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Roedunet International Conference (RoEduNet)

作者： Cristian Vizitiu Ioan Turcin Kevin Alexander Dominey Selver Softic Adrian-Cătălin Dinculescu Vlad I. Bocăneţ Cosmin Dugan Sorin-Aurel Moraru The Space Applications and Technologies Laboratory Institute of Space Science -Subsidiary of INFLPR Magurele Romania Department of Automatics and Information Technology Faculty of Electrical Engineering and Computer Science Transilvania University of Brasov Brasov Romania Department Automation Technology CAMPUS 02 University of Applied Sciences Graz Austria Department IT & Business Informatics CAMPUS 02 University of Applied Sciences Graz Austria Department of Manufacturing Engineering Faculty of Industrial Engineering Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (数字)9798331540388

ISBN: (纸本)9798331540395

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems Engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation.

关键词： Adaptation models Generative AI Psychology Human factors Machine learning Predictive models Physiology Digital twins Modeling Biomedical monitoring

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Bioluminescence tomography system for in vivo irradiation guidance

Bioluminescence tomography system for in vivo irradiation gu...

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Optical Tomography and Spectroscopy, OTS 2022

作者： Deng, Zijian Xu, Xiangkun Iordachita, Iulian Dehghani, Hamid Zhang, Bin Wong, John W. Wang, Ken Kang-Hsin Department of Radiation Oncology and Molecular Radiation Sciences Johns Hopkins University BaltimoreMD21287 United States Biomedical Imaging and Radiation Technology Laboratory Department of Radiation Oncology University of Texas Southwestern Medical Center DallasTX75390 United States Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom School of Biomedical Engineering Dalian University of Technology Liaoning Dalian116024 China

We constructed a bioluminescence tomography(BLT) to localize soft tissue targets for preclinical radiotherapy study. With the threshold and margin designed for target volume, BLT can provide opportunity to perform con... 详细信息

ISBN: (纸本)9781557528209

关键词： Irradiation

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Compositional Inversion for Stable Diffusion Models

arXiv

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arXiv 2023年

作者： Zhang, Xulu Wei, Xiao-Yong Wu, Jinlin Zhang, Tianyi Zhang, Zhaoxiang Lei, Zhen Li, Qing Department of Computing the Hong Kong Polytechnic University Hong Kong Center for Artificial Intelligence and Robotics HKISI CAS Hong Kong College of Computer Science Sichuan University Chengdu China School of Artificial Intelligence UCAS Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA Beijing China

Inversion methods, such as Textual Inversion, generate personalized images by incorporating concepts of interest provided by user images. However, existing methods often suffer from overfitting issues, where the dominant presence of inverted concepts leads to the absence of other desired concepts. It stems from the fact that during inversion, the irrelevant semantics in the user images are also encoded, forcing the inverted concepts to occupy locations far from the core distribution in the embedding space. To address this issue, we propose a method that guides the inversion process towards the core distribution for compositional embeddings. Additionally, we introduce a spatial regularization approach to balance the attention on the concepts being composed. Our method is designed as a post-training approach and can be seamlessly integrated with other inversion methods. Experimental results demonstrate the effectiveness of our proposed approach in mitigating the overfitting problem and generating more diverse and balanced compositions of concepts in the synthesized images. The source code is available at https://***/zhangxulu1996/Compositional-Inversion. © 2023, CC BY.

关键词： Semantics

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Distributed Twins in Edge Computing: Blockchain and IOTA

arXiv

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arXiv 2023年

作者： Sadad, Anwar Khan, Muazzam A. Ghaleb, Baraq Ali Khan, Fadia Driss, Maha Boulila, Wadii Ahmad, Jawad Department of Computer Science Quaid-i-Azam University Islamabad Pakistan School of Computing Engineering & the Built Environment Edinburgh Napier University Edinburgh United Kingdom Department of Mechatronics Engineering Wah Engineering College University of Wah Wah Pakistan Security Engineering Laboratory Ccis Prince Sultan University Riyadh Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh Saudi Arabia

Blockchain (BC) and Information for Operational and Tactical Analysis (IOTA) are distributed ledgers that record a huge number of transactions in multiple places at the same time using decentralized databases. Both BC and IOTA facilitate Internet-of-Things (IoT) by overcoming the issues related to traditional centralized systems, such as privacy, security, resources cost, performance, and transparency. Still, IoT faces the potential challenges of real-time processing, resource management, and storage services. Edge computing (EC) has been introduced to tackle the underlying challenges of IoT by providing real-time processing, resource management, and storage services nearer to IoT devices on the network's edge. To make EC more efficient and effective, solutions using BC and IOTA have been devoted to this area. However, BC and IOTA came with their pitfalls. This survey outlines the pitfalls of BC and IOTA in EC and provides research directions to be investigated further. © 2023, CC BY.

