Hidden Markov Chain Model Research Articles

Multi-Disjoint Path opportunistic networks with Hidden Markov Chain modeling

Advancing the efficiency and reliability of wireless sensor networks is a paramount pursuit in modern networking research. In this context, we introduce a groundbreaking approach based on Hidden Markov Chain (HMC) modeling with opportunistic routing, harnessed by the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism. Our innovative algorithm addresses key challenges in Wireless Body Area Networks, including delay, throughput, and bandwidth utilization. By strategically integrating the HMC model, which captures intricate state transitions and emission probabilities, our proposed method introduces a robust solution for optimizing node connections and routing decisions. This fusion of HMC and opportunistic routing capitalizes on the strengths of both paradigms, enhancing the network's ability to make intelligent decisions in dynamic scenarios. Through rigorous simulations, we showcase the algorithm's prowess in achieving efficient data transmission. The empirical evidence from these simulations underscores the algorithm's superiority when juxtaposed against state-of-the-art mechanisms such as Simple Multi-Packet Access (SMPA) and Priority-based Congestion-avoidance Routing Protocol (PCRP). Our algorithm not only outperforms existing solutions in terms of delay, throughput, and bandwidth utilization, but it also showcases its ability to adapt to varying network conditions, making it a versatile tool for diverse applications.

Non-stationary data segmentation with hidden evidential semi-Markov chains

Hidden Markov chains (HMCs) are widely used in unsupervised Bayesian hidden discrete data restoration. They are very robust and, in spite of their simplicity, they are sufficiently efficient in many cases. However, in complex situations, extensions of HMCs models are of interest. In particular, when sojourn time in hidden states is not geometrical, hidden semi-Markov chains (HSMCs) may work better. Besides, hidden evidential Markov chains (HEMCs) showed its interest in non-stationary situations. In this paper, we propose a new model simultaneously extending HSMCs and HEMCs. Based on triplet Markov chains (TMCs), it is used in an unsupervised framework, parameters being estimated with the Expectation-Maximization (EM) algorithm. We validate its interest through some experiments on hand-drawn images noised with artificial noises.

Unsupervised statistical image segmentation using bi-dimensional hidden Markov chains model with application to mammography images

Hidden Markov chain (HMC) models have been widely used in unsupervised image segmentation. In these models, there is a double process; a hidden one noted X and an observed one, which is often one-dimensional, noted Y. The latter is constituted by pixels of a noisy image after transforming its bi-dimensional form into a mono-dimensional sequence. In this context, these models run into a problem of relationships between pixels which is often solved by applying curves such as the Hilbert-Peano scan when modeling the image under study. We propose enriching the HMC model by introducing a second component to the observed process Y based on the average of two observations which are neighbors in the image but are not in the chain of each considered pixel. This gives a bi-dimensional HMC model which has the same structure as the classical model except for the two-dimensional case of the low modeling noise. The estimation of the parameters of this model is carried out by using a three-algorithm approach: Bayesian one based mainly on the Markov Chain Monte Carlo (MCMC) methods, Expectation–Maximization (EM), and Iterative Conditional Estimation (ICE). We apply the final Bayesian decision criteria Marginal Posterior Mode to come up with a final configuration of the result X. The proposed model is compared to the classical HMC model in combination with the Hilbert-Peano scan numerically through simulated data and visually through synthetic and mammogram images.

Hidden Markov Chain Model-Based Intelligent Distributed Optimization Scheduling Operation Error Prevention Method for Main Network

After the implementation of the intelligent distributed optimal dispatching mode of the main network, the number of operations is more frequent, resulting in the problem of operation error, which affects the reliability and stability of the power grid operation. In this paper, a hidden Markov chain (HMC) model-based intelligent distributed optimization scheduling operation error prevention method for the main network is proposed. In this study, the power grid topology model is established through continuous access, which lays the foundation for the dynamic tracking of dispatching operation instructions. The hidden Markov chain model is used to estimate the operational risk of scheduling. Combining with the association mining theory, the anti-error detection rules between risk and anti-error strategy are established, and the anti-error strategy is implemented. The results show that under the application of the operation error prevention method, the operation error prevention response time difference and the voltage fluctuation range of the power grid reach the minimum. Through this study, it is expected to provide a reliable reference for dispatchers to make more effective dispatching decisions and correct dispatching errors in time.

