IEEE Transactions on Cybernetics

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• Complex and Concurrent Negotiations for Multiple Interrelated e-Markets

Publication Year: 2013, Page(s):230 - 245
Cited by:  Papers (21)
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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 (16)
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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»

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

Publication Year: 2017, Page(s):81 - 92
Cited by:  Papers (13)
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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 (329)  |  Patents (5)
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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»

• An Adaptive Multiobjective Particle Swarm Optimization Based on Multiple Adaptive Methods

Publication Year: 2017, Page(s):2754 - 2767
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Multiobjective particle swarm optimization (MOPSO) algorithms have attracted much attention for their promising performance in solving multiobjective optimization problems (MOPs). In this paper, an adaptive MOPSO (AMOPSO) algorithm, based on a hybrid framework of the solution distribution entropy and population spacing (SP) information, is developed to improve the search performance in terms of co... View full abstract»

• Stacked Convolutional Denoising Auto-Encoders for Feature Representation

Publication Year: 2017, Page(s):1017 - 1027
Cited by:  Papers (13)
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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»

• Adaptive Trajectory Tracking of Nonholonomic Mobile Robots Using Vision-Based Position and Velocity Estimation

Publication Year: 2018, Page(s):571 - 582
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Despite tremendous efforts made for years, trajectory tracking control (TC) of a nonholonomic mobile robot (NMR) without global positioning system remains an open problem. The major reason is the difficulty to localize the robot by using its onboard sensors only. In this paper, a newly designed adaptive trajectory TC method is proposed for the NMR without its position, orientation, and velocity me... View full abstract»

• Automatic Facial Expression Recognition System Using Deep Network-Based Data Fusion

Publication Year: 2018, Page(s):103 - 114
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This paper presents a novel automatic facial expressions recognition system (AFERS) using the deep network framework. The proposed AFERS consists of four steps: 1) geometric features extraction; 2) regional local binary pattern (LBP) features extraction; 3) fusion of both the features using autoencoders; and 4) classification using Kohonen self-organizing map (SOM)-based classifier. This paper mak... View full abstract»

• Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons

Publication Year: 2017, Page(s):198 - 211
Cited by:  Papers (5)
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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»

• Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

Publication Year: 2018, Page(s):16 - 28
Cited by:  Papers (1)
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In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial ... 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 (8)
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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»

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

Publication Year: 2013, Page(s):1656 - 1671
Cited by:  Papers (130)
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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»

• Simulating Kinect Infrared and Depth Images

Publication Year: 2016, Page(s):3018 - 3031
Cited by:  Papers (2)
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With the emergence of the Microsoft Kinect sensor, many developer communities and research groups have found countless uses and have already published a wide variety of papers that utilize the raw depth images for their specific goals. New methods and applications that use the device generally require an appropriately large ensemble of data sets with accompanying ground truth for testing purposes,... View full abstract»

• Data-Driven Adaptive Probabilistic Robust Optimization Using Information Granulation

Publication Year: 2018, Page(s):450 - 462
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In this paper, we consider a generic class of adaptive optimization problems under uncertainty, and develop a data-driven paradigm of adaptive probabilistic robust optimization (APRO) in a robust and computationally tractable manner. The paradigm comprises two phases: 1) bilayer information granulation (IG), which involves the data-mining techniques and nested decomposition of convex sets that est... View full abstract»

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

Publication Year: 2017, Page(s):449 - 460
Cited by:  Papers (5)
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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»

• Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking

Publication Year: 2017, Page(s):4014 - 4024
Cited by:  Papers (4)
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How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. T... View full abstract»

• Trajectory Predictor by Using Recurrent Neural Networks in Visual Tracking

Publication Year: 2017, Page(s):3172 - 3183
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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»

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

Publication Year: 2016, Page(s):620 - 629
Cited by:  Papers (165)
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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»

• Asynchronous Periodic Edge-Event Triggered Control for Double-Integrator Networks With Communication Time Delays

Publication Year: 2018, Page(s):675 - 688
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This paper focuses on the average consensus of double-integrator networked systems based on the asynchronous periodic edge-event triggered control. The asynchronous property lies in the edge event-detecting procedure. For different edges, their event detections are performed at different times and the corresponding events occur independently of each other. When an event is activated, the two adjac... View full abstract»

• Coupled Deep Autoencoder for Single Image Super-Resolution

Publication Year: 2017, Page(s):27 - 37
Cited by:  Papers (12)
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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»

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

Publication Year: 2017, Page(s):1743 - 1756
Cited by:  Papers (3)
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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»

• Consensus of Leader-Following Multiagent Systems: A Distributed Event-Triggered Impulsive Control Strategy

Publication Year: 2018, Page(s):1 - 10
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This paper investigates the leader-following consensus problem of multiagent systems using a distributed event-triggered impulsive control method. For each agent, the controller is updated only when some state-dependent errors exceed a tolerable bound. The control inputs will be carried out by actor only at event triggering impulsive instants. According to the Lyapunov stability theory and impulsi... 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 (20)
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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»

• Event-Triggered Fault Detection of Nonlinear Networked Systems

Publication Year: 2017, Page(s):1041 - 1052
Cited by:  Papers (68)
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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»

