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IEEE International Conference on Robotics and Automation (ICRA)

作者： Artūras Straižys Michael Burke Subramanian Ramamoorthy School of Informatics University of Edinburgh Department of Electrical and Computer Systems Engineering Monash University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In surgery, the application of appropriate force levels is critical for the success and safety of a given procedure. While many studies are focused on measuring in situ forces, little attention has been devoted to relating these observed forces to surgical techniques. Answering questions like "Can certain changes to a surgical technique result in lower forces and increased safety margins?" could lead to improved surgical practice, and importantly, patient outcomes. However, such studies would require a large number of trials and professional surgeons, which is generally impractical to arrange. Instead, we show how robots can learn several variations of a surgical technique from a smaller number of surgical demonstrations and interpolate learnt behaviour via a parameterised skill model. This enables a large number of trials to be performed by a robotic system and the analysis of surgical techniques and their downstream effects on tissue. Here, we introduce a parameterised model of the elliptical excision skill and apply a Bayesian optimisation scheme to optimise the excision behaviour with respect to expert ratings, as well as individual characteristics of excision forces. Results show that the proposed framework can successfully align the generated robot behaviour with subjects across varying levels of proficiency in terms of excision forces.

关键词： Force measurement Force Surgery Safety Bayes methods Robots Optimization

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Virtual and augmented experience in cultural places: The perspective of integration in the learning process 7

Virtual and augmented experience in cultural places: The per...

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7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Bosmos, Fotios Tzallas, Alexandros T. Tsipouras, Markos G. Giannakeas, Nikolaos University of Ioannina Department of Informatics and Telecommunications Arta Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

The objective of this paper is the development and evaluation of mobile applications for learning tours in cultural and archaeological sites, using virtual and augmented reality. Firstly, the theoretical framework of virtual and augmented reality techniques is presented. Then the virtual model of the bridge of Arta is constructed. The model is to be integrated into the unity gaming machine to accompany a virtual navigation application. The application is enriched with multimedia elements and is connected to a corresponding augmented reality application and is evaluated by secondary school students. © 2022 IEEE.

关键词： E-learning

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Adversarial learning for Mirai botnet detection based on long short-term memory and XGBoost

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 153-160页

作者： Vajrobol, Vajratiya Gupta, Brij B. Gaurav, Akshat Chuang, Huan-Ming Institute of Informatics and Communication University of Delhi India International Center for AI and Cyber Security Research and Innovations. Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Ronin Institute MontclairNJ United States Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Dehradun India

In today's world, where digital threats are on the rise, one particularly concerning threat is the Mirai botnet. This malware is designed to infect and command a collection of Internet of Things (IoT) devices. The use of Mirai attacks has intensified in recent times, thus threatening the smooth operation of numerous devices that are connected to a network. Such attacks carry adverse consequences that include interference with services or the leakage of confidential information. To fight this growing threat, smart and flexible detection techniques are required to counter the new methods cyber attackers use. The aim of this research is to develop a resilient defense against Mirai botnet attacks. The Long Short Term Memory term (LSTM) and XGBoost combined have the best performance of 97.7% accuracy score. With this combination, the aim is to strengthen our cyber defenses against sophisticated and dynamically operating Mirai botnets to further enhance the security of our digital world. © 2024

关键词： Botnet

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Space Division Multiple Access for Cellular V2X Communications

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computers, Materials & Continua 2022年第10期73卷 1195-1206页

作者： Doaa Sami Khafaga Mohammad Zubair Khan Muhammad Awais Javed Amel Ali Alhussan Wael Said Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Information Taibah University Madinah Saudi Arabia Department of Electrical and Computer Engineering COMSATS University Islamabad45550Pakistan Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazig44511Egypt

Vehicular communication is the backbone of future Intelligent Transportation Systems(ITS).It offers a network-based solution for vehicle safety,cooperative awareness,and traffic management *** safety applications,Basic Safety Messages(BSM)containing mobility information is shared by the vehicles in their neighborhood to continuously monitor other nearby vehicles and prepare a local traffic *** are shared using mode 4 of Cellular V2X(C-V2X)communications in which resources are allocated in an ad hoc ***,the strict packet transmission requirements of BSM and hidden node problem causes packet collisions in a vehicular network,thus reducing the reliability of safety ***,as vehicles choose the transmission resources in a distributed manner in mode 4 of CV2X,the packet collision problem is further *** paper presents a novel solution in the form of a Space Division Multiple Access(SDMA)protocol that intelligently schedules BSM transmissions using vehicle position data to reduce concurrent transmissions from hidden node *** proposed protocol works by dividing road segments into clusters and *** sub-frames are allocated to a cluster and these sub-frames are reused after a certain *** a cluster,sub-channels are allocated to *** implement the proposed SDMA protocol and evaluate its performance in a highway vehicular *** results show that the proposed SDMA protocol outperforms standard Sensing-Based Semi Persistent Scheduling(SB-SPS)in terms of safety range and packet delay.