ESTEEM – Enhanced stability and throughput for energy efficient multihop routing based on Markov Chain Model in wireless body area networks

Wireless Medical Body Area Sensor Network (WMBASN) monitors vital human biological parameters used for various healthcare and fitness applications. The design and development of a reliable WMBASN routing protocol with improved network performance are significant tasks for researchers and healthcare industries. This work focuses on multi-hop routing to select a relay node to collect data from far away sensor nodes and to Central Coordinator Node (CCN). A novel approach for choosing best fitted Assistive Node (AN) by incorporating Hidden Markov Chain Model (HMCM) called Enhanced Stability and Throughput for Energy Efficient Multihop routing protocol (ESTEEM). Selection Function selects the best-fitted AN based on HMCM relating to maximum residual energy and minimum distance between the sensor node and CCN. The outcome of the proposed ESTEEM achieves increased throughput, better stability with the first dead node and extended network lifetime with the last dead node at 71% and 95.1% of total simulation time, respectively. In addition, the proposed work also concentrates on CCN placed at different positions on the human body to analyze and identify its best position for better routing. Overall, an improved multi-hop, energy efficiency and better quality of service are achieved based on specific network parameters with random postural movements through ESTEEM, and the results are superior to conventional routing protocols.

Computer network security evaluation method based on improved attack graph

ABSTRACT The traditional security detection and defense methods are relatively backward, and there are many problems, such as high false alarm and missed alarm rate, and detection lag, which have been difficult to meet the requirements of computer security defense. In order to strengthen the active defense of computer network security and improve the classification and prediction performance of computer network security events, a computer network security risk assessment model based on mrmr Ig feature selection model and attack graph model is proposed. The mrmr Ig feature selection model is used to classify the characteristics of security events, and the hidden Markov chain model and attack graph model are used to predict the attack intention. The experimental results show that the classification accuracy rate of the model is 99.79%, the false alarm rate and the false alarm rate are 0.11% and 0.18%, respectively. The model can accurately predict attacks in real time, and can provide reference for timely taking targeted computer network security reinforcement and active defense measures.

Anomaly Intrusion Detection of Wireless Communication Network-Based on Markov Chain Model

In order to solve the increasingly serious security problems of wireless networks, research on abnormal intrusion detection methods of wireless communication networks based on Markov chain model is proposed. What is usually observed is not the known intrusion behavior but the abnormal phenomenon in the communication process studied, which is completed by detecting the change of system behavior or usage. In this paper, the Markov chain model is used to detect the abnormal intrusion of wireless communication networks. Through the analysis and selection of parameters, the experimental results are ideal, and a variety of judgment methods are compared and analyzed. First, this method can easily distinguish between normal and abnormal data, which reduces the time by about 50% compared with the previous method; Second, the detection result of analysis method 2 is better than that of analysis method 1, and the accuracy is about 20%. The new method proposed in this paper has the characteristics of simple calculation, low algorithm complexity, and easy online detection. This method overcomes the disadvantage that the single-step Markov chain analysis and detection method cannot be strictly established in the nature of the Markov chain, has lower algorithm complexity than the multistep Markov chain analysis and detection method, and is simpler than the parameter calculation of hidden Markov chain model.

Altered Larval Yellow Perch Swimming Behavior Due to Methylmercury and PCB126 Detected Using Hidden Markov Chain Models.

Fish swimming behavior is a commonly measured response in aquatic ecotoxicology because behavior is considered a whole organism-level effect that integrates many sensory systems. Recent advancements in animal behavior models, such as hidden Markov chain models (HMM), suggest an improved analytical approach for toxicology. Using both new and traditional approaches, we examined the sublethal effects of PCB126 and methylmercury on yellow perch (YP) larvae (Perca flavescens) using three doses. Both approaches indicate larvae increase activity after exposure to either chemical. The middle methylmercury-dosed larvae showed multiple altered behavior patterns. First, larvae had a general increase in activity, typically performing more behavior states, more time swimming, and more swimming bouts per second. Second, when larvae were in a slow or medium swimming state, these larvae tended to switch between these states more often. Third, larvae swam slower during the swimming bouts. The upper PCB126-dosed larvae exhibited a higher proportion and a fast swimming state, but the total time spent swimming fast decreased. The middle PCB126-dosed larvae transitioned from fast to slow swimming states less often than the control larvae. These results indicate that developmental exposure to very low doses of these neurotoxicants alters YP larvae overall swimming behaviors, suggesting neurodevelopment alteration.

