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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Avanzato, Roberta Mandelli, Lorenzo Randieri, Cristian Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy ficonTEC Service GmbH Im Finigen 3 Achim 28832 Germany 22060 Italy

Image captioning represents a significant challenge within the field of computer Vision. This task involves processing an image as input, identifying objects within it, comprehending the relationships between these objects, including their implicit characteristics, and generating a concise description as output. Given the vast number of potential interactions, acquiring sufficient training examples is a formidable task. Prior research has demonstrated that objects and predicates, when involved in less common relationships, occur more frequently when considered independently. Consequently, the proposed solution involves training two distinct visual models for objects and predicates, which are subsequently combined to capture as many relationships as possible. This study aims to leverage the information obtained regarding detected objects and their relationships to generate a comprehensive description of the image. By facilitating user interaction through Visual Question Answering, a task that bridges computer Vision and Natural Language Processing, we can create an interactive approach to image captioning. Given a question and an image, the system is designed to reason based on both the image content and general knowledge, and generate an accurate answer. We believe that such a system provides an effective method for visually impaired individuals to understand the content of an image. © 2023 Copyright for this paper by its authors.

关键词： Natural language processing systems

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Biological Influence of Low-Frequency Electromagnetic Field Exposition Based on Selected Cell Death Markers 15

Biological Influence of Low-Frequency Electromagnetic Field ...

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15th International Conference Elektro, ELEKTRO 2024

作者： Cichorowski, Robert Kocikowska, Olga Bajtos, Marek Radil, Roman Janousek, Ladislav De Carrillo, Daria Gendosz Student, Sebastian Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Faculty of Automatic Control Electronics and Computer Science Department of Engineering and Systems Biology Gliwice Poland Silesian University of Medicine Faculty of Medical Sciences Department of Physiology Katowice Poland Faculty of Electrical Engineering and Informational Technology Department of Electromagnetic and Biomedical Engineering Žilina Slovakia Silesian University of Medicine Faculty of Medical Sciences in Zabrze Department of Histology Zabrze Poland Silesian University of Technology Biotechnology Center Gliwice Poland

ISBN: (纸本)9798350372359

This work presents the results of the examination of the HeLa cell line exposure on the ELF-EMF (extremely low-frequency electromagnetic field). In particular, the relationship between ELF-EMF exposition time and cell death. The examination of cell death hallmarks were examined by estimation levels of selected proteins - FACL4 (a protein that is a part of the ferroptosis pathway) and CK18 (Cytokeratin related to necrosis and apoptosis pathways) in the proposed model of workers exposed to ELF-EMF week. © 2024 IEEE.

关键词： Electromagnetic fields

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User Input Search - Custom Motion Estimation Algorithm Optimized for UAVs

User Input Search - Custom Motion Estimation Algorithm Optim...

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Proceedings of the International Convention MIPRO

作者： J. Benjak D. Hofman Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing Zagreb Croatia

Unmanned aerial vehicle technology is growing rapidly as it finds its application in the various industries, including military & defense, agriculture, logistics, transportation, healthcare, entertainment and many others. One of the fastest growing industries including drones is the entertainment industry. More specifically, First Person View (FPV) piloting has become a popular sport which attracts huge masses. In FPV systems, the pilot wears FPV goggles and controls the drone using a controller, and the drone transmits video data to the goggles in real time. Since drones usually fly very quickly, the video quality in terms of resolution, compression artefacts and end-to-end delay must be maintained. This paper explores the idea of optimizing the Motion Estimation (ME) algorithm of existing High Efficiency Video Coding (HEVC) algorithms by utilizing user input from the controller. For example, if the drone is directed to hover to the left, then it would make sense to search for the most similar blocks in the previous video frame only on the left side of the referent block. For the purpose of this research, a HEVC video coder was customized to receive additional input besides the video data – the user input from the controller, or in other words, drone movement directions. We call this ME algorithm User Input Search (UIS). Our UIS algorithm is compared with the standard ME algorithms and its efficiency is tested.

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Optical Data Center Implementation within Data Center Networks Context Based on Mininet 2

Optical Data Center Implementation within Data Center Networ...

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2nd International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Abdulmahdi, Esraa Ali Ali, Adnan Hussein Ibrahim, Raed Khalid Al-Askery, Ali Alhamdane, Hayder Jasim Mahmood, Amal Ibrahim Electrical Engineering College Middle Technical University Student at Dept. of Computer Engineering Techniques Baghdad Iraq Institute of Technology Middle Technical University Electrical Techniques Department Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad Iraq Electrical Engineering College Middle Technical University Department of Control and Automation Technical Engineering Baghdad Iraq College of Applied Arts Middle Technical University Baghdad Iraq Electrical Engineering College Middle Technical University Dept. of Computer Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350373363

The technology of reconfigurable optical networks (RONs) is growing to be a promising solution to effectively cater to the rapidly increasing traffic generated by the digital society. Optical technology emerges as a viable alternative because to its capacity to offer a substantial bandwidth, little latency, and enhanced overall performance. Nevertheless, it is imperative to effectively manage the physical resources comprising a data center. This study provides an experimental demonstration of the RONs, which is enhanced by a control plane. The implementation relies on the monitoring of real-time network statistics. This enables the execution of various activities, including as network slice provisioning and reconfiguration, assignment of packet priority classes, and dynamic operations. These actions are performed to ensure the attainment of the desired Quality of Service level. The experimental results show the successfully implementation of RONs using Mininet Emulator and sFlow traffic monitoring technology. The results approve that optical datacenter has minimum delay with low OSNR even with long distance and fast switching time. © 2023 IEEE.

