Cops and Robbers from a distance

Cops and Robbers is a pursuit and evasion game played on graphs that has received much attention. We consider an extension of Cops and Robbers, distance k Cops and Robbers, where the cops win if at least one of them is of distance at most k from the robber in G. The cop number of a graph G is the minimum number of cops needed to capture the robber in G. The distance k analogue of the cop number, written c_k(G), equals the minimum number of cops needed to win at a given distance k. We study the parameter c_k from algorithmic, structural, and probabilistic perspectives. We supply a classification result for graphs with bounded c_k(G) values and develop an O(n^2s+3) algorithm for determining if c_k(G)≤s for s fixed. We prove that if s is not fixed, then computing c_k(G) is NP-hard. Upper and lower bounds are found for c_k(G) in terms of the order of G. We prove that

     

Multiple robust H∞ controller design using the nonsmooth optimization method

This paper proposes a systematic technique to design multiple robust H_∞ controllers. The proposed technique achieves a desired robust performance objective, which is impossible to achieve with a single robust controller, by dividing the uncertainty set into several subsets and by designing a robust controller to each subset. To achieve this goal with a small number of divisions of the uncertainty set, an optimization problem is formulated. Since the cost function of this optimization problem is not a smooth function, a numerical nonsmooth optimization algorithm is proposed to solve this problem. This method avoids the use of Lyapunov variables, and therefore it leads to a moderate size optimization problem. A numerical example shows that the proposed multiple robust control method can improve the closed‐loop performance when a single robust controller cannot achieve satisfactory performance. Copyright © 2009 John Wiley & Sons, Ltd.     

Adjustment of drag coefficient correlations in three dimensional CFD simulation of gas-solid bubbling fluidized bed

Fluidized beds have been widely used in power generation and in chemical, biochemical, and petroleum industries. 3D simulation of commercial scale fluidized beds has been computationally impractical due to the required memory and processor speeds. In this study, 3D Computational Fluid Dynamics simulation of a gas-solid bubbling fluidized bed is performed to investigate the effect of using different inter-phase drag models. The drag correlations of Richardon and Zaki, Wen-Yu, Gibilaro, Gidaspow, Syamlal-O’Brien, Arastoopour, RUC, Di Felice, Hill Koch Ladd, Zhang and Reese, and adjusted Syamlal are reviewed using a multiphase Eulerian-Eulerian model to simulate the momentum transfer between phases. Furthermore, a method has been proposed to adjust the Di Felice drag model in a three dimensional domain based on the experimental value of minimum fluidization velocity as a calibration point. Comparisons are made with both a 2D Cartesian simulation and experimental data. The experiments are performed on a Plexiglas rectangular fluidized bed consisting of spherical glass beads and ambient air as the gas phase. Comparisons were made based on solid volume fractions, expansion height, and pressure drop inside the fluidized bed at different superficial gas velocities. The results of the proposed drag model were found to agree well with experimental data. The effect of restitution coefficient on three dimensional prediction of bed height is also investigated and an optimum value of restitution coefficient for modeling fluidized beds in a bubbling regime has been proposed. Finally sensitivity analysis is performed on the grid interval size to obtain an optimum mesh size with the objective of accuracy and time efficiency.     

Model-based PI–fuzzy control of four-wheeled omni-directional mobile robots

The purpose of this study is to suggest and examine a PI–fuzzy path planner and associated low-level control system for a linear discrete dynamic model of omni-directional mobile robots to obtain optimal inputs for drivers. Velocity and acceleration filtering is also implemented in the path planner to satisfy planning prerequisites and prevent slippage. Regulated drivers’ rotational velocities and torques greatly affect the ability of these robots to perform trajectory planner tasks. These regulated values are examined in this research by setting up an optimal controller. Introducing optimal controllers such as linear quadratic tracking for multi-input–multi-output control systems in acceleration and deceleration is one of the essential subjects for motion control of omni-directional mobile robots. The main topics presented and discussed in this article are improvements in the presented discrete-time linear quadratic tracking approach such as the low-level controller and combined PI–fuzzy path planner with appropriate speed monitoring algorithm such as the high-level one in conditions both with and without external disturbance. The low-level tracking controller presented in this article provides an optimal solution to minimize the differences between the reference trajectory and the system output. The efficiency of this approach is also compared with that of previous PID controllers which employ kinematic modeling. Utilizing the new approach in trajectory-planning controller design results in more precise and appropriate outputs for the motion of four-wheeled omni-directional mobile robots, and the modeling and experimental results confirm this issue.     

