A Structural Approach for Modelling Performance of Systems Using Skeletons

AbstractIn this paper, we discuss a structural approach to automatic performance modelling of skeleton based applications. This uses a synthesis of performance evaluation process algebra (pepa) and a pattern-oriented hierarchical expression scheme. Such approaches are important in parallel and distributed systems where the performance models must be updated regularly based on the current state of the resources

Microstructural abnormalities in deep and superficial white matter in youths with mild traumatic brain injury

BACKGROUND: Diffusion Tensor Imaging (DTI) studies of traumatic brain injury (TBI) have focused on alterations in microstructural features of deep white matter fibers (DWM), though post-mortem studies have demonstrated that injured axons are often observed at the gray-white matter interface where superficial white matter fibers (SWM) mediate local connectivity. OBJECTIVES: To examine microstructural alterations in SWM and DWM in youths with a history of mild TBI and examine the relationship between white matter alterations and attention. METHODS: Using DTIDWM fractional anisotropy (FA) and SWM FA in youths with mild TBI (TBI, n=63) were compared to typically developing and psychopathology matched control groups (n=63 each). Following tract-based spatial statistics, SWM FA was assessed by applying a probabilistic tractography derived SWM mask, and DWM FA was captured with a white matter fiber tract mask. Voxel-wise z-score calculations were used to derive a count of voxels with abnormally high and low FA for each participant. Analyses examined DWM and SWM FA differences between TBI and control groups, the relationship between attention and DWM and SWM FA and the relative susceptibility of SWM compared to DWM FA to alterations associated with mild TBI. RESULTS: Case-based comparisons revealed more voxels with low FA and fewer voxels with high FA in SWM in youths with mild TBI compared to both control groups. Equivalent comparisons in DWM revealed a similar pattern of results, however, no group differences for low FA in DWM were found between mild TBI and the control group with matched psychopathology. Slower processing speed on the attention task was correlated with the number of voxels with low FA in SWM in youths with mild TBI. CONCLUSIONS: Within a sample of youths with a history of mild TBI, this study identified abnormalities in SWM microstructure associated with processing speed. The majority of DTI studies of TBI have focused on long-range DWM fiber tracts, often overlooking the SWM fiber type

Integrated product relationships management : a model to enable concurrent product design and assembly sequence planning

The paper describes a novel approach to product relationships management in the context of concurrent engineering and product lifecycle management (PLM). Current industrial practices in product data management and manufacturing process management systems require better efficiency, flexibility, and sensitivity in managing product information at various levels of abstraction throughout its lifecycle. The aim of the proposed work is to manage vital yet complex and inherent product relationship information to enable concurrent product design and assembly sequence planning. Indeed, the definition of the product with its assembly sequence requires the management and the understanding of the numerous product relationships, ensuring consistency between the product and its components. This main objective stresses the relational design paradigm by focusing on product relationships along its lifecycle. This paper gives the detailed description of the background and models which highlight the need for a more efficient PLM approach. The proposed theoretical approach is then described in detail. A separate paper will focus on the implementation of the proposed approach in a PLM-based application, and an in-depth case study to evaluate the implementation of the novel approach will also be given

IMPLEMENTATION AND UNIFORM MANAGEMENT OF MODELLING ENTITIES IN A MASSIVELY FEATURE-OBJECT ORIENTED ADVANCED CAD ENVIRONMENT

Today we are spectators of the transition process in computer aided design from traditional geometry based on design systems to advanced computer-based engineering systems. The key is the feature technology that allows both integrating and managing modelling entities in a coherent way. Feature technology is developing rapidly. New research topics and contexts are emerging from time to time. This paper introduces concept, design and technological feature-objects to support operational, structural and morphological modelling of mechanical products. First, the feature-centred approaches to conceptual design are summarized and evaluated. Then an implementation of concept feature-objects and the methodology for using them is presented. The strength of concept feature-objects is in their morphology inclusive nature. They appear as parametrized three-dimensional skeletons providing geometrical representations for the modelled engineering conceptions. A concept feature-object models the physical ports, contact surfaces related to ports, bones between ports, DOF of ports, relevant physical parameters, scientific and empirical descriptions of intentional transformations and environmental effects. Concept feature-objects are related to design feature-objects that, in turn, are constructed of a relevant set of technological feature-entities. Concept feature-objects refer to the configurable and parametrized design feature-objects through an indexing mechanism. The conceptions have been tested during the programming and further development of the authors' PRODES system

Unsupervised Learning of Complex Articulated Kinematic Structures combining Motion and Skeleton Information

In this paper we present a novel framework for unsupervised kinematic structure learning of complex articulated objects from a single-view image sequence. In contrast to prior motion information based methods, which estimate relatively simple articulations, our method can generate arbitrarily complex kinematic structures with skeletal topology by a successive iterative merge process. The iterative merge process is guided by a skeleton distance function which is generated from a novel object boundary generation method from sparse points. Our main contributions can be summarised as follows: (i) Unsupervised complex articulated kinematic structure learning by combining motion and skeleton information. (ii) Iterative fine-to-coarse merging strategy for adaptive motion segmentation and structure smoothing. (iii) Skeleton estimation from sparse feature points. (iv) A new highly articulated object dataset containing multi-stage complexity with ground truth. Our experiments show that the proposed method out-performs state-of-the-art methods both quantitatively and qualitatively