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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on Data Science and systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Ziang Huang, Yuanyuan Zhou, Qiang Zhang, Yingli Zhou, xiaokang Zhou, Rui School of Information Science and Engineering Lanzhou University Lanzhou China State Grid Gansu Electric Power Research Institute Lanzhou China Shiga University Faculty of Data Science Hikone Japan Riken Center for Advanced Intelligence Project Tokyo Japan

ISBN: (纸本)9781665494571

With the energy revolution starting the third transformation to new energy, new energy has become essential for human energy. In order to better organize and utilize new energy, more and more research is committed to predicting the power generation capacity of new energy through the power generation data collected by intelligent systems to make better use of new energy. However, with the increase of new energy digital platforms and supporting infrastructure improvement, the distribution and management of relevant data also begin to face significant challenges. New energy data faces a substantial potential threat in distributing and using data to various digital platforms. Based on this, this paper designs and implements a new energy data call monitoring system based on blockchain to ensure the security of new energy data by tracking data operation information. © 2021 IEEE.

关键词： Smart cities Consensus algorithm Machine learning Blockchains Safety Recording Intelligent systems

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Decoding Driving Neural Dynamics: An Exploratory Survey of EEG applications in Unraveling Cognitive Processes in Virtual Reality Environments

Decoding Driving Neural Dynamics: An Exploratory Survey of E...

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IEEE SoutheastCon conference on Engineering the Future

作者： Sargolzaei, Saman Hatchett, Seth Okashita, Matthew Sargolzaei, Arman Univ Tennessee Dept Engn Martin TN 38238 USA Univ S Florida Dept Mech Engn Tampa FL USA

ISBN: (纸本)9798350317107;9798350317114

The exploratory survey embarks on the crossroads of neuroscience, virtual reality, and transportation research, exploring studies, located upon a systematic review, of the brain's cognitive processes, assessed by electroencephalography (EEG), within virtual reality (VR) based driving environments. In driving research, combining virtual reality with bio-physiological monitoring technology has recently opened up a revolutionary new field that promise never-before-seen insights into driving behaviors and cognitive processes and the creation of innovative technology that could significantly enhance road safety and driving experiences. The exploration found four major domains where the crucial link between human cognition and driving is studied. While most studies focused on driver fatigue detection, monitoring, and forecasting, studying the cognitive responses and brain dynamics in navigation, obstacle avoidance, and other distracting external stimuli are among the trends. Developing real-time advanced embedded monitoring systems technologies to guide future assisted and autonomous transportation technology was also noted. This survey aims to deepen our understanding of EEG applications in VR-based driving studies, unlock the potential for innovative interventions, and provide recommendations for prospective studies contributing to the evolution of intelligent transportation systems.

关键词： Brain analysis Cognitive Processes Driving behavior Electroencephalography (EEG) Intelligent Transportation systems Neuroscience Virtual Reality (VR)

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DD-MLFMA-slotFFT Framework for the Electromagnetic Solution of Mounted Finite Periodic Structures

DD-MLFMA-slotFFT Framework for the Electromagnetic Solution ...

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International conference on Electromagnetics in advanced applications (ICEAA)

作者： Victor F. Martin Alberto Serna Luis Landesa Fernando Obelleiro Marta G. Araujo Francesca Vipiana Jose M. Taboada University of Extremadura Caceres Spain University of Vigo Vigo Spain Politecnico di Torino Turin Italy

ISBN: (数字)9798350360974

ISBN: (纸本)9798350360981

The interest in the electromagnetic behavior of periodic structures has significantly increased due to their crucial role in advancing cutting-edge applications in nanoscale and metasurface devices in scattering networks, plasmonic crystals, and nanophotonics. The rise of these technologies makes the improvement of the state-of-the-art methods in computational electromagnetics for the efficient solution of these kind of periodic structures even more crucial than ever. Traditional approaches predominantly centered on methods tailored for infinite periodic structures based on Floquet’s theorem and the Ewald transformation. However, although these methods have been demonstrated as powerful tools in the design process, they are not suitable for integration with other systems and sensors due to the limitation of infinite periodic structures or the accurate modeling of complex physical phenomena like edge effects and standing waves, vital phenomena for certain applications.

