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IEEE International Conference on Automation science and Engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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Assessing Parkinson's Disease Severity Using Channel Attention and Graph Attention Networks on Kinect Skeleton Images

Assessing Parkinson's Disease Severity Using Channel Attenti...

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Iranian Conference on Intelligent Systems (ICIS)

作者： Fatemeh Fadaie Hamed Aghapanah Alireza Sedighifard Hadi Niknam Shirvan Department of Computer Engineering AI and Robotics University of Isfahan Isfahan Iran Department of Bioelectrics and Biomedical Engineering School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran Department of Artificial Intelligence Iran University of Science and Technology Isfahan Iran Faculty of New Sciences and Technologies University of Tehran Isfahan Iran

ISBN: (数字)9798331507565

ISBN: (纸本)9798331507572

Parkinson's Disease (PD), a prevalent global neurological disorder, presents with motor symptoms like rigidity and tremors. Accurate severity assessment is vital for intervention evaluation. This study uses computer vision, specifically Kinect cameras and deep learning, to develop a system for assessing the severity of Parkinson's disease. Leveraging Kinect cameras' Precision in tracking key motion patterns, the system utilizes convolutional neural networks, namely Spatial-Temporal Graph Convolutional Network (STGCN) and Two-Stream Adaptive Graph Convolutional Network (2SAGCN), to detect movement anomalies and provide quantitative severity measures. A novel model is proposed, representing an enhanced version of 2SAGCN featuring a channel and graph attention module. Parkinson's severity classification comprises 11 classes based on 9 motion categories, totaling 99 classes. Three-dimensional transfer learning and data augmentation improve model Accuracy. This research advances non-invasive, quantitative Parkinson's assessment using innovative technology and advanced neural network models. The program is hosted on GitHub for implementation and result observation. The 10-fold cross-validation results of the best model, incorporating 2SAGCN structure, Graph, and Channel Attention, yield Accuracy, Precision, Recall, and F1-Score of Confidence Intervals 94.12± 0.14, 99.6 ± 0.20, 98.9 ± 0.10,99.2 ± 0.80 respectively.

关键词： Adaptation models Solid modeling Attention mechanisms Accuracy Parkinson's disease Graph convolutional networks Tracking Transfer learning Cameras Data models

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A Regularization Approach to Analyze the Time-Optimal Motion of a Mobile Robot under State Constraints using Pontryagin’s Maximum Principle

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Procedia computer science 2021年 186卷 11-20页

作者： Fernando Lobo Pereira Roman Chertovskih Anna Daryina Askhat Diveev Dmitry Karamzin Elena Sofronova SYSTEC Faculty of Electrical Engineering Porto University and the Institute for Systems and Robotics 4200-465 Porto Portugal Federal Research Center “Computer Science and Control” of the Russian Academy of Science Moscow Russia

This work concerns the investigation of time-optimal control problem for a simplified mobile robot model subject to state constraints. In this type of problems, which involve the so-called unicycle dynamics, the well-established regularity conditions with respect to the state constraints are not fulfilled. This fact greatly complicates the analysis of the control problem bearing in mind the use of the maximum principle. Moreover, the difficulty is also due to the presence of a singular control mode with respect to the angular velocity. Herein, a regularization approach is proposed in order to overcome these obstacles. A certain tool for the numerical implementation is developed. The numerical analysis is provided for a sample problem.

关键词： Unicycle model state constraints regularization maximum principle

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Adaptive Non-Maximum Suppression to Improve Performance of Rumex Detection

SSRN

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SSRN 2022年

作者： Al-Badri, Ahmed Husham Ismail, Nor Azman Al-Dulaimi, Khamael Salman, Ghalib Ahmed Salam, Md Sah Bin Haji Al-Sabaawi, Aiman School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Computer Science Department College of Science Al-Nahrain University Baghdad Iraq School of Electrical Engineering and Robotics Queensland University of Technology BrisbaneQLD4000 Australia Department of Computer Science Middle Technical University Baghdad Iraq School of Computer Science Queensland University of Technology BrisbaneQLD4000 Australia

