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When dealing with complex nonlinear signals in intelligent system, by defining the inner product of two data vectors in the feature space, the kernel function can reflect the nonlinear mapping relationship between reproducing kernel Hilbert space (RKHS) and original data space. Therefore, by implementing classical linear adaptive filtering in RKHS space, the filtering operation can be expressed as a special relation of inner product with kernel function, which is referred to as “kernel trick.” As long as these algorithms can be expressed in the form of inner product, not only the convex least squares problem can be solved iteratively, but also the nonlinear adaptive filtering algorithms can be obtained, which have both general approximation characteristics and convexity. Therefore, the combination of kernel method and adaptive filtering algorithm is realized. On the other hand, since the Gram matrix is used, the dimension of kernel adaptive algorithm is determined by the number of data samples. When the number of observation sample point is increasing, the size of the state space increases exponentially with the growth of the dimension. Thereby, the kernel adaptive algorithm should solve the problem of online sparsification to avoid “curse of dimensionality.” As part II, based on online sparse kernel learning and the classical adaptive filtering algorithm, the kernel adaptive algorithms and online sparse algorithms are investigated in this paper. The main works of this paper have two aspects. First, combined with classical adaptive algorithm and kernel feature mapping, this paper investigates the basic concept of kernel adaptive algorithm and the realization mechanism of four kernel adaptive algorithm intensively: 1) kernel least mean squares; 2) kernel recursive least squares; 3) kernel affine projection algorithm; and 4) kernel principal component analysis. Second, in order to reduce the computational complexity, this paper studies some online sparse algorithms which include novel criterion, approximate linear dependency, sliding window, coherence criterion, surprise criterion, and so on. Finally, this paper summarizes the essence of existing conclusions of above algorithms and perspectives the future research direction. [ABSTRACT FROM AUTHOR]

Autonomous robotics working in the uncertain environment have drawn increasing interests from researchers. Here, an issue of online motion optimization under unknown environment is considered while preserving the safety and improving the flexible manoeuvrability of robot–environment interaction. This problem is addressed by improving the conventional dynamic movement primitives (DMPs) framework with force tracking constraints. First, an initial motion is learned through the DMPs. At the stage of skill generalization, a temporal coupling term combining with force constraints scheme which is inspired by the barrier Lyapunov function and finite‐time prescribed performance is deduced and adds to the original DMPs, so as to remain the contacting force staying within a predefined limit while aligning the motion along with surface of unknown environment adaptively. In this way, not only the contacting force can be guaranteed within a safe margin, but the shape of generalizing motion is preserved. Then the convergence and stability of the proposed DMPs are proved which is grounded on Laplace transformation‐based stability analysis to ensure the performance and safety. Finally, the proposed method is instantiated combined with conventional PID controller through the compared simulations to verify its effectiveness. [ABSTRACT FROM AUTHOR]

This study investigates the finite‐time consensus problem of leaderless and leader‐following multi‐agent systems. Two novel nonlinear consensus protocols are presented based on event‐driven strategies, which can significantly reduce the communication consumption and the frequency of the controller updates. It is shown that the multi‐agent system can achieve consensus in finite time under the proposed nonlinear consensus protocols. Moreover, the bound of the triggering interval is provided to illustrate that no Zeno behaviour exists. Finally, some simulations are employed to verify the effectiveness of the proposed consensus protocols. [ABSTRACT FROM AUTHOR]

