Finite element analysis on implicitly defined domains: An accurate representation based on arbitrary parametric surfaces

In this paper, we present some novel results and ideas for robust and accurate implicit representation of geometric surfaces in finite element analysis. The novel contributions of this paper are threefold: (1) describe and validate a method to represent arbitrary parametric surfaces implicitly; (2) represent arbitrary solids implicitly, including sharp features using level sets and boolean operations; (3) impose arbitrary Dirichlet and Neumann boundary conditions on the resulting implicitly defined boundaries. The methods proposed do not require local refinement of the finite element mesh in regions of high curvature, ensure the independence of the domain’s volume on the mesh, do not rely on boundary regularization, and are well suited to methods based on fixed grids such as the extended finite element method (XFEM). Numerical examples are presented to demonstrate the robustness and effectiveness of the proposed approach and show that it is possible to achieve optimal convergence rates using a fully implicit representation of object boundaries. This approach is one step in the desired direction of tying numerical simulations to computer aided design (CAD), similarly to the isogeometric analysis paradigm.     

SimClus: an effective algorithm for clustering with a lower bound on similarity

Clustering algorithms generally accept a parameter k from the user, which determines the number of clusters sought. However, in many application domains, like document categorization, social network clustering, and frequent pattern summarization, the proper value of k is difficult to guess. An alternative clustering formulation that does not require k is to impose a lower bound on the similarity between an object and its corresponding cluster representative. Such a formulation chooses exactly one representative for every cluster and minimizes the representative count. It has many additional benefits. For instance, it supports overlapping clusters in a natural way. Moreover, for every cluster, it selects a representative object, which can be effectively used in summarization or semi-supervised classification task. In this work, we propose an algorithm, SimClus, for clustering with lower bound on similarity. It achieves a O(log n) approximation bound on the number of clusters, whereas for the best previous algorithm the bound can be as poor as O(n). Experiments on real and synthetic data sets show that our algorithm produces more than 40% fewer representative objects, yet offers the same or better clustering quality. We also propose a dynamic variant of the algorithm, which can be effectively used in an on-line setting.     

A generalized least absolute deviation method for parameter estimation of autoregressive signals

This paper proposes a generalized least absolute deviation (GLAD) method for parameter estimation of autoregressive (AR) signals under non-Gaussian noise environments. The proposed GLAD method can improve the accuracy of the estimation of the conventional least absolute deviation (LAD) method by minimizing a new cost function with parameter variables and noise error variables. Compared with second- and high-order statistical methods, the proposed GLAD method can obtain robustly an optimal AR parameter estimation without requiring the measurement noise to be Gaussian. Moreover, the proposed GLAD method can be implemented by a cooperative neural network (NN) which is shown to converge globally to the optimal AR parameter estimation within a finite time. Simulation results show that the proposed GLAD method can obtain more accurate estimates than several well-known estimation methods in the presence of different noise distributions.     

Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach

In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, face recognition grand challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality     

Reinforcement Learning Based Algorithms for Average Cost Markov Decision Processes

This article proposes several two-timescale simulation-based actor-critic algorithms for solution of infinite horizon Markov Decision Processes with finite state-space under the average cost criterion. Two of the algorithms are for the compact (non-discrete) action setting while the rest are for finite-action spaces. On the slower timescale, all the algorithms perform a gradient search over corresponding policy spaces using two different Simultaneous Perturbation Stochastic Approximation (SPSA) gradient estimates. On the faster timescale, the differential cost function corresponding to a given stationary policy is updated and an additional averaging is performed for enhanced performance. A proof of convergence to a locally optimal policy is presented. Next, we discuss a memory efficient implementation that uses a feature-based representation of the state-space and performs TD(0) learning along the faster timescale. The TD(0) algorithm does not follow an on-line sampling of states but is observed to do well on our setting. Numerical experiments on a problem of rate based flow control are presented using the proposed algorithms. We consider here the model of a single bottleneck node in the continuous time queueing framework. We show performance comparisons of our algorithms with the two-timescale actor-critic algorithms of Konda and Borkar (1999) and Bhatnagar and Kumar (2004). Our algorithms exhibit more than an order of magnitude better performance over those of Konda and Borkar (1999).

