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In this thesis, we present MOSHPIT (Multi-allelic Outbred Strain Haplotype Phasing and Inference Tool), a novel framework for analyzing genomic datasets of outbred populations. MOSHPIT integrates different variant mutation models into a single Hidden Markov Model (HMM), enabling the simultaneous utilization of single nucleotide polymorphisms (SNPs), insertion/deletions (indels), and short tandem repeats (STRs). This multi-variant capability makes MOSHPIT the first tool of its kind. Extensive evaluations with real-world data and simulated genotypes demonstrate MOSHPIT's superior accuracy and runtime performance compared to existing methods.MOSHPIT allows comprehensive analysis of outbred populations at a high genomic resolution, facilitating investigations into genotype-phenotype associations in the outbred model. This enhanced understanding of genetic diversity and its impact on observable traits has significant potential for advancing our knowledge of biological processes, complex traits, and disease risk.In summary, MOSHPIT represents a significant advancement in population genetics analysis, enabling researchers to better understand outbred populations. By integrating multiple variant types and leveraging sophisticated computational techniques, MOSHPIT provides a powerful tool for unraveling the complexities of genetic variation and its relationships with various phenotypes.

We are in an era of rapid urbanization, technological advances, transportation transformation, and increasingly big data and computation power. We have witnessed how shared transportation (Uber, Lyft, Lime, Bird, etc) intrudes into daily lives in just a few years, how online shopping, including same-day grocery delivery, has changed day-to-day travel trajectories, and how the emerging work-from-home lifestyle would fundamentally change people's location choices. At the same time, large-scale data becomes more accessible than ever; so does the computation power needed to process such data. It is therefore a good time to retrospect existing paradigms of dynamic behavior models and keep exploring the potentials and new opportunities.While studies on long-term travel behavior, such as residential location choice and working location choice, have been the emphasis of a substantial body of prior work, most empirical studies adopt a static approach to behavior modeling. For the small body of work that allows for dynamic behavior modeling, only backward-looking behavior, i.e., time-dependency, is incorporated, and the role of forward-looking behavior, i.e. by considering future expectations in sequential decision-making, has long been neglected. This is with good reasons: the estimation of a truly dynamic choice model is extraordinarily difficult due to (a) computational tractability associated with big data and large-scale dynamic programming to accommodate forward-looking and (b) scarcity of longitudinal data on long-term travel behaviors, such as residential moving trajectories. Yet long-term travel behavior is inherently dynamic, and this has led to concerns that estimates from static models may be biased. In economics, dynamic discrete choice models (DDCM) have been used to model many aspects of transportation behaviors; however, this approach has several limitations, including its assumptions of optimal human behavior, conditional independence, extreme value distribution, etc. In the recent decade, advances in artificial intelligence, especially in inverse reinforcement learning (IRL), have inspired new approaches to solving complex dynamic behavioral problems. In particular, IRL can circumvent several assumptions common in DDCM, while still reconstructing problems and estimating models in a tractable way. However, the research worlds of economics and of artificial intelligence rarely reference each other; one objective of this dissertation is to bridge these two disciplines to address the challenging problem of modeling large-scale long-term forward-looking travel behavior.We do not necessarily need the forward-looking assumption in all situations. In practice, for short-term and medium-term behavior trajectories, such as mode choice, car usage and car ownership, the backward-looking assumption can be sufficient. This is because these choices are associated with much lower costs both financially and psychologically and the impact of future expectations can be trivial. There is a rich amount of literature on backward-looking dynamic models, including studies on identifying policy and environment triggers that shift travel behavior, studies that investigate the role of key life events on travel behavior change, and studies on lifestyle analysis which treat lifestyle transition as a higher-level orientation of behavioral change. However, most frameworks on backward-looking dynamic modeling concentrate on analysis of single-dimensional choice and ignore the interdependence and multi-dimensionality of travel behaviors. Furthermore, few prior work consolidates all these dynamics components in a single framework to analyze the joint effect of different sources of triggers. Therefore, another objective of this dissertation is to develop a unified modeling framework that accounts for time-varying economic and policy context (external dynamics), life events (internal dynamics), lifestyle, and multi-dimensional interrelated choices.The first component of this dissertation formulates a mathematical framework for representing long-term travel behaviors as sequential actions under the Inverse Reinforcement Learning (IRL) framework, which aims to address the forward-looking limitation. In the proposed framework, the individual observes the environment and takes action (i.e., move to a new location or not) accordingly by evaluating action-dependent future rewards received from the environment. The reward can be a function of built-in environment attributes, which shares a similar concept with the utility function in discrete choice models. Three highlights of the first component of this work are presented below.- In the classic IRL setting within the domain of artificial intelligence, the agents (usually robots) are often assumed to have homogeneous behavior and do not own any internal dynamics associated with the agents. Our work extends the IRL framework to accommodate heterogeneous household behavioral dynamics, and derives its corresponding learning algorithm to estimate the parameters associated with the attributes.- We provide an in-depth theoretical comparison between Dynamic Discrete Choice Model (DDCM) in economics and IRL in artificial intelligence from different aspects, including terminologies, assumptions, and model structures.- To validate the existence of forward-looking behavior and the methodological feasibility of the proposed framework, we use a large-scale infused data set of household relocation trajectories in Texas over a 7-year period (2005-2011).- The empirical results are three-fold. First, all households have a positive preference to locate in areas with higher degree of land-use mix, higher accessibility to jobs, and lower employment density. Our model also shows that low-income households focus more on current needs and are less forward-looking compared with households with higher income level. And low-income households present less willingness to pay for neighborhood amenities such as land-use mix and accessibility to jobs. In terms of goodness of fit, our proposed model outperforms the DDCM model (for high-income and low-income households), backward-looking model and the static model. The second component of this work addresses the limitation on backward-looking models. The HMM framework has gained increasing attention in the transportation arena (in applications from car ownership to mode choice) due to its latent hierarchical structure, favorable model performance, and intuitive interpretation. Highlights of this component are as follows. - We extend the framework of heterogeneous Hidden Markov Model (HMM) from single-dimensional discrete choice to multi-dimensional discrete and continuous choices, and derive its recursive parameter learning algorithm. Building on this framework, we propose a unified model that conjoins lifestyle, life events, external environment, and multi-dimensional travel behavior dynamics.- We evaluate the feasibility and robustness of the proposed methodology via a case study: a retrospective survey in the San Francisco Bay Area consisting of 830 households.- To fully explore the potentials of the proposed framework, we provide trend analysis of car ownership and mode use based on estimation results, and conduct sensitivity analysis of changes in fuel price and unemployment rate. - We identify four latent lifestyles: auto-oriented-2-car group with rare use of other travel modes, auto-oriented-1-car group with rare use of other travel modes, multi-modals group that own at least one car, and auto-free group that have the lowest car ownership and car usage. The results demonstrate how life events, policies, and the economic environment influence people on lifestyle transitions.In sum, this dissertation provides building blocks to evolve the field of dynamic behavior modeling by incorporating advances in artificial intelligence. Throughout this dissertation, when providing theoretical improvements building on each mathematical framework, we ground each methodology with case studies. Empirical results have shown our methodologies can effectively help quantify the triggers that prompt individuals and households to change their travel behavior, better predict the trend of future mobility, and help transportation planning and policy-making.