The wireless technology is regarded as a paradigm shifter in the process industry. A star-linear industry wireless sensor network (IWSN) for narrow process industries is proposed in this paper. Based on the proposed IWSN, we focus on time-efficient convergecast solutions. We present algorithms to achieve optimal convergecast performance in terms of time slots use. In the proposed IWSN, the field devices (FDs) constitute a set of TDMA (time division multiple access) based star topology clusters, and the cluster heads present a multihop linear backbone. Time slots are scarce communication resource for convergecast in a narrow IWSN. Aiming to use slots efficiently, we design optimal algorithms to improve the polling scheduling in the cluster and the packets forwarding over the backbone. In a cluster, we design a multicycle scheduling algorithm and a fair polling algorithm to improve slots utility of the communication reliability and integrity. Over the backbone, an optimal slots allocating algorithm is designed to maximize the slots performance in terms of the end-to-end communication reliability, based on which a slot-efficient multisuperframe scheduling algorithm is presented. Performance analysis and simulations show that our solution outperforms traditional ones in terms of communication reliability and real-time. [ABSTRACT FROM AUTHOR]

SUMMARY As considerable progress has been made in wireless sensor networks (WSNs), we can expect that sensor nodes will be applied in industrial applications. Most available techniques for WSNs can be transplanted to industrial wireless sensor networks (IWSNs). However, there are new requirements of quality of service (QoS), that is, real-time routing, energy efficiency, and transmission reliability, which are three main performance indices of routing design for IWSNs. As one-hop neighborhood information is often inadequate to data routing in IWSNs, it is difficult to use the conventional routing methods. In the paper, we propose the routing strategy by taking the real-time routing performance, transmission reliability, and energy efficiency (TREE, triple R and double E) into considerations. For that, each sensor node should improve the capability of search range in the phase of data route discovery. Because of the increase of available information in the enlarged search range, sensor node can select more suitable relay node per hop. The real-time data routes with lower energy cost and better transmission reliability will be used in our proposed routing guideline. By comparing with other routing methods through extensive experimental results, our distributed routing proposal can guarantee the diversified QoS requirements in industrial applications. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned with uplink interference suppression problem in two-tier femtocell networks through power control. Specifically, we consider the Quality-of-Service (QoS) of the macrocell user and femtocell users in terms of their received Signal to Interference-plus-Noise Ratios (SINRs) at macrocell base station (MBS) and femtocell base stations (FBSs), and we also take femtocell users’ power efficiency into consideration by designing an objective function, which is a weighted sum of transmission power and squared SINR difference between femtocell user's maximum SINR and actual SINR. Due to the error of the SINR at MBS caused by distance errors, a robust uplink power control problem is formulated, and it is equivalent to a robust convex optimization problem with femtocell users’ SINR constraints. Then, the robust convex optimization problem is converted into a general convex optimization problem. Moreover, a distributed power control algorithm combined with admission control is presented to obtain femtocell users’ optimal power allocation. Numerical results show the convergence and effectiveness of the proposed uplink power control algorithm. [Copyright &y& Elsevier]

This article investigates the problem of delay-dependent exponential stability in mean square for continuous-time linear stochastic systems with structured uncertainties and time-varying delays. By applying descriptor model transformation of the systems, a new type of Lyapunov-Krasovskii functional is constructed, and by introducing some free weighting matrices, some new delay-dependent and delay-independent stability criteria are derived respectively in terms of an LMI algorithm. The new stability criteria are less conservative than existing results. Numerical examples demonstrate that the new criteria are effective and are an improvement over existing results. [ABSTRACT FROM AUTHOR]

Xu Q, Guan X, Yao F. Welfare program participation among rural-to-urban migrant workers in China Int J Soc Welfare 2011: 20: 10-21 © 2010 The Author(s), Journal compilation © 2010 Blackwell Publishing Ltd and International Journal of Social Welfare. An estimated 225 million Chinese people have migrated to cities from China's rural areas over the past two decades. These rural-to-urban migrant workers have greatly challenged China's welfare system. The pre-reform welfare system was a duel scheme with an urban-rural distinction in which rural residents were not covered by state-run welfare programs and had to rely on their families and rural collectives. The development of employment-based social insurance programs in 1999 made social welfare programs available for rural-to-urban migrant workers. Using an anonymous survey conducted in seven cities across China in 2006, we found that social insurance program participation rates were low among rural-to-urban migrant workers. Individual factors, including lack of knowledge of welfare programs and of a willingness to participate, and macro-level factors, including type of employer and industry, are critical in determining migrant workers' participation in welfare programs. Implications for policies and practice are discussed. [ABSTRACT FROM AUTHOR]

