Continuous Hidden Markov Model Research Articles

Malicious attack detection based on continuous Hidden Markov Models in Wireless sensor networks

Alongside the rapid progress of Wireless sensor networks (WSNs) technologies, sensors and networks can rapidly be a victim of distributed attacks. Attackers can perform intrusions to break down the network during the routing process and intercept gathered data by dropping them for example. To prevent the increase of security issues in WSN, many attack identification methods and models were proposed in which detection systems are deployed to collect sensed data and categorize them using machine learning and stochastic binary-classification techniques. For this work, a new method is introduced to analyze and classify an own WSN dataset. We aim to design an anomaly identification approach to improve the sensor network security and its efficiency with high accuracy. To reach this goal, machine learning approaches are used to define a detection system that learns from routing datasets to identify network malicious entries. The proposed models are based on Hidden Markov Model (HMM), and Gaussian Mixture Model (GMM) stochastic assumptions. Also, the dimensionality reduction technique was deployed to select the most relevant features for the training. The experimentation phase was realized on its own and made the dataset that reflects different network situations of normal and attacked cases. The outcomes performances of the proposed method were obtained with a classification accuracy of 92.18% using a 2 HMM/3 GMM classifier. This result demonstrates the quality of our proposed approach compared with existing literature and its usefulness to improve the security of WSN.

Dynamic texture classification based on bag-of-models with mixture of student’s t-hidden Markov models

Hidden Markov model (HMM) is a popular statistical approach for modeling sequential data comprising continuous attributes, and it has been applied in finance, machine vision and other fields. Due to higher tolerance to outliers than Gaussian distribution, the multivariate student’s t-distribution is used to serve as the observation emission distribution in continuous HMM (SHMM), which has been exploited to improve the performance for modeling sequential data. In this paper, a bag-of-models method based on the mixture of SHMMs is proposed to describe dynamic texture for dynamic texture classification, in which codebook is constructed with SHMMs. Specifically, a novel mixture model, mixture of SHMMs, is proposed to model and cluster the observation sequences of the dynamic texture. The parameter learning method is derived by using the expectation maximization (EM) algorithm. Then all components of the mixture of SHMMs are assembled to obtain the codewords and the bag-of-models based features are extracted for describing the dynamic texture. Finally, we demonstrate the effectiveness of the bag-of-models based on the mixture of SHMMs for dynamic texture classification on different benchmark datasets. The experimental results show the potential performance of the proposed approach by comparing with some existing dynamic texture classification methods.

Lamb wave-based damage assessment for CFRP composite structures using a CHMM-based damage localization algorithm and a damage quantitative expression

Accurately and quantitatively monitoring the damages in composite structures is very challenging, but important for assessing their integrity. In this paper, an innovative damage location identification and size quantification assessment method based on Lamb wave is proposed for composite laminates. A continuous hidden Markov model (CHMM)-based damage scaling factor together with a weighted average imaging algorithm is adopted in damage location identification. To quantify the degree of structural deviation from the healthy state, the inconspicuous change of guided wave (GW) signal is converted to a CHMM-based damage scaling factor with significant change. Based on the damage scaling factors of all GW sensing paths, a weighted average imaging algorithm is performed to identify the most probable damage location. In addition, a damage quantitative mathematical expression is proposed to approximately estimate the size of circular-shaped damage founded on several different damage-related characterizations. The implementation of the proposed quantitative methodology is comparatively simple and straightforward for damage detection. Experiments on three composite panels with multiple damage cases are conducted to substantiate the proposed method. The sensor layers with embedded piezoelectric transducer (PZT) sensor networks surf-mounted on the structures are employed to excite and receive Lamb wave signals since their superiority of flexibility, electrical stability and apt for complex structures. The results demonstrate that the proposed algorithm is capable of pinpointing both the location and size of the circular-hole damage in composite structures.

Highway Lane-Changing Prediction Using a Hierarchical Software Architecture based on Support Vector Machine and Continuous Hidden Markov Model

Highway Lane-Changing Prediction Using a Hierarchical Software Architecture based on Support Vector Machine and Continuous Hidden Markov Model

Continuous Hidden Markov Model Based Spectrum Sensing with Estimated SNR for Cognitive UAV Networks.

