scholarly journals STBS-Stega: Coverless text steganography based on state transition-binary sequence

2020 ◽  
Vol 16 (3) ◽  
pp. 155014772091425
Author(s):  
Ning Wu ◽  
Zhongliang Yang ◽  
Yi Yang ◽  
Lian Li ◽  
Poli Shang ◽  
...  
Keyword(s):  
Markov Chain ◽  
State Transition ◽  
Markov Chain Model ◽  
Transition Probability ◽  
Binary Sequence ◽  
Chain Model ◽  
Sequence Diagrams ◽  
Hide Information ◽  
Proposed Model ◽  
Text Steganography

Information-hiding technology has recently developed into an area of significant interest in the field of information security. As one of the primary carriers in steganography, it is difficult to hide information in texts because there is insufficient information redundancy. Traditional text steganography methods are generally not robust or secure. Based on the Markov chain model, a new text steganography approach is proposed that focuses on transition probability, one of the most important concepts of the Markov chain model. We created a state transition-binary sequence diagrams based on the aforementioned concepts and used them to guide the generation of new texts with embedded secret information. Compared to other related works, the proposed method exploits the use of the transition probability in the process of steganographic text generation. The associated developed algorithm also encrypts the serial number of the state transition-binary sequence diagram needed by the receiver to extract the information, which further enhances the security of the steganography information. Experiments were designed to evaluate the proposed model. The results revealed that the model had higher concealment and hidden capacity compared to previous methods.

Predict the Volume of Passenger Transport of Railway Based on Grey Markov Chain Model

2014 ◽  
Vol 1030-1032 ◽  
pp. 2069-2072 ◽  
Author(s):  
Ying Li
Keyword(s):  
Markov Chain ◽  
Prediction Accuracy ◽  
State Transition ◽  
Markov Chain Model ◽  
Transition Probability ◽  
Chain Model ◽  
Grey Prediction ◽  
Passenger Transport ◽  
Passenger Flow ◽  
State Transition Probability

Combined with grey model and the characteristics of the Markov chain, based on the grey prediction model, calculating the state transition probability, grey Markov chain model is established. The results show that the grey Markov chain model has higher prediction accuracy than GM (1, 1) model, can offer references for passenger flow organization.

Multiple repair scenario of life cycle cost of RCC girder bridge using Markov chain model

2019 ◽  
Vol 10 (4) ◽  
pp. 75
Author(s):  
Md. Shafiqul Islam ◽  
Shayla Sharmin ◽  
Jebunnesa Islam
Keyword(s):  
Markov Chain ◽  
Life Cycle ◽  
Life Cycle Cost ◽  
Markov Chain Model ◽  
Transition Probability ◽  
Chloride Concentration ◽  
Chain Model ◽  
Actual Performance ◽  
Girder Bridge ◽  
Inspection Data

At present, many road authorities in the world face challenges in condition monitoring diagnosis of distress and forecasting deterioration, strengthening and convalescence of aging bridge structures. The accurate prediction of the future condition is crucial for optimizing the maintenance activities. It is very tough to predict the actual performance scenario or actual in–situ structures without carrying out inspection. Limited availability of detailed inspection data is considered as one of the major drawbacks in developing deterioration models. In State Based Markov deterioration (SNMD) modelling, the main job is to estimate transition probability matrixes (TPMs). In this paper, Markov Chain Monte Carlo (MCMC) is used to estimate TPMs. In Markov Chain Model, future conditions depend on only present bridge inspection data. Multiple repair options are adopted in order to optimize life cycle cost. Repairs are needed when the critical chloride concentration exceeds 0.2. Three distinct types of cost corresponding to each repair option is considered. The objective of this paper is to minimize the life cycle cost considering appropriate repair timings of mixed repair methods. Variation of life cycle cost of five different concretes (stronger to weaker) using three different repair option is shown in this paper. For specific normalized condition of concrete’s failure probability (0.3) and specific type of concrete, variation of life cycle cost using multiple repair options is also shown in this paper.