Abstract: A full delayed feedback controller (FDFC) design method is proposed to stabilize unstable fixed points (UFPs) for a class of continuous time-delay chaotic systems. Unlike the existing methods where the controller delay is usually determined by trial-and-error, it is shown in FDFC that both the upper bound of the controller delay and the controller gain can be obtained computationally. The simulation result demonstrates the effectiveness of the proposed FDFC design method. [Copyright &y& Elsevier]

Abstract: In this paper, the problem of decentralized robust model reference control for a class of interconnected time-delay systems is investigated. The interconnections with time-varying time delays considered are high order and the gains are not known. A class of decentralized adaptive feedback controllers are proposed, which can render the resulting closed-loop error system uniformly ultimately bounded stable. A numerical example is given to show the feasibility and effectiveness of the proposed design techniques. [Copyright &y& Elsevier]

Abstract: A full delayed feedback controller (FDFC) design method is proposed to stabilize the unstable fixed points (UFPs) for continuous chaotic systems. It is shown that the two parameters of a FDFC, i.e., the controller gain and the upper bound of controller time delay, can be simultaneously obtained by solving some linear matrix inequalities. Two examples are presented to show the effectiveness of proposed method. [Copyright &y& Elsevier]

Abstract: In this paper, the problem of control for a class of uncertain interconnected time-delay systems is studied. The interconnections of the system possess time-varying delay characteristics, and the bounds of uncertainties are not precisely known. A decentralized adaptive feedback controller is designed, which is independent of the delays. Lyapunov–Krasovskii approach is employed to show that the corresponding closed-loop system is asymptotically stable. A simulation example is given to demonstrate the potential of the proposed techniques. [Copyright &y& Elsevier]

Abstract: Synchronization problem of chaotic systems via observer method is investigated. In contrast to the results of the literatures, we consider the case that there exist noise disturbances in the output. Under this condition, it is not ideal to employ the classic observer to solve the synchronization problem. The proportional integral observer is proposed, which can render the error system stable with the noise in the output. Simulations on synchronizing Chua chaotic systems are done to verify the effectiveness of the main results. [Copyright &y& Elsevier]

In this paper, the problem of variable structure adaptive fuzzy control for a class of nonlinear time-delay systems is investigated. Sliding surface dependent of the time delay is constructed based on the Lyapunov–Krasovskii method. Two adaptive fuzzy logic systems are proposed to approximate two unknown continuous nonlinear functions containing the current state and the delayed state, respectively. The corresponding reaching law is designed, which can drive the state trajectory of the closed system onto the sliding surface within limited time. Simulation investigation verifies the effect of the main results of the paper. [Copyright &y& Elsevier]

The problem of robust stabilization for a class of uncertain dynamic systems with multiple delayed state perturbations is considered. It is assumed that perturbations of the time-delayed sections are not bounded by first-order linear functions, but bounded by high-order functions with unknown gains. And the time delay considered is time varying. Two classes of controllers are proposed. When the time derivative of each time-varying time delay is less than 1, a class of adaptive state feedback controllers are proposed based on Lyapunov–Krasovskii method, which can render the closed-loop systems uniformly ultimately bounded stable. Novel nonlinear feedback controllers are developed by employing Razumikhin lemma, and the controller also can render the closed-loop systems stable in the sense of uniform ultimate boundedness. Finally, several examples are given to show the potential of the proposed techniques. [Copyright &y& Elsevier]