Both DNA and RNA modifications play critical roles in cell regulation. Traditionally, a chemical selection process alters base pairing or sequencing coverage is used to sequence modified nucleotides. Therefore, a new chemical labeling process needs to be created for each modification. Currently, we do not have methods for sequencing the majority of the over 150 RNA and over 40 DNA modifications. However, with nanopore sequencing, we can directly detect modifications on native DNA or RNA reads without any selection or chemical labeling techniques. Nanopore sequencing measures the change in current across a nanopore as a polynucleotide threads through the nanopore and we can use this signal to identify modifications. In chapter 1, we present a framework for the unsupervised determination of the number of nucleotide modifications from nanopore sequencing readouts. We demonstrate the approach can effectively recapitulate the number of modifications, the corresponding ionic current signal levels, as well as mixing proportions under both DNA and RNA contexts. We further show, by integrating information from multiple detected modification regions, that the modification status of DNA and RNA molecules can be inferred. This method forms a key step of de novo characterization of nucleotide modifications.In chapter 2, we present a graph convolutional network-based deep learning framework for predicting the mean of kmer distributions from corresponding chemical structures. We show such a framework can generalize the chemical information of the 5-methyl group from thymine to cytosine by correctly predicting 5-methylcytosine-containing DNA 6mers.In chapter 3, using a combination of yeast genetics and nanopore direct RNA sequencing, we have developed a reliable method to track the modification status of single rRNA molecules at 37 sites in 18S rRNA and 73 sites in 25S rRNA. We use our method to characterize patterns of modification heterogeneity and identify concerted modification of nucleotides found near functional centers of the ribosome. Distinct undermodified subpopulations of rRNAs accumulate when ribosome biogenesis is compromised by loss of Dbp3 or Prp43-related RNA helicase function. Modification profiles are surprisingly resistant to change in response to many genetic and environmental conditions that affect translation, ribosome biogenesis, and pre-mRNA splicing. The ability to capture complete modification profiles for RNAs at single-molecule resolution will provide new insights into the roles of nucleotide modifications in RNA function.

In this study, the output-feedback control (OFC) strategy design problem is explored for a type of Takagi-Sugeno fuzzy singular perturbed system. To alleviate the communication load and improve the reliability of signal transmission, a novel stochastic communication protocol (SCP) is proposed. In particular, the SCP is scheduled based on a nonhomogeneous Markov chain, where the time-varying transition probability matrix is characterized by a polytope-structure-based set. Different from the existing homogeneous Markov SCP, a nonhomogeneous Markov SCP depicts the data transmission in a more reasonable manner. To detect the actual network mode, a hidden Markov process observer is addressed. By virtue of the hidden Markov model with partly unidentified detection probabilities, an asynchronous OFC law is formulated. By establishing a novel Lyapunov-Krasovskii functional with a singular perturbation parameter and a nonhomogeneous Markov process, a sufficient condition is exploited to guarantee the stochastic stability of the resulting system, and the solution for the asynchronous controller is portrayed. Eventually, the validity of the attained methodology is expressed through a practical example.

Parkinson’s disease (PD) is the second most common neurodegenerative disorder and the heterogeneity of early PD leads to inter-rater and intra-rater variability in observation-based clinical assessment. Thus, objective monitoring of PD-induced motor abnormalities has attracted significant attention to manage disease progression. Here, we proposed a hierarchical architecture to reliably detect abnormal characteristics and comprehensively quantify the multi-symptom severity in patients with PD. A novel wearable device was designed to measure motor features in fifteen PD patients and fifteen age-matched healthy subjects, while performing five types of motor tasks. The abnormality classes of multi-modal measurements were recognized by hidden Markov models (HMMs) in the first layer of the proposed architecture, aiming at motivating the evaluation of specific motor manifestations. Subsequently, in the second layer, three single-symptom models differentiated PD motor characteristics from normal motion patterns and quantified the severity of cardinal PD symptoms in parallel. In order to further analyze the disease status, the multi-level severity quantification was fused in the third layer, where machine learning algorithms were adopted to develop a multi-symptom severity score. Experimental results demonstrated that the quantification of three cardinal symptoms were highly accurate to distinguish PD patients from healthy controls. Furthermore, strong correlations were observed between the Unified Parkinson’s Disease Rating Scale (UPDRS) scores and the predicted sub-scores for tremor (R=0.75, P=1.40e-3), bradykinesia (R=0.71, P=2.80e-3) and coordination impairments (R=0.69, P=4.20e-3), and the correlation coefficient can be enhanced to 0.88 (P=1.26e-5) based on the fusion schemes. In conclusion, the proposed assessment architecture holds great promise to push forward the in-home monitoring of clinical manifestations, thus enabling the self-assessment of disease progression.

This article focuses on the investigation of finite-time dissipative state estimation for Markov jump neural networks. First, in view of the subsistent phenomenon that the state estimator cannot capture the system modes synchronously, the hidden Markov model with partly unknown probabilities is introduced in this article to describe such asynchronization constraint. For the upper limit of network bandwidth and computing resources, a novel dynamic event-triggered transmission mechanism, whose threshold parameter is constructed as an adjustable diagonal matrix, is set between the estimator and the original system to avoid data collision and save energy. Then, with the assistance of Lyapunov techniques, an event-based asynchronous state estimator is designed to ensure that the resulting system is finite-time bounded with a prescribed dissipation performance index. Ultimately, the effectiveness of the proposed estimator design approach combining with a dynamic event-triggered transmission mechanism is demonstrated by a numerical example.