• Output Consensus of Heterogeneous Linear Multi-Agent Systems by Distributed Event-Triggered/Self-Triggered Strategy

Publication Year: 2017, Page(s):1914 - 1924
Cited by:  Papers (2)
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This paper addresses the output consensus problem of heterogeneous linear multi-agent systems. We first propose a novel distributed event-triggered control scheme. It is shown that, with the proposed control scheme, the output consensus problem can be solved if two matrix equations are satisfied. Then, we further propose a novel self-triggered control scheme, with which continuous monitoring is av... 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 (7)
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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»

• Test Problems for Large-Scale Multiobjective and Many-Objective Optimization

Publication Year: 2017, Page(s):4108 - 4121
Cited by:  Papers (2)
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The interests in multiobjective and many-objective optimization have been rapidly increasing in the evolutionary computation community. However, most studies on multiobjective and many-objective optimization are limited to small-scale problems, despite the fact that many real-world multiobjective and many-objective optimization problems may involve a large number of decision variables. As has been... View full abstract»

• Progressive Semisupervised Learning of Multiple Classifiers

Publication Year: 2018, Page(s):689 - 702
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Semisupervised learning methods are often adopted to handle datasets with very small number of labeled samples. However, conventional semisupervised ensemble learning approaches have two limitations: 1) most of them cannot obtain satisfactory results on high dimensional datasets with limited labels and 2) they usually do not consider how to use an optimization process to enlarge the training set. ... View full abstract»

• Semi-Supervised Image-to-Video Adaptation for Video Action Recognition

Publication Year: 2017, Page(s):960 - 973
Cited by:  Papers (1)
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Human action recognition has been well explored in applications of computer vision. Many successful action recognition methods have shown that action knowledge can be effectively learned from motion videos or still images. For the same action, the appropriate action knowledge learned from different types of media, e.g., videos or images, may be related. However, less effort has been made to improv... View full abstract»

• Virtual Network Embedding via Monte Carlo Tree Search

Publication Year: 2018, Page(s):510 - 521
Cited by:  Papers (1)
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Network virtualization helps overcome shortcomings of the current Internet architecture. The virtualized network architecture enables coexistence of multiple virtual networks (VNs) on an existing physical infrastructure. VN embedding (VNE) problem, which deals with the embedding of VN components onto a physical network, is known to be NP-hard. In this paper, we propose two VNE algorithms: MaVEn-M ... View full abstract»

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

Publication Year: 2016, Page(s):148 - 157
Cited by:  Papers (75)
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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»

• Efficient Nondomination Level Update Method for Steady-State Evolutionary Multiobjective Optimization

Publication Year: 2017, Page(s):2838 - 2849
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Nondominated sorting (NDS), which divides a population into several nondomination levels (NDLs), is a basic step in many evolutionary multiobjective optimization (EMO) algorithms. It has been widely studied in a generational evolution model, where the environmental selection is performed after generating a whole population of offspring. However, in a steady-state evolution model, where a populatio... View full abstract»

• Adaptive Fuzzy Bounded Control for Consensus of Multiple Strict-Feedback Nonlinear Systems

Publication Year: 2018, Page(s):522 - 531
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This paper studies the adaptive fuzzy bounded control problem for leader-follower multiagent systems, where each follower is modeled by the uncertain nonlinear strict-feedback system. Combining the fuzzy approximation with the dynamic surface control, an adaptive fuzzy control scheme is developed to guarantee the output consensus of all agents under directed communication topologies. Different fro... View full abstract»

• Semi-Supervised and Unsupervised Extreme Learning Machines

Publication Year: 2014, Page(s):2405 - 2417
Cited by:  Papers (155)
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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»

• Observer-Based Event-Triggering Consensus Control for Multiagent Systems With Lossy Sensors and Cyber-Attacks

Publication Year: 2017, Page(s):1936 - 1947
Cited by:  Papers (3)
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In this paper, the observer-based event-triggering consensus control problem is investigated for a class of discrete-time multiagent systems with lossy sensors and cyber-attacks. A novel distributed observer is proposed to estimate the relative full states and the estimated states are then used in the feedback protocol in order to achieve the overall consensus. An event-triggered mechanism with st... View full abstract»

• Extreme Kernel Sparse Learning for Tactile Object Recognition

Publication Year: 2017, Page(s):4509 - 4520
Cited by:  Papers (2)
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Tactile sensors play very important role for robot perception in the dynamic or unknown environment. However, the tactile object recognition exhibits great challenges in practical scenarios. In this paper, we address this problem by developing an extreme kernel sparse learning methodology. This method combines the advantages of extreme learning machine and kernel sparse learning by simultaneously ... 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 (9)
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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»

• An Intelligent Actuator Fault Reconstruction Scheme for Robotic Manipulators

Publication Year: 2018, Page(s):639 - 647
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This paper investigates a difficult problem of reconstructing actuator faults for robotic manipulators. An intelligent approach with fast reconstruction property is developed. This is achieved by using observer technique. This scheme is capable of precisely reconstructing the actual actuator fault. It is shown by Lyapunov stability analysis that the reconstruction error can converge to zero after ... View full abstract»

