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( 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.
( Qiuhua Wang ),( Xiaoqin Ouyang ),( Jiacheng Zhan ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.7
With the rapid development of network, Intrusion Detection System(IDS) plays a more and more important role in network applications. Many data mining algorithms are used to build IDS. However, due to the advent of big data era, massive data are generated. When dealing with large-scale data sets, most data mining algorithms suffer from a high computational burden which makes IDS much less efficient. To build an efficient IDS over big data, we propose a classification algorithm based on data clustering and data reduction. In the training stage, the training data are divided into clusters with similar size by Mini Batch K-Means algorithm, meanwhile, the center of each cluster is used as its index. Then, we select representative instances for each cluster to perform the task of data reduction and use the clusters that consist of representative instances to build a K-Nearest Neighbor(KNN) detection model. In the detection stage, we sort clusters according to the distances between the test sample and cluster indexes, and obtain k nearest clusters where we find k nearest neighbors. Experimental results show that searching neighbors by cluster indexes reduces the computational complexity significantly, and classification with reduced data of representative instances not only improves the efficiency, but also maintains high accuracy.
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.
A New Low-BMR Quantization Method for Wireless Channel Characteristics-based Secret Key Generation
( Qiuhua Wang ),( Qiuyun Lyu ),( Xiaojun Wang ),( Jianrong Bao ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.10
Channel characteristics-based secret key generation is an effective physical-layer security method. The issues of how to remove the effect of random noise and to balance the key generation rate (KGR) and the bit mismatch rate (BMR) are needed to be addressed. In this paper, to reduce the effect of random noise and extract more secret bits, a new quantization scheme with high key generation rate and low bit mismatch rate is proposed. In our proposed scheme, we try to use all measurements and correct the differences caused by noise at the boundary regions instead of simply dropping them. We evaluate and discuss the improvements of our proposed scheme. The results show that our proposed scheme achieves lower bit mismatch rate as well as remaining high key generation rate.
Wang, Weina,Wu, Qiuhua,Zang, Xiaohuan,Wang, Chun,Wang, Zhi Korean Chemical Society 2012 Bulletin of the Korean Chemical Society Vol.33 No.10
In this paper, a layered-carbon-$Fe_3O_4$ (LC-$Fe_3O_4$) hybrid material was synthesized through a facile one-pot solvothermal method and used as the adsorbent for the preconcentration of some phthalate esters (dimethyl phthalate, diethyl phthalate, diallyl phthalate, diisobutyl phthalate and benzyl butyl phthalate) in water samples. The effects of the adsorbent dosage, extraction time, the solution pH and salinity on the adsorption of the phthalate esters (PAEs) were investigated. The magnetic nanocomposite adsorbent could remove and enrich the PAEs from water samples efficiently. After the adsorption, the analytes were desorbed and then determined by high performance liquid chromatography-ultraviolet detection. Under the optimum conditions, the enrichment factors of the method for the analytes were in the range from 161 to 180. A linear response with peak area as the quantification signal was observed in the concentration range from 0.5 to $100ng\;mL^{-1}$. The limits of detection (S/N = 3) of the method were between 0.08 and $0.1ng\;mL^{-1}$. The method was suitable for the determination of trace phthalate esters in environmental water samples.
Weina Wang,Qiuhua Wu,Xiaohuan Zang,Chun Wang,Zhi Wang 대한화학회 2012 Bulletin of the Korean Chemical Society Vol.33 No.10
In this paper, a layered-carbon-Fe3O4 (LC-Fe3O4) hybrid material was synthesized through a facile one-pot solvothermal method and used as the adsorbent for the preconcentration of some phthalate esters (dimethyl phthalate, diethyl phthalate, diallyl phthalate, diisobutyl phthalate and benzyl butyl phthalate) in water samples. The effects of the adsorbent dosage, extraction time, the solution pH and salinity on the adsorption of the phthalate esters (PAEs) were investigated. The magnetic nanocomposite adsorbent could remove and enrich the PAEs from water samples efficiently. After the adsorption, the analytes were desorbed and then determined by high performance liquid chromatography-ultraviolet detection. Under the optimum conditions, the enrichment factors of the method for the analytes were in the range from 161 to 180. A linear response with peak area as the quantification signal was observed in the concentration range from 0.5 to 100 ng mL−1. The limits of detection (S/N = 3) of the method were between 0.08 and 0.1 ng mL−1. The method was suitable for the determination of trace phthalate esters in environmental water samples.