关键词： Nanophotonics Electromagnetic scattering Crystals sensor phenomena and characterization sensor systems and applications Plasmons Metasurfaces Nanoscale devices Electromagnetics Periodic structures

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A pipelined image transmission protocol for WSNs utilizing LoRa networks 15

A pipelined image transmission protocol for WSNs utilizing L...

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15th International conference on Emerging Ubiquitous systems and Pervasive Networks / 14th International conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2024

作者： Ta, Quoc Hop Ta, Van Khoe Oh, Hoon University of Ulsan Ulsan Korea Republic of Le Quy Don Technical University Vietnam Hanoi Viet Nam

Industrial safety applications using wireless sensor networks (WSN) require fast transmission of still images to identify urgent situation in the target field. However, it is quite challenging to transmit a large number of image packets reliably and quickly through wireless multihop. Thus, the current approaches employ slot scheduling and pipelined transmission, but suffer from many control messages. The proposed protocol exploits LoRa networks for reliable and fast image transmission on WSNs so that it can remove most of control messages related to topology management, slot scheduling, time synchronization, and retransmission. According to experimental results, the proposed protocol improves image transmission delay by 25% compared to the recent pipelined protocol by transmitting one image of 100x80 pixels in about 0.3 seconds, and also achieves an image transfer rate of 100% under 1 image packet loss tolerance at an 8-hop distance. © 2024 The Authors.

关键词： Image communication systems

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A Novel Cyber-attack Leads Prediction System using Cascaded R2CNN Model

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INTERNATIONAL JOURNAL OF advanced COMPUTER SCIENCE AND applications 2022年第2期13卷 520-524页

作者： Prabha, P. Shanmuga Kumar, S. Magesh Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai 602105 Tamil Nadu India

Novel prediction systems are required in almost all internet-connected platforms to safeguard the user information to get hacked by intermediate peoples. Finding the real impacted factors associated with the Cyber-attack probes are being considered for research. The proposed methodology is derived from various literature studies that motivated to find the unique prediction model that shows improved accuracy and performance. The proposed model is represented as R2CNN that acts as the cascaded combination of Gradient boosted regression detector with recurrent convolution neural network for pattern prediction. The given input data is the collection of various applications engaged with the wireless sensor nodes in a smart city. Each user connected with a certain number of applications that access the authorization of the device owner. The dataset comprises device information, the number of connectivity, device type, simulation time, connectivity duration, etc. The proposed R2CNN extracts the features of the dataset and forms a feature mapping that related to the parameter being focused on. The features are tested for correlation with the trained dataset and evaluate the early prediction of Cyber-attacks in the massive connected IoT devices.

关键词： Cyber security in smart devices cyber security cyber-attacks internet of things IoT devices machine learning wireless sensor networks

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A Survey on Failure Prediction in Large-scale Computing systems 23

A Survey on Failure Prediction in Large-scale Computing Syst...

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作者： xia, Fei Song, Hu Yan, Long-Chuan Li, Yan Wang, Li-Jun State Grid Jiangsu Electric Power Co. Ltd. Information & Telecommunication Branch Nanjing China State Grid Information & Telecommunication Branch Beijing China State Grid Electric Power Research Institute Nanjing China

ISBN: (纸本)9781665494571

With the development of many data-intensive applications, large-scale systems have been widely used to solve advanced computation problems. As the increasing growth of complexity and scale, these systems are more likely to confront failure. Since remedial measures for failure take huge cost and effort at today's large-scale systems, fault tolerance which aims to decrease the impacts of fault on systems has become a necessity instead of an option. As one of the key techniques of fault tolerance, failure prediction has made itself an increasing important issue to improve the resource efficiency and the availability of systems. Over the past few years, a multitude of innovative failure prediction approaches has emerged such as mathematical and statistical modeling, machine learning techniques and so forth. Unfortunately, they are currently so poorly classified that it is difficult to figure out the wide spectrum of methods concerning with this area. To this end, we provide an extensive and comprehensive survey of existing research work in the area of failure prediction via exploring and analyzing over 20 various approaches. Also, we develop our own taxonomy assisting in classifying methods, which makes it easier to understand and compare the pros and cons of these methods in respective category. © 2021 IEEE.

