Showing papers in "IEEE Transactions on Systems, Man, and Cybernetics in 2019"

A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis

Abstract: Fault diagnosis plays an important role in modern industry. With the development of smart manufacturing, the data-driven fault diagnosis becomes hot. However, traditional methods have two shortcomings: 1) their performances depend on the good design of handcrafted features of data, but it is difficult to predesign these features and 2) they work well under a general assumption: the training data and testing data should be drawn from the same distribution, but this assumption fails in many engineering applications. Since deep learning (DL) can extract the hierarchical representation features of raw data, and transfer learning provides a good way to perform a learning task on the different but related distribution datasets, deep transfer learning (DTL) has been developed for fault diagnosis. In this paper, a new DTL method is proposed. It uses a three-layer sparse auto-encoder to extract the features of raw data, and applies the maximum mean discrepancy term to minimizing the discrepancy penalty between the features from training data and testing data. The proposed DTL is tested on the famous motor bearing dataset from the Case Western Reserve University. The results show a good improvement, and DTL achieves higher prediction accuracies on most experiments than DL. The prediction accuracy of DTL, which is as high as 99.82%, is better than the results of other algorithms, including deep belief network, sparse filter, artificial neural network, support vector machine and some other traditional methods. What is more, two additional analytical experiments are conducted. The results show that a good unlabeled third dataset may be helpful to DTL, and a good linear relationship between the final prediction accuracies and their standard deviations have been observed.

Blockchain-Enabled Smart Contracts: Architecture, Applications, and Future Trends

Abstract: In recent years, the rapid development of cryptocurrencies and their underlying blockchain technology has revived Szabo’s original idea of smart contracts, i.e., computer protocols that are designed to automatically facilitate, verify, and enforce the negotiation and implementation of digital contracts without central authorities. Smart contracts can find a wide spectrum of potential application scenarios in the digital economy and intelligent industries, including financial services, management, healthcare, and Internet of Things, among others, and also have been integrated into the mainstream blockchain-based development platforms, such as Ethereum and Hyperledger. However, smart contracts are still far from mature, and major technical challenges such as security and privacy issues are still awaiting further research efforts. For instance, the most notorious case might be “The DAO Attack” in June 2016, which led to more than $50 million Ether transferred into an adversary’s account. In this paper, we strive to present a systematic and comprehensive overview of blockchain-enabled smart contracts, aiming at stimulating further research toward this emerging research area. We first introduced the operating mechanism and mainstream platforms of blockchain-enabled smart contracts, and proposed a research framework for smart contracts based on a novel six-layer architecture. Second, both the technical and legal challenges, as well as the recent research progresses, are listed. Third, we presented several typical application scenarios. Toward the end, we discussed the future development trends of smart contracts. This paper is aimed at providing helpful guidance and reference for future research efforts.

EmotionMeter: A Multimodal Framework for Recognizing Human Emotions

Abstract: In this paper, we present a multimodal emotion recognition framework called EmotionMeter that combines brain waves and eye movements To increase the feasibility and wearability of EmotionMeter in real-world applications, we design a six-electrode placement above the ears to collect electroencephalography (EEG) signals We combine EEG and eye movements for integrating the internal cognitive states and external subconscious behaviors of users to improve the recognition accuracy of EmotionMeter The experimental results demonstrate that modality fusion with multimodal deep neural networks can significantly enhance the performance compared with a single modality, and the best mean accuracy of 8511% is achieved for four emotions (happy, sad, fear, and neutral) We explore the complementary characteristics of EEG and eye movements for their representational capacities and identify that EEG has the advantage of classifying happy emotion, whereas eye movements outperform EEG in recognizing fear emotion To investigate the stability of EmotionMeter over time, each subject performs the experiments three times on different days EmotionMeter obtains a mean recognition accuracy of 7239% across sessions with the six-electrode EEG and eye movement features These experimental results demonstrate the effectiveness of EmotionMeter within and between sessions

A Dynamic Event-Triggered Transmission Scheme for Distributed Set-Membership Estimation Over Wireless Sensor Networks

