Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance.The main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber security.Finally, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication constraints.These transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear dynamics.Through the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

Dynamic Event-Triggered Quadratic Nonfragile Filtering for Non-Gaussian Systems:Tackling Multiplicative Noises and Missing Measurements

This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises,multiple missing measurements as well as the dynamic event-triggered transmission scheme.The multiple missing measurements are characterized through random variables that obey some given probability distributions,and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary variable.Our attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance sense.To this end,the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers,thus the original design issue is reformulated as that of the augmented system.Subsequently,we analyze statistical properties of augmented noises as well as high-order moments of certain random parameters.With the aid of two well-defined matrix difference equations,we not only obtain upper bounds on filtering error covariances,but also minimize those bounds via carefully designing gain parameters.Finally,an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.

Recent progress and future prospects of high-entropy materials for battery applications

Due to the energy crisis caused by limited fossil fuel reserves,extensive use of the renewable energy sources such as wind or solar energy is deemed to replace the use of traditional fossil fuels in the future^([1−3]).However,most renewable energy sources face the same problem,which is the intermittency of energy.For example,solar energy cannot be utilized at night.That means the continuous energy demand required for large-scale power grids can’t be satisfied by a single solar panel model.

Encrypted Finite-Horizon Energy-to-Peak State Estimation for Time-Varying Systems Under Eavesdropping Attacks: Tackling Secrecy Capacity

This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying systems.Due to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential eavesdroppers.To avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption scheme.The purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is achieved.Sufficient conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state estimator.Finally,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.

Recent progress and future prospect of novel multi-ion storage devices

Rechargeable batteries,especially lithium-ion batteries(LIBs),have made rapid development since the 21st century,greatly facilitating people's lives[1−6].Based on considerations of cost and existing problems(such as safety issues due to LIBs stacking strategy and unsatisfactory performance for various applications),researchers have explored alternative technologies to LIBs to meet the needs for wide application scenarios[5].Among them,multi-ion storage devices such as dual-ion batteries(DIBs)and metal-ion hybrid capacitors(MIHCs)are considered promising alternative energy storage devices of LIBs due to their unique multi-ion storage mechanism.In a multi-ion storage device,cations and anions carry charges back and forth between the electrolyte and the electrodes at the same time,unlike the rocking chair mechanism of LIBs[7].Generally,the anodes of DIBs and MIHCs work in a similar mechanism to LIBs,storing charge through redox reactions.The main difference among them is the mechanism of the cathodes during charging and discharging[8].In DIBs,the battery-type cathode stores anions through the Faraday reaction.

Loading direction dependence of asymmetric response of pyramidal slip in rolled AZ31 magnesium alloy

Textured magnesium alloys usually exhibit anisotropic mechanical behavior due to the asymmetric activation of different twinning and slipping modes.This work focuses on the pyramidal slip responses of rolled AZ31 magnesium alloy under two loading conditions,compressive and tensile loading along the normal direction.Under the condition where the compressive loading direction is closely parallel to the c-axis of the unit cell,tensile twinning and basal slips are prohibited, dislocations then active and tend to accumulate at grain boundaries and form dislocation walls.Meanwhile,these dislocations exhibit zigzag morphologies,which result from the cross-slip from {10■1} first-order pyramidal plane to {11■2} second-order pyramidal plane,then back to {10■1} first-order pyramidal plane.Under the condition where tensile twins are prevalent,{10■1} first-order and {11■2} second-order pyramidal dislocations are favorable to be activated.Both types of dislocations behave climb-like dissociations onto the basal plane,forming zigzag dislocations.

Proximal Alternating-Direction-Method-of-Multipliers-Incorporated Nonnegative Latent Factor Analysis

High-dimensional and incomplete(HDI)data subject to the nonnegativity constraints are commonly encountered in a big data-related application concerning the interactions among numerous nodes.A nonnegative latent factor analysis(NLFA)model can perform representation learning to HDI data efficiently.However,existing NLFA models suffer from either slow convergence rate or representation accuracy loss.To address this issue,this paper proposes a proximal alternating-directionmethod-of-multipliers-based nonnegative latent factor analysis(PAN)model with two-fold ideas:(1)adopting the principle of alternating-direction-method-of-multipliers to implement an efficient learning scheme for fast convergence and high computational efficiency;and(2)incorporating the proximal regularization into the learning scheme to suppress the optimization fluctuation for high representation learning accuracy to HDI data.Theoretical studies verify that PAN converges to a Karush-KuhnTucker(KKT)stationary point of its nonnegativity-constrained learning objective with its learning scheme.Experimental results on eight HDI matrices from real applications demonstrate that the proposed PAN model outperforms several state-of-the-art models in both estimation accuracy for missing data of an HDI matrix and computational efficiency.

Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.

Research on the Impact Behavior of Molten Metal on the Mold Shell in Gravity Casting of Large Titanium alloy Castings

The impact behavior of molten metal on the mold shell in gravity casting of large titanium alloy castings by investment precision castings was studied.The physical and mechanical models of the impact of molten metal on the mold shell during the pouring process were constructed using numerical simulation.The effects of molten metal pouring rate and pouring weight on the maximum impact force of the mold shell were studied.The research results indicated that during the entire pouring process,the impact force of the first molten metal contacting the mold shell was higher than subsequent molten metal.The maximum impact force increased with the increase of pouring rate and pouring weight.The total impact force of the molten metal on the mold shell was composed of the instantaneous impact force converted from instantaneous impulse and itself gravity.The instantaneous impact force of the molten metal that first impacts the mold shell was much greater than its own gravity,while the impact force of the molten metal at the end of pouring was much less than its own gravity.The maximum impact force on the mold shell of a large casting with a pouring weight of 800kg was about three times higher than that of a medium-sized casting with a pouring weight of 80kg.The difference in the total impact force on the mold shell between them mainly comes from the instantaneous impact force converted from instantaneous impulse.

Set-Membership Filtering Subject to Impulsive Measurement Outliers:A Recursive Algorithm

This paper is concerned with the set-membership filtering problem for a class of linear time-varying systems with norm-bounded noises and impulsive measurement outliers.A new representation is proposed to model the measurement outlier by an impulsive signal whose minimum interval length(i.e.,the minimum duration between two adjacent impulsive signals)and minimum norm(i.e.,the minimum of the norms of all impulsive signals)are larger than certain thresholds that are adjustable according to engineering practice.In order to guarantee satisfactory filtering performance,a so-called parameter-dependent set-membership filter is put forward that is capable of generating a time-varying ellipsoidal region containing the true system state.First,a novel outlier detection strategy is developed,based on a dedicatedly constructed input-output model,to examine whether the received measurement is corrupted by an outlier.Then,through the outcome of the outlier detection,the gain matrix of the desired filter and the corresponding ellipsoidal region are calculated by solving two recursive difference equations.Furthermore,the ultimate boundedness issue on the time-varying ellipsoidal region is thoroughly investigated.Finally,a simulation example is provided to demonstrate the effectiveness of our proposed parameter-dependent set-membership filtering strategy.

An Overview of Recommendation Techniques and Their Applications in Healthcare

With the increasing amount of information on the internet,recommendation system(RS)has been utilized in a variety of fields as an efficient tool to overcome information overload.In recent years,the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health.This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare.More concretely,an overview is provided on three famous recommendation techniques,namely,content-based,collaborative filtering(CF)-based,and hybrid methods.Next,we provide a snapshot of five application scenarios about health RS,which are dietary recommendation,lifestyle recommendation,training recommendation,decision-making for patients and physicians,and disease-related prediction.Finally,some key challenges are given with clear justifications to this new and booming field.

Variance-Constrained Filtering Fusion for Nonlinear Cyber-Physical Systems With the Denial-of-Service Attacks and Stochastic Communication Protocol

In this paper,a new filtering fusion problem is studied for nonlinear cyber-physical systems under errorvariance constraints and denial-of-service attacks.To prevent data collision and reduce communication cost,the stochastic communication protocol is adopted in the sensor-to-filter channels to regulate the transmission order of sensors.Each sensor is allowed to enter the network according to the transmission priority decided by a set of independent and identicallydistributed random variables.From the defenders’view,the occurrence of the denial-of-service attack is governed by the randomly Bernoulli-distributed sequence.At the local filtering stage,a set of variance-constrained local filters are designed where the upper bounds(on the filtering error covariances)are first acquired and later minimized by appropriately designing filter parameters.At the fusion stage,all local estimates and error covariances are combined to develop a variance-constrained fusion estimator under the federated fusion rule.Furthermore,the performance of the fusion estimator is examined by studying the boundedness of the fused error covariance.A simulation example is finally presented to demonstrate the effectiveness of the proposed fusion estimator.

Toward High Carrier Mobility and Low Contact Resistance:Laser Cleaning of PMMA Residues on Graphene Surfaces

Poly(methyl methacrylate)(PMMA) is widely used for graphene transfer and device fabrication.However,it inevitably leaves a thin layer of polymer residues after acetone rinsing and leads to dramatic degradation of device performance.How to eliminate contamination and restore clean surfaces of graphene is still highly demanded.In this paper,we present a reliable and position-controllable method to remove the polymer residues on graphene films by laser exposure.Under proper laser conditions,PMMA residues can be substantially reduced without introducing defects to the underlying graphene.Furthermore,by applying this laser cleaning technique to the channel and contacts of graphene fieldeffect transistors(GFETs),higher carrier mobility as well as lower contact resistance can be realized.This work opens a way for probing intrinsic properties of contaminant-free graphene and fabricating high-performance GFETs with both clean channel and intimate graphene/metal contact.