关键词： ITS vehicular network cellular V2X SDMA

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CORRECTIVE MAINTENANCE TIME MODEL OF THE 2-PARALLEL CONFIGURATION OF THE EARTH STATION SYSTEM USING CURVE FITTING 51

CORRECTIVE MAINTENANCE TIME MODEL OF THE 2-PARALLEL CONFIGUR...

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51st International Conference on computers and Industrial engineering, CIE 2024

作者： Kamaruddin, Nur Fatin Syamimi Rahim, Nadirah Abdul Mansor, Mahayaudin M. Department of Electrical and Computer Engineering International Islamic University Malaysia Jalan Gombak Kuala Lumpur Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia

An Earth station system serves as a key hub in the ever-changing environment of satellite communication technology, allowing uninterrupted data interchange between the Earth and orbiting satellites. These systems are essential for various applications like telecommunications, remote sensing, and global positioning. In this research, a 2-parallel configuration is chosen for the Earth station system design because the literature suggests that it has the lowest operational cost and is reliably acceptable if compared to other parallel configurations. However, the 2-parallel configuration comes with the issue of care and upkeep, especially in the context of corrective maintenance time, which is the aim of this study. Corrective maintenance, or repairing components and systems after failure, is critical to the Earth station system operation. This study uses curve fitting techniques by using the Monte Carlo method from MATLAB software to foresee system component failures and predict their behavior over time. The useful insights can be extracted from previous maintenance data by using curve fitting, allowing one to make informed decisions regarding when and how to do maintenance. The results demonstrate the curve fitting of the combination of the corrective maintenance for seven subsystems with the mean time between failures (MTBF) of 1, 3, 5, 7, and 10 years, and it is shown that the first-degree polynomial has the lowest Root Mean Square Error (RMSE), which is 1.35. Furthermore, the corrective maintenance time model is developed not only to analyse component failures but also to assist in deriving appropriate maintenance guidelines in the future that can help system engineers repair the failed subsystem. © 2024 computers and Industrial engineering. All rights reserved.

关键词： MATLAB

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Cervical Cancer Classification From Pap Smear Using XCeption Model 10

Cervical Cancer Classification From Pap Smear Using XCeption...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Maesaroh, Ima Siti Syaja'ah, Khoerun Nisa Perkasa, Yudha Satya Hadianti, Sri Riana, Dwiza Nurmalasari, Rin Rin UIN Sunan Gunung Djati Department of Physics Bandung Indonesia Universitas Nusa Mandiri Department of Informatics Jakarta Indonesia Universitas Nusa Mandiri Department of Computer Science Jakarta Indonesia UIN Sunan Gunung Djati Department of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350377057

Cervical cancer is a disease that affects the reproductive organs of women and contributes to a high mortality rate among them. The rise in mortality can be attributed to delays in cancer screening, underscoring the importance of early prevention measures. While the Pap smear test is an effective method for early detection and diagnosis of cervical cancer, manual inspection of test results still has limitations in terms of observation and time-consuming laboratory processes. This study aims to implement the Convolutional Neural Network (CNN) model using transfer learning with the Xception architecture to classify cervical cancer based on Pap smear images, categorizing them into 2 or 4 classes. The dataset used in this study consists of 400 cervical cell images, including normal cells, koilocytes, L-Sil, and H-Sil, sourced from RepoMedUNM. The study involves several steps: data splitting, data resizing and augmentation, hyperparameter tuning, CNN model development using the Xception architecture, training, testing, and model evaluation. The research results indicate that the Convolutional Neural Network (CNN) with transfer learning using the Xception method can accurately classify cervical cancer based on Pap smear images, achieving an accuracy of 100% in the 2-class classification and 85% in the 4-class classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Ship Engine Data Analysis for the Application of Machine Learning Algorithms 8

Ship Engine Data Analysis for the Application of Machine Lea...

