Advances in 3G enhanced technologies for wireless communications

Boston, MA : Artech House, 2002 . xv, 350 p. : , Based on selected papers from the Fourth IEEE Workshop on Emerging Technologies in Circuits and Systems--Third Generation (3G) Mobile Technologies and Applications, held Nov. 29-Dec. 1, 2000, in Hong Kong. 24 cm.. 1580533027

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Environmental tracking for public health surveillance /

Leiden, Netherlands : CRC Press/Balkema, 2013 . xxvii, 419 p., [32] p. of plates : , H.B 26 cm.. 9780415584715 (hbk.) | 041558471X (hbk.) | 9780203093276 (eBook) | 0203093275 (eBook)

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Communication networks for smart grids : making smart grid real /

By Budka, Kenneth C.,. . xviii, 369 pages : 24 cm.. 9781447163015 | 144716301X

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Advancements in model-driven architecture in software engineering /

. xix, 287 pages : 29 cm. 9781799836612 | 9781799836629

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Addressing the Problem of Sloshing in a Liquid Carrying Mobile Robot through Artificial Intelligence /

By Khan, Khubaib Haider . . 90p. , Liquid sloshing presents a critical challenge for mobile robots tasked with transporting partially filled containers, as it destabilizes motion, reduces accuracy, and increases the risk of spillage. Traditional passive and active control methods—such as baffles, PID, and LQR—are limited in adaptability and computational efficiency when faced with nonlinear, time-varying fluid–structure interactions. This thesis addresses these challenges by formulating slosh suppression as a reinforcement learning problem, leveraging the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm integrated with a surrogate ball-in-container analogy in the Webots simulator. The surrogate transforms complex liquid dynamics into a tractable cart-pole inspired system, enabling efficient training of robust control policies without reliance on high-fidelity but computationally expensive CFD models. A custom simulation–learning pipeline was developed, coupling Webots with a PyTorchbased TD3 agent via JSON-based communication. State and action spaces were defined using the ball’s displacement ratio and robot velocity, while a multi-component reward function balanced slosh minimization, forward progress, and smooth energy-efficient motion. Training results demonstrated that the TD3 agent learned to achieve stable, sloshfree navigation over a 10 m trajectory, outperforming traditional controllers and earlier RL variants in stability and adaptability. Results shows that the Model Learns well after 600 Episodes attaining optimal velocity of 1.25 – 0.5 m/s keeping ball within 2 mm limit, maximizing the reward to around 1180 at 1700 Episodes, eventually reducing sloshing by 65 % during Training phase. While, during Test phase robot is tested for 0 to 4 m/s velocity with control and no control scenarios, showing the results RL algorithm suppress the ball displacement (liquid slosh) by approximately 45 %. The study validates reinforcement learning as a viable paradigm for real-time liquid slosh suppression in robotics, offering superior robustness and scalability. Contributions include an AI-driven control framework for slosh suppression, integrationxvii of surrogate modeling with DRL for real-time feasibility, and a reward design framework encoding domain-specific stability objectives. For Further Future working it is recommended to include extending to real liquid models and hardware validation, adopting symmetric action spaces and advanced RL methods, and integrating slosh control with autonomous navigation in dynamic environments. 30cm.

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Advances in 3G enhanced technologies for wireless communications

Environmental tracking for public health surveillance /

Communication networks for smart grids : making smart grid real /

Advancements in model-driven architecture in software engineering /

Addressing the Problem of Sloshing in a Liquid Carrying Mobile Robot through Artificial Intelligence /