Physics of Artificial Gravity

This chapter discusses potential technologies for achieving artificial gravity in a space vehicle. We begin with a series of definitions and a general description of the rotational dynamics behind the forces ultimately exerted on the human body during centrifugation, such as gravity level, gravity gradient, and Coriolis force. Human factors considerations and comfort limits associated with a rotating environment are then discussed. Finally, engineering options for designing space vehicles with artificial gravity are presented

Recommended Research on Artificial Gravity

Based on the summaries presented in the above sections of what is still to be learned on the effects of artificial gravity on human functions, this chapter will discuss the short- and long-term steps of research required to understand fundamentals and to validate operational aspects of using artificial gravity as an effective countermeasure for long-duration space travel

Tilt and Translation Motion Perception during Off Vertical Axis Rotation

The effect of stimulus frequency on tilt and translation motion perception was studied during constant velocity off-vertical axis rotation (OVAR), and compared to the effect of stimulus frequency on eye movements. Fourteen healthy subjects were rotated in darkness about their longitudinal axis 10deg and 20deg off-vertical at 0.125 Hz, and 20deg offvertical at 0.5 Hz. Oculomotor responses were recorded using videography, and perceived motion was evaluated using verbal reports and a joystick with four degrees of freedom (pitch and roll tilt, mediallateral and anteriorposterior translation). During the lower frequency OVAR, subjects reported the perception of progressing along the edge of a cone. During higher frequency OVAR, subjects reported the perception of progressing along the edge of an upright cylinder. The modulation of both tilt recorded from the joystick and ocular torsion significantly increased as the tilt angle increased from 10deg to 20deg at 0.125 Hz, and then decreased at 0.5 Hz. Both tilt perception and torsion slightly lagged head orientation at 0.125 Hz. The phase lag of torsion increased at 0.5 Hz, while the phase of tilt perception did not change as a function of frequency. The amplitude of both translation perception recorded from the joystick and horizontal eye movements was negligible at 0.125 Hz and increased as a function of stimulus frequency. While the phase lead of horizontal eye movements decreased at 0.5 Hz, the phase of translation perception did not vary with stimulus frequency and was similar to the phase of tilt perception during all conditions. During dynamic linear acceleration in the absence of other sensory input (canal, vision) a change in stimulus frequency alone elicits similar changes in the amplitude of both self motion perception and eye movements. However, in contrast to the eye movements, the phase of both perceived tilt and translation motion is not altered by stimulus frequency. We conclude that the neural processing to distinguish tilt and translation linear acceleration stimuli differs between eye movements and motion perception

Spatial Coding of Eye Movements Relative to Perceived Orientations During Roll Tilt with Different Gravitoinertial Loads

This purpose of this study was to examine the spatial coding of eye movements during roll tilt relative to perceived orientations while free-floating during the microgravity phase of parabolic flight or during head tilt in normal gravity. Binocular videographic recordings obtained in darkness from six subjects allowed us to quantify the mean deviations in gaze trajectories along both horizontal and vertical coordinates relative to the aircraft and head orientations. Both variability and curvature of gaze trajectories increased during roll tilt compared to the upright position. The saccades were less accurate during parabolic flight compared to measurements obtained in normal gravity. The trajectories of saccades along perceived horizontal orientations tended to deviate in the same direction as the head tilt, while the deviations in gaze trajectories along the perceived vertical orientations deviated in the opposite direction relative to the head tilt. Although subjects were instructed to look off in the distance while performing the eye movements, fixation distance varied with vertical gaze direction independent of whether the saccades were made along perceived aircraft or head orientations. This coupling of horizontal vergence with vertical gaze is in a consistent direction with the vertical slant of the horopter. The increased errors in gaze trajectories along both perceived orientations during microgravity can be attributed to the otolith's role in spatial coding of eye movements

The number of corner polyhedra graphs

Corner polyhedra were introduced by Eppstein and Mumford (2014) as the set of simply connected 3D polyhedra such that all vertices have non negative integer coordinates, edges are parallel to the coordinate axes and all vertices but one can be seen from infinity in the direction (1, 1, 1). These authors gave a remarkable characterization of the set of corner polyhedra graphs, that is graphs that can be skeleton of a corner polyhedron: as planar maps, they are the duals of some particular bipartite triangulations, which we call hereafter corner triangulations.In this paper we count corner polyhedral graphs by determining the generating function of the corner triangulations with respect to the number of vertices: we obtain an explicit rational expression for it in terms of the Catalan gen- erating function. We first show that this result can be derived using Tutte's classical compositional approach. Then, in order to explain the occurrence of the Catalan series we give a direct algebraic decomposition of corner triangu- lations: in particular we exhibit a family of almond triangulations that admit a recursive decomposition structurally equivalent to the decomposition of binary trees. Finally we sketch a direct bijection between binary trees and almond triangulations. Our combinatorial analysis yields a simpler alternative to the algorithm of Eppstein and Mumford for endowing a corner polyhedral graph with the cycle cover structure needed to realize it as a polyhedral graph

