![]() Having the incentive to evade further detection, content creators try to come up with a slightly modified version of the text (known as an attack with an adversarial example) that exploit the weaknesses of classifiers and result in a different output. Quite accurate models (likely based on deep neural networks) help in moderating public electronic platforms and often cause content creators to face rejection of their submissions or removal of already published texts. 19-23, Brussels, Belgium, April 24-26, 2017.Download a PDF of the paper titled Verifying the Robustness of Automatic Credibility Assessment, by Piotr Przybya and 2 other authors Download PDF Abstract:Text classification methods have been widely investigated as a way to detect content of low credibility: fake news, social media bots, propaganda, etc. Schoukens, F-16 aircraft benchmark based on ground vibration test data, 2017 Workshop on Nonlinear System Identification Benchmarks, pp. This zip-file contains a detailed system description, the estimation and test data sets, and some pictures of the setup. All the provided files and information on the F-16 aircraft benchmark system are available for download here. A preliminary investigation showed that the back connection of the right-wing-to-payload interface was the predominant source of nonlinear distortions in the aircraft dynamics, and is therefore the focus of this benchmark study.Ī detailed formulation of the identification problem can be found here. These interfaces consist of T-shaped connecting elements on the payload side, slid through a rail attached to the wing side. The dominant source of nonlinearity in the structural dynamics was expected to originate from the mounting interfaces of the two payloads. One shaker was attached underneath the right wing to apply input signals. The aircraft structure was instrumented with accelerometers. The experimental data made available to the Workshop participants were acquired on a full-scale F-16 aircraft on the occasion of the Siemens LMS Ground Vibration Testing Master Class, held in September 2014 at the Saffraanberg military basis, Sint-Truiden, Belgium.ĭuring the test campaign, two dummy payloads were mounted at the wing tips to simulate the mass and inertia properties of real devices typically equipping an F-16 in flight. The F-16 Ground Vibration Test benchmark features a high order system with clearance and friction nonlinearities at the mounting interface of the payloads. A more detailed study of this system will be published in the future. ![]() For now, this notebook is just a simple example of the performance of SysIdentPy on a real world dataset. ![]() ![]() Note: The reader is referred to the mentioned website for a complete reference concerning the experiment. The following text was taken from the link. Identification of an electromechanical system.Example: F-16 Ground Vibration Test benchmark Example: F-16 Ground Vibration Test benchmark Table of contents.Identification of an electromechanical system using Entropic Regression.Using the Accelerated Orthogonal Least-Squares algorithm for building Polynomial NARX models.Building NARX Neural Network using Sysidentpy.Meta-Model Structure Selection (MetaMSS) algorithm for building Polynomial NARX models.Building NARX models using general estimators.Example: N-steps-ahead prediction - F-16 Ground Vibration Test benchmark.Multiobjective Parameter Estimation for NARMAX models - An Overview.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |