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A mixture model Parameters Estimation Algorithm for Inter-Contact Times in Internet of Vehicles

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Computers, Materials & Continua 2021年第11期69卷 2445-2457页

作者： Cheng Gong Xinzhu Yang Wei Huangfu Qinghua Lu School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Advanced Innovation Center for Materials Genome Engineering School of Computer and Communication EngineeringUniversity of Science and Technology Beijing100083China Shunde Graduate School of University of Science and Technology Beijing Foshan City528300GuangdongChina Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services University of Science and Technology BeijingBeijing100083China Commonwealth Scientific and Industrial Research Organization Canberra2600Australia

Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles(IoV).mixture models are appropriate to describe complex spatial-temporal *** calculating the expectation of hidden variables in vehicle communication,Expectation Maximization(EM)algorithm solves the maximum likelihood estimation of parameters,and then obtains the mixture model of vehicle communication ***,the EM algorithm requires multiple iterations and each iteration needs to process all the *** its computational complexity is high.A parameter estimation algorithm with low computational complexity based on Bin Count(BC)and Differential Evolution(DE)(PEBCDE)is *** overcomes the disadvantages of the EM algorithm in solving mixture models for big *** order to reduce the computational complexity of the mixture models in the IoV,massive data are divided into relatively few time intervals and then *** to these few counted values,the parameters of the mixture model are obtained by using DE *** modeling and analysis of simulation data and instance data,the PEBCDE algorithm is verified and discussed from two aspects,i.e.,accuracy and *** numerical solution of the probability distribution parameters is obtained,which further provides a more detailed statistical model for the distribution of the opportunity interval of the IoV.

关键词： Internet of vehicles opportunistic networks inter-contact times mixture model parameters estimation

Multiphase Oil Simulation Based on mixture model

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Multiphase Oil Simulation Based on Mixture Model

Detecting compromised email accounts via login behavior characterization

第36届中国控制会议

作者： Zou Changjun Yin Yong Jing Qianfeng Marine Dynamic Simulation ＆ Control Lab Dalian Maritime University

ISBN: (纸本)9781538629185

The slip velocity associates the mixture velocity with the phase velocity,establishing vital relationship between the phase and *** are many slip velocity models exist,but the limitation among them is the complexity for real time simulation.A simplified slip velocity model is proposed in this *** of our slip velocity model coupled in the mixture model is conducted with satisfactory results.

关键词： Slip velocity model mixture model Multiphase oil simulation

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Cybersecurity 2024年第1期7卷 16-36页

作者： Jianjun Zhao Can Yang Di Wu Yaqin Cao Yuling Liu Xiang Cui Qixu Liu Institute of Information Engineering Chinese Academy of SciencesBeijing100085China School of Cyber Security University of Chinese Academy of SciencesBeijing100049China China Cybersecurity Review Technology and Certification Center Beijing100013China Zhongguancun Laboratory Beijing100089China

The illegal use of compromised email accounts by adversaries can have severe consequences for enterprises and *** compromised email accounts is more challenging than in the social network field,where email accounts have only a few interaction events(sending and receiving).To address the issue of insufficient features,we propose a novel approach to detecting compromised accounts by combining time zone differences and alternate logins to identify abnormal *** on this approach,we propose a compromised email account detection framework that relies on widely available and less sensitive login logs and does not require *** framework characterizes login behaviors to identify logins that do not belong to the account owner and outputs a list of account-subnet pairs ranked by their likelihood of having abnormal login *** approach reduces the number of account-subnet pairs that need to be investigated and provides a reference for investigation *** evaluation demonstrates that our method can detect most email accounts that have been accessed by disclosed malicious IP addresses and outperforms similar ***,our framework has the capability to uncover undisclosed malicious IP addresses.

关键词： Compromised account detection mixture model Login log analysis Attribution and forensic

Semi-supervised Artificial Immune mixture models Clustering

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Chinese Journal of Electronics 2016年第2期25卷 249-255页

作者： HUANG Wenlong WANG Xiaodan LI Guohong KANG Hongxia Air and Missile Defense College Air Force Engineering University State Key Laboratory of Astronautic Dynamics Xi'an Satellite Control Center

Learning with partly labeled data aims at combining labeled and unlabeled data in order to boost the accuracy of an algorithm. The traditional Expectation maximization(EM) algorithm only produces locally optimal solutions, it is sensitive to initialization, and the number of components of mixture model must be known in advance. We propose a novel semi-supervised clustering algorithm that uses Gaussian mixture models(GMM) as the underlying clustering model. A novel adaptive global search mechanism is introduced into semi-supervised gaussian mixture model-based clustering, where the EM algorithm is incorporated with the ideas of an immune clonal selection technique. The new algorithm overcomes the various problems associated with the traditional EM algorithm. And it can improve the effectiveness in estimating the parameters and determining the optimal number of clusters automatically. The experimental results illustrate the proposed algorithm provides significantly better clustering results, when compared with other methods of incorporating equivalence constraints.

