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IEEE Transactions on Cybernetics

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• Recent Development in Big Data Analytics for Business Operations and Risk Management

Publication Year: 2017, Page(s):81 - 92
Cited by:  Papers (6)
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“Big data” is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., w... View full abstract»

• Enhanced Computer Vision With Microsoft Kinect Sensor: A Review

Publication Year: 2013, Page(s):1318 - 1334
Cited by:  Papers (252)  |  Patents (4)
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With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision al... View full abstract»

• Coupled Deep Autoencoder for Single Image Super-Resolution

Publication Year: 2017, Page(s):27 - 37
Cited by:  Papers (6)
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Sparse coding has been widely applied to learning-based single image super-resolution (SR) and has obtained promising performance by jointly learning effective representations for low-resolution (LR) and high-resolution (HR) image patch pairs. However, the resulting HR images often suffer from ringing, jaggy, and blurring artifacts due to the strong yet ad hoc assumptions that the LR image patch r... View full abstract»

• Complex and Concurrent Negotiations for Multiple Interrelated e-Markets

Publication Year: 2013, Page(s):230 - 245
Cited by:  Papers (18)
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To date, most of the existing bargaining models are designed for supporting negotiation in only one market involving only two types of participants (buyers and sellers). This work devises a complex negotiation mechanism that supports negotiation activities among three types of participants in multiple interrelated markets. The complex negotiation mechanism consists of: 1) a bargaining-position-est... View full abstract»

• Genetic Learning Particle Swarm Optimization

Publication Year: 2016, Page(s):2277 - 2290
Cited by:  Papers (7)
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Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is ... View full abstract»

• Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems

Publication Year: 2017, Page(s):1743 - 1756
Cited by:  Papers (1)
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For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this ... View full abstract»

• Interrelationship-Based Selection for Decomposition Multiobjective Optimization

Publication Year: 2015, Page(s):2076 - 2088
Cited by:  Papers (18)
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Multiobjective evolutionary algorithm based on decomposition (MOEA/D), which bridges the traditional optimization techniques and population-based methods, has become an increasingly popular framework for evolutionary multiobjective optimization. It decomposes a multiobjective optimization problem (MOP) into a number of optimization subproblems. Each subproblem is handled by an agent in a collabora... View full abstract»

• Stacked Convolutional Denoising Auto-Encoders for Feature Representation

Publication Year: 2017, Page(s):1017 - 1027
Cited by:  Papers (5)
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Deep networks have achieved excellent performance in learning representation from visual data. However, the supervised deep models like convolutional neural network require large quantities of labeled data, which are very expensive to obtain. To solve this problem, this paper proposes an unsupervised deep network, called the stacked convolutional denoising auto-encoders, which can map images to hi... View full abstract»

• A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition

Publication Year: 2017, Page(s):1496 - 1509
Cited by:  Papers (2)
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This paper proposes a facial expression recognition system using evolutionary particle swarm optimization (PSO)-based feature optimization. The system first employs modified local binary patterns, which conduct horizontal and vertical neighborhood pixel comparison, to generate a discriminative initial facial representation. Then, a PSO variant embedded with the concept of a micro genetic algorithm... View full abstract»

• Data Randomization and Cluster-Based Partitioning for Botnet Intrusion Detection

Publication Year: 2016, Page(s):1796 - 1806
Cited by:  Papers (1)
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Botnets, which consist of remotely controlled compromised machines called bots, provide a distributed platform for several threats against cyber world entities and enterprises. Intrusion detection system (IDS) provides an efficient countermeasure against botnets. It continually monitors and analyzes network traffic for potential vulnerabilities and possible existence of active attacks. A payload-i... View full abstract»

• The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement

Publication Year: 2016, Page(s):284 - 297
Cited by:  Papers (33)
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Proper contrast change can improve the perceptual quality of most images, but it has largely been overlooked in the current research of image quality assessment (IQA). To fill this void, we in this paper first report a new large dedicated contrast-changed image database (CCID2014), which includes 655 images and associated subjective ratings recorded from 22 inexperienced observers. We then present... View full abstract»

• Event-Triggered Fault Detection of Nonlinear Networked Systems

Publication Year: 2017, Page(s):1041 - 1052
Cited by:  Papers (37)
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This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee ... View full abstract»