This work investigates the observer-based asynchronous control of discrete-time nonlinear systems with network-induced communication constraints. To avoid the data collisions and side effects in a constrained communication channel, a novel dynamic event-based weighted try-once-discard (DEWTOD) protocol is proposed. In contrast to the existing protocols, the DEWTOD scheduling regulates whether the sampling instant to release and which node to transmit the sampling instant simultaneously. In light of a hidden Markov model, the time-varying detection probability matrix is characterized by a polytopic set. By resorting to the polytopic-structured Lyapunov functional, sufficient conditions are derived such that the closed-loop dynamic is mean-square exponentially stable, and the observer-based controller is designed. In the end, two numerical examples are provided to explicate the validity of the attained methodology.

This article is concerned with the problem of compensation-based output feedback control for Takagi-Sugeno fuzzy Markov jump systems subject to packet losses. The phenomenon of packet losses is assumed to randomly occur in the feedback channel, which is modeled by a Bernoulli process. Employing the single exponential smoothing method as a compensation scheme, the missing measurements are predicted to help offset the impact of packet losses on system performance. Then, an asynchronous output feedback controller is designed by the hidden Markov model. Based on the mode-dependent Lyapunov function, some novel sufficient conditions on the controller existence are derived such that the closed-loop system is stochastically stable with strict dissipativity. Besides, an algorithm for determining the optimal smoothing parameter is proposed. Finally, the validity and advantages of the design approach are manifested by some simulation results.

With the enhancement of location-acquisition technologies, GPS trajectories play an essential role in data-driven intelligent transportation applications, which requires an accurate approach to match raw GPS trajectories to road segments on a digital map. However, for complex urban roads containing elevated roads and surface roads, map matching for low-frequency GPS data is still challenging. This paper aims to address the biases and instability problem in existing approaches. To this end, we combine the spatial-temporal characteristics of GPS data in complex roads with driving behaviours and present a novel global map matching method including truncated density clustering algorithm, statistic features based spatial-temporal analysis, and driving-behaviour-based track modification. Additionally, a weighted-matrix based interactive voting algorithm is proposed to select the best results from a global perspective. The experiments are conducted with two real GPS trajectory datasets under three road conditions. The result shows that our approach outperforms state-of-art approaches for urban complex road networks in both accuracy and efficiency.

One of the pillar generative machine learning approaches in time series data study and analysis is the hidden Markov model (HMM). Early research focused on the speech recognition application of the model with later expansion into numerous fields, including video classification, action recognition, and text translation. The recently developed generalized Dirichlet HMMs have proven efficient in proportional sequential data modeling. As such, we focus on investigating a maximum a posteriori (MAP) framework for the inference of its parameters. The proposed approach differs from the widely deployed Baum-Welch through the placement of priors that regularizes the estimation process. A feature selection paradigm is also integrated simultaneously in the algorithm. For validation, we apply our proposed approach in the classification of dynamic textures and the recognition of infrared actions.

Despite the tremendous growth of the transportation sector, the availability of systems that ensure safe, efficient, sustainable transportation reduces traffic congestion, maintenance costs, the off-road time of the vehicle, enhances driver's experiences, and ensures a more reliable journey are very limited. The fast evolution of our economy, lack of driver training, and the grown affordability of our society are reasons for this mismatch in developing economies. We think that the inconsistency will increase and unfavorably affect our traffic structure unless intelligent algorithm-based solutions are developed and deployed. This article presents a system for providing safe, accurate, comfortable, reliable, fuel-efficient, and economical driving behavior using Machine Learning techniques like the hidden Markov model (HMM). Our proposed system recommends subsequent trips using a multi-objective optimization (MOO) technique for the driver. It provides suggestions concerning speed limits and alerts based on the driver's behavior score and fuel efficiency. We used a publicly available UAH-DriveSet dataset captured by the driving monitoring app DriveSafe for all of our experiments. The results reveal that the proposed model predicts behavior with 95% accuracy and calculates fuel efficiency to improve driving quality and experience. This system recommends safer, more comfortable, more reliable, more efficient, and economical rides, beneficial for everyone in our society.

Outlier detection in multivariate data is one of the critical challenges in preprocessing phase. Many outlier detection methods have been emerged for the past few years to perform outlier detection efficiently in multivariate datasets. The prediction accuracy cannot be improved without proper outlier analysis and the prediction model might not confirm the expected behavior. The generation of huge data in real time applications makes the outlier detection process more crucial and challenging. Most of the currently available detection methods are based on mean and covariance that are not suitable for handling large volume of datasets, they are suitable for handlind static data and simple data to detect outliers. They cannot cope up with large scale data. So, there is a need for an efficient outlier detection model to detect the outliers in multivariate datasets. The primary objective of this research work is to develop a robust model for outlier detection in multivariate data. To achieve this, the work proposed an enhanced Hidden Semi-Markov Model (HSMM) which allows arbitrary time distribution in its states to detect outliers. The proposed work utilized six benchmark datasets and the performance is compared with several outlier detection algorithms such as HMM, iForest, FastABOD, and Expose. The work achieves 98.2 % of accuracy which is significantly better for detecting outliers in multivariate dataset. The proposed work improvised the percentage of acheivements between 2% to 25% than the currently available models.. The experimental analysis shows that the proposed model performs well than the currently available models in terms of accuracy, and receiver operation curve (ROC).

In this article, the stabilization problem of discrete-time power systems subject to random abrupt changes is studied via asynchronous control. In this regard, the transient faults in the power lines, and subsequent switching of associated circuit breakers are modeled as a Markov chain. Based on this, the power systems are described as discrete-time Markov jump systems. The focus is mainly to design the control for Markov jump-based power systems (MJPSs) when modes of the control asynchronously run with the modes of power systems. To do this, a hidden Markov model technique is used to characterize the nonsynchronization between the control and system. By constructing the mode-dependent stochastic Lyapunov function, the sufficient conditions are acquired in the form of linear matrix inequalities (LMIs), which ensure not only the stochastic stability of the resulting hidden MJPSs but also the existence of the desired control. Finally, the simulation example reveals the efficiency of the designed control law.

This article investigates the adaptive event-triggered finite-time dissipative filtering problems for the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy Markov jump systems (MJSs) with asynchronous modes. By designing a generalized performance index, the H∞, L₂-L∞, and dissipative fuzzy filtering problems with network transmission delay are addressed. The adaptive event-triggered scheme (ETS) is proposed to guarantee that the IT2 T-S fuzzy MJSs are finite-time boundedness (FTB) and, thus, lower the energy consumption of communication while ensuring the performance of the system with extended dissipativity. Different from the conventional triggering mechanism, in this article, the parameters of the triggering function are based on an adaptive law, which is obtained online rather than as a predefined constant. Besides, the asynchronous phenomenon between the plant and the filter is considered, which is described by a hidden Markov model (HMM). Finally, two examples are presented to show the availability of the proposed algorithms.

