Event-Triggered Bipartite Consensus Tracking and Vibration Control of Flexible Timoshenko Manipulators Under Time-Varying Actuator Faults

For bipartite angle consensus tracking and vibration suppression of multiple Timoshenko manipulator systems with time-varying actuator faults,parameter and modeling uncertainties,and unknown disturbances,a novel distributed boundary event-triggered control strategy is proposed in this work.In contrast to the earlier findings,time-varying consensus tracking and actuator defects are taken into account simultaneously.In addition,the constructed event-triggered control mechanism can achieve a more flexible design because it is not required to satisfy the input-to-state condition.To achieve the control objectives,some new integral control variables are given by using back-stepping technique and boundary control.Moreover,adaptive neural networks are applied to estimate system uncertainties.With the proposed event-triggered scheme,control inputs can reduce unnecessary updates.Besides,tracking errors and vibration states of the closed-looped network can be exponentially convergent into some small fields,and Zeno behaviors can be excluded.At last,some simulation examples are given to state the effectiveness of the control algorithms.

Adaptive Leader-Follower Consensus Control of Multiple Flexible Manipulators With Actuator Failures and Parameter Uncertainties

In this paper,the leader-follower consensus problem for a multiple flexible manipulator network with actuator failures,parameter uncertainties,and unknown time-varying boundary disturbances is addressed.The purpose of this study is to develop distributed controllers utilizing local interactive protocols that not only suppress the vibration of each flexible manipulator but also achieve consensus on joint angle position between actual followers and the virtual leader.Following the accomplishment of the reconstruction of the fault terms and parameter uncertainties,the adaptive neural network method and parameter estimation technique are employed to compensate for unknown items and bounded disturbances.Furthermore,the Lyapunov stability theory is used to demonstrate that followers’angle consensus errors and vibration deflections in closed-loop systems are uniformly ultimately bounded.Finally,the numerical simulation results confirm the efficacy of the proposed controllers.

Evaluation method and optimization strategies of resilience for air&space defense system of systems based on kill network theory and improved self-information quantity

Resilience of air&space defense system of systems(SoSs)is critical to national air defense security.However,the research on it is still scarce.In this study,the resilience of air&space defense SoSs is firstly defined and the kill network theory is established by combining super network and kill chain theory.Two cases of the SoSs are considered:(a)The kill chains are relatively homogenous;(b)The kill chains are relatively heterogenous.Meanwhile,two capability assessment methods,which are based on the number of kill chains and improved self-information quantity,respectively,are proposed.The improved self-information quantity modeled based on nodes and edges can achieve qualitative and quantitative assessment of the combat capability by using linguistic Pythagorean fuzzy sets.Then,a resilient evaluation index consisting of risk response,survivability,and quick recovery is proposed accordingly.Finally,network models for regional air defense and anti-missile SoSs are established respectively,and the resilience measurement results are verified and analyzed under different attack and recovery strategies,and the optimization strategies are also proposed.The proposed theory and method can meet different demands to evaluate combat capability and optimize resilience of various types of air&space defense and similar SoSs.

The Immense Impact of Reverse Edges on Large Hierarchical Networks

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so forth.The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group.This study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific paths.More surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork.The overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization processes.This finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks.Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

Radiomics approach for preoperative identification of stages Ⅰ-Ⅱand Ⅲ-Ⅳ of esophageal cancer

Objective： To predict preoperative staging using a radiomics approach based on computed tomography （CT）images of patients with esophageal squamous cell carcinoma （ESCC）.Methods： This retrospective study included 154 patients （primary cohort： n： t 14; validation cohort： n：40） withpathologically confirmed ESCC. All patients underwent a preoperative CT scan from the neck to abdomen. Highthroughput and quantitative radiomics features were extracted from the CT images for each patient. A radiomicssignature was constructed using the least absolute shrinkage and selection operator （Lasso）. Associations betweenradiomics signature, tumor volume and ESCC staging were explored. Diagnostic performance of radiomicsapproach and tumor volume for discriminating between stages Ⅰ-Ⅱand Ⅲ-Ⅳ was evaluated and compared usingthe receiver operating characteristics （ROC） curves and net reclassification improvement （NRI）.Results= A total of 9,790 radiomics features were extracted. Ten features were selected to build a radiomicssignature after feature dimension reduction. The radiomics signature was significantly associated with ESCCstaging （P〈0.001）, and yielded a better performance for discrimination of early and advanced stage ESCC comparedto tumor volume in both the primary [area under the receiver operating characteristic curve （AUC）： 0.795 vs. 0.694,P=0.003; NRI=0.424）] and validation cohorts （AUC： 0.762 vs. 0.624, P=0.035; NRI=0.834）.Conclusions： The quantitative approach has the potential to identify stage Ⅰ-Ⅱand Ⅲ-Ⅳ ESCC beforetreatment.