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8th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2023

作者： Dimitriou, Theodoros Skondras, Emmanouil Hitiris, Christos Gkola, Cleopatra Papapanagiotou, Ioannis S. Vergados, Dimitrios J. Vergopoulos, Constantinos Koumantakis, Stratos Michalas, Angelos Vergados, Dimitrios D. University of Piraeus Department of Informatics Piraeus Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece University of Western Macedonia Department of Informatics Kastoria Greece Internet Business Hellas Athens Greece Mas S.A. Athens Greece

ISBN: (纸本)9798350383676

In Machine Learning (ML) the analysis and the preparation of data before their use is considered an important task, in order to improve the performance of the ML algorithms. Techniques like clustering and dimensionality reduction are applied for the preparation of the data offering several advantages for the ML algorithms to which the data will be used. Some indicative advantages include the improved performance and the faster training of the ML algorithms, the handling of missing or corrupted data, the detection of data overfitting and the visualization of the data. In this paper, a dataset that contains plenty of ship engine's data is analyzed. Specifically, methodologies for density estimation, clustering and dimensionality reduction are studied. Subsequently, such methodologies are applied to the aforementioned dataset, providing useful results about the structure of the dataset, about the correlation of its data, as well as about the importance of each feature included in the dataset. © 2023 IEEE.

关键词： Machine learning

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Motor Imagery Approach for BCI Game Development 7

Motor Imagery Approach for BCI Game Development

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7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Prapas, Georgios Glavas, Kosmas Tzallas, Alexandros T. Tzimourta, Katerina D. Giannakeas, Nikolaos Tsipouras, Markos G. University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece University of Ioannina Department of Informatics and Telecommunication Arta Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

Brain-computer interface (BCI) is a rapidly growing field with various applications in many domains such as medical, gaming and lifestyle. This paper presents a 3D non-invasive BCI game. Muse 2 headband is used for acquiring electroencephalogram (EEG) data and OpenViBE platform for processing the raw signals and classification. The game is developed in Unity game Engine. Several subjects are included in the study and EEG signals are recorded for three different mental states i.e. left and right Motor Imagery and eye blink, before playing the game for ten times, aiming to collect coins. Average classification result is 94.86% and average coins collected from the users is 30.8 out of 50 coins. Furthermore, longer periods of playing the game leads to increased control over the game. © 2022 IEEE.

关键词： Electroencephalography

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A Network Simulator as a Service for Demanding Performance Evaluation of Wireless and IoT Scenarios 2

A Network Simulator as a Service for Demanding Performance E...

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2nd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2022

作者： Antonopoulos, Konstantinos Ringas, Dimitrios Christopoulou, Eleni Keramidas, Georgios Antonopoulos, Christos Voros, Nikolaos University of Peloponesse Electrical and Computer Engineering Department Patras Greece Ionian University Department of Informatics Corfu Greece School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9781665498258

ISBN: (纸本)9781665498258

This paper presents the design, development and evaluation of a novel Network Simulator as a Service infrastructure. It is based on a prominent open-source discrete-event network simulator which is extended to offer advanced batch simulation execution, results acquisition, post-processing analysis and presentation features, as well as machine learning optimisations to reduce the overall processing load. In that respect, we have developed a software system, which allows researchers to define simulation scenarios along with a set of parameters and to commission simulation executions in large scale thus delivering to the end user a service that is novel, robust, extendible and extensible. It has been thoroughly evaluated within ESDA Lab (UoP), proving its feasibility and demonstrating its ability to leverage machine learning techniques in order to automatically identify and exclude simulations that are classified as ones that will not meet defined performance requirements, thus drastically reducing simulation workload, delay and resource utilisation. © 2022 IEEE.

关键词： Wireless networks

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EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation

IEEE Open Journal of Signal Processing

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IEEE Open Journal of Signal Processing 2021年 2卷 1页

作者： Kaselimi, Maria Doulamis, Nikolaos Voulodimos, Athanasios Doulamis, Anastasios Protopapadakis, Eftychios Department of Electrical and Computer Engineering National Technical University of Athens Zografou15780 Greece Department of Informatics and Computer Engineering University of West Attica Athens12243 Greece

Energy disaggregation, namely the separation of the aggregated household energy consumption signal into its additive sub-components, bears resemblance to the signal (source) separation problem and poses several challenges, not only as an ill-posed problem, but also, due to unsteady appliance signatures, abnormal behaviour that is usually detected in appliances operation and the existence of noise in the aggregated signal. In this paper, we propose EnerGAN++, a model based on Generative Adversarial Networks (GAN) for robust energy disaggregation. We attempt to unify the autoencoder (AE) and GAN architectures into a single framework, in which the autoencoder achieves a non-linear power signal source separation. EnerGAN++ is trained adversarially using a novel discriminator, to enhance robustness to noise. The discriminator performs sequence classification, using a recurrent convolutional neural network to handle the temporal dynamics of an appliance energy consumption time series. In particular, the proposed architecture of the discriminator leverages the ability of Convolutional Neural Networks (CNN) in rapid processing and optimal feature extraction, among with the need to infer the data temporal character and time dependence. Experimental results indicate the proposed method's superiority compared to the current state of the art. © 2020 IEEE.

关键词： Generative adversarial networks

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