# IEEE Transactions on Cybernetics

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• ### Enhanced Computer Vision With Microsoft Kinect Sensor: A Review

Publication Year: 2013, Page(s):1318 - 1334
Cited by:  Papers (329)  |  Patents (25)
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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»

• ### 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»

• ### An Overview of Recent Advances in Event-Triggered Consensus of Multiagent Systems

Publication Year: 2018, Page(s):1110 - 1123
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Event-triggered consensus of multiagent systems (MASs) has attracted tremendous attention from both theoretical and practical perspectives due to the fact that it enables all agents eventually to reach an agreement upon a common quantity of interest while significantly alleviating utilization of communication and computation resources. This paper aims to provide an overview of recent advances in e... 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»

• ### 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»

• ### A Generic Deep-Learning-Based Approach for Automated Surface Inspection

Publication Year: 2018, Page(s):929 - 940
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Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning ne... 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»

• ### 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»

• ### Clustering by Local Gravitation

Publication Year: 2018, Page(s):1383 - 1396
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The objective of cluster analysis is to partition a set of data points into several groups based on a suitable distance measure. We first propose a model called local gravitation among data points. In this model, each data point is viewed as an object with mass, and associated with a local resultant force (LRF) generated by its neighbors. The motivation of this paper is that there exist distinct d... 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»

• ### Stacked Convolutional Denoising Auto-Encoders for Feature Representation

Publication Year: 2017, Page(s):1017 - 1027
Cited by:  Papers (13)
| | PDF (1793 KB) | HTML

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»

• ### Locally Weighted Ensemble Clustering

Publication Year: 2018, Page(s):1460 - 1473
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Due to its ability to combine multiple base clusterings into a probably better and more robust clustering, the ensemble clustering technique has been attracting increasing attention in recent years. Despite the significant success, one limitation to most of the existing ensemble clustering methods is that they generally treat all base clusterings equally regardless of their reliability, which make... 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»

• ### Interrelationship-Based Selection for Decomposition Multiobjective Optimization

Publication Year: 2015, Page(s):2076 - 2088
Cited by:  Papers (35)
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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»

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

Publication Year: 2017, Page(s):1743 - 1756
Cited by:  Papers (3)
| | PDF (1832 KB) | HTML Media

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 Linear Multi-Agent Systems by Distributed Event-Triggered Strategy

Publication Year: 2016, Page(s):148 - 157
Cited by:  Papers (75)
| | PDF (1008 KB) | HTML

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»

• ### Transfer Independently Together: A Generalized Framework for Domain Adaptation

Publication Year: 2018, Page(s):1 - 12
| | PDF (2454 KB)

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 t... 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»

• ### Fuzzy Adaptive Output Feedback Control of Uncertain Nonlinear Systems With Prescribed Performance

Publication Year: 2018, Page(s):1342 - 1354
| | PDF (1960 KB) | HTML

This paper investigates the tracking control problem for a family of strict-feedback systems in the presence of unknown nonlinearities and immeasurable system states. A low-complexity adaptive fuzzy output feedback control scheme is proposed, based on a backstepping method. In the control design, a fuzzy adaptive state observer is first employed to estimate the unmeasured states. Then, a novel err... 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)
| | PDF (1536 KB) | HTML

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»

• ### 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)
| | PDF (785 KB) | HTML

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»

• ### Learning and Recognition of Clothing Genres From Full-Body Images

Publication Year: 2018, Page(s):1647 - 1659
| | PDF (2664 KB) | HTML

According to the theory of clothing design, the genres of clothes can be recognized based on a set of visually differentiable style elements, which exhibit salient features of visual appearance and reflect high-level fashion styles for better describing clothing genres. Instead of using less-discriminative low-level features or ambiguous keywords to identify clothing genres, we proposed a novel ap... View full abstract»

• ### Adaptive Critic Designs for Event-Triggered Robust Control of Nonlinear Systems With Unknown Dynamics

Publication Year: 2018, Page(s):1 - 13
| | PDF (1152 KB)

This paper develops a novel event-triggered robust control strategy for continuous-time nonlinear systems with unknown dynamics. To begin with, the event-triggered robust nonlinear control problem is transformed into an event-triggered nonlinear optimal control problem by introducing an infinite-horizon integral cost for the nominal system. Then, a recurrent neural network (RNN) and adaptive criti... 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)
| | PDF (1260 KB) | HTML

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»

• ### 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»

• ### Model-Free Adaptive Control for Unknown Nonlinear Zero-Sum Differential Game

Publication Year: 2018, Page(s):1633 - 1646
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In this paper, we present a new model-free globalized dual heuristic dynamic programming (GDHP) approach for the discrete-time nonlinear zero-sum game problems. First, the online learning algorithm is proposed based on the GDHP method to solve the Hamilton-Jacobi-Isaacs equation associated with H∞ optimal regulation control problem. By setting backward one step of the definition ... View full abstract»

