Sikojen perinnölliset viat

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Preliminary design study of a lateral-directional control system using thrust vectoring

A preliminary design of a lateral-directional control system for a fighter airplane capable of controlled operation at extreme angles of attack is developed. The subject airplane is representative of a modern twin-engine high-performance jet fighter, is equipped with ailerons, rudder, and independent horizontal-tail surfaces. Idealized bidirectional thrust-vectoring engine nozzles are appended to the mathematic model of the airplane to provide additional control moments. Optimal schedules for lateral and directional pseudo control variables are calculated. Use of pseudo controls results in coordinated operation of the aerodynamic and thrust-vectoring controls with minimum coupling between the lateral and directional airplane dynamics. Linear quadratic regulator designs are used to specify a preliminary flight control system to improve the stability and response characteristics of the airplane. Simulated responses to step pilot control inputs are stable and well behaved. For lateral stick deflections, peak stability axis roll rates are between 1.25 and 1.60 rad/sec over an angle-of-attack range of 10 deg to 70 deg. For rudder pedal deflections, the roll rates accompanying the sideslip responses can be arrested by small lateral stick motions

Improving Viewpoint Robustness for Visual Recognition via Adversarial Training

Viewpoint invariance remains challenging for visual recognition in the 3D world, as altering the viewing directions can significantly impact predictions for the same object. While substantial efforts have been dedicated to making neural networks invariant to 2D image translations and rotations, viewpoint invariance is rarely investigated. Motivated by the success of adversarial training in enhancing model robustness, we propose Viewpoint-Invariant Adversarial Training (VIAT) to improve the viewpoint robustness of image classifiers. Regarding viewpoint transformation as an attack, we formulate VIAT as a minimax optimization problem, where the inner maximization characterizes diverse adversarial viewpoints by learning a Gaussian mixture distribution based on the proposed attack method GMVFool. The outer minimization obtains a viewpoint-invariant classifier by minimizing the expected loss over the worst-case viewpoint distributions that can share the same one for different objects within the same category. Based on GMVFool, we contribute a large-scale dataset called ImageNet-V+ to benchmark viewpoint robustness. Experimental results show that VIAT significantly improves the viewpoint robustness of various image classifiers based on the diversity of adversarial viewpoints generated by GMVFool. Furthermore, we propose ViewRS, a certified viewpoint robustness method that provides a certified radius and accuracy to demonstrate the effectiveness of VIAT from the theoretical perspective.Comment: 14 pages, 12 figures. arXiv admin note: substantial text overlap with arXiv:2307.1023

Towards Viewpoint-Invariant Visual Recognition via Adversarial Training

Visual recognition models are not invariant to viewpoint changes in the 3D world, as different viewing directions can dramatically affect the predictions given the same object. Although many efforts have been devoted to making neural networks invariant to 2D image translations and rotations, viewpoint invariance is rarely investigated. As most models process images in the perspective view, it is challenging to impose invariance to 3D viewpoint changes based only on 2D inputs. Motivated by the success of adversarial training in promoting model robustness, we propose Viewpoint-Invariant Adversarial Training (VIAT) to improve viewpoint robustness of common image classifiers. By regarding viewpoint transformation as an attack, VIAT is formulated as a minimax optimization problem, where the inner maximization characterizes diverse adversarial viewpoints by learning a Gaussian mixture distribution based on a new attack GMVFool, while the outer minimization trains a viewpoint-invariant classifier by minimizing the expected loss over the worst-case adversarial viewpoint distributions. To further improve the generalization performance, a distribution sharing strategy is introduced leveraging the transferability of adversarial viewpoints across objects. Experiments validate the effectiveness of VIAT in improving the viewpoint robustness of various image classifiers based on the diversity of adversarial viewpoints generated by GMVFool.Comment: Accepted by ICCV 202

Heart-Lung Interactions in Aerospace Medicine

Few of the heart-lung interactions that are discussed have been studied in any detail in the aerospace environment, but is seems that many such interactions must occur in the setting of altered accelerative loadings and pressure breathing. That few investigations are in progress suggests that clinical and academic laboratory investigators and aerospace organizations are further apart than during the pioneering work on pressure breathing and acceleration tolerance in the 1940s. The purpose is to reintroduce some of the perennial problems of aviation physiology as well as some newer aerospace concerns that may be of interest. Many possible heart-lung interactions are pondered, by necessity often drawing on data from within the aviation field, collected before the modern understanding of these interactions developed, or on recent laboratory data that may not be strictly applicable. In the field of zero-gravity effects, speculation inevitably outruns the sparse available data

