Obstructive sleep apnea (OSA) syndrome is a common sleep disorder suffered by an increasing number of people worldwide. As an alternative to polysomnography (PSG) for OSA diagnosis, the automatic OSA ...detection methods used in the current practice mainly concentrate on feature extraction and classifier selection based on collected physiological signals. However, one common limitation in these methods is that the temporal dependence of signals are usually ignored, which may result in critical information loss for OSA diagnosis. In this study, we propose a novel OSA detection approach based on ECG signals by considering temporal dependence within segmented signals. A discriminative hidden Markov model (HMM) and corresponding parameter estimation algorithms are provided. In addition, subject-specific transition probabilities within the model are employed to characterize the subject-to-subject differences of potential OSA patients. To validate our approach, 70 recordings obtained from the Physionet Apnea-ECG database were used. Accuracies of 97.1% for per-recording classification and 86.2% for per-segment OSA detection with satisfactory sensitivity and specificity were achieved. Compared with other existing methods that simply ignore the temporal dependence of signals, the proposed HMM-based detection approach delivers more satisfactory detection performance and could be extended to other disease diagnosis applications.
We propose a novel context-dependent (CD) model for large-vocabulary speech recognition (LVSR) that leverages recent advances in using deep belief networks for phone recognition. We describe a ...pre-trained deep neural network hidden Markov model (DNN-HMM) hybrid architecture that trains the DNN to produce a distribution over senones (tied triphone states) as its output. The deep belief network pre-training algorithm is a robust and often helpful way to initialize deep neural networks generatively that can aid in optimization and reduce generalization error. We illustrate the key components of our model, describe the procedure for applying CD-DNN-HMMs to LVSR, and analyze the effects of various modeling choices on performance. Experiments on a challenging business search dataset demonstrate that CD-DNN-HMMs can significantly outperform the conventional context-dependent Gaussian mixture model (GMM)-HMMs, with an absolute sentence accuracy improvement of 5.8% and 9.2% (or relative error reduction of 16.0% and 23.2%) over the CD-GMM-HMMs trained using the minimum phone error rate (MPE) and maximum-likelihood (ML) criteria, respectively.
This paper focuses on the issue of the finite-region H∞ asynchronous control scheme, which is devoted to the transient behavior of a class of two-dimensional Markov jump systems. The disquisitive ...system is characterized based upon a Roesser model. Considering the fact that there exists an asynchronous phenomenon between the controlled plant and the controller, we introduce a hidden Markov model to address the non-synchronization. By resorting to a given conditional probability matrix, the mode jumps between the controlled plant and the controller are determined. To conclude, an example is employed to illustrate the potential applications of the devised approach.
The issue of asynchronous passive control is addressed for Markov jump systems in this technical note. The asynchronization phenomenon appears between the system modes and controller modes, which is ...described by a hidden Markov model. Accordingly, a hidden Markov jump model is used to name the resultant closed-loop system. By utilizing the matrix inequality technique, three equivalent sufficient conditions are obtained, which can guarantee the hidden Markov jump systems to be stochastically passive. Based on the established conditions, the design of asynchronous controller, which covers the well-known mode-independent controller and synchronous controller as special cases, is addressed. The DC motor device is applied to demonstrate the practicability of the derived asynchronous synthesis scheme.
The problem of asynchronous and resilient filtering for discrete-time Markov jump neural networks subject to extended dissipativity is investigated in this paper. The modes of the designed resilient ...filter are assumed to run asynchronously with the modes of original Markov jump neural networks, which accord well with practical applications and are described through a hidden Markov model. Due to the fluctuation of the filter parameters, a resilient filter taking into account parameter uncertainty is adopted. Being different from the norm-bound type of uncertainty which has been studied in a considerable number of the existing literatures, the interval type of uncertainty is introduced so as to describe uncertain phenomenon more accurately. By means of convex optimal method, the gains of filter are derived to guarantee the stochastic stability and extended dissipativity of the filtering error system under the wave of the filter parameters. Considering the limited computing power of MATLAB solver, a relatively simple simulation is exploited to verify the effectiveness and merits of the theoretical findings where the relationships among optimal performance index, uncertain parameter <inline-formula> <tex-math notation="LaTeX">\sigma </tex-math></inline-formula>, and asynchronous rate are revealed.