In this article, the hidden Markov model (HMM)-based fuzzy control problem is addressed for slow sampling model nonlinear Markov jump singularly perturbed systems (SPSs), in which the general transition and mode detection information issue is considered. The general information issue is formulated as the one with not only the transition probabilities (TPs) and the mode detection probabilities (MDPs) being partly known but also with the certain estimation errors existing in the known elements of them. This formulation covers the cases with both the TPs and the MDPs being fully known, or one of them being fully known but another being partly known, or both them being partly known but without the certain estimation errors, which were considered in some previous literature. By utilizing the HMM with general information, some strictly stochastic dissipativity analysis criteria are derived for the slow sampling model nonlinear Markov jump SPSs. In addition, a unified HMM-based fuzzy controller design methodology is established for slow sampling model nonlinear Markov jump SPSs such that a fuzzy controller can be designed depending on whether the fast dynamics of the systems are available or not. A numerical example and a tunnel diode circuit are finally used to illustrate the validity of the obtained results.

Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but existing methods for mobility prediction often overlook the underlying activity patterns. Knowledge of activity patterns can improve the performance and interpretability of existing individual mobility models, leading to more informed policy design and better user experience in intelligent transportation systems. This study develops an activity-based modeling framework for individual mobility prediction in mass transit systems. Specifically, an input-output hidden Markov model (IOHMM) approach is proposed to simultaneously predict the (continuous) time and (discrete) location of an individual's next trip using transit smart card data. The prediction task can be transformed into predicting the hidden activity duration and end location. Based on a case study of Hong Kong's metro system, we show that the proposed model can achieve similar prediction performance as the state-of-the-art long short-term memory (LSTM) model. Unlike LSTM, the proposed IOHMM approach can also be used to analyze hidden activity patterns, which provides meaningful behavioral interpretation for why an individual makes a certain trip. Therefore, the activity-based prediction framework offers a way to preserve the predictive power of advanced machine learning methods while enhancing our ability to generate insightful behavioral explanations, which is useful for user-centric policy design and intelligent transportation applications such as personalized traveler information.

This article studies the problem of multiobjective control design for a class of human–machine systems (HMSs) with safety performance constraints containing state constraints and input constraints. The HMSs under consideration not only monitor the human but also need to take proper actions to both the human and machine. A model of controlled hidden Markov jump system (CHMJS) is applied to represent the HMSs. Based on the CHMJS model, a sufficient condition for the practical mean square stability of the unconstrained HMSs is first derived using a stochastic Lyapunov functional. A sufficient condition for ensuring the safety performance constraints of the HMSs is also deduced by employing reachability analysis and set invariance theory. Subsequently, a bilinear matrix inequality-based control design is presented to guarantee both the practical mean square stability and safety performance constraints of the HMSs. A multiobjective optimization problem (MOP) is then formulated to determine a feedback controller for the human and a human-assistance controller for the machine such that both the practical mean square stability and safety performance constraints as well as the less human intervention can be satisfied. An algorithm that mixes the multiobjective particle swarm optimization and linear matrix inequality technique is developed to solve this MOP. Finally, a lane departure example is given to illustrate its effectiveness.

A timely understanding of target vehicles (TVs) lateral behavior is essential for the decision-making and control of host vehicle. Existing physical model-based methods such as motion-based method and multiple centerline-based method are generally constructed based on TV pose and longitudinal velocity, and tend to ignore TV preview driving characteristic and other useful information such as lateral velocity and yaw rate. To address these issues, a driver preview and multiple centerline model-based probabilistic behavior recognition architecture is proposed for timely and accurate TV lateral behavior prediction. Firstly, a driver preview model is used to describe vehicle preview driving characteristic, and TV preview lateral offset and preview lateral velocity are calculated with TV states and road reference information. Then, the preview lateral offset and preview lateral velocity are combined with multiple centerline model for TV lateral behavior reasoning based on the interacting multiple model-based probabilistic behavior recognition algorithm. With this method, TV preview driving characteristic and lateral motion states are combined for precise TV lateral behavior description. Furthermore, to predict short-term lateral behavior, a preview lateral velocity-dependent transition probability matrix model constructed with Gaussian cumulative distribution function is proposed. Simulation and experimental results show that the proposed method considering vehicle preview driving characteristic predicts TV lateral behavior earlier than the conventional method.

This paper investigates the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy asynchronous controller design problem for nonlinear multiagent systems via a dynamic event-triggered scheme in the discrete-time context. Essentially different from most current literature, considering a realistic situation that the system mode and the anticipant controller mode are hardly to maintain synchronization at any time, the problem is characterized by means of hidden Markov model (HMM). The primary attention is focused on devising a feasible dynamic event-triggered strategy with novel threshold parameters to mitigate the communication burden efficiently. On this occasion, the information renewal of the controller is aperiodic. Furthermore, the nonlinear characteristics are effectually disposed through utilizing an unique IT2 T-S fuzzy model, which is with mismatched membership functions (MFs). As a result, the derived closed-loop fuzzy multiagent systems are accompanied by mismatched MFs and asynchronous modes. Whereafter, via solving the convex optimization problem, the desired controller gains are acquired. Eventually, the validity and practicability of the developed control scheme is illustrated by two examples.

Based on the interval type-2 fuzzy (IT2F) approach, this paper investigates the fault detection filter design problem for a class of nonhomogeneous higher-level Markov jump systems with uncertain transition probabilities. Considering that the mode information of the system cannot be obtained synchronously by the filter, the hidden Markov model can be seen as a detector to handle this asynchronous problem, and the parameter uncertainty can be processed by the IT2F approach with the lower and upper membership functions. Then, the asynchronous IT2F filter is designed to deal with the fault detection problem. Furthermore, the Gaussian transition probability density function is introduced to describe the uncertainty transition probabilities of the system and the filter. Based on Lyapunov theory, the existence of the designed asynchronous IT2F filter and the dissipativity of the filter error system can be well ensured. The simulation study on a quarter-car suspension system verifies that the designed asynchronous IT2F filter can detect faults without error alarms.

