry to enable rapid changes of cooling air path configuration. The coolant delivery system allows swift and accurate changes to the flow settings such that thermal transients may be performed. Particular attention has been focused on obtaining high accuracy data, using a radio telemetry system, as well as thorough through-calibration practices. Temperature measurements can now be made on both rotating and stationary discs with a long term uncertainty in the region of 0.3 K. A gas concentration measurement system has also been developed to obtain direct measurement of re-ingestion and rim seal exchange flows. High resolution displacement sensors have been installed in order to measure hot running geometry. This paper documents the commissioning of a test facility which is unique in terms of rapid configuration changes, non-dimensional engine matching and the instrumentation density and resolution. Example data for each of the measurement systems is presented. This includes the effect of coolant flow rate on the metal temperatures within the upstream cavity of the turbine stator well, the axial displacement of the rotor assembly during a commissioning test, and the effect of coolant flow rate on mixing in the downstream cavity of the stator well. Copyright © 2010 by ASME.

Warped Mixtures for Nonparametric Cluster Shapes

A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allows us to summarize the properties of the high-dimensional clusters (or density manifolds) describing the data. The number of manifolds, as well as the shape and dimension of each manifold is automatically inferred. We derive a simple inference scheme for this model which analytically integrates out both the mixture parameters and the warping function. We show that our model is effective for density estimation, performs better than infinite Gaussian mixture models at recovering the true number of clusters, and produces interpretable summaries of high-dimensional datasets.

Gaussian Process Vine Copulas for Multivariate Dependence

Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, it is common to assume that each of these conditional bivariate copulas is independent from its conditioning variables. In this paper, we relax this assumption by discovering the latent functions that specify the shape of a conditional copula given its conditioning variables We learn these functions by following a Bayesian approach based on sparse Gaussian processes with expectation propagation for scalable, approximate inference. Experiments on real-world datasets show that, when modeling all conditional dependencies, we obtain better estimates of the underlying copula of the data.

Low-rank optimization for distance matrix completion

This paper addresses the problem of low-rank distance matrix completion. This problem amounts to recover the missing entries of a distance matrix when the dimension of the data embedding space is possibly unknown but small compared to the number of considered data points. The focus is on high-dimensional problems. We recast the considered problem into an optimization problem over the set of low-rank positive semidefinite matrices and propose two efficient algorithms for low-rank distance matrix completion. In addition, we propose a strategy to determine the dimension of the embedding space. The resulting algorithms scale to high-dimensional problems and monotonically converge to a global solution of the problem. Finally, numerical experiments illustrate the good performance of the proposed algorithms on benchmarks. © 2011 IEEE.

Anisotropy preserving DTI processing

Statistical analysis of diffusion tensor imaging (DTI) data requires a computational framework that is both numerically tractable (to account for the high dimensional nature of the data) and geometric (to account for the nonlinear nature of diffusion tensors). Building upon earlier studies exploiting a Riemannian framework to address these challenges, the present paper proposes a novel metric and an accompanying computational framework for DTI data processing. The proposed approach grounds the signal processing operations in interpolating curves. Well-chosen interpolating curves are shown to provide a computational framework that is at the same time tractable and information relevant for DTI processing. In addition, and in contrast to earlier methods, it provides an interpolation method which preserves anisotropy, a central information carried by diffusion tensor data. © 2013 Springer Science+Business Media New York.

Cancer subtypes recognition and its gene expression profiles analysis based on geometrical learning

The accurate recognition of cancer subtypes is very significant in clinic. Especially, the DNA microarray gene expression technology is applied to diagnosing and recognizing cancer types. This paper proposed a method of that recognized cancer subtypes based on geometrical learning. Firstly, the cancer genes expression profiles data was pretreated and selected feature genes by conventional method; then the expression data of feature genes in the training samples was construed each convex hull in the high-dimensional space using training algorithm of geometrical learning, while the independent test set was tested by the recognition algorithm of geometrical learning. The method was applied to the human acute leukemia gene expression data. The accuracy rate reached to 100%. The experiments have proved its efficiency and feasibility.

Keyword spotting system based on biomimetic pattern recognition

The mandarin keyword spotting system was investigated, and a new approach was proposed based on the principle of homology continuity and point location analysis in high-dimensional space geometry theory which are both parts of biomimetic pattern recognition theory. This approach constructed a hyper-polyhedron with sample points in the training set and calculated the distance between each test point and the hyper-polyhedron. The classification resulted from the value of those distances. The approach was tested by a speech database which was created by ourselves. The performance was compared with the classic HMM approach and the results show that the new approach is much better than HMM approach when the training data is not sufficient.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

An algorithm of analysis tools on points distribution in high dimension space: The distance of a point and an infinite sub-space

This paper discusses the algorithm on the distance from a point and an infinite sub-space in high dimensional space With the development of Information Geometry([1]), the analysis tools of points distribution in high dimension space, as a measure of calculability, draw more attention of experts of pattern recognition. By the assistance of these tools, Geometrical properties of sets of samples in high-dimensional structures are studied, under guidance of the established properties and theorems in high-dimensional geometry.

