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作者： E. Silva D. Viegas A. Martins J. Almeida C. Almeida B. Neves P. Madureira A.J. Wheeler G. Salavasidis A. Phillips A. Schaap B. Murton A. Berry A. Weir G. Dooly E. Omerdic D. Toal P.C. Collins M. Miranda C. Petrioli C. Barrera Rodríguez D. Demoor C. Drouet G.El Serafy S.M. Jesus J. Dañobeitia V. Tegas S. Cusi L. Lopes B. Bodo L. Beguery S. VanDam J. Dumortier L. Neves V. Srivastava T.G. Dahlgren J. Thomassen Hestetun R. Eiras R. Caldeira C. Rossi J. Spearman L. Somoza F.J. González R. Bartolomé P. Bahurel INESC TEC Porto Portugal Instituto Superior Engenharia do Porto ISEP Portugal Portuguese Task Group for the Extension of the Continental Shelf EMEPC Portugal Department of Geosciences and Institute of Earth Sciences University of Évora Portugal SFI Centre for Research in Applied Geosciences School of Biological Earth & Environmental Sciences University College Cork Ireland Marine Autonomous and Robotic Systems National Oceanography Centre NOC United Kingdom Ocean Technology and Engineering NOC United Kingdom Ocean BioGeosciences NOC United Kingdom Marine Institute Ireland University of Limerick Centre for Robotics & Intelligent Systems CRIS Ireland School of Biological Science Queen’s University Belfast Northern Ireland Instituto Português do Mar e Atmosfera IPMA Portugal WSENSE Italy Head of Vehicle Base VIMAS Consortium for the Design Equipping and Exploitation of the Oceanic Platform of the Canary Islands Spain GREENOV-ITES France Unit of Coastal and Sea Systems Stichting Deltares& TU Delft The Netherlands LARSys University of Algarve Portugal EMSO-ERIC Italy Marine Technology Unit UTM-CSIC Spain La Palma Research Centre Spain Alseamar France Agora Partners Malha Technology Israel Timelex Belgium Gesi Belgium NORCE Norwegian Research Centre Norway Forum Oceano UPTEC POLO DO MAR Portugal ARDITI Portugal Center for Automation and Robotics UPM-CSIC Universidad Politécnica de Madrid Spain HRWallingford United Kingdom Geological Survey of Spain Marine Geology Resources and Extreme Environments IGME-CSIC Spain Institute for Marine Science ICM-CSIC Spain Mercator Ocean France

By creating a dependable, transparent, and cost-effective system for forecasting and ongoing environmental impact monitoring of exploration and exploitation activities in the deep sea, TRIDENT seeks to contribute to the sustainable exploitation of seabed mineral resources. In order to operate autonomously in remote locations under harsh conditions and send real-time data to authorities in charge of granting licenses and providing oversight, this system will create and integrate new technology and innovative solutions. The efficient monitoring and inspection system that will be created will abide by national and international legal frameworks. At the sea surface, mid-water, and the bottom, TRIDENT will identify all pertinent physical, chemical, geological, and biological characteristics that must be monitored. It will also look for data gaps and suggest procedures for addressing them. These are crucial actions to take in order to produce accurate indicators of excellent environmental status, statistically robust environmental baselines, and thresholds for significant impact, allowing for the standardization of methods and tools. In order to monitor environmental parameters on mining and reference areas at representative spatial and temporal scales, the project consortium will thereafter develop and test an integrated system of stationary and mobile observatory platforms outfitted with the most recent automatic sensors and samplers. The system will incorporate high-capacity data processing pipelines able to gather, transmit, process, and display monitoring data in close to real-time to facilitate prompt actions for preventing major harm to the environment. Last but not least, it will offer systemic and technological solutions for predicting probable impacts of applying the developed monitoring and mitigation techniques.

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Image Segmentation of Throat Swab Sampling Based on Mask R-CNN

Image Segmentation of Throat Swab Sampling Based on Mask R-C...

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Chinese automation Congress (CAC)

作者： Xinbing Xu Yongming Yang Yuanyuan Zhou Huanqi Cui Shaoqiang Li Wenliang Guo Shiyue Li Hao Liu School of Information Science and Engineering Northeastern University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Minimally Invasive Surgical Robot Shenyang China State Key Laboratory of Respiratory Diseases National Clinical Research Center for Respiratory Disease Guangzhou Institute of Respiratory Health The First Affiliated Hospital of Guangzhou Medical University Guangzhou China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This study aims to provide a feasibility basis for image processing for automatic throat swab robot sampling. For target segmentation and identification in the oral environment, we use a robust instance segmentation algorithm Mask R-CNN. In order to solve the insufficiency of the data used, we use data augmentation methods based on the original data to increase the available effective data. Experiments have proved that good results have been achieved for the segmentation of the throat swab and the posterior pharyngeal wall.

