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Exploring Augmented Reality Triage Tools to Support Mass Casualty Incidents

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2022年第1期66卷 1664 - 1666页

作者： Cassidy Rae Nelson Joseph Conwell Samuel Lally Madelyn Cohn Kyle Tanous Jason Moats Joseph L. Gabbard Department of Industrial and Systems Engineering Virginia Tech Department of Mathematics Virginia Tech Electrical and Computer Engineering Virginia TechSchool of Visual Arts Virginia Tech Department of Computer Science Virginia Tech Texas A&M Engineering Extension Service Texas A&M University

ARTTS is a head-worn Augmented Reality (AR) Triage Tool Suite containing an initial sorting tool, virtual assessment tool, and virtual triage tag to assist first responders in mass casualty incidents. The initial sorting tool prompts stunned responders through first-wave tasks to aid recalibration from shock to triage. The virtual assessment tool provides novice responders, potentially confused by the chaos, with a walkthrough of the SALT triage flowchart. Finally, current emergency medical triage processes leverage static paper tags susceptible to loss or illegible damage. ARTTS’ virtual triage tags are dynamic, can be updated automatically with vital sensors or manually through responder interaction, and employ user interface emergent features based on individual patient conditions. This paper describes the UX process used to develop ARTTS.

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Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

Learning for Interval Prediction of Electricity Demand: A Cluster-Based Bootstrapping Approach

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SSRN

SSRN 2024年

作者： Dube, Rohit Gautam, Natarajan Banerjee, Amarnath Nagarajan, Harsha Industrial and Systems Engineering Texas A & M University College StationTX77843 United States Electrical Engineering and Computer Science Syracuse University SyracuseNY13210 United States Applied Mathematics and Plasma Physics Los Alamos National Laboratory Los Alamos NM87545 United States

Accurate predictions of electricity demands are necessary for managing operations in a small aggregation load setting like a Microgrid. Due to low aggregation, the electricity demands can be highly stochastic and point estimates would lead to inflated errors. Interval estimation in this scenario, would provide a range of values within which the future values might lie and helps quantify the errors around the point estimates. This paper introduces a residual bootstrap algorithm to generate interval estimates of day-ahead electricity demand. A machine learning algorithm is used to obtain the point estimates of electricity demand and respective residuals on the training set. The obtained residuals are stored in memory and the memory is further partitioned. Days with similar demand patterns are grouped in clusters using an unsupervised learning algorithm and these clusters are used to partition the memory. The point estimates for test day are used to find the closest cluster of similar days and the residuals are bootstrapped from the chosen cluster. This algorithm is evaluated on the real electricity demand data from EULR (End Use Load Research) and is compared to other bootstrapping methods for varying confidence intervals. © 2024, The Authors. All rights reserved.

关键词： Machine learning

Posterior sampling via Langevin dynamics based on generative priors

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arXiv 2024年

作者： Purohit, Vishal Repasky, Matthew Lu, Jianfeng Qiu, Qiang Xie, Yao Cheng, Xiuyuan Elmore Family School of Electrical and Computer Engineering Purdue University United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology United States Department of Mathematics Duke University United States Department of Physics Department of Chemistry Duke University United States

Posterior sampling in high-dimensional spaces using generative models holds significant promise for various applications, including but not limited to inverse problems and guided generation tasks. Despite many recent developments, generating diverse posterior samples remains a challenge, as existing methods require restarting the entire generative process for each new sample, making the procedure computationally expensive. In this work, we propose efficient posterior sampling by simulating Langevin dynamics in the noise space of a pre-trained generative model. By exploiting the mapping between the noise and data spaces which can be provided by distilled flows or consistency models, our method enables seamless exploration of the posterior without the need to re-run the full sampling chain, drastically reducing computational overhead. Theoretically, we prove a guarantee for the proposed noise-space Langevin dynamics to approximate the posterior, assuming that the generative model sufficiently approximates the prior distribution. Our framework is experimentally validated on image restoration tasks involving noisy linear and nonlinear forward operators applied to LSUN-Bedroom (256 x 256) and ImageNet (64 x 64) datasets. The results demonstrate that our approach generates high-fidelity samples with enhanced semantic diversity even under a limited number of function evaluations, offering superior efficiency and performance compared to existing diffusion-based posterior sampling techniques. © 2024, CC BY.

