Retraction of “Using ergonomic digital human modeling in evaluation of workplace design and prevention of workrelated musculoskeletal disorders onboard small fishing vessel”

The following article from Human Factors and Ergonomics in Manufacturing & Service Industries, “Using ergonomic digital human modeling in evaluation of workplace design and prevention of workrelated musculoskeletal disorders onboard small fishing vessel,” by Enrique Álvarez‐Casado, Bing Zhang, Sonia Tello Sandoval and Mondelo Pedro, published online on October 11, 2011 in Wiley Online Library ( www.onlinelibrary.wiley.com ), has been retracted by agreement between the authors, the journal Editors‐in‐Chief, Waldemar Karwowski and Gavriel Salvendy, and Publisher Wiley Periodicals, Inc. The retraction has been agreed as these articles were mistakenly published after being rejected.     

Detection of sudden pedestrian crossings for driving assistance systems

In this paper, we study the problem of detecting sudden pedestrian crossings to assist drivers in avoiding accidents. This application has two major requirements: to detect crossing pedestrians as early as possible just as they enter the view of the car-mounted camera and to maintain a false alarm rate as low as possible for practical purposes. Although many current sliding-window-based approaches using various features and classification algorithms have been proposed for image-/video-based pedestrian detection, their performance in terms of accuracy and processing speed falls far short of practical application requirements. To address this problem, we propose a three-level coarse-to-fine video-based framework that detects partially visible pedestrians just as they enter the camera view, with low false alarm rate and high speed. The framework is tested on a new collection of high-resolution videos captured from a moving vehicle and yields a performance better than that of state-of-the-art pedestrian detection while running at a frame rate of 55 fps.     

An integrated head pose and eye gaze tracking approach to non-intrusive visual attention measurement for wide FOV simulators

Eye gaze tracking is very useful for quantitatively measuring visual attention in virtual environments. However, most eye trackers have a limited tracking range, e.g., ±35° in the horizontal direction. This paper proposed a method to combine head pose tracking and eye gaze tracking together to achieve a large range of tracking in virtual driving simulation environments. Multiple parallel multilayer perceptrons were used to reconstruct the relationship between head images and head poses. Head images were represented with the coefficients extracted from Principal Component Analysis. Eye gaze tracking provides precise results on the front view, while head pose tracking is more suitable for tracking areas of interest than for tracking points of interest on the side view.     

A space and time efficient algorithm for SimRank computation

SimRank has become an important similarity measure to rank web documents based on a graph model on hyperlinks. The existing approaches for conducting SimRank computation adopt an iteration paradigm. The most efficient deterministic technique yields O(n³)O\left(n^3\right) worst-case time per iteration with the space requirement O(n²)O\left(n^2\right), where n is the number of nodes (web documents). In this paper, we propose novel optimization techniques such that each iteration takes O (min{ n ·m , n^r })O \left(\min \left\{ n \cdot m , n^r \right\}\right) time and O ( n + m )O \left( n + m \right) space, where m is the number of edges in a web-graph model and r ≤ log₂ 7. In addition, we extend the similarity transition matrix to prevent random surfers getting stuck, and devise a pruning technique to eliminate impractical similarities for each iteration. Moreover, we also develop a reordering technique combined with an over-relaxation method, not only speeding up the convergence rate of the existing techniques, but achieving I/O efficiency as well. We conduct extensive experiments on both synthetic and real data sets to demonstrate the efficiency and effectiveness of our iteration techniques.     

Optimal Performance in Tracking Stochastic Signal Under Disturbance Rejection

The analysis method of optimal tracking performance is proposed for multiple‐input multiple‐output (MIMO) linear time‐invariant (LTI) systems under disturbance rejection. An H₂ criterion of the error signal between the output of the plant and the reference signal is used as a measure for the tracking performance. Spectral factorization is applied to obtain the optimal solution of the system tracking error. The explicit expressions are derived for this minimal tracking error with respect to random reference signals under disturbance rejection. It is shown that the nonminimum phase zeros, the zero direction, the unstable poles, the pole direction of a given plant, statistical characteristics of the reference input signal, and disturbance signal have a negative effect on a feedback system's ability to reduce the system error with disturbance rejection. The results show that the optimal tracking performance will further be damaged because of disturbance rejection. Some typical examples are given to illustrate the theoretical results.     

