Statistical approach and benchmarking for modeling of multi-dimensional behavior in TRISO-coated fuel particles

The fundamental design for a gas-cooled reactor relies on the behavior of the coated particle fuel. The coating layers, termed the TRISO coating, act as a mini-pressure vessel that retains fission products. Results of US irradiation experiments show that many more fuel particles have failed than can be attributed to one-dimensional pressure vessel failures alone. Post-irradiation examinations indicate that multi-dimensional effects, such as the presence of irradiation-induced shrinkage cracks in the inner pyrolytic carbon layer, contribute to these failures. To address these effects, the methods of prior one-dimensional models are expanded to capture the stress intensification associated with multi-dimensional behavior. An approximation of the stress levels enables the treatment of statistical variations in numerous design parameters and Monte Carlo sampling over a large number of particles. The approach is shown to make reasonable predictions when used to calculate failure probabilities for irradiation experiments of the New Production - Modular High Temperature Gas Cooled Reactor Program.     

Port Placement Planning in Robot-Assisted Coronary Artery Bypass

Properly selected port sites for robot-assisted coronary artery bypass graft (CABG) improve the efficiency and quality of these procedures. In clinical practice, surgeons select port locations using external anatomic landmarks to estimate a patient's internal anatomy. This paper proposes an automated approach to port selection based on a preoperative image of the patient, thus avoiding the need to estimate internal anatomy. Using this image as input, port sites are chosen from a grid of surgeon-approved options by defining a performance measure for each possible port triad. This measure seeks to minimize the weighted squared deviation of the instrument and endoscope angles from their optimal orientations at each internal surgical site. This performance measure proves insensitive to perturbations in both its weighting factors and moderate intraoperative displacements of the patient's internal anatomy. A validation study of this port site selection was performed. cardiac algorithm also Six surgeons dissected model vessels using the port triad selected by this algorithm with performance compared to dissection using a surgeon-selected port triad and a port triad template described by Tabaie et al., 1999. With the algorithm-selected ports, dissection speed increased by up to 43% (p = 0.046) with less overall vessel trauma. Thus, this algorithmic approach to port site selection has important clinical implications for robot-assisted CABG which warrant further investigation.     

Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data

Siberia's boreal forests represent an economically and ecologically precious resource, a significant part of which is not monitored on a regular basis. Synthetic aperture radars (SARs), with their sensitivity to forest biomass, offer mapping capabilities that could provide valuable up-to-date information, for example about fire damage or logging activity. The European Commission SIBERIA project had the aim of mapping an area of approximately 1 million km² in Siberia using SAR data from two satellite sources: the tandem mission of the European Remote Sensing Satellites ERS-1/2 and the Japanese Earth Resource Satellite JERS-1. Mosaics of ERS tandem interferometric coherence and JERS backscattering coefficient show the wealth of information contained in these data but they also show large differences in radar response between neighbouring images. To create one homogeneous forest map, adaptive methods which are able to account for brightness changes due to environmental effects were required. In this paper an adaptive empirical model to determine growing stock volume classes using the ERS tandem coherence and the JERS backscatter data is described. For growing stock volume classes up to 80 m³/ha, accuracies of over 80% are achieved for over a hundred ERS frames at a spatial resolution of 50 m.     

Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation

The Moderate Resolution Imaging Radiometer (MODIS) is the primary instrument in the NASA Earth Observing System for monitoring the seasonality of global terrestrial vegetation. Estimates of 8-day mean daily gross primary production (GPP) at the 1 km spatial resolution are now operationally produced by the MODIS Land Science Team for the global terrestrial surface using a production efficiency approach. In this study, the 2001 MODIS GPP product was compared with scaled GPP estimates (25 km²) based on ground measurements at two forested sites. The ground-based GPP scaling approach relied on a carbon cycle process model run in a spatially distributed mode. Land cover classification and maximum annual leaf area index, as derived from Landsat ETM+ imagery, were used in model initiation. The model was driven by daily meteorological observations from an eddy covariance flux tower situated at the center of each site. Model simulated GPPs were corroborated with daily GPP estimates from the flux tower. At the hardwood forest site, the MODIS GPP phenology started earlier than was indicated by the scaled GPP, and the summertime GPP from MODIS was generally lower than the scaled GPP values. The fall-off in production at the end of the growing season was similar to the validation data. At the boreal forest site, the GPP phenologies generally agreed because both responded to the strong signal associated with minimum temperature. The midsummer MODIS GPP there was generally higher than the ground-based GPP. The differences between the MODIS GPP products and the ground-based GPPs were driven by differences in the timing of FPAR and the magnitude of light use efficiency as well as by differences in other inputs to the MODIS GPP algorithm—daily incident PAR, minimum temperature, and vapor pressure deficit. Ground-based scaling of GPP has the potential to improve the parameterization of light use efficiency in satellite-based GPP monitoring algorithms.     