Abstract: This paper is concerned with the distributed set-membership estimation for a discrete-time linear time-varying system over a resource-constrained wireless sensor network under the influence of unknown-but-bounded (UBB) process and measurement noise. Sensors collaborate among themselves by exchanging local measurements with only neighboring sensors in their sensing ranges. First, a new dynamic event-triggered transmission scheme (ETS) is developed to schedule the transmission of each sensor’s local measurement. In contrast with the majority of existing static ETSs, the newly proposed dynamic ETS can result in larger average interevent times and thus less totally released data packets. Second, a criterion for designing desired event-triggered set-membership estimators is derived such that the system’s true state always resides in each sensor’s bounding ellipsoidal estimation set regardless of the simultaneous presence of UBB process and measurement noise. Third, a recursive convex optimization algorithm is presented to determine optimal ellipsoids as well as the estimator gain parameters and the event triggering weighting matrix parameter. Furthermore, the proposed dynamic ETS is applied to address the distributed set-membership estimation problem for a discrete-time linear time-varying system with a nonlinearity satisfying a sector constraint. Finally, an illustrative example is given to show the effectiveness and advantage of the developed approach.

Spatial–Temporal Recurrent Neural Network for Emotion Recognition

Abstract: In this paper, we propose a novel deep learning framework, called spatial–temporal recurrent neural network (STRNN), to integrate the feature learning from both spatial and temporal information of signal sources into a unified spatial–temporal dependency model. In STRNN, to capture those spatially co-occurrent variations of human emotions, a multidirectional recurrent neural network (RNN) layer is employed to capture long-range contextual cues by traversing the spatial regions of each temporal slice along different directions. Then a bi-directional temporal RNN layer is further used to learn the discriminative features characterizing the temporal dependencies of the sequences, where sequences are produced from the spatial RNN layer. To further select those salient regions with more discriminative ability for emotion recognition, we impose sparse projection onto those hidden states of spatial and temporal domains to improve the model discriminant ability. Consequently, the proposed two-layer RNN model provides an effective way to make use of both spatial and temporal dependencies of the input signals for emotion recognition. Experimental results on the public emotion datasets of electroencephalogram and facial expression demonstrate the proposed STRNN method is more competitive over those state-of-the-art methods.

Neural Networks-Based Adaptive Finite-Time Fault-Tolerant Control for a Class of Strict-Feedback Switched Nonlinear Systems

Abstract: This paper concentrates upon the problem of finite-time fault-tolerant control for a class of switched nonlinear systems in lower-triangular form under arbitrary switching signals. Both loss of effectiveness and bias fault in actuator are taken into account. The method developed extends the traditional finite-time convergence from nonswitched lower-triangular nonlinear systems to switched version by designing appropriate controller and adaptive laws. In contrast to the previous results, it is the first time to handle the fault tolerant problem for switched system while the finite-time stability is also necessary. Meanwhile, there exist unknown internal dynamics in the switched system, which are identified by the radial basis function neural networks. It is proved that under the presented control strategy, the system output tracks the reference signal in the sense of finite-time stability. Finally, an illustrative simulation on a resistor-capacitor-inductor circuit is proposed to further demonstrate the effectiveness of the theoretical result.

Resilient Event-Triggered Controller Synthesis of Networked Control Systems Under Periodic DoS Jamming Attacks

Abstract: In this paper, the event-based controller synthesis problem for networked control systems under the resilient event-triggering communication scheme (RETCS) and periodic denial-of-service (DoS) jamming attacks is studied. First, a new periodic RETCS is designed under the assumption that the DoS attacks imposed by power-constrained pulsewidth-modulated jammers are partially identified, that is, the period of the jammer and a uniform lower bound on the jammer’s sleeping periods are known. Second, a new state error-dependent switched system model is constructed, including the impacts of the RETCS and DoS attacks. According to this new model, the exponential stability criteria are derived by using the piecewise Lyapunov functional. In these criteria, the relationship among DoS parameters, the triggering parameters, the sampling period, and the decay rate is quantitatively characterized. Then, a criterion is also proposed to obtain the explicit expressions of the triggering parameter and event-based state feedback controller gain simultaneously. Finally, the obtained theoretical results are verified by a satellite yaw-angles control system.

