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Copyright of Journal of Southeast University (English Edition) is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract In this paper, a new approach for dealing with multiple tracking tasks during physical interaction is proposed. By using this method, multiple tasks are accomplished based on the assigned priority in addition to a compliant behavior in the null-space of the main tasks. This issue is critical when robots are employed for complex manipulation in unknown environments and in the presence of human. During the manipulation in the dynamic environments, different objects may collide with the robot body and disturb its manipulation. In these cases, the robot is expected to continue execution of the tasks, accurately. Meanwhile, the robot should be compliant to ensure the safety during the interaction. A nonlinear controller-observer is proposed for tracking the desired trajectory based on a preallocated hierarchy. The suggested controller-observer estimates the external torques applied to the robot body without using joint torque measurements and compensates its projection on the task spaces. Asymptotic stability of the task space errors, the null-space velocity and the external interaction estimation error during accomplishing multiple tracking tasks are shown analytically. Finally, the algorithm performance is shown through experiments on a 7-DOF KUKA LWR robot arm. [ABSTRACT FROM AUTHOR]

We present an improvement of existing methods for globally solving optimal experimental design (OED) for bounded-error estimation based on a bilevel formulation from Mukkala et al. (2017). The proposed solution method for the min–max program is based on our method for generalized semi-infinite programs (via restriction of the right-hand side). The algorithm employed has the advantage that it guarantees a global solution for the OED assuming the global solution of two subproblems. To obtain a feasible solution only the lower-level problem has to be solved globally. In case of a local solution of the upper-level problem, the solution is still feasible though it is an upper bound of the global solution. The min–max method for OED is illustrated with four examples: two simple chemical reactions, BET-adsorption and a reformulated predator-prey system. The benefits of global methods are shown along with the limitations of state-of-the-art global solvers. [ABSTRACT FROM AUTHOR]

This paper is concerned with the consensus problem for continuous time linear multi-agent systems with directed communication topology. An estimation error dynamic is proposed to design the reduced-order observer for dealing with unknown coefficient matrices. To guarantee the consensus, control protocols based on the reduced-order observer are designed to utilize the relative outputs of neighboring agents whose communication topology contains a directed spanning tree. Then the algorithms are extended to the model reference consensus case, where the case of zero control input for reference node is not assumed. Numerical examples are given to illustrate the effectiveness of proposed approaches. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal linear estimation problem for a class of networked descriptor systems with multiple packet dropouts, measurement multiplicative noises and finite-step correlated process and measurement noises. Based on a fast–slow subsystem decomposition approach (FSD), the descriptor system is transformed into two reduced-order linear nonsingular subsystems with finite-step correlated noises. Optimal linear estimators including filter, predictor and smoother with corresponding estimation error covariance matrices for the states and noises of new systems are developed via the innovation analysis approach. Then, the optimal linear estimators are obtained for the original descriptor system. An example shows the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of persistently covering an environment with a group of mobile robots. In contrast to traditional coverage, in our scenario the coverage level of the environment is always changing. For this reason, the robots have to continually move to maintain a desired coverage level. In this context, our contribution is a complete approach to the problem, including distributed estimation of the coverage and control of the motion of the robots. First, we present an algorithm that allows every robot to estimate the global coverage function only with local information. We pay special attention to the characterization of the algorithm, establishing bounds on the estimation error, and we demonstrate that the algorithm guarantees a perfect estimation in particular areas. Second, we introduce a new function to determine the possible improvement of the coverage at each point of the environment. Upon this metric, we build a motion control strategy that drives the robots to the points of the highest improvement while following the direction of the gradient of the function. Finally, we simulate the proposal to test its correctness and performance. [ABSTRACT FROM AUTHOR]

A kind of adaptive filter algorithm based on the estimation of the unknown input is proposed for studying the adaptive adjustment of process noise variance of boost phase trajectory. Polynomial model is used as the motion model of the boost trajectory, truncation error is regarded as an equivalent to the process noise and the unknown input and process noise variance matrix is constructed from the estimation value of unknown input according to the quantitative relationship among the unknown input, the state estimation error, and optimal process noise variance. The simulation results show that in the absence of prior information, the unknown input is estimated effectively in terms of magnitude, a positive definite matrix of process noise covariance which is close to the optimal value is constructed real-timely, and the state estimation error approximates the error lower bound of the optimal estimation. The estimation accuracy of the proposed algorithm is similar to that of the current statistical model algorithm using accurate prior information. [ABSTRACT FROM AUTHOR]

