This paper gives a general overview of hidden Markov model (HMM)-based speech synthesis, which has recently been demonstrated to be very effective in synthesizing speech. The main advantage of this ...approach is its flexibility in changing speaker identities, emotions, and speaking styles. This paper also discusses the relation between the HMM-based approach and the more conventional unit-selection approach that has dominated over the last decades. Finally, advanced techniques for future developments are described.
This paper focuses on the state estimator design problem for a switched neural network (SNN) with probabilistic quantized outputs, where the switching process is governed by a sojourn probability. It ...is assumed that both packet dropouts and signal quantization exist in communication channels. Asynchronous estimator and quantification function are described by two different hidden Markov model between the SNNs and its estimator. To deal with the small uncertain of estimators in a random way, a probabilistic nonfragile state estimator is introduced, where uncertain information is described by the interval type of gain variation. A sufficient condition on mean square stable of the estimation error system is obtained and then the desired estimator is designed. Finally, a simulation result is provided to verify the effectiveness of the proposed design method.
Positioning is envisioned as an essential enabler of future fifth generation (5G) mobile networks due to the massive number of use cases that would benefit from knowing users' positions. In this ...work, we propose a particle filter-based reinforcement learning (PFRL) approach for the robust wireless indoor positioning system. Our algorithm integrates information of indoor zone prediction, inertial measurement units, wireless radio-based ranging, and floor plan into an particle filter. The zone prediction method is designed with an ensemble learning algorithm by integrating individual discriminative learning methods and Hidden Markov Models. Further, we integrate the particle filter approach with a reinforcement learning-based resampling method to provide robustness against localization failure problems such as the kidnapping robot problem. The PFRL approach is validated on a two-tier architecture, in which distributed machine learning tasks are hosted at client and edge layer. Experiment results show that our system outperforms traditional terminal-based approaches in both stability and accuracy.
Misunderstanding of driver correction behaviors is the primary reason for false warnings of lane-departure-prediction systems. We proposed a learning-based approach to predict unintended ...lane-departure behaviors and chances of drivers to bring vehicles back to the lane. First, a personalized driver model for lane-departure and lane-keeping behavior is established by combining the Gaussian mixture model and the hidden Markov model. Second, based on this model, we developed an online model-based prediction algorithm to predict the forthcoming vehicle trajectory and judge whether the driver will act a lane departure behavior or correction behavior. We also develop a warning strategy based on the model-based prediction algorithm that allows the lane-departure warning system to be acceptable for drivers according to the predicted trajectory. In addition, the naturalistic driving data of ten drivers were collected to train the personalized driver model and validate this approach. We compared the proposed method with a basic time-to-lane-crossing (TLC) method and a TLC-directional sequence of piecewise lateral slopes (TLC-DSPLS) method. Experimental results show that the proposed approach can reduce the false-warning rate to 3.13% on average at 1-s prediction time.
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.
Sleep apnea (SA) is a harmful respiratory disorder that has caused widespread concern around the world. Considering that electrocardiogram (ECG)-based SA diagnostic methods were effective and ...human-friendly, many machine learning or deep learning methods based on ECG have been proposed by prior works. However, these methods are based on feature engineering or supervised and semisupervised learning techniques, and the feature sets are always incomplete, subjective, and highly dependent on labeled data. In addition, some related studies ignored the data imbalance problem which leads to poor performance of classifier on minority classes. In this study, an SA detection model based on frequential stacked sparse auto-encoder (FSSAE) and time-dependent cost-sensitive (TDCS) classification model was proposed. The FSSAE extracts feature set automatically with unsupervised learning technique, and the TDCS classification model is proposed by combining the hidden Markov model (HMM) and the MetaCost algorithm to improve the performance of the classifier by considering temporal dependence and the imbalance problem. In the test set, the result of per-segment classification achieved 85.1%, 86.2%, and 84.4% for accuracy, sensitivity, and specificity, respectively, proving that our method is helpful for SA detection.
The brief studies the asynchronous observer-based sliding mode control (SMC) for Markov jump systems (MJSs) with actuator failures. Considering the phenomena of unmeasurable states and the case that ...the controller/observer to be devised have different modes from the original systems, a hidden Markov model (HMM) is used to construct an asynchronous observer and the corresponding sliding surface is designed. Then, the asynchronous SMC strategy is developed to guarantee the reachability of the predetermined sliding surface in a limited time. A sufficient condition is established for the mean-square stability of the overall closed-loop systems and the desired controller is designed. Moreover, when the conditional probabilities describing the mode asynchronism are only partially known for the HMM in the systems, the related results are also given. Finally, simulation results show the usefulness of the developed techniques.
Temporal data clustering can provide underpinning techniques for the discovery of intrinsic structures, which proved important in condensing or summarizing information demanded in various fields of ...information sciences, ranging from time series analysis to sequential data understanding. In this paper, we propose a novel hidden Markov model (HMM)-based hybrid meta-clustering ensemble with bi-weighting scheme to solve the problems of initialization and model selection associated with temporal data clustering. To improve the performance of the ensemble techniques, the proposed bi-weighting scheme adaptively examines the partition process and hence optimizes the fusion of consensus functions. Specifically, three consensus functions are used to combine the input partitions, generated by HMM-based <inline-formula> <tex-math notation="LaTeX">{K} </tex-math></inline-formula>-models under different initializations, into a robust consensus partition. An optimal consensus partition is then selected from the three candidates by a normalized mutual information-based objective function. Finally, the optimal consensus partition is further refined by the HMM-based agglomerative clustering algorithm in association with dendrogram-based similarity partitioning algorithm, leading to the advantage that the number of clusters can be automatically and adaptively determined. Extensive experiments on synthetic data, time series, and real-world motion trajectory datasets illustrate that our proposed approach outperforms all the selected benchmarks and hence providing promising potentials for developing improved clustering tools for information analysis and management.
With the growing use of crowdsourced location data from smartphones for transportation applications, the task of map-matching raw location sequence data to travel paths in the road network becomes ...more important. High-frequency sampling of smartphone locations using accurate but power-hungry positioning technologies is not practically feasible as it consumes an undue amount of the smartphone's bandwidth and battery power. Hence, there exists a need to develop robust algorithms for map-matching inaccurate and sparse location data in an accurate and timely manner. This paper addresses the above-mentioned need by presenting a novel map-matching solution that combines the widely used approach based on a hidden Markov model (HMM) with the concept of drivers' route choice. Our algorithm uses an HMM tailored for noisy and sparse data to generate partial map-matched paths in an online manner. We use a route choice model, estimated from real drive data, to reassess each HMM-generated partial path along with a set of feasible alternative paths. We evaluated the proposed algorithm with real world as well as synthetic location data under varying levels of measurement noise and temporal sparsity. The results show that the map-matching accuracy of our algorithm is significantly higher than that of the state of the art, especially at high levels of noise.
This paper is concerned with event-based H ∞ control for a class of networked Markov jump systems (MJSs) with missing measurements. The phenomenon of asynchronism occurs in both the controller and ...the actuator-failure model, which is estimated by the hidden Markov model, is taken into consideration. In addition, to reduce the burden of data transmission, a mode-dependent event-triggered mechanism (ETM) is proposed. Together with ETM, a network-induced delay is introduced. Subsequently, to guarantee that the MJS is stochastically stable, an event-based asynchronous controller is designed. Finally, to reveal the effectiveness of the proposed method, a simulation example of pulse width-modulation-driven boost converter is considered.
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