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

Publication Year: 2017, Page(s):449 - 460
Cited by:  Papers (5)
| | PDF (2541 KB) | HTML

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»

• ### A New Approach to Stabilization of Chaotic Systems With Nonfragile Fuzzy Proportional Retarded Sampled-Data Control

Publication Year: 2018, Page(s):1 - 12
| | PDF (2114 KB)

This paper is concerned with the problem of stabilization of chaotic systems via nonfragile fuzzy proportional retarded sampled-data control. Compared with existing sampled-data control schemes, a more practical nonfragile fuzzy proportional retarded sampled-data controller is designed, which involves not only a signal transmission delay but also uncertainties. Based on the Wirtinger inequality, a... View full abstract»

• ### Secure Estimation for Cyber-Physical Systems via Sliding Mode

Publication Year: 2018, Page(s):1 - 12
| | PDF (1454 KB)

This paper is concerned with the problem of secure state reconstruction for cyber-physical systems (CPSs). CPSs are more vulnerable to the cyber world yet to attackers, who can attack any sensor of the considered systems and modify values of attacked sensors to be arbitrary ones. In the design process, both malicious attacks on sensors and unknown input are taken into consideration. First, a linea... 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»

• ### A New Differential Evolution Algorithm for Minimax Optimization in Robust Design

Publication Year: 2018, Page(s):1355 - 1368
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Minimax optimization, which is actively involved in numerous robust design problems, aims at pursuing the solutions with best worst-case performances. Although considerable research has been devoted to the development of minimax optimization algorithms, there still exist several fundamental limitations for existing approaches, e.g., restriction on problem types, excessively high computational cost... View full abstract»

• ### A Q-Learning Approach to Flocking With UAVs in a Stochastic Environment

Publication Year: 2017, Page(s):186 - 197
Cited by:  Papers (3)
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In the past two decades, unmanned aerial vehicles (UAVs) have demonstrated their efficacy in supporting both military and civilian applications, where tasks can be dull, dirty, dangerous, or simply too costly with conventional methods. Many of the applications contain tasks that can be executed in parallel, hence the natural progression is to deploy multiple UAVs working together as a force multip... 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)
| | PDF (1591 KB) | HTML

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»

• ### Body Joint Guided 3-D Deep Convolutional Descriptors for Action Recognition

Publication Year: 2018, Page(s):1095 - 1108
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3-D convolutional neural networks (3-D CNNs) have been established as a powerful tool to simultaneously learn features from both spatial and temporal dimensions, which is suitable to be applied to video-based action recognition. In this paper, we propose not to directly use the activations of fully connected layers of a 3-D CNN as the video feature, but to use selective convolutional layer activat... View full abstract»

• ### Distributed Algorithms for Searching Generalized Nash Equilibrium of Noncooperative Games

Publication Year: 2018, Page(s):1 - 10
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In this paper, the distributed Nash equilibrium (NE) searching problem is investigated, where the feasible action sets are constrained by nonlinear inequalities and linear equations. Different from most of the existing investigations on distributed NE searching problems, we consider the case where both cost functions and feasible action sets depend on actions of all players, and each player can on... View full abstract»

• ### Aperiodic Robust Model Predictive Control for Constrained Continuous-Time Nonlinear Systems: An Event-Triggered Approach

Publication Year: 2018, Page(s):1397 - 1405
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The event-triggered control is a promising solution to cyber-physical systems, such as networked control systems, multiagent systems, and large-scale intelligent systems. In this paper, we propose an event-triggered model predictive control (MPC) scheme for constrained continuous-time nonlinear systems with bounded disturbances. First, a time-varying tightened state constraint is computed to achie... View full abstract»

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

Publication Year: 2018, Page(s):1 - 10
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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 ... View full abstract»

• ### A Self-Adaptive Sleep/Wake-Up Scheduling Approach for Wireless Sensor Networks

Publication Year: 2018, Page(s):979 - 992
| | PDF (1063 KB) | HTML Media

Sleep/wake-up scheduling is one of the fundamental problems in wireless sensor networks, since the energy of sensor nodes is limited and they are usually unrechargeable. The purpose of sleep/wake-up scheduling is to save the energy of each node by keeping nodes in sleep mode as long as possible (without sacrificing packet delivery efficiency) and thereby maximizing their lifetime. In this paper, a... View full abstract»

• ### Neural-Learning-Based Telerobot Control With Guaranteed Performance

Publication Year: 2017, Page(s):3148 - 3159
Cited by:  Papers (9)
| | PDF (3029 KB) | HTML