关键词： Semi-supervised clustering mixture model Immune clonal selection Globally optimal solution

Data-driven Operation Risk Assessment of Wind-integrated Power Systems via mixture models and Importance Sampling

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Journal of Modern Power Systems and Clean Energy 2020年第3期8卷 437-445页

作者： Osama Aslam Ansari Yuzhong Gong Weijia Liu Chi Yung Chung Department of Electrical and Computer Engineering University of SaskatchewanSaskatoonCanada

The increasing penetration of highly intermittent wind generation could seriously jeopardize the operation reliability of power systems and increase the risk of electricity outages. To this end, this paper proposes a novel data-driven method for operation risk assessment of wind-integrated power systems. Firstly, a new approach is presented to model the uncertainty of wind power in lead time. The proposed approach employs k-means clustering and mixture models(MMs) to construct time-dependent probability distributions of wind *** proposed approach can also capture the complicated statistical features of wind power such as multimodality. Then, a nonsequential Monte Carlo simulation(NSMCS) technique is adopted to evaluate the operation risk indices. To improve the computation performance of NSMCS, a cross-entropy based importance sampling(CE-IS) technique is applied. The CE-IS technique is modified to include the proposed model of wind *** method is validated on a modified IEEE 24-bus reliability test system(RTS) and a modified IEEE 3-area RTS while employing the historical data of wind generation. The simulation results verify the importance of accurate modeling of shortterm uncertainty of wind power for operation risk *** case studies have been performed to analyze the impact of transmission systems on operation risk indices. The computational performance of the framework is also examined.

关键词： Cross entropy mixture model Monte Carlo simulation operation risk power system reliability

Simulation of abrasive flow machining process for 2D and 3D mixture models

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Frontiers of Mechanical Engineering 2015年第4期10卷 424-432页

作者： Rupalika DASH Kalipada MAITY Mechanical Engineering Department National Institute of TechnologyRourkela 769008 India

Improvement of surface finish and material removal has been quite a challenge in a finishing operation such as abrasive flow machining （AFM）. Factors that affect the surface finish and material removal are media viscosity, extrusion pressure, piston velocity, and particle size in abrasive flow machining process. Performing experiments for all the parameters and accurately obtaining an optimized parameter in a short time are difficult to accomplish because the operation requires a precise finish. Computational fluid dynamics （CFD） simulation was employed to accurately determine optimum parameters. In the current work, a 2D model was designed, and the flow analysis, force calculation, and material removal prediction were performed and compared with the available experi- mental data. Another 3D model for a swaging die finishing using AFM was simulated at different viscosities of the media to study the effects on the controlling parameters. A CFD simulation was performed by using commercially available ANSYS FLUENT. Two phases were considered for the flow analysis, and multiphase mixture model was taken into account. The fluid was considered to be a Newtonian fluid and the flow laminar with no wall slip.

关键词： abrasive flow machining (AFM) computational fluid dynamics (CFD) modeling mixture model

Generalized fiducial methods for testing quantitative trait locus effects in genetic backcross studies

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Statistical Theory and Related Fields 2022年第2期6卷 148-160页

作者： Pengcheng Ren Guanfu Liu Xiaolong Pu Yan Li KLATASDS-MOE School of StatisticsEast China Normal UniversityShanghaiPeople’s Republic of China School of Statistics and Information Shanghai University of International Business and EconomicsShanghaiPeople’s Republic of China

In this paper,we propose generalized fiducial methods and construct four generalized p-values to test the existence of quantitative trait locus effects under phenotype distributions from a location-scale *** with the likelihood ratio test based on simulation studies,our methods perform better at controlling type I errors while retaining comparable power in cases with small or moderate sample *** four generalized fiducial methods support varied sce-narios:two of them are more aggressive and powerful,whereas the other two appear more conservative and robust.A real data example involving mouse blood pressure is used to illustrate our proposed methods.

关键词： Generalized fiducial inference quantitative trait locus mixture model Gibbs algorithm likelihood ratio test

mixture models for Web Page Classification

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Mixture Models for Web Page Classification

Effects of porous material on transient natural convection heat transfer of nano-fluids inside a triangular chamber

2010 Second Asia-Pacific Conference on Information Processing (APCIP 2010)

作者： Bai JingHuaand Zhang XiaoXian School of Computer Science and technology a Beijing Institute of Technology a Beijing P.R.China a Li ZhiXin b and Li XiaoPing a Department of software b Changchun Institute of Technology b Chang Chun P.R.China b

This paper presents a method for designing semisupervised classifier trained on labeled and unlabeled *** explore the trade-off maximizing a generative likelihood of labeled and unlabeled ***,mixture models are an interesting and flexible model *** different uses of mixture models include for example generative models and density *** paper investigates semi-supervised learning of mixture models using a unified objective function taking both labeled and unlabeled data into *** conducted experiments on the WebKB and *** results show that unlabeled data results in improvement in classification accuracy over the supervised model.

关键词： semi-supervised learning mixture model generative model

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Chinese Journal of Chemical Engineering 2020年第5期28卷 1203-1213页

作者： Mohsen Izadi Mechanical Engineering Department Faculty of EngineeringLorestan UniversityKhorramabadIran

This study numerically investigates the impact of porous materials,nano-particle types,and their concentrations on transient natural convection heat transfer of nano-fluid inside a porous chamber with a triangular *** governing equations of the two-phase mixture model are separated on the computational domain and solved using the Finite Volume Method,taking into account the Darcy–Brinkman model for porous *** was observed that convection heat transfer inside the triangular chamber consists of three stages named initial,transient,and *** features of each step are provided in *** results suggested that the use of a hybrid nano-fluid(water/aluminum oxide-cooper)inside a porous glass material and an increase in volume fraction of nano-particles have adverse effects on heat transfer *** contrast,as the nano-particle volume fraction of the single nano-fluid(water/aluminum oxide)inside the chamber increased,convection heat transfer rate *** the same time,it was observed that the use of both nano-fluids(single and hybrid)in the porous environment of the aluminum foam could improve convection.

关键词： Transient natural convection mixture model Darcy-Brinlanan model Rayleigh-Benard instability Triangular chamber