Soft computing-based approaches to predict energy consumption and stability margin of six-legged robots moving on gradient terrains

Soft computing-based approaches have been developed to predict specific energy consumption and stability margin of a six-legged robot ascending and descending some gradient terrains. Three different neuro-fuzzy and one neural network-based approaches have been developed. The performances of these approaches are compared among themselves, through computer simulations. Genetic algorithm-tuned multiple adaptive neuro-fuzzy inference system is found to perform better than other three approaches for predicting both the outputs. This could be due to a more exhaustive search carried out by the genetic algorithm in comparison with back-propagation algorithm and the use of two separate adaptive neuro-fuzzy inference systems for two different outputs. A designer may use the developed soft computing-based approaches in order to predict specific energy consumption and stability margin of the robot for a set of input parameters, beforehand.     

Effects of turning gait parameters on energy consumption and stability of a six-legged walking robot

Minimization of energy consumption plays a key role in the locomotion of a multi-legged robot used for various purposes. Turning gaits are the most general and important factors for omni-directional walking of a six-legged robot. This paper presents an analysis on energy consumption of a six-legged robot during its turning motion over a flat terrain. An energy consumption model is developed for statically stable wave gaits in order to minimize dissipating energy for optimal feet forces distributions. The effects of gait parameters, namely angular velocity, angular stroke and duty factors are studied on energy consumption, as the six-legged robot walks along a circular path of constant radius with wave gait. The variations of average power consumption and energy consumption per unit weight per unit traveled length with the angular velocity and angular stroke are compared for the turning gaits of a robot with four different duty factors. Computer simulations show that wave gait with a low duty factor is more energy-efficient compared to that with a high duty factor at the highest possible angular velocity. A stability analysis based on normalized energy stability margin is performed for turning motion of the robot with four duty factors for different angular strokes.     

SMARTINT: using mined attribute dependencies to integrate fragmented web databases

Many web databases can be seen as providing partial and overlapping information about entities in the world. To answer queries effectively, we need to integrate the information about the individual entities that are fragmented over multiple sources. At first blush this is just the inverse of traditional database normalization problem—rather than go from a universal relation to normalized tables, we want to reconstruct the universal relation given the tables (sources). The standard way of reconstructing the entities will involve joining the tables. Unfortunately, because of the autonomous and decentralized way in which the sources are populated, they often do not have Primary Key–Foreign Key relations. While tables may share attributes, naive joins over these shared attributes can result in reconstruction of many spurious entities thus seriously compromising precision. Our system, SmartInt is aimed at addressing the problem of data integration in such scenarios. Given a query, our system uses the Approximate Functional Dependencies (AFDs) to piece together a tree of relevant tables to answer it. The result tuples produced by our system are able to strike a favorable balance between precision and recall.     

Analytic Tangent Irradiance Environment Maps for Anisotropic Surfaces

Environment‐mapped rendering of Lambertian isotropic surfaces is common, and a popular technique is to use a quadratic spherical harmonic expansion. This compact irradiance map representation is widely adopted in interactive applications like video games. However, many materials are anisotropic, and shading is determined by the local tangent direction, rather than the surface normal. Even for visualization and illustration, it is increasingly common to define a tangent vector field, and use anisotropic shading. In this paper, we extend spherical harmonic irradiance maps to anisotropic surfaces, replacing Lambertian reflectance with the diffuse term of the popular Kajiya‐Kay model. We show that there is a direct analogy, with the surface normal replaced by the tangent. Our main contribution is an analytic formula for the diffuse Kajiya‐Kay BRDF in terms of spherical harmonics; this derivation is more complicated than for the standard diffuse lobe. We show that the terms decay even more rapidly than for Lambertian reflectance, going as l^–3, where l is the spherical harmonic order, and with only 6 terms (l = 0 and l = 2) capturing 99.8% of the energy. Existing code for irradiance environment maps can be trivially adapted for real‐time rendering with tangent irradiance maps. We also demonstrate an application to offline rendering of the diffuse component of fibers, using our formula as a control variate for Monte Carlo sampling.     

Automatic extraction of free-form surface features (FFSFs)

This paper presents a new algorithm for extracting Free-Form Surface Features (FFSFs) from a surface model. The extraction algorithm is based on a modified taxonomy of FFSFs from that proposed in the literature. A new classification scheme has been proposed for FFSFs to enable their representation and extraction. The paper proposes a separating curve as a signature of FFSFs in a surface model. FFSFs are classified based on the characteristics of the separating curve (number and type) and the influence region (the region enclosed by the separating curve). A method to extract these entities is presented. The algorithm has been implemented and tested for various free-form surface features on different types of free-form surfaces (base surfaces) and is found to correctly identify and represent the features irrespective of the type of underlying surface. The representation and extraction algorithm are both based on topology and geometry. The algorithm is data-driven and does not use any pre-defined templates. The definition presented for a feature is unambiguous and application independent. The proposed classification of FFSFs can be used to develop an ontology to determine semantic equivalences for the feature to be exchanged, mapped and used across PLM applications.     

Artificial Neural Network Model for Predicting Stable and Unstable Regions in Cu-Zn Alloys

Processing maps are developed using the Dynamic Materials Model (DMM) and instability criterion, which help in choosing optimum process parameters for hot-working of materials. Certain high-level expertise is required to interpret and extract the information on instability regimes to be avoided during processing. In recent years, Artificial Neural Network (ANN) models have been developed to predict flow stress by using the input vector; namely, temperature, strain rate and strain. In this study, using the available Cu-Zn alloy data, ANN model has been developed to classify the hot-working process parameters, such as temperature, strain rate and flow stress for instability regime, directly from the corrected flow stress data without applying the DMM. This model uses 10 compositions of Cu-Zn system, ranging from 3% Zn to 51% Zn. The developed ANN model has been able to learn the nonlinear classifier, which separates unstable region from the stable region in the Cu-Zn alloy system with zinc content less than 40%.     

Sex Differences and Individual Differences in Cognitive Performance and Their Relationship to Endogenous Gonadal Hormones and Gonadotropins.

Sexually dimorphic cognitive performance in men (n=42) and women (n=42) was related to testosterone, estradiol, progesterone, luteinizing hormone, follicle-stimulating hormone, and sex hormone binding globulin, measured in 10-ml blood samples collected between 0900 and 1030 and, among women, during the follicular phase of the menstrual cycle. Significant sex differences favored men on spatial tasks (Mental Rotation and Judgment of Line Orientation) and on an inhibition task and favored women on a verbal task (category fluency). However, there were no significant relationships between any of the hormones and cognitive performance, suggesting that there are few, if any, consistent, substantial relationships between endogenous, nonfluctuating levels of gonadal hormones or gonadotropins and these cognitive abilities in men or women. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Designing communication strategies for heterogeneous parallel systems

This paper explores the suitability of the emerging passive star-coupled optical interconnection using wavelength division multiplexing as the system interconnect to provide high bandwidth (Gbits/sec) communication demanded by heterogeneous systems. Several different communication strategies (combinations of communication topologies and protocols) are investigated under a representative master-slave computational model. The interplay between system speed, network speed, task granularity, and degree of parallelism is studied using both analytical modeling and simulations. It is shown that a hierarchical ALOHA-based communication strategy between the master and the slaves, implemented on top of the passive star-coupled network, leads to a considerable reduction in channel contention and provides 50–80% reduction in task completion time for applications with medium to high degrees of coarse grain parallelism. Comparable reduction in channel contention is also shown to be achieved by using tunable acoustooptic filters at master nodes.