This paper discusses an adaptive fuzzy controller design method for chaotic systems in the presence of completely unknown nonlinearities and disturbances. All the unknown nonlinear functions are estimated by dynamic fuzzy approximators so that the adaptive fuzzy control approach can be employed in the chaotic systems. Moreover, H∞ control method is applied to attenuate the effect of the modeling errors and disturbances in tracking control. An example is given to illustrate the effectiveness of this approach. [Copyright &y& Elsevier]

Robust control problem of nonlinear time-delay chaotic systems is investigated. For such uncertain systems, we propose adaptive feedback controller and novel nonlinear feedback controller. They are both independent of the time delay and can render the corresponding closed-loop systems globally uniformly ultimately bounded stable. The simulations on controlling logistic system are made and the results show the controllers are feasible. [Copyright &y& Elsevier]

This paper addresses time-delayed feedback control (DFC) of time-delay chaotic systems. To extend the DFC approach to time-delay chaotic system, alter having been successfully used in chaotic systems without time-delays, the standard feedback control (SFC) method is firstly employed to show the main control technique in this paper based on one error control system. Then sufficient conditions for stabilization and tracking problems via DFC are derived from the results based on SFC. Also, the systematic and analytic controller design method can be obtained to stabilize the system to an unstable fixed point and to tracking an unstable periodic orbit, respectively. Some numerical examples are provided to demonstrate the effectiveness of the presented method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the robust control for uncertain discrete systems with delay using dynamic output feedback. The parametric uncertainties considered in this paper are norm-bounded and time-varying. It is shown that the feasibility of a linear matrix inequality (LMI) guarantees the solvability of the addressed problem. A numerical example is given to illustrate the potential of the proposed techniques. [ABSTRACT FROM AUTHOR]

In this paper, the problem of memoryless static state feedback control for a class of discrete-time uncertain systems with delayed state is considered. The uncertain time-delay system under consideration is described by state difference equation with time-varying unknown, norm-bounded parameter uncertainty. Necessary and sufficient condition for the existence of the desired control law is provided. Based on the discrete Lyapunov functional technique combined with linear matrix inequality (LMI) approach, a new design method is proposed for synthesizing robust control laws for the uncertain discrete time-delay systems. It is shown that the construction of the stabilizing controller involves solving a certain LMI. [ABSTRACT FROM AUTHOR]

This paper offers a wide-ranging review of social policy trends and developments in China. It details the characteristics of what had been traditional social polity during the first 40 years of the People's Republic. It then looks at the pressures on this system in the context of Market Reform and the Open Door policy, and comments on the changing roles of government, NGOs and urban employment units, in the societalization of social policy away from the state welfare model. The general direction of reform is portrayed as neo-liberal, geared to reducing the role of government in the provision of welfare and to increasing individual responsibility for social security and well-being. However, there is as yet no prospect of a fully privatized social welfare system, owing to the continuing weak state of the non-governmental sector. In conclusion, prospects for the future are analysed and summed up in the light of the government's two overriding policy objectives: economic development, coupled with the maintenance of social/political stability. [ABSTRACT FROM AUTHOR]

In this paper, a distributed nonsingular terminal sliding mode control (NTSMC) is proposed for vehicular platoons subjected to nonlinear uncertainty, external disturbance, and input saturation. Due to the presence of input saturation, the platoon control becomes more complicated. However, only considering the impact of uncertainty on the system, input saturation will usually lead to a decline in the driving performance, even lead to string instability. First, the input saturation is compensated by a single parameter, which is simple and direct. The radial basis function neural network (RBFNN) based on disturbance observer is employed to compensate the nonlinear uncertainty and external disturbance, respectively. The basis function of neural network is only related to the velocity and acceleration of the leader. Therefore, the nonlinearity of the vehicle systems does not need to meet the matching conditions. Then, a distributed NTSMC is designed to realize the internal stability, which weakens the chattering of traditional sliding mode control (SMC) to some extent. In addition, the nonsingular problem in terminal sliding mode control (TSMC) is solved. The string stability is realized by employing a coupled sliding mode control (CSMC). Finally, simulation results demonstrate the effectiveness and feasibility of the proposed strategy. [ABSTRACT FROM AUTHOR]