关键词： Image segmentation Robots Tongue Feature extraction Image color analysis Training Annotations

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Diagnostic and Prediction of Machines Health Status as Exemplary Best Practice for Vehicle Production System

Diagnostic and Prediction of Machines Health Status as Exemp...

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IEEE Vehicular Technology Conference

作者： Ren C. Luo Hao Wang National Taiwan University International Center of Excellence in Intelligent Robotics and Automation Research Taipei Taiwan

ISBN: (纸本)9781538663592;9781538663585

Diagnosis and prediction of the health status of vehicle components production line machine is the core requirement for global manufacturing system. With the development of Internet of things (IoT), there are enormous big data of production line could be collected quickly and stored in large quantities. The development of artificial intelligence makes it possible to deal with big data efficiently. Due to the industrial requirement of health self-diagnosis for vehicle production line, this paper presents a method based on Artificial Neural Network (ANN) to diagnose the health status of production line machines using the data produced by the machines. The PID control parameters of motors are segmented to simulate the health status of the machines in a long duration. We use three kinds of artificial neural network (ANN) methods to train the model of the relationship between the large data trend and the diagnostic score of the machine, it is demonstrated that it becomes more efficient than traditional empirical analysis to improve the speed and accuracy for diagnostic and prediction of machines health status.

关键词： artificial intelligence automobile industry automotive components Big Data condition monitoring Internet of Things manufacturing systems neural nets production engineering computing three-term control big data Health Status Three term control systems production engineering computing Internet of things Artificial Intelligence machine Automobile Components Manufacturing systems automobile industry Neural network Condition monitoring machinery Diagnosis Artificial neural networks

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A Scalable Deep Attention Mechanism of Instance Segmentation for the Investigation of Chromosome

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SLAS technology 2025年 100306页

作者： Neelam Umbreen Sara Ali Hasan Sajid Yasar Ayaz Shrooq Alsenan Yunyoung Nam So Yeon Kim Muhammad Baber Sial National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: neelamumbreen.phd@smme.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan Suzhou Industrial Park (SIP) Monash Research Institute of Science and Technology Monash University 8Q9C+R6W Suzhou Jiangsu China. School of Mechanical and Manufacturing Engineering (SMME) National University of Sciences and Technology (NUST) Islamabad NUST-COVENTRY Human-Robot Interaction (NC-HRI) Laboratory Pakistan Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: sarababer@smme.nust.edu.pk. National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: hasan.sajid@smme.nust.edu.pk. Intelligent Field Robotics Laboratory (IFRL) National Center for Artificial Intelligence (NCAI) National University of Sciences and Technology (NUST) Islamabad Pakistan. Electronic address: yasar@smme.nust.edu.pk. Information Systems Department College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia. Electronic address: shalsenan@pnu.edu.sa. Emotional and Intelligent Child Care Convergence Research Center Soonchunhyang University Asan 31538 Korea. Electronic address: ynam@sch.ac.kr. Department of Artificial Intelligence Department of Software and Computer Engineering Ajou University. Electronic address: jebi1771@ajou.ac.kr. School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China. Electronic address: babersial@***.

Chromosome segmentation in metaphase images is a critical yet challenging task in cytogenetics and genomics due to the inherent complexity, variability in chromosome shapes, and the scarcity of high-quality annotated datasets. This study proposes a robust instance segmentation framework that integrates an automated annotation pipeline with an enhanced deep learning architecture to address these challenges. A novel dataset is introduced, comprising metaphase images and corresponding karyograms, annotated with precise instance segmentation information across 24 chromosome classes in COCO format. To overcome the labor-intensive manual annotation process, a feature-based image registration technique leveraging SIFT and homography is employed, enabling the accurate mapping of chromosomes from karyograms to metaphase images and significantly improving annotation quality and segmentation performance. The proposed framework includes a custom Mask R-CNN model enhanced with an Attention-based Feature Pyramid Network (AttFPN), spatial attention mechanisms, and a LastLevelMaxPool block for superior multi-scale feature extraction and focused attention on critical regions of the image. Experimental evaluations demonstrate the model's efficacy, achieving a mean average precision (mAP) of 0.579 at IoU = 0.50:0.95, surpassing the baseline Mask R-CNN and Mask R-CNN with AttFPN by 3.94% and 5.97% improvements in mAP and AP50, respectively. Notably, the proposed architecture excels in segmenting small and medium-sized chromosomes, addressing key limitations of existing methods. This research not only introduces a state-of-the-art segmentation framework but also provides a benchmark dataset, setting a new standard for chromosome instance segmentation in biomedical imaging. The integration of automated dataset creation with advanced model design offers a scalable and transferable solution, paving the way for tackling similar challenges in other domains of biomedical and cytogenetic imagi

关键词： Chromosome segmentation Instance segmentation, cytogenetics Mask R-CNN attention mechanism biomedical imaging multi-scale feature fusion

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Quadrotor Control based on Self-Tuning LQR

