ORCID Profile
0000-0001-7828-0741
Current Organisations
University of South Australia
,
University of Queensland
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Publisher: Field Robotics Publication Society
Date: 10-03-2022
DOI: 10.55417/FR.2022034
Abstract: Ground-penetrating radar mounted on a micro aerial vehicle (MAV) is a promising tool to assist humanitarian landmine clearance. However, the quality of synthetic aperture radar images depends on accurate and precise motion estimation of the radar antennas as well as generating informative viewpoints with the MAV. This paper presents a complete and automatic airborne ground-penetrating synthetic aperture radar (GPSAR) system. The system consists of a spatially calibrated and temporally synchronized industrial grade sensor suite that enables navigation above ground level, radar imaging, and optical imaging. A custom mission planning framework allows generation and automatic execution of stripmap and circular GPSAR trajectories controlled above ground level as well as aerial imaging survey flights. A factor graph based state estimator fuses measurements from dual receiver real-time kinematic (RTK) global navigation satellite system (GNSS) and an inertial measurement unit (IMU) to obtain precise, high-rate platform positions and orientations. Ground truth experiments showed sensor timing as accurate as 0.8 µs and as precise as 0.1 µs with localization rates of 1 kHz. The dual position factor formulation improves online localization accuracy up to 40 % and batch localization accuracy up to 59 % compared to a single position factor with uncertain heading initialization. Our field trials validated a localization accuracy and precision that enables coherent radar measurement addition and detection of radar targets buried in sand. This validates the potential as an aerial landmine detection system.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 04-02-2020
DOI: 10.1007/S10514-020-09903-2
Abstract: Unmanned aerial vehicles represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general informative path planning framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori. The approach is capable of learning and focusing on regions of interest via adaptation to map either discrete or continuous variables on the terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a coarse grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and a proof of concept for an agricultural monitoring task.
Publisher: ONERA
Date: 2014
Publisher: IEEE
Date: 24-10-2020
Publisher: Springer Science and Business Media LLC
Date: 21-08-2018
Publisher: JMIR Publications Inc.
Date: 18-02-2022
DOI: 10.2196/35558
Abstract: Workplace bullying and violence (WBV) are well-documented issues in the midwifery profession. Negative workplace culture, conflict, and bullying are the most common forms of workplace violence experienced by midwives. Workplace violence increases the risk of midwives experiencing burnout, compassion fatigue, psychological trauma, poor mental health, absenteeism, loss of passion for the midwifery profession, job dissatisfaction, and poor job retention. Midwifery students describe workplace violence in the form of physical, emotional, or verbal abuse, and bullying. Therefore, there is a justification to develop conflict resolution strategies and resilience in midwifery students prior to graduation. Our aim is to develop and facilitate a bespoke education program for South Australian midwifery students to enable them to develop skills in conflict resolution, build resilience, and identify self-care strategies. This study will undertake a preparatory phase summarizing the body of literature on midwifery students’ knowledge, understanding, and experiences of WBV. Following this, a 3-phase sequential mixed methods research design study will be undertaken. In Phase 1, quantitative data will be collected via a semistructured questionnaire and a validated conflict measurement tool, before and after attending an education workshop, and will be analyzed using descriptive and inferential statistics. Results from Phase 1 will inform and guide the development of an interview schedule for Phase 2. In Phase 2, qualitative data will be gathered by facilitating one-to-one interviews and a thematic analysis will be undertaken to gain a deeper understanding of midwifery students’ experiences of WBV. In Phase 3, data integration using triangulation will be undertaken and meta-inferences will be developed via the integration of results and findings from Phases 1 and 2. The preparatory phase will commence in October 2021. Phase 1 will commence in 2022 with analysis of pre- and posteducation results anticipated to be completed by December 2022. Phase 2 will be developed from findings of the preparatory phase and results of Phase 1. An interpretation of verbatim interview transcripts is estimated to be undertaken by April 2023. Phase 3 of the study is expected to commence in May 2023, and this will involve the analysis of collective evidence gathered from Phases 1 and 2. The anticipated completion date for the study is December 2023. The outcomes of this research will provide insights into the prevalence and impact of WBV experienced by midwifery students. The findings of the research will report on levels of knowledge, skills, and confidence, and will assess the impact of a bespoke conflict resolution and resilience education workshop for midwifery students in managing WBV. PRR1-10.2196/35558
Publisher: IEEE
Date: 11-07-2021
Publisher: IEEE
Date: 09-2015
Publisher: IEEE
Date: 24-10-2020
Publisher: IEEE
Date: 30-05-2021
Publisher: IEEE
Date: 08-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: SAGE Publications
Date: 20-06-2018
Abstract: In this paper, we introduce a novel algorithm for incorporating uncertainty into lookahead planning. Our algorithm searches through connected graphs with uncertain edge costs represented by known probability distributions. As a robot moves through the graph, the true edge costs of adjacent edges are revealed to the planner prior to traversal. This locally revealed information allows the planner to improve performance by predicting the benefit of edge costs revealed in the future and updating the plan accordingly in an online manner. Our proposed algorithm, risk-aware graph search (RAGS), selects paths with high probability of yielding low costs based on the probability distributions of in idual edge traversal costs. We analyze RAGS for its correctness and computational complexity and provide a bounding strategy to reduce its complexity. We then present results in an ex le search domain and report improved performance compared with traditional heuristic search techniques. Lastly, we implement the algorithm in both simulated missions and field trials using satellite imagery to demonstrate the benefits of risk-aware planning through uncertain terrain for low-flying unmanned aerial vehicles.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: IEEE
Date: 10-2016
Publisher: IEEE
Date: 05-2013
Publisher: Elsevier BV
Date: 12-2023
Publisher: MIT Press
Date: 12-2019
DOI: 10.1162/EVCO_A_00239
Abstract: We present Modular Memory Units (MMUs), a new class of memory-augmented neural network. MMU builds on the gated neural architecture of Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTMs), to incorporate an external memory block, similar to a Neural Turing Machine (NTM). MMU interacts with the memory block using independent read and write gates that serve to decouple the memory from the central feedforward operation. This allows for regimented memory access and update, giving our network the ability to choose when to read from memory, update it, or simply ignore it. This capacity to act in detachment allows the network to shield the memory from noise and other distractions, while simultaneously using it to effectively retain and propagate information over an extended period of time. We train MMU using both neuroevolution and gradient descent, and perform experiments on two deep memory benchmarks. Results demonstrate that MMU performs significantly faster and more accurately than traditional LSTM-based methods, and is robust to dramatic increases in the sequence depth of these memory benchmarks.
