Autonomous flight cycles and extreme landings of airliners beyond the current limits and capabilities using artificial neural networks

We describe the Intelligent Autopilot System (IAS), a fully autonomous autopilot capable of piloting large jets such as airliners by learning from experienced human pilots using Artificial Neural Networks. The IAS is capable of autonomously executing the required piloting tasks and handling the different flight phases to fly an aircraft from one airport to another including takeoff, climb, cruise, navigate, descent, approach, and land in simulation. In addition, the IAS is capable of autonomously landing large jets in the presence of extreme weather conditions including severe crosswind, gust, wind shear, and turbulence. The IAS is a potential solution to the limitations and robustness problems of modern autopilots such as the inability to execute complete flights, the inability to handle extreme weather conditions especially during approach and landing where the aircraft’s speed is relatively low, and the uncertainty factor is high, and the pilots shortage problem compared to the increasing aircraft demand. In this paper, we present the work done by collaborating with the aviation industry to provide training data for the IAS to learn from. The training data is used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when executing all the piloting tasks required to pilot an aircraft between two airports. In addition, we introduce new ANNs trained to control the aircraft’s elevators, elevators’ trim, throttle, flaps, and new ailerons and rudder ANNs to counter the effects of extreme weather conditions and land safely. Experiments show that small datasets containing single demonstrations are sufficient to train the IAS and achieve excellent performance by using clearly separable and traceable neural network modules which eliminate the black-box problem of large Artificial Intelligence methods such as Deep Learning. In addition, experiments show that the IAS can handle landing in extreme weather conditions beyond the capabilities of modern autopilots and even experienced human pilots. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots and beyond

A new method for the spectroscopic identification of stellar non-radial pulsation modes. II. Mode identification of the Delta Scuti star FG Virginis

We present a mode identification based on new high-resolution time-series spectra of the non-radially pulsating Delta Scuti star FG~Vir (HD 106384, V = 6.57, A5V). From 2002 February to June a global Delta Scuti Network (DSN) campaign, utilizing high-resolution spectroscopy and simultaneous photometry has been conducted for FG~Vir in order to provide a theoretical pulsation model. In this campaign we have acquired 969 Echelle spectra covering 147 hours at six observatories. The mode identification was carried out by analyzing line profile variations by means of the Fourier parameter fit method, where the observational Fourier parameters across the line are fitted with theoretical values. This method is especially well suited for determining the azimuthal order m of non-radial pulsation modes and thus complementary with the method of Daszynska-Daszkiewicz (2002) which does best at identifying the degree l. 15 frequencies between 9.2 and 33.5 c/d were detected spectroscopically. We determined the azimuthal order m of 12 modes and constrained their harmonic degree l. Only modes of low degree (l <= 4) were detected, most of them having axisymmetric character mainly due to the relatively low projected rotational velocity of FG Vir. The detected non-axisymmetric modes have azimuthal orders between -2 and 1. We derived an inclination of 19 degrees, which implies an equatorial rotational rate of 66 km/s.Comment: 14 pages, 26 figure

Peripheral and central mechanisms involved in hormonal control of male and female reproduction

Reproduction involves the integration of hormonal signals acting across multiple systems togenerate a synchronized physiological output. A critical component of reproduction is the luteinizinghormone (LH) surge, which is mediated by estradiol (E2) and neuroprogesterone interacting tostimulate kisspeptin release in the rostral periventricular nucleus of the third ventricle in rats. Recentevidence has shown that both classical and membrane E2 and progesterone signaling is involved inthis pathway. A metabolite of gonadotropin-releasing hormone (GnRH), GnRH-(1-5), has been shownto stimulate GnRH expression, secretion, and has a role in the regulation of lordosis. Additionally,gonadotropin-inhibitory hormone (GnIH) projects to and influences the activity of GnRH neurons inbirds. Stress-induced changes in GnIH have been shown to alter breeding behaviors in birds,demonstrating another molecular control of reproduction. Peripherally, paracrine and autocrineactions within the gonad have been suggested as therapeutic targets for infertility in both males andfemales. Dysfunction of testicular prostaglandin synthesis is a possible cause of idiopathic maleinfertility. Indeed, local production of melatonin and corticotropin-releasing hormone (CRH) couldinfluence spermatogenesis via immune pathways in the gonad. In females, vascular endothelialgrowth factor A (VEGF-A) has been implicated in an angiogenic process that mediates developmentof the corpus luteum and thus fertility via the Notch signaling pathway. Age-induced decreases infertility involve ovarian kisspeptin and its regulation of ovarian sympathetic innervation. Finally,morphological changes in the arcuate nucleus of the hypothalamus influence female sexualreceptivity in rats. The processes mediating these morphological changes have been shown toinvolve rapid effects of E2 controlling synaptogenesis in this hypothalamic nucleus. Together, thisreview highlights new research in these areas, focusing on recent findings in the molecularmechanisms of central and peripheral hormonal control of reproduction.Fil: Rudolph, L. M.. University of California at Los Angeles; Estados UnidosFil: Bentley, G. E.. University of California Berkeley; Estados UnidosFil: Calandra, Ricardo Saul. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Paredes, A. H.. Universidad de Chile; ChileFil: Tesone, Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Wu, T. J.. Uniformed Services University; Estados UnidosFil: Micevych, P. E.. University of California at Los Angeles; Estados Unido

