Matching Items (3)
Description
For a conventional quadcopter system with 4 planar rotors, flight times vary between 10 to 20 minutes depending on the weight of the quadcopter and the size of the battery used. In order to increase the flight time, either the weight of the quadcopter should be reduced or the battery size should be increased. Another way is to increase the efficiency of the propellers. Previous research shows that ducting a propeller can cause an increase of up to 94 % in the thrust produced by the rotor-duct system. This research focused on developing and testing a quadcopter having a centrally ducted rotor which produces 60 % of the total system thrust and 3 other peripheral rotors. This quadcopter will provide longer flight times while having the same maneuvering flexibility in planar movements.
ContributorsLal, Harsh (Author) / Artemiadis, Panagiotis (Thesis advisor) / Lee, Hyunglae (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2019
Description
This work has improved the quality of the solution to the sparse rewards problemby combining reinforcement learning (RL) with knowledge-rich planning. Classical
methods for coping with sparse rewards during reinforcement learning modify the
reward landscape so as to better guide the learner. In contrast, this work combines
RL with a planner in order to utilize other information about the environment. As
the scope for representing environmental information is limited in RL, this work has
conflated a model-free learning algorithm – temporal difference (TD) learning – with
a Hierarchical Task Network (HTN) planner to accommodate rich environmental
information in the algorithm. In the perpetual sparse rewards problem, rewards
reemerge after being collected within a fixed interval of time, culminating in a lack of a
well-defined goal state as an exit condition to the problem. Incorporating planning in
the learning algorithm not only improves the quality of the solution, but the algorithm
also avoids the ambiguity of incorporating a goal of maximizing profit while using
only a planning algorithm to solve this problem. Upon occasionally using the HTN
planner, this algorithm provides the necessary tweak toward the optimal solution. In
this work, I have demonstrated an on-policy algorithm that has improved the quality
of the solution over vanilla reinforcement learning. The objective of this work has
been to observe the capacity of the synthesized algorithm in finding optimal policies to
maximize rewards, awareness of the environment, and the awareness of the presence
of other agents in the vicinity.
ContributorsNandan, Swastik (Author) / Pavlic, Theodore (Thesis advisor) / Das, Jnaneshwar (Thesis advisor) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2022
Description
Asphalt is a widely used mixture in the paving and roofing industries and its sales are expected to increase by 57% in the next eight years alone (Mandaokar, 2024). However, it is known to have highly toxic constituents such as benzo[a]pyrene (BaP) and catechol, (National Institute, 1977, Hazard Review, 2000, Neghab et al., 2015, and Rozewski et al., 2023). Lemon juice, which is an inexpensive and easily accessible natural substance that is shown to have health benefits such as increasing insulin sensitivity, aiding with weight loss, and preventing heart disease (Tejpal et al., 2020), may counteract the effects of asphalt. The question of what the biological effects of asphalt, lemon juice, and the combination of the two on adipocytes was tested via computational analysis and experiments. It was predicted that catechol and lemon juice components will show biological effects in adipocytes that could be opposing, additive, or synergistic. A computational analysis involving the docking of fourteen components of asphalt and thirty-five components of lemon juice constituents to a targetome of 7,529 proteins (Ovanessians et al., 2024) suggests that asphalt and lemon juice components have many possible protein targets. Experiments were carried out with 3T3L1 mouse adipocytes to study five different lemon extracts (crude, hexane organic and aqueous, and ether organic and aqueous), and two components of asphalt (catechol and BaP): 1) Thiazolyl Blue Tetrazolium Bromide (MTT) cell viability and toxicity assay, 2) reactive oxygen species fluorescence assay, 3) Nile red staining assay, 4) red oil o staining assay, and a 5) lipidomics analysis on the hexane and ether organic extracts of lemon juice. This study has shown that asphalt components BaP and catechol and lemon juice components combined have the following biological effects on adipocytes: 1) Of the 5 lemon extracts tested, the organic layer of the hexane extract solubilized in DMSO (LE4) decreases differentiation the most. 2) Nile red staining is inhibited by 0.1 mg/mL of LE4, 1 µM BaP, and 20 µM catechol at a similar level. 3) Cell morphology differs between LE4, BaP, and catechol. Future work will include an insulin sensitivity assay to confirm the indicative inhibitory relationship found between lemon juice and asphalt. Expanding upon the lipidomic results of the lemon juices, as well as maximizing the potential of dockings by connecting results with the experiments, may also prove to be useful in future studies.
ContributorsImtiaz, Shazeen (Author) / Klein-Seetharaman, Judith (Thesis director) / Wang, Shu (Committee member) / Singharoy, Abhishek (Committee member) / Barrett, The Honors College (Contributor) / School of Molecular Sciences (Contributor)
Created2024-05