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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Georgi Martinov Petr A. Nikishechkin Department of Computer-Architecture Control Systems Moscow State University of Technology "STANKIN" Moscow Russia

The process of developing a PLC program for controlling the electromechanical units of modern machine tools with computer numerical control (CNC) has been formalized. The architecture of a two-computer CNC system with an integrated SoftPLC is given and the specifics of using the touch operator panel and virtual machine panel are considered. A set of libraries developed in the FBD (Functional Block Diagram) language for controlling the electrical automation units of a turning machine is proposed. An example of controlling the electric automation of a training lathe via the high-speed EtherCAT field bus using relatively inexpensive third-party solutions for servo drives and I/O modules that provide the required accuracy and speed is given.

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Enhanced Federated Learning for Iot Systems

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SSRN 2023年

作者： Mora-Gimeno, Francisco José Elouali, Aya Mora, Higinio Mora Szymanski, Julian Department of Computer Science Technology and Computation University of Alicante Alicante Spain Department of Computer Architecture Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Gdańsk Poland

Federated learning consists on training several local models on edge devices where data is collected. These models are then shared with the cloud where the general model is calculated and transmitted back to the edge. This process is repeated several times until the general model achieves the desired accuracy. This technique permits training a model on the largest possible quantity of data without having to share the device’s private data with other devices or the cloud. However, the main drawback of this method is transmitting big size models multiple time on the network from and to every edge device participating in the training. In this paper, we propose a data transmission reduction method in federated learning context which, based on data similarity, reduces the amount of weights to be transmitted to the cloud. Several experiments has been performed to prove the effectiveness of the method and define the best components to use it such as data representation method and change calculation method. The performed experiments revealed that the proposed solution reduces data transmission up to 75% while having only 0.02% accuracy deterioration © 2023, The Authors. All rights reserved.

关键词： Edge computing

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FUCOM-Kendall Model for Evaluating E-Scooter Adoption: A Case Study in Catania, Italy

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Transportation Research Procedia 2025年 86卷 1-8页

作者： Vincenza Torrisi Rosalia Camporeale Sarbast Moslem Tiziana Campisi Department of Electric Electronic and Computer Engineering Catania Italy Department of Technology and Society Division of Transport and Roads Lund Sweden School of Architecture Planning and Environmental Policy University College Dublin Ireland Faculty of Engineering and Architecture University of Enna KORE Enna Italy

Electric scooters (e-scooters) have emerged as a fundamental component of urban micro-mobility, particularly in European cities during the Covid-19 pandemic. This study employs the Full Consistency Method (FUCOM) and Kendall rank correlation method to assess the preferences and priorities of university students in Catania, Italy, regarding e-scooter adoption. By analyzing influencing factors, this research offers a comprehensive understanding of student attitudes towards e-scooters and their adoption, contributing to strategies for enhancing sustainable urban mobility. The findings highlight significant preferences for factors such as parking ease, portability, and intermodality, while also identifying safety concerns and the need for better integration with public transport. These insights underscore the necessity for targeted improvements in e-scooter infrastructure and policies, aiming to optimize urban mobility and meet the specific needs of potential users. Further research will focus on comparative analysis across different urban settings, investigating addition user-centric factors to capture a more comprehensive view of e-scooter adoption.

关键词： e-scooter multi-criteria decision making FUCOM method Kendall rank correlation urban mobility

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A new clustering-based semi-supervised method to restrict the users from anomalous electricity consumption: supporting urbanization

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Electrical Engineering 2024年第5期106卷 6431-6448页

作者： Aslam, Zeeshan Javaid, Nadeem Javed, Muhammad Umar Aslam, Muhammad Aldegheishem, Abdulaziz Alrajeh, Nabil Bredesen Center for Interdisciplinary Research and Graduate Education The University of Tennessee TN37996 United States Department of Computer Science COMSATS University Islamabad Islamabad44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Artificial Intelligence Sejong University Seoul05006 Korea Republic of Department of Urban Planning College of Architecture and Planning King Saud University Riyadh11574 Saudi Arabia Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia

One of the crucial issues for power grids in strengthening the urbanization around the world is imbalance between supply and demand, which leads the users to consume electricity in an anomalous manner without paying for it. Electricity theft plays a pivotal role in cutting down on the electricity bills. The existing data-oriented approaches for electricity theft detection (ETD) in the smart cities have limited ability to handle noisy high-dimensional data and features’ associations. These limitations raise the misclassification rate, which makes some of the approaches unacceptable for electric utilities. A new twofold end-to-end methodology is proposed for ETD. In the first fold, it groups the similar electricity consumption (EC) cases through grey wolf optimization (GWO)-based clustering mechanism;clustering by fast search and find of density peaks (CFSFDP), we named it GC. In the second fold, a new relational stacked denoising autoencoder (RSDAE)-based semi-supervised generative adversarial network (GAN), termed as RGAN, is used for ETD. The combined methodology is named as GC-RGAN. In the methodology, RSDAE acts as both feature extraction technique and generator sub-model of the proposed RGAN. The proposed methodology utilizes the advantages of clustering, adversarial learning and semi-supervised EC data. Besides, to validate the effectiveness of the proposed solution, extensive simulations are performed using smart meter data. Simulation results validate the excellent ETD performance of the proposed GC-RGAN against existing ETD schemes, such as random forest and semi-supervised support vector machine. In comparison, GC-RGAN covers the ETD score of 98% that shows its suitability for real-world scenarios. The proposed solution has extraordinary performance for ETD as compared to traditional solutions, which shows its superiority and usefulness for real-world applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Na

