We suggest and compare different methods for the numerical solution of Lyapunov like equations with application to control of Markovian jump linear systems. First, we consider fixed point iterations and associated Krylov subspace formulations. Second, we reformulate the equation as an optimization problem and consider steepest descent, conjugate gradient, and a trust-region method.Numerical experiments illustrate that for large-scale problems the trust-region method is more effective than the steepest descent and the conjugate gradient methods. The fixedpoint approach, however, is superior to the optimization methods. As an application we consider a networked control system, where the Markov jumps are induced by the wireless communication protocol.
In this paper, we propose an approach and workflow in order to detect humans in the environment around a crane with Monocular Images. The considered area is split up into a zone around the crane truck and one around the load. The load will be monitored with an optical zoom camera where we can control the zoom. We discretize the zoom levels and a Convolutional Neural Network for each zoom level is trained. Afterwards a Meta Convolutional Neural Network is trained in order to select the next zoom level. Since there are no public datasets available for this kind of task we propose to generate the needed data with a photorealistic simulation.
This paper presents two variations of architecture referred to as RANet and BIRANet. The proposed architecture aims to use radar signal data along with RGB camera images to form a robust detection network that works efficiently, even in variable lighting and weather conditions such as rain, dust, fog, and others. First, radar information is fused in the feature extractor network. Second, radar points are used to generate guided anchors. Third, a method is proposed to improve region proposal network [1] targets. BIRANet yields 72.3/75.3% average AP/AR on the NuScenes [2] dataset, which is better than the performance of our base network Faster-RCNN with Feature pyramid network(FFPN) [3]. RANet gives 69.6/71.9% average AP/AR on the same dataset, which is reasonably acceptable performance. Also, both BI-RANet and RANet are evaluated to be robust towards the noise.
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