Wang, Dan,Liu, Wenjun,Ren, Yan,De, Liangliang,Zhang, Donglei,Yang, Yanrong,Bao, Qiuhua,Zhang, Heping,Menghe, Bilige Korean Society for Food Science of Animal Resource 2016 한국축산식품학회지 Vol.36 No.4
In this study, traditional culture method and 16S rRNA gene analysis were applied to reveal the composition and diversity of lactic acid bacteria (LAB) of fermented cow milk, huruud and urum from Baotou and Bayannur of midwestern Inner Mongolia. Also, the quantitative results of dominant LAB species in three different types of dairy products from Baotou and Bayannur were gained by quantitative polymerase chain reaction (q-PCR) technology. Two hundred and two LAB strains isolated from sixty-six samples were identified and classified into four genera, namely Enterococcus, Lactococcus, Lactobacillus, Leuconostoc, and twenty-one species and subspecies. From these isolates, Lactococcus lactis subsp. lactis (32.18%), Lactobacillus plantarum (12.38%) and Leuconosto mesenteroides (11.39%) were considered as the dominated LAB species under the condition of cultivating in MRS and M17 medium. And the q-PCR results revealed that the number of dominant species varied from samples to samples and from region to region. This study clearly shows the composition and diversity of LAB existing in fermented cow milk, huruud and urum, which could be considered as valuable resources for LAB isolation and further probiotic selection.
Dan Wang,Wenjun Liu,Yan Ren,Liangliang De,Donglei Zhang,Yanrong Yang,Qiuhua Bao,Heping Zhang,Bilige Menghe 한국축산식품학회 2016 한국축산식품학회지 Vol.36 No.4
In this study, traditional culture method and 16S rRNA gene analysis were applied to reveal the composition and diversity of lactic acid bacteria (LAB) of fermented cow milk, huruud and urum from Baotou and Bayannur of midwestern Inner Mongolia. Also, the quantitative results of dominant LAB species in three different types of dairy products from Baotou and Bayannur were gained by quantitative polymerase chain reaction (q-PCR) technology. Two hundred and two LAB strains isolated from sixty-six samples were identified and classified into four genera, namely Enterococcus, Lactococcus, Lactobacillus, Leuconostoc, and twenty-one species and subspecies. From these isolates, Lactococcus lactis subsp. lactis (32.18%), Lactobacillus plantarum (12.38%) and Leuconosto mesenteroides (11.39%) were considered as the dominated LAB species under the condition of cultivating in MRS and M17 medium. And the q-PCR results revealed that the number of dominant species varied from samples to samples and from region to region. This study clearly shows the composition and diversity of LAB existing in fermented cow milk, huruud and urum, which could be considered as valuable resources for LAB isolation and further probiotic selection.
Chunxia Wu,Bin Zhao,Qiuhua Wu,Chun Wang,Zhi Wang,Yingli Li 대한화학회 2011 Bulletin of the Korean Chemical Society Vol.32 No.3
A dispersive liquid-liquid microextraction based on solidification of floating organic droplet (DLLME-SFO)has been developed as a new approach for the extraction of trace copper in water and beverage samples followed by the determination with flame atomic absorption spectrometry. In the DLLME-SFO, 8-hydroxy quinoline, 1-dodecanol, and methanol were used as chelating agent, extraction solvent and dispersive solvent,respectively. The experimental parameters related to the DLLME-SFO such as the type and volume of the extraction and dispersive solvent, extraction time, sample volume, the concentration of chelating agent and salt addition were investigated and optimized. Under the optimum conditions, the enrichment factor for copper was 122. The method was linear in the range from 0.5 to 300 ng mL^(−1) of copper in the samples with a correlation coefficient (r) of 0.9996 and a limit of detection of 0.1 ng mL^(−1). The method was applied to the determination of copper in water and beverage samples. The recoveries for the spiked water and beverage samples at the copper concentration levels of 5.0 and 10.0 ng mL^(−1) were in the range between 92.0% and 108.0%. The relative standard deviations (RSD) varied from 3.0% to 5.6%.
Wu, Chunxia,Zhao, Bin,Li, Yingli,Wu, Qiuhua,Wang, Chun,Wang, Zhi Korean Chemical Society 2011 Bulletin of the Korean Chemical Society Vol.32 No.3
A dispersive liquid-liquid microextraction based on solidification of floating organic droplet (DLLME-SFO) has been developed as a new approach for the extraction of trace copper in water and beverage samples followed by the determination with flame atomic absorption spectrometry. In the DLLME-SFO, 8-hydroxy quinoline, 1-dodecanol, and methanol were used as chelating agent, extraction solvent and dispersive solvent, respectively. The experimental parameters related to the DLLME-SFO such as the type and volume of the extraction and dispersive solvent, extraction time, sample volume, the concentration of chelating agent and salt addition were investigated and optimized. Under the optimum conditions, the enrichment factor for copper was 122. The method was linear in the range from 0.5 to $300\;ng\;mL^{-1}$ of copper in the samples with a correlation coefficient (r) of 0.9996 and a limit of detection of $0.1\;ng\;mL^{-1}$. The method was applied to the determination of copper in water and beverage samples. The recoveries for the spiked water and beverage samples at the copper concentration levels of 5.0 and $10.0\;ng\;mL^{-1}$ were in the range between 92.0% and 108.0%. The relative standard deviations (RSD) varied from 3.0% to 5.6%.