• Trajectory Predictor by Using Recurrent Neural Networks in Visual Tracking

Publication Year: 2017, Page(s):1 - 12
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Motion models have been proved to be a crucial part in the visual tracking process. In recent trackers, particle filter and sliding windows-based motion models have been widely used. Treating motion models as a sequence prediction problem, we can estimate the motion of objects using their trajectories. Moreover, it is possible to transfer the learned knowledge from annotated trajectories to new ob... View full abstract»

• An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine

Publication Year: 2017, Page(s):920 - 933
Cited by:  Papers (2)
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This paper proposes a computationally efficient method for traffic sign recognition (TSR). This proposed method consists of two modules: (1) extraction of histogram of oriented gradient variant (HOGv) feature and (2) a single classifier trained by extreme learning machine (ELM) algorithm. The presented HOGv feature keeps a good balance between redundancy and local details such that it can represen... View full abstract»

• Cross-Modal Retrieval With CNN Visual Features: A New Baseline

Publication Year: 2017, Page(s):449 - 460
Cited by:  Papers (1)
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Recently, convolutional neural network (CNN) visual features have demonstrated their powerful ability as a universal representation for various recognition tasks. In this paper, cross-modal retrieval with CNN visual features is implemented with several classic methods. Specifically, off-the-shelf CNN visual features are extracted from the CNN model, which is pretrained on ImageNet with more than o... View full abstract»

• Finite Time Fault Tolerant Control for Robot Manipulators Using Time Delay Estimation and Continuous Nonsingular Fast Terminal Sliding Mode Control

Publication Year: 2017, Page(s):1681 - 1693
Cited by:  Papers (1)
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In this paper, a novel finite time fault tolerant control (FTC) is proposed for uncertain robot manipulators with actuator faults. First, a finite time passive FTC (PFTC) based on a robust nonsingular fast terminal sliding mode control (NFTSMC) is investigated. Be analyzed for addressing the disadvantages of the PFTC, an AFTC are then investigated by combining NFTSMC with a simple fault diagnosis ... View full abstract»

• Consensus of Linear Multi-Agent Systems by Distributed Event-Triggered Strategy

Publication Year: 2016, Page(s):148 - 157
Cited by:  Papers (30)
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This paper studies the consensus problem of multi-agent systems with general linear dynamics. We propose a novel event-triggered control scheme with some desirable features, namely, distributed, asynchronous, and independent. It is shown that consensus of the controlled multi-agent system can be reached asymptotically. The feasibility of the event-triggered strategy is further verified by the excl... View full abstract»

• Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach

Publication Year: 2013, Page(s):1656 - 1671
Cited by:  Papers (96)
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Classification problems often have a large number of features in the data sets, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the performance. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main conflicting objectives of maxim... View full abstract»

• Sparse Extreme Learning Machine for Classification

Publication Year: 2014, Page(s):1858 - 1870
Cited by:  Papers (59)
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Extreme learning machine (ELM) was initially proposed for single-hidden-layer feedforward neural networks (SLFNs). In the hidden layer (feature mapping), nodes are randomly generated independently of training data. Furthermore, a unified ELM was proposed, providing a single framework to simplify and unify different learning methods, such as SLFNs, least square support vector machines, proximal sup... View full abstract»

• Time-Delay Neural Network for Continuous Emotional Dimension Prediction From Facial Expression Sequences

Publication Year: 2016, Page(s):916 - 929
Cited by:  Papers (4)
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Automatic continuous affective state prediction from naturalistic facial expression is a very challenging research topic but very important in human-computer interaction. One of the main challenges is modeling the dynamics that characterize naturalistic expressions. In this paper, a novel two-stage automatic system is proposed to continuously predict affective dimension values from facial expressi... View full abstract»

• Semi-Supervised and Unsupervised Extreme Learning Machines

Publication Year: 2014, Page(s):2405 - 2417
Cited by:  Papers (111)
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Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization,... View full abstract»

• Weighted Joint Sparse Representation for Removing Mixed Noise in Image

Publication Year: 2017, Page(s):600 - 611
Cited by:  Papers (3)
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Joint sparse representation (JSR) has shown great potential in various image processing and computer vision tasks. Nevertheless, the conventional JSR is fragile to outliers. In this paper, we propose a weighted JSR (WJSR) model to simultaneously encode a set of data samples that are drawn from the same subspace but corrupted with noise and outliers. Our model is desirable to exploit the common inf... View full abstract»

• Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints

Publication Year: 2016, Page(s):620 - 629
Cited by:  Papers (73)
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This paper studies the tracking control problem for an uncertain ${n}$ -link robot with full-state constraints. The rigid robotic manipulator is described as a multiinput and multioutput system. Adaptive neural network (NN) control for the robotic system with full-state constraints is designed. In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances. The... View full abstract»

• From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms

Publication Year: 2014, Page(s):1001 - 1013
Cited by:  Papers (78)
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Image denoising is a well explored topic in the field of image processing. In the past several decades, the progress made in image denoising has benefited from the improved modeling of natural images. In this paper, we introduce a new taxonomy based on image representations for a better understanding of state-of-the-art image denoising techniques. Within each category, several representative algor... View full abstract»

• Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function

Publication Year: 2017, Page(s):1641 - 1651
Cited by:  Papers (4)
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In this paper, we consider the trajectory tracking of a marine surface vessel in the presence of output constraints and uncertainties. An asymmetric barrier Lyapunov function is employed to cope with the output constraints. To handle the system uncertainties, we apply adaptive neural networks to approximate the unknown model parameters of a vessel. Both full state feedback control and output feedb... View full abstract»

• Design of Fuzzy Functional Observer-Controller via Higher Order Derivatives of Lyapunov Function for Nonlinear Systems

Publication Year: 2017, Page(s):1630 - 1640
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In this paper, we investigate the stability of Takagi-Sugeno fuzzy-model-based (FMB) functional observer-control system. When system states are not measurable for state-feedback control, a fuzzy functional observer is designed to directly estimate the control input instead of the system states. Although the fuzzy functional observer can reduce the order of the observer, it leads to a number of obs... View full abstract»

• Joint Dictionary Learning for Multispectral Change Detection

Publication Year: 2017, Page(s):884 - 897
Cited by:  Papers (7)
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Change detection is one of the most important applications of remote sensing technology. It is a challenging task due to the obvious variations in the radiometric value of spectral signature and the limited capability of utilizing spectral information. In this paper, an improved sparse coding method for change detection is proposed. The intuition of the proposed method is that unchanged pixels in ... View full abstract»

• Finite-Horizon H∞ Consensus for Multiagent Systems With Redundant Channels via An Observer-Type Event-Triggered Scheme

Publication Year: 2017, Page(s):1 - 10
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This paper is concerned with the finite-horizon H∞ consensus problem for a class of discrete time-varying multiagent systems with external disturbances and missing measurements. To improve the communication reliability, redundant channels are introduced and the corresponding protocol is constructed for the information transmission over redundant channels. An event-triggered scheme is adopte... View full abstract»

• Energy-Efficient Distributed Filtering in Sensor Networks: A Unified Switched System Approach

Publication Year: 2017, Page(s):1618 - 1629
Cited by:  Papers (1)
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This paper is concerned with the energy-efficient distributed filtering in sensor networks, and a unified switched system approach is proposed to achieve this goal. For the system under study, the measurement is first sampled under nonuniform sampling periods, then the local measurement elements are selected and quantized for transmission. Then, the transmission rate is further reduced to save con... View full abstract»

• Disturbance Observer-Based Fuzzy Control of Uncertain MIMO Mechanical Systems With Input Nonlinearities and its Application to Robotic Exoskeleton

Publication Year: 2017, Page(s):984 - 994
Cited by:  Papers (1)
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We develop a novel disturbance observer-based adaptive fuzzy control approach in this paper for a class of uncertain multi-input-multi-output mechanical systems possessing unknown input nonlinearities, i.e., deadzone and saturation and time-varying external disturbance. It is shown that the input nonlinearities can be represented by a nominal part and a nonlinear disturbance term. High-dimensional... View full abstract»

• A Hierarchical Auction-Based Mechanism for Real-Time Resource Allocation in Cloud Robotic Systems

Publication Year: 2017, Page(s):473 - 484
Cited by:  Papers (2)
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Cloud computing enables users to share computing resources on-demand. The cloud computing framework cannot be directly mapped to cloud robotic systems with ad hoc networks since cloud robotic systems have additional constraints such as limited bandwidth and dynamic structure. However, most multirobotic applications with cooperative control adopt this decentralized approach to avoid a single point ... View full abstract»