In this paper, to enhance the spectrum utilization in cognitive unmanned aerial vehicle networks (CUAVNs), we propose a cooperative spectrum sensing scheme based on a continuous hidden Markov model (CHMM) with a novel signal-to-noise ratio (SNR) estimation method. First, to exploit the Markov property in the spectrum state, we model the spectrum states and the corresponding fusion values as a hidden Markov model. A spectrum prediction is obtained by combining the parameters of CHMM and a preliminary sensing result (obtained from a clustered heterogeneous two-stage-fusion scheme), and this prediction can further guide the sensing detection procedure. Then, we analyze the detection performance of the proposed scheme by deriving its closed-formed expressions. Furthermore, considering imperfect SNR estimation in practical applications, we design a novel SNR estimation scheme which is inspired by the reconstruction of the signal on graphs to enhance the proposed CHMM-based sensing scheme with practical SNR estimation. Simulation results demonstrate the proposed CHMM-based cooperative spectrum sensing scheme outperforms the ones without CHMM, and the CHMM-based sensing scheme with the proposed SNR estimator can outperform the existing algorithm considerably.

Hidden Markov Model based Stochastic Resonance and its Application to Bearing Fault Diagnosis

Rolling bearings are crucial components in rotating machinery and the detection of damage at early stages is pivotal to ensure the life of the machine. Thus, developing accurate methods to identify the presence of faults captured using vibration measurements early on are necessary. Stochastic resonance (SR) is an interesting technique for bearing fault diagnosis, which by adding proper amount of noise to the signal the faulty signal can be extracted. However, traditional SR methods require that the analyst already know the characteristic frequency associated with the fault to be able to extract the corresponding weak signal. This paper overcomes this shortage by adaptively searching the SR system parameters by using Box-Cox sparse measures (BCSM) – based continuous hidden Markov model (CHMM) to determine the characteristic frequency directly from measured data. The method consists of a training phase that uses a simulated outer race fault to determine the optimal CHMM parameters and an application phase where the CHMM is used to determine the characteristic frequency directly from the unprocessed measured signal. The proposed method is verified using three well-known real bearing data sets and is found to be successful in all applications considered here.

Research on HMM-Based Efficient Stock Price Prediction

Stock market is one of the most important parts of the investment market. Compared with other industries, the stock market not only has a higher rate of return on investment but also has a higher risk, and stock price prediction has always been a close concern of investors. Therefore, the research on stock price prediction methods and how to reduce the error of stock price prediction has become a hot topic for many scholars at home and abroad. In recent years, the development of computer technology such as machine learning and econometric method makes the stock price prediction more reliable. Due to the hidden Markov nature of stock price, this paper proposes a stock price prediction method based on hidden Markov model (HMM). To be specific, since the data of stock price have continuity in time series, it is necessary to extend the discrete HMM to the continuous HMM, and then put forward the up and down trend prediction model based on the continuous HMM. The first-order continuous HMM is extended to the second-order continuous HMM, and the stock price is predicted by combining the prediction method of fluctuation range. As a result, the proposed second-order continuous HMM-based stock price prediction model is simulated on Hang Seng Index (HSI), one of the earliest stock market indexes in Hong Kong. The evaluation results on six months HSI show that the predicted value of the proposed model is very close to the actual value and outperforms three benchmarks in terms of RMSE, MAE, and R2.

A Method for Predicting Diverse Lane-Changing Trajectories of Surrounding Vehicles Based on Early Detection of Lane Change

The trajectory prediction of surrounding vehicles is the basis for reasonable decision-making of autonomous vehicles (AV), which is helpful for improving their safety and comfort. Aiming to predict lane-changing trajectories, we propose a behavior-based method of predicting diverse lane-changing trajectories of surrounding vehicles, which includes two parts: lane-changing behavior recognition and diverse lane-changing trajectory prediction. Firstly, a lane-changing behavior recognition model based on the Continuous Hidden Markov Model (CHMM) is established to identify the lane-changing behavior of surrounding vehicles. Secondly, considering the driving styles will lead to diverse lane-changing patterns, a diverse lane-changing trajectory prediction method based on LSTM is proposed to predict three lane-changing trajectories when the driving style is unknown, which is composed of three LSTM trajectory generators representing three lane-changing patterns. Finally, the Next Generation Simulation (NGSIM) dataset is used to train, validate and test the behavior recognition model and the trajectory prediction model. The results show good accuracy and anticipative ability of the behavior recognition model. The average accuracy of surrounding vehicle behavior detection is 98.98%, the accuracy of surrounding vehicle behavior detection in 2s before lane change point is above 95%, the average anticipation time of left and right lane-changing behavior recognition is 3.24s and 3.71s, the average proportion of anticipation time in the lane-changing duration time is 46.78% and 55.54%. In the trajectory prediction section, with considering the diversity of lane changing trajectory caused by driving style, the proposed method for predicting diverse lane-changing trajectories reduces the error between the predicted and actual trajectories. The Root Mean Square Error (RMSE) and the Final Displacement Error (FDE) of the longitudinal and lateral positions are reduced by more than 21% over a 5s time horizon. In conclusion, the diverse trajectory prediction method based on the early detection of lane-changing behavior can provide AV with future trajectory of other vehicle under different driving styles, which is conducive to a more comprehensive and accurate driving risk assessment.