Development and validation of a CT-based radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma

Objectives:To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma(GA).Methods:This retrospective study enrolled 592 patients with clinicopathologically confirmed GA(low-grade:n=154;high-grade:n=438)from January 2008 to March 2018 who were divided into training(n=450)and validation(n=142)sets according to the time of computed tomography(CT)examination.Radiomic features were extracted from the portal venous phase CT images.The Mann-Whitney U test and the least absolute shrinkage and selection operator(LASSO)regression model were used for feature selection,data dimension reduction and radiomics signature construction.Multivariable logistic regression analysis was applied to develop the prediction model.The radiomics signature and independent clinicopathologic risk factors were incorporated and presented as a radiomics nomogram.The performance of the nomogram was assessed with respect to its calibration and discrimination.Results:A radiomics signature containing 12 selected features was significantly associated with the histologic grade of GA(P<0.001 for both training and validation sets).A nomogram including the radiomics signature and tumor location as predictors was developed.The model showed both good calibration and good discrimination,in which C-index in the training set,0.752[95%confidence interval(95%CI):0.701-0.803];C-index in the validation set,0.793(95%CI:0.711-0.874).Conclusions:This study developed a radiomics nomogram that incorporates tumor location and radiomics signatures,which can be useful in facilitating preoperative individualized prediction of histologic grade of GA.

A review on DFACS(II):Modeling and analysis of disturbances and noises

This paper presents Part II of a review on DFACS,which specifically focuses on the modeling and analysis of disturbances and noises in DFACSs.In Part I,the system composition and dynamics model of the DFACS were presented.In this paper,we discuss the effects of disturbance forces and noises on the system,and summarize various analysis and modeling methods for these interferences,including the integral method,frequency domain analysis method,and magnitude evaluation method.By analyzing the impact of disturbances and noises on the system,the paper also summarizes the system’s performance under slight interferences.Additionally,we highlight current research difficulties in the field of DFACS noise analysis.Overall,this paper provides valuable insights into the modeling and analysis of disturbances and noises in DFACSs,and identifies key areas for future research.

A novel adaptive mutative scale optimization algorithm based on chaos genetic method and its optimization efficiency evaluation

By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [-1,1].Some measures in the optimization algorithm,such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline,were taken to ensure its speediness and veracity in seeking the optimization process.The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision.Furthermore,the average truncated generations,the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally.It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm.

Distributed Robust Containment Control of Linear Heterogeneous Multi-Agent Systems:An Output Regulation Approach

In this paper,we consider the robust output containment problem of linear heterogeneous multi-agent systems under fixed directed networks.A distributed dynamic observer based on the leaders’measurable output was designed to estimate a convex combination of the leaders’states.First,for the case of followers with identical state dimensions,distributed dynamic state and output feedback control laws were designed based on the state-coupled item and the internal model compensator to drive the uncertain followers into the leaders’convex hull within the output regulation framework.Subsequently,we extended theoretical results to the case where followers have nonidentical state dimensions.By establishing virtual errors between the dynamic observer and followers,a new distributed dynamic output feedback control law was constructed using only the states of the compensator to solve the robust output containment problem.Finally,two numerical simulations verified the effectiveness of the designed schemes.