New IT Driven Service-Oriented Smart Manufacturing: Framework and Characteristics

Abstract: Recently, along with the wide application of new generation information technologies (New IT) in manufacturing, many countries issued their national advanced manufacturing development strategies, such as Industrial Internet, Industry 4.0, and Made in China 2025. One common aim of these strategies is to achieve smart manufacturing, which demands the interoperation, integration, and fusion of the physical world and the cyber world of manufacturing. As well, New IT [such as Internet of Things (IoT), cloud computing, big data, mobile Internet, and cyber-physical systems (CPS)] have played pivotal roles in promoting smart manufacturing. Data generated in the physical world can be sensed and transfered to the cyber world through IoT and the Internet, and be processed and analyzed by cloud computing, big data technologies to adjust the physical world. The physical world and the cyber world of manufacturing are integrated based on CPS. On the other hand, servitization has become a prominent trend in the manufacturing. Embracing the concept of “Manufacturing-as-a-Service,” manufacturing is provided as service for users. Because of the characteristics of interoperability and platform independence, services pave the way for large-scale smart applications and manufacturing collaboration. Combining New IT and services, this paper proposes a framework—New IT driven service-oriented smart manufacturing (SoSM). SoSM aims at facilitating the visions of smart manufacturing by making full use of New IT and services. Complementary to the framework of SoSM, the New IT driven typical characteristics of SoSM are also investigated and discussed, respectively.

Synchronization Control for A Class of Discrete Time-Delay Complex Dynamical Networks: A Dynamic Event-Triggered Approach

Abstract: This paper is concerned with the synchronization control problem for a class of discrete time-delay complex dynamical networks under a dynamic event-triggered mechanism. For the efficiency of energy utilization, we make the first attempt to introduce a dynamic event-triggering strategy into the design of synchronization controllers for complex dynamical networks. A new discrete-time version of the dynamic event-triggering mechanism is proposed in terms of the absolute errors between control input updates. By constructing an appropriate Lyapunov functional, the dynamics of each network node combined with the introduced event-triggering mechanism are first analyzed, and a sufficient condition is then provided under which the synchronization error dynamics is exponentially ultimately bounded. Subsequently, a set of the desired synchronization controllers is designed by solving a matrix inequality. Finally, a simulation example is provided to verify the effectiveness of the proposed dynamic event-triggered synchronization control scheme.

Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications

Abstract: Convolutional neural networks (CNNs) have yielded state-of-the-art performance in image classification and other computer vision tasks. Their application in fire detection systems will substantially improve detection accuracy, which will eventually minimize fire disasters and reduce the ecological and social ramifications. However, the major concern with CNN-based fire detection systems is their implementation in real-world surveillance networks, due to their high memory and computational requirements for inference. In this paper, we propose an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire. It uses smaller convolutional kernels and contains no dense, fully connected layers, which helps keep the computational requirements to a minimum. Despite its low computational needs, the experimental results demonstrate that our proposed solution achieves accuracies that are comparable to other, more complex models, mainly due to its increased depth. Moreover, this paper shows how a tradeoff can be reached between fire detection accuracy and efficiency, by considering the specific characteristics of the problem of interest and the variety of fire data.

Event-Triggered $H_\infty$ Load Frequency Control for Multiarea Power Systems Under Hybrid Cyber Attacks

Abstract: This paper investigates the problem of event-triggered ${H_\infty }$ load frequency control (LFC) for multiarea power systems under hybrid cyber attacks, including denial-of-service (DoS) attacks and deception attacks. An event-triggered transmission scheme is developed under the DoS attacks to lighten the load of network bandwidth while preserving a satisfactory system performance. Then, a new switched system model accounting for the simultaneous presence of DoS attacks and stochastic deception attacks is established with respect to the LFC for multiarea power system. On the basis of the new model, sufficient conditions ensuring multiarea power system exponentially mean-square stable with prescribed ${H_\infty }$ performance are obtained by using Lyapunov stability theory. Furthermore, criteria for simultaneously designing the weighting matrix in event-triggered scheme and the controller gain matrix are derived by utilizing the linear matrix inequality technique. Finally, a three-area power system is simulated to demonstrate the usefulness of the approaches proposed in this paper.

An Adaptive Backstepping Nonsingular Fast Terminal Sliding Mode Control for Robust Fault Tolerant Control of Robot Manipulators

Abstract: This paper develops a novel control methodology for tracking control of robot manipulators based on a novel adaptive backstepping nonsingular fast terminal sliding mode control (ABNFTSMC). In this approach, a novel backstepping nonsingular fast terminal sliding mode controller (BNFTSMC) is developed based on an integration of integral nonsingular fast terminal sliding mode surface and a backstepping control strategy. The benefits of this approach are that the proposed controller can preserve the merits of the integral nonsingular fast terminal sliding mode control (NFTSMC) in terms of high robustness, fast transient response, and finite-time convergence, as well as backstepping control strategy in terms of globally asymptotic stability based on Lyapunov criterion. However, the major limitation of the proposed BNFTSMC is that its design procedure is dependent on the prior knowledge of the bound value of the disturbance and uncertainties. In order to overcome this limitation, an adaptive technique is employed to approximate the upper bound value; yielding an ABNFTSMC is recommended. The proposed controller is then applied for tracking control of a PUMA560 robot and compared with other state-of-the-art controllers, such as computed torque controller, PID controller, conventional PID-based sliding mode controller, and NFTSMC. The comparison results demonstrate the superior performance of the proposed approach.

Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image

Abstract: Hyperspectral image (HSI) contains a large number of spatial-spectral information, which will make the traditional classification methods face an enormous challenge to discriminate the types of land-cover. Feature learning is very effective to improve the classification performances. However, the current feature learning approaches are mostly based on a simple intrinsic structure. To represent the complex intrinsic spatial-spectral of HSI, a novel feature learning algorithm, termed spatial-spectral hypergraph discriminant analysis (SSHGDA), has been proposed on the basis of spatial-spectral information, discriminant information, and hypergraph learning. SSHGDA constructs a reconstruction between-class scatter matrix, a weighted within-class scatter matrix, an intraclass spatial-spectral hypergraph, and an interclass spatial-spectral hypergraph to represent the intrinsic properties of HSI. Then, in low-dimensional space, a feature learning model is designed to compact the intraclass information and separate the interclass information. With this model, an optimal projection matrix can be obtained to extract the spatial-spectral features of HSI. SSHGDA can effectively reveal the complex spatial-spectral structures of HSI and enhance the discriminating power of features for land-cover classification. Experimental results on the Indian Pines and PaviaU HSI data sets show that SSHGDA can achieve better classification accuracies in comparison with some state-of-the-art methods.

Iterative Learning Control for a Flapping Wing Micro Aerial Vehicle Under Distributed Disturbances

Abstract: This paper addresses a flexible micro aerial vehicle (MAV) under spatiotemporally varying disturbances, which is composed of a rigid body and two flexible wings. Based on Hamilton’s principle, a distributed parameter system coupling in bending and twisting, is modeled. Two iterative learning control (ILC) schemes are designed to suppress the vibrations in bending and twisting, reject the distributed disturbances and regulate the displacement of the rigid body to track a prescribed constant trajectory. At the basis of composite energy function, the boundedness and the learning convergence are proved for the closed-loop MAV system. Simulation results are provided to illustrate the effectiveness of the proposed ILC laws.

Energy Crowdsourcing and Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids

Abstract: The power grid is rapidly transforming, and while recent grid innovations increased the utilization of advanced control methods, the next-generation grid demands technologies that enable the integration of distributed energy resources (DERs)—and consumers that both seamlessly buy and sell electricity. This paper develops an optimization model and blockchain-based architecture to manage the operation of crowdsourced energy systems (CESs), with peer-to-peer (P2P) energy trading transactions (ETTs). An operational model of CESs in distribution networks is presented considering various types of ETT and crowdsourcees. Then, a two-phase operation algorithm is presented: Phase I focuses on the day-ahead scheduling of generation and controllable DERs, whereas Phase II is developed for hour-ahead or real-time operation of distribution networks. The developed approach supports seamless P2P energy trading between individual prosumers and/or the utility. The presented operational model can also be used to operate islanded microgrids. The CES framework and the operation algorithm are then prototyped through an efficient blockchain implementation, namely, the IBM Hyperledger Fabric. This implementation allows the system operator to manage the network users to seamlessly trade energy. Case studies and prototype illustration are provided.

Distributed Adaptive Event-Triggered Fault-Tolerant Consensus of Multiagent Systems With General Linear Dynamics

Abstract: In this paper, the distributed adaptive event-triggered fault-tolerant consensus of general linear multiagent systems (MASs) is considered. First, in order to deal with multiplicative fault, a distributed event-triggered consensus protocol is designed. Using distributed adaptive online updating strategies, the computation of the minimum eigenvalue of Laplacian matrix is avoided. Second, some adaptive parameters are introduced in trigger function to improve the self-regulation ability of event-triggered mechanism. The new trigger threshold is both state-dependent and time-dependent, which is independent of the number of agents. Then sufficient conditions are derived to guarantee the leaderless and leader-following consensus. On the basis of this, the results are extended to the case of actuator saturation. It is proved the Zeno-behavior of considered event-triggered mechanism is avoided. At last, the effectiveness of the proposed methods are demonstrated by three simulation examples.