Encoding-Decoding-Based Control and Filtering of Networked Systems： Insights, Developments and Opportunities

In order to make the information transmission more efficient and reliable in a digital communication channel with limited capacity, various encoding-decoding techniques have been proposed and widely applied in many branches of the signal processing including digital communications, data compression,information encryption, etc. Recently, due to its promising application potentials in the networked systems(NSs), the analysis and synthesis issues of the NSs under various encoding-decoding schemes have stirred some research attention. However, because of the network-enhanced complexity caused by the limited network resources, it poses new challenges to the design of suitable encoding-decoding procedures to meet certain control or filtering performance for the NSs. In this survey paper, our aim is to present a comprehensive review of the encoding-decodingbased control and filtering problems for different types of NSs.First, some basic introduction with respect to the coding-decoding mechanism is presented in terms of its engineering insights,specific properties and theoretical formulations. Then, the recent representative research progress in the design of the encodingdecoding protocols for various control and filtering problems is discussed. Some possible further research topics are finally outlined for the encoding-decoding-based NSs.

Robust control for a class of nonlinear networked systems with stochastic communication delays via sliding mode conception

This paper deals with the robust control problem for a class of uncertain nonlinear networked systems with stochastic communication delays via sliding mode conception （SMC）. A sequence of variables obeying Bernoulli distribution are employed to model the randomly occurring communication delays which could be different for different state variables. A discrete switching function that is different from those in the existing literature is first proposed. Then, expressed as the feasibility of a linear matrix inequality （LMI） with an equality constraint, sufficient conditions are derived in order to ensure the globally mean-square asymptotic stability of the system dynamics on the sliding surface. A discrete-time SMC controller is then synthesized to guarantee the discrete-time sliding mode reaching condition with the specified sliding surface. Finally, a simulation example is given to show the effectiveness of the proposed method.

The effect of far field flow on a spherical crystal growth in the undercooled melt

The effect of convective flow on a spherical crystal growth in the undercooled melt with a moderate far field flow is studied. The asymptotic solution of the evolution of the interface of the spherical crystal growth is obtained by the matched asymptotic expansion method. The analytic result shows that the convective flow in the undercooled melt has a strong effect on the evolution of spherical crystal growth. The convective flow induced by the far field flow makes the interface of the growing spherical crystal enhance its growth velocity in the upstream direction of the far field flow and inhibit growth in the downstream direction, and the interface of the decaying spherical crystal further decay in the upstream direction and inhibit decay in the downstream direction. The maximum growth velocity of the interface of the spherical crystal influenced by the far field flow is obtained.

The 20-nm Skyrmion Generated at Room Temperature by Spin-Orbit Torques

The discovery of magnetic skyrmions provides a promising pathway for developing functional spintronic memory and logic devices. Towards the future high-density memory application, nanoscale skyrmions with miniaturized diameters, ideally down to 20 nm are required. Using x-ray magnetic circular dichroism transmission microscopy,nanoscale skyrmions are observed in the [Pt/Co/Ir]15 multilayer at room temperature. In particular, small skyrmions with minimum diameters approaching 20 nm could be generated by the current-induced spin-orbit torques. Through implementing material specific parameters, the dynamic process of skyrmion generation is further investigated by performing micromagnetic simulations. According to the simulation results, we find that both the tube-like Neel-type skyrmions and the bobber-like Neel-type skyrmions can be electrically generated. In particular, the size of the bobber-like Neel-type skyrmions can be effectively reduced by the spin-orbit torques,which leads to the formation of 20 nm Neel-type skyrmions. Our findings could be important for understanding the formation dynamics of nanoscale Neel-type spin textures, skyrmions and bobber in particular, which could also be useful for promoting nanoscale skyrmionic memories and logic devices.

Temperature distribution in front of the liquid-solid interface in the undercooled pure melt influenced by a transverse far field flow

The directional solidification in the undercooled pure melt influenced by a transverse far field flow was studied by using the multiple scale method. The result shows that in the boundary layer near the liquid-solid interface, when affected by a transverse far field flow, the temperature distribution in the direction of crystal growth presents an oscillatory and decay front in the side of liquid phase. The crucial distinguishing feature of a temperature pattern due to the transverse convection is the additional periodic modulation of the pattern in the growth direction. The wave number and eigenvalue that satisfy the Mullins-Sekerka dispersion relation are suppressed by the transverse far field flow.