Modeling and simulation of structural components in recursive closed-loop multibody systems

Passenger cars, transit buses, railroad vehicles, off-highway trucks, earth moving equipment and construction machinery contain structural and light-fabrications (SALF) components that are prone to excessive vibration due to rough terrains and work-cycle loads’ excitations. SALF components are typically modeled as flexible components in the multibody system allowing the analysts to predict elastic deformation and hence the stress levels under different loading conditions. Including SALF component in the multibody system typically generates closed-kinematic loops. This paper presents an approach for integrating SALF modeling capabilities as a flexible body in a general-purpose multibody dynamics solver that is based on joint-coordinates formulation with the ability to handle closed-kinematic loops. The spatial algebra notation is employed in deriving the spatial multibody dynamics equations of motion. The system kinematic topology matrix is used to project the Cartesian quantities into the joint subspace, leading to a condensed set of nonlinear equations with minimum number of generalized coordinates. The proposed flexible body formulation utilizes the component mode synthesis approach to reduce the large number of finite element degrees of freedom to a small set of generalized modal coordinates. The resulting reduced flexible body model has two main characteristics: the stiffness matrix is constant while the mass matrix depends on the elastic modal coordinates. A consistent set of pre-computed inertia shape integrals are identified and used to update the modal mass matrix at each time step. The implementation of the component mode synthesis approach in a closed-loop recursive multibody formulation is presented. The kinematic equations are modified to include the effect of the flexible body modal elastic coordinates. Also, modified constraint equations that include the effect of flexibility at the joint connections and the necessary details of the Jacobian matrix are presented. Baumgarte stabilization approach is used to stabilize the constraint equations without using iterative schemes. A sample results for flexible body impeded in a closed system will be presented to demonstrate the above mentioned approach.     

Efficient schemes for similarity-aware refinement of aggregation queries

Interactive data exploration platforms in Web, business and scientific domains are becoming increasingly popular. Typically, users without prior knowledge of data interact with these platforms in an exploratory manner hoping they might retrieve the results they are looking for. One way to explore large-volume data is by posing aggregate queries which group values of multiple rows by an aggregate operator to form a single value: an aggregated value. Though, when a query fails, i.e., returns undesired aggregated value, users will have to undertake a frustrating trial-and-error process to refine their queries, until a desired result is attained. This data exploration process, however, is growing rather difficult as the underlying data is typically of large-volume and high-dimensionality. While heuristic-based techniques are fairly successful in generating refined queries that meet specified requirements on the aggregated values, they are rather oblivious to the (dis)similarity between the input query and its corresponding refined version. Meanwhile, enforcing a similarity-aware query refinement is rather a non-trivial challenge, as it requires a careful examination of the query space while maintaining a low processing cost. To address this challenge, we propose an innovative scheme for efficient Similarity-Aware Refinement of Aggregation Queries called (EAGER) which aims to balance the tradeoff between satisfying the aggregate and similarity constraints imposed on the refined query to maximize its overall benefit to the user. To achieve that goal, EAGER implements efficient strategies to minimize the costs incurred in exploring the available search space by utilizing similarity-based and monotonic-based pruning techniques to bound the search space and quickly find a refined query that meets users’ expectations. Our extensive experiments show the scalability exhibited by EAGER under various workload settings, and the significant benefits it provides.     

Fusion of thermal infrared and visible spectra for robust moving object detection

The detection of moving objects is a crucial step for many video surveillance applications whether using a visible camera (VIS) or an infrared (IR) one. In order to profit from both types, several fusion methods were proposed in the literature: low-level fusion, medium-level fusion and high-level fusion. The first one is the most used for moving objects’ detection in IR and VIS spectra. In this paper, we present an overview of the different moving object detection methods in IR and VIS spectra and a state of the art of the low-level fusion techniques. Moreover, we propose a new method for moving object detection using low-level fusion of IR and VIS spectra. In order to evaluate quantitatively and qualitatively our proposed method, three series of experiments were carried out using two well-known datasets namely “OSU Color-Thermal Database” and “INO-Database”; the results of these evaluations show promising results and demonstrate the effectiveness of the proposed method.     

Help-Training for semi-supervised support vector machines

In this paper, we propose to reinforce the Self-Training strategy in semi-supervised mode by using a generative classifier that may help to train the main discriminative classifier to label the unlabeled data. We call this semi-supervised strategy Help-Training and apply it to training kernel machine classifiers as support vector machines (SVMs) and as least squares support vector machines. In addition, we propose a model selection strategy for semi-supervised training. Experimental results on both artificial and real problems demonstrate that Help-Training outperforms significantly the standard Self-Training. Moreover, compared to other semi-supervised methods developed for SVMs, our Help-Training strategy often gives the lowest error rate.     

