Observer-based sliding mode control with adaptive perturbation estimation for micropositioning actuators

Control of piezoelectric actuators is under the effects of hysteresis that could affect actuators micropositioning accuracy. In this paper a modified Prandtl-Ishlinskii (PI) operator and its inverse is utilized for both identification and real time compensation of the hysteresis effect. As a result, the actuator dynamic model would be transformed to the second order linear dynamic model. Considering the parametric uncertainties, PI estimation error and probably unmodeled dynamics, a variable structure controller coupled with adaptive perturbation estimation is proposed for trajectory tracking of the piezoelectric position. Considering the very noisy output of the actuator, a high-gain observer would estimate full states from the only measurable position trajectory. The stability of the controller in the presence of the estimated state is demonstrated with the Lyapunov criterion. Comparing to the widely used proportional-integral controller, the experimental results depicts that the proposed approach is greatly achieved in precisely tracking of multiple frequency trajectories.     

Automation of Tracking Trajectories in a Crowded Situation

Studies on pedestrians using microscopic simulation require large amounts of trajectory data from real-world pedestrian crowds. The collection of such data, if done manually, involves tremendous efforts and is very time-consuming. Although many studies have asserted the possibility of automating this task using video cameras, we have found that only a few have demonstrated good performance in very crowded situations or from a top-angled view scene. This paper deals with tracking pedestrian crowd under heavy occlusion and from an angular scene using only a single non-stereo video camera. Our automated tracking system consists of three modules that are performed sequentially. The first module detects moving objects as blobs. The second module computes feature values from the blob information in order to generate what we call a possibility matrix. The third module is a tracking system, which employs a Bayesian update of the probability tree derived from the possibility matrix and from the detection of each pedestrian, in order to track the next position of the pedestrian. The result of such tracking is a database of pedestrian trajectories over time and space. With certain prior information, we show that the system is able to track a large number of people under occlusion and clutter scene.     

Deformation analysis of large aerospace components during assembly

The civil aircraft industry often assembles large deformable aluminium components within the fuselage and wing structures. Part-to-part assembly of these compliant components regularly causes difficulties associated with dimensional variations. This paper describes a methodology to predict overall deformation between the alignment of the rib foot and the skin panel. The assembly operations and conditions encountered on the real production jigs were conducted under simulated laboratory conditions. A table method for prediction was developed to reduce uncertainties in positioning between the skin panel and rib foot. Finite element model of the rib was created to further enhance the prediction method. Results obtained from the predicted method were evaluated against real factory data. The validation performed against the assembly plant showed good performance of the prediction method in forecasting the positional variations occurring during the simulation of the assembly. The prediction method was, therefore, proven to be reliable.     

Optimal design of nonuniform multirate filter banks

The design of general nonuniform filter banks is studied. Contrary to uniform filter banks, in nonuniform filter banks, it may not be possible to achieve perfect reconstruction, but in some cases by using optimization techniques, we can design acceptable filter banks. Here, the initial finite impulse response (FIR) analysis filters are designed according to the characteristics of the input. By the design procedure, the FIR synthesis filters are found so that theH-norm of an error system is minimized over all synthesis filters that have a prespecified order. Then, the synthesis filters obtained in the previous step are fixed, and the analysis filters are found similarly. By iteration, theH-norm of the error system decreases until it converges to its final value. At each iteration, the coefficients of the analysis or synthesis filters are obtained by finding the least squares solution of a system of linear equations. If necessary, the frequency characteristics of the filters can be altered by adding penalty terms to the objective function.This research was supported by the Natural Sciences and Engineering Research Council of Canada.     

Next Branch Multicast (NBM) routing protocol

It is well known that IP multicast suffers from deployment issues. The problem mainly originates from the multicast routing complexities in the inter-domain level and state-full nature of current solutions. To cope with the problem, many alternative group communication methods have been proposed. Among them, branching point (BP) based approaches have promising features like incremental deployment, high tree availability, low memory requirement and, hence, high scalability. However, current BP-based methods suffer from two major inefficiencies namely the tree construction difficulties and presence of excessive lookups in the forwarding process of unicast and multicast data packets. We propose a new BP-based protocol named NBM (Next Branch Multicast) to avoid the existing drawbacks. NBM constructs the multicast distribution tree in the forward direction and has a fault-detection and repair mechanism which protects the tree against BPs failures. NBM detects the failure of a higher level BP in the tree sooner than a lower level BP. NBM does not maintain any type of control state in non-branching routers. Our simulation results show that NBM memory requirement for maintaining multicast forwarding states is approximately less than half when compared to the traditional approach. In addition, the NBM tree is more available than the traditional one at least by a factor of 2.     

