Chemical Vapor Deposition Growth of Graphene Domains Across the Cu Grain Boundaries

Yang Wang,Yu Cheng,Yunlu Wang,Shuai Zhang,Chen Xu,Xuewei Zhang,Miao Wang,Yang Xia,Qunyang Li,Pei Zhao,Hongtao Wang 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2018 NANO Vol.13 No.08

Many aspects in the chemical vapor deposition (CVD) growth of graphene remain unclear such as its behavior near the catalyst grain boundaries. Here we investigate the CVD growth mechanism of graphene across the Cu grain boundaries using unidirectional aligned graphene domains, which simplifies the analysis of both graphene and Cu to a large extent. We found that for a graphene domain grown across the Cu grain boundary, the domain orientation is determined by the Cu grain where the domain nucleation center is located, and the Cu grain boundary will not change the growth behavior for this graphene domain. This growth mechanism is consistent with the Custep-attached nucleation and edge-attachment-limited growth mechanism for H-terminated graphene domains and will provide more guidance for the synthesis of high-quality graphene with less domain boundaries.

On the Application of Channel Characteristic-Based Physical Layer Authentication in Industrial Wireless Networks

Wang, Qiuhua,Kang, Mingyang,Yuan, Lifeng,Wang, Yunlu,Miao, Gongxun,Choo, Kim-Kwang Raymond Korean Society for Internet Information 2021 KSII Transactions on Internet and Information Syst Vol.15 No.7

Channel characteristic-based physical layer authentication is one potential identity authentication scheme in wireless communication, such as used in a fog computing environment. While existing channel characteristic-based physical layer authentication schemes may be efficient when deployed in the conventional wireless network environment, they may be less efficient and practical for the industrial wireless communication environment due to the varying requirements. We observe that this is a topic that is understudied, and therefore in this paper, we review the constructions and performance of several commonly used test statistics and analyze their performance in typical industrial wireless networks using simulation experiments. The findings from the simulations show a number of limitations in existing channel characteristic-based physical layer authentication schemes. Therefore, we believe that it is a good idea to combine machine learning and multiple test statistics for identity authentication in future industrial wireless network deployment. Four machine learning methods prove that the scheme significantly improves the authentication accuracy and solves the challenge of choosing a threshold.

On the Application of Channel Characteristic-Based Physical Layer Authentication in Industrial Wireless Networks

( Qiuhua Wang ),( Mingyang Kang ),( Lifeng Yuan ),( Yunlu Wang ),( Gongxun Miao ),( Kim-kwang Raymond Choo ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.6

Channel characteristic-based physical layer authentication is one potential identity authentication scheme in wireless communication, such as used in a fog computing environment. While existing channel characteristic-based physical layer authentication schemes may be efficient when deployed in the conventional wireless network environment, they may be less efficient and practical for the industrial wireless communication environment due to the varying requirements. We observe that this is a topic that is understudied, and therefore in this paper, we review the constructions and performance of several commonly used test statistics and analyze their performance in typical industrial wireless networks using simulation experiments. The findings from the simulations show a number of limitations in existing channel characteristic-based physical layer authentication schemes. Therefore, we believe that it is a good idea to combine machine learning and multiple test statistics for identity authentication in future industrial wireless network deployment. Four machine learning methods prove that the scheme significantly improves the authentication accuracy and solves the challenge of choosing a threshold.

A Hybrid Filtering Stage Based Quasi-type-1 PLL under Distorted Grid Conditions

Yunlu Li,Dazhi Wang,Wei Han,Zhenao Sun,Tianqing Yuan 전력전자학회 2017 JOURNAL OF POWER ELECTRONICS Vol.17 No.3

For three-phase synchronization applications, the synchronous reference frame phase-locked loop (SRF-PLL) is probably the most widely used technique due to its ease of implementation and satisfactory phase tracking performance under ideal grid conditions. However, under unbalanced and distorted grid conditions, its performance tends to worsen. To deal with this problem, a variety of filtering stages have been proposed and used in SRF-PLLs for the rejection of disturbance components at the cost of degrading the dynamic performance. In this paper, to improve dynamic performance without compromising the filtering capability, an effective hybrid filtering stage is proposed and incorporated into the inner loop of a quasi-type-1 PLL (QT1-PLL). The proposed filtering stage is a combination of a moving average filter (MAF) and a modified delay signal cancellation (DSC) operator in cascade. The time delay caused by the proposed filtering stage is smaller than that in the conventional MAF-based and DSC-based PLLs. A small-signal model of the proposed PLL is derived. The stability is analyzed and parameters design guidelines are given. The effectiveness of the proposed PLL is confirmed through experimental results.

Intrusion Detection System Combining Misuse Detection and Anomaly Detection Using Genetic Network Programming

Yunlu Gong,Shingo Mabu,Ci Chen,Yifei Wang,Kotaro Hirasawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8

In this paper, a class association rule mining approach based on Genetic Network Programming(GNP) for detecting network intrusion combining misuse detection and anomaly detection is proposed. The proposed approach is an extension of the intrusion detection approach using GNP, so it can detectand distinguish normal, known in trusion and unknown intrusion. The simulation result shows that the detection rate is improved compared with traditional intrusion detection approach, and normal, known intrusion and unknown intrusion are distinguished with high accuracy.