Uniform mems chip temperatures in the nucleate boiling heat transfer region by selecting suitable, medium boiling number range

The not only lower but also uniform MEMS chip temperatures can he reached by selecting suitable boiling number range that ensures the nucleate boiling heat transfer. In this article, boiling heat transfer experiments in 10 silicon triangular microchannels with the hydraulic diameter of 55.4 mu m were performed using acetone as the working fluid, having the inlet liquid temperatures of 24-40 degrees C, mass fluxes of 96-360 kg/m(2)s, heat fluxes of 140-420 kW/m(2), and exit vapor mass qualities of 0.28-0.70. The above data range correspond to the boiling number from 1.574 x 10(-3) to 3.219 x 10(-3) and ensure the perfect nucleate boiling heat transfer region, providing a very uniform chip temperature distribution in both streamline and transverse directions. The boiling heat transfer coefficients determined by the infrared radiator image system were found to he dependent on the heat Axes only, not dependent on the mass Axes and the vapor mass qualities covering the above data range. The high-speed flow visualization shows that the periodic flow patterns take place inside the microchannel in the time scale of milliseconds, consisting of liquid refilling stage, bubble nucleation, growth and coalescence stage, and transient liquid film evaporation stage in a full cycle. The paired or triplet bubble nucleation sites can occur in the microchannel corners anywhere along the flow direction, accounting for the nucleate boiling heat transfer mode. The periodic boiling process is similar to a series of bubble nucleation, growth, and departure followed by the liquid refilling in a single cavity for the pool boiling situation. The chip temperature difference across the whole two-phase area is found to he small in a couple of degrees, providing a better thermal management scheme for the high heat flux electronic components. Chen's [11 widely accepted correlation for macrochannels and Bao et al.'s [21 correlation obtained in a copper capillary tube with the inside diameter of 1.95 mm using R11 and HCFC123 as working fluids can predict the present experimental data with accepted accuracy. Other correlations fail to predict the correct heat transfer coefficient trends. New heat transfer correlations are also recommended.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

离群模糊核聚类算法

一般说来,离群点是远离其他数据点的数据,但很可能包含着极其重要的信息.提出了一种新的离群模糊核聚类算法来发现样本集中的离群点.通过Mercer核把原来的数据空间映射到特征空间,并为特征空间的每个向量分配一个动态权值,在经典的FCM模糊聚类算法的基础上得到了一个特征空间内的全新的聚类目标函数,通过对目标函数的优化,最终得到了各个数据的权值,根据权值的大小标识出样本集中的离群点.仿真实验的结果表明了该离群模糊核聚类算法的可行性和有效性.

工程地质三维建模与可视化技术应用开发

Synthetic Geology Information System (SGIS) is an important constituent part of the theory of Engineering Geomechanics Mate-Synthetic (EGMS), and is the information system more suited for the collection, storage, management, analysis and processing to the information coming from engineering geology,' geological engineering and geotechnical engineering. Its contents involve various works and methods of the investigation, design, and construction in different stages of the geological engineering. Engineering geological and three-dimensional modeling and visualization is the fundamental part of the SGIS, and is a theory, method and technique by which, adopting the computer graphics and image processing techniques, the data derived from engineering geological survey and the calculated results obtained from the geomechanical numerical simulation and analysis are converted to the graphics and images displayed on the computer screen and can be processed interactively. In this paper, the significance and realizing approaches of the three-dimensional modeling and visualization for the complex geological mass in the engineering geology are discussed and the methods of taking advantage of the interpolation and fitting for the scattered and field-surveyed data to simulate the geological layers, such as the topography and earth surface, the groundwater table and the stratum boundary, are researched into. At the mean time, in mind the characteristics of the structure of the basic data for three-dimensional modeling, its visual management can be resolved into the engineering surveyed database management module, plot parameter management module and data output module and the requirement for basic data management can be fulfilled. In the paper, the establishment and development of the three-dimensional geological information system are probed tentatively, and an instance of three-dimensional visual Engineering Distribution Information System (EDIS), theConstruction Management Information System for an airport, in which the functions, such as the real-time browse among the three-dimensional virtual-reality landscapes of the airport construction from start to finish, the information query to the airport facility and the building in the housing district and the recording and playback of the animation sets for the browse and the takeoff and landing of the planes, is developed by applying the component-mode three-dimensional virtual-reality geological information system (GIS) software development kits (SDK), so the three-dimensional visual management platform is provided for the airport construction. Moreover, in the gaper, integrated with the three-dimensional topography visualization and its application in the Sichuan-Tibet Highways, the method of the digital elevation model (DEM) data collection from the topographic maps is described, and the three-dimensional visualization and the roaming about the terrain along the highway are achieved through computer language programming. Understanding to the important role played by the varied and unique topographical condition in the gestation and germination of the highly-dense, frequently-arising and severely-endangered geological hazards can be deepened.

Shape Recipes: Scene Representations that Refer to the Image

The goal of low-level vision is to estimate an underlying scene, given an observed image. Real-world scenes (e.g., albedos or shapes) can be very complex, conventionally requiring high dimensional representations which are hard to estimate and store. We propose a low-dimensional representation, called a scene recipe, that relies on the image itself to describe the complex scene configurations. Shape recipes are an example: these are the regression coefficients that predict the bandpassed shape from bandpassed image data. We describe the benefits of this representation, and show two uses illustrating their properties: (1) we improve stereo shape estimates by learning shape recipes at low resolution and applying them at full resolution; (2) Shape recipes implicitly contain information about lighting and materials and we use them for material segmentation.

A Radial Basis Function Approach to Financial Time Series Analysis

Nonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of practical techniques to address important estimation and confidence issues for Radial Basis Function networks arising from such a data driven approach, including efficient methods for parameter estimation and pruning, a pointwise prediction error estimator, and a methodology for controlling the "data mining'' problem. Novel applications in the finance area are described, including customized, adaptive option pricing and stock price prediction.