Nanoscale Research Letters,2012年7(1):2-19 ISSN：1931-7573
[Chen, Xiangdong; Yao, Yao; Li, Xiaoyu; Wu, Zuquan] School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;[Zhu, Jinfeng; Zeng, Baoqing] National Key Laboratory of Science and Technology on Vacuum Electronics, School of Physical Electronics, University of Electronic Science and Technology of China, Chengdu 610054, China
[Yao, Yao] School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031, People's Republic of China.
We investigate the effect of water adsorption on the electrical properties of graphene oxide (GO) films using the direct current (DC) measurement and alternating current (AC) complex impedance spectroscopy. GO suspension synthesized by a modified Hummer's method is deposited on Au interdigitated electrodes. The strong electrical interaction of water molecules with GO films was observed through electrical characterizations. The DC measurement results show that the electrical properties of GO films are humidity- and applied voltage amplitude-dependent. The AC complex impedance spectroscopy method is used to analyze the mechanism of electrical interaction between water molecules and GO films in detail. At low humidity, GO films exhibit poor conductivity and can be seen as an insulator. However, at high humidity, the conductivity of GO films increases due to the enhancement of ion conduction. Our systematic research on this effect provides the fundamental supports for the development of graphene devices originating from solution-processed graphene oxide.
Rough set theory;Incremental learning;Knowledge discovery;Set-valued information system;Approximations
Incremental learning is an efficient technique for knowledge discovery in a dynamic database, which enables acquiring additional knowledge from new data without forgetting prior knowledge. Rough set theory has been successfully used in information systems for classification analysis. Set-valued information systems are generalized models of single-valued information systems, which can be classified into two categories: disjunctive and conjunctive. Approximations are fundamental concepts of rough set theory, which need to be updated incrementally while the object set varies over time in the set-valued information systems. In this paper, we analyze the updating mechanisms for computing approximations with the variation of the object set. Two incremental algorithms for updating the approximations, in disjunctive/conjunctive set-valued information systems are proposed, respectively. Furthermore, extensive experiments are carried out on several data sets to verify the performance of the proposed algorithms. The results indicate the incremental approaches significantly outperform non-incremental approaches with a dramatic reduction in the computational speed. (C) 2013 The Authors. Published by Elsevier B.V. All rights reserved.
Fuzzy rough sets;Incremental learning;Feature selection;Hybrid information systems;Big data
In real-applications, there may exist many kinds of data (e.g., boolean, categorical, real-valued and set-valued data) and missing data in an information system which is called as a Hybrid Information System (HIS). A new Hybrid Distance (HD) in HIS is developed based on the value difference metric, and a novel fuzzy rough set is constructed by combining the HD distance and the Gaussian kernel. Considering the information systems often vary with time, the updating mechanisms for attribute reduction (feature selection) are analyzed with the variation of the attribute set. Fuzzy rough set approaches for incremental feature selection on HIS are presented. Then two corresponding incremental algorithms are proposed, respectively. Finally, extensive experiments on eight datasets from UCI and an artificial dataset show that the incremental approaches significantly outperform non-incremental approaches with feature selection in the computational time.
Rough set theory;Incomplete information systems;Incremental learning;Knowledge discovery
With the rapid growth of data sets nowadays, the object sets in an information system may evolve in time when new information arrives. In order to deal with the missing data and incomplete information in real decision problems, this paper presents a matrix based incremental approach in dynamic incomplete information systems. Three matrices (support matrix, accuracy matrix and coverage matrix) under four different extended relations (tolerance relation, similarity relation, limited tolerance relation and characteristic relation), are introduced to incomplete information systems for inducing knowledge dynamically. An illustration shows the procedure of the proposed method for knowledge updating. Extensive experimental evaluations on nine UCI datasets and a big dataset with millions of records validate the feasibility of our proposed approach. (C) 2014 Elsevier Inc. All rights reserved.
Joint remote state preparation;Quantum circuit;Photon circuit;KAK decomposition
Motivated by some previous joint remote preparation schemes, we first propose some quantum circuits and photon circuits that two senders jointly prepare an arbitrary one-qubit state to a remote receiver via GHZ state. Then, by constructing KAK decomposition of some transformation in SO(4), one quantum circuit is constructed for jointly preparing an arbitrary two-qubit state to the remote receiver. Furthermore, some deterministic schemes of jointly preparing one-qubit and two-qubit states are presented. Besides, the proposed schemes are extended to multi-sender and the partially entangled quantum resources.