Direction-Dependent Learning Approach for Radial Basis Function Networks

Direction-dependent scaling, shaping, and rotation of Gaussian basis functions are introduced for maximal trend sensing with minimal parameter representations for input output approximation. It is shown that shaping and rotation of the radial basis functions helps in reducing the total number of function units required to approximate any given input-output data, while improving accuracy. Several alternate formulations that enforce minimal parameterization of the most general radial basis functions are presented. A novel "directed graph" based algorithm is introduced to facilitate intelligent direction based learning and adaptation of the parameters appearing in the radial basis function network. Further, a parameter estimation algorithm is incorporated to establish starting estimates for the model parameters using multiple windows of the input-output data. The efficacy of direction-dependent shaping and rotation in function approximation is evaluated by modifying the minimal resource allocating network and considering different test examples. The examples are drawn from recent literature to benchmark the new algorithm versus existing methods     

Endothelial selectins and vascular cell adhesion molecule-1 promote hematopoietic progenitor homing to bone marrow

The adhesive mechanisms allowing hematopoietic progenitor cells (HPC) homing to the bone marrow (BM) after BM transplantation are poorly understood. We investigated the role of endothelial selectins and vascular cell adhesion molecule-1 (VCAM-1) in this process. Lethally irradiated recipient mice deficient in both P-and E-selectins (P/E-/-), reconstituted with minimal numbers (相似文献   

Mechanical properties of biorenewable fiber/plastic composites

Plastic fiber composites, consisting of polypropylene (PP) or polyethylene (PE), and pinewood, big blue stem (BBS), soybean hulls, or distillers dried grain and solubles (DDGS), were prepared by extrusion. Young's modulus, tensile and flexural strengths, melt flow, shrinkage, and impact energy, with respect to the type, amount, and size of fiber on composites, were evaluated. Young's moduli under tensile load of wood, BBS, and soybean‐hull fiber composites, compared with those of pure plastic controls, were either comparable or higher. Tensile strength significantly decreased for all the PP/fiber composites when compared with that of the control. Strength of BBS fiber composites was higher than or comparable to that of wood. When natural fibers were added there was a significant decrease in the melt flow index for both plastic/fiber composites. There was no significant difference in the shrinkage of all fiber/plastic composites compared to that of controls. BBS/PE plastic composites resulted in higher notched impact strength than that of wood or soybean‐hull fiber composites. There was significant reduction in the unnotched impact strength compared to that of controls. BBS has the potential to be used as reinforcing materials for low‐cost composites. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 93: 2484–2493, 2004     

Preparation and surface- active properties of polyoxyethylene-glycol (600) monoesters of fatty acids

Polyoxyethyleneglycol (PEG-600) monoesters of undecylenic, lauric, myristic, palmitic, stearic, oleic, ricinoleic and 12-hydroxystearic acids were prepared in 80-85% yield by reacting PEG with boric acid, esterifying the resultant borate with fatty acid, and selectively hydrolyzing the borate ester; their surface active properties were evaluated. Increase in acyl chain length increased the surface tension. The presence of a double bond or hydroxyl group in C₁₈ series improved the wetting and emulsifying proper-ties. Unsaturation in the fatty acid chain reduced the foaming power.     

The impact of multi‐group multi‐layer network structure on the performance of distributed consensus building strategies

We consider a structural approach to the consensus building problem in multi‐group multi‐layer (MGML) distributed sensor networks (DSNs) common in many natural and engineering applications. From among the possible network structures, we focus on bipartite graph structure as it represents a typical MGML structure and has a wide applicability in the real world. We establish exact conditions for consensus and derive a precise relationship between the consensus value and the degree distribution of nodes in a bipartite MGML DSN. We also demonstrate that for subclasses of connectivity patterns, convergence time and simple characteristics of network topology can be captured by explicit algebra. Direct inference of the convergence behavior of consensus strategies from MGML DSN structure is the main contribution of this paper. The insights gained from our analysis facilitate the design and development of large‐scale DSNs that meet specific performance criteria. Copyright © 2012 John Wiley & Sons, Ltd.     

Sequential Monte Carlo in reachability heuristics for probabilistic planning

Some of the current best conformant probabilistic planners focus on finding a fixed length plan with maximal probability. While these approaches can find optimal solutions, they often do not scale for large problems or plan lengths. As has been shown in classical planning, heuristic search outperforms bounded length search (especially when an appropriate plan length is not given a priori). The problem with applying heuristic search in probabilistic planning is that effective heuristics are as yet lacking.In this work, we apply heuristic search to conformant probabilistic planning by adapting planning graph heuristics developed for non-deterministic planning. We evaluate a straight-forward application of these planning graph techniques, which amounts to exactly computing a distribution over many relaxed planning graphs (one planning graph for each joint outcome of uncertain actions at each time step). Computing this distribution is costly, so we apply Sequential Monte Carlo (SMC) to approximate it. One important issue that we explore in this work is how to automatically determine the number of samples required for effective heuristic computation. We empirically demonstrate on several domains how our efficient, but sometimes suboptimal, approach enables our planner to solve much larger problems than an existing optimal bounded length probabilistic planner and still find reasonable quality solutions.     

H-Infinity Static Output-feedback Control for Rotorcraft

The problem of stabilization of an autonomous rotorcraft platform in a hover configuration subject to external disturbances is addressed. Necessary and sufficient conditions are presented for static output-feedback control of linear time-invariant systems using the H-Infinity approach. Simplified conditions are given which only require the solution of two coupled matrix design equations. This paper also proposes a numerically efficient solution algorithm for the coupled design equations to determine the output-feedback gain. A major contribution is that an initial stabilizing gain is not needed. The efficacy of the control law and the disturbance accommodation properties are shown on a rotorcraft design example. The helicopter dynamics do not decouple as in the fixed-wing aircraft case, so that the design of helicopter flight controllers with a desirable intuitive structure is not straightforward. In this paper an output feedback approach is given that allows one to selectively close prescribed multivariable feedback loops using a reduced set of the states. Shaping filters are added that improve performance and yield guaranteed robustness and speed of response. This gives direct control over the design procedure and performance. Accurate identification of the System parameters is a challenging task for rotorcraft control, addition of loop shaping facilitates implementation engineers to counteract unmodeled high frequency dynamics. The net result yields control structures that have been historically accepted in the flight control community.