Guaranteed cost control for discrete-time networked control systems with random Markov delays

The guaranteed cost control for a class of uncertain discrete-time networked control systems with random delays is addressed. The sensor-to-controller （S-C） and contraller-to-actuator （C-A） random network-induced delays are modeled as two Markov chains. The focus is on the design of a two-mode-dependent guar- anteed cost controller, which depends on both the current S-C delay and the most recently available C-A delay. The resulting closed-loop systems are special jump linear systems. Sufficient conditions for existence of guaranteed cost controller and an upper bound of cost function are established based on stochastic Lyapunov-Krasovakii functions and linear matrix inequality （LMI） approach. A simulation example illustrates the effectiveness of the proposed method.

A Survey of Output Feedback Robust MPC for Linear Parameter Varying Systems

For constrained linear parameter varying(LPV)systems,this survey comprehensively reviews the literatures on output feedback robust model predictive control(OFRMPC)over the past two decades from the aspects on motivations,main contributions,and the related techniques.According to the types of state observer systems and scheduling parameters of LPV systems,different kinds of OFRMPC approaches are summarized and compared.The extensions of OFRMPC for LPV systems to other related uncertain systems are also investigated.The methods of dealing with system uncertainties and constraints in different kinds of OFRMPC optimizations are given.Key issues on OFRMPC optimizations for LPV systems are discussed.Furthermore,the future research directions on OFRMPC for LPV systems are suggested.

Closed-loop P-type Iterative Learning Control of Uncertain Linear Distributed Parameter Systems

An iterative learning control problem for a class of uncertain linear parabolic distributed parameter systems is discussed,which covers many processes such as heat and mass transfer,convection diffusion and transport.Under condition of allowing system state initially to have error in the iterative process a closed-loop P-type iterative learning algorithm is presented,and the sufficient condition of tracking error convergence in L2 norm is given.Next,the convergence of the tracking error in L2 and W1,2 space is proved by using Gronwall-Bellman inequality and Sobolev inequality.In the end,a numerical example is given to illustrate the effectiveness of the proposed method.

EFFICIENT TIME DOMAIN ANALYSIS of ARBITRARILY SHAPED CONDUCTING WIRE STRUCTURES

We developed an efficient analysis the current induced in the wire structure. The analysis based on the time-Domain Integral Equation, in which a thin wire approximation is used. The time-domain electric field integral equation is used with the moment method to develop a numerical procedure for treating problems of scattering by arbitrary shaped bodies. We present an efficient numerical method for calculating the electromagnetic scattering from arbitrary shaped conducting bodies in the time domain with a comprehensive treatment of a single, straight thin wire. A time domain electric field integral equation is formulated for the problem of an arbitrary shape. The solution method is based on the moment method to solve the straight thin-wire problem.

Axial Assembled Correspondence Network for Few-Shot Semantic Segmentation

Few-shot semantic segmentation aims at training a model that can segment novel classes in a query image with only a few densely annotated support exemplars.It remains a challenge because of large intra-class variations between the support and query images.Existing approaches utilize 4D convolutions to mine semantic correspondence between the support and query images.However,they still suffer from heavy computation,sparse correspondence,and large memory.We propose axial assembled correspondence network(AACNet)to alleviate these issues.The key point of AACNet is the proposed axial assembled 4D kernel,which constructs the basic block for semantic correspondence encoder(SCE).Furthermore,we propose the deblurring equations to provide more robust correspondence for the aforementioned SCE and design a novel fusion module to mix correspondences in a learnable manner.Experiments on PASCAL-5~i reveal that our AACNet achieves a mean intersection-over-union score of 65.9%for 1-shot segmentation and 70.6%for 5-shot segmentation,surpassing the state-of-the-art method by 5.8%and 5.0%respectively.

Dynamical pattern recognition for univariate time series and its application to an axial compressor

In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurements of general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe the univariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is then adopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and the modeling results in the form of radial basis function network (RBFN) models are stored in a pattern library. Subsequently, multiple observer-based dynamical estimators containing the RBFN models in the pattern library are constructed for a test univariate time series, and a recognition decision scheme is proposed by the derived recognition indicator. On this basis, more concise recognition conditions are provided, which is beneficial for verifying the recognition results. Finally, simulation studies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach.

