Extending twin support vector machine classifier for multi-category classification problems

© 2013 – IOS Press and the authors. All rights reservedTwin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification problems. TWSVM not only overcomes the difficulties in handling the problem of exemplar unbalance in binary classification problems, but also it is four times faster in training a classifier than classical support vector machines. This paper proposes one-versus-all twin support vector machine classifiers (OVA-TWSVM) for multi-category classification problems by utilizing the strengths of TWSVM. OVA-TWSVM extends TWSVM to solve k-category classification problems by developing k TWSVM where in the ith TWSVM, we only solve the Quadratic Programming Problems (QPPs) for the ith class, and get the ith nonparallel hyperplane corresponding to the ith class data. OVA-TWSVM uses the well known one-versus-all (OVA) approach to construct a corresponding twin support vector machine classifier. We analyze the efficiency of the OVA-TWSVM theoretically, and perform experiments to test its efficiency on both synthetic data sets and several benchmark data sets from the UCI machine learning repository. Both the theoretical analysis and experimental results demonstrate that OVA-TWSVM can outperform the traditional OVA-SVMs classifier. Further experimental comparisons with other multiclass classifiers demonstrated that comparable performance could be achieved.This work is supported in part by the grant of the Fundamental Research Funds for the Central Universities of GK201102007 in PR China, and is also supported by Natural Science Basis Research Plan in Shaanxi Province of China (Program No.2010JM3004), and is at the same time supported by Chinese Academy of Sciences under the Innovative Group Overseas Partnership Grant as well as Natural Science Foundation of China Major International Joint Research Project (NO.71110107026)

Escherichia coli of sequence type 3835 carrying blaNDM-1, blaCTX-M-15, blaCMY-42 and blaSHV-12

New Delhi metallo-β-lactamase (NDM) represents a serious challenge for treatment and public health. A carbapenem-resistant Escherichia coli clinical strain WCHEC13-8 was subjected to antimicrobial susceptibility tests, whole genome sequencing and conjugation experiments. It was resistant to imipenem (MIC, >256 μg/ml) and meropenem (MIC, 128 μg/ml) and belonged to ST3835. blaNDM-1 was the only carbapenemase gene detected. Strain WCHEC13-8 also had a plasmid-borne AmpC gene (blaCMY-42) and two extended-spectrum β-lactamase genes (blaCTX-M-15 and blaSHV-12). blaNDM-1 and blaSHV-12 were carried by a 54-kb IncX3 self-transmissible plasmid, which is identical to plasmid pNDM-HF727 from Enterobacter cloacae. blaCMY-42 was carried by a 64-kb IncI1 plasmid and blaCTX-M-15 was located on a 141-kb plasmid with multiple F replicons (replicon type: F36:A4:B1). blaCMY-42 was in a complicated context and the mobilisation of blaCMY-42 was due to the transposition of IS Ecp1 by misidentifying its right-end boundary. Genetic context of blaNDM-1 in strain WCHEC13-8 was closely related to those on IncX3 plasmids in various Enterobacteriaceae species in China. In conclusion, a multidrug-resistant ST3835 E. coli clinical strain carrying blaNDM-1, blaCTX-M-15, blaCMY-42 and blaSHV-12 was identified. IncX3 plasmids may be making a significant contribution to the dissemination of blaNDM among Enterobacteriaceae in China

An Ultra-fast DOA Estimator with Circular Array Interferometer Using Lookup Table Method

The time-consuming phase ambiguity resolution makes the uniform circular array (UCA) interferometer not suitable for real-time direction-of-arrival (DOA) estimation. This paper introduces the lookup table (LUT) method to solve this problem. The key of the method is that we look up the ambiguity numbers instead of the eventual DOA from the table, and then the DOA is obtained by relatively small amount of calculation. This makes it possible that we are able to shrink the table size while maintain the DOA estimation accuracy. The table addresses cover all possible measured phase differences (PDs), which enables the method to be free of spatial scanning. Moreover, without adding frequency index to the lookup table, the estimator can realize wideband application. As an example, a field-programmable gate array (FPGA) based DOA estimator with the estimation time of 180 ns is presented, accompanied by the measured results. This method possesses the advantages of ultra-high speed, high accuracy and low memory usage

A data driven deep neural network model for predicting boiling heat transfer in helical coils under high gravity

In this article, a deep artificial neural network (ANN) model has been proposed to predict the boiling heat transfer in helical coils under high gravity conditions, which is compared with experimental data. A test rig is set up to provide high gravity up to 11 g with a heat flux up to 15100 W/m 2 and the mass velocity range from 40 to 2000 kg m −2 s −1. In the current work, a total 531 data samples have been used in the ANN model. The proposed model was developed in a Python Keras environment with Feed-forward Back-propagation (FFBP) Multi-layer Perceptron (MLP) using eight features (mass flow rate, thermal power, inlet temperature, inlet pressure, direction, acceleration, tube inner surface area, helical coil diameter) as the inputs and two features (wall temperature, heat transfer coefficient) as the outputs. The deep ANN model composed of three hidden layers with a total number of 1098 neurons and 300,266 trainable parameters has been found as optimal according to statistical error analysis. Performance evaluation is conducted based on six verification statistic metrics (R 2, MSE, MAE, MAPE, RMSE and cosine proximity) between the experimental data and predicted values. The results demonstrate that a 8-512-512-64-2 neural network has the best performance in predicting the helical coil characteristics with (R 2=0.853, MSE=0.018, MAE=0.074, MAPE=1.110, RMSE=0.136, cosine proximity=1.000) in the testing stage. It is indicated that with the utilisation of deep learning, the proposed model is able to successfully predict the heat transfer performance in helical coils, and especially achieved excellent performance in predicting outputs that have a very large range of value differences