This paper proposes a new avoidance control model for automatic vehicle in facing dangerous lane-changing behavior. Firstly, the new lane-changing probability factor based on Gaussian-mixture-based hidden Markov model is constructed to predict the lateral-vehicle lane-changing probability and output the pre-control parameters. Secondly, the back propagation neural network avoidance model, which combined with driver's avoidance behavior, is developed for achieving the instantaneous collision avoidance control. Moreover, the optimal solution between control parameters and vehicle stability is obtained by using linear quadratic regulator. Finally, the accuracy of the avoidance model is verified by the semi-physical driver-in-the-loop simulation based on PreScan/Simulink. Results show that the automatic vehicle with the proposed avoidance model can accurately and effectively take pre-braking and micro-steering behavior. The proposed model can greatly reduce vehicle collision probability and effectively take both safety and comfort of collision avoidance into account. In addition, the robustness of the control model under different network penetration is discussed.

Aircraft operations in the terminal area rely heavily on voice communications between pilots and air traffic controllers. This paper proposes a novel aircraft trajectory simulation framework by guiding the trajectory simulation following the voice command from controllers. Bayesian model selection is used for checking pilot compliances to controller commands with observed trajectories. This framework is named as Voice Communication-Augmented Simulation. The goal of the proposed study is to enable accurate trajectory predictions. The framework can act as a computer assistant for controllers to monitor pilot compliances and ensure safe operations. The proposed method is tested and validated with actual trajectory data from Sherlock Data Warehouse. The tests showed that the proposed framework can accurately simulate and monitor the flight level change of aircraft and update the approach procedure.

Recurrent neural networks (RNNs) can be used to operate over sequences of vectors and have been successfully applied to a variety of problems. However, it is hard to use RNNs to model the variable dwell time of the hidden state underlying an input sequence. In this article, we interpret the typical RNNs, including original RNN, standard long short-term memory (LSTM), peephole LSTM, projected LSTM, and gated recurrent unit (GRU), using a slightly extended hidden Markov model (HMM). Based on this interpretation, we are motivated to propose a novel RNN, called explicit duration recurrent network (EDRN), analog to a hidden semi-Markov model (HSMM). It has a better performance than conventional LSTMs and can explicitly model any duration distribution function of the hidden state. The model parameters become interpretable and can be used to infer many other quantities that the conventional RNNs cannot obtain. Therefore, EDRN is expected to extend and enrich the applications of RNNs. The interpretation also suggests that the conventional RNNs, including LSTM and GRU, can be made small modifications to improve their performance without increasing the parameters of the networks.

Glance behavior is significant because whether and how the driver is scanning and observing the driving scene is closely related to driving safety. This paper aims to improve the accuracy of glance behavior modeling and realize the spatiotemporal representation and visualization of glance behavior. Forty subjects were recruited to perform a freeway driving task using a driving simulator. The vehicle data were collected by the simulator. Drivers' gaze points were collected by an eye tracker. The prior knowledge on gaze points obtained through a statistical analysis were provided for K-means (KM) to form a semi-supervised K-means (SSKM), which classifies gaze points into different fixation zones. The classification results were compared with the results of KM. Furthermore, a clustering center-based piecewise aggregate representation (CCPAR) was proposed to characterize glance behavior. Maneuvers identification was taken as a case to evaluate the proposed method. The k-nearest neighbour (KNN) based on the similarity of CCPAR identified driving maneuvers into lane-keeping, left lane change, and right lane change. The identification results were compared with the results of the Hidden Markov Model (HMM). The average classification accuracies of KM and SSKM were 55.28% and 94.75%, respectively. The accuracies of maneuvers identified by CCPAR-KNN and by HMM were 87.50% and 85.83%, respectively. The results indicate that SSKM and CCPAR are feasible for glance behavior modeling. SSKM eliminates the randomness of initial cluster center selection and improves the accuracy of gaze points classification. CCPAR is intuitive and convenient to describe and visualize the spatiotemporal characteristics of glance behavior.

Speech conveys not only the verbal communication, but also emotions, manifested as facial expressions of the speaker. In this work, we present deep learning frameworks that directly infer facial expressions from just speech signals. Specifically, the time-varying contextual non-linear mapping between audio stream and micro facial movements is realized by our proposed recurrent neural networks to drive a 3D blendshape face model in real-time. Our models not only activate appropriate facial action units (AUs), defined as 3D expression blendshapes in the FaceWarehouse database, to depict different utterance generating actions in the form of lip movements, but also, without any assumption, automatically estimate emotional intensity of the speaker and reproduces her ever-changing affective states by adjusting strength of related facial unit activations. In the baseline models, conventional handcrafted acoustic features are utilized to predict facial actions. Furthermore, we show that it is more advantageous to learn meaningful acoustic feature representation from speech spectrograms with convolutional nets, which subsequently improves the accuracy of facial action synthesis. Experiments on diverse audiovisual corpora of different actors across a wide range of facial actions and emotional states show promising results of our approaches. Being speaker- independent, our generalized models are readily applicable to various tasks in human-machine interaction and animation.

Collaborative robots are widely utilized in intelligent manufacturing to cooperate with the human to accomplish different assembly tasks. To improve the efficiency of human-robot cooperation, robots should be able to recognize human intentions and provide necessary assistance proactively. The major challenge for current human intention recognition methods is that they only deal with known human intentions of predefined tasks and lack of ability to learn unknown intentions corresponding to new tasks. This article introduces an evolving hidden Markov model (EHMM)-based approach to learn new human intentions incrementally by carrying out structure and parameter updating based on the observed sequence, in parallel with the recognition. The incremental learning ability makes it applicable in dynamic environments with changing tasks. A set of assistive execution policies has been developed for the robot to provide appropriate assistance to the human partner based on the intention recognition results in real time. Experiments have been carried out to verify the effectiveness of our approach in human-robot cooperative assembly tasks. The results show very high recognition accuracy (≥95.45%), and the human subjects show their high satisfaction with the intention learning ability of the proposed approach.

Cyber-threats are continually evolving and growing in numbers and extreme complexities with the increasing connectivity of the Internet of Things (IoT). Existing cyber-defense tools seem not to deter the number of successful cyber-attacks reported worldwide. If defense tools are not seldom, why does the cyber-chase trend favor bad actors? Although cyber-defense tools monitor and try to diffuse intrusion attempts, research shows the required agility speed against evolving threats is way too slow. One of the reasons is that many intrusion detection tools focus on anomaly alerts’ accuracy, assuming that pre-observed attacks and subsequent security patches are adequate. Well, that is not the case. In fact, there is a need for techniques that go beyond intrusion accuracy against specific vulnerabilities to the prediction of cyber-defense performance for improved proactivity. This paper proposes a combination of cyber-attack projection and cyber-defense agility estimation to dynamically but reliably augur intrusion detection performance. Since cyber-security is buffeted with many unknown parameters and rapidly changing trends, we apply a machine learning (ML) based hidden markov model (HMM) to predict intrusion detection agility. HMM is best known for robust prediction of temporal relationships mid noise and training brevity corroborating our high prediction accuracy on three major open-source network intrusion detection systems, namely Zeek, OSSEC, and Suricata. Specifically, we present a novel approach for combined projection, prediction, and cyber-visualization to enable precise agility analysis of cyber defense. We also evaluate the performance of the developed approach using numerical results.