Diagnosis of rolling element bearing based on multifractal detrended fluctuation analyses and continuous hidden markov model

Diagnosis of rolling element bearing based on multifractal detrended fluctuation analyses and continuous hidden markov model

Simultaneous positive sequential vectors modeling and unsupervised feature selection via continuous hidden Markov models

Since positive data vectors are often naturally generated in various real-life applications, positive vectors modeling has become an important research topic. In this article, we tackle the problem of modeling positive sequential vectors through continuous hidden Markov models (HMMs). Motivated by several recent studies in which the generalized inverted Dirichlet (GID) distribution has provided better performance than the Gaussian distribution for modeling positive data, instead of adopting Gaussian mixture models (GMM) as the emission density for conventional continuous HMMs, we theoretically propose a novel HMM by considering the mixture of GID distributions as the emission density. Moreover, to cope with high-dimensional data which may contain irrelevant features, an unsupervised localized feature selection method is incorporated with our model, which results in a unified framework that can simultaneously perform positive sequential data modeling and feature selection. To learn the proposed model, we develop a convergence-guaranteed algorithm based on variational Bayes. The advantages of our model are demonstrated through both simulated data sets and a real-life application about human action recognition.

Discounted optimal stopping problems in continuous hidden Markov models

We study a two-dimensional discounted optimal stopping problem related to the pricing of perpetual commodity equities in a model of financial markets in which the behaviour of the underlying asset price follows a generalized geometric Brownian motion and the dynamics of the convenience yield are described by an unobservable continuous-time Markov chain with two states. It is shown that the optimal time of exercise is the first time at which the commodity spot price paid in return to the fixed coupon rate hits a lower stochastic boundary being a monotone function of the running value of the filtering estimate of the state of the chain. We rigorously prove that the optimal stopping boundary is regular for the stopping region relative to the resulting two-dimensional diffusion process and the value function is continuously differentiable with respect to the both variables. It is verified by means of a change-of-variable formula with local time on surfaces that the value function and the boundary are determined as a unique solution of the associated parabolic-type free-boundary problem. We also give a closed-form solution to the optimal stopping problem for the case of an observable Markov chain.

An identity authentication method for ubiquitous electric power internet of things based on dynamic gesture recognition

This paper presents a novel algorithm for gesture recognition and identity authentication based on continuous hidden Markov model (CHMM) and optical flow method. This study aims to solve the information security problems about ubiquitous electric power internet of things. In this system, the optical flow method is used to segment and extract the features of the preprocessed dynamic gesture information to obtain the features of the dynamic gesture motion track, and the CHMM is chosen to establish a valid user dynamic gesture model, which leads to ensuring the dynamic gestures are accurately recognised. The proposed method is test on accurately recognise the dynamic gestures and the result is compared with the dynamic time warpring (DTW) algorithm and practical swarm optimisation-radial basis function network (PSO-RBFN) algorithm. The result of the comparisons illuminates the superiority of the proposed method in terms of accuracy of identity authentication.

Human Movement Representation on Multivariate Time Series for Recognition of Professional Gestures and Forecasting Their Trajectories.

Human-centered artificial intelligence is increasingly deployed in professional workplaces in Industry 4.0 to address various challenges related to the collaboration between the operators and the machines, the augmentation of their capabilities, or the improvement of the quality of their work and life in general. Intelligent systems and autonomous machines need to continuously recognize and follow the professional actions and gestures of the operators in order to collaborate with them and anticipate their trajectories for avoiding potential collisions and accidents. Nevertheless, the recognition of patterns of professional gestures is a very challenging task for both research and the industry. There are various types of human movements that the intelligent systems need to perceive, for example, gestural commands to machines and professional actions with or without the use of tools. Moreover, the interclass and intraclass spatiotemporal variances together with the very limited access to annotated human motion data constitute a major research challenge. In this paper, we introduce the Gesture Operational Model, which describes how gestures are performed based on assumptions that focus on the dynamic association of body entities, their synergies, and their serial and non-serial mediations, as well as their transitioning over time from one state to another. Then, the assumptions of the Gesture Operational Model are translated into a simultaneous equation system for each body entity through State-Space modeling. The coefficients of the equation are computed using the Maximum Likelihood Estimation method. The simulation of the model generates a confidence-bounding box for every entity that describes the tolerance of its spatial variance over time. The contribution of our approach is demonstrated for both recognizing gestures and forecasting human motion trajectories. In recognition, it is combined with continuous Hidden Markov Models to boost the recognition accuracy when the likelihoods are not confident. In forecasting, a motion trajectory can be estimated by taking as minimum input two observations only. The performance of the algorithm has been evaluated using four industrial datasets that contain gestures and actions from a TV assembly line, the glassblowing industry, the gestural commands to Automated Guided Vehicles as well as the Human–Robot Collaboration in the automotive assembly lines. The hybrid approach State-Space and HMMs outperforms standard continuous HMMs and a 3DCNN-based end-to-end deep architecture.

