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7th International Conference on Integrated Modeling and Analysis in Applied control and Automation, IMAACA 2013, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2013

作者： Gregor, Michal Groumpos, Peter P. Department of Control and Information Systems Faculty of Electrical Engineering University of Žilina Slovakia Laboratory for Automation and Robotics Department of Electrical and Computer Engineering University of Patras Greece

ISBN: (纸本)9781629934884

The paper proposes a new learning method for fuzzy cognitive maps, which makes it possible to encode an attractor into the map. The method is based on the principle of backpropagation through time known from the theory of artificial neural networks. Simulation results are presented to show how well the method performs. It is shown that the results are superior to those achieved using Hebbian learning approaches such as nonlinear Hebbian learning. Some lines for possible future research and development are given.

关键词： Fuzzy Cognitive Maps

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Physical Layer Authentication Using Ensemble Learning Technique in Wireless Communications

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Computers, Materials & Continua 2022年第12期73卷 4489-4499页

作者： Muhammad Waqas Shehr Bano Fatima Hassan Shanshan Tu Ghulam Abbas Ziaul Haq Abbas Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan School of Engineering Edith Cowan UniversityJoondalup Perth6027WA Australia Faculty of Electrical Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan

Cyber-physical wireless systems have surfaced as an important data communication and networking research *** is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking *** to their high reliability,sensitivity and connectivity,their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping,spoofing,botnets,man-in-the-middle attack,denial of service(DoS)and distributed denial of service(DDoS)and *** methods use physical layer authentication(PLA),themost promising solution to detect ***,the cyber-physical systems(CPS)have relatively large computational requirements and require more communication resources,thus making it impossible to achieve a low latency *** methods perform well but only in stationary *** have extracted the relevant features from the channel matrices using discrete wavelet transformation to improve the computational time required for data processing by considering mobile *** features are fed to ensemble learning algorithms,such as AdaBoost,LogitBoost and Gentle Boost,to classify *** authentication of the received signal is considered a binary classification *** transmitted data is labeled as legitimate information,and spoofing data is illegitimate ***,this paper proposes a threshold-free PLA approach that uses machine learning algorithms to protect critical data from spoofing *** detects the malicious data packets in stationary scenarios and detects them with high accuracy when receivers are *** proposed model achieves better performance than the existing approaches in terms of accuracy and computational time by decreasing the processing time.

关键词： Physical layer authentication machine learning cyber-physical systems security

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Autonomus Systems control Design Using Neuro-Evolution 31

Autonomus Systems Control Design Using Neuro-Evolution

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31st International Conference on Cybernetics and Informatics, K and I 2022

作者： Zubek, Filip Melichar, Ales Kenicky, Ivan Korosi, Ladislav Sekaj, Ivan Slovak University of Technology Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology Bratislava Slovakia

ISBN: (数字)9781665487757

ISBN: (纸本)9781665487757

This paper aims to design and implement a neuro-evolution for the purpose of an autonomous vehicle moving on the road, where its control is performed by an MLP-type feed-forward artificial neural network. The network perceives the environment and controls the angle of the vehicle's wheels as well as its speed in a 2D simulation environment. The vehicle itself consists of a four-wheeled chassis with rotatable front wheels with its own dynamics. The neural network is represented by a four-layer architecture and its parameters are optimized using a parallel genetic algorithm to minimize the chosen performance criterion for controlling the motion of the vehicle. © 2022 IEEE.

关键词： Autonomous vehicles

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Laguerre network implementation in industrial control systems

Laguerre network implementation in industrial control system...

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15th International Carpathian control Conference, ICCC 2014

作者： Mrosko, M. Škultéty, J. Miklovičová, E. Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9781479935284

Discrete Laguerre filters are a simple and efficient tool for approximating stable dynamical systems. They can be used not only to model the system dynamics but also to express the controller structure. In this paper the real-time implementation of a Laguerre network-based control system using a micro programmable logic controller (PLC) is presented. Generally the PC is not needed for the real-time control execution, but it may be used to support the control design, signal processing and visualization. The communication between the PLC and PC is based on the OPC communication protocol. © 2014 IEEE.

关键词： Programmable logic controllers

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Two approaches to improve robot capabilitiese 23

Two approaches to improve robot capabilitiese

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23rd International Conference on robotics in Alpe-Adria-Danube Region, IEEE RAAD 2014

作者： Vitko, Anton Babinec, Andrej Dekan, Martin Paulusova, Jana Dubravska, Maria Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9781479967988

The paper presents two approaches to instil advanced capabilities to the industrial and mobile robot. The first one is related to the redundant industrial robot, motion of which is to be optimized under kinematic restrictions caused by the robot operation in a tight space. To solve the task the solution of inverse kinematics problem was converted into a closed-loop optimization problem, due to which the control algorithm provided high precise and robust solution w.r.t outer influences. The algorithm was verified by simulation and at present runs its implementation on ABB IRB 4600 robot. The second one is related to learning fuzzy-neural navigation of a mobile robot in unknown environment cluttered with obstacles. The learning process runs in two modes. First the parameters of membership functions (MF) are updated, and then the walls of MFs are adaptively deformed so as to remove transversal swings from the robot trajectory. The navigation algorithm was verified by simulation and validated by the real mobile robot. © 2014 IEEE.

