Research and Design of Consistent Project Cases for the Software Engineering Course Group

In order to enhance the abilities of practical innovation and solving complex engineering problems of students in the engineering context,we design a course cluster teaching model based on a unified enterprise-level project case.The program divides the knowledge points required by the project into the corresponding courses,and divides their realization into the practical teaching cases,so as to realize the design of teaching practice cases embodied in the unified project framework.This model allows students to practice projects based on the unified project background while learning knowledge from different courses.It not only allows students to learn abstract,fragmented,and difficult-to-understand knowledge systems thoroughly,but also integrates the knowledge into the practice of the enterprise-level project development,helping students experience the value of knowledge in complex engineering projects and thus improving their ability to solve complex engineering problems while learning theoretical knowledge.

Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

Set-Membership Filtering Approach to Dynamic Event-Triggered Fault Estimation for a Class of Nonlinear Time-Varying Complex Networks

The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks,utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism(DETM).In order to optimize communication resource utilization,the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not.To guarantee a satisfactory estimation performance for the fault signal,an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals.The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains.The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator,ensuring that the specified performance requirements are met under certain conditions.Then,the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided.Finally,a numerical example is conducted to demonstrate the effectiveness of the designed estimator.

A Survey of Multi-robot Regular and Adversarial Patrolling

Multi-robot systems can be applied to patrol a concerned environment for security purposes.According to different goals,this work reviews the existing researches in a multi-robot patrolling field from the perspectives of regular and adversarial patrolling.Regular patrolling requires robots to visit important locations as frequently as possible and a series of deterministic strategies are proposed,while adversarial one focuses on unpredictable robots’moving patterns to maximize adversary detection probability.Under each category,a systematic survey is done including problem statements and modeling,patrolling objectives and evaluation criteria,and representative patrolling strategies and approaches.Existing problems and open questions are presented accordingly.

Maximum Correntropy Kalman Filtering for Non-Gaussian Systems With State Saturations and Stochastic Nonlinearities

This paper tackles the maximum correntropy Kalman filtering problem for discrete time-varying non-Gaussian systems subject to state saturations and stochastic nonlinearities. The stochastic nonlinearities, which take the form of statemultiplicative noises, are introduced in systems to describe the phenomenon of nonlinear disturbances. To resist non-Gaussian noises, we consider a new performance index called maximum correntropy criterion(MCC) which describes the similarity between two stochastic variables. To enhance the “robustness” of the kernel parameter selection on the resultant filtering performance, the Cauchy kernel function is adopted to calculate the corresponding correntropy. The goal of this paper is to design a Kalman-type filter for the underlying systems via maximizing the correntropy between the system state and its estimate. By taking advantage of an upper bound on the one-step prediction error covariance, a modified MCC-based performance index is constructed. Subsequently, with the assistance of a fixed-point theorem, the filter gain is obtained by maximizing the proposed cost function. In addition, a sufficient condition is deduced to ensure the uniqueness of the fixed point. Finally, the validity of the filtering method is tested by simulating a numerical example.

Resilient Event-Triggered Control of Connected Automated Vehicles Under Cyber Attacks

Dear Editor,This letter contributes to designing a resilient event-triggered controller for connected automated vehicles under cyber attacks,including denial-of-service(DoS)and deception attacks.To characterize the effect of DoS attacks,the effective intervals of the attack are redivided based on the sampling period.

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.

Distributed Secure State Estimation of Multi-Agent Systems Under Homologous Sensor Attacks

This paper addresses the problem of distributed secure state estimation for multi-agent systems under homologous sensor attacks.Two types of secure Luenberger-like distributed observers are proposed to estimate the system state and attack signal simultaneously.Specifically,the proposed two observers are applicable to deal with the cases in the presence and absence of time delays during network communication.It is also shown that the proposed observers can ensure the attack estimations from different agents asymptotically converge to the same value.Sufficient conditions for guaranteeing the asymptotic convergence of the estimation errors are derived.Simulation examples are finally provided to demonstrate the effectiveness of the proposed results.

