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Self-optimizing control is a strategy for selecting controlled variables, where the economic objective guides the selection and design of controlled variables, with the expectation that maintaining the controlled variables at constant…

Autonomous modeling of artificial swarms is necessary because manual creation is a time intensive and complicated procedure which makes it impractical. An autonomous approach employing deep reinforcement learning is presented in this study…

The techniques of precision agriculture include the possibility to execute crop monitoring tasks through the application of Unmanned Aerial Vehicles (UAVs). These platforms are flexible, easy to use and low-cost, and they are the best…

Seasonal forecasting remains challenging due to the inherent chaotic nature of atmospheric dynamics. This paper introduces DeepSeasons, a novel deep learning approach designed to enhance the accuracy and reliability of seasonal forecasts.…

Merchant shipping is responsible for more than 90% of the global trade and has a significant environmental impact, accounting for over 2% of global greenhouse gas emissions. Therefore, fuel-saving measures are becoming increasingly…

Renewable energy brings huge uncertainties to the power system, which challenges the traditional power system operation with limited flexible resources. One promising solution is to introduce dynamic pricing to more consumers, which, if…

Optimal control problems driven by evolutionary partial differential equations arise in many industrial applications and their numerical solution is known to be a challenging problem. One approach to obtain an optimal feedback control is…

This paper proposed a discrete stochastic dynamic programming (SDP) model for sustainable ecosystem (SE) planning of the Loess Plateau in Northwestern, China, and analyzed the ecological resource planning by the evolutionary game model in…

We consider an energy storage problem involving a wind farm with a forecasted power output, a stochastic load, an energy storage device, and a connection to the larger power grid with stochastic prices. Electricity prices and wind power…

Expensive sensors and inefficient algorithmic pipelines significantly affect the overall cost of autonomous machines. However, affordable robotic solutions are essential to practical usage, and their financial impact constitutes a…

Real-time altitude control of airborne wind energy (AWE) systems can improve performance by allowing turbines to track favorable wind speeds across a range of operating altitudes. The current work explores the performance implications of…

DC marine architecture integrated with variable speed diesel generators (DGs) has garnered the attention of the researchers primarily because of its ability to deliver fuel efficient operation. This paper aims in modeling and to…

A prominent challenge to the safe and optimal operation of the modern power grid arises due to growing uncertainties in loads and renewables. Stochastic optimal power flow (SOPF) formulations provide a mechanism to handle these…

Sea-level rise poses considerable risks to coastal communities, ecosystems, and infrastructure. Decision makers are faced with uncertain sea-level projections when designing a strategy for coastal adaptation. The traditional methods are…

Wireless power transfer (WPT) is a promising technology that provides the network a way to replenish the batteries of the remote devices by utilizing RF transmissions. We study a class of harvest-first-transmit-later type of WPT policy,…

Inspired by the octopus and other animals living in water, soft robots should naturally lend themselves to underwater operations, as supported by encouraging validations in deep water scenarios. This work deals with equipping soft arms with…

In this work the problem of path planning for an autonomous vehicle that moves on a freeway is considered. The most common approaches that are used to address this problem are based on optimal control methods, which make assumptions about…

Dynamic wind farm control is a new strategy that aims to apply time-varying, often periodic, control signals on upstream wind turbines to increase the wake mixing behind the turbine. As a result, wake recovery is accelerated, leading to a…

Marine heatwaves (MHWs), an extreme climate phenomenon, pose significant challenges to marine ecosystems and industries, with their frequency and intensity increasing due to climate change. This study introduces an integrated deep learning…

The exceptional benefits of wind power as an environmentally responsible renewable energy resource have led to an increasing penetration of wind energy in today's power systems. This trend has started to reshape the paradigms of power…