Short-term wind power forecast (WPF) depends highly on the wind speed forecast (WSF), which is the prime contributor to the forecasting error. To achieve more accurate WPF results, this article ...proposes a wind speed correction method to improve the WSF result obtained by using the weather research and forecasting (WRF) model. First, the WRF model is constructed to forecast the wind speed, and its performance is analyzed. Second, a novel hidden Markov model (HMM) is developed to explore both the temporal autocorrelation of WSF error and the nonlinear correlation between the WSF result and the error. In the model, the fuzzy C-means cluster is introduced to properly divide the hidden state space of HMM and the emission probability of HMM is improved as continuous by the kernel density estimation (KDE) to make full use of the observation information. The proposed HMM model is better at wind speed correction through modification. Third, the HMM is solved by the Viterbi algorithm and the minimum mean-square error regulation to correct the predicted wind speed. Finally, the deterministic and probabilistic WPF results are obtained by using another KDE model, the proposed method is demonstrated to be superior to the benchmarks in case studies.
This paper aims to design an asynchronous state feedback controller for Markov jump time-delay systems. The highlight of this work lies in that the state feedback is quantized by a logarithmic ...quantizer, and both the controller and quantizer are asynchronous with the controlled systems. By Lyapunov–Krasovskii functional, a sufficient condition is presented to ensure that the resulting closed-loop system is stochastically mean square stable with a prescribed H∞ performance index. Finally, an example is presented to illustrate the effectiveness and new features of proposed design method.
Correctly anticipating load characteristics of low voltage level is getting increased interest by distribution network operators. Energy disaggregation could be one of the potential approaches to ...exploit the massive amount of smart meter data to fulfill the task. Proper individual home appliance modeling is critical to the performance of NILM. In this paper, a hierarchical hidden Markov model (HHMM) framework to model home appliances is proposed. This model aims to provide better representation for those appliances that have multiple built-in modes with distinct power consumption profiles, such as washing machines and dishwashers. The dynamic Bayesian network representation of such an appliance model is built. A forward-backward algorithm, which is based on the framework of expectation maximization, is formalized for the HHMM fitting process. Tests on publically available data show that the HHMM and proposed algorithm can effectively handle the modeling of appliances with multiple functional modes, as well as better representing a general type of appliances. A disaggregation test also demonstrates that the fitted HHMM can be easily applied to a general inference solver to outperform conventional hidden Markov model in the estimation of energy disaggregation.
In this article, the passivity-based control problem is addressed for hidden Markov jump systems with singular perturbations and partially unknown probabilities. The hidden Markov model (HMM) with ...partially unknown probabilities is introduced, where the partially unknown probabilities may exist in either the transition probability matrix of Markov chain, the observation probability matrix of observed signal, or in both of them. Thus, the underlying HMM is more general than the one in some existing works. By using this HMM, some passivity analysis criteria are established for Markov jump singularly perturbed systems with partial unknown probabilities. Based on these criteria, a unified method is presented for the design of controllers to ensure the passivity of the system. A numerical example and an armature controlled dc motor model are given to illustrate the efficiency of the proposed method.
Vehicle speed prediction provides important information for many intelligent vehicular and transportation applications. Accurate on-road vehicle speed prediction is challenging, because an individual ...vehicle speed is affected by many factors, e.g., the traffic condition, vehicle type, and driver's behavior, in either deterministic or stochastic way. This paper proposes a novel data-driven vehicle speed prediction method in the context of vehicular networks, in which the real-time traffic information is accessible and utilized for vehicle speed prediction. It first predicts the average traffic speeds of road segments by using neural network models based on historical traffic data. Hidden Markov models (HMMs) are then utilized to present the statistical relationship between individual vehicle speeds and the traffic speed. Prediction for individual vehicle speeds is realized by applying the forward-backward algorithm on HMMs. To evaluate the prediction performance, simulations are set up in the SUMO microscopic traffic simulator with the application of a real Luxembourg motorway network and traffic count data. The vehicle speed prediction result shows that our proposed method outperforms other ones in terms of prediction accuracy.