Multi-Robot Collaborative Hunting in Cluttered Environments With Obstacle-Avoiding Voronoi Cells

This work proposes an online collaborative hunting strategy for multi-robot systems based on obstacle-avoiding Voronoi cells in a complex dynamic environment. This involves firstly designing the construction method using a support vector machine(SVM) based on the definition of buffered Voronoi cells(BVCs). Based on the safe collision-free region of the robots, the boundary weights between the robots and the obstacles are dynamically updated such that the robots are tangent to the buffered Voronoi safety areas without intersecting with the obstacles. Then, the robots are controlled to move within their own buffered Voronoi safety area to achieve collision-avoidance with other robots and obstacles. The next step involves proposing a hunting method that optimizes collaboration between the pursuers and evaders. Some hunting points are generated and distributed evenly around a circle. Next, the pursuers are assigned to match the optimal points based on the Hungarian algorithm.Then, a hunting controller is designed to improve the containment capability and minimize containment time based on collision risk. Finally, simulation results have demonstrated that the proposed cooperative hunting method is more competitive in terms of time and travel distance.

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.

A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts

Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto fronts.To remedy this issue,a large body of research has been performed in recent years and many new algorithms have been proposed.This paper provides a comprehensive survey of the research on MOPs with irregular Pareto fronts.We start with a brief introduction to the basic concepts,followed by a summary of the benchmark test problems with irregular problems,an analysis of the causes of the irregularity,and real-world optimization problems with irregular Pareto fronts.Then,a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed with a discussion of their strengths and weaknesses.Finally,open challenges are pointed out and a few promising future directions are suggested.

Parameters Tuning of Model Free Adaptive Control Based on Minimum Entropy

Dynamic linearization based model free adaptive control(MFAC) algorithm has been widely used in practical systems, in which some parameters should be tuned before it is successfully applied to process industries. Considering the random noise existing in real processes, a parameter tuning method based on minimum entropy optimization is proposed,and the feature of entropy is used to accurately describe the system uncertainty. For cases of Gaussian stochastic noise and non-Gaussian stochastic noise, an entropy recursive optimization algorithm is derived based on approximate model or identified model. The extensive simulation results show the effectiveness of the minimum entropy optimization for the partial form dynamic linearization based MFAC. The parameters tuned by the minimum entropy optimization index shows stronger stability and more robustness than these tuned by other traditional index,such as integral of the squared error(ISE) or integral of timeweighted absolute error(ITAE), when the system stochastic noise exists.

Improving Link Prediction Accuracy of Network Embedding Algorithms via Rich Node Attribute Information

Complex networks are widely used to represent an abundance of real-world relations ranging from social networks to brain networks. Inferring missing links or predicting future ones based on the currently observed network is known as the link prediction task. Recent network embedding based link prediction algorithms have demonstrated ground-breaking performance on link prediction accuracy. Those algorithms usually apply node attributes as the initial feature input to accelerate the convergence speed during the training process. However, they do not take full advantage of node feature information. In this paper, besides applying feature attributes as the initial input, we make better utilization of node attribute information by building attributable networks and plugging attributable networks into some typical link prediction algorithms and name this algorithm Attributive Graph Enhanced Embedding (AGEE). AGEE is able to automatically learn the weighting trades-off between the structure and the attributive networks. Numerical experiments show that AGEE can improve the link prediction accuracy by around 3% compared with SEAL, Variational Graph AutoEncoder (VGAE), and node2vec.

Stochastic Iterative Learning Control With Faded Signals

Stochastic iterative learning control(ILC) is designed for solving the tracking problem of stochastic linear systems through fading channels. Consequently, the signals used in learning control algorithms are faded in the sense that a random variable is multiplied by the original signal. To achieve the tracking objective, a two-dimensional Kalman filtering method is used in this study to derive a learning gain matrix varying along both time and iteration axes. The learning gain matrix minimizes the trace of input error covariance. The asymptotic convergence of the generated input sequence to the desired input value is strictly proved in the mean-square sense. Both output and input fading are accounted for separately in turn, followed by a general formulation that both input and output fading coexists.Illustrative examples are provided to verify the effectiveness of the proposed schemes.

