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Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks

International Journal of Intelligent Networks 2024年第1期5卷 255-266页

作者： Ahmad, Rizwana Anwar, Dil Nashin Bany Salameh, Haythem Elgala, Hany Ayyash, Moussa Almajali, Sufyan El-Khazali, Reyad Electronic and Electrical Engineering Department University of Cambridge United Kingdom Electrical and Computer Engineering Department University at Albany-State University of New York United States Artificial Intelligence Research Center Al Ain University Al Ain United Arab Emirates Telecommunications Engineering Department Yarmouk University Irbid Jordan Department of Computing Information and Technology Chicago State University ChicagoIL United States Department of Computer Science Princess Sumaya University for Technology Amman Jordan Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates

Advancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN-capable indoor flying networks (IFNs). To achieve this, a unified physical layer (UniPHY) capable of simultaneous control communication, data transfer, and sensing is crucial. However, traditional block-based decoders, designed independently for LiFi and WiFi, perform poorly in complex and hybrid LiFi-WiFi-enabled UniPHY systems. In this study, we propose an end-to-end learning framework based on convolutional neural networks (CNNs) for UniPHY, which can be trained to serve hybrid LiFi-WiFi transmissions to improve error performance and simplify UAV hardware. In this work, the performance of the proposed framework is assessed and compared with that of the conventional independent block-based communication system. Furthermore, a comprehensive summary of optimal hyper-parameters for efficient training of our learning framework has been provided. It is shown that, with optimal hyper-parameters, the proposed CNN-based framework outperforms the conventional block-based approach by providing a signal-to-noise ratio gain of approximately 7 dB for the LiFi channel and 23 dB for the WiFi channel. In addition, we evaluate the complexity and training convergence for loss and accuracy. © 2024 The Authors

关键词： Data transfer

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Improved Half-Bridge Z-Source Inverter Based Gamma Structure with Continuous Input Current 65

Improved Half-Bridge Z-Source Inverter Based Gamma Structure...

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65th IEEE Annual International Scientific Conference on Power and electrical engineering of Riga Technical University, RTUCON 2024

作者： Bahador, Asghar Babaei, Ebrahim Chauhan, Sachin University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran Tallinn University of Technology Dept. of Electrical Power Engineering and Mechatronics Tallinn Estonia

ISBN: (纸本)9798350365771

In this paper, a new structure of the impedance source inverter based on the gamma half-bridge structure is proposed. In addition to having the advantages of the conventional gamma impedance source inverter, the proposed inverter also has more advantages. The boost factor has been improved in the proposed structure. Also, the number of passive components has decreased, which will increase the efficiency of the inverter. To reduce the size and weight of the proposed inverter, the voltage stress on the components of the proposed structure is reduced. Another advantage of the proposed structure is the continuity of the input current along with the improvement of the input current ripple. The continuity of input current is very important in applications of renewable energy sources such as solar energy. The continuity of the input current makes the maximum use of the input energy source. To see the advantages of the proposed inverter compared to other common inverters, a comparison between the proposed inverter and other common structures will be made and the results of the comparison will be expressed. Finally, PSCAD/EMTDC simulation software will be used to confirm the correctness of the calculated theoretical relationships. © 2024 IEEE.

关键词： Renewable energy

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Multi-Agent H-Learning Based Cooperative Spectrum Sensing for Cognitive Radio Networks 32

Multi-Agent H-Learning Based Cooperative Spectrum Sensing fo...

