NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-rl:"Advanced.")'

Engineering circuit analysis (EBOOK)

By Hayt, William Hart,. New York : McGraw-Hill, 2012 . xxi, 852 p. : 26 cm.. 0071317066 (international ed. : pbk.) | 0073529575 | 9780071317061 (international ed. : pbk.) | 9780073529578

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Understanding advanced statistical methods /

By Westfall, Peter H.,. . xxv, 543 pages : 26 cm.. 9781466512108 (hardback) | 1466512105 (hardback)

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Renewable and sustainable energy : selected, peer reviewed papers from the 2011 International Conference on Energy, Environment and Sustainable Development (ICEESD 2011), October 21-23, 2011, Shanghai, China /

Durnten-Zurich ; | Enfield, NH : Trans Tech Publications, 2012 . 7 v. (xlix, 4170 p.) : 24 cm.. 9783037852651 | 3037852658

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Ceramic materials for energy applications III : a collection of papers presented at the 37th International Conference on Advanced Ceramics and Composites, January 27-February 1, 2013, Daytona Beach, Florida /

. xi, 161 pages : , "This proceedings issue contains contributions from three energy related symposia and the Engineering Ceramics Summit of the America that were part of the 37th International Conference on Advanced Ceramics and Composites (ICACC) ... The symposia include Advanced Materials and Technologies for Energy Generation and Rechargeable Energy Storage; Advanced Ceramics and Composites for Sustainable Nuclear Energy and Fusion Energy; and Advanced Ceramic Materials and Processing for Photonics and Energy."--Preface. | "The American Ceramic Society."--Title page. 25 cm.. 9781118807583 | 1118807588

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Modern RF and microwave measurement techniques /

. xxv, 447 pages : 26 cm.. 9781107036413 (hardback)

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The art of memory forensics : detecting malware and threats in Windows, Linux, and Mac memory /

By Ligh, Michael Hale.. . xxiii, 886 pages : 24 cm. 9781118825099 (pbk.) | 1118825098 (pbk.) | 9781118825044 (ebk.) | 1118825047 (ebk.) | 9781118824993 (ebk.) | 1118824997 (ebk.)

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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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Machine learning for dummies /

By Mueller, John,. . xii, 410 pages : , "Learning made easy"--Cover. | Includes index. 24 cm.. 9781119245513 (pbk.) | 1119245516

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Probability models and applications /

By Olkin, Ingram,. . xv,715p.; 9789813202030 | 9789813202047

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Traffic Signal Control using Reinforcement Learning /

By Umer Jamil, Qazi . . 90p. ; 30cm..

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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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