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arXiv 2020年

作者： Do, Ton Duc Gui, Meei Mei Ng, Kok Yew Department of Robotics and Mechatronics School of Engineering and Digital Sciences Nazarbayev University Nur-Sultan Kazakhstan School of Chemistry and Chemical Engineering Queen's University Belfast Belfast United Kingdom Engineering Research Institute University of Ulster Belfast United Kingdom Electrical and Computer Systems Engineering School of Engineering Monash University Malaysia Bandar Sunway Malaysia

This paper presents the assessment of time-dependent national-level restrictions and control actions and their effects in fighting the COVID-19 pandemic. By analysing the transmission dynamics during the first wave of COVID-19 in the country, the effectiveness of the various levels of control actions taken to flatten the curve can be better quantified and understood. This in turn can help the relevant authorities to better plan for and control the subsequent waves of the pandemic. To achieve this, a deterministic population model for the pandemic is firstly developed to take into consideration the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measures used to describe the transmission dynamics of this pandemic. The reproduction number alongside other key parameters of the model can then be estimated by fitting the model to real-world data using numerical optimisation techniques or by inducing ad-hoc control actions as recorded in the news platforms. In this paper, the model is verified using a case study based on the data from the first wave of COVID-19 in the Republic of Kazakhstan. The model is fitted to provide estimates for two settings in simulations;time-invariant and time-varying (with bounded constraints) parameters. Finally, some forecasts are made using four scenarios with time-dependent control measures so as to determine which would reflect on the actual situations better. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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Mobile Robot Localization with Reinforcement Learning Map Update Decision aided by an Absolute Indoor Positioning System

Mobile Robot Localization with Reinforcement Learning Map Up...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Luís Garrote Miguel Torres Tiago Barros João Perdiz Cristiano Premebida Urbano J. Nunes Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal Dept. of Aeronautical and Automotive Engineering Loughborough University UK

This paper introduces a new mobile robot localization solution consisting of two main modules: a Particle-Filter based Localization (PFL) and a Reinforcement-Learning based map updating, integrating relative measurements and absolute indoor positioning sensor (A-IPS) data. Concerning localization using 2D-LiDARs, featureless areas are known to be problematic. To solve this problem a classic PFL approach was modified to incorporate A-IPS position measurements in the prediction and update stages. The localization approach has the particularity of including the possibility of updating the map whenever major modifications are detected in the environment in relation to the current localization map. Due to the random sampling-based nature of the PFL, an associated map update solution is not trivial since small inconsistencies in the estimated pose can lead to erroneous map associations. The proposed method learns to decide by assigning higher rewards the greater is the overlap between the map and the 2DLIDAR scans, via RL, and then a proper update of the map is achieved. Validation of the proposed pipeline was carried out in a differential drive platform with algorithms developed in ROS. Tests were performed in two scenarios in order to assess the performance of both the localization module and the map update stage. The results show that the proposed localization method offers improvements in relation to known approaches, and consequently suggest promising perspectives for the proposed map update decision framework.

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GardnerNet: An Interpretable Deep Learning Model for Quantitative Human Blastocyst Evaluation - A Retrospective Model Development and Validation Study