This article is devoted to the investigation of reduced-order dissipative filtering for Takagi-Sugeno (T-S) fuzzy Markov jump systems with the event-triggered mechanism. For the proposed event-triggered mechanism, its threshold parameter is constructed as a special diagonal matrix which can improve system performance by flexibly adjusting the matrix elements. Due to the impact of the sampling behaviors and the environmental disturbance, the asynchronization between the filter and the estimated system is considered in this article, which can be characterized by the hidden Markov model. Through handling the linear matrix inequalities (LMIs) with some slack matrices, event-triggered fuzzy filters are designed to guarantee the resulting system is stochastically stable and strictly dissipative. The proposed filter parameters are obtained by solving LMIs. Ultimately, both the effectiveness and advantages of the proposed reduced-order filter with the event-triggered mechanism are verified by a practical example.

This paper introduces novel approaches to forecast pooling methods based on a nonparametric prior for a weight vector combining predictive densities. The first approach places a Dirichlet process prior on the weight vector and generalizes the static linear pool. The second approach uses a hierarchical Dirichlet process prior to allow the weight vector to follow an infinite hidden Markov chain. This generalizes dynamic prediction pools to the nonparametric setting. Efficient posterior simulation based on MCMC methods is also examined. Detailed applications to short-term interest rates, realized covariance matrices and asset pricing models demonstrate that the nonparametric pool forecasts well. The paper concludes with extensions and an application for calibrating and combining predictive densities.

Supervised dimensionality reduction for sequence data learns a transformation that maps the observations in sequences onto a low-dimensional subspace by maximizing the separability of sequences in different classes. It is typically more challenging than conventional dimensionality reduction for static data, because measuring the separability of sequences involves non-linear procedures to manipulate the temporal structures. In this paper, we propose a linear method, called order-preserving Wasserstein discriminant analysis (OWDA), and its deep extension, namely DeepOWDA, to learn linear and non-linear discriminative subspace for sequence data, respectively. We construct novel separability measures between sequence classes based on the order-preserving Wasserstein (OPW) distance to capture the essential differences among their temporal structures. Specifically, for each class, we extract the OPW barycenter and construct the intra-class scatter as the dispersion of the training sequences around the barycenter. The inter-class distance is measured as the OPW distance between the corresponding barycenters. We learn the linear and non-linear transformations by maximizing the inter-class distance and minimizing the intra-class scatter. In this way, the proposed OWDA and DeepOWDA are able to concentrate on the distinctive differences among classes by lifting the geometric relations with temporal constraints. Experiments on four 3D action recognition datasets show the effectiveness of OWDA and DeepOWDA.

This paper deals with the modeling and estimation of the sociological phenomena called echo chambers and segregation in social networks. Specifically, we present a novel community-based graph model that represents the emergence of segregated echo chambers as a Markov bridge process. A Markov bridge is a one-dimensional Markov random field that facilitates modeling the formation and disassociation of communities at deterministic times which is important in social networks with known timed events. We justify the proposed model with six real world examples and examine its performance on a recent Twitter dataset. We provide model parameter estimation algorithm based on maximum likelihood and, a Bayesian filtering algorithm for recursively estimating the level of segregation using noisy samples obtained from the network. Numerical results indicate that the proposed filtering algorithm outperforms the conventional hidden Markov modeling in terms of the mean-squared error. The proposed filtering method is useful in computational social science where data-driven estimation of the level of segregation from noisy data is required.

Exergames have beneficial effects on the player's motivation to exercise. However, many current games lack accurate full-body motion recognition, resulting in players not performing the physical exercise the game requires. Therefore, we aim to develop an immersive virtual reality exergame that simultaneously recognizes and reconstructs full-body movements to motivate players to learn and practice yoga. The system analyzes the entire movement execution and identifies the player's execution errors to provide appropriate feedback so that players can then improve their movements. Such a system can be used in exergames designed for rehabilitation purposes to assist patients or to monitor their improvement. To access recognition performance, we trained and tested hidden Markov models and applied the leave-one-out cross-validation. The results show that the system achieves an F1-score of 0.79 for yoga warrior I, 0.85 for yoga warrior II, and 0.66 for extended side angle. A user study with 32 participants revealed that the game was fun and that the players enjoyed it. Moreover, performance results show that players needed fewer attempts to correctly perform a pose as the exergame progressed.

Incipient faults in running gear systems corrupt the overall performance of high-speed trains, increasing the necessity of fault detection and diagnosis whose purpose is to maintain the safe and stable operation of high-speed trains. For this purpose, a novel data-driven method, that utilizes Hellinger distance and slow feature analysis, is proposed in this study. By integrating Hellinger distance into slow feature analysis, a new test statistic is defined for detecting incipient faults in running gear systems. Furthermore, the hidden Markov method is developed for performing reliable fault diagnosis tasks. The salient strengths of the proposed method lie in its satisfactory fault detectability on the one hand and the considerable robustness against high-level noises on the other hand. Finally, the effectiveness of the proposed method is verified through a numerical example and a running gear system of high-speed trains under actual working conditions.

This article is concerned with the asynchronous reliable extended dissipative filtering problem for a class of continuous-time T-S fuzzy Markov jump systems. The modes of the encountered sensor failures and the designed filter are considered to be asynchronous with the original systems, which can be described by two mutually independent hidden Markov processes. By proposing double variables-based decoupling principle and variable substitution principle, a new condition is presented to guarantee the filtering error system to be stochastically stable and extended dissipative. Compared with the existing works, the proposed method does not impose constraints on Lyapunov variables and slack variables, and some unnecessary constraints on the system structure are removed. These directly lead to less conservative and more general results. An example is provided to illustrate the effectiveness of the proposed design method.