This paper formulates optimal bilinear observers for bilinear state-space models. Observers in bilinear form, as opposed to other nonlinear forms, are required to develop an extension of observer/Kalman filter identification for simultaneous identification of a bilinear state-space model and an associated bilinear observer from noisy input–output measurements. The paper establishes the relationship between the bilinear observer gains and the interaction matrices which are used to convert the original bilinear state-space model to a form that simplifies the identification of such a model. Techniques to find the interaction matrices are developed. In the absence of noises, these matrices produce the gains of the fastest converging observer. In the presence of noises, they minimise the state estimation error in the same manner as a standard steady-state Kalman filter. Numerical examples illustrate both the theoretical and computational aspects of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

Covariance-based DoA estimation (CB-DoA) algorithms represent lower computational complexity alternatives to the traditional ESPRIT approach. This paper investigates CB-DoA using Krylov-subspace techniques (including Arnoldi's and Lanczos' updates) with respect to the resulting computational cost and estimation error performance. The proposed modifications also allow an automatic estimation of the number of sources. Computational analyses performed for the resulting CB-DoA algorithm indicate cost savings above $$60\,\%$$ in comparison with the standard CB-DoA implementation, which already represents a $$20\,\%$$ improvement upon its ESPRIT counterpart, at an equivalent mean squared error level, as verified in numerical simulations. [ABSTRACT FROM AUTHOR]

With the increasing popularity of XML database applications, the use of efficient XML query optimizers is becoming very essential. The performance of an XML query optimizer depends heavily on the query selectivity estimators it uses to find the best possible query execution plan. In this work, we propose and evaluate a novel selectivity estimator, based on a structural synopsis, called SynopTech. The main idea of SynopTech is the generation of a summary tree by labeling the nodes of the source XML data tree using a fingerprint function and merging subtrees with similar structures. The generated summary tree is then used by SynopTech to estimate the selectivity of given queries. We experimented the proposed approach with four benchmark datasets of different structural characteristics and using different types of queries. Comparing with the Sampling algorithm, one of the state-of-the-art algorithms for selectivity estimations, SynopTech achieved lower selectivity estimation error rates, yet with very low memory budget. For example, for linear and existential queries, SynopTech had perfect estimations whereas the Sampling algorithm had an error rate of up to 70 %. For regular twig queries, SynopTech had a maximum error rate of 4.12 % whereas the Sampling algorithm had more than 55 %. [ABSTRACT FROM AUTHOR]

Applying the typical active noise control (ANC) algorithm, namely the filtered-x least mean square (FxLMS) algorithm, to the non-Gaussian impulsive noise will experience difficulty. This is because the FxLMS algorithm is based upon the minimization of variance of the error signal, which is normally with the assumption of Gaussian distribution. To deal with this issue, the filtered-x least mean M -estimate (FxLMM) algorithm has been proposed based on the robust statistics theory to minimize the objective M -estimator error function. The Hampel’s three-part M -estimator has been applied to provide thresholds for detecting and rejecting the outliers/impulses in the error signal. To further enhance the robustness of the conventional FxLMM algorithm, in this study a modified FxLMM (MFxLMM) algorithm is proposed, where the reference signal is fed through another Hampel’s three-part M -estimator by setting thresholds for the impulsive samples. Numerous computer simulations are performed to validate the performance of the proposed algorithm. The generic impulsive noise is synthesized by using the contaminated Gaussian (CG) noise model, where certain number of impulses with different amplitudes and widths can be added in the background white Gaussian noise to represent different impulsive characteristics. In addition, extensive laboratory tests are carried out to further demonstrate the enhanced efficacy of the algorithm in a real-time environment. Results indicate that the proposed MFxLMM algorithm with online-threshold estimation is a very promising alternative for ANC of impulsive noise, and it shows similar convergence performance as the prevalent FxLMS algorithm for noise not corrupted by impulses. [ABSTRACT FROM AUTHOR]

In compressive sensing, generalized Orthogonal Matching Pursuit (gOMP) algorithm generalizes OMP algorithm by selecting N ( N ≥ 1 ) atoms in each iteration. In this paper, we propose restricted isometry constant based sufficient conditions for gOMP algorithm to correctly recover the support of significant components of signal when both measurement dictionary and measurement signal are contaminated with noise. Bound of estimation error is also derived. Upper bound of restricted isometry constant could be relaxed if the original sparse signal is strong decaying. When sparsity of original sparse signal is unavailable, we give a stopping criterion of gOMP algorithm to ensure correct support recovery. [ABSTRACT FROM AUTHOR]