scholarly journals A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yajun Zhou ◽  
Lilei Wang ◽  
Rong Zhong ◽  
Yulong Tan
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Prediction Method ◽  
Chain Model ◽  
Daily Production ◽  
Forecasting Accuracy ◽  
Traffic Demand ◽  
Demand Prediction ◽  
Proposed Model ◽  
Bike Sharing

Accurate transfer demand prediction at bike stations is the key to develop balancing solutions to address the overutilization or underutilization problem often occurring in bike sharing system. At the same time, station transfer demand prediction is helpful to bike station layout and optimization of the number of public bikes within the station. Traditional traffic demand prediction methods, such as gravity model, cannot be easily adapted to the problem of forecasting bike station transfer demand due to the difficulty in defining impedance and distinct characteristics of bike stations (Xu et al. 2013). Therefore, this paper proposes a prediction method based on Markov chain model. The proposed model is evaluated based on field data collected from Zhongshan City bike sharing system. The daily production and attraction of stations are forecasted. The experimental results show that the model of this paper performs higher forecasting accuracy and better generalization ability.

scholarly journals Insight into earthquake sequencing: analysis and interpretation of time-series constructed from the directed graph of the Markov chain model

2015 ◽  
Vol 2 (1) ◽  
pp. 399-424
Author(s):  
M. S. Cavers ◽  
K. Vasudevan
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Directed Graph ◽  
State Transition ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Fano Factor ◽  
Chain Model ◽  
Intrinsic Mode Functions ◽  
Allan Factor

Abstract. Directed graph representation of a Markov chain model to study global earthquake sequencing leads to a time-series of state-to-state transition probabilities that includes the spatio-temporally linked recurrent events in the record-breaking sense. A state refers to a configuration comprised of zones with either the occurrence or non-occurrence of an earthquake in each zone in a pre-determined time interval. Since the time-series is derived from non-linear and non-stationary earthquake sequencing, we use known analysis methods to glean new information. We apply decomposition procedures such as ensemble empirical mode decomposition (EEMD) to study the state-to-state fluctuations in each of the intrinsic mode functions. We subject the intrinsic mode functions, the orthogonal basis set derived from the time-series using the EEMD, to a detailed analysis to draw information-content of the time-series. Also, we investigate the influence of random-noise on the data-driven state-to-state transition probabilities. We consider a second aspect of earthquake sequencing that is closely tied to its time-correlative behavior. Here, we extend the Fano factor and Allan factor analysis to the time-series of state-to state transition frequencies of a Markov chain. Our results support not only the usefulness the intrinsic mode functions in understanding the time-series but also the presence of power-law behaviour exemplified by the Fano factor and the Allan factor.

scholarly journals Vehicle Driving Risk Prediction Based on Markov Chain Model

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoxia Xiong ◽  
Long Chen ◽  
Jun Liang
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Transition Probability ◽  
False Positive Rate ◽  
Estimation Method ◽  
Prediction Algorithm ◽  
Chain Model ◽  
Risk Status ◽  
Risk Levels ◽  
Driving Risk

A driving risk status prediction algorithm based on Markov chain is presented. Driving risk states are classified using clustering techniques based on feature variables describing the instantaneous risk levels within time windows, where instantaneous risk levels are determined in time-to-collision and time-headway two-dimension plane. Multinomial Logistic models with recursive feature variable estimation method are developed to improve the traditional state transition probability estimation, which also takes into account the comprehensive effects of driving behavior, traffic, and road environment factors on the evolution of driving risk status. The “100-car” natural driving data from Virginia Tech is employed for the training and validation of the prediction model. The results show that, under the 5% false positive rate, the prediction algorithm could have high prediction accuracy rate for future medium-to-high driving risks and could meet the timeliness requirement of collision avoidance warning. The algorithm could contribute to timely warning or auxiliary correction to drivers in the approaching-danger state.