A Hybrid Filtering Stage Based Quasi-type-1 PLL under Distorted Grid Conditions

Li, Yunlu,Wang, Dazhi,Han, Wei,Sun, Zhenao,Yuan, Tianqing The Korean Institute of Power Electronics 2017 JOURNAL OF POWER ELECTRONICS Vol.17 No.3

For three-phase synchronization applications, the synchronous reference frame phase-locked loop (SRF-PLL) is probably the most widely used technique due to its ease of implementation and satisfactory phase tracking performance under ideal grid conditions. However, under unbalanced and distorted grid conditions, its performance tends to worsen. To deal with this problem, a variety of filtering stages have been proposed and used in SRF-PLLs for the rejection of disturbance components at the cost of degrading the dynamic performance. In this paper, to improve dynamic performance without compromising the filtering capability, an effective hybrid filtering stage is proposed and incorporated into the inner loop of a quasi-type-1 PLL (QT1-PLL). The proposed filtering stage is a combination of a moving average filter (MAF) and a modified delay signal cancellation (DSC) operator in cascade. The time delay caused by the proposed filtering stage is smaller than that in the conventional MAF-based and DSC-based PLLs. A small-signal model of the proposed PLL is derived. The stability is analyzed and parameters design guidelines are given. The effectiveness of the proposed PLL is confirmed through experimental results.

FLOURY ENDOSPERM8, encoding the UDP-glucose pyrophosphorylase 1, affects the synthesis and structure of starch in rice endosperm

Wuhua Long,Bangning Dong,Yihua Wang,Pengyi Pan,Yunlong Wang,Linglong Liu,Xiaoli Chen,Xi Liu,Shijia Liu,Yunlu Tian,Liangming Chen,Jianmin Wan 한국식물학회 2017 Journal of Plant Biology Vol.60 No.5

Cereal opaque-kernel mutants are ideal geneticmaterials for studying the mechanism of starch biosynthesisand amyloplast development. Here we isolated and identifiedtwo allelic floury endosperm 8 (flo8) mutants of rice, namedflo8-1 and flo8-2. In the flo8 mutant, the starch content wasdecreased and the normal physicochemical features ofstarch were altered. Map-based cloning and subsequentDNA sequencing analysis revealed a single nucleotidesubstitution and an 8-bp insertion occurred in UDP-glucosepyrophosphorylase 1 (Ugp1) gene in flo8-1 and flo8-2,respectively. Complementation of the flo8-1 mutant restorednormal seed appearance by expressing full length codingsequence of Ugp1. RT-qPCR analysis revealed that Ugp1was ubiquitously expressed. Mutation caused the decreasedUGPase activity and affected the expression of most of genesassociated with starch biosynthesis. Meanwhile, western blotand enzyme activity analyses showed the comparability ofprotein levels and enzyme activity of most tested starchbiosynthesis related genes. Our results demonstrate thatUgp1 plays an important role for starch biosynthesis in riceendosperm.

Genetic dissection of leaf-related traits using 156 chromosomal segment substitution lines

Xi Liu,Linglong Liu,Yinhui Xiao,Shijia Liu,Yunlu Tian,Liangming Chen,Zhiquan Wang,Ling Jiang,Zhigang Zhao,Jianmin Wan 한국식물학회 2015 Journal of Plant Biology Vol.58 No.6

A two-line super-hybrid rice (Oryza sativa L.) variety [Liangyoupei9 (LYP9)] demonstrated superiority over its both parents, viz. elite inbred lines 93-11 and Pei-ai64S (PA64S), as well as other conventional hybrids, and had long been exploited in China. However, the genetic basis of its leaf-related traits, supposed to be an important component for yield potential, remains elusive. Here, initially a set of chromosome segment substitution lines (CSSLs) was constructed, in which the genome of Pei-ai64S has been introgressed into the background of 93-11. This set was developed by marker aided selection, based on 123 polymorphic SSR markers. The introgressed chromosomal segments presented in the 156 CSSLs covered 96.46% of Pei-ai64S genome. Afterwards, the CSSLs were deployed to assess the genetic basis of leaf size (length and width) and chlorophyll content of top three leaves across five different environments. The CSSLs showed transgressive segregation for all of the traits, and significant correlations were detected among most of the traits. A total of 27 quantitative trait loci (QTL) were identified on ten chromosomes, and three QTL cluster affecting related traits were found on chromosome 3, 6, and 8, respectively. Remarkably, two key QTLs, qALW3-1 and qALW3-2, both controlling the antepenultimate leaf width, were identified in all five environments, and their effect were further validated by CSSLs harboring the two QTL alleles. Our results indicate that developing CSSLs is a powerful tool for genetic dissection of quantitative traits. Meanwhile, the QTLs controlling leaf-related traits uncovered here provide useful information for marker-assisted selection in improving the performance of leaf morphology and photosynthetic ability.