SSRN

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SSRN 2024年

作者： Liu, Hang Chen, Longbin Shan, Guanqiao Sun, Chen Liao, Hongqing Lu, Changfu Zhang, Shuoping Dong, Shaonan Xu, Xinxin Yan, Qiuyun Gong, Fei Zhang, Zhuoran Dai, Changsheng Chen, Wenyuan Song, Haocong Chen, Lei Sun, Haixiang Wang, Shanshan Lin, Ge Sun, Yu Gu, Yifan Department of Mechanical and Industrial Engineering University of Toronto 5 King’s College Road TorontoONM5S 3G8 Canada Institute of Reproductive and Stem Cell Engineering School of Basic Medical Science Central South University 172 Tongzipo Road Hunan Province Changsha China Hengyang Nanhua-Xinghui Reproductive Health Hospital 30 Jiefang Road Hunan Province Hengyang China The second Affiliated Hospital Hengyang Medical School University of South China 30 Jiefang Road Hunan Province Hengyang City China Reproductive & Genetic Hospital of CITIC-Xiangya 567 Tongzipo Road Hunan Province Changsha China NHC Key Laboratory of Human Stem Cell and Reproductive Engineering School of Basic Medical Science Central South University 172 Tongzipo Road Hunan Province Changsha China Clinical Research Center for Reproduction and Genetics in Hunan Province Reproductive & Genetic Hospital of CITIC-Xiangya 567 Tongzipo Road Hunan Province Changsha China School of Science and Engineering The Chinese University of Hong Kong-Shenzhen 2001 Longxiang Boulevard Longgang District Shenzhen China Institute of Robotics and Intelligent Systems Dalian University of Technolgy 2 Linggong Road Liaoning Province Dalian China Department of Computer Science University of Toronto 40 St George Street TorontoONM5S 2E4 Canada Center for Reproductive Medicine and Obstetrics and Gynecology Nanjing Drum Tower Hospital Nanjing University Medical School 321 Zhongshan Road Jiangsu Province Nanjing China National Engineering and Research Center of Human Stem Cell 8 Luyun Road Jiangsu Province Changsha China Department of Electrical and Computer Engineering 10 King’s College Road TorontoONM5S 3G8 Canada Institute of Biomedical Engineering University of Toronto 164 College Street TorontoONM5S 3G9 Canada

Background: Selecting the blastocyst with the highest potential for live birth from a cohort is crucial for the success of in vitro fertilization (IVF). While the Gardner and Schoolcraft morphological grading system, commonly known as the Gardner grading system, is widely used by embryologists worldwide, blastocysts having similar or the same inner cell mass (ICM) and trophectoderm (TE) grades cause challenges for embryologists in decision-making. Furthermore, human assessment is subjective and inconsistent in predicting which blastocysts have higher potential to result in live birth. This study aims to develop an interpretable, quantitative method aligned with the Gardner grading system, providing continuous scores that correlate with live birth outcomes, thereby assisting embryologists in improving blastocyst selection. Methods: In this retrospective study, we developed GardnerNet, an interpretable deep learning model that outputs expansion degree grade and continuous scores quantifying a blastocyst's ICM morphology and TE morphology, based on the Gardner grading system. The continuous ICM and TE scores were calculated by weighting each base grade's predicted probability and summing the predicted probabilities. To represent each blastocyst's overall potential for live birth, we combined the ICM and TE scores using their odds ratios (ORs) for live birth. We further assessed the correlation between the ICM score, TE score, and the OR-combined score (adjusted for expansion degree) with live birth rates by applying GardnerNet to blastocysts with known live birth outcomes. To test its generalizability, we also applied GardnerNet to an external IVF institution, accounting for variations in imaging conditions, live birth rates, and embryologists' experience levels. Findings: GardnerNet was developed using data from 2760 blastocysts with majority-voted grades for expansion degree, ICM, and TE. The model achieved mean area under the receiver operating characteristic curve

关键词： Risk assessment

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Online multi-object tracking and segmentation with GMPHD filter and mask-based affinity fusion

arXiv

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arXiv 2020年

作者： Song, Young-Min Yoon, Young-Chul Yoon, Kwangjin Jeon, Moongu Lee, Seong-Whan Pedrycz, Witold The School of Electrical Engineering and Computer Science Gwangju Institute of Technology Gwangju61005 Korea Republic of The Robotics Laboratory Hyundai Motor Company Gyeonggi-do 16082 Korea Republic of SI Analytics Co. Ltd. Daejeon34051 Korea Republic of The Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of The Department of Brain and Cognitive Engineering Korea University Seoul02841 Korea Republic of The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2H5 Canada The Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia The Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

In this paper, we propose a highly practical fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input. The proposed method is based on the Gaussian mixture probability hypothesis density (GMPHD) filter, a hierarchical data association (HDA), and a mask-based affinity fusion (MAF) model to achieve high-performance online tracking. The HDA consists of two associations: segment-to-track and track-to-track associations. One affinity, for position and motion, is computed by using the GMPHD filter, and the other affinity, for appearance is computed by using the responses from a single object tracker such as a kernalized correlation filter. These two affinities are simply fused by using a score-level fusion method such as min-max normalization referred to as MAF. In addition, to reduce the number of false positive segments, we adopt mask IoU-based merging (mask merging). The proposed MOTS framework with the key modules: HDA, MAF, and mask merging, is easily extensible to simultaneously track multiple types of objects with CPU-only execution in parallel processing. In addition, the developed framework only requires simple parameter tuning unlike many existing MOTS methods that need intensive hyperparameter optimization. In the experiments on the two popular MOTS datasets, the key modules show some improvements. For instance, ID-switch decreases by more than half compared to a baseline method in the training sets. In conclusion, our tracker achieves state-of-the-art MOTS performance in the test sets. Copyright © 2020, The Authors. All rights reserved.

