Search

With the increasing maturity of speech synthesis technology, on the one hand, it has been more and more widely used in people's lives; on the other hand, it also brings more and more convenience to people. The requirements for speech synthesis systems are getting higher and higher. Therefore, advanced technology is used to improve and update the accent recognition system. This paper mainly introduces the word stress annotation technology combined with neural network speech synthesis technology. In Chinese speech synthesis, prosodic structure prediction has a great influence on naturalness. The purpose of this paper is to accurately predict the prosodic structure, which has become an important problem to be solved in speech synthesis. Experimental data show that the average error of samples in the network training process is lel/85, and the minimum value of the training error after 500 steps is 0.00013127, so the final sample average error is lel = 85 ∗ 0.0013127 = 0.112 < 0.5, and use the deep neural network (DNN) to train different parameters to obtain the conversion model, and then synthesize these conversion models, and finally achieve the effect of improving the synthesized sound quality. [ABSTRACT FROM AUTHOR]

Clustering is an important ingredient of unsupervised learning; classical clustering methods include K-means clustering and hierarchical clustering. These methods may suffer from instability because of their tendency prone to sink into the local optimal solutions of the nonconvex optimization model. In this paper, we propose a new convex clustering method for high-dimensional data based on the sparse group lasso penalty, which can simultaneously group observations and eliminate noninformative features. In this method, the number of clusters can be learned from the data instead of being given in advance as a parameter. We theoretically prove that the proposed method has desirable statistical properties, including a finite sample error bound and feature screening consistency. Furthermore, the semiproximal alternating direction method of multipliers is designed to solve the sparse group lasso convex clustering model, and its convergence analysis is established without any conditions. Finally, the effectiveness of the proposed method is thoroughly demonstrated through simulated experiments and real applications. [ABSTRACT FROM AUTHOR]

When designing the sample scheme, it is important to determine the sample size. The survey accuracy and cost of survey and sampling method should be considered comprehensively. In this article, we discuss the method of determining the sample size of complex successive sampling with rotation sample for sensitive issue and deduce the formulas for the optimal sample size under two-stage sampling and stratified two-stage sampling by using Cauchy-Schwartz inequality, respectively, so as to minimize the cost for given sampling errors and to minimize the sampling errors for given cost. [ABSTRACT FROM AUTHOR]

This paper studies the distributed estimation problem of sensor networks, in which each node is periodically sensing and broadcasting in order. A consensus estimation algorithm is applied, and a weight design approach is proposed. The weights are designed based on an adjusting parameter and the nodes’ lengths of their shortest paths to the target node. By introducing a (T+2)-partite graph of the time-varying networks over a time period [0,T] and studying the relationships between the product of the time-sequence estimation error system matrices and the sequences of edges in the (T+2)-partite graph, a sufficient condition in terms of the observer gain and the adjusting parameter for the stability of the estimation error system is proposed. A simulation example is given to illustrate the results. [ABSTRACT FROM AUTHOR]

We consider a mathematical model which describes a static frictional contact between a piezoelectric body and a thermally conductive obstacle. The constitutive law is supposed to be thermo-electro-elastic and the contact is modeled with normal compliance and a version of Coulomb’s friction law. We derive a variational formulation of the problem and we prove the existence and uniqueness of its solution. The proof is based on some results of elliptic variational inequalities and fixed point arguments. Furthermore, a finite element approximation and a priori error estimates are obtained. [ABSTRACT FROM AUTHOR]

This work, under the optimal experimental design framework, investigates the sensor placement problem that aims to guide electromagnetic induction (EMI) sensing of multiple objects. We use the linearized model covariance matrix as a measure of estimation error to present a sequential experimental design (SED) technique. The technique recursively minimizes data misfit to update model parameters and maximizes an information gain function for a future survey relative to previous surveys. The fundamental process of the SED seeks to increase weighted sensitivities to targets when placing sensors. The synthetic and field experiments demonstrate that SED can be used to guide the sensing process for an effective interrogation. It also can serve as a theoretic basis to improve empirical survey operation. We further study the sensitivity of the SED to the number of objects within the sensing range. The tests suggest that an appropriately overrepresented model about expected anomalies might be a feasible choice. [ABSTRACT FROM AUTHOR]

