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22th Annual Conference Towards Autonomous Robotic systems, TAROS 2021

作者： Haggart, Rory Aitken, Jonathan M. Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9783030891763

Simultaneous localisation and mapping (SLAM) relies on low-cost on-board sensors such as cameras and inertial measurement units. It is crucial that the surroundings are visible to the cameras to maximise the accuracy of the system. An estimation strategy is proposed to augment ORB-SLAM2 that considers feature extraction capability, distribution of the extracted features in the image frame, and the ability of the algorithm to track features over time. The method is tested on challenging datasets, and the output is evaluated against different visibility conditions. The proposed method is shown to react appropriately and consistently to ‘less visible’ conditions such as fog, sunlight, and rapid motion in real time, with minimal computational load. © 2021, Springer Nature Switzerland AG.

关键词： Cameras

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A Minimalist Solution to the Multi-robot Barrier Coverage Problem 22nd

A Minimalist Solution to the Multi-robot Barrier Coverage Pr...

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22th Annual Conference Towards Autonomous Robotic systems, TAROS 2021

作者： Green, Thomas Kamel, Kevin Li, Siyuan Shinn, Christopher Toscano, Paolo Wang, Xintong Ye, Yuchen Groß, Roderich Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9783030891763

This paper addresses the multi-robot barrier coverage problem. It presents a group of memory-less robots that encircle a group of herd agents, by moving along a polygonal barrier. The results, produced from simulations in CoppeliaSim, demonstrate high retention of herd agents, and robust performance across a range of simulated scenarios. © 2021, Springer Nature Switzerland AG.

关键词： Multipurpose robots

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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CDQKL: Consensus-Based Distributed Quantum Kernel Learning

CDQKL: Consensus-Based Distributed Quantum Kernel Learning

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Wenxuan Ma Mengxiang Liu Ruilong Deng State Key Laboratory of Industrial Control Technology and the College of Control Science and Engineering Zhejiang University Hangzhou China Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

ISBN: (数字)9798350366778

ISBN: (纸本)9798350366785

The field of quantum computing has developed rapidly in recent years due to its promising trend of surpassing traditional machine learning in terms of speed and effectiveness. Quantum kernel learning is one of the paradigms of quantum machine learning, but the training of quantum kernel is time consuming. Therefore, this work makes the first attempt to introduce a consensus-based distributed approach to quantum kernel learning - named CDQKL - that only requires to exchange model parameter information between adjacent nodes while avoiding the need of sharing local training data. Through comparative experimental studies, the advantages of CDQKL in classification accuracy and convergence speed are verified. Considering the popularization of quantum computing cloud service and miniaturization of quantum terminals, the CDQKL adapting to this trend is able to play a vital role in data security, which implies the far-reaching significance of this work. Our code is available at https://***/Leisurivan/CDOKL.

关键词： Training Adaptation models Quantum computing Accuracy Training data Machine learning Market research

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Fuzzy System of IT-Project Works Priority 14

Fuzzy System of IT-Project Works Priority

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14th International Conference on Advanced computer Information Technologies, ACIT 2024

作者： Vasylkiv, Nadia Dubchak, Lesia Karpinski, Mikolaj Turchenko, Iryna Flud, Liubomyr Nadvynychna, Tetiana West Ukrainian National University Department of Information Computer Systems and Control Ternopil Ukraine West Ukrainian National University Department of Computer Engineering Ternopil Ukraine University of the National Education Commission Department of Cyber Security Krakow Poland Ukrainian National Forestry University Department of Information Systems and Computer Modeling Lviv Ukraine West Ukrainian National University Subdepartment of Psychology and Social Work Ternopil Ukraine

ISBN: (纸本)9798350350036

A fuzzy system for setting the priority of IT project work has been developed, in which a preliminary analysis of the metrics of readiness, permission and complexity of tasks is carried out, which makes it possible to determine the priority of the project work. Based on this, the business analyst can effectively make decisions about the workload of the project team. The results of the work can be successfully applied to the effective management of the implementation of IT projects and the proactive management of project teams. © 2024 IEEE.

关键词： Project management

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Human-Machine Interaction in E-commerce: a Multi-Faceted Examination of CAPTCHA Effect

Human-Machine Interaction in E-commerce: a Multi-Faceted Exa...

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2024 Conference of Young Researchers in Electrical and Electronic engineering, ElCon 2024

作者： Okoli, Kingsley Joseph, Innocent Chijioke, Famous Bekeneva, Yana Chijioke, Amadi Kodondo, Ibrahim Saint Petersburg Electrotechnical University 'LETI' Department of Computer Science and Engineering Saint Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Department of Control in Technical Systems Saint Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Department of Information Systems and Technologies Saint Petersburg Russia

ISBN: (纸本)9798350360646

The growing prevalence of automated threats in e-commerce necessitates effective security measures, exemplified by CAPTCHAs (Completely Automated Public Turing test to tell computers and Humans Apart). However, the use of CAPTCHAs introduces challenges related to user perception, engagement, and frustration. This study aims to comprehensively analyze the impact of CAPTCHAs on ecommerce transactions, including their influence on user behavior, security effectiveness, and the complexity of their implementation. Through a structured questionnaire-based approach, we gathered insights from users, assessing their experiences and perceptions of CAPTCHAs during online shopping. Our analysis involved quantitative data, utilizing statistical software, to provide a holistic understanding of these security measures in the e-commerce domain. Users support the effort to make CAPTCHAs engaging, and many find gamified elements positive. A substantial number express high frustration with multiple CAPTCHAs, affecting their willingness to return to e-commerce platforms. This study outlines the importance of optimizing CAPTCHAs to balance security and user experience in e-commerce. © 2024 IEEE.