On Scalable Particle Markov Chain Monte Carlo

Particle Markov Chain Monte Carlo (PMCMC) is a general approach to carry out Bayesian inference in non-linear and non-Gaussian state space models. Our article shows how to scale up PMCMC in terms of the number of observations and parameters by expressing the target density of the PMCMC in terms of the basic uniform or standard normal random numbers, instead of the particles, used in the sequential Monte Carlo algorithm. Parameters that can be drawn efficiently conditional on the particles are generated by particle Gibbs. All the other parameters are drawn by conditioning on the basic uniform or standard normal random variables; e.g. parameters that are highly correlated with the states, or parameters whose generation is expensive when conditioning on the states. The performance of this hybrid sampler is investigated empirically by applying it to univariate and multivariate stochastic volatility models having both a large number of parameters and a large number of latent states and shows that it is much more efficient than competing PMCMC methods. We also show that the proposed hybrid sampler is ergodic

Pintaporalaitteiden sähkövikatyypit ja -testaus

Opinnäytetyö tehtiin toimeksiantona Sandvik Mining and Constructionin Tampereen toimipisteen pintaporalaitteiden sähkö- ja automaatiosuunnittelutiimille. Työssä tutkittiin uusissa pintaporalaitteissa ilmeneviä sähkövikoja. Lisäksi haluttiin selvittää, miksi osa näistä sähkövioista ilmenee vasta käyttöönotossa tai hieman sen jälkeen. Työn tavoitteena oli myös selvittää poralaitteille tehtäviä sähkötestauksia. Työssä on kaksi osaa. Ensimmäisen osan tarkoituksena oli selvittää Sandvikin omista laatutietokannoista sähköiset vikatyypit, joita uusissa pintaporalaitteissa vasta asiakkaalla ilmenee. Näistä koostettiin jokaiselle laitetyypille oma kaavionsa, sillä kaikilla laitetyypeillä oli omat ongelmakohtansa. Siksi täysin identtisiä vertailuja ei laitteiden kesken kyetty tekemään. Toisessa osassa tutkittiin pintaporalaitteiden sähkökokoonpanojen hajautettua sähkötestausta. Pintaporalaitteille tehtävistä sähkötestauksista ei sähkö- ja automaatiosuunnittelutiimillä ollut minkäänlaista tietoa tai dokumentointia, joten sähkötestaukseen tutustuminen oli merkittävä osa työtä. Lisäksi tutkittiin ja mietittiin mahdollisia sähkötestauksen kehitys- ja ongelmakohtia, kun vertailukohtana sähkötestaustietämykseen oli Sandvikin omat laatutietokannat. Kummatkin työn osiot onnistuivat hyvin, ja niistä saatiin käyttökelpoista tietoa tulevaisuutta varten. Lisäksi työ lisäsi huomattavasti ymmärrystä ja tietämystä Sandvikilla, sillä kokoonpanojen sähkötestaustavat ja -menetelmät kävivät hyvin selkeiksi sähkötes-taukseen tutustumisen myötä.This thesis was commissioned by the electrical designer team of surface drills in Sandvik Mining and Construction Tampere facility. The purpose was to find out why some faults occur shortly after implementation. Every surface drill will be electrically tested before being delivered to a customer. This thesis has two parts. In the first part all electrical fault types which occur after delivery to a customer were determined. The details for this part were obtained from quality data bases of Sandvik. Charts were made to every drill family with all electrical fault types included. Because the fault types are not identical in all drill families, the charts are not totally comparable. In the second part the distributed testing of electrical assemblies of surface drills was studied. The electrical designer team did not have any previous data or documentation about this electrical testing in Sandvik. The data were needed, because troubleshooting is easier with some knowledge about testing. Also problems and improvements in electrical testing were studied, based on the quality data bases of Sandvik. Both parts of this thesis were successful and gave useful information for the future. This thesis also gave a great deal of information and understanding to Sandvik. After inspecting the electrical testing, the information about testing and the methods used in testing electrical assemblies became clear