97 research outputs found

    Mobile Robot Path Planning using Q-Learning with Guided Distance and Moving Target Concept

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    Classical Q-learning algorithm is a reinforcement of learning algorithm that has been applied in path planning of mobile robots. However, classical Q-learning suffers from slow convergence rate and high computational time. This is due to the random decision making for direction during the early stage of path planning. Such weakness curtails the ability of mobile robot to make instantaneous decision in real world application. In this study, the distance aspect and moving target concept were added to Q-learning in order to enhance the direction decision making ability and bypassing dead end. With the addition of these features, Q-learning is able to converge faster and generate shorter path. Consequently, the proposed improved Q-learning is able to achieve average improvement of 29.34-94.85%, 18.29-29.69% and 75.76-99.50% in time used, shortest distance and total distance used, respectively

    Slices of the Kerr ergosurface

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    The intrinsic geometry of the Kerr ergosurface on constant Boyer-Lindquist (BL), Kerr, and Doran time slices is characterized. Unlike the BL slice, which had been previously studied, the other slices (i) do not have conical singularities at the poles (except the Doran slice in the extremal limit), (ii) have finite polar circumference in the extremal limit, and (iii) for sufficiently large spin parameter fail to be isometrically embeddable as a surface of revolution above some latitude. The Doran slice develops an embeddable polar cap for spin parameters greater than about 0.96.Comment: 13 pages, 6 figures; v.2: minor editing for clarification, references added, typos fixed, version published in Classical and Quantum Gravit

    Online social support for single mothers in Japan

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    Master'sMASTER OF ART

    Modified Q-learning with distance metric and virtual target on path planning of mobile robot

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    Path planning is an essential element in mobile robot navigation. One of the popular path planners is Q-learning – a type of reinforcement learning that learns with little or no prior knowledge of the environment. Despite the successful implementation of Q-learning reported in numerous studies, its slow convergence associated with the curse of dimensionality may limit the performance in practice. To solve this problem, an Improved Q-learning (IQL) with three modifications is introduced in this study. First, a distance metric is added to Q-learning to guide the agent moves towards the target. Second, the Q function of Q-learning is modified to overcome dead-ends more effectively. Lastly, the virtual target concept is introduced in Q-learning to bypass dead-ends. Experi�mental results across twenty types of navigation maps show that the proposed strategies accelerate the learning speed of IQL in comparison with the Q-learning. Besides, performance comparison with seven well-known path planners indicates its efficiency in terms of the path smoothness, time taken, shortest distance and total distance used

    Mobile robot path planning using q-learning with guided Distance

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    In path planning for mobile robot, classical Q-learning algorithm requires high iteration counts and longer time taken to achieve conver-gence. This is due to the beginning stage of classical Q-learning for path planning consists of mostly exploration, involving random di-rection decision making. This paper proposed the addition of distance aspect into direction decision making in Q-learning. This feature is used to reduce the time taken for the Q-learning to fully converge. In the meanwhile, random direction decision making is added and activated when mobile robot gets trapped in local optima. This strategy enables the mobile robot to escape from local optimal trap. The results show that the time taken for the improved Q-learning with distance guiding to converge is longer than the classical Q-learning. However, the total number of steps used is lower than the classical Q-learning

    Prevalence of MRSA and Antimicrobial Resistance of Staphylococcus aureus in Maryland Ground Meat Products

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    Gemstone Team Antibiotic ResistanceThe aim of this study was to evaluate the risk of exposure to antimicrobial-resistant Staphylococcus aureus from food-grade raw ground meat products in Maryland. Samples of ground beef (n = 198), pork (n = 300), and turkey (n = 196), were collected by random sampling from March-August, 2008. All isolates were tested for resistance to methicillin and confirmed S. aureus isolates (n = 200) were tested for susceptibility to 21 additional antimicrobials. Overall, turkey- and pork-derived isolates were more likely to be resistant to commonly used antimicrobials. One isolate from pork was confirmed to be the USA100 strain of MRSA and was resistant to 10 antibiotics. In addition, antibiotic-resistant non-S. aureus isolates were characterized and may represent a source for the transfer of resistance genes to S. aureus. Our findings suggest that meat production practices may impact the prevalence and antimicrobial resistance of S. aureus in ground meat
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