关键词： CAPTCHA E-commerce Gamified Security User-Centric

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A Heuristically Accelerated Reinforcement Learning-Based Neurosurgical Path Planner

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Cyborg and Bionic systems 2023年第1期4卷 298-308页

作者： Guanglin Ji Qian Gao Tianwei Zhang Lin Cao Zhenglong Sun School of Science and Engineering The Chinese University of Hong KongShenzhenChina Shenzhen Institute of Artificial Intelligence and Robotics for Society ShenzhenChina Department of Automatic Control and Systems Engineering The University of SheffieldUK

The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain;with proper path planning,it can also minimize the potential damage by setting constraints and optimizing the insertion ***,reinforcement learning(RL)-based path planning algorithm has shown promising results in neurosurgery,but because of the trial and error mechanism,it can be computationally expensive and insecure with low training *** this paper,we propose a heuristically accelerated deep Q network(DQN)algorithm to safely preoperatively plan a needle insertion path in a neurosurgical ***,a fuzzy inference system is integrated into the framework as a balance of the heuristic policy and the RL *** are conducted to test the proposed method in comparison to the traditional greedy heuristic searching algorithm and DQN *** showed promising results of our algorithm in saving over 50 training episodes,calculating path lengths of 0.35 after normalization,which is 0.61 and 0.39 for DQN and traditional greedy heuristic searching algorithm,***,the maximum curvature during planning is reduced to 0.046 from 0.139 mm−1 using the proposed algorithm compared to DQN.

关键词： needle surgical insertion

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Adaptability analysis on the measurement device of UACV special machinery unlocking force

Adaptability analysis on the measurement device of UACV spec...

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2023 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2023

作者： Yang, Mo Cai, Zizhuo Lin, Jing Li, Jiahao Ren, Haiyue Liu, Qiang School of Mechanical Engineering Nanjing University of Science and Technology 200 Xiaolingwei Street Nanjing210094 China Department of Automatic Control Systems Bauman Moscow State Technical University ul. Baumanskaya 2-ya 5/1 Moscow105005 Russia

ISBN: (纸本)9781510674608

During the unlocking process of the rotating-bolt locking special machinery head, there is a significant resistance. The aim is to select a motor for unmanned aerial combat vehicle (UACV) mounted external power special machinery drives, taking the special machinery unlocking force as the research subject. Adaptive analysis is conducted on the patented structure of the special machinery unlocking force measurement device previously designed. A virtual prototype model is established using the dynamic simulation software ADAMS to apply driving, constraints, and simulation parameters based on the model's principles. Addressing the phenomenon of the detachment of the guide rod slider from the tested bolt carrier during the operation of the special machinery unlocking force measurement device, it is hypothesized that the detachment time is related to the stiffness of the spring and the velocity of the lead screw slider. Through multiple comparative simulations, the correctness of the hypothesis is proven. It is demonstrated that increasing the velocity of the lead screw slider can completely eliminate the detachment time, whereas increasing the stiffness of the spring, although reducing the detachment time, still results in secondary detachment. Consequently, the conclusion is drawn that increasing the velocity of the lead screw slider is the way to resolve the detachment issue of the guide rod slider from the tested bolt carrier in the experiment. © 2024 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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The Emergence of Artificial Intelligence in Education and its Impact on Individual Literacy in Higher Education 7

The Emergence of Artificial Intelligence in Education and it...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Farkas, Imre Kovari, Attila Rajcsanyi-Molnar, Monika Institute of Computer Engineering University of Dunaujvaros Department of Computer Systems and Control Technology Dunaujvaros Hungary Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Institute of Electronics and Communication Systems Kandó Kalmán Faculty of Electrical Engineering Obuda University Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Social Sciences University of Dunaujvaros Department of Economics and Management Dunaujvaros Hungary

ISBN: (纸本)9798331531119

In the last three years, a new partner has emerged for teachers and educators in the field of education. The mushrooming of applications based on large language models has greatly shaped the educational development fields of the present era. The use of various AI applications has become commonplace among students and teachers alike. Many questions are being raised by researchers in this field. What does the rapid development of AI applications bring to the field of pedagogy? Should the products of such applications be compared with human performance? The form presented in this thesis seeks to answer similar questions. What is the impact of frequent use of these applications on the literacy level of individuals? In which areas do students tend to use these applications? The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change. However, 88 % of students have already dealt with AI-based applications, with 64 % using them several times or more, suggesting a high level of integration into their educational processes. © 2024 IEEE.

关键词： Students

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Adaptive Iterative Learning control for Robotic Manipulators

Adaptive Iterative Learning Control for Robotic Manipulators

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UKACC International Conference on control (control)

作者： Yu Dou Emmanuel Prempain Lanlan Su School of Engineering University of Leicester Leicester United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

A refined control scheme is presented to optimise the trajectory-tracking performance of robotic manipulators. This strategy integrates a linear feedback controller and a feedforward learning controller. The former mitigates unknown disturbances and desensitises the system to unidentified parameters, while the latter enhances tracking efficiency by incorporating past tracking errors. Traditionally, a fixed learning gain is used in the learning law to update the control input. However, we modify the learning law in this study by applying an adaptive learning gain. Our simulations on a robotic manipulator demonstrate that the adaptive ILC algorithm surpasses the classical ILC algorithm concerning convergence speed. These findings highlight the advantages of our approach, showcasing its extensive applicability in trajectory tracking.

关键词： Adaptive learning Adaptation models Trajectory tracking Manipulators Feedforward systems Adaptive control Convergence

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