This paper deals with the problem of dissipativity-based asynchronous fault detection (FD) for Takagi-Sugeno fuzzy Markov jump systems with network data dropouts. It is assumed that data dropouts ...happen intermittently from the plant to the FD filter, which is described by Bernoulli process. The hidden Markov model is employed to describe the asynchronous phenomenon between the plant and filter. Based on Lyapunov theory, a sufficient condition is developed to guarantee that the FD system is stochastically stable with strictly dissipative performance. By choosing an appropriate Lyapunov function with the slack matrix technique and Finsler's Lemma, two approaches are proposed to compute filter gains by solving linear matrix inequalities. Finally, an example is provided to illustrate the usefulness and effectiveness of the proposed design methods.
This paper addresses the dissipative asynchronous filtering problem for a class of Takagi-Sugeno fuzzy Markov jump systems in the continuous-time domain. The hidden Markov model is applied to ...describe the asynchronous situation between the designed filter and the original system. Based on the stochastic Lyapunov function, a sufficient condition is developed to guarantee the stochastic stability of the filtering error systems with a given dissipative performance. Two different methods for the existence of desired filter are established. Due to the Finsler's lemma, the second approach has fewer variables to decide and brings less conservatism than the first one. Finally, an example is provided to demonstrate the correctness and advantage of the proposed approaches.
The problem of secure state estimation and attack detection in cyber-physical systems is considered in this paper. A stochastic modeling framework is first introduced, based on which the attacked ...system is modeled as a finite-state hidden Markov model with switching transition probability matrices controlled by a Markov decision process. Based on this framework, a joint state and attack estimation problem is formulated and solved. Utilizing the change of probability measure approach, we show that an unnormalized joint state and attack distribution conditioned on the sensor measurement information evolves in a linear recursive form, based on which the optimal estimates can be further calculated by evaluating the normalized marginal conditional distributions. The estimation results are further applied to secure estimation of stable linear Gaussian systems, and extensions to more general systems are also discussed. The effectiveness of the results are illustrated by numerical examples and comparative simulation.
Appliance-level load models are expected to be crucial to future smart grid applications. Unlike direct appliance monitoring approaches, it is more flexible and convenient to mine smart meter data to ...generate load models at device level nonintrusively and generalise to all households with smart meter ownership. This paper proposes a comprehensive and extensible framework to solve the load disaggregation problem for residential households. Our approach examines both the modelling of home appliances as hidden Markov models and the solving of non-intrusive load monitoring based on segmented integer quadratic constraint programming to disaggregate a household power profile into the appliance level. Structure of our approach to be implemented with current smart meter infrastructure is given and simulations are performed based on public datasets. All data are down-sampled to the rate that is consistent with the Australia smart meter infrastructure minimum functionality. The results demonstrate that our approach is able to work with existing smart meters to generate device level load model for other smart grid research and applications.
Dissolved gases analysis (DGA) provides widely recognized practice for oil-immersed power transformers, and it is mainly interpreted for fault diagnosis. In order to accurately estimate the health ...index state of power transformers and predict the incipient operation failure, a dynamic fault prediction technique based on hidden Markov model (HMM) of DGA is proposed in this paper. Gaussian mixture model, as a soft clustering method, is used to extract the static features of different health states from a DGA dataset of 65 in-service power transformers with 1600 days operation. Especially, a sub-health state is introduced to enrich the health index and aging stages of power transformers. The static features between health states and concentrations of dissolved gases are built, and the effectiveness of clustering is cross validated. Furthermore, taking time sequence into consideration, transition probability of power transformer between different health states based on the HMM model is calculated and analyzed. The effectiveness of dynamic early warning and incipient fault prediction in sub-health status of in-service power transformers has been proved. Moreover, the dynamic fault prediction is able to provide decision-making basis for practical condition-based operation and maintenances.
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