In this paper, a neural networks (NNs) enhanced telerobot control system is designed and tested on a Baxter robot. Guaranteed performance of the telerobot control system is achieved at both kinematic and dynamic levels. At kinematic level, automatic collision avoidance is achieved by the control design at the kinematic level exploiting the joint space redundancy, thus the human operator would be a... View full abstract»

• ### A Novel Deep Learning-Based Collaborative Filtering Model for Recommendation System

Publication Year: 2018, Page(s):1 - 13
| | PDF (2465 KB)

The collaborative filtering (CF) based models are capable of grasping the interaction or correlation of users and items under consideration. However, existing CF-based methods can only grasp single type of relation, such as restricted Boltzmann machine which distinctly seize the correlation of user-user or item-item relation. On the other hand, matrix factorization explicitly captures the interact... View full abstract»

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

Publication Year: 2018, Page(s):1 - 14
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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 most based on a simple intrinsic structure. To represent the c... View full abstract»

• ### An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts

Publication Year: 2016, Page(s):421 - 437
Cited by:  Papers (23)
| | PDF (2626 KB) | HTML

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This pape... View full abstract»

• ### An Open Framework for Constructing Continuous Optimization Problems

Publication Year: 2018, Page(s):1 - 15
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Many artificial benchmark problems have been proposed for different kinds of continuous optimization, e.g., global optimization, multimodal optimization, multiobjective optimization, dynamic optimization, and constrained optimization. However, there is no unified framework for constructing these types of problems and possible properties of many problems are not fully tunable. This will cause diffi... View full abstract»

• ### Robust Gait Recognition by Integrating Inertial and RGBD Sensors

Publication Year: 2018, Page(s):1136 - 1150
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Gait has been considered as a promising and unique biometric for person identification. Traditionally, gait data are collected using either color sensors, such as a CCD camera, depth sensors, such as a Microsoft Kinect, or inertial sensors, such as an accelerometer. However, a single type of sensors may only capture part of the dynamic gait features and make the gait recognition sensitive to compl... View full abstract»

• ### Classifier Fusion With Contextual Reliability Evaluation

Publication Year: 2018, Page(s):1605 - 1618
| | PDF (1468 KB) | HTML Media

Classifier fusion is an efficient strategy to improve the classification performance for the complex pattern recognition problem. In practice, the multiple classifiers to combine can have different reliabilities and the proper reliability evaluation plays an important role in the fusion process for getting the best classification performance. We propose a new method for classifier fusion with cont... View full abstract»

• ### No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization

Publication Year: 2017, Page(s):4559 - 4565
Cited by:  Papers (7)
| | PDF (990 KB) | HTML

The general purpose of seeing a picture is to attain information as much as possible. With it, we in this paper devise a new no-reference/blind metric for image quality assessment (IQA) of contrast distortion. For local details, we lirst roughly remove predicted regions in an image since unpredicted remains are of much information. We then compute entropy of particular unpredicted areas of maximum... View full abstract»

• ### Trajectory Planning and Optimized Adaptive Control for a Class of Wheeled Inverted Pendulum Vehicle Models

Publication Year: 2013, Page(s):24 - 36
Cited by:  Papers (88)
| | PDF (701 KB) | HTML

In this paper, we investigate optimized adaptive control and trajectory generation for a class of wheeled inverted pendulum (WIP) models of vehicle systems. Aiming at shaping the controlled vehicle dynamics to be of minimized motion tracking errors as well as angular accelerations, we employ the linear quadratic regulation optimization technique to obtain an optimal reference model. Adaptive contr... View full abstract»

• ### Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-occurrence Data

Publication Year: 2018, Page(s):1263 - 1275
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Using online consumer reviews as electronic word of mouth to assist purchase-decision making has become increasingly popular. The Web provides an extensive source of consumer reviews, but one can hardly read all reviews to obtain a fair evaluation of a product or service. A text processing framework that can summarize reviews, would therefore be desirable. A subtask to be performed by such a frame... View full abstract»

• ### A Generalized Laplacian of Gaussian Filter for Blob Detection and Its Applications

Publication Year: 2013, Page(s):1719 - 1733
Cited by:  Papers (36)
| | PDF (3549 KB) | HTML

In this paper, we propose a generalized Laplacian of Gaussian (LoG) (gLoG) filter for detecting general elliptical blob structures in images. The gLoG filter can not only accurately locate the blob centers but also estimate the scales, shapes, and orientations of the detected blobs. These functions can be realized by generalizing the common 3-D LoG scale-space blob detector to a 5-D gLoG scale-spa... View full abstract»

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

Publication Year: 2018, Page(s):1 - 12
| | PDF (2063 KB)

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 distribut... 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