This paper studies the problem of fault tolerant control by trajectory tracking for a class of linear constant time-delay systems. The aim is to design a control law by considering the fault detected by the observer to make the faulty system track the reference model even if faults occur. By considering two kinds of actuator faults, one constant and another time-varying, the corresponding proportional integral observers and active FTC control laws are designed, respectively. State tracking error, state estimation error, output estimation error, and fault estimation error are combined into a descriptor system. Based on Lyapunov-Krasovskii functional approach stability problems of the descriptor system are easily solved in terms of the Linear Matrix Inequalities (LMI). Finally, a numerical example is considered to prove the effectiveness in both cases. [ABSTRACT FROM AUTHOR]

This paper presents a sensorless speed control strategy for a permanent-magnet synchronous motor (PMSM) based on an adaptive nonlinear extended state observer (ANLESO). In this paper, an extended state observer (ESO), which takes back-EMF (back electromotive force) as an extended state, is used to estimate the rotor position and the rotor speed because of its simpler structure and higher accuracy. Both linear ESO (LESO) and nonlinear ESO (NLESO) are considered to estimate the back-EMF of PMSM, and NLESO is finally implemented due to its obvious advantage in convergence. The convergence characteristics of the estimation error of the observer are analyzed by the Lyapunov theory. In order to take both stability and steady-state error into consideration, an adaptive NLESO is proposed, which adaptively adjusts the parameters of NLESO to a compromised value. The performance of the proposed method was demonstrated by simulations and experiments. [ABSTRACT FROM AUTHOR]

Time-interleaved technique is widely used to increase the sampling rate of analog-to-digital converter (ADC). However, the channel mismatches degrade the performance of time-interleaved ADC (TIADC). Therefore, a statistic-based calibration method for TIADC is proposed in this paper. The average value of sampling points is utilized to calculate offset error, and the summation of sampling points is used to calculate gain error. After offset and gain error are obtained, they are calibrated by offset and gain adjustment elements in ADC. Timing skew is calibrated by an iterative method. The product of sampling points of two adjacent subchannels is used as a metric for calibration. The proposed method is employed to calibrate mismatches in a four-channel 5 GS/s TIADC system. Simulation results show that the proposed method can estimate mismatches accurately in a wide frequency range. It is also proved that an accurate estimation can be obtained even if the signal noise ratio (SNR) of input signal is 20 dB. Furthermore, the results obtained from a real four-channel 5 GS/s TIADC system demonstrate the effectiveness of the proposed method. We can see that the spectra spurs due to mismatches have been effectively eliminated after calibration. [ABSTRACT FROM AUTHOR]

Traditionally, to implement the first-best marginal cost pricing scheme in a traffic network requires the information on the exact demand function or true origin-destination demand, which, however, is rarely available in practice. To overcome this dilemma, the trial-and-error method has been proposed to find the marginal cost toll through an iterative process using the observed traffic volumes. This method guarantees the convergence of tolls and flows to the system optimal state under the assumption of deterministic traffic conditions. However, in reality, the uncertainty of transportation network has been recognized well that induces the variability of link flow and travel time. Therefore, this paper proposes an evolutionary implementation method that iteratively finds the first-best marginal cost toll pattern according to the observed stochastic link flow information and the known travel time functions. The proof of the convergence of the iterative algorithm is presented. The paper also analyzes the effect of the sampling error of the link flow data on the convergence of the algorithm and shows that the biases from the flow observation will not affect the convergence. The numerical tests are provided for the illustration of the algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the robust estimation fusion problem for distributed multisensor systems 8/9with uncertain correlations of local estimation errors. For an uncertain class characterized by the Kullback-Leibler (KL) divergence from the actual model to nominal model of local estimation error covariance, the robust estimation fusion problem is formulated to find a linear minimum variance unbiased estimator for the least favorable model. It is proved that the optimal fuser under nominal correlation model is robust while the estimation error has a relative entropy uncertainty. [ABSTRACT FROM AUTHOR]