Metal-on-metal hip simulator study of increased wear particle surface area due to 'severe' patient activity

This study investigated changes in metal-on-metal (MOM) hip wear and wear particle characteristics arising from a more aggressive patient activity level compared with normal walking. The test hypothesis was that 'severe'-gait conditions will change wear, wear particle sizes, and morphology owing to a decline in joint lubrication. Four carbon MOM hip bearings 40 mm high were subjected to normal-walking and fast-jogging simulations in an orbital hip joint simulator with 25 per cent alpha-calf serum as a lubricant. Co-Cr-Mo wear particles were extracted using an enzymatic method, and prolate ellipsoid equations were used to estimate particle volume and surface area. Fast-jogging simulations generated a sevenfold increase in volumetric wear, a 33 per cent increase in mean wear particle size, and a threefold increase in the number of larger (needle) particles compared with walking. This resulted in a twentyfold increase in total wear particle surface area per 10(6) cycles compared with walking, thereby confirming our hypothesis. The clinical significance of this result suggests that highly active MOM patients may exhibit greater ion release than less active patients.     

Continuous relaxation and local maxima selection: conditions for equivalence

Relaxation labeling processes are a class of iterative algorithms for using contextual information to reduce local ambiguities. This paper introduces a new perspective toward relaxation-that of considering it as a process for reordering labels attached to nodes in a graph. This new perspective is used to establish the formal equivalence between relaxation and another widely used algorithm, local maxima selection. The equivalence specifies conditions under which a family of cooperative relaxation algorithms, which generalize the well-known ones, decompose into purely local ones. Since these conditions are also sufficient for guaranteeing the convergence of relaxation processes, they serve as stopping criteria. We feel that equivalences such as these are necessary for the proper application of relaxation and maxima selection in complex speech and vision understanding systems.     

Bathymetric study of Lake Hayq,Ethiopia

The generation of scientific information for improved understating of the physical dynamics of a lake is fundamental for guiding lake stakeholders and managers at the local level to implement best management practices and help design effective management strategies and policies at higher levels. Multitemporal bathymetric information on lakes is very important in hydrology and sediment studies to more clearly indicate environmental changes and to understand the effects of land processes on the hydrology of lakes. Accordingly, the purpose of this study was to map bathymetric charts of Lake Hayq ¹ in Ethiopia and to derive morphometric parameters, including depth, volume, area, width and length, and to plot curves illustrating the relationships between these parameters. The bathymetric survey was carried out using a combination of a SonarLite Portable Echo Sounder and Global Positioning System (GPS) to generate three‐dimensional (XYZ) hydrographic data. Surfer 8.01 and ArcGIS 9.3 software program were used for surface, gridding and morphometric analyses. Comparison of the results of this study with a previous study conducted in 1941 indicated the lake has experienced changes in depth and surface area. To reduce the negative impacts of human‐induced activities on the ecohydrology of the lake, and to maintain its ecological integrity, appropriate and integrated lake management practices must be adopted. This will necessitate policy formulation, active lake basin stakeholder involvement and implementation of basin‐wide lake management to ensure sustainable use of the lake and its basin resources.     

Removal of mercaptans from a gas stream using continuous adsorption-regeneration

The removal of the mercaptan, 1-methyl-1-propanethiol, from aqueous solutions using a non-porous, electrically conducting carbon-based adsorbent (Nyex 1000) was investigated. The adsorption process was found to be rapid (equilibrium capacity achieved within 5 minutes) with low adsorptive capacity (of the order of 0.4 mg g(-1)) when compared with activated carbon. Electrochemical regeneration of the Nyex 1000 in a simple divided electrochemical cell within a sequential batch treatment unit restored 100% of the adsorbent's adsorptive capacity using treatment times as low as 20 minutes by passing a current of 0.5 A. The sorptive characteristics of a Nyex-water slurry were also modelled and investigated both in a bubble column and in a continuous adsorption-regeneration treatment system. It was demonstrated that the continuous removal-destruction system could achieve a step reduction in challenge gas concentration of approximately 75% for a period of 35 minutes with a current of 5 Amps. This was attributed to mass transfer enhanced by a combination of adsorption and chemical reaction with free chlorine species generated in the electrochemical process.