Drawing Arrangement Graphs In Small Grids, Or How To Play Planarity

We describe a linear-time algorithm that finds a planar drawing of every graph of a simple line or pseudoline arrangement within a grid of area O(n^{7/6}). No known input causes our algorithm to use area \Omega(n^{1+\epsilon}) for any \epsilon>0; finding such an input would represent significant progress on the famous k-set problem from discrete geometry. Drawing line arrangement graphs is the main task in the Planarity puzzle.Comment: 12 pages, 8 figures. To appear at 21st Int. Symp. Graph Drawing, Bordeaux, 201

Solving kk-means on High-dimensional Big Data

In recent years, there have been major efforts to develop data stream algorithms that process inputs in one pass over the data with little memory requirement. For the $k$-means problem, this has led to the development of several $(1+\varepsilon)$-approximations (under the assumption that $k$ is a constant), but also to the design of algorithms that are extremely fast in practice and compute solutions of high accuracy. However, when not only the length of the stream is high but also the dimensionality of the input points, then current methods reach their limits. We propose two algorithms, piecy and piecy-mr that are based on the recently developed data stream algorithm BICO that can process high dimensional data in one pass and output a solution of high quality. While piecy is suited for high dimensional data with a medium number of points, piecy-mr is meant for high dimensional data that comes in a very long stream. We provide an extensive experimental study to evaluate piecy and piecy-mr that shows the strength of the new algorithms.Comment: 23 pages, 9 figures, published at the 14th International Symposium on Experimental Algorithms - SEA 201

Efficient Processing of Spatial Joins Using R-Trees

Abstract: In this paper, we show that spatial joins are very suitable to be processed on a parallel hardware platform. The parallel system is equipped with a so-called shared virtual memory which is well-suited for the design and implementation of parallel spatial join algorithms. We start with an algorithm that consists of three phases: task creation, task assignment and parallel task execu-tion. In order to reduce CPU- and I/O-cost, the three phases are processed in a fashion that pre-serves spatial locality. Dynamic load balancing is achieved by splitting tasks into smaller ones and reassigning some of the smaller tasks to idle processors. In an experimental performance compar-ison, we identify the advantages and disadvantages of several variants of our algorithm. The most efficient one shows an almost optimal speed-up under the assumption that the number of disks is sufficiently large. Topics: spatial database systems, parallel database systems

Flare energetics

In this investigation of flare energetics, researchers sought to establish a comprehensive and self-consistent picture of the sources and transport of energy within a flare. To achieve this goal, they chose five flares in 1980 that were well observed with instruments on the Solar Maximum Mission, and with other space-borne and ground-based instruments. The events were chosen to represent various types of flares. Details of the observations available for them and the corresponding physical parameters derived from these data are presented. The flares were studied from two perspectives, the impulsive and gradual phases, and then the results were compared to obtain the overall picture of the energics of these flares. The role that modeling can play in estimating the total energy of a flare when the observationally determined parameters are used as the input to a numerical model is discussed. Finally, a critique of the current understanding of flare energetics and the methods used to determine various energetics terms is outlined, and possible future directions of research in this area are suggested

The Wow Factor? A Comparative Study of the Development of Student Music Teachers' Talents in Scotland and Australia

For some time there has been debate about differing perspectives on musical gift and musical intelligence. One view is that musical gift is innate: that it is present in certain individuals from birth and that the task of the teacher is to develop the potential which is there. A second view is that musical gift is a complex concept which includes responses from individuals to different environments and communities (Howe and Sloboda, 1997). This then raises the possibility that musical excellence can be taught. We have already explored this idea with practising musicians (Stollery and McPhee, 2002). Our research has now expanded to include music teachers in formation, and, in this paper, we look at the influences in their musical development which have either 'crystallised' or 'paralysed' the musical talent which they possess. Our research has a comparative dimension, being carried out in Scotland and in Australia. We conclude that there are several key influences in the musical development of the individual, including home and community support, school opportunities and teaching styles and that there may be education and culture-specific elements to these influences