Search

  • Select Article Type
  • Abstract Supplements
  • Blood Group Review
  • Call to Arms
  • Hypothesis
  • In Memoriam
  • Interview
  • Introduction
  • Letter to the Editor
  • Short Report
  • abstract
  • Abstracts
  • Article
  • book-review
  • case-report
  • case-study
  • Clinical Practice
  • Commentary
  • Conference Presentation
  • conference-report
  • congress-report
  • Correction
  • critical-appraisal
  • Editorial
  • Editorial Comment
  • Erratum
  • Events
  • Letter
  • Letter to Editor
  • mini-review
  • minireview
  • News
  • non-scientific
  • Obituary
  • original-paper
  • original-report
  • Original Research
  • Pictorial Review
  • Position Paper
  • Practice Report
  • Preface
  • Preliminary report
  • Product Review
  • rapid-communication
  • Report
  • research-article
  • Research Communicate
  • research-paper
  • Research Report
  • Review
  • review -article
  • review-article
  • review-paper
  • Review Paper
  • Sampling Methods
  • Scientific Commentary
  • short-communication
  • short-report
  • Student Essay
  • Varia
  • Welome
  • Select Journal
  • In Jour Smart Sensing And Intelligent Systems
  • International Journal Advanced Network Monitoring Controls
  • Statistics In Transition

 

Article | 01-October-2019

Searchable Re-encryption Cloud Storage Method Based on Markov Chain

paper, SReCSM, Searchable Re-encryption Cloud Storage Method is proposed using the idea of Markov chain. In this method, instead of using traditional storage, a proactive storage approach is adopted to avoid delayed storage. In addition, the SReCSM predicts periodically to increase the effiency of cloud storage. The major contribution of our work includes: Searchable Re-encryption Cloud Storage Method based on Markov chain is proposed and built as a SReCSM model. In order to find out the optimal

Wang Hui, Wang Zhong Sheng, Li Jinguang

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 3, 29–46

Article | 14-October-2020

Demand Forecast of Weapon Equipment Spare Parts Based on Improved Gray-Markov Model

randomness and fluctuation Therefore, equipment managers urgently need to find a method to predict the random demand for repair spare parts. At present, there are many technologies for demand planning, forecasting and decision-making, mainly including time series forecasting models, regression analysis methods, support vector machines, neural networks, gray forecasting and decision making, Markov forecasting, Combined optimization decision-making, and their Between each other. Among them, the method

Ou Li, Bailin Liu, Chenhao Li, Dan Gao

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 3, 47–56

research-article | 30-November-2020

Optimization Method of Equipment Maintenance Resource Scheduling Based on Hidden Semi-Markov Model

I. INTRODUCTION In order to solve the problems of uneven distribution of maintenance resources, poor planning, and inaccurate forecasts in the process of coordinated maintenance of complex equipment, a complex equipment maintenance resource scheduling optimization method based on hidden semi-Markov model is proposed. Establish the transportation time matrix between the support points and the maintenance points, and propose the maintenance resource scheduling plan between the maintenance points

Pengrui Wang, Bailin Liu, Tao Zhao, Pengxiang Cao

International Journal of Advanced Network, Monitoring and Controls, Volume 6 , ISSUE 2, 23–31

Article | 30-November-2019

Research on Blockchain Availability Modeling in P2P Network

mainly focus on the reliability and maintainability of the engineering capability of complex systems, and also on the availability of mission capability. Lianhong Zhou established the availability model of optical fiber communication system by using the state transfer equation[1]. Hailin Feng used Markov theory to study the steady-state availability of repairable network system, and analyzed the fuzzy availability of repairable network system and continuous kn(F) network. Fenghua Xie studied the

Zan Wang, Yanfang Fu, Lianjiong Zhong, Fei Dai

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 1, 36–43

Article | 05-September-2021

Bayesian estimation and prediction based on Rayleigh record data with applications

Based on a record sample from the Rayleigh model, we consider the problem of estimating the scale and location parameters of the model and predicting the future unobserved record data. Maximum likelihood and Bayesian approaches under different loss functions are used to estimate the model’s parameters. The Gibbs sampler and Metropolis-Hastings methods are used within the Bayesian procedures to draw the Markov Chain Monte Carlo (MCMC) samples, used in turn to compute the Bayes estimator

Raed R. Abu Awwad, Omar M. Bdair, Ghassan K. Abufoudeh

Statistics in Transition New Series, Volume 22 , ISSUE 3, 59–79

Research paper | 01-September-2014

POSTERIOR BELIEF CLUSTERING ALGORITHM FOR ENERGY-EFFICIENT TRACKING IN WIRELESS SENSOR NETWORKS

In this paper, we propose a novel posterior belief clustering (PBC) algorithm to solve the tradeoff between target tracking performance and sensors energy consumption in wireless sensor networks. We model the target tracking under dynamic uncertain environment using partially observable Markov decision processes (POMDPs), and transform the optimization of the tradeoff between tracking performance and energy consumption into yielding the optimal value function of POMDPs. We analyze the error of

Bo Wu, Yanpeng Feng, Hongyan Zheng

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 628–641

Article | 01-December-2015

PREDICTION AND ANALYSIS OF URBAN HEAT ISLAND EFFECT IN DANGSHAN BY REMOTE SENSING

island effect from 2000 to 2013. In addition, area of the heat island and strong heat island increased was observed to increase rapidly, while the area of the green island and strong green island reduced by 46%. Also, using the Markov model, urban heat island effect in Dangshan County was predicted over the next 40 years. This model was feasible in predicting the urban heat island effect with small errors. Finally, it was determine that heat island effect was in negative correlation to the vegetation

Gang Fang

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 4, 2195–2211

Article | 06-July-2017

INFORMATIVE VERSUS NON-INFORMATIVE PRIOR DISTRIBUTIONS AND THEIR IMPACT ON THE ACCURACY OF BAYESIAN INFERENCE

In this study the benefits arising from the use of the Bayesian approach to predictive modelling will be outlined and exemplified by a linear regression model and a logistic regression model. The impact of informative and non-informative prior on model accuracy will be examined and compared. The data from the Central Statistical Office of Poland describing unemployment in individual districts in Poland will be used. Markov Chain Monte Carlo methods (MCMC) will be employed in modelling.