关键词： Generative adversarial networks

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Deep Learning based vehicle image detection using Yolo V5 with Region-Based Convolutional Neural Network 3

Deep Learning based vehicle image detection using Yolo V5 wi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Rani, S. Swapna Mudigonda, Aditya Hemanth, S.V. Sundararajan Reddy, G. Vinoda Amirthayogam, G. Engineering College Department of Electronics and Communication Engineering Hyderabad India JNIAS School of Planning and Architecture Telangana Hyderabad500034 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Telangana Hyderabad501401 India Hindustan Institute of Technology and Science Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

Detecting and classifying vehicles is a modern technology with numerous uses. Administration and regulation of traffic is one of the primary uses. Projects utilizing image processing to prevent traffic accidents heavily rely on vehicle monitoring and detection. Monitoring and recording human movement in surveillance situations require the ability to follow moving objects. Considering its significance, it provides a valuable image processing-based vehicle detection technique - a vehicle tracking and detection system based on images. The recently released high-resolution road vehicle dataset supports deep learning-based vehicle detection and monitoring on the Python platform. It includes over 100 well-defined pictures taken from movies from various locations. This data is produced using a range of image processing methods. The most recent iteration of the YOLO model, the v5 model, was employed for detection. R-CNN is additionally utilized for model detection. The four stages in the Region-Based Convolutional Neural Network (RCNN) process are preprocessing, segmentation, feature selection, and classification. The first step is identifying moving vehicles accurately. Preprocessing the original image is necessary. This enhances the image's quality, valuable information extracted, or unwanted areas cropped out. Segmentation is the second step. The region's borders are established, and the entire image is divided into numerous smaller areas using a threshold. The vehicle image's most pertinent aspects or characteristics (features) that aid in precise detection are found in the third stage, feature selection. A region-based convolutional neural network (RCNN) is employed to determine the categorization. The Yolov5 method can be used to identify and categorize items. The Region-based Convolutional Neural Network (RCNN) approach enhances accuracy and temporal complexity over current segmentation algorithms. © 2024 IEEE.

关键词： Vehicles

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A Lottery-based Spherical Evolution Algorithm with Elite Retention Strategy 14

A Lottery-based Spherical Evolution Algorithm with Elite Ret...

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14th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2022

作者： Li, Jiayi Zhang, Zihang Lei, Zhenyu Yi, Junyan Gao, Shangce University of Toyama Faculty of Engineering Toyama930-8555 Japan Beijing University of Civil Engineering and Architecture Department of Computer Science and Technology Beijing100044 China

ISBN: (数字)9781665461696

ISBN: (纸本)9781665461696

Spherical evolution (SE) is a recently proposed meta-heuristic algorithm. Its special search approach has been proved to be very effective in exploring the search space. SE is very powerful for optimization, but still has room for improvement due to some promising solutions usually fail to survive into the next generation. To alleviate this issue, we innovatively design a novel lottery-based elite retention strategy and propose a lottery elite spherical evolution algorithm (LESE). To verify the effectiveness of the proposed LESE, we experimentally compare it with the original SE algorithm and other representative meta-heuristics algorithms. We use 30 benchmark functions from the IEEE CEC2017 benchmark as the test set for our experiments. The effectiveness of LESE is demonstrated by analyzing the experimental results from perspectives of solution accuracy, convergence speed, solution distribution, and search dynamics. © 2022 IEEE.

关键词： Spheres

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A research infrastructure for generating and sharing diversity-aware data 2

A research infrastructure for generating and sharing diversi...

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Workshops at the 2nd International Conference on Hybrid Human-Artificial Intelligence, HHAI-WS 2023

作者： Busso, Matteo Abente Acosta, Ronald Chenu de Götzen, Amalia Department of Information Engineering and Computer Science University of Trento Via Sommarive 9 Trento30123 Italy Department of Architecture Design and Media Technology Aalborg University A. C. Meyers Vænge 15 København2450 Denmark

The intensive flow of personal data associated with the trend of computerizing aspects of people’s diversity in their daily lives is associated with issues concerning not only people protection and their trust in new technologies, but also bias in the analysis of data and problems in their management and reuse. Faced with a complex problem, the strategies adopted, including technologies and services, often focus on individual aspects, which are difficult to integrate into a broader framework, which can be of effective support for researchers and developers. Therefore, we argue for the development of an end-to-end research infrastructure (RI) that enables trustworthy diversity-aware data within a citizen science community. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Data privacy

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OntoBlock: a novel ontological-based and blockchain enabled spectrum sensing framework for detection of malicious users in cognitive radio internet of things (CR-IoT) networks

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International Journal of Information technology (Singapore) 2024年第6期16卷 3913-3921页

作者： Marriwala, Nikhil Kumar Shukla, Vinod Kumar Raju, Ayalapogu Ratna Panda, Sunita S, Suma Purad, Hemavati C. Electronics and Communication Engineering Department University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra India Information Technology Head of Academics–School of Engineering Architecture Interior Design Amity University Dubai Dubai International Academic City Dubai United Arab Emirates Department of Computer Science and Engineering Mahatma Gandhi Institute of Technology Hyderabad India Department of Electrical Electronics and Communication Engineering GITAM School of Technology Bengaluru Campus Karnataka Bengaluru India CMR Technical Campus Hyderabad India Department of AIML BITM Karnataka Ballari India

CR-IoT networks can benefit from the idea of blockchain technology to achieve accurate decisions in order to provide very high-accuracy spectrum sensing in the network. It allows for multi-user cooperation as the sensing readings are being exchanged in the spectrum. It leads to security issues in CR-IoT networks. Thus, in order to increase the quality of decision-making in spectrum sensing, the given paper introduces an ontological framework with a blockchain-enabled system to detect malicious users in networks. The proposed framework first captures network packets from available Wi-Fi, followed by the creation of blocks with private, public, and hash keys. The keys are used to encode information about the nodes, such as confidential information and their location, data relative to users, their position where packets are delivered, etc. This is subsequently followed by the generation of network ontology, which includes parsing, computation of similarity, and reduction in clustered packets. Finally, the performance of the suggested mechanism is validated using a set of evaluation measures such as authentication rate, intrusion rate, average network throughput time consumption, and energy efficiency. As seen from the findings, it is evident that the proposed framework performs better than existing recent studies, thereby improving the performance of CR-IoT networks. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blockchain Cognitive radio Internet of things (IoT) Ontology Spectrum sensing

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Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

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Bone Research 2024年第1期12卷 1-19页

作者： Xiwei Fan Antonia Rujia Sun Reuben S.E.Young Isaac O.Afara Brett R.Hamilton Louis Jun Ye Ong Ross Crawford Indira Prasadam Centre for Biomedical Technologies Queensland University of TechnologyBrisbaneQLDAustralia School of Mechanical Medical&Process EngineeringQueensland University of TechnologyBrisbaneQLDAustralia Central Analytical Research Facility Queensland University of TechnologyBrisbaneQLDAustralia Molecular Horizons University of WollongongWollongongNSWAustralia Department of Technical Physics University of Eastern FinlandKuopioFinland School of Electrical Engineering and Computer Science Faculty of EngineeringArchitecture and Information TechnologyUniversity of QueenslandBrisbaneQLDAustralia Centre for Microscopy and Microanalysis University of QueenslandBrisbaneQLDAustralia The Prince Charles Hospital BrisbaneQLDAustralia

Osteoarthritis(OA)is a debilitating degenerative disease affecting multiple joint tissues,including cartilage,bone,synovium,and adipose *** presents diverse clinical phenotypes and distinct molecular endotypes,including inflammatory,metabolic,mechanical,genetic,and synovial ***,innovative technologies are needed to support the development of effective diagnostic and precision therapeutic *** analysis of bulk OA tissue extracts has limitations due to technical constraints,causing challenges in the differentiation between various physiological and pathological phenotypes in joint *** issue has led to standardization difficulties and hindered the success of clinical *** insights into the spatial variations of the cellular and molecular structures in OA tissues,encompassing DNA,RNA,metabolites,and proteins,as well as their chemical properties,elemental composition,and mechanical attributes,can contribute to a more comprehensive understanding of the disease *** resolved biology enables biologists to investigate cells within the context of their tissue microenvironment,providing a more holistic view of cellular *** advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various-omics lenses,such as genomics,transcriptomics,proteomics,and metabolomics,with spatial *** fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial ***,advanced imaging techniques,including high-resolution microscopy,hyperspectral imaging,and mass spectrometry imaging,enable the visualization and analysis of the spatial distribution of biomolecules,cells,and *** these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease *** review summarizes the rec

关键词： insight Spatial enable

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Relationship between the Nasal Region on Facial Thermal Images and the Flow Velocity of Nasal Breathing 4

Relationship between the Nasal Region on Facial Thermal Imag...

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4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022

作者： Hanawa, Dai Oguchi, Kimio Nagoya City University Graduate School of Design and Architecture Nagoya Japan National Taiwan University of Science and Technology Department of Electronics and Computer Engineering Taipei Taiwan

ISBN: (纸本)9781665419048

In this research, two experiments were performed to develop a noncontact measurement method for flow velocity in nasal breathing. On the basis of the results of our preliminary study, we first compared the instantaneous flow velocity of nasal breathing and the air temperature of inhalation and exhalation. Next, we compared the air temperature and the nasal regions on facial thermal images. Results showed that the correlation between flow velocity, air temperature, and the nasal region can be improved using a smoothing filter and our modified normalization procedure. © 2022 IEEE.

关键词： Flow velocity

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