Object detection is one significant field of computer vision. The imbalance problem exerts negative effects on achieving satisfactory performance. We reveal two sources of imbalance in existing object detection methods. Correspondingly, we propose our methods in terms of the model architecture and optimization target. Different from general object detection benchmarks, the location distribution of objects with different sizes is unbalanced in many practical applications. In addition, the representation information of different categories of objects is unbalanced. In this paper, we propose a location scale equilibrium module to utilize the prior location scale information and generate more balanced feature maps. More appropriate feature maps are selected and merged for different locations. After merging, feature maps become more consistent in terms of representation content, exerting positive effects on the following classification and regression tasks. For the imbalance caused by similar objects, we propose the repulsive loss to strengthen the punishment. Our method will not treat all categories of objects equally since we take the imbalance between them into consideration. With the enhanced supervision, the training will pay more attention to similar objects. Our proposed model is evaluated on the VisDrone benchmark and UAVDT benchmark. Sufficient experiments are conducted. Our model achieves the highest precision on most evaluation metrics, outperforming the other strong models. [ABSTRACT FROM AUTHOR]

A new event-triggered iterative learning control method is proposed for handling the distributed containment control problem of model-free multiagent systems under a fixed directed graph. The designed controller merely uses the input and output signals, controlled model information is not required. At first, the unknown dynamic is transformed into the linearization model upon the base of pseudo partial derivative. Secondly, the novel distributed containment controller is proposed for each follower by use of iterative learning algorithm. Moreover, a new trigger mechanism is designed to save energy of the systems, such that the updating number of the proposed controller can be reduced greatly. Mathematical deduction shows that the controller can render the outputs of the followers converge to a convex hull formed by the outputs of leaders. Finally, simulation examples are given for verifying the significance of proposed method. [ABSTRACT FROM AUTHOR]

This article investigates the consensus problem of multiagent systems (MASs) with the reset control approach. Reset control has been shown to possess great potential to improve transient system performance, but reset-induced state jump dynamics bring difficulties for consensus analysis, especially under the network environment. In this article, a novel reset element is developed, and time regularization is utilized to exclude Zeno behavior. The case with continuous communication is first considered, a distributed proportional integral + reset consensus protocol is proposed, which consists of the proportional-integral controller and the reset mechanism. The results are further generalized to the case with asynchronous sampling, which relaxes the requirement for continuous communication and clock synchronization. A hybrid model of the MAS is constructed to describe the system dynamics, and a hybrid systems approach is proposed to handle the reset- and sampling-induced jump dynamics in a unified framework. Moreover, a novel Lyapunov function is proposed and LMI-based stability conditions are given. Finally, an example is provided to show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Slender objects are long and thin objects. Existing object detection networks are not specially designed for detecting slender objects. We propose a method to detect slender objects. We represent slender objects with a keypoint-displacement pattern instead of using axis-aligned bounding boxes, avoiding problems like orientation confusion and wrong elimination. In our network, three parallel branches predict keypoint heatmaps, displacement vector field, and displacement uncertainty heatmap respectively. We add the uncertainty branch to enable our network to give uncertainty together with detection results. The predicted uncertainty provides a continuous criterion to evaluate whether detection results are reliable. In addition, the uncertainty branch can lower the weight of ambiguous training samples, leading to more accurate detection results. We employ our proposed method in two typical practical applications. Edges of electrode sheets and pins of electronic chips are correctly detected as slender objects. Manufacturing quality is evaluated through analyzing the detection results, including keypoint number, displacement property, and uncertainty value. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced model-free adaptive iterative learning control (EMFAILC) method is proposed, which is applied for a class of nonlinear discrete-time systems with load disturbance and random data dropout. This method is a data-driven control strategy and only the I/O data are required for the controller design. Data are lost at every time instance and iteration instance independently, which allows successive data dropout both in time and iterative axes. By compensating the missing data, the proposed EMFAILC algorithm can track the desired time-varying trajectory. The convergence and effectiveness of the proposed approach are verified by both the rigorous mathematical analysis and the simulation results. [ABSTRACT FROM AUTHOR]