Self-synchronizing watermarking scheme for an arbitrarily shaped object

To protect the copyright of an arbitrarily shaped object, an oblivious and robust watermarking scheme is proposed. Different from other methods, an inertia ellipse is constructed to estimate the state of attack channel, and barycenter matching is employed to achieve fast self-synchronizing watermark detection. Watermark verification can be performed without the original image. Experimental results prove the robustness of our scheme against object manipulations, such as rotation, translation, scaling, etc.     

Modular Reconfigurable Robots in Space Applications

Robots used for tasks in space have strict requirements. Modular reconfigurable robots have a variety of attributes that are well suited to these conditions, including: serving as many different tools at once (saving weight), packing into compressed forms (saving space) and having high levels of redundancy (increasing robustness). In addition, self-reconfigurable systems can self-repair and adapt to changing or unanticipated conditions. This paper will describe such a self-reconfigurable modular robot: PolyBot. PolyBot has significant potential in the space manipulation and surface mobility class of applications for space.     

A flight control system for aerial robots: algorithms and experiments

This paper presents a hierarchical flight control system for unmanned aerial vehicles. The proposed system executes high-level mission objectives by progressively substantiating them into machine-level commands. The acquired information from various sensors is propagated back to the higher layers for reactive decision making. Each vehicle is connected via standardized wireless communication protocol for scalable multi-agent coordination. The proposed system has been successfully implemented on a number of small helicopters and validated in various applications. Results from waypoint navigation, a probabilistic pursuit-evasion game and vision-based target tracking demonstrate the potential of the proposed approach toward intelligent flying robots.     

A Generative Probabilistic Oriented Wavelet Model for Texture Segmentation

This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album [1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (i) sea surface pollution detection from intensity images and (ii) image segmentation of the still images with varying illumination across the scene.     

A Simulation Framework for Heterogeneous Agents

We introduce a generic simulation framework suitable for agent-based simulations featuring the support of heterogeneous agents, hierarchical scheduling, and flexible specification of design parameters. One key aspect of this framework is the design specification: we use a format based on the Extendible Markup Language (XML) that is simple-structured yet still enables the design of flexible models. Another issue in agent-based simulations, especially when ready-made components are used, is the heterogeneity arising from both the agents' implementations and the underlying platforms. To tackle such obstacles, we introduce a wrapper technique for mapping the functionality of agents living in an interpreter-based environment to a standardized JAVA interface, thus facilitating the task for any control mechanism (like a simulation manager) because it has to handle only one set of commands for all agents involved. Again, this mapping is made by an XML-based definition format. We demonstrate the technique by applying it to a simple sample simulation of two mass marketing firms operating in an artificial consumer environment.     

Covariant-Conics Decomposition of Quartics for 2D Shape Recognition and Alignment

This paper outlines a new geometric parameterization of 2D curves where parameterization is in terms of geometric invariants and parameters that determine intrinsic coordinate systems. This new approach handles two fundamental problems: single-computation alignment, and recognition of 2D shapes under Euclidean or affine transformations. The approach is model-based: every shape is first fitted by a quartic represented by a fourth degree 2D polynomial. Based on the decomposition of this equation into three covariant conics, we are able, in both the Euclidean and the affine cases, to define a unique intrinsic coordinate system for non-singular bounded quartics that incorporates usable alignment information contained in the polynomial representation, a complete set of geometric invariants, and thus an associated canonical form for a quartic. This representation permits shape recognition based on 11 Euclidean invariants, or 8 affine invariants. This is illustrated in experiments with real data sets.