Transfer Independently Together: A Generalized Framework for Domain Adaptation

Abstract: Currently, unsupervised heterogeneous domain adaptation in a generalized setting, which is the most common scenario in real-world applications, is under insufficient exploration. Existing approaches either are limited to special cases or require labeled target samples for training. This paper aims to overcome these limitations by proposing a generalized framework, named as transfer independently together (TIT). Specifically, we learn multiple transformations, one for each domain (independently) , to map data onto a shared latent space, where the domains are well aligned. The multiple transformations are jointly optimized in a unified framework (together) by an effective formulation. In addition, to learn robust transformations, we further propose a novel landmark selection algorithm to reweight samples, i.e., increase the weight of pivot samples and decrease the weight of outliers. Our landmark selection is based on graph optimization. It focuses on sample geometric relationship rather than sample features. As a result, by abstracting feature vectors to graph vertices, only a simple and fast integer arithmetic is involved in our algorithm instead of matrix operations with float point arithmetic in existing approaches. At last, we effectively optimize our objective via a dimensionality reduction procedure. TIT is applicable to arbitrary sample dimensionality and does not need labeled target samples for training. Extensive evaluations on several standard benchmarks and large-scale datasets of image classification, text categorization and text-to-image recognition verify the superiority of our approach.

Observer-Based Adaptive Fuzzy Fault-Tolerant Optimal Control for SISO Nonlinear Systems

Abstract: This paper investigates adaptive fuzzy output feedback fault-tolerant optimal control problem for a class of single-input and single-output nonlinear systems in strict feedback form. The considered nonlinear systems contain unknown nonaffine nonlinear faults and unmeasured states. Fuzzy logic systems are used to approximate cost function and unknown nonlinear functions, respectively. It is assumed that the states of the systems to be controlled are unmeasurable, thus an adaptive state observer is developed. To solve the nonaffine nonlinear fault control design problem, filtered signals are introduced into the adaptive backstepping control design procedures, and in the framework of adaptive critic technique and fault-tolerant control technique, a novel adaptive fuzzy fault-tolerant optimal control scheme is developed. The stability of the closed-loop system is proved by using Lyapunov stability theory. The simulation results verify the effectiveness of the proposed control strategy.

Prescribed-Time Consensus and Containment Control of Networked Multiagent Systems

Abstract: In this paper, we present a new prescribed-time distributed control method for consensus and containment of networked multiple systems. Different from both regular finite-time control (where the finite settling time is not uniform in initial conditions) and the fixed-time control (where the settling time cannot be preassigned arbitrarily), the proposed one is built upon a novel scaling function, resulting in prespecifiable convergence time (the settling time can be preassigned as needed within any physically allowable range). Furthermore, the developed control scheme not only ensures that all the agents reach the average consensus in prescribed finite time under undirected connected topology, but also ensures that all the agents reach a prescribed-time consensus with the root’s state being the group decision value under the directed topology containing a spanning tree with the root as the leader. In addition, we extend the result to prescribed-time containment control involving multiple leaders under directed communication topology. Numerical examples are provided to verify the effectiveness and the superiority of the proposed control.

Deep Attention-Based Spatially Recursive Networks for Fine-Grained Visual Recognition

Abstract: Fine-grained visual recognition is an important problem in pattern recognition applications. However, it is a challenging task due to the subtle interclass difference and large intraclass variation. Recent visual attention models are able to automatically locate critical object parts and represent them against appearance variations. However, without consideration of spatial dependencies in discriminative feature learning, these methods are underperformed in classifying fine-grained objects. In this paper, we present a deep attention-based spatially recursive model that can learn to attend to critical object parts and encode them into spatially expressive representations. Our network is technically premised on bilinear pooling, enabling local pairwise feature interactions between outputs from two different convolutional neural networks (CNNs) that correspond to distinct region detection and relevant feature extraction. Then, spatial long-short term memory (LSTMs) units are introduced to generate spatially meaningful hidden representations via the long-range dependency on all features in two dimensions. The attention model is leveraged between bilinear outcomes and spatial LSTMs for dynamic selection on varied inputs. Our model, which is composed of two-stream CNN layers, bilinear pooling, and spatial recursive encoding with attention, is end-to-end trainable to serve as the part detector and feature extractor whereby relevant features are localized, extracted, and encoded spatially for recognition purpose. We demonstrate the superiority of our method over two typical fine-grained recognition tasks: fine-grained image classification and person re-identification.