Summary: This article studied the global output feedback regulation problem for a class of uncertain nonlinear time delay systems subject to unknown measurement faults on sensors. Different from the existing works, we consider the unknown time‐varying delays on the system states and relax their conservative condition on nonlinear functions. By introducing two novel time‐varying gains, a new global output feedback regulation algorithm is proposed, which ensures control parameters can be chosen flexibly. The proposed linear‐like controller is independent of the unknown time‐varying delays. Moreover, it has a simple structure, which is convenient for the implementation in practice. Based on the Lyapunov stability theory, it is strictly proved that all signals of the resulting closed‐loop system are globally bounded with the designed controller. Finally, a simulation example is presented to illustrate the effectiveness of the proposed output feedback regulation algorithm. [ABSTRACT FROM AUTHOR]

In this study, the visual servoing control problem is investigated, and a fixed time based visual servoing control scheme is proposed for quadrotor unmanned aerial vehicles. First, based on the perspective projection principle and target points, effective image features are defined on an image plane called the virtual image plane. Then, the image feature dynamics and QUAV dynamics are combined to derive a coupled non‐linear system, and the non‐linear system is decoupled into two subsystems under inner–outer loop control framework. Furthermore, the novel sliding mode surfaces based fixed‐time controllers are designed, and the resulting closed‐loop systems are proved to be stable. Finally, the numerical simulations and experiments are conducted to validate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Summary: This paper deals with the funnel‐like prescribed tracking control problem for a class of uncertain nonlinear stochastic switched systems. An improved performance technique is developed to restrain the fluctuation at the moment of switches and a new algorithm is proposed to address the funnel‐like prescribed tracking problem. First, a dynamic gain‐based switched K‐filter is constructed to estimate the unmeasured state information of the switched system. Subsequently, the performance technique is applied to prescribe output tracking error and restrain fluctuations of the system. Thereafter, the dynamic output feedback switched controller is designed by the use of the backstepping method. Moreover, based on the Lyapunov stability theory, it is proved strictly that all signals of the resulting closed‐loop system are bounded in probability if the switching signal satisfies the average dwell time. Finally, a numerical simulation is presented to illustrate the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

Observers have broad applications in power systems, whereas observer models are hard to obtain when the system is unknown. This article considers the observer design problems for unknown noisy linear time‐invariant systems based on biased dynamics estimations. Unlike the unbiased methods, biased methods have stronger generalization ability, which benefits obtaining stable estimations for noisy systems. However, the biased estimation's influence on observer design still needs to be investigated. To analyze the influence, we exploit the impact of estimation bias‐variance trade‐off to observer design. Specifically, we propose a support vector regression (SVR) based estimator to provide biased estimations for the system identification of unknown linear systems. The sample complexity results of SVR with bias‐variance trade‐offs are analyzed and used for observer design and performance analysis. Then, a stable observer gain design algorithm is developed based on biased estimation. The observation performance is evaluated by the mean square observation error, which is shown to be adjustable by tuning the trade‐off between bias and variance, thus achieving higher scalability than the unbiased methods. Finally, observing performance analysis demonstrates the influence of the bias‐variance trade‐off for the observer. Extensive simulation validations are conducted to verify the computed estimation error and performance optimality with different bias‐variance trade‐offs and noise settings. [ABSTRACT FROM AUTHOR]

