During the last five years, several convex optimization algorithms have been proposed for solving inverse problems. Most of the time, they allow us to minimize a criterion composed of two terms one of which permits to...
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ISBN:
(纸本)9781617388767
During the last five years, several convex optimization algorithms have been proposed for solving inverse problems. Most of the time, they allow us to minimize a criterion composed of two terms one of which permits to "stabilize" the solution. Different choices are possible for the so-called reg-ularization term, which plays a prominent role for solving ill-posed problems. While a total variation regularization introduces staircase effects, a wavelet regularization may bring other kinds of visual artefacts. A compromise can be envisaged combining these regularization functions. In the context of Poisson data, we propose in this paper an algorithm to achieve the minimization of the associated (possibly constrained) convex optimization problem.
Reducing fuel consumption while coping with continually increasing customer demands with regard to driving dynamics, is a conflict of objectives in vehicle development. At the same time, hybrid vehicles offer a chance...
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ISBN:
(纸本)9781510836334
Reducing fuel consumption while coping with continually increasing customer demands with regard to driving dynamics, is a conflict of objectives in vehicle development. At the same time, hybrid vehicles offer a chance to meet this challenge. For calibrating the hybrid car's operational strategy, realistic driving cycles are of great importance. Studies have shown that the driver's driving style as well as the traffic situation have a considerable influence on fuel consumption and pollutant emission [1]. Deriving replacement cycles from extensive customer data to support the vehicle calibration in consideration of driving styles and traffic situations seems to be an approach. This contribution describes the generation of these replacement cycles by making use of stochastic models and cluster analysis. Therefore, extensive customer records can be summarised and complex signal sequences can be obtained. Cluster analysis allow for arranging extensive data sets in similar groups according to driving style and traffic situation. Following this, an optimisation algorithm assembles several generated velocity progressions to one replacement cycle. The aim is to keep important characteristics in relation to the consumption and the customer behaviour. The resulting replacement cycles offer the possibility to calibrate a hybrid car's operational strategy to the market in a better way and to conduct sensitivity analysis as well as consumption forecasts [2].
It is presented a monocular RGB vision system to estimate the pose (3D position and orientation) of a fixed-wing Unmanned Aerial Vehicle (UAV) concerning the camera reference frame. Using this estimate, a Ground Contr...
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ISBN:
(纸本)9781728114521
It is presented a monocular RGB vision system to estimate the pose (3D position and orientation) of a fixed-wing Unmanned Aerial Vehicle (UAV) concerning the camera reference frame. Using this estimate, a Ground Control Station (GCS) can control the UAV trajectory during landing on a Fast Patrol Boat (FPB). A ground-based vision system makes it possible to use more sophisticated algorithms since we have more processing power available. The proposed method uses a 3D model-based approach based on a Particle Filter (PF) divided into five stages: (i) frame capture, (ii) target detection, (iii) distortion correction, (iv) appearance-based pose sampler, and (v) pose estimation. In the frame capture stage, we obtain a new observation (a new frame). In the target detection stage, we detect the UAV region on the captured frame using a detector based on a Deep Neural Network (DNN). In the distortion correction stage, we correct the frame radial and tangential distortions to obtain a better estimate. In the appearance-based pose sampler stage, we use a synthetically generated pre-trained database for a rough pose initialization. In the pose estimation stage, we apply an optimization algorithm to be able to obtain a UAV pose estimate in the captured frame with low error. The overall system performance is increased using the Graphics Processing Unit (GPU) for parallel processing. Results show that the GPU computational resources are essential to obtain a real-time pose estimation system.
Proximal splitting-based convex optimization is a promising approach to linear inverse problems because we can use some prior knowledge of the unknown variables explicitly. An understanding of the behavior of the opti...
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This study presents an optimal algorithm for the Collatz conjecture, examining its convergence properties and connecting it to the original conjecture through an equation that calculates the steps needed for the Colla...
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Consider the problems of computing the Augustin information and a Rényi information measure of statistical independence, previously explored by Lapidoth and Pfister (IEEE Information Theory Workshop, 2018) and To...
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Many techniques for real-time trajectory optimization and control require the solution of optimization problems at high frequencies. However, ill-conditioning in the optimization problem can significantly reduce the s...
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Multi-task optimisation is a new research topic in the area of evolutionary computing, and it has received extensive attention from researchers since it was proposed. It can exploit the potential synergistic facilitat...
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Bilevel optimization problems, which are problems where two optimization problems are nested, have more and more applications in machine learning. In many practical cases, the upper and the lower objectives correspond...
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Unbalanced optimal transport (UOT) has recently gained much attention due to its flexible framework for handling un-normalized measures and its robustness properties. In this work, we explore learning (structured) spa...
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