The observed pattern and hidden process of female reproductive trajectories across the life span in a non-human primate.

Age-specific fertility trajectories are fundamental to understanding population structure and the evolutionary ecology of diverse life histories. However, characterizing reproductive ageing has been difficult with cross-sectional data, where senescence especially late in life can be confounded by selective disappearance. Addressing such challenge requires longitudinal data tracking the reproductive life span of known individuals, but such data are rare, especially for very long-lived species such as primates. We analyse the entire life span trajectory of annual fertility, from reproductive maturity to death, for 673 free-ranging female rhesus macaques, Macaca mulatta, on Cayo Santiago, Puerto Rico. Using generalized linear mixed-effects models (GLMMs), we first tested if time to death explains the ageing pattern independently of and additionally to chronological age, and if so, whether there is interaction between them. While GLMM captures the patterns in the data well, it is not a generative model. For example, given the GLMM and an individual's reproductive trajectory up to a given age, we cannot directly predict the probability of reproduction or death in the next year. For this reason, we further fitted a hidden Markov chain model (HMM) which allows just such a prediction, and additionally helps infer the process underlying the observed trajectory. We show that, after accounting for individual differences in fertility, reproductive ageing exhibits both age-dependent decline and also an abrupt terminal decline independently of age at death. We infer from the HMM that the underlying process of reproductive trajectory is where individuals cycle between reproductive bouts until they enter an irreversible frail condition that constrains fertility. The findings provide valuable insights into the longitudinal progression of reproductive trajectories in primates, by revealing both age-dependent and age-independent patterns and processes of ageing, and contribute to a growing body of literature on reproductive ageing and senescence across animal taxa.

A Repeated Commuting Driving Cycle Dataset With Application to Short-Term Vehicle Velocity Forecasting

Abstract Vehicle velocity forecasting plays a critical role in operation scheduling of varying systems and devices for a passenger vehicle. The forecasted information serves as an indispensable prerequisite for vehicle energy management via predictive control algorithms or vehicle ecosystem control Co-design. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to simulate a daily commuting route and serves as a base for further energy management study. To explore the dynamic properties, these driving cycles are piecewise divided into cycle segments via intersection/stop identification. A vehicle velocity forecasting model pool is then developed for each segment, including the hidden Markov chain model, long short-term memory network, artificial neural network, support vector regression, and similarity methods. To further improve the forecasting performance, higher-level algorithms like localized model selection, ensemble approaches, and a combination of them are investigated and compared. Results show that (i) the segment-based forecast improves the forecasting accuracy by up to 20.1%, compared to the whole cycle-based forecast, and (ii) the hybrid localized model framework that combines dynamic model selection and an ensemble approach could further improve the accuracy by 9.7%. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.

Predicting Sequential Design Decisions Using the Function-Behavior-Structure Design Process Model and Recurrent Neural Networks

Abstract In engineering systems design, designers iteratively go back and forth between different design stages to explore the design space and search for the best design solution that satisfies all design constraints. For complex design problems, human has shown surprising capability in effectively reducing the dimensionality of design space and quickly converging it to a reasonable range for algorithms to step in and continue the search process. Therefore, modeling how human designers make decisions in such a sequential design process can help discover beneficial design patterns, strategies, and heuristics, which are essential to the development of new algorithms embedded with human intelligence to augment the computational design. In this paper, we develop a deep learning-based approach to model and predict designers’ sequential decisions in the systems design context. The core of this approach is an integration of the function-behavior-structure (FBS) model for design process characterization and the long short-term memory unit (LSTM) model for deep leaning. This approach is demonstrated in two case studies on solar energy system design, and its prediction accuracy is evaluated benchmarking on several commonly used models for sequential design decisions, such as the Markov Chain model, the Hidden Markov Chain model, and the random sequence generation model. The results indicate that the proposed approach outperforms the other traditional models. This implies that during a system design task, designers are very likely to rely on both short-term and long-term memory of past design decisions in guiding their future decision-making in the design process. Our approach can support human–computer interactions in design and is general to be applied in other design contexts as long as the sequential data of design actions are available.

