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Optimal control in pandemics [PDF]
Physical Review E, 2021 During a pandemic, there are conflicting demands arising from public health and economic cost. Lockdowns are a common way of containing infections, but they adversely affect the economy. We study the question of how to minimise the economic damage of a lockdown while still containing infections.
Supurna Sinha +2 more
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Optimal Coverage Path Planning for Agricultural Vehicles with Curvature Constraints
Agriculture, 2023 Complete coverage path planning (CCPP) is vital in mobile robot applications. Optimizing CCPP is particularly significant in precision agriculture, where it enhances resource utilization, reduces soil compaction, and boosts crop yields.
Maria Höffmann +2 more
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Algorithms, 2022 The article offers a possible treatment for the numerical research of tasks which require searching for an absolute optimum. This approach is established by employing both globalized nature-inspired methods as well as local descent methods for ...
Pavel Sorokovikov, Alexander Gornov
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Low Cost Evolutionary Neural Architecture Search (LENAS) Applied to Traffic Forecasting
Machine Learning and Knowledge Extraction, 2023 Traffic forecasting is an important task for transportation engineering as it helps authorities to plan and control traffic flow, detect congestion, and reduce environmental impact.
Daniel Klosa, Christof Büskens
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Optimally Controlling an Epidemic [PDF]
SSRN Electronic Journal, 2020 We propose a exible model of infectious dynamics with a single endogenous state variable and economic choices. We characterize equilibrium, optimal outcomes, static and dynamic externalities, and prove the following: (i) A lockdown generically is followed by policies to stimulate activity.
Gonzalez-Eiras, Martín, Niepelt, Dirk
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Remote Sensing, 2023 Obstacle detection is the primary task of the Advanced Driving Assistance System (ADAS). However, it is very difficult to achieve accurate obstacle detection in complex traffic scenes.
Wenyan Ci +5 more
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Communications in Mathematical Sciences, 2022 Least squares Monte Carlo methods are a popular numerical approximation method for solving stochastic control problems. Based on dynamic programming, their key feature is the approximation of the conditional expectation of future rewards by linear least squares regression.
Bayer, Christian +5 more
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Optimal control and inverse optimal control by distribution matching [PDF]
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016 Optimal control is a powerful approach to achieve optimal behavior. However, it typically requires a manual specification of a cost function which often contains several objectives, such as reaching goal positions at different time steps or energy efficiency.
Arenz, O., Abdulsamad, H., Neumann, G.
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Mathematics, 2023 This study investigates the problem of time-varying formation tracking (TVFT) control involving event-triggered and switching topological mechanisms.
Xiaoya Chen, Huiying Chen
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