关键词： Inverse problems

Effects of Human Personal Space on the Robot Obstacle Avoidance Behavior: A Human-in-the-loop Assessment 65th

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Effects of Human Personal Space on the Robot Obstacle Avoida...

65th Human Factors and Ergonomics Society Annual Meeting, HFES 2021

作者： Chen, Yuhao Smith, Trevor Hewitt, Nathan Gu, Yu Hu, Boyi Department of Industrial and Systems Engineering University of Florida GainesvilleFL United States Department of Mechanical and Aerospace Engineering West Virginia University MorgantownWV United States Department of Electrical and Computer Engineering University of North Carolina at Charlotte CharlotteNC United States

ISBN: (纸本)9781713844129

To ensure both the physical and mental safety of humans during human-robot interaction (HRI), a rich body of literature has been accumulated, and the notion of socially acceptable robot behaviors has arisen. To be specific, it requires the motion of robots not only to be physically collision-free but also to consider and respect the social conventions developed and enforced in the human social contexts. Among these social conventions, personal space, or proxemics, is one of the most commonly considered in the robot behavioral design. Nevertheless, most previous research efforts assumed that robots could generate human-like motions by merely mimicking a human. Rarely are the robot’s behavioral algorithms assessed and verified by human participants. Therefore, to fill the research gap, a Turing-like simulation test, which contains the interaction of two agents (each agent could be a human or a robot) in a shared space was conducted. Participants (33 in total) were asked to identify and label the category of those agents followed by questionnaires. Results revealed that people who had different attitudes and prior expectations of appropriate robot behaviors responded to the algorithm differently, and their identification accuracy varied significantly. In general, by considering personal space in the robot obstacle avoidance algorithm, robots could demonstrate more humanlike motion behaviors which are confirmed by human experiments. © 2021 by Human Factors and Ergonomics Society. All rights reserved.

关键词： Machine design

Experimental implementation of chaos-based video encryption using visible light communication technology

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Applied Optics 2025年第9期64卷 C101-C118页

作者： Fotso, Sorel Bagio Nono Mbé, Jimmi Hervé Talla Takotue, Jean Ernest Takougang, Armand Cyrille Nzeukou Ndjiongue, Alain Richard Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang P.O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang P.O. Box: 134 Bandjoun Cameroon Laboratory of Industrial Systems and Environmental Engineering Fotso Victor University Institute of Technology University of Dschang P.O. Box 134 Bandjoun Cameroon School of Electrical and Information Engineering Faculty of Engineering and the Built Environment University of the Witwatersrand Johannesburg South Africa

Simulations are commonly used to develop and evaluate video encryption algorithms. Although these approaches are useful for demonstrating theoretical feasibility and algorithm performance, they neglect practical challenges and real-world communication conditions. This oversight creates a critical gap in evaluating their effectiveness and security in practical applications. In this paper, we describe a novel, to the best of our knowledge, video encryption scheme that employs a modified chaotic Colpitts oscillator, a customized Josephus problem, and ribonucleic acid (RNA) operations, and is implemented in visible light communication (VLC) environment. The modified Colpitts oscillator reveals significant phenomena, the most prominent for this study being chaos. The encryption scheme involves block scrambling and pixel-level permutation employing a customized Josephus problem and a column and row shifting technique. Diffusion is then achieved through a custom Josephus problem-oriented RNA operation combined with basic RNA operations and cipher block chaining (CBC). Based on the results, we found that the customized Josephus problem-based permutation performed more efficiently than traditional methods, whereas the column and row shifting permutation was more robust. By eliminating key sequence coding and item-to-item operations, the proposed RNA operation reduces computational overhead. This video encryption scheme is proven to be effective and resilient at securing video data transmitted over the VLC channel, and it constitutes a significant advancement in areas such as surveillance, medical imaging, and military communication requiring robust secured video data. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Medical imaging

Use and usability of software verification methods to detect behaviour interference when teaching an assistive home companion robot: A proof-of-concept study

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Paladyn 2021年第1期12卷 402-422页

作者： Koay, Kheng Lee Webster, Matt Dixon, Clare Gainer, Paul Syrdal, Dag Fisher, Michael Dautenhahn, Kerstin Department of Computer Science School of Physics Engineering and Computer Science University of Hertfordshire College Lane Hertfordshire HatfieldAL10 9AB United Kingdom School of Computer Science and Mathematics Liverpool John Moores University LiverpoolL2 2QP United Kingdom Department of Computer Science University of Manchester ManchesterM13 9PL United Kingdom Department of Computer Science University of Liverpool Liverpool United Kingdom Department of Computer Science School of Physics Engineering and Computer Science University of Hertfordshire College Lane Hertfordshire HatfieldAL10 9AB United Kingdom Departments of Electrical and Computer Engineering and Systems Design Engineering University of Waterloo ON Canada

When studying the use of assistive robots in home environments, and especially how such robots can be personalised to meet the needs of the resident, key concerns are issues related to behaviour verification, behaviour interference and safety. Here, personalisation refers to the teaching of new robot behaviours by both technical and non-technical end users. In this article, we consider the issue of behaviour interference caused by situations where newly taught robot behaviours may affect or be affected by existing behaviours and thus, those behaviours will not or might not ever be executed. We focus in particular on how such situations can be detected and presented to the user. We describe the human-robot behaviour teaching system that we developed as well as the formal behaviour checking methods used. The online use of behaviour checking is demonstrated, based on static analysis of behaviours during the operation of the robot, and evaluated in a user study. We conducted a proof-of-concept human-robot interaction study with an autonomous, multi-purpose robot operating within a smart home environment. Twenty participants individually taught the robot behaviours according to instructions they were given, some of which caused interference with other behaviours. A mechanism for detecting behaviour interference provided feedback to participants and suggestions on how to resolve those conflicts. We assessed the participants' views on detected interference as reported by the behaviour teaching system. Results indicate that interference warnings given to participants during teaching provoked an understanding of the issue. We did not find a significant influence of participants' technical background. These results highlight a promising path towards verification and validation of assistive home companion robots that allow end-user personalisation. © 2021 Kheng Lee Koay et al., published by De Gruyter.

关键词： Human robot interaction

Codes for Correcting t Limited-Magnitude Sticky Deletions

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arXiv 2023年

作者： Wang, Shuche Vu, Van Khu Tan, Vincent Y.F. Institute of Operations Research and Analytics National University of Singapore Singapore Department of Mathematics National University of Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Industrial Systems Engineering and Management National University of Singapore Singapore

Codes for correcting sticky insertions/deletions and limited-magnitude errors have attracted significant attention due to their applications of flash memories, racetrack memories, and DNA data storage systems. In this paper, we first consider the error type of t-sticky deletions with -limited-magnitude and propose a non-systematic code for correcting this type of error with redundancy 2t(1 − 1/p) · log(n + 1) + O(1), where p is the smallest prime larger than + 1. Next, we present a systematic code construction with an efficient encoding and decoding algorithm with redundancy ⌈2t(1−1/p)⌉·⌈log p⌉ log(n+1)+O(log log n), log p where p is the smallest prime larger than + 1. Copyright © 2023, The Authors. All rights reserved.

关键词： Redundancy

Codes for Correcting Asymmetric Adjacent Transpositions and Deletions

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arXiv 2023年

Codes in the Damerau–Levenshtein metric have been extensively studied recently owing to their applications in DNA-based data storage. In particular, Gabrys, Yaakobi, and Milenkovic (2017) designed a length-n code correcting a single deletion and s adjacent transpositions with at most (1 + 2s) log n bits of redundancy. In this work, we consider a new setting where both asymmetric adjacent transpositions (also known as right-shifts or left-shifts) and deletions may occur. We present several constructions of the codes correcting these errors in various cases. In particular, we design a code correcting a single deletion, s+ right-shift, and s− left-shift errors with at most (1 + s) log(n + s + 1) + 1 bits of redundancy where s = s+ + s−. In addition, we investigate codes correcting t 0-deletions, s+ right-shift, and s− left-shift errors with both uniquely-decoding and list-decoding algorithms. Our main contribution here is the construction of a list-decodable code with list size O(nmin{s+1,t}) and with at most (max{t, s + 1}) log n + O(1) bits of redundancy, where s = s+ + s−. Finally, we construct both non-systematic and systematic codes for correcting blocks of 0-deletions with `-limited-magnitude and s adjacent transpositions. Copyright © 2023, The Authors. All rights reserved.

关键词： Redundancy