Wioletta Grzenda

Statistics in Transition New Series, Volume 17 , ISSUE 4, 763–780

Article | 01-December-2012

DISTRIBUTED TRUST INFERENCE MODEL BASED ON PROBABILITY AND BALANCE THEORY FOR PEER-TO-PEER SYSTEMS

inference network based on direct trust network. In order to discover trusted evidence chains during complex relations, we design two inference rules and propose mathematics models to infer indirect trust value based on Markov chain theory. Simulations proved the rightness and effectiveness in intensive trust relations environment and intensive distrust environment.

Zhenhua Tan, Guangming Yang, Wei Cheng

International Journal on Smart Sensing and Intelligent Systems, Volume 5 , ISSUE 4, 1063–1080

Research Article | 01-September-2014

CONDITION BASED PREVENTIVE MAINTENANCE CONTROL STRATEGY DESIGN

H. S. Su, Y. Q. Kang

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 967–1003

Article | 01-June-2015

AUTOMATIC SEGMENTATION OF BRAIN TUMOR MAGNETIC RESONANCE IMAGING BASED ON MULTI-CONSTRAINS AND DYNAMIC PRIOR

robustly is very difficult. In this paper, we propose an image segmentation algorithm based on multiconstrains and dynamic prior. Through introducing a novel big scale constrain into Markov random filed model from magnetic resonance image we realize automatic segmentation under the principle of maximum a Posterior and a modified expectation-maximization algorithm according to the Bayesian frame. Finally, a set of human body detection and tracking experiments are designed to demonstrate the

Liu Erlin, Wang Meng, Teng Jianfeng, Li Jianjian

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 2, 1031–1049

Article | 20-December-2020

A new generalization of the Pareto distribution and its applications

estimation. Approximate confidence intervals are obtained by means of an asymptotic property of the maximum likelihood and maximum product spacings methods, while the Bayes credible intervals are found by using the Monte Carlo Markov Chain method under different loss functions. A simulation analysis is conducted to compare the estimation methods. Finally, the application of the proposed new distribution to three real-data examples is presented and its goodness-of-fit is demonstrated. In addition

Ehab M. Almetwally, Hanan A. Haj Ahmad

Statistics in Transition New Series, Volume 21 , ISSUE 5, 61–84

Research Article | 03-March-2021

Analysis of Polish mutual funds performance: a Markovian approach

Dariusz Filip, Tomasz Rogala

Statistics in Transition New Series, Volume 22 , ISSUE 1, 115–130

Article | 06-July-2017

VARIATIONAL APPROXIMATIONS FOR SELECTING HIERARCHICAL MODELS OF CIRCULAR DATA IN A SMALL AREA ESTIMATION APPLICATION

number of models in this context, by dramatically speeding up computations relative to the fast Markov Chain Monte Carlo method while giving virtually identical results.

Daniel Hernandez-Stumpfhauser, F. Jay Breidt, Jean D. Opsomer

Statistics in Transition New Series, Volume 17 , ISSUE 1, 91–104

Research Article | 02-November-2017

MULTI-TARGET, MULTI-SENSOR TRACKING BASED ON QUALITY-OF-INFORMATION AND FORMAL BAYESIAN FRAMEWORKS

. Toward this end, we exploit random set theory, a statistical tool based on Bayesian framework, for establishing generalized likelihood and Markov density functions to yield an iterative filtering procedure. We conduct a study regarding how the design of distributed detection has impact on the result of system level information fusion. The sources of analyzed data include (a) simulation-based sensor readings through bi-directional sensing/communication; and (b) practical images taken by multiple

Juo-Yu Lee, Kung Yao

International Journal on Smart Sensing and Intelligent Systems, Volume 1 , ISSUE 4, 842–857

Article | 01-March-2015

NEW STEREO MATCHING METHOD BASED ON IMPROVED BP ALGORITHM

Qian. Zhang, Shaomin Li, Y. Zhang, P. Wang, JF. Huang

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 1, 464–479

Article | 05-June-2013

UTILITY BASED DATA GATHERING IN MOBILE SENSOR NETWORK

Liu Jieyan, Wu Lei, Gong Haigong

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 3, 953–972

Research paper | 01-November-2017

Triple-goal estimation of unemployment rates for U.S. states using the U.S. Current Population Survey data

Daniel Bonnéry, Yang Cheng, Neung Soo Ha, Partha Lahiri

Statistics in Transition New Series, Volume 16 , ISSUE 4, 511–522

Article | 15-March-2019

BAYESIAN SPATIAL ANALYSIS OF CHRONIC DISEASES IN ELDERLY CHINESE PEOPLE USING A STAR MODEL

Ping Gao, Hikaru Hasegawa

Statistics in Transition New Series, Volume 19 , ISSUE 4, 645–670

Research Communicate | 27-May-2018

SOME RESULTS FROM THE 2013 INTERNATIONAL YEAR OF STATISTICS

Jan Kordos

Statistics in Transition New Series, Volume 19 , ISSUE 1, 149–158

No Record Found..
Page Actions