Switched-capacitor Based Seven-level Boost Inverter with a Reduced Number of Devices

The conventional multilevel inverters(MLIs)have the disadvantages of numerous devices,incapacity of boost,unbalance for capacitor’s voltage,high complexity for control,and etc.Motivated by this issue,a seven-level boost inverter(7LBI)based on a switched capacitor is presented for singlephase applications in this paper.The proposed 7LBI using only seven transistors can achieve seven output levels,1.5 voltage gain,and natural balance of capacitors’voltages without sensors or other auxiliary methods,which illustrates its suitability for the applications of renewable energy generation.The configuration of topology and operating principles are illustrated in detail.The natural balance of capacitors and capacitance calculations are deduced as well.Moreover,the comparative study is conducted for different types of MLIs.The results illustrate the merits of the proposed 7LBI with respect to reduced devices,lower voltage stress,and less power loss.Finally,a simulation for the proposed 7LBI with PWM modulation is realized based on the theoretical analysis;an experimental prototype is also implemented,verifying multilevel output,boost ability,natural balance for switched capacitors,and performance of transient response.

An overview on the distributed internal model approach and its applications

The cooperative output regulation problem has been studied by two approaches:the distributed observer(DO)approach and the distributed internal model(DIM)approach,respectively.Each of these two approaches has its own merits and weaknesses.Recently,we presented an overview on the cooperative output regulation problem by the DO approach.This paper further surveys the cooperative output regulation problem by the DIM approach.We first summarize the constructions and the roles of two different versions of the internal models:the distributed p-copy internal model and the distributed canonical internal model.Then,we describe an integrated framework that combines the DO approach and the DIM approach.Extensions and variants of the DIM and their applications will also be highlighted.

A review on DFACS(I):System design and dynamics modeling

The Drag-Free and Attitude Control System(DFACS)is a critical platform for various space missions,including high precision satellite navigation,geoscience and gravity field measurement,and space scientific experiments.This paper presents a comprehensive review of over sixty years of research on the design and dynamics model of DFACS.Firstly,we examine the open literature on DFACS and its applications in Drag-Free missions,providing readers with necessary background information on the field.Secondly,we analyze the system configurations and main characteristics of different DFACSs,paying particular attention to the coupling mechanism between the system configuration and dynamics model.Thirdly,we summarize the dynamics modeling methods and main dynamics models of DFACS from multiple perspectives,including common fundamentals and specific applications.Lastly,we identify current challenges and technological difficulties in the system design and dynamics modeling of DFACS,while suggesting potential avenues for future research.This paper aims to provide readers with a comprehensive understanding of the state-of-the-art in DFACS research,as well as the future prospects and challenges in this field.

Navigating the future:Embracing resilience and interoperability in the industrial internet

In the midst of the fourth industrial revolution,the convergence of the digital,physical,and biological realms is propelling industrial innovation to new heights.At the heart of this transformative era lies the Industrial Internet,a pivotal technology reshaping our industries.This powerful force establishes an all-encompassing network[1-2].

A Novel Method for Night-Time Single Image Dehazing

Images acquired under deprived weather environment are frequently corrupted due to the presence of haze, mist, fog or other aerosols in a form of noise. Haze elimination is essential in computer vision and computational photography applications. Generally, there is the existence of numerous approaches towards haze removal which are mostly meant for hazy images under daytime environments. Although the potency of these proposed approaches has been comprehensively established on daylight hazy images. However these procedures inherit significant limitations on images influenced by night-time hazy environments. Since night time haze removal dehazing remains an ill-posed problem, we proposed a novel method for night-time single image dehazing which is efficient under night-time environments. The proposed scheme is a dark channel-based local image dehazing procedure that locally estimates the atmospheric intensity for each selected mask on a corrupted image independently and not the entire image. This is done in order to overcome the challenge of night-scenes that are exposed to multiple/artificial lights source and spatially non-uniform environmental illumination. We performed an adaptive filtering on the combined dehazed masks to improve the degraded image. We validated the supremacy of the proposed approach in terms of speed and robustness through computer-based experiments. Conclusively, we displayed comparison results with state-of-the-art and extensively emphasized the comparative advantage of our scheme.