MARKOV CHAIN MODELING AND ANALYSIS OF COMPLICATED PHENOMENA IN COUPLED CHAOTIC OSCILLATORS

2010 ◽  
Vol 19 (04) ◽  
pp. 801-818 ◽  
Author(s):  
YOSHIFUMI NISHIO ◽  
YUTA KOMATSU ◽  
YOKO UWATE ◽  
MARTIN HASLER
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Chain Model ◽  
Chaotic Oscillators ◽  
Modeling And Analysis ◽  
Markov Chain Models ◽  
Simulation Results ◽  
Markov Chain Modeling

In this paper, we propose a Markov chain modeling of complicated phenomena observed from coupled chaotic oscillators. Once we obtain the transition probability matrix from computer simulation results, various statistical quantities can be easily calculated from the model. It is shown that various statistical quantities are easily calculated by using the Markov chain model. Various features derived from the Markov chain models of chaotic wandering of synchronization states and switching of clustering states are compared with those obtained from computer simulations of original circuit equations.

Degradation modelling and life expectancy using Markov chain model for carriageway

2018 ◽  
Vol 35 (6) ◽  
pp. 1268-1288 ◽  
Author(s):  
Kong Fah Tee ◽  
Ejiroghene Ekpiwhre ◽  
Zhang Yi
Keyword(s):  
Markov Chain ◽  
Life Expectancy ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Transition Probability ◽  
Condition Assessment ◽  
Cumulative Distribution ◽  
Chain Model ◽  
Content Type ◽  
Pairwise Correlation

PurposeAutomated condition surveys have been recently introduced for condition assessment of highway infrastructures worldwide. Accurate predictions of the current state, median life (ML) and future state of highway infrastructures are crucial for developing appropriate inspection and maintenance strategies for newly created as well as existing aging highway infrastructures. The paper aims to discuss these issues.Design/methodology/approachThis paper proposes Markov Chain based deterioration modelling using a linear transition probability (LTP) matrix method and a median life expectancy (MLE) algorithm. The proposed method is applied and evaluated using condition improvement between the two successive inspections from the Surface Condition Assessment of National Network of Roads survey of the UK Pavement Management System.FindingsThe proposed LTP matrix model utilises better insight than the generic or decoupling linear approach used in estimating transition probabilities formulated in the past. The simulated LTP predicted conditions are portrayed in a deterioration profile and a pairwise correlation. The MLs are computed statistically with a cumulative distribution function plot.Originality/valueThe paper concludes that MLE is ideal for projecting half asset life, and the LTP matrix approach presents a feasible approach for new maintenance regime when more certain deterioration data become available.

scholarly journals Brief Communication: Earthquake sequencing: analysis of time series constructed from the Markov chain model

2015 ◽  
Vol 22 (5) ◽  
pp. 589-599 ◽  
Author(s):  
M. S. Cavers ◽  
K. Vasudevan
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
State Transition ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Fano Factor ◽  
Chain Model ◽  
Sequencing Analysis ◽  
Intrinsic Mode Functions ◽  
Allan Factor

Abstract. Directed graph representation of a Markov chain model to study global earthquake sequencing leads to a time series of state-to-state transition probabilities that includes the spatio-temporally linked recurrent events in the record-breaking sense. A state refers to a configuration comprised of zones with either the occurrence or non-occurrence of an earthquake in each zone in a pre-determined time interval. Since the time series is derived from non-linear and non-stationary earthquake sequencing, we use known analysis methods to glean new information. We apply decomposition procedures such as ensemble empirical mode decomposition (EEMD) to study the state-to-state fluctuations in each of the intrinsic mode functions. We subject the intrinsic mode functions, derived from the time series using the EEMD, to a detailed analysis to draw information content of the time series. Also, we investigate the influence of random noise on the data-driven state-to-state transition probabilities. We consider a second aspect of earthquake sequencing that is closely tied to its time-correlative behaviour. Here, we extend the Fano factor and Allan factor analysis to the time series of state-to-state transition frequencies of a Markov chain. Our results support not only the usefulness of the intrinsic mode functions in understanding the time series but also the presence of power-law behaviour exemplified by the Fano factor and the Allan factor.

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