A distributed sliding mode control based on neural network and disturbance observer is investigated for heterogeneous vehicle systems in this paper. The vehicle systems in this paper consider both parameter uncertainty and disturbance. First, the radial basis function neural network is applied to estimate the parameter uncertainty owing to its universal approximation ability, and the disturbance observer is designed to compensate for the external disturbance. Compared with the existing adaptive methods, the disturbance observer can estimate the external disturbance directly and effectively. Then, constant time headway policy is employed to regulate the inter-vehicle distance and improve the string stability. Unlike most existing sliding mode control strategy for vehicle platoons, the string stability is achieved via employing sufficient conditions on the control parameters rather than employing coupled sliding mode control. Afterward, modified constant time headway policy is designed to reduce the effect of nonzero initial inter-vehicle spacing and improve the traffic density. Finally, the simulation results with constant time headway policy and modified constant time headway policy are provided to demonstrate the effectiveness and advantages of the proposed approaches. [ABSTRACT FROM AUTHOR]

This study considers the tracking control problem of an uncertain quadrotor unmanned aerial vehicle with model uncertainties and wind gust disturbances, and a novel robust dynamic surface control based multiple state variables constrained control scheme is proposed. Under the presented control framework, the overall quadrotor system is decoupled into a translational subsystem and a rotational subsystem. These two subsystems are connected to each other through common attitude extraction algorithms. For the translational subsystem, the novel robust multiple state variables constrained control inputs are designed to ensure the state variables within the prescribed constraints. For the rotational subsystem, the dynamic surface control based tracking controllers are proposed to track the desired attitudes. In the end, the resulting closed‐loop system is proved to be stable in the sense of uniform ultimate boundness, and numerical simulations and experiments are conducted to validate the feasibility and effectiveness of the designed control scheme. [ABSTRACT FROM AUTHOR]

Network systems often rely on distributed algorithms to achieve a global computation goal with iterative local information exchanges between neighbor nodes. To preserve data privacy, a node may add a random noise to its original data for information exchange at each iteration. Nevertheless, an eavesdropping node can estimate other’s original data based on the information it received. The estimation accuracy and data privacy can be measured in terms of $(\epsilon, \delta)$ -data-privacy, defined as the probability of $\epsilon $ -accurate estimate (the difference of an estimation and the original data is within $\epsilon $ ) is no larger than $\delta $ (the disclosure probability). How to optimize the estimation and analyze data privacy is a critical and open issue. In this paper, a theoretical framework is developed to investigate how to optimize the estimation of neighbor’s original data using the local information received, named optimal distributed estimation. Then, we study the disclosure probability under the optimal estimation for data privacy analysis. We further apply the developed framework to analyze the data privacy of the privacy-preserving average consensus algorithm and identify the optimal noises for the algorithm. [ABSTRACT FROM AUTHOR]

For autonomous vehicles, the acquisition frequency difference between LiDAR (10–20 Hz) and camera (over 100 Hz) makes simultaneous update of two perceptive systems (2D/3D) less efficient. Nowadays, frame interpolation is in urgent need for increasing frame rate of point cloud sequences obtained by LiDAR. However, a major limitation of current full supervised methods is that high frame rate ground truth sequences are hard to access. We propose a novel Self-supervised Point Cloud Frame Interpolation Network (SPINet) accommodating with variable motion situation, retaining geometric consistency, but without the necessity of utilizing G.T. data. Extensive experiments show that our proposed SPINet outperforms the current full supervised methods. [ABSTRACT FROM AUTHOR]