Quadrotor Control based on Self-Tuning LQR

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第37届中国控制会议

作者： Kaiwen Pan Yang Chen Zhengxi Wang Huaiyu Wu Lei Cheng Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology

Recently, quadrotor has become one of the hotspots in robot research. To design an efficient controller for a quadrotor UAV is always challenging to for its under-actuated, strong coupling and nonlinearity. In this paper, a double closed-loop cascaded linear quadratic regulator(LQR) controller is proposed to track the referred position and attitude respectively. In order to solve the problem of tedious parameter setting, a parameter self-tuning control structure based on Simultaneous Perturbation Stochastic Approximation(SPSA) is designed and the parameters in the position and attitude controller are optimized respectively. By comparing with the original LQR controller, the simulation results show that the parameter-optimized controller improves the dynamic performance and control effect both in position control and attitude control, which verifies the correctness and feasibility of the proposed method.

关键词： Quadrotor UAVs LQR SPSA Parameter Self-Tuning

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Path Planning for the Marsupial double-UAVs System in Air-ground Collaborative Application

Path Planning for the Marsupial double-UAVs System in Air-gr...

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第37届中国控制会议

作者： Shiwen Ren Yang Chen Ling Xiong Zhihuan Chen Mengqing Chen Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology

This paper considers the path planning problem lor deployment and collection of a marsupial vehicle system which consists of an unmanned ground vehicle（UGV） and two flying *** UGV,as a carrier,is able to deploy and retrieve the flying robots while each of the latter usually takes off and lands on the *** work is motivated by cooperative search and reconnaissance missions in field of heterogeneous robot system.A sequence of tasks,whose positions are known as prior knowledge,are expected to be accessed by the flying robots in a given *** to their limited duration in the air,the aerial robots,usually unmanned aerial vehicles（UAVs）,should return to the UGV timely and land for its energy-saving and *** paper mainly contributes on the mathematical model for path planning of the special marsupial *** real constraints including the maximum velocity of the UAV and the UGV,the maximum hovering time and so on are taken into account in the *** objective function is constructed by minimizing the time for completing the whole *** order to clarify the novel method,a single task point model first is proposed and then extended to multi tasks ***,particle swarm optimization（PSO） method is used to solve the path planning *** simulations verily the feasibility and effectiveness of the proposed method.

关键词： Heterogeneous multi-robot system path planning air-ground cooperation marsupial robots

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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An Early Stage researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization

arXiv

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arXiv 2018年

作者： Lundell, Jens Krug, Robert Schaffernicht, Erik Stoyanov, Todor Kyrki, Ville Intelligent Robotics Group Department of Electrical Engineering and Automation Aalto University Finland AASS Research Center Örebro University Sweden Robotics Learning and Perception lab KTH Royal Institute of Technology Sweden

Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict the consequences of the exploratory actions it takes. Therefore, exploration can lead to collisions with the potential to harm the robot and/or the environment. In this work we address the safety aspect by constraining the exploration to happen in safe-to-explore state spaces. These are formed by decomposing target skills (e.g., grasping) into higher ranked sub-tasks (e.g., collision avoidance, joint limit avoidance) and lower ranked movement tasks (e.g., reaching). Sub-tasks are defined as concurrent controllers (policies) in different operational spaces together with associated Jacobians representing their joint-space mapping. Safety is ensured by only learning policies corresponding to lower ranked sub-tasks in the redundant null space of higher ranked ones. As a side benefit, learning in sub-manifolds of the state-space also facilitates sample efficiency. Reaching skills performed in simulation and grasping skills performed on a real robot validate the usefulness of the proposed approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

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Silk Ball Pitching Detection Strategy based on Binocular Vision

Silk Ball Pitching Detection Strategy based on Binocular Vis...

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第37届中国控制会议

作者： Chan Li Lei Cheng Yating Dai Rui Peng Yang Chen Huaiyu Wu Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology

Most of the research on intelligent pitching robots only focuses on robot control, ball recognition and tracking. In this paper, silk ball pitching detection strategy is proposed. Color recognition, ellipse fitting and binocular distance measurement are combined to realize recognition and position and detection of silk ball pitching. When silk ball is pitched, ribbons interfere with the shape of the ball, thus affecting the recognition. Color recognition avoids this problem effectively. Firstly, the silk ball and the ball frame are respectively identified through color recognition and ellipse fitting. Then, it is judged whether the silk ball appears in the frame area by the acquired image. In this case, the silk ball judged in the frame area may not be in the frame area actually,maybe in front of the frame area or behind the frame area. Secondly, the three-dimensional coordinates of the silk ball in the frame area are calculated according to the binocular distance measurement principle and coordinate transform ***, whether the silk ball appears in the frame area is judged through the calculated silk ball's actual position. Through many experiments, a higher accuracy rate is obtained, meanwhile, the rationality and feasibility of the method are also proved.

关键词： Binocular vision silk ball pitching detection color recognition ellipse fitting coordinate transform

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