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A novel second-order cone programming support vector machine model for binary data classification

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第3期39卷 4505-4513页

作者： Dong, Guishan Mu, Xuewen Xidian Univ Sch Math & Stat Xian Shaanxi Peoples R China

The support vector machine is a classification approach in machine learning. The second-order cone optimization formulation for the soft-margin support vector machine can ensure that the misclassification rate of data points do not exceed a given value. In this paper, a novel second-order cone programming formulation is proposed for the soft-margin support vector machine. The novel formulation uses the l(2)-norm and two margin variables associated with each class to maximize the margin. Two regularization parameters alpha and beta are introduced to control the trade-off between the maximization of margin variables. Numerical results illustrate that the proposed second-order cone programming formulation for the soft-margin support vector machine has a better prediction performance and robustness than other second-order cone programming support vector machine models used in this article for comparision.

关键词： Support vector machine second-order cone programming binary data classification

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Automatic binary data classification Using a Modified Allen-Cahn Equation

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2021年第4期35卷 2150013-2150013页

作者： Kim, Sangkwon Kim, Junseok Korea Univ Dept Math Seoul 02841 South Korea

In this paper, we propose an automatic binary data classification method using a modified Allen-Cahn (AC) equation. The modified AC equation was originally developed for image segmentation. The equation consists of the AC equation with a fidelity term which enforces the solution to be the given data. In the proposed method, we start from a coarse grid and refine the grid until the accuracy of the data classification reaches a given tolerance. Therefore, we can avoid a laborious trial and error procedure. For a numerical method for the modified AC equation, we use a recently developed explicit hybrid scheme. We perform several 2D and 3D computational tests to demonstrate the performance of the proposed method. The computational results confirm that the proposed algorithm is automatic.

关键词： binary data classification modified Allen– Cahn equation operator splitting method

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Blockage and cavitation detection in centrifugal pumps from dynamic pressure signal using deep learning algorithm

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MEASUREMENT 2021年 173卷 108676-108676页

作者： Tiwari, Rajiv Bordoloi, D. J. Dewangan, Aakash Indian Inst Technol Guwahati Dept Mech Engn Gauhati 781039 India

Centrifugal pumps play an important role in many industrial applications even in harsh environment for prolong duration. High efficiency with very low power consumption makes them very popular in industry. However, during their operation, they may fail due to some operationally developed faults, which may subsequently lead to the interruption in the continuous operation of pumps. Therefore, monitoring the health status of the centrifugal pumps is essential to prevent unwanted stoppage, which may further lead to the breakdown of the whole system. The main focus of this study is to propose a methodology to identify the presence and severity of blockages, and cavitation in the centrifugal pump using fluid pressure, which is very vital for fluid related faults. To simulate the blockage in the pump, the flow area of the suction pipe is restricted by dividing into six equal intervals (i.e., 0%, 16.7%, 33.3%, 50%, 66.6% and 83.33%) using a mechanical modulating valve. Due to blockage and cavitation, the main parameter which directly gets affected is the fluid dynamic pressure. Hence, in the present study, pressure signatures were captured at different blockage levels and at different running speeds with the help of a pressure transducer, which was mounted on the circumference of the centrifugal pump casing. Deep learning based binary data classification methodology is used to classify the data acquired from the pressure transducer. To get better performance of the data classifier, statistical features are extracted from time domain pressure signals. In order to identify the severity of the faults, binary classification of the data is performed at different blockage levels and running speeds. Finally, based on the results obtained from the classifier, existence of the faults (i.e., blockage and the cavitation), their severity levels are presented.Y

关键词： Centrifugal pump Blockage Cavitation Pressure signature Modulating valve Deep learning binary data classification

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