Neural Networks-Based Adaptive Control for Nonlinear State Constrained Systems With Input Delay

Abstract: This paper addresses the problem of adaptive tracking control for a class of strict-feedback nonlinear state constrained systems with input delay. To alleviate the major challenges caused by the appearances of full state constraints and input delay, an appropriate barrier Lyapunov function and an opportune backstepping design are used to avoid the constraint violation, and the Pade approximation and an intermediate variable are employed to eliminate the effect of the input delay. Neural networks are employed to estimate unknown functions in the design procedure. It is proven that the closed-loop signals are semiglobal uniformly ultimately bounded, and the tracking error converges to a compact set of the origin, as well as the states remain within a bounded interval. The simulation studies are given to illustrate the effectiveness of the proposed control strategy in this paper.

A Survey on Security Communication and Control for Smart Grids Under Malicious Cyber Attacks

Abstract: Smart grids (SGs), which can be classified into a class of networked distributed control systems, are designed to deliver electricity from various plants through a communication network to serve individual consumers. Due to the complexity of environments, the distribution of the spatial locations and vulnerability of the communication networks, cyber security emerges to be a critical issue because millions of electronic devices are interconnected via communication networks throughout critical power facilities. This paper addresses a comprehensive security understanding of the SGs framework, attacks scenarios, detection/protection methods, estimation and control strategies from both communication and control viewpoints. Also, some potential challenges and solution approaches are discussed to deal with the threat issues of SGs. At last, some conclusions and highlight future research directions are presented.

Distributed Resilient Filtering for Power Systems Subject to Denial-of-Service Attacks

Abstract: This paper addresses the distributed resilient filtering problem for a class of power systems subject to denial-of-service (DoS) attacks. A novel distributed filter is first constructed to practically reflect the impact from both cyber-attacks and gain perturbations. For all possible occurrence of DoS attacks and gain perturbations, an upper bound of filtering error covariance is derived by resorting to some typical matrix inequalities. Furthermore, the desired filter gain relying on the solution of two Riccati-like difference equations is obtained with the help of the gradient-based approach and the mathematical induction. The developed algorithm with a recursive form is independent of the global information and thus satisfies the requirements of scalability and distributed implementation online. Finally, a benchmark simulation test is exploited to check the usefulness of the designed filter.

Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI

Abstract: Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain–computer interface (BCI) application. The effectiveness of CSP is highly affected by the frequency band and time window of EEG segments. Although numerous algorithms have been designed to optimize the spectral bands of CSP, most of them selected the time window in a heuristic way. This is likely to result in a suboptimal feature extraction since the time period when the brain responses to the mental tasks occurs may not be accurately detected. In this paper, we propose a novel algorithm, namely temporally constrained sparse group spatial pattern (TSGSP), for the simultaneous optimization of filter bands and time window within CSP to further boost classification accuracy of MI EEG. Specifically, spectrum-specific signals are first derived by bandpass filtering from raw EEG data at a set of overlapping filter bands. Each of the spectrum-specific signals is further segmented into multiple subseries using sliding window approach. We then devise a joint sparse optimization of filter bands and time windows with temporal smoothness constraint to extract robust CSP features under a multitask learning framework. A linear support vector machine classifier is trained on the optimized EEG features to accurately identify the MI tasks. An experimental study is implemented on three public EEG datasets (BCI Competition III dataset IIIa, BCI Competition IV datasets IIa, and BCI Competition IV dataset IIb) to validate the effectiveness of TSGSP in comparison to several other competing methods. Superior classification performance (averaged accuracies are 88.5%, 83.3%, and 84.3% for the three datasets, respectively) based on the experimental results confirms that the proposed algorithm is a promising candidate for performance improvement of MI-based BCIs.

Fixed-Time Leader–Follower Output Feedback Consensus for Second-Order Multiagent Systems

Abstract: This paper addresses the fixed-time leader–follower consensus problem for second-order multiagent systems without velocity measurement. A new continuous fixed-time distributed observer-based consensus protocol is developed to achieve consensus in a bounded finite time fully independent of initial condition. A rigorous stability proof of the multiagent systems by output feedback control is presented based on the bi-limit homogeneity and the Lyapunov technique. Finally, the efficiency of the proposed methodology is illustrated by numerical simulation.