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32nd International Conference on electrical engineering, ICEE 2024

作者： Fard, E. Karimpour Nouri, M. Behroozi, H. Sobhi-Givi, S. Sharif University of Technology Dept. of Electrical Engineering Tehran Iran University of Mohaghegh Ardabili Dept. of Electrical and Computer Engineering Ardabil Iran

ISBN: (纸本)9798350376340

Fast and accurate methods for spectrum sensing (SS) are the key elements in cognitive radio networks (CRNs) that achieve high SS. This paper proposes a reinforcement learning (RL) scheme for secondary users (SUs) in a massive multiple-input multiple-output (MIMO) CRN to determine occupation and emptiness of scanned wideband channels. In the proposed H-learning method, the clustered neighboring SUs collect data to check the status of channels that improve reliability of measured sensing. Then, they share information about different frequency channels with other clusters and the status of channel occupation is determined for all SUs. This model-based learning method increases finding free channels and enhances spectral efficiency (SE) in presence of large probability of missing detection. Furthermore, each SU learns the presence pattern of primary user (PU) and gather a dynamic scan priority list to decrease scan overhead and access delays. Simulation results demonstrate the multi-agent reinforcement modified H-learning (MH-learning) method can achieve better performance in terms of PU usage percent, number of attempts, call block rate, and probability of detection in comparison to H-learning and some state-of-art learning methods. © 2024 IEEE.

关键词： Reinforcement learning

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Virtual Synchronous Generator-Based Positive Negative Sequence Control Scheme for Voltage Unbalance Mitigation in Low Inertia Islanded Microgrid 1

Virtual Synchronous Generator-Based Positive Negative Sequen...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Sinha, Suruchi Ali, Nada Kumar, Deepak Anwar, Shamama Birla Institue of Technology Mesra Electrical and Electronics Engineering Ranchi India Birla Institute of Technology Mesra Computer Science and Engineering Ranchi India

ISBN: (纸本)9798350384246

The depletion of traditional energy sources, particularly fossil fuels, and increasing energy demands have prompted a combination of renewable energy sources (RES), Distributed Generation (DG) units, and the extensive uptake of micro grids are shaping the energy landscape. A micro grid operates either independently or in conjunction with the main power grid. In islanded mode, micro sources manage to supply power to loads while maintaining voltage and frequency within acceptable limits. However, islanded microgrids often face challenges related to poor power quality, with voltage unbalance being an important concern. Also, renewable energy systems lack inertia as they do not have any rotating parts. Therefore, this paper addresses the issue by proposing a positive-sequence (PS) in parallel with the negative sequence (NS) controller for a Voltage-Source Converter (VSC)-based DG unit for voltage unbalance reduction in microgrids. Additionally, to intensify the stability of the system, this paper also introduces a Virtual Synchronous Generator (VSG) control framework, utilizing inverter-interfaced distributed energy resources to impart virtual inertia to the system. The proposed controller's design concept is validated via simulation in Simulink/MATLAB, with the results confirming the efficacy of the controller as demonstrated. © 2024 IEEE.

关键词： Synchronous generators

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Lightweight Parallel CNN To Classify COVID-19 Associated Pneumonia From Chest X-Ray 20

Lightweight Parallel CNN To Classify COVID-19 Associated Pne...

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20th International Conference on electrical engineering, Computing Science and Automatic Control, CCE 2023

作者： Kibria, Hafsa Binte Hossain, Md. Ali Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350306767

The global outbreak of the novel Coronavirus disease (COVID-19) has impacted millions of individuals, leading to widespread health, economic, and social consequences. Additionally, the overwhelming number of COVID-19 cases has strained healthcare facilities, underscoring the crucial need for timely and accurate detection of infected individuals to mitigate the pandemic's impact. Inspired by the fact that existing deep learning models often rely on millions of parameters, rendering them unsuitable for resource-constrained devices, we developed a lightweight Parallel Convolutional Neural Network (PCNN) in this research. The model addresses the necessity for efficient COVID-19 pneumonia diagnostics from chest X-ray images by significantly reducing the number of parameters, resulting in a notable accuracy of 99.25% with 313k parameters. To further reduce parameters, separable convolution blocks were introduced, yielding an accuracy of 99.21% with 224k parameters. Despite their lightweight nature, our model demonstrates competitive performance compared to heavyweight models, emphasizing an accuracy rate of 99.21%. By minimizing computational costs and memory requirements, our model offers a cost-effective solution that can be effortlessly deployed on devices with limited resources. © 2023 IEEE.