Publisher: IEEE
Date: 05-2020
Publisher: IEEE
Date: 24-10-2020
Publisher: ACM
Date: 07-2017
Publisher: IEEE
Date: 09-2015
Publisher: IEEE
Date: 30-05-2021
Publisher: JMIR Publications Inc.
Date: 09-12-2021
Abstract: orkplace bullying and violence (WBV) are well-documented issues in the midwifery profession. Negative workplace culture, conflict, and bullying are the most common forms of workplace violence experienced by midwives. Workplace violence increases the risk of midwives experiencing burnout, compassion fatigue, psychological trauma, poor mental health, absenteeism, loss of passion for the midwifery profession, job dissatisfaction, and poor job retention. Midwifery students describe workplace violence in the form of physical, emotional, or verbal abuse, and bullying. Therefore, there is a justification to develop conflict resolution strategies and resilience in midwifery students prior to graduation. ur aim is to develop and facilitate a bespoke education program for South Australian midwifery students to enable them to develop skills in conflict resolution, build resilience, and identify self-care strategies. his study will undertake a preparatory phase summarizing the body of literature on midwifery students’ knowledge, understanding, and experiences of WBV. Following this, a 3-phase sequential mixed methods research design study will be undertaken. In Phase 1, quantitative data will be collected via a semistructured questionnaire and a validated conflict measurement tool, before and after attending an education workshop, and will be analyzed using descriptive and inferential statistics. Results from Phase 1 will inform and guide the development of an interview schedule for Phase 2. In Phase 2, qualitative data will be gathered by facilitating one-to-one interviews and a thematic analysis will be undertaken to gain a deeper understanding of midwifery students’ experiences of WBV. In Phase 3, data integration using triangulation will be undertaken and meta-inferences will be developed via the integration of results and findings from Phases 1 and 2. he preparatory phase will commence in October 2021. Phase 1 will commence in 2022 with analysis of pre- and posteducation results anticipated to be completed by December 2022. Phase 2 will be developed from findings of the preparatory phase and results of Phase 1. An interpretation of verbatim interview transcripts is estimated to be undertaken by April 2023. Phase 3 of the study is expected to commence in May 2023, and this will involve the analysis of collective evidence gathered from Phases 1 and 2. The anticipated completion date for the study is December 2023. he outcomes of this research will provide insights into the prevalence and impact of WBV experienced by midwifery students. The findings of the research will report on levels of knowledge, skills, and confidence, and will assess the impact of a bespoke conflict resolution and resilience education workshop for midwifery students in managing WBV. RR1-10.2196/35558
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 30-05-2021
Publisher: IEEE
Date: 30-05-2021
Publisher: IEEE
Date: 30-05-2021
Publisher: Springer Science and Business Media LLC
Date: 21-01-2020
Publisher: Springer Singapore
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2017
Publisher: SAGE Publications
Date: 16-12-2014
Abstract: This paper examines temporal difference reinforcement learning with adaptive and directed exploration for resource-limited missions. The scenario considered is that of an unpowered aerial glider learning to perform energy-gaining flight trajectories in a thermal updraft. The presented algorithm, eGP-SARSA( λ), uses a Gaussian process regression model to estimate the value function in a reinforcement learning framework. The Gaussian process also provides a variance on these estimates that is used to measure the contribution of future observations to the Gaussian process value function model in terms of information gain. To avoid myopic exploration we developed a resource-weighted objective function that combines an estimate of the future information gain using an action rollout with the estimated value function to generate directed explorative action sequences. A number of modifications and computational speed-ups to the algorithm are presented along with a standard GP-SARSA( λ) implementation with [Formula: see text]-greedy exploration to compare the respective learning performances. The results show that under this objective function, the learning agent is able to continue exploring for better state-action trajectories when platform energy is high and follow conservative energy-gaining trajectories when platform energy is low.
Publisher: IEEE
Date: 05-2019
Publisher: Springer Science and Business Media LLC
Date: 26-02-2018
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 12-2020
Publisher: IEEE
Date: 05-2020
Publisher: IEEE
Date: 12-2017
Location: Switzerland
No related grants have been discovered for Jen Jen Chung.