ALBERT

Description: Sensors, Vol. 18, Pages 1909: Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework Sensors doi: 10.3390/s18061909 Authors: Changjian Deng Kun Lv Debo Shi Bo Yang Song Yu Zhiyi He Jia Yan In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect.

Description: A novel compound-structure transverse-flux permanent-magnet synchronous machine (CS-TFPMSM) is proposed in this paper, which is used in hybrid electric vehicles (HEVs) to fulfill the power-split function. The key component of the CS-TFPMSM is a brushless transverse-flux dual rotor machine (TFDRM). The TFDRM originates from the transverse-flux machines, and is capable of speed adjustment between the transverse-flux rotor and the permanent-magnet rotor without using any brushes. The structure and principle of the TFDRM are described. The torque equations of the TFDRM are deduced, which are different from those of traditional machines. Based on the investigation, the TFDRM tends to have high leakage and a poor power factor. The method to obtain high power factor is discussed. The back electromotive force (BEMF) and torque of the TFDRM are simulated with the variation of parameters, such as pole-pair number, width of the permanent magnets (PMs), and so on. A prototype of a 10kW TFDRM is designed.

Description: Sensors, Vol. 18, Pages 388: Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing Sensors doi: 10.3390/s18020388 Authors: Tailai Wen Jia Yan Daoyu Huang Kun Lu Changjian Deng Tanyue Zeng Song Yu Zhiyi He The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods.