425 research outputs found
Outstanding supercapacitive properties of Mn-doped TiO2 micro/nanostructure porous film prepared by anodization method.
Mn-doped TiO2 micro/nanostructure porous film was prepared by anodizing a Ti-Mn alloy. The film annealed at 300 °C yields the highest areal capacitance of 1451.3 mF/cm(2) at a current density of 3 mA/cm(2) when used as a high-performance supercapacitor electrode. Areal capacitance retention is 63.7% when the current density increases from 3 to 20 mA/cm(2), and the capacitance retention is 88.1% after 5,000 cycles. The superior areal capacitance of the porous film is derived from the brush-like metal substrate, which could greatly increase the contact area, improve the charge transport ability at the oxide layer/metal substrate interface, and thereby significantly enhance the electrochemical activities toward high performance energy storage. Additionally, the effects of manganese content and specific surface area of the porous film on the supercapacitive performance were also investigated in this work
Fault Diagnosis for Wireless Sensor by Twin Support Vector Machine
Various data mining techniques have been applied to fault diagnosis for wireless sensor because of the advantage of discovering useful knowledge from large data sets. In order to improve the diagnosis accuracy of wireless sensor, a novel fault diagnosis for wireless sensor technology by twin support vector machine (TSVM) is proposed in the paper. Twin SVM is a binary classifier that performs classification by using two nonparallel hyperplanes instead of the single hyperplane used in the classical SVM. However, the parameter setting in the TSVM training procedure significantly influences the classification accuracy. Thus, this study introduces PSO as an optimization technique to simultaneously optimize the TSVM training parameter. The experimental results indicate that the diagnosis results for wireless sensor of twin support vector machine are better than those of SVM, ANN
Radioactivity and heavy metal concentrations and assessment of hazard indices in sediments from Zhushan Bay at Taihu Lake, China
Bi-Directional Generation for Unsupervised Domain Adaptation
Unsupervised domain adaptation facilitates the unlabeled target domain
relying on well-established source domain information. The conventional methods
forcefully reducing the domain discrepancy in the latent space will result in
the destruction of intrinsic data structure. To balance the mitigation of
domain gap and the preservation of the inherent structure, we propose a
Bi-Directional Generation domain adaptation model with consistent classifiers
interpolating two intermediate domains to bridge source and target domains.
Specifically, two cross-domain generators are employed to synthesize one domain
conditioned on the other. The performance of our proposed method can be further
enhanced by the consistent classifiers and the cross-domain alignment
constraints. We also design two classifiers which are jointly optimized to
maximize the consistency on target sample prediction. Extensive experiments
verify that our proposed model outperforms the state-of-the-art on standard
cross domain visual benchmarks.Comment: 9 pages, 4 figure
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