The recognition of online handwriting is a vital application of pattern recognition, which involves the extraction of spatial and temporal information of handwritten patterns, and understanding the handwritten text while writing on the digital surface. Although, online handwriting recognition is a mature but exciting and fast developing field of pattern recognition, the same is not true for many of the Indic scripts. Gurmukhi is one of such popular scripts of India, and online handwriting recognition issues for larger units as words or sentences largely remained unexplored for this script till date. The existing study and first ever attempt for online handwritten Gurmukhi word recognition has relied upon the widely used hidden Markov model. This existing study evaluated against and performed very well in their chosen metrics. But, the available online handwritten Gurmukhi word recognition system could not obtain more than 90% recognition accuracy in data dependent environment too. The present study provided benchmark results for online handwritten Gurmukhi word recognition using deep learning architecture convolutional neural network, and obtained above 97% recognition accuracy in data dependent mode of handwriting. The previous Gurmukhi word recognition system followed the stroke based class labeling approach, whereas the present study has followed the word based class labeling approach. Present Online handwritten Gurmukhi word recognition results are quite satisfactory. Moreover, the proposed architecture can be used to improve the benchmark results of online handwriting recognition of several major Indian scripts. Experimental results demonstrated that the deep learning system achieved great results in Gurmukhi script and outperforms existing results in the literature.

The processing and analysis of large-scale journey trajectory data is becoming increasingly important as vehicles become ever more prevalent and interconnected. Mapping these trajectories onto a road network is a complex task, largely due to the inevitable measurement error generated by GPS sensors. Past approaches have had varying degrees of success, but achieving high accuracy has come at the expense of performance, memory usage, or both.In this paper, we solve these issues by proposing a map matching algorithm based on Hidden Markov Models (HMM). The proposed method is shown to be more efficient when compared against a traditional HMM based map matching method, whilst maintaining high accuracy and eschewing any requirements for CPU-intensive and memory-expensive pre-processing. The proposed algorithm offers a method for significantly accelerating transition-probability calculations using instances of high data-availability, which have previously been a large bottleneck in map matching algorithm performance. It is shown that this can be accomplished with the application of road-network segmentation combined with a spatially-aware heuristic. Experiments are performed using two different datasets, with over 9 hours of GPS samples. We show that the proposed framework is able to offer a reduction in run-time of over 90% with no significant effect on the algorithm's accuracy when compared against the traditional HMM approach.

Problem definition: We address the problem of how to estimate lost sales for substitutable products when there is no reliable on-shelf availability (OSA) information. Academic/practical relevance: We develop a novel approach to estimating lost sales using only sales data, a market share estimate, and an estimate of overall availability. We use the method to illustrate the negative consequences of using potentially inaccurate inventory records as indicators of availability. Methodology: We suggest a partially hidden Markov model of OSA to generate probabilistic choice sets and incorporate these probabilistic choice sets into the estimation of a multinomial logit demand model using a nested expectation-maximization algorithm. We highlight the importance of considering inventory reliability problems first through simulation and then by applying the procedure to a data set from a major U.S. retailer. Results: The simulations show that the method converges in seconds and produces estimates with similar or lower bias than state-of-the-art benchmarks. For the product category under consideration at the retailer, our procedure finds lost sales of around 3.0% compared with 0.2% when relying on the inventory record as an indicator of availability. Managerial implications: The method efficiently computes estimates that can be used to improve inventory management and guide managers on how to use their scarce resources to improve stocking execution. The research also shows that ignoring inventory record inaccuracies when estimating lost sales can produce substantially inaccurate estimates, which leads to incorrect parameters in supply chain planning.

In this article, asynchronous sliding-mode control (SMC) is investigated for 2-D discrete-time Markov jump systems. As the system modes are not always accessible to the controller, the hidden Markov model is employed to describe the asynchronization between the system modes and controller. A new 2-D sliding surface is constructed and the corresponding asynchronous SMC law is designed under the framework of the hidden Markov model. By Lyapunov function and linear matrix inequality (LMI) approaches, the reachability of system dynamics to the predefined sliding surface is investigated, and sufficient conditions are established to guarantee that the underlying 2-D system is asymptotically mean-square stable (AMSS) with an H∞ disturbance attenuation performance. Then, an algorithm is provided to derive the asynchronous 2D-SMC law. Finally, an example is given to verify the validity and effectiveness of the new SMC law design algorithm.

Current research and development in recognizing and predicting driving behaviors plays an important role in the development of Advanced Driver Assistance Systems (ADAS). For this reason, many machine learning approaches have been developed and applied. Hidden Markov Model (HMM) is a suitable algorithm due to its ability to handle time series data and state transition descriptions. Therefore, this contribution will focus on a review of HMM and its applications. The aim of this contribution is to analyze the current state of various driving behavior models and related HMM-based algorithms. By examining the current available approaches, a review is provided with respect to: i) influencing factors of driving behaviors corresponding to the research objectives of different driving models, ii) summarizing HMM related methods applied to driving behavior studies, and iii) discussing limitations, issues, and future potential works of the HMM-based algorithms. Conclusions with respect to the development of intelligent driving assistant system and vehicle dynamics control systems are given.

This article focuses on investigating the synchronization and position/force tracking performance of the multilateral teleoperation system with asymmetric time delays. First, the synchronization and tracking error signals are proposed to transform the nonlinear dynamic system into a closed-loop system governed by the Markovian jumping parameter. Because of the presence of time delays, the information regarding the system states might become unavailable. Owing to this, a feedback control based on the hidden Markov model is developed through which the information on the current state of the actual system can be accessed through a series of observations. Moreover, the forward and backward delays of the master and slave manipulators are assumed to be asymmetric and varying with time. For the stability analysis, the Lyapunov-Krasovskii technique has been adopted for which its derivatives are dealt by employing Jensens' inequality and extended reciprocal convex matrix inequality. Finally, simulation results were provided to validate the proposed methodology guaranteeing the closed-loop system to be asymptotically stable.

This paper proposes a new design of an event-based dissipative asynchronous controller for the interval type2 (IT2) fuzzy Markov jump systems (MJSs) subject to sensor saturation and actuator nonlinearity. By resorting to a generalized performance index, the $H_{\infty}$, passive, and dissipative fuzzy control problems are solved in a unified framework. The event-based scheme is developed for the IT2 fuzzy MJSs subject to sensor saturation and actuator nonlinearity, and the energy consumption of communication can be reduced. Moreover, the system and controller modes are asynchronous, and a hidden Markov model (HMM) is employed to observe the modes of the original system. The Membership-Function-Dependent (MFD) approach is applied to analyze the stability of the closed-loop system. Finally, two examples are given to demonstrate the effectiveness of the proposed algorithms.

