With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of th...With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of these idle UPS resources is an urgent problem that must be addressed.Based on the energy storage type of the UPS(EUPS)and using renewable sources,a solution for IDCs is proposed in this study.Subsequently,an EUPS cluster classification method based on the concept of shared mechanism niche(CSMN)was proposed to effectively solve the EUPS control problem.Accordingly,the classified EUPS aggregation unit was used to determine the optimal operation of the IDC.An IDC cost minimization optimization model was established,and the Quantum Particle Swarm Optimization(QPSO)algorithm was adopted.Finally,the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.展开更多
Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to ident...Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean(FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups.The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently,the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures.展开更多
Sea Level Pressure（SLP） data for the period 1950–2012 at 61 stations located in or around the Balkan Peninsula was used. The main concept is that intra-annual course of SLP represents the best different air masses ...Sea Level Pressure（SLP） data for the period 1950–2012 at 61 stations located in or around the Balkan Peninsula was used. The main concept is that intra-annual course of SLP represents the best different air masses that are situated over the Balkan Peninsula during the year. The method for differentiation of climatic zones is cluster analysis. A hierarchical clustering technique–average linkage between groups with Pearson correlation for measurement of intervals was employed in the research. The climate of the Balkan Peninsula is transitional between oceanic and continental and also between subtropical and temperate climates. Several major changes in atmospheric circulation over the Balkan Peninsula have happened over the period 1950–2012. There is a serious increase of the influence of the Azores High in the period January–Marchwhich leads to an increase of SLP and enhances oceanic influence. There is an increase of the influence of the north-west extension of the monsoonal low in the period June–September. This leads to more continental climatebut also to more tropical air masses over the Balkan Peninsula. Accordinglythe extent of subtropical climate widens in northern direction. There is an increase of the influence of the Siberian High in the period October–December. This influence covers central and eastern part of the peninsula in October and Novemberand it reaches western parts in December. Thusthe climate becomes more continental.展开更多
Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process.In recent times,the most complex task in Software Defined Network(SDN)is security,w...Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process.In recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable controller.Therefore,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment.Consequently,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five algorithms.Furthermore,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)dataset.First,the utilized dataset is clustered into normal and four major attack categories via the clustering process.Then,a deep learning method is projected for building an efficient SDN-based intrusion detection system.The results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD dataset.Similarly,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing techniques.For example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.展开更多
An integrated fuzzy min-max neural network(IFMMNN) is developed to avoid the classification result influenced by the input sequence of training samples, and the learning algorithm can be used as pure clustering,pure c...An integrated fuzzy min-max neural network(IFMMNN) is developed to avoid the classification result influenced by the input sequence of training samples, and the learning algorithm can be used as pure clustering,pure classification, or a hybrid clustering classification. Three experiments are designed to realize the aim. The serial input of samples is changed to parallel input, and the fuzzy membership function is substituted by similarity matrix. The experimental results show its superiority in contrast with the original method proposed by Simpson.展开更多
In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific...In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network(CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count(CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.展开更多
Soil is an important source to other environ- mental media and organisms for organochlorine pesticides （OCPs） bioaccumulation. Twenty-four representative sur- face soil samples were collected from the lower reaches ...Soil is an important source to other environ- mental media and organisms for organochlorine pesticides （OCPs） bioaccumulation. Twenty-four representative sur- face soil samples were collected from the lower reaches of the Jiulong River, China, in 2009. The concentrations of hexachlorocyclohexane isomers （HCHs） ranged from 0.38 to 39.52 ng.g~, with a mean value of 9.51 ng.g-~. The concentrations of dichlorodiphenyltrichloroethanes （DDTs） and their metabolites were within the ranges of 0.94-700.99 ng. gl, with a mean value of 71.17 ng. g-1. The concentrations of HCHs and DDTs in the soil were lower than the first grade level （50 ng. g-a） of the Chinese Environmental Quality Standard （GB 15618-1995）. Hier- archical Cluster Analysis （HCA） and Pearson＇s bivariate Correlations Analysis （PCA） were used to analyse the distribution and contamination levels of OCPs in this region. The results showed that DDTs were the major contaminants and there were no significant correlations between various OCPs concentrations and the total organic carbon （TOC） contents. A significant positive correlation was observed between HCHs and DDTs （p 〈 0.01）, which indicates that HCHs and DDTs may have similar sources and fate in the study area.展开更多
One-class support vector machines （one-class SVMs） are powerful tools that are widely used in many applications. This paper describes a semi-supervised one-class SVM that uses supervision in terms of relative compar...One-class support vector machines （one-class SVMs） are powerful tools that are widely used in many applications. This paper describes a semi-supervised one-class SVM that uses supervision in terms of relative comparisons. The analysis uses a hypersphere version of one-class SVMs with a penalty term appended to the objective function. The method simultaneously finds the minimum sphere in the feature space that encloses most of the target points and considers the relative comparisons. The result is a standard convex quadratic programming problem, which can be solved by adapting standard methods for SVM training, i.e., sequential minimal optimization. This one-class SVM can be applied to semi-supervised clustering and multi-classification problems. Tests show that this method achieves higher accuracy and better generalization performance than previous SVMs.展开更多
基金supported by the Key Technology Projects of the China Southern Power Grid Corporation(STKJXM20200059)the Key Support Project of the Joint Fund of the National Natural Science Foundation of China(U22B20123)。
文摘With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of these idle UPS resources is an urgent problem that must be addressed.Based on the energy storage type of the UPS(EUPS)and using renewable sources,a solution for IDCs is proposed in this study.Subsequently,an EUPS cluster classification method based on the concept of shared mechanism niche(CSMN)was proposed to effectively solve the EUPS control problem.Accordingly,the classified EUPS aggregation unit was used to determine the optimal operation of the IDC.An IDC cost minimization optimization model was established,and the Quantum Particle Swarm Optimization(QPSO)algorithm was adopted.Finally,the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.