Sequentially spherical data modeling with hidden Markov models and its application to fMRI data analysis

Due to the reason that spherical data (i.e. L2 normalized vectors) are often involved with various real-life applications (such as anomaly detection, gesture recognition, intrusion detection in networks, gene expression data analysis, etc.), spherical data modeling has recently become an important research topic. In this work, we address the problem of modeling sequentially spherical data through continuous hidden Markov models (HMMs). Instead of adopting Gaussian mixture models (GMMs) as the emission distributions as in common continuous HMMs, we propose a continuous HMM by considering the mixture of von Mises–Fisher (VMF) distributions as its emission densities. Then, we systematically propose an effective method based on variational Bayes (VB) to learn the VMF-based HMM. The developed learning method has the following merits: (1) It is convergence-guaranteed; (2) It can be optimized with closed-form solutions. The proposed VMF–HMM with VB learning is validated by conducting experiments on both simulated sequential spherical data and a real application about fMRI data analysis.

Multi metric functional connectivity analysis based on continuous hidden Markov model with application in early diagnosis of Alzheimer’s disease

Functional connectivity (FC) is referred to as statistical dependencies between regions of interest. To investigate brain functional connectivity, there are many different connectivity metrics and researches show that the choice of the connectivity metric influences the results of the study and there is no golden rule of choosing the best connectivity metric.It is assumed that functional connectivity has a neural basis, and therefor is related to a variety of different neurological disorders like Alzheimer’s disease (AD) and Parkinson. AD is the most common neurodegenerative disorder. Cerebral cortex damage and synaptic plasticity disturbance in AD cause a decrease in functional connectivity. Mild Cognitive Impairment (MCI) is the first stage in AD progression and causes measurable decline in memory and cognitive abilities.In this study, a novel methodology is presented to combine several connectivity metrics with the goal of improving between-class discrimination. In the proposed method, temporal changes of multiple functional connectivity metrics are investigated along sliding windows by modeling it as the observation vector of a continuous observation hidden Markov model (HMM). The performance of the proposed method is evaluated using resting state eyes-closed EEG data from 7 MCI patients and 7 age-matched normal controls (NC).Group differences were investigated in five different frequency bands: delta, theta, alpha, beta, and gamma. Method analysis revealed that NC subjects and MCI patients are discriminated with accuracy of %95.9 ± 0.4 and %97.2 ± 0.5 over the alpha and gamma frequency bands respectively, using leave one subject out cross validation. These results indicate the proficiency of the connectivity metrics combination in distinguishing MCI from NC.

Degradation Estimation of Electro-Mechanical Actuator With Multiple Failure Modes Using Integrated Health Indicators

Electro-Mechanical Actuator (EMA) is a key component in the flight control actuation of the more electric aircraft. The degradation of EMA has a significant influence on its performance. Hence, the degradation estimation of EMA needs to be studied, which is an important part of the health monitoring of EMA. However, the multiple failure modes of EMA bring much difficulty for implementing degradation estimation. To solve this problem, a fusion degradation estimation method based on Continuous Hidden Markov Model (CHMM) and Generalized Linear Model (GLM) (i.e., Multiple CHMM-GLM) is proposed for EMA with multiple failure modes. Firstly, Health Indicator (HI) extraction models are formulated based on CHMM for multiple failure modes to extract HIs, which can be used to indicate the degradation degree of EMA. Secondly, the fusion degradation model for EMA with multiple failure modes is formulated based on the extracted HIs and GLM, in which the nonlinearity of a single HI and the coupling among multiple HIs are both considered. Finally, the output of the fusion degradation model is regarded as a HI to indicate the degradation degree of EMA. This study provides a new way for the degradation estimation of EMA with multiple failure modes, which can contribute to the Prognostics and Health Management (PHM) development of EMA. To evaluate the effectiveness of the proposed method, experiments are conducted utilizing EMA simulation and practical data. Experimental results show that the proposed method has a good performance in the degradation estimation for EMA with multiple failure modes.