关键词： Edge computing

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A Routing-based Strategy to Socially Approach Multiple Individuals in Cluttered Environments

A Routing-based Strategy to Socially Approach Multiple Indiv...

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IEEE Latin American robotics Symposium, LARS

作者： Aline F. F. Silva Douglas G. Macharet Department of Computer Science Computer Vision and Robotics Laboratory (VeRLab) Universidade Federal de Minas Gerais Brazil Laboratório de Inteligência Computacional e Robótica (LICRo) Instituto Federal do Triângulo Mineiro (IFTM) Campus Patrocínio Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

The use of autonomous mobile robots is expected to continue increasing in different parts of society in the next years. In this context, when not restricted to industrial environments only, it is essential to provide such devices with the ability to behave in a so-called socially acceptable manner. In the Human-Robot Interaction (HRI) field, a fundamental problem is to approach people, i.e., try to initiate and maintain an interaction. This task involves different challenges since some implicit nonverbal social rules must be respected, especially those concerning people's personal spaces (called proxemics). Considering the presence of multiple individuals to be approached, a combinatorial optimization aspect must also be tackled, where an optimal sequence of visits must be selected. In this paper, we formulate this problem as a Generalized Travelling Salesman Problem (GTSP) and combine it with a Probabilistic Roadmap (PRM) planner in order to account for obstacles in the environment. Numerical simulations show that the methodology produces length-optimized paths that are feasible, safe, and respect the social conventions.

关键词： Conferences Education Human-robot interaction Probabilistic logic Numerical simulation Space exploration Mobile robots

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KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation

arXiv

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arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

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Colibri5: Real-Time Monocular 5-DoF Trocar Pose Tracking for Robot-Assisted Vitreoretinal Surgery

Colibri5: Real-Time Monocular 5-DoF Trocar Pose Tracking for...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Shervin Dehghani Michael Sommersperger Mahdi Saleh Alireza Alikhani Benjamin Busam Peter Gehlbach Iulian Iordachita Nassir Navab M. Ali Nasseri Department of Computer Science Technische Universität München München Germany Augenklinik und Poliklinik Klinikum Rechts der Isar der Technische Universität München München Germany Wilmer Eye Institute Johns Hopkins Hospital Baltimore MD USA Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Retinal surgery is a complex medical procedure that requires high precision dexterity to perform delicate instrument maneuvers with sub-millimeter accuracy. Minimizing the manual tremor and achieving precise and repeatable execution of surgical tasks has motivated the development of robotic platforms to overcome the limitations of manual surgery. However, specific tasks, such as instrument insertion through the trocar, are more challenging in robotic surgery than in conventional manual procedures since the robot control is often optimized for navigation inside the eye. This challenges the integration of robotic systems, creating a high cognitive load on the operator and prolonging the surgery time. Moreover, misalignment of the robot’s remote center of motion (RCM) and trocar position during the procedure can lead to excessive forces between the instrument and the trocar, potentially causing patient trauma. Precise and rapid localization of the trocars enables the automation of the insertion procedure and dynamic compensation of eye *** this work, we present a real-time marker-less method for 3D pose tracking of trocar, achieved with only a single monocular camera. Our experiments show promising results towards real-time trocar pose estimation and tracking, achieving an average error of 3 ◦ in trocar orientation estimation, with an average processing time of 15 fps. This could serve as a foundation to improve robotic systems’ automation, integration, and efficiency of robotic systems for retinal surgery. The dataset created for this work is made publicly available.