With the increase of sensors' resolution, traditional object tracking technology, which ignores object's physical extension, gradually becomes inappropriate. Extended object tracking (EOT) technology is able to obtain more information about the object through jointly estimating both centroid's dynamic state and physical extension of the object. Random matrix based approach is a promising method for EOT. It uses ellipse/ellipsoid to describe the physical extension of the object. In order to reduce the physical extension estimation error when object maneuvers, the relationship between ellipse/ellipsoid and symmetrical positive definite matrix is analyzed at first. On this basis, ellipse/ellipsoid fitting based approach (EFA) for EOT is proposed based on the measurement model and centroid's dynamic model of random matrix based EOT approach. Simulation results show that EFA is effective. The physical extension estimation error of EFA is lower than those of random matrix based approaches when object maneuvers. Besides, the estimation error of centroid's dynamic state of EFA is also lower. [ABSTRACT FROM AUTHOR]

The UWB channel estimation and multi user detection problem are investigated. The information symbol and channel parameter are considered as unknown variables. The multi user detector and UWB channel estimator are designed jointly. For symbol detection, the one-step predictor of channel parameter is used and the estimation error is treated as a multiplicative noise; then a Riccati equation and a Lyapunov equation will be needed. If the transmitted symbols are uncorrelated and identically distributed random variables with zero mean and unit variance, only a Riccati equation needs to be solved. For UWB channel estimation, the one-step predictor of information symbol is used and the estimation error is also considered as a multiplicative noise. The solutions to the above two problems are obtained by solving a couple of Riccati equations together with two Lyapunov equations. [ABSTRACT FROM AUTHOR]

We need to predict mathematical model of the system and a priori knowledge of the noise statistics when traditional simultaneous localization and mapping (SLAM) solutions are used. However, in many practical applications, prior statistics of the noise are unknown or time-varying, which will lead to large estimation errors or even cause divergence. In order to solve the above problem, an innovative cubature Kalman filter-based SLAM (CKF-SLAM) algorithm based on an adaptive cubature Kalman filter (ACKF) was established in this paper. The novel algorithm estimates the statistical parameters of the unknown system noise by introducing the Sage-Husa noise statistic estimator. Combining the advantages of the CKF-SLAM and the adaptive estimator, the new ACKF-SLAM algorithm can reduce the state estimated error significantly and improve the navigation accuracy of the SLAM system effectively. The performance of this new algorithm has been examined through numerical simulations in different scenarios. The results have shown that the position error can be effectively reduced with the new adaptive CKF-SLAM algorithm. Compared with other traditional SLAM methods, the accuracy of the nonlinear SLAM systemis significantly improved. It verifies that the proposed ACKF-SLAM algorithm is valid and feasible. [ABSTRACT FROM AUTHOR]

This paper studies the remote filtering problem over a packet-dropping network. Ageneral multiple-input-multiple-output (MIMO) discrete-time system is considered. The multiple measurements are sent over different communication channels every time step, and the packet loss phenomenon in every communication channel is described by an independent and identically distributed (i.i.d) Bernoulli process. A suboptimal filter is obtained which can minimize the mean squared estimation error. The convergence properties of the estimation error covariance are studied, and mean square stability of the sub optimal filter is proved under standard assumptions. A simulation example is exploited to demonstrate the effectiveness of the results. [ABSTRACT FROM AUTHOR]