Estimating required ‘lockdown’ cycles before immunity to SARS-CoV-2: model-based analyses of susceptible population sizes, ‘S0’, in seven European countries, including the UK and Ireland

Background: Following stringent social distancing measures, some European countries are beginning to report a slowed or negative rate of growth of daily case numbers testing positive for the novel coronavirus. The notion that the first wave of infection is close to its peak begs the question of whether future peaks or ‘second waves’ are likely. We sought to determine the current size of the effective (i.e. susceptible) population for seven European countries—to estimate immunity levels following this first wave. Methods: We used Bayesian model inversion to estimate epidemic parameters from the reported case and death rates from seven countries using data from late January 2020 to April 5th 2020. Two distinct generative model types were employed: first a continuous time dynamical-systems implementation of a Susceptible-Exposed-Infectious-Recovered (SEIR) model, and second a partially observable Markov Decision Process or hidden Markov model (HMM) implementation of an SEIR model. Both models parameterise the size of the initial susceptible population (‘S0’), as well as epidemic parameters. Results: Both models recapitulated the dynamics of transmissions and disease as given by case and death rates. Crucially, maximum a posteriori estimates of S0 for each country indicated effective population sizes of below 20% (of total population size), under both the continuous time and HMM models. Using a Bayesian weighted average across all seven countries and both models, we estimated that 6.4% of the total population would be immune. From the two models, the maximum percentage of the effective population was estimated at 19.6% of the total population for the UK, 16.7% for Ireland, 11.4% for Italy, 12.8% for Spain, 18.8% for France, 4.7% for Germany and 12.9% for Switzerland. Conclusion: Our results indicate that after the current wave, a large proportion of the total population will remain without immunity.

Trip Purpose Identification of Docked Bike-Sharing From IC Card Data Using a Continuous Hidden Markov Model

It is different from the previous supervised learning algorithm based on personal travel questionnaire, the aim of this study is to develop an unsupervised learning methodology to estimate the docked bike-sharing users’ trip purposes using IC card data, which trip purposes were unknown from the dataset. The present study is able to extract the trip-chains, which is used to understand the complete individual trip process. A rigorous method is then proposed to interpret the purpose of each leg of the trip-chain using a continuous hidden Markov model (CHMM). This method effectively combines the Gaussian mixture model and the hidden Markov model, and realizes the inference based on trip-chains. It is intended to enhance the understanding of docked bike-sharing users’ transfer intention, which is different from most trip motivation recognition methods. The Gaussian mixture layer uses the feature space constructed by the spatial and temporal information on trip-chains from the IC card data, as well as the land-use characteristics of the docked bike-sharing docking stations to complete the transfer of the trip-chains to the trip modes. The hidden Markov structure can realize the process from the trip modes to the trip purposes. The IC card data of docked bike-sharing usage in Nanjing, China is used to interpret the specific steps of the proposed model. A questionnaire survey is conducted to obtain the real trip purposes, which is compared with the estimated results from the model to verify the effectiveness of the model. The results show that the accuracies of single trip recognition and chain trip recognition are 0.770 and 0.756, respectively. Compared with the baseline algorithm, the model also shows good performance. Therefore, the proposed approach can be used to discover and interpret the trip purpose using the IC card data.

Recognition of classroom student state features based on deep learning algorithms and machine learning

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Measurement and Characterization of Electromagnetic Noise in Edge Computing Networks for the Industrial Internet of Things.

Edge computing and the Internet of Things (IOT) provide the technological basis for the development of intelligent manufacturing nowadays. In order to support the intelligent interconnection and application of all kinds of equipment in the industrial field, edge computing should be equipped close to or embedded in all kinds of equipment nodes in the industrial wireless network. Therefore, it is meaningful to investigate the wireless network design of the Industrial Internet of Things. Low power wireless sensor devices are widely used in the Industrial Internet of Things (IIoT), which are sensitive to electromagnetic noise. The electromagnetic noises in industrial scenarios are significantly different from the conventional assumed white noise. In this paper, the measurement results of electromagnetic noises at three different test positions are given in an automobile factory. The spectrum occupancy of the factory wireless environment in the 300 MHz–3 GHz band was obtained by frequency domain measurement. In the time domain measurement, four statistical parameters of the three bands of 315 MHz, 433 MHz, and 916 MHz were measured, and the electromagnetic noise distributions in different plant areas and different frequency bands were analyzed. According to the measurement results, the time-varying characteristics of electromagnetic noise can be characterized by continuous hidden Markov models (CHMM). These results are informative to the design and optimization for the edge computing networks for IIoT.