关键词： Tracking Navigation Instruments Robot vision systems Surgery Manuals Retina

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Grand Challenges in the Verification of Autonomous Systems

arXiv

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arXiv 2024年

作者： Leahy, Kevin Asgari, Hamid Dennis, Louise A. Feather, Martin S. Fisher, Michael Ibanez-Guzman, Javier Logan, Brian Olszewska, Joanna I. Redfield, Signe Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States Complex and Autonomous Systems Research Group Thales UK Research Reading United Kingdom Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Department of Computer Science University of Manchester Manchester United Kingdom Renault Guyancourt France Department of Computing Science University of Aberdeen Aberdeen United Kingdom Department of Information and Computing Sciences Utrecht University Utrecht Netherlands School of Computing and Engineering University of the West of Scotland Glasgow United Kingdom U.S. Naval Research Laboratory WashingtonDC United States

Autonomous systems use independent decisionmaking with only limited human intervention to accomplish goals in complex and unpredictable environments. As the autonomy technologies that underpin them continue to advance, these systems will find their way into an increasing number of applications in an ever wider range of settings. If we are to deploy them to perform safety-critical or mission-critical roles, it is imperative that we have justified confidence in their safe and correct operation. Verification is the process by which such confidence is established. However, autonomous systems pose challenges to existing verification practices. This paper highlights viewpoints of the Roadmap Working Group of the IEEE robotics and Automation Society Technical Committee for Verification of Autonomous Systems, identifying these grand challenges, and providing a vision for future research efforts that will be needed to address them. © 2024, CC BY.

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Line Drawing Guided Progressive Inpainting of Mural Damage

arXiv

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arXiv 2022年

作者： Li, Luxi Zou, Qin Zhang, Fan Yu, Hongkai Chen, Long Song, Chengfang Huang, Xianfeng Wang, Xiaoguang Li, Qingquan Department of Computer Science Technology United International College of Beijing Normal University HongKong Baptist University Zhuhai China Machine Vision and Robotics Laboratory School of Computer Science Wuhan University Wuhan China State Key Laboratory of Surveying Mapping and Remote Sensing Information Engineering Wuhan University Wuhan China Department of Electrical Engineering and Computer Science Cleveland State University OH United States Institute of Automation Chinese Academy of Sciences Beijing China Cultural Heritage Intelligent Computing Laboratory Wuhan University Wuhan China Guangming Laboratory Shenzhen University Shenzhen China

Mural image inpainting is far less explored compared to its natural image counterpart and remains largely unsolved. Most existing image-inpainting methods tend to take the target image as the only input and directly repair the damage to generate a visually plausible result. These methods obtain high performance in restoration or completion of some pre-defined objects, e.g., human face, fabric texture, and printed texts, etc., however, are not suitable for repairing murals with varying subjects and large damaged areas. Moreover, due to discrete colors in paints, mural inpainting may suffer from apparent color bias. To this end, in this paper, we propose a line drawing guided progressive mural inpainting method. It divides the inpainting process into two steps: structure reconstruction and color correction, implemented by a structure reconstruction network (SRN) and a color correction network (CCN), respectively. In structure reconstruction, SRN utilizes the line drawing as an assistant to achieve large-scale content authenticity and structural stability. In color correction, CCN operates a local color adjustment for missing pixels which reduces the negative effects of color bias and edge jumping. The proposed approach is evaluated against the current state-of-the-art image inpainting methods. Qualitative and quantitative results demonstrate the superiority of the proposed method in mural image inpainting. The codes and data are available at https://***/qinnzou/mural-image-inpainting . © 2022, CC BY-NC-ND.

关键词： Restoration

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IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction

arXiv

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arXiv 2023年

作者： Yang, Jing Chen, Yuehai Du, Shaoyi Chen, Badong Principe, Jose C. School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong Xi'An710049 China Institute of Articial Intelligence and Robotics College of Articial Intelligence Xi'an Jiaotong University Shanxi Xi'An710049 China The Computational NeuroEngineering Laboratory Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians around is beneficial for policy decision to avoid collision. It is a challenging issue because humans have different walking motions, and the interactions between humans and objects in the current environment, especially between humans themselves, are complex. Previous researchers focused on how to model human-human interactions but neglected the relative importance of interactions. To address this issue, a novel mechanism based on correntropy is introduced. The proposed mechanism not only can measure the relative importance of human-human interactions but also can build personal space for each pedestrian. An interaction module including this data-driven mechanism is further proposed. In the proposed module, the data-driven mechanism can effectively extract the feature representations of dynamic human-human interactions in the scene and calculate the corresponding weights to represent the importance of different interactions. To share such social messages among pedestrians, an interaction-aware architecture based on long short-term memory network for trajectory prediction is designed. Experiments are conducted on two public datasets. Experimental results demonstrate that our model can achieve better performance than several latest methods with good performance. © 2023, CC BY-NC-ND.

关键词： Long short-term memory

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