• Discrete-Time Deterministic $Q$ -Learning: A Novel Convergence Analysis

Publication Year: 2017, Page(s):1224 - 1237
Cited by:  Papers (5)
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In this paper, a novel discrete-time deterministic Q-learning algorithm is developed. In each iteration of the developed Q-learning algorithm, the iterative Q function is updated for all the state and control spaces, instead of updating for a single state and a single control in traditional Q-learning algorithm. A new convergence criterion is established to guarantee that the iterative Q function ... View full abstract»

• Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems With Arbitrary Switchings and Unmodeled Dynamics

Publication Year: 2017, Page(s):403 - 414
Cited by:  Papers (26)
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This paper deals with the problem of adaptive fuzzy output feedback control for a class of stochastic nonlinear switched systems. The controlled system in this paper possesses unmeasured states, completely unknown nonlinear system functions, unmodeled dynamics, and arbitrary switchings. A state observer which does not depend on the switching signal is constructed to tackle the unmeasured states. F... View full abstract»

• Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons

Publication Year: 2017, Page(s):198 - 211
Cited by:  Papers (2)
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Dynamic multiobjective optimization (DMO) has received growing research interest in recent years since many real-world optimization problems appear to not only have multiple objectives that conflict with each other but also change over time. The time-varying characteristics of these DMO problems (DMOPs) pose new challenges to evolutionary algorithms. Considering the importance of a representative ... View full abstract»

• A Recommendation System to Facilitate Business Process Modeling

Publication Year: 2017, Page(s):1380 - 1394
Cited by:  Papers (1)
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This paper presents a system that utilizes process recommendation technology to help design new business processes from scratch in an efficient and accurate way. The proposed system consists of two phases: 1) offline mining and 2) online recommendation. At the first phase, it mines relations among activity nodes from existing processes in repository, and then stores the extracted relations as patt... View full abstract»

• Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach

Publication Year: 2016, Page(s):158 - 170
Cited by:  Papers (28)
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Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the motion feature descriptor on a populatio... View full abstract»

• FUIQA: Fetal Ultrasound Image Quality Assessment With Deep Convolutional Networks

Publication Year: 2017, Page(s):1336 - 1349
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The quality of ultrasound (US) images for the obstetric examination is crucial for accurate biometric measurement. However, manual quality control is a labor intensive process and often impractical in a clinical setting. To improve the efficiency of examination and alleviate the measurement error caused by improper US scanning operation and slice selection, a computerized fetal US image quality as... View full abstract»

• Game Design and Analysis for Price-Based Demand Response: An Aggregate Game Approach

Publication Year: 2017, Page(s):720 - 730
Cited by:  Papers (2)
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In this paper, an aggregate game is adopted for the modeling and analysis of energy consumption control in smart grid. Since the electricity users' cost functions depend on the aggregate energy consumption, which is unknown to the end users, an average consensus protocol is employed to estimate it. By neighboring communication among the users about their estimations on the aggregate energy consump... View full abstract»

• Exploring Representativeness and Informativeness for Active Learning

Publication Year: 2017, Page(s):14 - 26
Cited by:  Papers (6)
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How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network mode... View full abstract»

• Robust Object Tracking via Key Patch Sparse Representation

Publication Year: 2017, Page(s):354 - 364
Cited by:  Papers (2)
| | PDF (2406 KB) | HTML

Many conventional computer vision object tracking methods are sensitive to partial occlusion and background clutter. This is because the partial occlusion or little background information may exist in the bounding box, which tends to cause the drift. To this end, in this paper, we propose a robust tracker based on key patch sparse representation (KPSR) to reduce the disturbance of partial occlusio... View full abstract»

• Data-Driven Tracking Control With Adaptive Dynamic Programming for a Class of Continuous-Time Nonlinear Systems

Publication Year: 2017, Page(s):1460 - 1470
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A data-driven adaptive tracking control approach is proposed for a class of continuous-time nonlinear systems using a recent developed goal representation heuristic dynamic programming (GrHDP) architecture. The major focus of this paper is on designing a multivariable tracking scheme, including the filter-based action network (FAN) architecture, and the stability analysis in continuous-time fashio... View full abstract»

• Event-Triggered Schemes on Leader-Following Consensus of General Linear Multiagent Systems Under Different Topologies

