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- 저자 : ( Qingyu Mao ) (검색결과 2 건)
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Characteristics of Interface States in One-dimensional Composite Photonic Structures
Qingyue Zhang,Weitao Mao,Qiuling Zhao,Maorong Wang,Xia Wang,Wing Yim Tam 한국광학회 2022 Current Optics and Photonics Vol.6 No.3
Based on the transfer-matrix method (TMM), we report the characteristics of the interface states in one-dimensional (1D) composite structures consisting of two photonic crystals (PCs) composed of binary dielectrics A and B, with unit-cell configurations ABA (PC I) and BAB (PC II). The dependence of the interface states on the number of unit cells N and the boundary factor x are displayed. It is verified that the interface states are independent of N when the PC has inversion symmetry (x = 0.5). Besides, the composite structures support the formation of interface states independent of the PC symmetry, except that the positions of the interface states will be varied within the photonic band gaps. Moreover, the robustness of the interface states against nonuniformities is investigated by adding Gaussian noise to the layer thickness. In the case of inversion symmetry (x = 0.5) the most robust interface states are achieved, while for the other cases (x ≠ 0.5) interface states decay linearly with position inside the band gap. This work could shed light on the development of robust photonic devices.
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DroidVecDeep: Android Malware Detection Based on Word2Vec and Deep Belief Network
( Tieming Chen ),( Qingyu Mao ),( Mingqi Lv ),( Hongbing Cheng ),( Yinglong Li ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.4
With the proliferation of the Android malicious applications, malware becomes more capable of hiding or confusing its malicious intent through the use of code obfuscation, which has significantly weaken the effectiveness of the conventional defense mechanisms. Therefore, in order to effectively detect unknown malicious applications on the Android platform, we propose DroidVecDeep, an Android malware detection method using deep learning technique. First, we extract various features and rank them using Mean Decrease Impurity. Second, we transform the features into compact vectors based on word2vec. Finally, we train the classifier based on deep learning model. A comprehensive experimental study on a real sample collection was performed to compare various malware detection approaches. Experimental results demonstrate that the proposed method outperforms other Android malware detection techniques.
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