An Intelligent Robust Tracking Control for a Class of Electrically Driven Mobile Robots

This paper addresses the problem of designing robust tracking control for a class of uncertain wheeled mobile robots actuated by brushed direct current motors. This class of electrically‐driven mechanical systems consists of the robot kinematics, the robot dynamics, and the wheel actuator dynamics. Via the backstepping technique, an intelligent robust tracking control scheme that integrates a kinematic controller and an adaptive neural network‐based (or fuzzy‐based) controller is developed such that all of the states and signals of the closed‐loop system are bounded and the tracking error can be made as small as possible. Two adaptive approximation systems are constructed to learn the behaviors of unknown mechanical and electrical dynamics. The effects of both the approximation errors and the unmodeled time‐varying perturbations in the input and virtual‐input weighting matrices are counteracted by suitably tuning the control gains. Consequently, the robust control scheme developed here can be employed to handle a broader class of electrically‐driven wheeled mobile robots in the presence of high‐degree time‐varying uncertainties. Finally, a simulation example is given to demonstrate the effectiveness of the developed control scheme.     

Input‐to‐state stability of min‐max MPC scheme for nonlinear time‐varying delay systems

This paper studies the robustness problem of the min–max model predictive control (MPC) scheme for constrained nonlinear time‐varying delay systems subject to bounded disturbances. The notion of the input‐to‐state stability (ISS) of nonlinear time‐delay systems is introduced. Then by using the Lyapunov–Krasovskii method, a delay‐dependent sufficient condition is derived to guarantee input‐to‐state practical stability (ISpS) of the closed‐loop system by way of nonlinear matrix inequalities (NLMI). In order to lessen the online computational demand, the non‐convex min‐max optimization problem is then converted to a minimization problem with linear matrix inequality (LMI) constraints and a suboptimal MPC algorithm is provided. Finally, an example of a truck‐trailer is used to illustrate the effectiveness of the proposed results. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

A new anti‐windup strategy for PID controllers with derivative filters

This paper presents a new strategy for suppressing the windup effect caused by actuator saturation in proportional–integral–derivative (PID) controlled systems. In the proposed approach, the windup effect is modeled as an external disturbance imported to the PID controller and an observer‐based auxiliary controller is designed to minimize the difference between the controller output signal and the system input signal in accordance with an H‐infinite optimization criterion. It is shown that the proposed anti‐windup (AW) scheme renders the performance of the controlled system more robust toward the effects of windup than conventional PID AW schemes and provides a better noise rejection capability. In addition, the proposed PID AW scheme is system independent and is an explicit function of the parameters of the original PID controller. As a result, the controller is easily implemented using either digital or analog circuits and facilitates a rapid, on‐line tuning of the controller parameters as required in order to prevent the windup effect. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Intelligent control of doubly‐fed induction generator systems using PIDNNs

An intelligent control for a stand‐alone doubly‐fed induction generator (DFIG) system using a proportional‐integral‐derivative neural network (PIDNN) is proposed in this study. This system can be applied as a stand‐alone power supply system or as the emergency power system when the electricity grid fails for all sub‐synchronous, synchronous, and super‐synchronous conditions. The rotor side converter is controlled using field‐oriented control to produce 3‐phase stator voltages with constant magnitude and frequency at different rotor speeds. Moreover, the grid side converter, which is also controlled using field‐oriented control, is primarily implemented to maintain the magnitude of the DC‐link voltage. Furthermore, the intelligent PIDNN controller is proposed for both the rotor and grid side converters to improve the transient and steady‐state responses of the DFIG system for different operating conditions. Both the network structure and online learning algorithm are introduced in detail. Finally, the feasibility of the proposed control scheme is verified through experimentation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society