In this study, the ultimate boundedness control for a type of networked singularly perturbed systems (SPSs) with communication constraints and deception attacks is explored. To improve the observer performance, the measurement outputs are quantized with the aid of a logarithmic quantizer. Meanwhile, the Markovian communication protocol (MCP) is forwarded to schedule the transmission sequence of the quantized signals. Unlike the conventional MCP, a novel nonhomogeneous MCP is proposed to further alleviate the communication bandwidth usage, whose transition probabilities are time-varying. By virtue of the hidden Markov model, the mismatches between the transmission mode over nonhomogeneous MCP and its detected ones are revealed, and an asynchronous observer-based controller is formed. Then, by establishing a novel singular-perturbation-parameter-based polytope Lyapunov-Krasovskii functional, the ultimate boundedness of the augmented networked SPS with randomly occurring deception attacks is elicited, and the local minimization of the controlled output is guaranteed. Finally, the effectiveness and applicability of the propounded control strategy are validated by two simulation examples.

Cross conditions prediction is a prevalent problem in manufacturing area, where tool wear prediction is a typical one. Existing data-driven methods for tool wear prediction mainly focus on cutting conditions with small variations, which encounters much difficulty under cross conditions with large variations, and the essential is the difference of both marginal distribution and conditional distribution of the data under cross conditions. To address this issue, this article proposes a meta-invariant feature space (MIFS) learning method, where invariant feature space is constructed for paired tasks to close marginal distribution, whose nature law under cross conditions is learned by meta-learning, i.e., MIFS, which can be adapted to achieve accurate tool wear prediction under cross conditions with a small number of new samples. Experimental results provided positive confirmation on the feasibility and accuracy of the proposed method, which can also be readily extended to regression and classification problems in other fields.

In this paper, we investigate the interference cancellation problem for asynchronous cognitive Internet of Things (C-IoT) in the concurrent spectrum access (CSA) model. An iterative receiver together with a new clustering algorithm is employed to cancel the interference and recovers the desired signal. Since the C-IoT device and the primary user (PU) are non-cooperative, they are generally asynchronous at the symbol level and the interference signal received by the secondary user (SU) receiver (Rx) possesses a first-order Markov property over time. Therefore, we propose a new clustering algorithm named hidden Morkov model (HMM) based affinity propagation (AP) algorithm that combines the HMM and the AP algorithm to realize the clustering module. The proposed HMM-AP algorithm exploits the temporal-correlation information to improve the performance of the iterative receiver. We show that the receiver with the HMM-AP algorithm can work well in non-cooperative system and has a much better performance than the original AP.

Markov switching (MS-)GARCH(1,1) models allow for structural changes in volatility dynamics between a finite number of regimes. Since the regimes are not observed, computation of the likelihood requires integrating over an exponentially increasing number of regime paths, which is intractable. An existing smooth likelihood estimation procedure for sequential Monte Carlo (SMC), that is currently limited to hidden Markov models with a one-dimensional state variable, is modified to enable likelihood estimation and maximisation for MS-GARCH(1,1) models, a model which requires two dimensions, volatility and regime, to evolve its hidden state process. Furthermore, the modified SMC procedure is shown to be easily adapted to fitting MS-GARCH(1,1) models even when there are missing observations. The proposed methodology is validated with simulated data and is also illustrated with analysis of two financial time series, the daily returns on the S&P 500 index and on the Henry Hub natural gas spot price, with the latter series containing a gap caused by shutdown in response to hurricane Rita in 2005.

The region of speech acknowledgment is one of the fascinating fields with regard to speech signal handling. Accomplishing precision and strength is an extremely troublesome limitation to different natural elements. Reformist work and audits in the speech acknowledgment application have been received utilizing fuzzy HMM, as one of the procedures to improve the acknowledgment exactness. This survey paper audits the different ideas of fuzzy HMM strategy and its applications to speech signal handling territory. Since the idea of a speech signal is dubious, it doesn't handle consistency at untouched stretches. To manage this dubiousness and vulnerabilities, numerous scientists have proposed fuzzy HMM is one of the better strategy to break down the speech signals. This paper presents the writing work accessible identified with speech acknowledgment utilizing fuzzy HMM procedures.

Nonintrusive load monitoring (NILM) is a technique to disaggregate an appliance's load consumption from the aggregate load in a house. Monitoring the energy behavior has become increasingly important for home energy management. For many machine learning-based models, model training needs enough, and diverse appliance-level labeled data from different houses, which is very time-consuming, expensive, and unacceptable for users. In this article, we propose an algorithm based on the adversarial network and the joint adaptation network for energy disaggregation to decrease the distribution gaps of both the feature space and the label space between the source and target domains. With only very limited labeled data in the source domain and enough unlabeled data in the target domain, our proposed algorithm can obtain satisfactory accuracy results for NILM. Extensive experiments for intradomain and interdomain demonstrate that the proposed algorithm can significantly improve the domain adaptation. Comparing with the baseline method that without any domain adaptation, the improvement on mean absolute error with the proposed algorithm can reach 67.72%, 67.53%, and 66.56% for the washing machine (W.M), the dishwasher (D.W), and the microwave (M.V), respectively.

With the rapid development of computing, communication, and control technologies, cyber–physical systems that integrate physical space, information space, and social space have emerged and are widely used in various important infrastructures. This article introduces the composition and basic algorithm of the hidden Markov model, and gives the mathematical description of the hidden Markov model. Since the hidden Markov model can deduce the hidden state of the observation object through the observed feature values, a device operating state cognition scheme based on the hidden Markov model is proposed. A method for analyzing cascading failures is proposed, and the critical threshold value of cyber–physical system under random attack is obtained. It is verified by simulation experiments, and the changes of system critical thresholds under different network parameters are compared and analyzed. We mainly use several sets of simulation experiments to verify the reliability of the critical threshold, and then verify near the critical threshold. Before simulating the cascading failure process, we first construct two random networks based on the average degree and the number of nodes. According to the previous description of the cyber–physical system model, a node in network B is randomly connected with three nodes in network A, so that the two networks are connected together to form a coupled system. Random attack or failure is represented by randomly deleting nodes. In the simulation experiment, we will simulate the process of cascading failure at each step, and after each step of cascading failure, we output and save the number of remaining nodes. When no nodes in the two networks are deleted, the cascading failure will stop, and then we will verify the critical threshold through the data obtained from the analysis. This provides the support of related theories and methods for the design of stable and reliable cyber–physical systems.