Hidden Markov model for malicious hosts detection in a computer network

The problem of malicious host detection in a computer network is reviewed. Activity of computer network hosts is tracking by a noisy detector. The paper suggests method for detection malicious hosts using activity timeseries classification. The approach is based on hidden Markov chain model that analyses timeseries and consecutive search of the most probable final state of the model. Efficiency of the approach is based on assumption that advanced persisted threats are localised in time, therefore malicious hosts in a computer network can be detected by virtue of activity comparison with reliable safe hosts.

Emotional Interactive Simulation System of English Speech Recognition in Virtual Context

With the development of virtual scenes, the degree of simulation and functions of virtual reality have been very complete, providing a new platform and perspective for teaching design. Firstly, the hidden Markov chain model is used to perform emotion recognition on English speech signals. English speech emotion recognition and speech semantic recognition are essentially the same. Hidden Markov style has been widely used in English speech semantic recognition. The experiments of feature extraction and pattern recognition of speech samples prove that Hidden Markovian has higher recognition rate and better recognition effect in speech emotion recognition. Secondly, combining the human pronunciation model and the hearing model, by analyzing the impact of the glottis feature on the human ear hearing-model feature, the research application of the English speech recognition emotion interactive simulation system uses the glottis feature to compensate the human ear, hearing feature is proposed by compensated English speech recognition, and emotion interaction simulation system is used in the English speech emotion experiment, which has obtained a high recognition rate and showed excellent performance.

Conditional Random Fields with Least Absolute Shrinkage and Selection Operator to Classifying the Barley Genes Based on Expression Level Affected by the Fungal Infection.

The classical methods for the classification problem include hypothesis test with the Benjamini-Hochberg method, hidden Markov chain model, and support vector machine. One major application of the classification problem is gene expression analysis, for example, detecting the host genes having interaction with pathogen. The classical methods can be applied and have a good performance when the number of genes having interaction with the pathogen is not sparse with respect to the candidate genes. However, conditional random field (CRF), with an appropriate design, can be applied and have good performance even when it is sparse. In this work, we proposed a modified CRF with a baseline to reduce the number of parameters in CRF. Moreover, we show an application of CRF with the least absolute shrinkage and selection operator (LASSO) to classifying barley genes of its reaction to the pathogen.

Real-time headway state identification and saturation flow rate estimation: a hidden Markov Chain model

Saturation flow rate (SFR) denotes the maximum sustainable flow rate during the green signal. Calibration of SRF is not a problem that can be solved once and for all. Due to various reasons such as degrading infrastructure or changes in the surrounding environment, a well-calibrated SFR could become outdated and it is expensive to recalibrate following traditional methods. This manuscript proposes a model to calculate saturation flow rate in a real-time fashion from loop detector-data that is readily available. The problem is formulated as a Markov Chain model with the goal of identifying traffic headway states. A total of five states and the transitional behavior are defined. The distribution of headway given the underlying state is also presented. The SFR estimation is converted to the identification of stable headway. The proposed model is tested and validated, which shows the proposed model is able to generate satisfactory results.

Distilling Large Information Sets to Forecast Commodity Returns: Automatic Variable Selection or Hidden Markov Models?

We investigate the out-of-sample, recursive predictive accuracy for (fully hedged) commodity future returns of two sets of forecasting models, i.e., hidden Markov chain models in which the coefficients of predictive regressions follow a regime switching process and stepwise variable selection algorithms in which the coefficients of predictors not selected are set to zero. We perform the analysis under four alternative loss functions, i.e., squared and the absolute value, and the realized, portfolio Sharpe ratio and MV utility when the portfolio is built upon optimal weights computed solving a standard MV portfolio problem. We find that neither HMM or stepwise regressions manage to systematically (or even just frequently) outperform a plain vanilla AR benchmark according to RMSFE or MAFE statistical loss functions. However, in particular stepwise variable selection methods create economic value in out-of-sample meanvariance portfolio tests. Because we impose transaction costs not only ex post but also ex ante, so that an investor uses the forecasts of a model only when they increase expected utility,

Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains

Most multi-target tracking filters assume that one target and its observation follow a Hidden Markov Chain (HMC) model, but the implicit independence assumption of the HMC model is invalid in many practical applications, and a Pairwise Markov Chain (PMC) model is more universally suitable than the traditional HMC model. A set of weighted particles is used to approximate the probability hypothesis density of multi-targets in the framework of the PMC model, and a particle probability hypothesis density filter based on the PMC model (PF-PMC-PHD) is proposed for the nonlinear multi-target tracking system. Simulation results show the effectiveness of the PF-PMC-PHD filter and that the tracking performance of the PF-PMC-PHD filter is superior to the particle PHD filter based on the HMC model in a scenario where we kept the local physical properties of nonlinear and Gaussian HMC models while relaxing their independence assumption.

Monthly precipitation modeling using Bayesian Non-homogeneous Hidden Markov Chain

Abstract Monthly precipitation modeling is important in various applications, e.g. streamflow forecasts and water resources management. This paper develops an operational precipitation forecasting scheme, using Bayesian Non-homogeneous Hidden Markov Chain (NHMM) model and teleconnection index. Although the Hidden Markov Chain model has been investigated before in similar studies, the NHMM algorithm employed in this study allows modeling both non-stationary transition probabilities and emission matrix. Climatic teleconnection that affect precipitation is used to drive changes in transition probabilities of different states in the Markov model. The proposed framework is illustrated for multiple-station precipitation analysis in NingXiang County, a southern inland area in China with a high population density. A simulation model is constructed to examine the model's capacity in capturing variabilities and temporal-spatial characteristics exhibiting in monthly precipitation data during 1961–2013. Results indicate that the proposed NHMM model captures the precipitation characteristics at different stations well. Spearman correlation between conditional mean of simulated ensembles and observed data is 0.87–0.9, with few variations at distinct stations. The proposed framework has general applications and can be applied to simulate and generate stochastic monthly precipitation. Further application of the method is also described in the paper.

Machine Learning for Volatility Trading (Presentation Slides)

Academics and practitioners have developed many models for volatility measurement and forecast – I estimate that the total number of available models to be about 200-300 if we count all modifications of intraday estimators, GARCH-type and continuous-time models. In practice, the estimate and forecast of the volatility serves provide vital inputs to many applications ranging from signal construction to algorithmic strategies and quantitative methods for portfolio allocation. By applying machine learning to the volatility modeling, we can reduce the back-test bias and, as a result, improve the performance of live strategies. First, I implemented about 40 different volatility models from 4 separate model classes including intraday estimators, GARCH-type and Bayesian models, and Hidden Markov Chain (HMC) models. Then, I applied the supervised learning for each of the volatility models with the goal is to analyze the out-of-sample fit of the model prediction to the time series data. I propose a few regression-based tests which are applied to gauge the performance of all volatility models. The final step is the reinforcement learning that includes aggregation and analysis of the test results from the supervised learning. The goal is to dynamically select the best model out of 40 that provides the best predicative power out-of-sample. I use the analogy to the web-search to weight the importance of the test results when producing volatility forecasts for specific trading algorithms. One of key discoveries is that Hidden Markov Chain model is one of the best model for volatility forecast across many asset classes. I also observe the cyclical pattern in the rankings of the best models. On one hand, Hidden Chain models perform the best in periods with strong trends. On the other hand, simple intraday estimators perform the best in periods with range-bound markets. The machine learning enables to dynamically choose the best model for the present cycle.

Track Extraction With Hidden Reciprocal Chains

This technical note (TN) develops Bayesian track extraction algorithms for targets modeled as hidden reciprocal chains (HRC). HRC are a class of finite-state random process models that generalize the familiar hidden Markov chains (HMC). HRC are able to model the “intention” of a target to proceed from a given origin to a destination, behavior that cannot be properly captured by an HMC. While Bayesian estimation problems for HRC have previously been studied, this TN focuses principally on the problem of track extraction, that is, confirming target existence in a set of observations of uncertain origin. Simulation examples are presented, which show that the additional model information contained in an HRC, measured in terms of Kullback–Leibler divergence, improves detection performance when compared to HMC models.