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SmartAlert: Machine Learning-Based Patient-Ventilator Asynchrony Detection System in Intensive Care Units

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computer Methods and Programs in Biomedicine 2025年

作者： Jaroslav Pažout Milan Němý Jakub Mikeš Jan Jirman Jan Kubr Eliška Niebauerová Miroslav Macík Michal Pech Michal Štajnrt Jakub Vaněk Petr Waldauf Václav Zvoníček Lenka Vysloužilová Robert Babuška František Duška Department of Anesthesiology and Intensive Care 3rd Faculty of Medicine Charles University and Kralovske Vinohrady University Hospital in Prague Šrobárova 50 100 34 Prague Czech Republic Division of Clinical Geriatrics Center for Alzheimer Research Department of Neurobiology Care Sciences and Society Karolinska Institute Blickagången 16 14183 Huddinge Sweden Department of Cognitive Systems and Neurosciences Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Jugoslávských partyzánů 1580/3 160 00 Prague Czech Republic Department of Computer Graphics and Interaction Faculty of Electrical Engineering Czech Technical University Karlovo náměstí 13 121 35 Prague Czech Republic Department of Artificial Intelligence Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Jugoslávských partyzánů 1580/3 160 00 Prague Czech Republic Cognitive Robotics Faculty of 3mE Delft University of Technology Mekelweg 2 2628 CD Delft The Netherlands

Background and Objective Patient-ventilator asynchronies (PVA) are associated with ventilator-induced lung injury and increased mortality. Current detection methods rely on static thresholds, extensive preprocessing, or proprietary ventilator data. This study aimed to develop and validate a fully online, real-time system that detects and classifies PVAs directly from ventilator screen data while alerting clinicians based on severity. Methods The SmartAlert system was developed using ventilator screen recordings from ICU patients. It extracts pressure and flow waveforms from video recordings, converts them into time-series data, and employs deep neural networks to classify asynchronies and assign alarm levels from no urgency to most urgent. A dataset of 381,280 double-breath units was independently annotated by two expert intensivists. Two deep learning models were trained: one for alarm prediction and another for asynchrony classification (ineffective triggering, double cycling, high inspiratory effort, no asynchrony). Performance was evaluated using accuracy, sensitivity, specificity, and AUC-ROC, compared to expert consensus. Results SmartAlert demonstrated strong performance for alarm level prediction (overall accuracy: 83.8%, weighted AUC-ROC: 0.943 [95% CI: 0.941–0.945]) and PVA classification (weighted accuracy: 89.3%, weighted AUC-ROC: 0.951 [95% CI: 0.950–0.953]). It showed high specificity for urgent alarms (99.9% for level 3) and PVA types (98.5% for ineffective triggering, 96.9% for double cycling, 94.8% for high inspiratory effort). Conclusions We developed and internally validated SmartAlert, an automated system that detects PVAs, classifies severity, and alerts clinicians in real time. Its potential to reduce alarm fatigue, optimize ventilator settings, and improve patient outcomes remains to be tested in clinical trials.

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Adaptive Backstepping of Synergistic Hybrid Feedbacks with Application to Obstacle Avoidance

Adaptive Backstepping of Synergistic Hybrid Feedbacks with A...

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American Control Conference

作者： Pedro Casau Ricardo G. Sanfelice Carlos Silvestre Institute for Systems and Robotics (ISR) Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal University of California Santa Cruz CA 95064 USA Department of Electrical and Computer Engineering of the Faculty of Science and Technology of the University of Macau Macau China

ISBN: (纸本)9781538679012;9781538679265

In this paper, we show that the hybrid controller that is induced by a Synergistic Lyapunov Function and Feedback (SLFF) pair relative to a compact set, can be extended to the case where the original affine control system is subject to a class of additive disturbances known as matched uncertainties, provided that the estimator dynamics do not add new equilibria to the closed-loop system. We also show that the proposed adaptive hybrid controller is amenable to backstepping. Finally, we apply the proposed hybrid control strategy to the problem of global asymptotic stabilization of a compact set in the presence of an obstacle and we illustrate this application by means of simulation results.