Publication Year: 2017, Page(s):212 - 223
Cited by:  Papers (7)
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This paper investigates the leader-following consensus for multiagent systems with general linear dynamics by means of event-triggered scheme (ETS). We propose three types of schemes, namely, distributed ETS (distributed-ETS), centralized ETS (centralized-ETS), and clustered ETS (clustered-ETS) for different network topologies. All these schemes guarantee that all followers can track the leader ev... View full abstract»

• Multimodal Estimation of Distribution Algorithms

Publication Year: 2017, Page(s):636 - 650
Cited by:  Papers (3)
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Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) a... View full abstract»

• Cooperative Output Regulation of Heterogeneous Linear Multi-Agent Systems by Event-Triggered Control

Publication Year: 2017, Page(s):105 - 116
Cited by:  Papers (3)
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In this paper, we consider the cooperative output regulation problem of heterogeneous linear multi-agent systems (MASs) by event-triggered control. We first develop an event-triggering mechanism for leader-following consensus of homogeneous MASs. Then by proposing an internal reference model for each agent, a novel distributed event-triggered control scheme is developed to solve the cooperative ou... View full abstract»

• Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering

Publication Year: 2017, Page(s):1053 - 1066
Cited by:  Papers (5)
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Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i.e., intrasubspace data points). Recent works achieve good performance by modeling errors into their objective functions to remove the errors ... View full abstract»

• Distributed Fault-Tolerant Control of Networked Uncertain Euler–Lagrange Systems Under Actuator Faults

Publication Year: 2017, Page(s):1706 - 1718
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This paper investigates the distributed fault-tolerant control problem of networked Euler-Lagrange systems with actuator and communication link faults. An adaptive fault-tolerant cooperative control scheme is proposed to achieve the coordinated tracking control of networked uncertain Lagrange systems on a general directed communication topology, which contains a spanning tree with the root node be... View full abstract»

• Robust Face Recognition via Adaptive Sparse Representation

Publication Year: 2014, Page(s):2368 - 2378
Cited by:  Papers (43)
| | PDF (2324 KB) | HTML

Sparse representation (or coding)-based classification (SRC) has gained great success in face recognition in recent years. However, SRC emphasizes the sparsity too much and overlooks the correlation information which has been demonstrated to be critical in real-world face recognition problems. Besides, some paper considers the correlation but overlooks the discriminative ability of sparsity. Diffe... View full abstract»

• Learning Sampling Distributions for Efficient Object Detection

Publication Year: 2017, Page(s):117 - 129
Cited by:  Papers (1)
| | PDF (1981 KB) | HTML

Object detection is an important task in computer vision and machine intelligence systems. Multistage particle windows (MPW), proposed by Gualdi et al., is an algorithm of fast and accurate object detection. By sampling particle windows (PWs) from a proposal distribution (PD), MPW avoids exhaustively scanning the image. Despite its success, it is unknown how to determine the number of stages and t... View full abstract»

• A New Sensor Fault Isolation Method for T-S Fuzzy Systems

Publication Year: 2017, Page(s):1 - 11
| | PDF (675 KB)

This paper is concerned with the fault isolation problem for T-S fuzzy systems with sensor faults. With the help of a set theoretic description of T-S fuzzy models, a new fault isolation scheme is proposed. It consists of a set of fuzzy observers and each of them corresponds to a specified sensor, where the antecedent and consequent parts of the observer are independent on the sensor output. Diffe... View full abstract»

• A Robust and Fast Method for Sidescan Sonar Image Segmentation Using Nonlocal Despeckling and Active Contour Model

Publication Year: 2017, Page(s):855 - 872
Cited by:  Papers (1)
| | PDF (6391 KB) | HTML

Sidescan sonar image segmentation is a very important issue in underwater object detection and recognition. In this paper, a robust and fast method for sidescan sonar image segmentation is proposed, which deals with both speckle noise and intensity inhomogeneity that may cause considerable difficulties in image segmentation. The proposed method integrates the nonlocal means-based speckle filtering... View full abstract»

Aims & Scope

The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Prof. Jun Wang
Dept. of Computer Science
City University of Hong Kong
Kowloon Tong, Kowloon, Hong Kong
Tel: +852 34429701
Email: jwang.cs@cityu.edu.hk