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Journal ArticleDOI

Identification Method of Gas-Liquid Two-phase Flow Regime Based on Image Multi-feature Fusion and Support Vector Machine

TLDR
Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented in this paper, where the image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques.
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This article is published in Chinese Journal of Chemical Engineering.The article was published on 2008-12-01. It has received 40 citations till now. The article focuses on the topics: Flow (mathematics) & Two-phase flow.

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Citations
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Journal ArticleDOI

Study on two-phase flow regime visualization and identification using 3D electrical capacitance tomography and fuzzy-logic classification

TL;DR: This paper presents a preliminary study on automated two-phase gas–liquid flow pattern identification based on a fuzzy evaluation of series of reconstructed 3D ECT volumetric images using nonlinear electrical capacitance tomography reconstruction algorithms.
Journal ArticleDOI

Metrological evaluation of a 3D electrical capacitance tomography measurement system for two-phase flow fraction determination

TL;DR: In this article, a 3D capacitance tomography measurement system was proposed for non-invasive void fraction calculation and flow structure identification in vertical and horizontal pipelines, which includes the 3D ECT sensor structure optimization process, the metrological evaluation of the image reconstruction accuracy and the developed liquid void fractions calculation method efficiency, in comparison with other common techniques.
Journal ArticleDOI

Wet Gas Metering Using a Revised Venturi Meter and Soft-Computing Approximation Techniques

TL;DR: In this article, a novel approach is presented to the measurement of wet gas flows by using a throat-extended Venturi meter (TEVM) and soft-computing approximation techniques.

Wet Gas Metering Using a Revised Venturi Meter

TL;DR: Results obtained by using an industrial-scale test rig suggest that the flow rate of wet gas flowing in the TEVM is related not only to the static features but also to the dynamic features of the differential pressures across the converging and the extended throat sections of the Venturi meter.
Journal ArticleDOI

Fast recognition of global flow regime in pipeline-riser system by spatial correlation of differential pressures

TL;DR: A fast recognition method through the interrelation of differential pressures representing phase distribution along different sections is attempted, by which the regime category can be highly decoupled from time, and eventually sample length can be reduced remarkably with less complicated signal and data processing.
References
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Book

The Nature of Statistical Learning Theory

TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI

Textural Features for Image Classification

TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Book

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
Journal ArticleDOI

Visual pattern recognition by moment invariants

TL;DR: It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished and it is indicated that generalization is possible to include invariance with parallel projection.
Book

Rough Sets: Theoretical Aspects of Reasoning about Data

TL;DR: Theoretical Foundations.
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