The unscented Kalman filter (UKF) became very attractive for the navigation sensors data fusion, because of algorithm significant accuracy and implementation advantages. The unscented Kalman filter is based on the unscented transform (UT) to perform the estimation of the system states. The main idea of the unscented transformation is following. It's more effective to approximate probability distribution function than arbitrary transformation or nonlinear function. The developed sensors data fusion algorithm using the UKF is considered in this work. This algorithm was applied for the state estimation of the loosely coupled GPS/INS integrated navigation system. GPS/INS integrated navigation system contains low cost inertial sensors and low cost GPS receiver. To demonstrate the estimation performance, the processing of sensors data was done using linear Kalman filter (KF), extended Kalman filter (EKF) and UKF. As a result, UKF has lower velocity estimation error than EKF during simulated GPS signal outage. [ABSTRACT FROM AUTHOR]

An ongoing change in the theoretical framework from deterministic to probabilistic satellite rainfall estimations emerges from applications that require an error associated with rain estimates. The error budget for accumulated rainfall consists of several terms; these terms are related to sampling, algorithmic and calibration errors. From a number of studies, various errors were derived which have improved our understanding of the different terms in this error budget. In this paper, a methodological effort leading to the evaluation of a Tropical Amount of Precipitation with an Estimation of ERrors (TAPEER) is presented. It involves first merging passive microwave instantaneous rain rates from the BRAIN algorithm together with infrared imagery to build rain accumulations, and then evaluating the different terms of the error budget using two techniques. A dedicated error model is used to evaluate sampling errors and a forward error propagation approach is used for the estimation of algorithmic and calibration errors. One of the main findings in this study is the large contribution of the sampling errors and the algorithmic errors of BRAIN on medium rain rates (2-10 mm h−1) in the total error budget. This methodology leads to the formulation of a satellite rainfall product called TAPEER-BRAIN. This product will be one of the operational rainfall products for the Megha-Tropiques mission and will provide 1°/1-day accumulated rainfall estimations and associated error over the whole tropical belt. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The development of handheld mobile terminals (MT) capable of operating over both Wireless Local Area Networks (WLAN) and the cellular media is an important step toward the evolution of next-generation integrated networks. For real-time applications like voice, efficient vertical handover (VH) algorithms are required in maintaining a seamless connectivity and an acceptable level of quality for mobile users in heterogeneous environments. This paper proposes an analytical method for evaluating the performance of a VH algorithm that relies on the Received Signal Strength Indicator (RSSI) samples. The system model is portrayed upon addressing relevant factors that affect the quality and continuity of a voice call, and a set of performance metrics is proposed. A rigorous signal strength time series is utilized in characterizing the instantaneous decision metrics, and a novel intermediary system model, namely the N-model, is proposed to capture the large-scale shadowing effects. The performance of a generic algorithm that relies on the RSSI, which itself is susceptible to estimation error, is evaluated for an MT roaming in and out of the WLAN coverage area. Results obtained using the analytical expressions are validated by comparing them to that obtained through Monte Carlo simulation. [ABSTRACT FROM PUBLISHER]

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]

The most critical component of any adaptive numerical quadrature routine is the estimation of the integration error. Since the publication of the first algorithms in the 1960s, many error estimation schemes have been presented, evaluated, and discussed. This article presents a review of existing error estimation techniques and discusses their differences and their common features. Some common shortcomings of these algorithms are discussed, and a new general error estimation technique is presented. [ABSTRACT FROM AUTHOR]

We explore the theoretical framework as well as the associated algorithms for the problem of optimally placing mobile observation platforms to maximise the improvement of estimation accuracy. The approach in this study is based on the concept of observability, which is a quantitative measure of the information provided by sensor data and user-knowledge. To find the optimal sensor locations, the observability is maximised using a gradient projection method. The Burgers equation is used to verify this approach. To prove the optimality of the sensor locations, Monte Carlo experimentations are carried out using standard 4D-Var algorithms based on two sets of data, one from equally spaced sensors and the other from the optimal sensor locations. The results show that, relative to equally spaced sensors, the 4D-Var data assimilation achieves significantly improved estimation accuracy if the sensors are placed at the optimal locations. A robustness study is also carried out in which the error covariance matrix is varied by 50% and the sensor noise covariance is varied by 100%. In addition, both Gaussian and uniform probability distributions are used for the sensor noise and initial estimation errors. In all cases, the optimal sensor locations result in significantly improved estimation accuracy. [ABSTRACT FROM AUTHOR]

Abstract: This submission is a communication related to a recently published article in Computer-Aided Design journal, titled “Shortest path in a multiply-connected domain having curved boundaries”. We point out an error in estimating the time complexity of the algorithm proposed in that paper, using a simple example. We also illustrate, with a different example, that an ostensibly time-saving scheme used by that algorithm, called exterior region elimination, cannot be applied in general to derive the correct shortest interior path. Finally, we propose an alternate algorithm that solves the same problem with an improved worst-case running time. [Copyright &y& Elsevier]