A novel unsupervised classification approach for network anomaly detection by k-Means clustering and ID3 decision tree learning methods

This paper presents a novel host-based combinatorial method based on k-Means clustering and ID3 decision tree learning algorithms for unsupervised classification of anomalous and normal activities in computer network ARP traffic. The k-Means clustering method is first applied to the normal training instances to partition it into k clusters using Euclidean distance similarity. An ID3 decision tree is constructed on each cluster. Anomaly scores from the k-Means clustering algorithm and decisions of the ID3 decision trees are extracted. A special algorithm is used to combine results of the two algorithms and obtain final anomaly score values. The threshold rule is applied for making the decision on the test instance normality. Experiments are performed on captured network ARP traffic. Some anomaly criteria has been defined and applied to the captured ARP traffic to generate normal training instances. Performance of the proposed approach is evaluated using five defined measures and empirically compared with the performance of individual k-Means clustering and ID3 decision tree classification algorithms and the other proposed approaches based on Markovian chains and stochastic learning automata. Experimental results show that the proposed approach has specificity and positive predictive value of as high as 96 and 98%, respectively.     

Land-use and land-cover classification in semi-arid regions using independent component analysis (ICA) and expert classification

This study was focused on addressing the need for accurate land-use/land-cover classification (LULC) maps in Iran and in other similarly developing countries. To generate and validate a new LULC map for northeastern Iran’s 2037.5 km² Hable-roud watershed, a step-by-step process was developed and implemented, consisting of image preprocessing, extraction of training and reference sampling locations, decomposition of multi-spectral thematic mapper bands into features by independent component analysis methods, classification using these features and slope maps, enhancement of land-use classes through image segmentation and zonal statistics, then through consideration of normalized difference vegetation index and climatic zones, followed by ground truthing. This newly developed approach provided maps that distinguished dryland farming, irrigated farmland, forest plantations, and low-, medium-, and high-vegetation density rangelands, while currently available maps for the watershed lef t 39% of lands unclassified or in combined classes. The new maps’ ground-truthing-based overall accuracy and kappa coefficient were 88.3% and 0.83, respectively. In order to develop such an improved LULC map, it was necessary to go beyond the mere analysis of reflectance information, to incorporating climatic and topographic data through this newly proposed step-by-step approach.     

Stability and stabilization in switched discrete‐time systems

In this paper, we investigate the stability and stabilization problem for discrete‐time switched systems. We consider a probabilistic case where the system is switched among different subsystems, and the probability of each subsystem being active is defined as its occurrence probability. The relationship between the developed model of the switched system and the Markovian jump system is analyzed. For a switched system with a known subsystem occurrence probabilities, we give a stochastic stability criterion in terms of a linear matrix inequality. Then, we extend the results to a more practical case where the subsystem occurrence probabilities of switching are known to be constant, but their specific values are only known with some uncertainty. A new iterative approach is employed to choose the switching law between the subsystems. For unstable switched systems, mode‐dependent state feedback and static output feedback controllers are developed to achieve the stabilization objective. Finally, several simulation examples are presented to show the efficacy of the proposed criteria and methods. Copyright © 2012 John Wiley & Sons, Ltd.     

Mechanical and hydrodynamic analyses of helical strake-like ridges in a glass sponge

From the discovery of functionally graded laminated composites, to near-structurally optimized diagonally reinforced square lattice structures, the skeletal system of the predominantly deep-sea sponge Euplectella aspergillum has continued to inspire biologists, materials scientists and mechanical engineers. Building on these previous efforts, in the present study, we develop an integrated finite element and fluid dynamics approach for investigating structure–function relationships in the complex maze-like organization of helical ridges that surround the main skeletal tube of this species. From these investigations, we discover that not only do these ridges provide additional mechanical reinforcement, but perhaps more significantly, provide a critical hydrodynamic benefit by effectively suppressing von Kármán vortex shedding and reducing lift forcing fluctuations over a wide range of biologically relevant flow regimes. By comparing the disordered sponge ridge geometry to other more symmetrical strake-based vortex suppression systems commonly employed in infrastructure applications ranging from antennas to underwater gas and oil pipelines, we find that the unique maze-like ridge organization of E. aspergillum can completely suppress vortex shedding rather than delaying their shedding to a more downstream location, thus highlighting their potential benefit in these engineering contexts.