Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications

We present an overview and evaluation of a new, systematic approach for generation of highly realistic, annotated synthetic data for training of deep neural networks in computer vision tasks. The main contribution is a procedural world modeling approach enabling high variability coupled with physically accurate image synthesis, and is a departure from the hand-modeled virtual worlds and approximate image synthesis methods used in real-time applications. The benefits of our approach include flexible, physically accurate and scalable image synthesis, implicit wide coverage of classes and features, and complete data introspection for annotations, which all contribute to quality and cost efficiency. To evaluate our approach and the efficacy of the resulting data, we use semantic segmentation for autonomous vehicles and robotic navigation as the main application, and we train multiple deep learning architectures using synthetic data with and without fine tuning on organic (i.e. real-world) data. The evaluation shows that our approach improves the neural network's performance and that even modest implementation efforts produce state-of-the-art results.Comment: The project web page at http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the paper with high-resolution images as well as additional materia

Особенности подготовки врачей на цикле специализации по специальности «Инфекционные болезни»

МЕДИЦИНСКИЕ УЧЕБНЫЕ ЗАВЕДЕНИЯОБРАЗОВАНИЕ МЕДИЦИНСКОЕСТУДЕНТЫ МЕДИЦИНСКИХ УЧЕБНЫХ ЗАВЕДЕНИЙПОДГОТОВКА ВРАЧЕЙСПЕЦИАЛИЗАЦИЯИНФЕКЦИОННЫЕ БОЛЕЗН

Learning to Control Planar Hitting Motions of a Robotic Arm in a Mini-Golf-like Task

In this thesis we tackle the problem of goal-oriented adaptation of a robot hitting motion. We propose the parameters that must be learned in order to use and adapt a basic hitting motion to play minigolf. Then, two different statistical methods are used to learn these parameters. The two methods are evaluated and compared. To validate the proposed approach, a minigolf control module is developed for a robotic arm. Using the different learning techniques, we show that a robot can learn the non-trivial task of deciding how the ball should be hit for a given position on a minigolf field. The result is a robust minigolf-playing system that outperforms most human players using only a small set of training examples

Control and Learning of Compliant Manipulation Skills

Humans demonstrate an impressive capability to manipulate fragile objects without damaging them, graciously controlling the force and position of hands or tools. Traditionally, robotics has favored position control over force control to produce fast, accurate and repeatable motion. For extending the applicability of robotic manipulators outside the strictly controlled environments of industrial work cells, position control is inadequate. Tasks that involve contact with objects whose positions are not known with perfect certainty require a controller that regulates the relationship between positional deviations and forces on the robot. This problem is formalized in the impedance control framework, which focuses the robot control problem on the interaction between the robot and its environment. By adjusting the impedance parameters, the behavior of the robot can be adapted to the need of the task. However, it is often difficult to specify formally how the impedance should vary for best performance. Furthermore, fast it can be shown that careless variation of the impedance can lead to unstable regulation or tracking even in free motion. In the first part of the thesis, the problem of how to define a varying impedance for a task is addressed. A haptic human-robot interface that allows a human supervisor to teach impedance variations by physically interacting with the robot during task execution is introduced. It is shown that the interface can be used to enhance the performance in several manipulation tasks. Then, the problem of stable control with varying impedance is addressed. Along with a theoretical discussion on this topic, a sufficient condition for stable varying stiffness and damping is provided. In the second part of the thesis, we explore more complex manipulation scenarios via online generation of the robot trajectory. This is done along two axes 1) learning how to react to contact forces in insertion tasks which are crucial for assembly operations and 2) autonomous Dynamical Systems (DS) for motion representation with the capability to encode a family of trajectories rather than a fixed, time-dependent reference. A novel framework for task representation using DS is introduced, termed Locally Modulated Dynamical Systems (LMDS). LMDS differs from existing DS estimation algorithms in that it supports non-parametric and incremental learning all the while guaranteeing that the resulting DS is globally stable at an attractor point. To combine the advantages of DS motion generation with impedance control, a novel controller for tasks described by first order DS is proposed. The controller is passive, and has the properties of an impedance controller with the added flexibility of a DS motion representation instead of a time-indexed trajectory

Impact of Correlated Failures in 5G Dual Connectivity Architectures for URLLC Applications

Achieving end-to-end ultra-reliability and resiliency in mission critical communications is a major challenge for future wireless networks. Dual connectivity has been proposed by 3GPP as one of the viable solutions to fulfill the reliability requirements. However, the potential correlation in failures occurring over different wireless links is commonly neglected in current network design approaches. In this paper, we investigate the impact of realistic correlation among different wireless links on end-to-end reliability for two selected architectures from 3GPP. In ultra-reliable use-cases, we show that even small values of correlation can increase the end-to-end error rate by orders of magnitude. This may suggest alternative feasible architecture designs and paves the way towards serving ultra-reliable communications in 5G networks.Comment: Accepted in 2019 IEEE Globecom Workshops (GC Wkshps

Стенд для проведения вибродиагностики двигателя

Материалы III Респ. науч.-метод. конф., Гомель, 31 окт.–1 нояб. 2013 г

Joint power, Rate, and Channel Allocation in Multilink (Cognitive) Radio System

We consider multi-constrained power, rate and channelallocation crafted for low power consumption, delaytolerant traffic, and under interfering link conditions thatmay be used in a cognitive radio system. Specifically, aniterative distributed algorithm, based on a sum-powerconstrained sum-rate maximization with upper (andlower) per user and channel power and rate constraints,as well as upper per user sum-power and sum-rateconstraints is developed. The feasibility and performanceof the algorithm is demonstrated by simulation in acellular system. Simulations show that the multipleconstraints are handled while improving the sum-rate vs. sum-power relative an “equal power adaptive rate” RRM approach.© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Qc 2012020