This paper presents a method for pedestrian activity classification and gait analysis based on the microelectromechanical-systems inertial measurement unit (IMU). The work targets two groups of ...applications, including the following: 1) human activity classification and 2) joint human activity and gait-phase classification. In the latter case, the gait phase is defined as a substate of a specific gait cycle, i.e., the states of the body between the stance and swing phases. We model the pedestrian motion with a continuous hidden Markov model (HMM) in which the output density functions are assumed to be Gaussian mixture models. For the joint activity and gait-phase classification, motivated by the cyclical nature of the IMU measurements, each individual activity is modeled by a "circular HMM." For both the proposed classification methods, proper feature vectors are extracted from the IMU measurements. In this paper, we report the results of conducted experiments where the IMU was mounted on the humans' chests. This permits the potential application of the current study in camera-aided inertial navigation for positioning and personal assistance for future research works. Five classes of activity, including walking, running, going upstairs, going downstairs, and standing, are considered in the experiments. The performance of the proposed methods is illustrated in various ways, and as an objective measure, the confusion matrix is computed and reported. The achieved relative figure of merits using the collected data validates the reliability of the proposed methods for the desired applications.
Hidden Markov models are widely used for target tracking, where the process and measurement noises are usually modeled as independent Gaussian distributions for mathematical simplicity. However, the ...independence and Gaussian assumptions do not always hold in practice. For example, in a typical target tracking application, a radar is utilized to track a non-cooperative target. Measurement noise is correlated over time since the sampling frequency of a radar is usually far greater than the bandwidth of measurement noise. Besides, when target is maneuvering, the process and measurement noises are heavy-tailed and non-Gaussian due to intrinsic data generation mechanism. In this paper, we consider a linear pairwise Markov model (PMM) with Student's t noise to model non-cooperative single target tracking without clutter and missed detections. A PMM is more general than an HMM and can be used to model correlated measurement noise or correlated process and measurement noises. The Student's t distribution is one of the most commonly used heavy-tailed distribution and can be used to address irregular target motion. We mainly focus on solving the filtering problems for the model. First, we develop a filter for the case where noise statistics are accurately known. Second, we further consider the case of non-stationary Student's t noise, and propose a novel robust filter by employing a variational Bayesian method. Finally, the effectiveness of the proposed filters is illustrated via simulation results.
Existing static grid resource scheduling algorithms, which are limited to minimizing the makespan, cannot meet the needs of resource scheduling required by cloud computing. Current cloud ...infrastructure solutions provide operational support at the level of resource infrastructure only. When hardware resources form the virtual resource pool, virtual machines are deployed for use transparently. Considering the competing characteristics of multi-tenant environments in cloud computing, this paper proposes a cloud resource allocation model based on an imperfect information Stackelberg game (CSAM-IISG) using a hidden Markov model (HMM) in a cloud computing environment. CSAM-IISG was shown to increase the profit of both the resource supplier and the applicant. Firstly, we used the HMM to predict the service provider's current bid using the historical resources based on demand. Through predicting the bid dynamically, an imperfect information Stackelberg game (IISG) was established. The IISG motivates service providers to choose the optimal bidding strategy according to the overall utility, achieving maximum profits. Based on the unit prices of different types of resources, a resource allocation model is proposed to guarantee optimal gains for the infrastructure supplier. The proposed resource allocation model can support synchronous allocation for both multi-service providers and various resources. The simulation results demonstrated that the predicted price was close to the actual transaction price, which was lower than the actual value in the game model. The proposed model was shown to increase the profits of service providers and infrastructure suppliers simultaneously.
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.
This work investigates the observer-based asynchronous fault detection problem for a class of nonlinear Markov jumping systems. The conic-type nonlinearities hold such a restrictive condition that ...locates in a known hypersphere with an undefined centre. In order to guarantee the observer modes run synchronously with the system modes, we introduce a hidden Markov model to deal with this difficulty. Utilizing H ∞ and H _ performance index, a multi-targets strategy of asynchronous fault detection problem is formulated. Via linear matrix inequality, sufficient conditions for the presence of the asynchronous fault detection observer are derived respectively. Then an asynchronous fault detection algorithm is formulated. Finally, the application of dynamic equivalent circuit of separately excited DC motor with three cases is employed to illustrate that the devised asynchronous fault detection observer is able to detect the faults after the appearances in the absence of any incorrect alarm.
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