IEEE Transactions on Cybernetics

This paper suggests a new generation of optimal PI controllers for a class of servo systems characterized by saturation and dead zone static nonlinearities and second-order models with an integral component. The objective functions are expressed as the integral of time multiplied by absolute error plus the weighted sum of the integrals of output sensitivity functions of the state sensitivity model... View full abstract»

Resilient monitoring systems, considered in this paper, are sensor networks that degrade gracefully under malicious attacks on their sensors, causing them to project misleading information. The goal of this paper is to design, analyze, and evaluate the performance of a resilient monitoring system intended to monitor plant conditions (normal or anomalous). The architecture developed consists of fou... View full abstract»

The ability to maintain state awareness in the face of unexpected and unmodeled errors and threats is a defining feature of a resilient control system. Therefore, in this paper, we study the problem of distributed fault detection and isolation (FDI) in large networked systems with uncertain system models. The linear networked system is composed of interconnected subsystems and may be represented a... View full abstract»

In this paper, we study the problem of reaching a consensus among all the agents in the networked control systems (NCS) in the presence of misbehaving agents. A reputation-based resilient distributed control algorithm is first proposed for the leader-follower consensus network. The proposed algorithm embeds a resilience mechanism that includes four phases (detection, mitigation, identification, an... View full abstract»

Resiliency is one of the most critical objectives found in complex industrial applications today and designing control systems to provide resiliency is an open problem. This paper proposes resilient control design guidelines for industrial systems that can be modeled as networked multiagent consensus systems subject to disturbances or noise. We give a general analysis of multiagent consensus netwo... View full abstract»

Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifie... View full abstract»

Control systems with long distance sensor and actuator wiring have the problem of high system cost and increased sensor noise. Wireless sensor network (WSN)-based control systems are an alternative solution involving lower setup and maintenance costs and reduced sensor noise. However, WSN-based control systems also encounter problems such as possible data loss, irregular sampling periods (due to t... View full abstract»

Machine learning based on graph representation, or manifold learning, has attracted great interest in recent years. As the discrete approximation of data manifold, the graph plays a crucial role in these kinds of learning approaches. In this paper, we propose a novel learning method for graph construction, which is distinct from previous methods in that it solves an optimization problem with the a... View full abstract»

Human body segmentation is a challenging and important problem in computer vision. Existing methods usually entail a time-consuming training phase for prior knowledge learning with complex shape matching for body segmentation. In this paper, we propose a data-driven method that integrates top-down body pose information and bottom-up low-level visual cues for segmenting humans in static images with... View full abstract»

Planetary surface science operations performed by robotic space systems frequently require pointing cameras at various objects and moving a robotic arm end effector tool toward specific targets. Earlier NASA Mars Exploration Rovers did not have the ability to compute actual coordinates for given object coordinate frame names and had to be provided with explicit coordinates. Since it sometimes take... View full abstract»

In vision and learning, low computational complexity and high generalization are two important goals for video object detection. Low computational complexity here means not only fast speed but also less energy consumption. The sliding window object detection method with linear support vector machines (SVMs) is a general object detection framework. The computational cost is herein mainly paid in co... View full abstract»

Long term tracking is a challenging task for many applications. In this paper, we propose a novel tracking approach that can adapt various appearance changes such as illumination, motion, and occlusions, and owns the ability of robust reacquisition after drifting. We utilize a condensation-based method with an online support vector machine as a reliable observation model to realize adaptive tracki... View full abstract»

A novel framework of learning-based superresolution is proposed by employing the process of learning from the estimation errors. The estimation errors generated by different learning-based super-resolution algorithms are statistically shown to be sparse and uncertain. The sparsity of the estimation errors means most of estimation errors are small enough. The uncertainty of the estimation errors me... View full abstract»

Point pattern matching plays a prominent role in the fields of computer vision and pattern recognition. A technique combining the circular onion peeling and the radial decomposition is proposed to analyze the topology structure of a point pattern. The analysis derives a feature which records the topological structure of a point pattern. This novel feature is free from isometric assumption. It can ... View full abstract»

Recently, the hashing techniques have been widely applied to approximate the nearest neighbor search problem in many real applications. The basic idea of these approaches is to generate binary codes for data points which can preserve the similarity between any two of them. Given a query, instead of performing a linear scan of the entire data base, the hashing method can perform a linear scan of th... View full abstract»

Evolutionary algorithms (EAs) are widely employed for solving optimization problems with rugged fitness landscapes. Opposition-based learning (OBL) is a recent tool developed to improve the convergence rate of EAs. In this paper, we derive the probabilities that distances between OBL points and the optimization problem solution are less than the distance between a given EA individual and the optim... View full abstract»

In this paper, the problem of adaptive active fault-tolerant control for a class of nonlinear systems with unknown actuator fault is investigated. The actuator fault is assumed to have no traditional affine appearance of the system state variables and control input. The useful property of the basis function of the radial basis function neural network (NN), which will be used in the design of the f... View full abstract»

Stochastic point location (SPL) deals with the problem of a learning mechanism (LM) determining the optimal point on the line when the only input it receives are stochastic signals about the direction in which it should move. One can differentiate the SPL from the traditional class of optimization problems by the fact that the former considers the case where the directional information, for exampl... View full abstract»

Many real world optimization problems involve uncertainties. A solution for such a problem is expected to be robust to these uncertainties. Commonly, robustness is attained by choosing the solution's parameters such that the solution's performance is less influenced by negative effects of the uncertain parameters' variations. This robustness may be viewed as a passive robustness, because once the ... View full abstract»

This paper presents a multiple-feature and multiple-kernel support vector machine (MFMK-SVM) methodology to achieve a more reliable and robust segmentation performance for humanoid robot. The pixel wise intensity, gradient, and C1 SMF features are extracted via the local homogeneity model and Gabor filter, which would be used as inputs of MFMK-SVM model. It may provide multiple features of the sam... View full abstract»