Weed detection methods based on Region-Convolutional Neural Network (R-CNN) frameworks still suffer from a lack of detection rate due to overlapping and occlusion leaves issues. This paper presents an Ensemble-Region Convolutional Neural Networks (E-RCNN) model of three state-of-the-art networks to detect Rumex obtusifolius L. ( R. obtu .) weeds under various condition especially overlapping. The proposed E-RCNN model is used due to its novelty of using ensemble classifiers with the combination of three extractors at its backbone. The Adaptive Non-Maximum Suppression (ANMS) is proposed with the Region Proposal Network (RPN) to enhance the detection performance of overlapping and occluded objects by overcoming the drawbacks of the conventional Non-Maximum Suppression (NMS). Three main stages are proposed as a framework to achieve the classification and detection goals, including pre-processing stage, classification stage, and detection stage. A hybrid model of three CNN extractor networks is used as the backbone in the classification stage. Thus, integrating three networks into one robust model increases the recognition capability by extracting additional useful features more efficiently than those from an individual network. For detection, RPN is used to generate multi-proposed boxes, whereas ANMS is used to select the best box that has a high score rate to match the target object. Our proposed model has trained and tested two standard benchmarking datasets of Rumex weeds under real-world data. The proposed model tested each dataset separately to evaluate the detection rate in terms of Intersection over Union (IoU). To evaluate the detection rate, AlexNet, Single-Shot Detector (SSD), DetectNet and Faster R-CNN with conventional NMS models are used to compare the results. The evaluation performances of AlexNet, SSD, DetectNet, and Faster R-CNN on the IoU metric are 0.81, 0.83, 0.84, and 0.88, respectively. The proposed model achieves the highest score in localization

关键词： Convolutional neural networks

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Identifying Degradation Indicators for Electric Vehicle Battery Based on Field Testing Data

Identifying Degradation Indicators for Electric Vehicle Batt...

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IEEE Canada Electric Power Conference, EPEC

作者： Kei Long Wong Ka Seng Chou Davide Aguiari Rita Tse Su-Kit Tang Giovanni Pau Faculty of Applied Sciences Macao Polytechnic University Macao SAR China Department of Computer Science and Engineering Alma Mater Studiorum University of Bologna Bologna IT Autonomous Robotics Research Center Technology Innovation Institute UAE UCLA Samueli Computer Science University of California Los Angeles USA

ISBN: (数字)9781665463188

ISBN: (纸本)9781665463195

State of health estimation of battery is crucial to ensure the safety and durability of electric vehicles. This paper presents six methods to extract the battery health indicator from electric vehicle field testing data. The methods for extracting health indicators from the discharge cycle show the ability to cope with the variable driving condition. In total, 157 health indicators are extracted from the collected data. Pearson correlation coefficient and Spearman's rank correlation coefficient are used to measure the correlation between the health indicators and the state of health. The results suggest that health indicators extracted by the presented methods have high correlations to the battery state of health.

关键词： Correlation coefficient Degradation Correlation Estimation Electric vehicles Discharges (electric) Batteries

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Controller and Trajectory Optimization for a Quadrotor UAV with Parametric Uncertainty

Controller and Trajectory Optimization for a Quadrotor UAV w...

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

作者： Ali Srour Antonio Franchi Paolo Robuffo Giordano CNRS Univ Rennes Inria IRISA Campus de Beaulieu 35042 Rennes Cedex France Robotics and Mechatronics Department Electrical Engineering Mathematics Computer Science (EEMCS) Faculty University of Twente Enschede The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy LAAS-CNRS Université de Toulouse Toulouse France

In this work, we exploit the recent notion of closed-loop state sensitivity to critically compare three typical controllers for a quadrotor UAV with the goal of evaluating the impact of controller choice, gain tuning and shape of the reference trajectory in minimizing the sensitivity of the closed-loop system against uncertainties in the model parameters. To this end, we propose a novel optimization problem that takes into account both the shape of the reference trajectory and the controller gains. We then run a large statistical campaign for comparing the performance of the three controllers which provides some interesting insight for the goal of increasing closed-loop robustness against parametric uncertainties.

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Generative Adversarial Networks for Heterogeneous Unsupervised Domain Adaptation Detection

Generative Adversarial Networks for Heterogeneous Unsupervis...

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IEEE International Conference on Intelligent Engineering Systems (INES)

作者： Homayoun Safarpour Motealegh Mahalegi Amirfarhad Farhadi György Molnár Enikő Nagy Institute of Mechatronics and Vehicle Engineering Óbuda University Budapest Hungary Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (数字)9798350367591

ISBN: (纸本)9798350367607

Domain adaptation, a subset of transfer learning, involves generating examples from two related but differently distributed source and target domains. This paper introduces an incremental adversarial learning method for unsupervised domain adaptation, where the source data is labeled, and the target data remains unlabeled. We employ a discriminative adversarial strategy as the primary technique for unsupervised domain adaptation to minimize the significant distributional variance between the source and target domains. Traditional domain adaptation methods typically train the source domain to align with a fixed target domain distribution. However, in many real-world scenarios, the target domain's distribution continuously evolves, leading to a lack of generalizability in the trained model for subsequent domains. To tackle this issue, we suggest using a continuous sequential domain adaptation method. This involves including instances from several related target domains in the training process in a sequential and continuous manner. This process ensures that all receiving domains are correctly aligned with their respective sources, and to maintain sequence continuity, the source domain is incrementally updated by incorporating the most reliable data from the current target domain. This enhancement aims to improve the network's training for forthcoming domain data. Leveraging the effectiveness of deep neural networks, our method utilizes deep GANs for domain adaptation. The approach enhances domain matching by augmenting the source with selected target data and introduces a novel strategy for continuous sequential domain matching. This involves a progressive update of source domain samples with reliable data from the current target domain, facilitating seamless adaptation to successive domain shifts. The proposed method demonstrated a high classification accuracy, achieving an impressive 99% and 95% in different settings. Furthermore, it outperforms previous studies rega