This study investigates the stabilisation problem for a class of discrete‐time two‐dimensional (2D) switched systems. Here the 2D systems mean that the state evolves following two independent directions, which are usually represented as horizontal states and vertical states. The considered systems are represented by Roesser model and incorporate both stable and unstable modes. Based on a switching signal regulation approach, which restricted the switching number as well as the running time ratio between the unstable and stable modes, the exponential stability condition is established for the 2D switched systems with synchronous switching. Besides, solving the proposed stabilisation results in linear matrix inequality form, the stabilising controller gains can be obtained. Moreover, considering the time delay in the controllers when the switching occurs, the proposed results are extended to the stabilisation problem for the 2D switched systems under asynchronous switching. A numerical example further demonstrates the validity of the developed results. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of decentralized event-triggered control for a class of interconnected time delay stochastic nonlinear systems with unmodeled dynamics. In order to ensure that the outputs of systems satisfie the prescribed performance, a funnel performance variable is introduced. By using neural network approximation theory and backstepping method, the controller and its event triggered mechanism are co-designed. Based on Lyapunov stability theory and changing supply functions idea, it is proved that all the signals of the overall closed-loop system with the designed controller are bounded in probability. Finally, a numerical simulation is presented to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

5G communication technologies are expected to provide high rate and low delay services. To meet the requirements, more base stations (BS), including macrocell BS (MacBS) and microcell BS (MicBS), have to be deployed. In this dense multi-tier heterogeneous networks, the user quality of service (QoS) can be significantly improved by shortening communication distance between base stations and users. However, the network energy consumptions of base stations have been growing quickly. How to save energy consumption in these dense layered network has become a problem we have to face. In this paper, we proposed a microcell BS (MicBS) switch algorithm to reduce the network energy consumption. The BS energy consumption is associated with traffic load, which is denoted as the number of users a BS serves. Considering the time-varying traffic load, we proposed a metric named coverage ratio to characterize how many users can enjoy the services. When the coverage ratio exceeds the upper threshold, a switch off algorithm is activated. MicBSs whose energy costs are higher than their economic profit will be switched off one by one. On the contrary, if this metric is below the lower threshold, a switch on algorithm is activated. A group of inactive MicBSs surrounded by multiple unserved users will be switched on simultaneously. After the switching operations, the network coverage ratio is expected to fall between the upper and lower bounds. Simulation results show that the coverage ratio is kept with the desired level. Compared to some existing algorithms, the proposed algorithm shows more flexible switching operation and more effective energy saving. [ABSTRACT FROM AUTHOR]

In this paper, the robust delay-dependent stability problem is investigated for discrete-time recurrent neural networks (DRNNs) with time-varying delays and nonlinear perturbations. A novel summation inequality is proposed, which takes information on the double summation of system state into consideration and further extends the discrete Wirtinger-based inequality. By utilizing technique of the novel inequality and Lyapunov–Krasovskii functionals, a sufficient condition on robust stability of DRNNs with time-varying delays and nonlinear perturbations is obtained in terms of linear matrix inequality. The numerical example is included to show that the proposed method is effective and provides less conservative results. [ABSTRACT FROM AUTHOR]

This article studies the joint localization and tracking issue for the autonomous underwater vehicle (AUV), with the constraints of asynchronous time clock in cyberchannels and model uncertainty in physical channels. More specifically, we develop a reinforcement learning (RL)-based asynchronous localization algorithm to localize the position of AUV, where the time clock of AUV is not required to be well synchronized with the real time. Based on the estimated position, a scalable sampling strategy called multivariate probabilistic collocation method with orthogonal fractional factorial design (M-PCM-OFFD) is employed to evaluate the time-varying uncertain model parameters of AUV. After that, an RL-based tracking controller is designed to drive AUV to the desired target point. Besides that, the performance analyses for the integration solution are also presented. Of note, the advantages of our solution are highlighted as: 1) the RL-based localization algorithm can avoid local optimal in traditional least-square methods; 2) the M-PCM-OFFD-based sampling strategy can address the model uncertainty and reduce the computational cost; and 3) the integration design of localization and tracking can reduce the communication energy consumption. Finally, simulation and experiment demonstrate that the proposed localization algorithm can effectively eliminate the impact of asynchronous clock, and more importantly, the integration of M-PCM-OFFD in the RL-based tracking controller can find accurate optimization solutions with limited computational costs. [ABSTRACT FROM AUTHOR]