Evolutionary Multitasking via Explicit Autoencoding

Abstract: Evolutionary multitasking (EMT) is an emerging research topic in the field of evolutionary computation. In contrast to the traditional single-task evolutionary search, EMT conducts evolutionary search on multiple tasks simultaneously. It aims to improve convergence characteristics across multiple optimization problems at once by seamlessly transferring knowledge among them. Due to the efficacy of EMT, it has attracted lots of research attentions and several EMT algorithms have been proposed in the literature. However, existing EMT algorithms are usually based on a common mode of knowledge transfer in the form of implicit genetic transfer through chromosomal crossover. This mode cannot make use of multiple biases embedded in different evolutionary search operators, which could give better search performance when properly harnessed. Keeping this in mind, this paper proposes an EMT algorithm with explicit genetic transfer across tasks, namely EMT via autoencoding, which allows the incorporation of multiple search mechanisms with different biases in the EMT paradigm. To confirm the efficacy of the proposed EMT algorithm with explicit autoencoding, comprehensive empirical studies have been conducted on both the single- and multi-objective multitask optimization problems.

Consensus of Heterogeneous Linear Multiagent Systems Subject to Aperiodic Sampled-Data and DoS Attack

Abstract: In this paper, the robust output consensus problem for a class of heterogeneous linear multiagent systems (MASs) in presence of aperiodic sampling and random deny-of-service (DoS) attack is investigated. A novel distributed output-feedback control strategy is proposed so that the controlled MAS achieves the objective of output consensus in spite of aperiodic sampling and DoS attack. By assuming that the sampling process is nonuniform and the consecutive attack duration is upper bounded, the closed-loop control system is first described as a discrete-time switched stochastic delay system. Some sufficient conditions are then obtained for the solvability of the secure consensus problem. Furthermore, a constructive design procedure for the proposed controller is then presented. Finally, a simulation example is introduced to illustrate the effectiveness of controller design.

Distributed Formation Control of Multiple Quadrotor Aircraft Based on Nonsmooth Consensus Algorithms

Abstract: The problem of distributed formation control for multiple quadrotor aircraft in the form of leader–follower structure is considered in this paper. Based on a nonsmooth backstepping design, a novel consensus formation control algorithm is proposed and utilized. First, for the position control subsystem, based on the linear quadratic regulator optimal design method, a formation control law for multiple quadrotor aircraft is designed such that the positions of all the quadrotor aircraft converge to the desired formation pattern. The designed formation control law for position systems will generate the desired attitude for the attitude control systems. Second, for the attitude control subsystem described by unit quaternion, by employing the technique of finite-time control and switch control, a global bounded finite-time attitude tracking controller is designed such that the desired attitude can be tracked by the multiple quadrotor aircraft in finite time. Finally, numerical example is performed to demonstrate that all quadrotor aircraft converge to the desired formation pattern in the 3-D-space.

Improving Metaheuristic Algorithms With Information Feedback Models

Abstract: In most metaheuristic algorithms, the updating process fails to make use of information available from individuals in previous iterations. If this useful information could be exploited fully and used in the later optimization process, the quality of the succeeding solutions would be improved significantly. This paper presents our method for reusing the valuable information available from previous individuals to guide later search. In our approach, previous useful information was fed back to the updating process. We proposed six information feedback models. In these models, individuals from previous iterations were selected in either a fixed or random manner. Their useful information was incorporated into the updating process. Accordingly, an individual at the current iteration was updated based on the basic algorithm plus some selected previous individuals by using a simple fitness weighting method. By incorporating six different information feedback models into ten metaheuristic algorithms, this approach provided a number of variants of the basic algorithms. We demonstrated experimentally that the variants outperformed the basic algorithms significantly on 14 standard test functions and 10 CEC 2011 real world problems, thereby, establishing the value of the information feedback models.

Fixed-Time Event-Triggered Consensus for Nonlinear Multiagent Systems Without Continuous Communications

Abstract: In this paper, we study the fixed-time event-triggered consensus problem of the uncertain nonlinear multiagent systems. Two fixed-time event-triggered consensus controllers are proposed. In contrast to finite-time results, the convergence time of fixed-time results is independent of initial conditions. Furthermore, continuous communications can be avoided both in the update of controllers and in the triggering condition monitoring. It is proved that there is no Zeno behavior under the fixed-time event-triggered consensus control strategies. The availability of the control algorithms is verified by numerical simulations.