关键词： Fault tolerance Correlation Fault tolerant systems Taxonomy Neural networks Hidden Markov models Probability

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Design and Analysis of Hybrid Inset Feed and Probe Feed Microstrip Patch Antenna Array for Sub-6GHz applications 7

Design and Analysis of Hybrid Inset Feed and Probe Feed Micr...

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7th IEEE conference on Information and Communication Technology, CICT 2023

作者： Gupta, Aditya Singh, Amit K. Jharia, Bhavna Jabalpur Engineering College Department of Electronics and Communication Engineering Jabalpur India Indian Institute of Technology Patna Department of Electrical Engineering Patna India

ISBN: (纸本)9798350305173

This research work presents the design and comparative analysis of the performance of a hybrid feed 64 elements low-profile rectangular microstrip patch antenna (RMPA) array on a low-cost FR-4 substrate of thickness 1.6mm to achieve high gain and directivity at 3.5 GHz frequency. At Sub-6 GHz band a single RMPA, 2×2, 4×4, and 8×8 array is designed, and a T-junction equal power splitting power divider is used to feed the array network. The overall size of the proposed 8×8 array is 550×550×1.6mm3 and it achieves the highest gain among all also achieves a directivity of 25.01 dBi, outperforming 2×2, 4×4 arrays, and an RMPA. The hybrid feeding mechanism, which includes inset feeding and probe feeding was found to achieve a wider bandwidth and a more uniform radiation pattern, making it a promising option for advanced antenna arrays. On analyzing the proposed arrays in terms of reflection coefficient, VSWR, gain, directivity, and radiation patterns, it was found that the gain and directivity increased and side lobe levels (SLL) decreased with the increasing number of elements in the array. This study's results can guide the design and optimization of RMPAs for various applications in wireless communication systems such as satellite communication, wireless sensor networks, and radar systems. © 2023 IEEE.

关键词： Feeding

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advanced ELECTRICAL ENERGY STORAGE TECHNOLOGIES AND THEIR applications ON CUSTOMER SIDE 27

ADVANCED ELECTRICAL ENERGY STORAGE TECHNOLOGIES AND THEIR AP...

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27th International conference on Electricity Distribution, CIRED 2023

作者： Christian, Noce Luigi, Lanuzza Martina, Radicioni Enel X SRL Italy

ISBN: (纸本)9781839539596

Because of power connection limits, most parts of distribution grid customers are residential, small commercial or industrial. In these fields, the Electrical Energy Storage systems (EESS) are becoming key factors for the decarbonization goals. Lithium-Ions based EESS, in particular, is increasing even more its market share, in some cases "Lithium-Ions" is becoming a synonym of "battery";also, network codes are influenced by this technology. However new advanced EES technologies are emerging thanks to their ability to cover the intrinsic Lithium-Ions batteries (LIBs) gaps in term of safety, duty cycles, environmental conditions, costs etc. The subject addressed in this paper is to present the most attractive "LIBs beyond" solutions already available (or close to be available) on the market and, therefore, for grid connection. In particular: Energy intensive supercapacitors (SCs), Na-Ion or Sodium-Ion batteries (SIBs), Zn-Ion or Zinc-Ion batteries (ZIBs). © The Institution of Engineering and Technology 2023.

