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2020 International Conference on Virtual Reality and intelligent Systems, ICVRIS 2020

作者： Zhao, Haibo Li, Xiangjun Ren, Yancheng Yan'An University School of Physics and Electronic Information Shaanxi Yan'an716000 China Shaanxi Key Laboratory of Intelligent Processing of Big Energy Data Shaanxi Yan'an716000 China

ISBN: (纸本)9781728196367

In order to forecast Short-Term Load Forecast (STLF) for individual electric customers and then intelligently and flexibly utilize renewable energy, a big data framework is proposed that collects the data from smart meters and weather forecasting, pre-processes and loads it into a NoSQL database that is capable to store and further process large volumes of heterogeneous data. Then, a Long Short-Term Memory (LSTM) recurrent neural network is designed and implemented to determine the load profiles and forecast the electricity consumption for residential community for the next 24 hours. The proposed framework is tested with a publicly available smart meter dataset of a residential community, of which LSTM's performance is compared with two benchmark algorithms in terms of Root Mean Square Errors (RMSE) and Mean Absolute Percentage Errors (MAPE). © 2020 IEEE.

关键词： big data

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Investigation of the Salinity Distribution of the Soil along West Main Canal in ningxia, China

Investigation of the Salinity Distribution of the Soil along...

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2021 International Conference on Agriculture Science and Water Resource, ASWR 2021

作者： Wu, Juntao Jing, Hefang Wang, Weihong Guo, Xiaoxiao Guo, Xinxia Research Institute of Numerical Computation and Engineering Applications North Minzu University Yinchuan China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province North Minzu University Yinchuan China School of Mathematics and Information Science North Minzu University Yinchuan China

As one of the main irrigation canals in ningxia, West Main Canal plays a very important role in the irrigation of the land nearby the canal, such as farms, meadows, and tree farms. Because of the long-term use of the Yellow River water for irrigation, the soil salinization in this area become more serious. In this paper, the problem of soil salinization along the West Main Canal in ningxia is taken as the research object. 82 sample points in the studied area are chosen and a total number of 164 soil samples are measured for the eight main salt ions and pH value. Correlation analysis and principal component analysis are employed to study the distribution of the soil salinity ions and pH value. In addition, two dimensional interpolation is applied to obtain the spatial distribution of the total saltiness. It can be found that Cl-, SO42-, Ca2+, Na+ and HCO3- are the main salt ions and the main soluble salts are NaCl, KCl, CaSO4, Na2SO4 and CaCl2 in the soil of the studied area. The spatial distribution of the total soil salt is mainly in the patterns of north high and south low, far high and near low, up high and down low. © Published under licence by IOP Publishing Ltd.

关键词： Sodium chloride

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IGED:Towards intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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Experimental Study on Flow Characteristics in a Compound Open Channel with Flexible Vegetation

Experimental Study on Flow Characteristics in a Compound Ope...

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2021 International Conference on Agriculture Science and Water Resource, ASWR 2021

作者： Guo, Xiaoxiao Jing, Hefang Wang, Weihong Wu, Juntao Zhang, Qiaoling Research Institute of Numerical Computation and Engineering Applications North Minzu University Yinchuan China The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province North Minzu University Yinchuan China School of Mathematics and Information Science North Minzu University Yinchuan China

In order to study the effect of flexible vegetation for the flow in open channels with flood plains, a series of experiments were carried out in a laboratory flume with a two-stage floodplain. Four cases, i.e., dense parallel arrangement, normal parallel arrangement, sparse parallel arrangement and no vegetation condition, were designed with fixed water depth and discharge. By using three dimensional (3D) laser Doppler velocimeter (LDV) and other instruments, under the condition of the four cases,3D flow velocities, water level and turbulence intensity were measured, The experimental results show that, compared with the non-vegetation situation, the arrangement of grasses on the floodplain will increase the water surface gradient, hydraulic gradient and turbulence intensity to a certain extent. © Published under licence by IOP Publishing Ltd.

关键词： Vegetation

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A Local Regression-Based SLA-Aware Energy-Efficient Virtual Machine Consolidation Algorithm in data Centers 9

A Local Regression-Based SLA-Aware Energy-Efficient Virtual ...

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9th International Conference on big data Computing and Communications, bigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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A Recommendation Algorithm Based on Belief Propagation and Probability Matrix Factorization

A Recommendation Algorithm Based on Belief Propagation and P...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Juan Li Xiao-Feng Wang Wan-Jing Feng North Minzu University Yinchuan China Ningxia Key Laboratory of Intelligent Information and Big Data Processing Yinchuan China

ISBN: (数字)9781728166094

ISBN: (纸本)9781728166100

Collaborative filtering recommendation algorithm is the most important recommended application methods at present. The selection of users (items) similarity measurement methods in this method may affect the final recommendation effect. Probability matrix factorization algorithm is one of the more and more methods applied in collaborative filtering algorithms. This paper proposes to convert the users (items) consumption network into a bipartite graph and use belief propagation algorithm to obtain fuzzy nearest neighbor set, improving users (items) similarity measure and incorporating it into probability matrix factorization. Experimental tests on the data set showed that the proposed method performance was good.