关键词： Optical switches

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Rule-Based Out-of-Distribution Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2627-2637页

作者： De Bernardi, Giacomo Narteni, Sara Cambiaso, Enrico Mongelli, Maurizio CNR-Istituto di Elettronica Ingegneria Dell' Informazione e Delle Telecomunicazioni Genoa16152 Italy Universitá Degli Studi di Genova The Electrical Electronics and Telecommunication Engineering and Naval Architecture Department Genova16145 Italy CNR-Istituto di Elettronica Ingegneria dell'Informazione e Delle Telecomunicazioni Genoa16152 Italy Politecnico di Torino Department of Control and Computer Engineering Turin10129 Italy

Out-of-distribution detection is one of the most critical issue in the deployment of machine learning. The data analyst must assure that data in operation should be compliant with the training phase as well as understand if the environment has changed in a way that autonomous decisions would not be safe anymore. The method of the paper is based on eXplainable Artificial Intelligence (XAI);it takes into account different metrics to identify any resemblance between in-distribution and out of, as seen by the XAI model. The approach is nonparametric and distributional assumption free. The validation over complex scenarios (predictive maintenance, vehicle platooning, covert channels in cybersecurity) corroborates both precision in detection and evaluation of training-operation conditions proximity. © 2020 IEEE.

关键词： Probability distributions

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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Architecture of AC-DC Energy Nodes in Supergrids: A Technical Foundation for Future Renewable Integration

Architecture of AC-DC Energy Nodes in Supergrids: A Technica...

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Applied Research in electrical engineering (AREE), National and International Conference on

作者： Mohammad Hossein Mousavi Hassan Moradi Kumars Rouzbehi Department of Computer and Electrical Engineering Razi University Kermanshah Iran Department of System Engineering and Automatic Control University of Seville Seville Spain

ISBN: (数字)9798350377170

ISBN: (纸本)9798350377187

The AC-DC Energy Nodes (ADENs) concept offers a transformative approach to modernizing power grids, particularly in the context of supergrids. By centralizing power flows from diverse renewable energy sources, such as offshore wind farms, ADENs enable efficient long-distance power transmission through HVDC technology. This design enhances the balance of supply and demand across regions and countries, significantly improving grid stability and resilience. However, the implementation of ADENs presents substantial technical, regulatory, and cybersecurity challenges. This study delves into these challenges while also addressing the geopolitical complexities of transnational connections, underscoring the critical importance of policy alignment for successful deployment.

关键词： Renewable energy sources Supply and demand Protocols HVDC transmission Wind farms Power system stability Stability analysis computer security Protection Resilience

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Nonlinear Motion control of a Multirotor Slung Load System: Experimental Results

Nonlinear Motion Control of a Multirotor Slung Load System: ...

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American control Conference (ACC)

作者： Zifei Jiang Yanwen Yu Alan Lynch Department of Electrical and Computer Engineering Applied Nonlinear Control Lab University of Alberta AB CA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper considers the motion control of a multirotor slung load system (SLS) which is capable of tracking time-varying payload reference position trajectories. The method applies a quasi-static feedback (QSF) linearization to obtain linear tracking error dynamics for the outer-loop. QSF has the practical benefit of simple static dependence on state and reference input. Further, LTI error dynamics simplify gain tuning and the stability proof. Accurate software-in-the-loop (SITL) simulation and flight tests validate tracking performance. Open-source software and hardware are used in the experiments, and source code is available.

关键词： Linear systems State feedback Tracking Dynamics Stability analysis Trajectory Nonlinear dynamical systems

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Attention-based Vision Knowledge Adaptation for Constrained Continual Learning

Attention-based Vision Knowledge Adaptation for Constrained ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Bicheng Guo Conghao Zhou Haoyu Liu Shibo He Jiming Chen Xuemin Sherman Shen College of Control Science and Engineering Zhejiang University China Department of Electrical and Computer Engineering University of Waterloo Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The demand for continual machine learning in the context of limited computational resources and data availability is critical in the evolving landscape of the connected digital world. Current network applications predominantly rely on deep learning models that require labor/computation-intensive training processes. These models often struggle to effectively adapt to new data while preserving performance on previously acquired knowledge. In this paper, we introduce a lightweight framework for continual knowledge adaptation and learning designed to address these challenges. To prevent disruption of existing services, we propose an attention-based adapter that integrates seamlessly with the existing vision model to encode new incoming data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between existing knowledge and information from new data. Our framework is modular, enabling flexible deployment across distributed devices. The adapter and knowledge fusion module are implemented at each stage with minimal trainable parameters, optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed framework.

关键词： Continuing education Training Deep learning Adaptation models Multi-access edge computing Computational modeling Data models Global communication Convergence

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Mask R-CNN Based Real Time near Drowning Person Detection System in Swimming Pools

Mask R-CNN Based Real Time near Drowning Person Detection Sy...

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2022 Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2022

作者： Hayat, Muhammad Aftab Yang, Goutian Iqbal, Atif School of Control & Computer Engineering North China Electric Power University Beijing China The University of Lahore Department of Electrical Engineering Lahore Pakistan

ISBN: (数字)9781665475198

ISBN: (纸本)9781665475198

To find the drowning person in time in swimming pool to reduce the drowning person mortality rate. We used the Mask R-CNN algorithm, and optimizing the convolution backbone of the traditional Mask R-CNN algorithm by adding features of cascaded with pyramid model to design a swimmer drowning detection system. Through real-time recognition of the posture of swimmers in the swimming pool, it can determine the drowning person and alert in time. The system's proposed algorithm has been put to the test on multiple real-world video sequences taken in swimming pools, and the findings show that it is very accurate and capable of monitoring people in real time. The experimental results show that the detection speed of the system is 6 FPS, while the detection rate is 94.1 %, while the false detection rate is 5.9%. The effect is good, which satisfying the anticipated requirements. © 2022 IEEE.

关键词： Swimming pools

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