关键词： Lithium-ion batteries

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Evaluating Tree-based Ensemble Strategies for Imbalanced Network Attack Classification

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INTERNATIONAL JOURNAL OF advanced COMPUTER SCIENCE AND applications 2024年第1期15卷 1124-1134页

作者： Soon, Hui Fern Amir, Amiza Nishizaki, Hiromitsu Zahri, Nik Adilah Hanin Kamarudin, Latifah Munirah Azemi, Saidatul Norlyana Univ Malaysia Perlis Fac Elect Engn & Technol Arau Perlis Malaysia Univ Yamanashi Kofu Yamanashi Japan Univ Malaysia Perlis Fac Elect Engn & Technol Ctr Excellence Adv Comp ADVCOMP Arau 02600 Perlis Malaysia Univ Yamanashi Integrated Grad Sch Med Engn & Agr Sci Kofu Yamanashi Japan Univ Malaysia Perlis Fac Elect Engn & Technol Ctr Excellence Adv Sensor Technol CEASTECH Arau 02600 Perlis Malaysia Univ Malaysia Perlis Fac Elect Engn & Technol Ctr Excellence Adv Commun Engn ACE Arau 02600 Perlis Malaysia

With the continual evolution of cybersecurity threats, the development of effective intrusion detection systems is increasingly crucial and challenging. This study tackles these challenges by exploring imbalanced multiclass classification, a common situation in network intrusion datasets mirroring real-world scenarios. The paper aims to empirically assess the performance of diverse classification algorithms in managing imbalanced class distributions. Experiments were conducted using the UNSW-NB15 network intrusion detection benchmark dataset, comprising ten highly imbalanced classes. The evaluation includes basic, traditional algorithms like the Decision Tree, K-Nearest Neighbor, and Gaussian Naive Bayes, as well as advanced ensemble methods such as Gradient Boosted Decision Trees (GraBoost) and AdaBoost. Our findings reveal that the Decision Tree surpassed the Multi-Layer Perceptron, K-Nearest Neighbor, and Naive Bayes in terms of overall F1-score. Furthermore, thorough evaluations of nine tree-based ensemble algorithms were performed, showcasing their varying efficacy. Bagging, Random Forest, ExtraTrees, and xGBoost achieved the highest F1-scores. However, in individual class analysis, xGBoost demonstrated exceptional performance relative to the other algorithms. This is confirmed by achieving the highest F1-scores in eight out of the ten classes within the dataset. These results establish xGBoost as a predominant method for handling multiclass imbalance classification with Bagging being the closest feasible alternative, as Bagging gains an almost similar accuracy and F1-score as xGBoost.

关键词： Multiclass imbalanced classification ensemble algorithm network attack UNSW-NB15 dataset F1-score

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SwarmCatcher: An AI-Powered Anti-Drone Surveillance System for Public Safety Over 5G and Beyond Ecosystems 28

SwarmCatcher: An AI-Powered Anti-Drone Surveillance System f...

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28th conference on Innovation in Clouds, Internet and Networks, ICIN 2025

作者： Kalafatidis, Sarantis Tsiktsiris, Dimitrios Trakos, Dimitrios Lalas, Antonios Votis, Konstantinos Tzovaras, Dimitrios Centre for Research and Technology Hellas Information Technologies Institute Greece

ISBN: (纸本)9798331542399

The proliferation of unmanned aerial vehicles (UAVs) presents significant challenges to public safety and critical infrastructure protection. This paper introduces SwarmCatcher, an innovative AI-powered anti-drone and surveillance system designed to detect and classify UAV threats in real-time. SwarmCatcher integrates multi-sensor technologies, including optical, thermal, RF, and acoustic systems, with advanced AI algorithms to provide thorough situational awareness. Utilizing the low-latency, high-throughput capabilities of cloud-native architecture and 5G networks, the system supports scalable deployment across diverse environments, offering rapid threat mitigation. Real trials were conducted, utilizing 5G infrastructures in the FIDAL and CERTH testbeds, evaluating and demonstrating SwarmCatcher’s potential to address the requirements of public safety agencies and pave the way for integration with 6G networks. The paper investigates SwarmCatcher’s architecture, key performance metrics, and its role in advancing next-generation vertical-specific applications for public safety. © 2025 IEEE.

关键词： 5G mobile communication systems

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