Attention control with reinforcement learning for face recognition under partial occlusion

In this paper a new method for handling occlusion in face recognition is presented. In this method the faces are partitioned into blocks and a sequential recognition structure is developed. Then, a spatial attention control strategy over the blocks is learned using reinforcement learning. The outcome of this learning is a sorted list of blocks according to their average importance in the face recognition task. In the recall mode, the sorted blocks are employed sequentially until a confident decision is made. Obtained results of various experiments on the AR face database demonstrate the superior performance of proposed method as compared with that of the holistic approach in the recognition of occluded faces.     

Hamshahri: A standard Persian text collection

The Persian language is one of the dominant languages in the Middle East, so there are significant amount of Persian documents available on the Web. Due to the different nature of the Persian language compared to the other languages such as English, the design of information retrieval systems in Persian requires special considerations. However, there are relatively few studies on retrieval of Persian documents in the literature and one of the main reasons is the lack of a standard test collection. In this paper, we introduce a standard Persian text collection, named Hamshahri, which is built from a large number of newspaper articles according to TREC specifications. Furthermore, statistical information about documents, queries and their relevance judgments are presented in this paper. We believe that this collection is the largest Persian text collection, so far.     

A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.     

Hierarchical Classifiers for Robust Topological Robot Localization

This paper presents a novel appearance-based technique for topological robot localization and place recognition. A vocabulary of visual words is formed automatically, representing local features that frequently occur in the set of training images. Using the vocabulary, a spatial pyramid representation is built for each image by repeatedly subdividing it and computing histograms of visual words at increasingly fine resolutions. An information maximization technique is then applied to build a hierarchical classifier for each class by learning informative features. While top-level features in the hierarchy are selected from the coarsest resolution of the representation, capturing the holistic statistical properties of the images, child features are selected from finer resolutions, encoding more local characteristics, redundant with the information coded by their parents. Exploiting the redundancy in the data enables the localization system to achieve greater reliability against dynamic variations in the environment. Achieving an average classification accuracy of 88.9% on a challenging topological localization database, consisting of twenty seven outdoor places, demonstrates the advantages of our hierarchical framework for dealing with dynamic variations that cannot be learned during training.     

Classification of primitive shapes using brain–computer interfaces

Brain–computer interfaces (BCIs) are recent developments in alternative technologies of user interaction. The purpose of this paper is to explore the potential of BCIs as user interfaces for CAD systems. The paper describes experiments and algorithms that use the BCI to distinguish between primitive shapes that are imagined by a user. Users wear an electroencephalogram (EEG) headset and imagine the shape of a cube, sphere, cylinder, pyramid or a cone. The EEG headset collects brain activity from 14 locations on the scalp. The data is analyzed with independent component analysis (ICA) and the Hilbert–Huang Transform (HHT). The features of interest are the marginal spectra of different frequency bands (theta, alpha, beta and gamma bands) calculated from the Hilbert spectrum of each independent component. The Mann–Whitney U-test is then applied to rank the EEG electrode channels by relevance in five pair-wise classifications. The features from the highest ranking independent components form the final feature vector which is then used to train a linear discriminant classifier. Results show that this classifier can discriminate between the five basic primitive objects with an average accuracy of about 44.6% (compared to naïve classification rate of 20%) over ten subjects (accuracy range of 36%–54%). The accuracy classification changes to 39.9% when both visual and verbal cues are used. The repeatability of the feature extraction and classification was checked by conducting the experiment on 10 different days with the same participants. This shows that the BCI holds promise in creating geometric shapes in CAD systems and could be used as a novel means of user interaction.     

Failure analysis of unidirectional polymeric matrix composites with two serial pin loaded-holes

The objective of this study is to investigate the effects of geometrical and physical parameters on failure modes and failure loads in unidirectional polymeric matrix composites with two serial pin loaded holes, analytically and experimentally. It is assumed that all of unidirectional fibers in the laminate lie in one direction while loaded by a load p ₀ at infinity, parallel to the direction of the fibers. To derive equilibrium equations based on a Shear-Lag theory, a rectangular arrangement of fibers is considered and with the proper use of boundary and boundness conditions, stress and displacement fields are computed within the laminate, along with the surrounding pinholes. Finally by using the Hashin criterion failure modes and failure loads are estimated. To validate analytical results based on shear-lag theory, an experimental program is carried out. A very good agreement is observed between two procedures. Based on results, in small sizes of two pins, the dominant failure mode is bearing and with the increasing of hole sizes, failure modes are changed to tension and shear modes.