关键词： Training Adaptation models Machine learning algorithms Accuracy Transfer learning Clustering algorithms Sensitivity and specificity

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Research and Analysis of IT Specifications of Good Practices in the Area of Artificial Intelligence

Research and Analysis of IT Specifications of Good Practices...

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International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service (TELSIKS)

作者： I. Kostadinova G. Dimitrov V. Martsenyuk D. Rancic I. Dirgova-Luptakova I. Jovancevic P. Tsvetkova K. Rasheva - Yordanova P. Petrova P. Petrov Faculty of Information Science of University of Library Studies and Information Technologies (ULSIT) Sofia Bulgaria Institute of Robotics Bulgarian Academy of Sciences Acad. Bulgaria Department of Computer Science and Automatics University of Bielsko-Biala (UBB) Poland Faculty of Electronic Engineering University of Nis (UNI) Serbia Faculty of Natural Sciences University of Ss. Cyril and Methodius in Trnava (UCM) Slovakia University of Montenegro (UoM) Montenegro University of Economics - Varna Bulgaria UE

This article is a contribution within the Erasmus+ project titled "The Future Lies in Applied Artificial Intelligence" (FAAI) and examines research of collected IT specifications of good practices in Area of Artificial Intelligence (AAI). The article describes research conducted, the purpose of which is to find IT specifications of good practices in AI and describe their characteristics, like an area of implementation of the AI solution, the result of processing the data, the source of data, Data processing, and quality, what tools are used for processing data, and others. AAI application cases and the technologies used for implementation are reviewed. The specifics of the data and the applications used are described. The examination of these technologies will provide insight into which ones are favored and provide an overview of what is commonly referred to as "best practices" in this particular *** research encompassed a global examination of cases. The analysis of the data offers valuable insights in various directions:•Application area of ML/AI•Type of machine learning problems in described good practices in Artificial Intelligence•Type of models were developed within the projects•What is the area of implementation of AI solution•Used AI libraries (frameworks).•Source of data•Data characteristics•Tools are used to store data•What platform solution is used•What type of storage is usedThe research and analysis done provide a clear picture of the used IT specifications of good practices in AI. The obtained results will guide in what areas to organize the practical training in the preparation of specialists in the AAI. Also, this research would help future AI application developers.

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Cooperative Control Strategy of Dual Manipulators Based on Model Reference Adaptive Admittance Control

Cooperative Control Strategy of Dual Manipulators Based on M...

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Automation, Control and robotics Engineering (CACRE), International Conference on

作者： Hongmin Wang Meng Jiang Longhua Wu Junlin Huang Zengxi Pan Dagang Li Yingying Song Hui Huang School of Mechanical and Automation Engineering Institute of Carbon Peaking and Carbon Neutralization Wuyi University Jiang Men China School of Mechanical and Automation Engineering Wuyi University Jiang Men China Robotics and Welding Automation Laboratory University of Wollongong Australia School of Computer Science and Engineering Macau University of Science and Technology Macau China Faculty of Innovation and Entrepreneurship Wuyi University Jiang Men China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

This study aims to solve the dual robotic arm collaboration problem by proposing a method based on an adaptive conductivity control algorithm for nondestructive grasping and handling of unknown manipulated objects by dual robotic arms. The method is capable of dynamically tracking the intended force of the unknown entity while maintaining the desired trajectory and compensating for the error in the state of the unknown object. In this paper, adopting a two-arm collaborative robotic arm motion planning method oriented to the manipulated object, the kinematics of a singular appendage is calculated by closed-loop constraints. The collaborative control strategy of the dual robotic arms for the operated object is established, which facilitating the pursuit of the intended force and the rectification of the positional path deviation induced by external interference. Through simulation and experimental verification, the method demonstrates satisfactory performance regarding the error in tracking position and the steady state deviation in force, offering a viable solution for attaining heightened precision in tracking both position and force.

关键词： Trajectory tracking Force Collaboration Kinematics Manipulators Trajectory Steady-state

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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

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computer Systems science & Engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human *** produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and *** can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia ***,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for ***,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and *** authors have proposed automated detection systems for leukemia diagnosis to overcome these *** have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast ***,these systems are more efficient,reliable,and fast than previous manual diagnosing ***,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class *** this paper,we have proposed a robust automated system to diagnose leukemia and its *** have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better *** have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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