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

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

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

Master'sMASTER OF ART

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

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

Effects of moisture and pressure cycling on sorption capacity of coals

Variability of the data for CO2 absorption on coal reported by different research groups suggests that it strongly depends on experimental conditions. We investigated the effects of moisture content and pressure cycling history on temporal changes in the coal sorptive capacity for Pocahontas #3, Illinois #6, and Beulah Zap powders of Argonne premium coals. The samples were tested as received and moisture equilibrated at 96-97% RH and 55°C for 48 hours. It was demonstrated that the magnitude and dynamics of the changes are affected by the coal type (maceral) and rank. Correlation between the sample volume change (swelling/shrinkage) and the variations in absorption-desorption patterns may indicate the relationship between coal structural relaxation and kinetics of CO2 absorption. Experimental and theoretical methods are proposed to study these effects

Mobile robot path planning using q-learning with guided Distance

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

Roles and recommendations from primary care physicians towards managing low-risk breast cancer survivors in a shared-care model with specialists in Singapore-a qualitative study.

BackgroundBreast cancer is prevalent and has high cure rates. The resultant increase in numbers of breast cancer survivors (BCS) may overwhelm the current oncology workforce in years to come. We postulate that primary care physicians (PCPs) could play an expanded role in comanaging survivors, provided they are given the appropriate tools and training to do so.ObjectiveTo explore the perspectives of PCPs towards managing BCS in a community-based shared-care programme with oncologists.MethodsEleven focus groups and six in-depth interviews were conducted with seventy PCPs recruited by purposive sampling. All sessions were audio-recorded, transcribed verbatim and coded by three independent investigators. Thematic data analysis was performed and the coding process facilitated by NVivo 12.ResultsMajority of PCPs reported currently limited roles in managing acute and non-cancer issues, optimizing comorbidities and preventive care. PCPs aspired to expand their role to include cancer surveillance, risk assessment and addressing unmet psychosocial needs. PCPs preferred to harmonize cancer survivorship management of their primary care patients who are also BCS, with defined role distinct from oncologists. Training to understand the care protocol, enhancement of communication skills, confidence and trust were deemed necessary. PCPs proposed selection criteria of BCS and adequacy of their medical information; increased consultation time; contact details and timely access to oncologists (if needed) in the shared-care programme.ConclusionsPCPs were willing to share the care of BCS with oncologists but recommended role definition, training, clinical protocol, resources and access to oncologist's consultation to optimize the programme implementation

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

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

CO2 Sequestration in Unmineable Coal Seams: Potential Environmental Impacts

An initial investigation into the potential environmental impacts of CO2 sequestration in unmineable coal seams has been conducted, focusing on changes in the produced water during enhanced coalbed methane (ECBM) production using a CO2 injection process (CO2-ECBM). Two coals have been used in this study, the medium volatile bituminous Upper Freeport coal (APCS 1) of the Argonne Premium Coal Samples series, and an as-mined Pittsburgh #8 coal, which is a high volatile bituminous coal. Coal samples were reacted with either synthetic produced water or field collected produced water and gaseous carbon dioxide at 40 οC and 50 bar to evaluate the potential for mobilizing toxic metals during CO2-ECBM/sequestration. Microscopic and x-ray diffraction analysis of the post-reaction coal samples clearly show evidence of chemical reaction, and chemical analysis of the produced water shows substantial changes in composition. These results suggest that changes to the produced water chemistry and the potential for mobilizing toxic trace elements from coalbeds are important factors to be considered when evaluating deep, unmineable coal seams for CO2 sequestration

A secondary Fracture Prevention Programme to reduce fractures, hospital admissions, and mortality rates

Conference Theme: Happy Staff - Healthy People (開心員工 - 共建民康)published_or_final_versionThe Hospital Authority Convention, Hong Kong, 10-11 May 2010