关键词： Collaborative filtering Computational modeling Probability data models Bipartite graph Matrix converters Belief propagation

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Simulation Research on Nonlinear Modeling and Control of Switched Reluctance Motor 32

Simulation Research on Nonlinear Modeling and Control of Swi...

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32nd Chinese Control and Decision Conference, CCDC 2020

作者： Ru, Luan Zou, Hongjian Guo, Jinru Beijing University of Civil Engineering and Architecture School of Electrical and Information Engineering Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

ISBN: (纸本)9781728158549

The model of a switched reluctance motor (SRM) directly influences it's performance and control effect. However, the doubly salient structure, switching power supply and seriously saturated magnetic circuit, make it difficult to accurately model. Aiming to solve this problem, this paper presents a new simulation method based on BP neural network to establish the current and torque models of a SRM. Using a great deal of current, flux linkage, torque numerical solution data obtained by a finite element software as the sample data, a kind of BP neural network is trained offline, and all the expected data sets, which form a table, are outputted. By looking-up the table the nonlinear models of flux linkage and torque of the SRM are obtained. On the basis of the nonlinear models, the current closed-loop control is formed by using the torque sharing function and the fuzzy switching angles to reduce the torque ripple. The simulation results show that the proposed nonlinear models not only have a fast calculation speed, but also are high precision and can meet the requirements of real-time control. The proposed control method can effectively reduce the torque ripple of the SRM. © 2020 IEEE.

关键词： Fuzzy control

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Selection of wavelet generating function in voltage interruption detection 3

Selection of wavelet generating function in voltage interrup...

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2023 3rd International Conference on Electrical Engineering and Computer Technology, ICEECT 2023

作者： Gong, Jing Zhong, Siyu School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044 China

When using wavelet for voltage interruption detection, improper selection can lead to detection failure due to the differences in wavelet characteristics and the diversity of the generating function. Based on the analysis of wavelet characteristics, the modulus maximum principle is used to compare the detail coefficients after wavelet decomposition, and a method that should be followed for selecting the generating function is proposed. Here are four important points to consider: First, choose orthogonal wavelets that can decrease redundancy. Second, symmetry does not have a major effect. Third, higher-order vanishing moments and longer support lengths result in better singularity detection. Finally, the order of vanishing moments is more important than the support length. We establish a disturbance signal model with interruption occurring at the time of voltage zero crossing and select six types of wavelets: db1, db3, db6, db8, coif2, and coif3 for four-scale wavelet decomposition. The experimental results demonstrate the accuracy of the proposed method in this paper. © Published under licence by IOP Publishing Ltd.

关键词： Wavelet decomposition

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A GENERALIZED PENALTY METHOD FOR DIFFERENTIAL VARIATIONAL-HEMIVARIATIONAL INEQUALITIES

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Acta Mathematica Scientia 2022年第1期42卷 247-264页

作者： Liang LU Lijie LI Mircea SOFONEA Guangxi Key Laboratory of Cross-border E-commerce Intelligent Information Processing Guangxi University of Finance and EconomicsNanning 530003China Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal UniversityYulin 537000China Laboratoire de Mathématiques et Physique Universitéde Perpignan Via Domitia52 Avenue Paul Alduy66860 PerpignanFrance

We consider a differential variational-hemivariational inequality with constraints,in the framework of reflexive Banach *** existence of a unique mild solution of the inequality,together with its stability,was proved in[1].Here,we complete these results with existence,uniqueness and convergence results for an associated penalty-type *** this end,we construct a sequence of perturbed differential variational-hemivariational inequalities governed by perturbed sets of constraints and penalty *** prove the unique solvability of each perturbed inequality as well as the convergence of its solution to the solution of the original ***,we consider a mathematical model which describes the equilibrium of a viscoelastic rod in unilateral *** weak formulation of the model is in a form of a differential variational-hemivariational inequality in which the unknowns are the displacement field and the history of the *** apply our abstract penalty method in the study of this inequality and provide the corresponding mechanical interpretations.

关键词： differential variational-hemivariational inequality generalized penalty method Mosco convergence viscoelastic rod unilateral constraint

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Empirical Comparisons of Deep Learning Networks on Liver Segmentation

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Computers, Materials & Continua 2020年第3期62卷 1233-1247页

作者： Yi Shen Victor S.Sheng Lei Wang Jie Duan Xuefeng Xi Dengyong Zhang Ziming Cui School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhouChina Department of Computer Science University of Central ArkansasConwayArkansasUSA Huan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangshaChina

Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver *** recent years,due to the great improvement of hard device,many deep learning based methods have been proposed for automatic liver *** them,there are the plain neural network headed by FCN and the residual neural network headed by Resnet,both of which have many *** have achieved certain achievements in medical image *** this paper,we firstly select five representative structures,i.e.,FCN,U-Net,Segnet,Resnet and Densenet,to investigate their performance on liver *** original Resnet and Densenet could not perform image segmentation directly,we make some adjustments for them to perform live *** experimental results show that Densenet performs the best on liver segmentation,followed by *** perform much better than Segnet,U-Net,and *** Segnet,U-Net,and FCN,U-Net performs the best,followed by *** performs the worst.

关键词： Liver segmentation deep learning FCN U-Net Segnet Resnet Densenet

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