Patterns for multigrid equidistributed functions: Application to general parabolas and length estimation

We show that for some special functions (called k-multigrid equidistributed functions), we can compute the limit of the frequency of patterns in the discretization of their graph, when the resolution tends to zero. This result is applied to parabolas. We deduce also that local length estimators almost never converge to the length for the parabolas.     

Multi-data source fusion

This paper describes a new approach of heterogeneous data source fusion. Data sources are either static or active: static data sources can be structured or semi-structured, whereas active sources are services. In order to develop data sources fusion systems in dynamic contexts, we need to study all issues raised by the matching paradigms. This challenging problem becomes crucial with the dominating role of the internet. Classical approaches of data integration, based on schemas mediation, are not suitable to the World Wide Web (WWW) environment where data is frequently modified or deleted. Therefore, we develop a loosely integrated approach that takes into consideration both conflict management and semantic rules which must be enriched in order to integrate new data sources. Moreover, we introduce an XML-based Multi-data source Fusion Language (MFL) that aims to define and retrieve conflicting data from multiple data sources. The system, which is developed according to this approach, is called MDSManager (Multi-Data Source Manager). The benefit of the proposed framework is shown through a real world application based on web data sources fusion which is dedicated to online markets indices tracking. Finally, we give an evaluation of our MFL language. The results show that our language improves significantly the XQuery language especially considering its expressiveness power and its performances.     

Constructing malware normalizers using term rewriting

A malware mutation engine is able to transform a malicious program to create a different version of the program. Such mutation engines are used at distribution sites or in self-propagating malware in order to create variation in the distributed programs. Program normalization is a way to remove variety introduced by mutation engines, and can thus simplify the problem of detecting variant strains. This paper introduces the “normalizer construction problem” (NCP), and formalizes a restricted form of the problem called “NCP=”, which assumes a model of the engine is already known in the form of a term rewriting system. It is shown that even this restricted version of the problem is undecidable. A procedure is provided that can, in certain cases, automatically solve NCP= from the model of the engine. This procedure is analyzed in conjunction with term rewriting theory to create a list of distinct classes of normalizer construction problems. These classes yield a list of possible attack vectors. Three strategies are defined for approximate solutions of NCP=, and an analysis is provided of the risks they entail. A case study using the virus suggests the approximations may be effective in practice for countering mutated malware. R. Mathur is presently at McAfee AVERT Labs.     

The utility of a virtual reality locomotion interface for studying gait behavior

OBJECTIVE: To investigate the effect of optic flow on gait behavior during treadmill walking using an immersive virtual reality (VR) setup and compare it with conventional treadmill walking (TW) and overground walking (OW). BACKGROUND: Previous research comparing TW with OW speculated that a lack of optic flow (relative visual movement between a walker and the environment) during TW may have led to perceptual cue conflicts, resulting in differences in gait behavior, as compared with OW. METHOD: Participants walked under three locomotion conditions (OW, TW, and TW with VR [TWVR]) under three walking constraint conditions (no constraint, a temporal/pacing constraint, and a spatial/path-following constraint). Presence questionnaires (PQs) were administered at the close of the TWVR trials. Trials were subjected to video analysis to determine spatiotemporal and kinematics variables used for comparison of locomotion conditions. RESULTS: ANOVA revealed gait behavior during TWVR to be between that of OW and TW. Speed and cadence during TWVR were significantly different from those of TW, whereas knee angle was comparable to that of OW. Correlation analysis of PQ scores with gait measures revealed a positive linear association of the distraction subfactor of the PQ with walking speed during TWVR, suggesting an increase in the sense of presence in the virtual environment led to increases in walking speed. CONCLUSION: The results demonstrate that providing optic flow during TW through VR has an impact on gait behavior. APPLICATION: This study provides a basis for developing simple VR locomotion interface setups for gait research.     

Convex Drawings of 3-Connected Plane Graphs

We use Schnyder woods of 3-connected planar graphs to produce convex straight-line drawings on a grid of size The parameter depends on the Schnyder wood used for the drawing. This parameter is in the range The algorithm is a refinement of the face-counting algorithm; thus, in particular, the size of the grid is at most The above bound on the grid size simultaneously matches or improves all previously known bounds for convex drawings, in particular Schnyder's and the recent Zhang and He bound for triangulations and the Chrobak and Kant bound for 3-connected planar graphs. The algorithm takes linear time. The drawing algorithm has been implemented and tested. The expected grid size for the drawing of a random triangulation is close to For a random 3-connected plane graph, tests show that the expected size of the drawing is