文摘Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean(FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups.The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently,the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures.
文摘Sea Level Pressure（SLP） data for the period 1950–2012 at 61 stations located in or around the Balkan Peninsula was used. The main concept is that intra-annual course of SLP represents the best different air masses that are situated over the Balkan Peninsula during the year. The method for differentiation of climatic zones is cluster analysis. A hierarchical clustering technique–average linkage between groups with Pearson correlation for measurement of intervals was employed in the research. The climate of the Balkan Peninsula is transitional between oceanic and continental and also between subtropical and temperate climates. Several major changes in atmospheric circulation over the Balkan Peninsula have happened over the period 1950–2012. There is a serious increase of the influence of the Azores High in the period January–Marchwhich leads to an increase of SLP and enhances oceanic influence. There is an increase of the influence of the north-west extension of the monsoonal low in the period June–September. This leads to more continental climatebut also to more tropical air masses over the Balkan Peninsula. Accordinglythe extent of subtropical climate widens in northern direction. There is an increase of the influence of the Siberian High in the period October–December. This influence covers central and eastern part of the peninsula in October and Novemberand it reaches western parts in December. Thusthe climate becomes more continental.
文摘Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic process.In recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable controller.Therefore,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN environment.Consequently,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five algorithms.Furthermore,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)dataset.First,the utilized dataset is clustered into normal and four major attack categories via the clustering process.Then,a deep learning method is projected for building an efficient SDN-based intrusion detection system.The results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD dataset.Similarly,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing techniques.For example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.
基金the National Natural Science Foundation of China(No.61402280)
文摘An integrated fuzzy min-max neural network(IFMMNN) is developed to avoid the classification result influenced by the input sequence of training samples, and the learning algorithm can be used as pure clustering,pure classification, or a hybrid clustering classification. Three experiments are designed to realize the aim. The serial input of samples is changed to parallel input, and the fuzzy membership function is substituted by similarity matrix. The experimental results show its superiority in contrast with the original method proposed by Simpson.
基金Project supported by the National Natural Science Foundation of China(No.61379074)the Zhejiang Provincial Natural Science Foundation of China(Nos.LZ12F02003 and LY15F020035)
文摘In some image classification tasks, similarities among different categories are different and the samples are usually misclassified as highly similar categories. To distinguish highly similar categories, more specific features are required so that the classifier can improve the classification performance. In this paper, we propose a novel two-level hierarchical feature learning framework based on the deep convolutional neural network(CNN), which is simple and effective. First, the deep feature extractors of different levels are trained using the transfer learning method that fine-tunes the pre-trained deep CNN model toward the new target dataset. Second, the general feature extracted from all the categories and the specific feature extracted from highly similar categories are fused into a feature vector. Then the final feature representation is fed into a linear classifier. Finally, experiments using the Caltech-256, Oxford Flower-102, and Tasmania Coral Point Count(CPC) datasets demonstrate that the expression ability of the deep features resulting from two-level hierarchical feature learning is powerful. Our proposed method effectively increases the classification accuracy in comparison with flat multiple classification methods.
文摘Soil is an important source to other environ- mental media and organisms for organochlorine pesticides （OCPs） bioaccumulation. Twenty-four representative sur- face soil samples were collected from the lower reaches of the Jiulong River, China, in 2009. The concentrations of hexachlorocyclohexane isomers （HCHs） ranged from 0.38 to 39.52 ng.g~, with a mean value of 9.51 ng.g-~. The concentrations of dichlorodiphenyltrichloroethanes （DDTs） and their metabolites were within the ranges of 0.94-700.99 ng. gl, with a mean value of 71.17 ng. g-1. The concentrations of HCHs and DDTs in the soil were lower than the first grade level （50 ng. g-a） of the Chinese Environmental Quality Standard （GB 15618-1995）. Hier- archical Cluster Analysis （HCA） and Pearson＇s bivariate Correlations Analysis （PCA） were used to analyse the distribution and contamination levels of OCPs in this region. The results showed that DDTs were the major contaminants and there were no significant correlations between various OCPs concentrations and the total organic carbon （TOC） contents. A significant positive correlation was observed between HCHs and DDTs （p 〈 0.01）, which indicates that HCHs and DDTs may have similar sources and fate in the study area.
基金Supported by the National Natural Science Foundation of China(No. 60872070)
文摘One-class support vector machines （one-class SVMs） are powerful tools that are widely used in many applications. This paper describes a semi-supervised one-class SVM that uses supervision in terms of relative comparisons. The analysis uses a hypersphere version of one-class SVMs with a penalty term appended to the objective function. The method simultaneously finds the minimum sphere in the feature space that encloses most of the target points and considers the relative comparisons. The result is a standard convex quadratic programming problem, which can be solved by adapting standard methods for SVM training, i.e., sequential minimal optimization. This one-class SVM can be applied to semi-supervised clustering and multi-classification problems. Tests show that this method achieves higher accuracy and better generalization performance than previous SVMs.