Localization is one of the most significant technologies in wireless sensor networks (WSNs) since it plays a critical role in many applications. The main idea in most localization methods is to estimate the sensor-anchor distances that are used by sensors to locate themselves. However, the distance information is always imprecise due to the measurement or estimation errors. In this work, a novel algorithm called neighbor constraint assisted distributed localization (NCA-DL) is proposed, which introduces the application of geometric constraints to these distances within the algorithm. For example, in the case presented here, the assistance provided by a neighbor will consist in formulating a linear equality constraint. These constraints can be further used to formulate optimization problems for distance estimation. Then through some optimization methods, the imprecise distances can be refined and the localization precision is improved. [ABSTRACT FROM AUTHOR]

A novel method of robust trajectory linearization control for a class of nonlinear systems with uncertainties based on disturbance rejection is proposed. Firstly, on the basis of trajectory linearization control (TLC) method, a feedback linearization based control law is designed to transform the original tracking error dynamics to the canonical integral-chain form. To address the issue of reducing the influence made by uncertainties, with tracking error as input, linear extended state observer (LESO) is constructed to estimate the tracking error vector, as well as the uncertainties in an integrated manner. Meanwhile, the boundedness of the estimated error is investigated by theoretical analysis. In addition, decoupled controller (which has the characteristic of well-tuning and simple form) based on LESO is synthesized to realize the output tracking for closed-loop system. The closed-loop stability of the system under the proposed LESO-based control structure is established. Also, simulation results are presented to illustrate the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed fault estimation for a class of nonlinear networked systems, where the T-S fuzzy model is utilized to approximate the nonlinear plant and the whole fault estimation task is operated by a wireless sensor network. Due to the limited power in sensors, signal is quantized before transmission. Based on the Lyapunov stability theory and the robust control approach, a sufficient condition is obtained such that the estimation error system is asymptotic stable with a prescribed H∞ performance level. Finally, a case study on the actuator fault estimation of robotic manipulator is given to show the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

This work proposes an improved unscented Kalman filter (UKF)-based simultaneous localization andmapping (SLAM) algorithm based on an adaptive unscented Kalman filter (AUKF) with a noise statistic estimator. The algorithm solves the issue that conventional UKF-SLAMalgorithms have declining accuracy, with divergence occurring when the prior noise statistic is unknown and time-varying. The new SLAM algorithm performs an online estimation of the statistical parameters of unknown system noise by introducing a modified Sage-Husa noise statistic estimator. The algorithm also judges whether the filter is divergent and restrains potential filtering divergence using a covariance matchingmethod. This approach reduces state estimation error, effectively improving navigation accuracy of the SLAM system. A line feature extraction is implemented through a Hough transform based on the ranging sonar model. Test results based on unmanned underwater vehicle (UUV) sea trial data indicate that the proposed AUKF-SLAM algorithm is valid and feasible and provides better accuracy than the standard UKF-SLAM system. [ABSTRACT FROM AUTHOR]

This paper presents a fault diagnosis algorithm to estimate the fault for a class of linear discrete systems based on an adaptive fault estimation observer. And observer gain matrix and adaptive adjusting rule of the fault estimator are designed. Furthermore, the adaptive regulating algorithm can guarantee the first-order difference of a Lyapunov discrete function to be negative, so that the observer is ensured to be stable and fault estimation errors are convergent. Finally, simulation results of an aircraft F-16 illustrate the advantages of the theoretic results that are obtained in this paper. [ABSTRACT FROM AUTHOR]

The problem of robust H∞ filtering is investigated for the class of uncertain two-dimensional (2D) discrete systems described by a Roesser state-space model. The main contribution is a systematic procedure for generating conditions for the existence of a 2D discrete filter such that, for all admissible uncertainties, the error system is asymptotically stable, and the H∞ norm of the transfer function from the noise signal to the estimation error is below a prespecified level. These conditions are expressed as parameter-dependent linear matrix inequalities. Using homogeneous polynomially parameter-dependent filters of arbitrary degree on the uncertain parameters, the proposed method extends previous results in the quadratic framework and the linearly parameter dependent framework, thus reducing its conservatism. Performance of the proposed method, in comparison with that of existing methods, is illustrated by two examples. [ABSTRACT FROM AUTHOR]