Recursive Filtering for Nonlinear Systems With Self-Interferences Over Full-Duplex Relay Networks

Dear Editor,In this paper,a recursive filtering problem(RRP)is addressed for nonlinear systems over full-duplex relay(FDR)networks.A FDR is adopted to forward measurements of the sensor to the filter.Because of concurrently transmitting and receiving,the FDR is interfered by the signals from itself,thereby exhibiting self-interference(SI).

Adaptive Iterative Learning Control Based on Unfalsified Strategy for Chylla-Haase Reactor

An adaptive iterative learning control based on unfalsified strategy is proposed to solve high precision temperature tracking of the Chylla-Haase reactor, in which iterative learning is the main control method and the unfalsified strategy is adapted to adjust the learning rate adaptively. It is encouraged that the unfalsified control strategy is extended from time domain to iterative domain, and the basic definition and mathematics description of unfalsified control in iterative domain are given.The proposed algorithm is a kind of data-driven method, which does not need an accurate system model. Process data are used to construct fictitious reference signal and switch function in order to handle different process conditions. In addition, the plant data are also used to build the iterative learning control law. Here the learning rate in a different error level is adjusted to ensure the convergent speed and stability, rather than keeping constant in traditional iterative learning control. Furthermore,two important problems in iterative learning control, i.e., the initial control law and convergence analysis, are discussed in detail. The initial input of first iteration is arranged according to a mechanism model, which can assure a good produce quality in the first iteration and a fast convergence speed of tracking error. The convergence condition is given which is obviously relaxed compared with the tradition iterative learning control.Simulation results show that the proposed control algorithm is effective for the Chylla-Haase problem with good performance in both convergent speed and stability.

Behavior Model Construction for Client Side of Modern Web Applications

Most of the behavior models with respect to Web applications focus on sequencing of events,without regard for the changes of parameters or elements and the relationship between trigger conditions of events and Web pages.As a result,these models are not sufficient to effectively represent the dynamic behavior of the Web2.0 application.Therefore,in this paper,to appropriately describe the dynamic behavior of the client side of Web applications,we define a novel Client-side Behavior Model(CBM)for Web applications and present a user behavior trace-based modeling method to automatically generate and optimize CBMs.To verify the effectiveness of our method,we conduct a series of experiments on six Web applications according to three types of user behavior traces.The experimental results show that our modeling method can construct CBMs automatically and effectively,and the CBMs built are more precise to represent the dynamic behavior of Web applications.

Motion analysis of passive dynamic walking with a rigorously constraint model: A necessary condition for maintaining period-1 gait

The friction force is an important environmental factor that influences dynamic walking.While most of the related works simply assume static friction or Coulomb friction,we use the LuGre friction,which accounts for both static and dynamic effects,to model the horizontal ground reaction force of passive dynamic walking.We present a detailed mathematical modeling method and perform numerical simulations using it.Furthermore,we analyze the ground surface cases of the Coulomb friction condition and static friction condition to verify the model’s generalization.We discover the required condition for the existence of the period-1 gait through investigation.Our mathematical model and theoretical analysis add to our understanding of passive dynamic walking,which helps to positively utilize the natural dynamics of the legged locomotion system in control design.

Vulnerable Public Keys in NTRU Cryptosystem

In this paper the authors give an efficient bounded distance decoding(BDD for short)algorithm for NTRU lattices under some conditions about the modulus number q and the public key h.They then use this algorithm to give plain-text recovery attack to NTRUEncrypt and forgery attack on NTRUSign.In particular the authors figure out a weak domain of public keys such that the recent transcript secure version of NTRU signature scheme NTRUMLS with public keys in this domain can be forged.