关键词： Obstacle avoidance Backstepping Feedback Collision avoidance Liapunov function control systems Closed loop systems Self tuning Controllers

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Twin Kinematics Approach for Robotic-Assisted Tele-Echography

Twin Kinematics Approach for Robotic-Assisted Tele-Echograph...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Luis Santos Rui Cortesão João Quintas Luís Santos is with the Institute of Systems and Robotics University of Coimbra Portugal Rui Cortesão is with the Electrical and Computer Engineering Department Institute of Systems and Robotics University of Coimbra Portugal João Quintas is with the Instituto Pedro Nunes Coimbra Portugal

This paper discusses a new teleoperation approach for robotic-assisted tele-echography. A teleoperation architecture in the joint space is presented, taking advantage of kinematic similarity between master and slave manipulators. Haptic force feedback is provided based on the slave control command torque, without using force/torque sensing data. The slave manipulator is controlled in the joint space using computed torque techniques and featuring Kalman Active Observers (AOBs). Experiments in a typical telemedice scenario, assess and validate the teleoperation architecture in a clinical context, with a radiologist performing a robotic-assisted abdominal ultrasound examination on a healthy volunteer.

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Age of Information for Single Buffer systems with Vacation Server

arXiv

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arXiv 2020年

作者： Xu, Jin Hou, I-Hong Gautam, Natarajan School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China University of Science and Technology of China Anhui Hefei China Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Department of Industrial and Systems Engineering Texas A&M University College StationTX United States

In this research, we study the information freshness in M/G/1 queueing system with a single buffer and the server taking multiple vacations. This system has wide applications in communication systems. We aim to evaluate the information freshness in this system with both i.i.d. and non-i.i.d. vacations under three different scheduling policies, namely Conventional Buffer System (CBS), Buffer Relaxation System (BRS), and Conventional Buffer System with Preemption in Service (CBS-P). For the systems with i.i.d. vacations, we derive the closed-form expressions of information freshness metrics such as the expected Age of Information (AoI), the expected Peak Age of Information (PAoI), and the variance of peak age under each policy. For systems with non-i.i.d. vacations, we use the polling system as an example and provide the closed-form expression of its PAoI under each policy. We explore the conditions under which one of these policies has advantages over the others for each information freshness metric. We further perform numerical studies to validate our results and develop insights. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric circuit breakers

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Robot-Assisted Navigation for a Robotic Walker with Aided User Intent

Robot-Assisted Navigation for a Robotic Walker with Aided Us...

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IEEE International Symposium on Robot and Human Interactive Communication

作者： Luis Garrote Joao Paulo Joao Perdiz Paulo Peixoto Urbano J. Nunes Institute of Systems and Robotics (ISR-UC) Electrical and Computer Engineering Department (DEEC-UC) University of Coimbra

ISBN: (纸本)9781538679814

This paper presents an approach to robot-assisted navigation on a mobility assistance context, by learning from the user while helping him navigate efficiently and safely in complex environments. Assistive robots such as robotic walkers provide the ability to support a user's body weight on the upper limbs while walking. However, walkers can add an extra layer of distress due to their specific manipulation constraints. For the users of such devices, lack of dexterous upper limb control can be a considerable problem as it means they may be unable to operate these devices efficiently;users may also have visual impairments that reduce their navigational efficiency. The proposed approach uses a Reinforcement Learning (RL) model and a dynamic window-based local motion planning algorithm. It aims to learn needed corrections in the motion command based on the surrounding environment and the user's intent. The proposed solution handles corrections and guides the user through the environment without collisions while learning and aiding the user when he is unable to operate the device efficiently. The RL based approach was tested in indoor scenarios with a robotic walker platform showing preliminary promising results.

关键词： Consumers Walkers Learning Upper Extremity Visual Impairment ambient environment navigation Pedestrians Distress Stressing Environments

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