Apprentissage de co-similarités pour la classification automatique de données monovues et multivues

L'apprentissage automatique consiste à concevoir des programmes informatiques capables d'apprendre à partir de leurs environnement, ou bien à partir de données. Il existe différents types d'apprentissage, selon que l'on cherche à faire apprendre au programme, et également selon le cadre dans lequel il doit apprendre, ce qui constitue différentes tâches. Les mesures de similarité jouent un rôle prépondérant dans la plupart de ces tâches, c'est pourquoi les travaux de cette thèse se concentrent sur leur étude. Plus particulièrement, nous nous intéressons à la classification de données, qui est une tâche d'apprentissage dit non supervisé, dans lequel le programme doit organiser un ensemble d'objets en plusieurs classes distinctes, de façon à regrouper les objets similaires ensemble. Dans de nombreuses applications, ces objets (des documents par exemple) sont décrits à l'aide de leurs liens à d'autres types d'objets (des mots par exemple), qui peuvent eux-même être classifiés. On parle alors de co-classification, et nous étudions et proposons dans cette thèse des améliorations de l'algorithme de calcul de co-similarités XSim. Nous montrons que ces améliorations permettent d'obtenir de meilleurs résultats que les méthodes de l'état de l'art. De plus, il est fréquent que ces objets soient liés à plus d'un autre type d'objets, les données qui décrivent ces multiples relations entre différents types d'objets sont dites multivues. Les méthodes classiques ne sont généralement pas capables de prendre en compte toutes les informations contenues dans ces données. C'est pourquoi nous présentons dans cette thèse l'algorithme de calcul multivue de similarités MVSim, qui peut être vu comme une extension aux données multivues de l'algorithme XSim. Nous montrons que cette méthode obtient de meilleures performances que les méthodes multivues de l'état de l'art, ainsi que les méthodes monovues, validant ainsi l'apport de l'aspect multivue. Finalement, nous proposons également d'utiliser l'algorithme MVSim pour classifier des données classiques monovues de grandes tailles, en les découpant en différents ensembles. Nous montrons que cette approche permet de gagner en temps de calcul ainsi qu'en taille mémoire nécessaire, tout en dégradant relativement peu la classification par rapport à une approche directe sans découpage.Machine learning consists in conceiving computer programs capable of learning from their environment, or from data. Different kind of learning exist, depending on what the program is learning, or in which context it learns, which naturally forms different tasks. Similarity measures play a predominant role in most of these tasks, which is the reason why this thesis focus on their study. More specifically, we are focusing on data clustering, a so called non supervised learning task, in which the goal of the program is to organize a set of objects into several clusters, in such a way that similar objects are grouped together. In many applications, these objects (documents for instance) are described by their links to other types of objects (words for instance), that can be clustered as well. This case is referred to as co-clustering, and in this thesis we study and improve the co-similarity algorithm XSim. We demonstrate that these improvements enable the algorithm to outperform the state of the art methods. Additionally, it is frequent that these objects are linked to more than one other type of objects, the data that describe these multiple relations between these various types of objects are called multiview. Classical methods are generally not able to consider and use all the information contained in these data. For this reason, we present in this thesis a new multiview similarity algorithm called MVSim, that can be considered as a multiview extension of the XSim algorithm. We demonstrate that this method outperforms state of the art multiview methods, as well as classical approaches, thus validating the interest of the multiview aspect. Finally, we also describe how to use the MVSim algorithm to cluster large-scale single-view data, by first splitting it in multiple subsets. We demonstrate that this approach allows to significantly reduce the running time and the memory footprint of the method, while slightly lowering the quality of the obtained clustering compared to a straightforward approach with no splitting.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Artificial Gravity Research Project Overview: International Countermeasures Research Working Group Meeting, Prague - June 30th, 2015

No abstract availabl

Artificial Gravity for Protection of Human Health During Long-Duration Spaceflight

No abstract availabl