关键词： Mobile robots

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Neuro-evolution of mobile robot controller 25

Neuro-evolution of mobile robot controller

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25th International Conference on Soft Computing, MENDEL 2019

作者： Sekaj, Ivan Cíferský, Ladislav Hvozdík, Milan Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Bratislava Slovakia Institute of Informatics and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Bratislava Slovakia

We present a neuro-evolution design for control of a mobile robot in 2D simulation environment. The mobile robot is moving in unknown environment with obstacles from the start position to the goal position. The trajectory of the robot is controlled by a neural network – based controller which inputs are information from several laser beam sensors. The learning of the neural network controller is based on an evolutionary approach, which is provided by genetic algorithm. © 2019, Brno University of Technology. All rights reserved.

关键词： Mobile robots

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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Radiomics model based on computed tomography images for prediction of radiation-induced optic neuropathy following radiotherapy of brain and head and neck tumors

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Heliyon 2025年第1期11卷 e41409页

作者： Nafchi, Elham Raiesi Fadavi, Pedram Amiri, Sepideh Cheraghi, Susan Garousi, Maryam Nabavi, Mansoureh Daneshi, Iman Gomar, Marzieh Molaie, Malihe Nouraeinejad, Ali Department of Radiation Sciences Faculty of Allied Medicine Iran University of Medical Sciences Tehran Iran Department of Radiation Oncology School of Medicine Iran University of Medical Science Tehran Iran Department of Information Technology Faculty of Electrical and Computer Engineering University of Tehran Tehran Iran Radiation Biology Research Center Iran University of Medical Sciences Tehran Iran Radiation Oncology Research Center (RORC) Cancer Institute Tehran University of Medical Sciences Tehran Iran Department of Clinical Oncology Haft-e-Tir Hospital Iran University of Medical Science Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Optometry and Vision Science School of Rehabilitation Tehran University of Medical Science Tehran Iran

Purpose: We aimed to build a machine learning-based model to predict radiation-induced optic neuropathy in patients who had treated head and neck cancers with radiotherapy. Materials and methods: To measure radiation-induced optic neuropathy, the visual evoked potential values were obtained in both case and control groups and compared. Radiomics features were extracted from the area segmented which included the right and left optic nerves and chiasm. We integrated CT image features with dosimetric and clinical data subsequently, ranked 5 supervised ML models Bernoulli Naive Bayes, Decision Tree, Gradient Boosting Decision Trees, K-Nearest Neighbor, and Random Forest on 4 input datasets to predict radiation-induced visual complications classifiers by implementing 5-fold cross-validation. The F1 score, accuracy, sensitivity, specificity, and area under the ROC curve were compared to access prediction capability. Results: radiation-induced optic neuropathy affected 31 % of the patients. 856 radiomic characteristics were extracted and selected from each segmented area. Decision Tree and Random Forest with the highest AUC (97 % and 95 % respectively) among the five classifiers were the most powerful algorithms to predict radiation-induced optic neuropathy. Chiasm with higher sensitivity and precision was able to predict radiation-induced optic neuropathy better than right or left optic nerve or combination of all radiomic features. Conclusion: We found that combination of radiomic, dosimetric, and clinical factors can predict radiation-induced optic neuropathy after radiation treatment with high accuracy. To acquire more reliable results, it is recommended to conduct visual evoked potential tests before and after radiation therapy, with multiple follow-up courses and more patients. © 2024 The Authors

关键词： Computed tomography Machine learning Radiation-induced optic neuropathy Radiomics Visual evoked potential

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Algorithms Analysis of Mobile Robot SLAM based on Kalman and Particle Filter 9th

Algorithms Analysis of Mobile Robot SLAM based on Kalman and...

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2017 9th International Conference on Modelling, Identification and control (ICMIC 2017)

作者： Feng Zhang Siqi Li Shuai Yuan Enze Sun Languang Zhao Information & Control Engineering Faculty Shenyang Jianzhu University State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781509065769;9781509065752

Many algorithms of mobile robot SLAM(Simultaneous Localization and Mapping) have been researched at present,however,the SLAM algorithm of mobile robot based on probability is often used in the unknown *** this paper,two kinds of SLAM algorithms based on probability are analyzed and *** kind is the SLAM algorithm based on Kalman filter: extended Kalman filter SLAM(EKF-SLAM)and unscented Kalman filter SLAM algorithm(UKF-SLAM).Another kind is the SLAM algorithm based on Particle filter:Fast SLAM and unscented Fast SLAM(UFast SLAM) *** difference from the four algorithms of SLAM is illustrated in terms of principle and calculation ***,the simulation results show that the UFast SLAM algorithm is superior to other algorithms in robot path and landmark estimation.

关键词： mobile robot Simultaneous Localization and Mapping EKF-SLAM UKF-SLAM Fast SLAM UFast SLAM

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The Inherent Velocity Limitation in Robust Robot control 21

The Inherent Velocity Limitation in Robust Robot Control

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21st IEEE International Carpathian control Conference, ICCC 2020

作者： Kardos, Jan Information Technology Slovak University of Technology in Bratislava Bratislava Institute of Robotics and Cybernetics Faculty of Electrical Engineering Slovakia

ISBN: (纸本)9781728119519

The aim of this paper is to show an original method of the velocity limitation in robust MIMO motion system based on the theory of variable structure control. The advantage of the presented approach is in its usability for both the fixed target control and the command following one. Furthermore, we provide a continuous modification of the control algorithm suitable for engineering practice, where the motion velocity limitation for the safety and integrity of the technology is required. Numeric simulation on the robot dynamic model illustrates the quality and the reliability of the presented control method. © 2020 IEEE.

关键词： Velocity

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