• Large Scale Spectral Clustering Via Landmark-Based Sparse Representation

Publication Year: 2015, Page(s):1669 - 1680
Cited by:  Papers (23)
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Spectral clustering is one of the most popular clustering approaches. However, it is not a trivial task to apply spectral clustering to large-scale problems due to its computational complexity of O(n3) , where n is the number of samples. Recently, many approaches have been proposed to accelerate the spectral clustering. Unfortunately, these methods usually sacrifice quite a lot informat... View full abstract»

• Cooperative Robots to Observe Moving Targets: Review

Publication Year: 2018, Page(s):187 - 198
Cited by:  Papers (1)
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The deployment of multiple robots for achieving a common goal helps to improve the performance, efficiency, and/or robustness in a variety of tasks. In particular, the observation of moving targets is an important multirobot application that still exhibits numerous open challenges, including the effective coordination of the robots. This paper reviews control techniques for cooperative mobile robo... View full abstract»

• Actor-Critic Off-Policy Learning for Optimal Control of Multiple-Model Discrete-Time Systems

Publication Year: 2018, Page(s):29 - 40
Cited by:  Papers (1)
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In this paper, motivated by human neurocognitive experiments, a model-free off-policy reinforcement learning algorithm is developed to solve the optimal tracking control of multiple-model linear discrete-time systems. First, an adaptive self-organizing map neural network is used to determine the system behavior from measured data and to assign a responsibility signal to each of system possible beh... View full abstract»

• A Multiobjective Evolutionary Algorithm Based on Similarity for Community Detection From Signed Social Networks

Publication Year: 2014, Page(s):2274 - 2287
Cited by:  Papers (43)
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Various types of social relationships, such as friends and foes, can be represented as signed social networks (SNs) that contain both positive and negative links. Although many community detection (CD) algorithms have been proposed, most of them were designed primarily for networks containing only positive links. Thus, it is important to design CD algorithms which can handle large-scale SNs. To th... View full abstract»

• Decentralized Adaptive Fuzzy Secure Control for Nonlinear Uncertain Interconnected Systems Against Intermittent DoS Attacks

Publication Year: 2018, Page(s):1 - 12
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Cyber-physical systems (CPSs) are naturally highly interconnected and complexly nonlinear. This paper investigates the problem of decentralized adaptive output feedback control for CPSs subject to intermittent denial-of-service (DoS) attacks. The considered CPSs are modeled as a class of nonlinear uncertain strict-feedback interconnected systems. When a DoS attack is active, all the state variable... View full abstract»

• Sampled-Data-Based Event-Triggered Active Disturbance Rejection Control for Disturbed Systems in Networked Environment

Publication Year: 2017, Page(s):1 - 11
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This paper develops a methodology on sampled-data-based event-triggered active disturbance rejection control (ET-ADRC) for disturbed systems in networked environment when only using measurable outputs. By using disturbance/uncertainty estimation and attenuation technique, an event-based sampled-data composite controller is proposed together with a discrete-time extended state observer. Under the p... View full abstract»

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

Publication Year: 2016, Page(s):284 - 297
Cited by:  Papers (49)
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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»

• Adaptive Neural Network Control of a Flapping Wing Micro Aerial Vehicle With Disturbance Observer

Publication Year: 2017, Page(s):3452 - 3465
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The research of this paper works out the attitude and position control of the flapping wing micro aerial vehicle (FWMAV). Neural network control with full state and output feedback are designed to deal with uncertainties in this complex nonlinear FWMAV dynamic system and enhance the system robustness. Meanwhile, we design disturbance observers which are exerted into the FWMAV system via feedforwar... View full abstract»

• Expanding Training Data for Facial Image Super-Resolution

Publication Year: 2018, Page(s):716 - 729
| |PDF (7656 KB) | HTML Media

The quality of training data is very important for learning-based facial image super-resolution (SR). The more similarity between training data and testing input is, the better SR results we can have. To generate a better training set of low/high resolution training facial images for a particular testing input, this paper is the first work that proposes expanding the training data for improving fa... View full abstract»

• Compressive-Sensing-Based Structure Identification for Multilayer Networks

Publication Year: 2018, Page(s):754 - 764
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The coexistence of multiple types of interactions within social, technological, and biological networks has motivated the study of the multilayer nature of real-world networks. Meanwhile, identifying network structures from dynamical observations is an essential issue pervading over the current research on complex networks. This paper addresses the problem of structure identification for multilaye... View full abstract»

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

Publication Year: 2018, Page(s):1 - 13
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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... View full abstract»

• Exact and Approximate Stability of Solutions to Traveling Salesman Problems

Publication Year: 2018, Page(s):583 - 595
| |PDF (572 KB) | HTML

This paper presents the stability analysis of an optimal tour for the symmetric traveling salesman problem (TSP) by obtaining stability regions. The stability region of an optimal tour is the set of all cost changes for which that solution remains optimal and can be understood as the margin of optimality for a solution with respect to perturbations in the problem data. It is known that it is not p... View full abstract»

Aims & Scope

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

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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