关键词： COVID-19

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Denial-of-Service Attack Detection and Mitigation Strategy for Synchrophasor Data Flowing in Power Transmission System 4

Denial-of-Service Attack Detection and Mitigation Strategy f...

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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Nath, Soumya Ranjan Bhalja, Bhavesh R. Sidhu, Tarlochan S. Indian Institute of Technology Dept. of Electrical Engineering Roorkee India Ontario Tech University Dept. of Electrical and Computer Engineering Oshawa Canada

ISBN: (纸本)9798350383997

The coupling of the legacy power infrastructure with complex computer networks and the integration of communication devices has led to the problem of cyberattacks, which in turn may induce partial/full blackouts. In this regard, an approach based on a Decision Tree for the detection and mitigation of Denial-of-Service (DoS) attacks is proposed in this paper. Based on the patterns and effects observed during DoS attacks on the Synchrophasor data flowing from the Phasor Measurement Unit (PMU) to the Phasor Data Concentrator (PDC), various supervised machine learning models are employed, which classify healthy and attack data and detect DoS attacks. Thereafter, a recurrent deep learning network employing Gated Recurrent Unit (GRU) architecture is utilized to reconstruct the lost data, which in turn is helpful in mitigating the impacts of a DoS attack. The suggested algorithms are validated using a real-world scenario of a communication dataflow between multiple PMUs placed at different buses of a power transmission network and a PDC present at a control center, representing a wide-area network, and are incorporated using Real Time Digital Simulator and openPDC. By comparing various metrics of ML classification, the results indicate that the decision tree based model gives better accuracy along with the least detection time compared to other machine learning-based models. The suggested GRU-based mitigation algorithm can reconstruct the DoS-impacted lost data from the available pre-attack phasors. © 2024 IEEE.

关键词： Phasor measurement units

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Systematic Literature Review on Deep Learning for Weather Phenomenon Classification 6

Systematic Literature Review on Deep Learning for Weather Ph...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Hasanah, Uswatun Liu, Chuan-Ming College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9798331539603

Nowadays, weather image classification is very important, it can help many sectors, such as daily weather forecasting, disaster control, agriculture, aviation, etc. Deep learning has emerged as an effective method for image classification, providing significant increases in efficiency and accuracy. This study presents a thorough examination of the application of deep learning in weather image classification, with an emphasis on methodology, challenges, and future works. The PRISMA approach was used to systematically search and filter 25377 publications (2018-2024) from IEEE, Elsevier, Springer, Wiley Library, and MDPI. The result was a selection of 16 important studies that showcase cutting-edge methodologies and the effectiveness of those measures. Our goal is to present a comprehensive overview of how deep learning methods have improved weather prediction while identifying potential topics for future research. © 2024 IEEE.

关键词： climate prediction deep learning image classification Weather phenomena

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Nanogenerator-based bidirectional pressure sensor array and its demonstration in underwater invasive species detection

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Nano Research 2023年第9期16卷 11822-11831页

作者： Yunqi Cao Hongyang Shi Xiaobo Tan Nelson Sepúlveda State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMichigan 48824USA

Lakes areas,which cause catastrophic damages in both commercial fishery and ecological ***,current assessment strategies may pose challenges for lake-wide abundance estimation and non-target anadromous species ***,we demonstrate an efficacious species-specific non-destructive sensing system based on porous ferroelectret nanogenerator for in-situ monitoring of lamprey spawning migration using their unique suction *** show that the porous structure enables a redistribution of surface charges under bidirectional deformations,which allows the detection of both positive and negative *** quasi-piezoelectric effect is further validated by quantitative analysis in a wide pressure range of−50 to 60 kPa,providing detailed insights into transduction working *** reliable lamprey detection,a 4×4-pixel sensor array is developed and integrated with a complementary metal-oxide-semiconductor(CMOS)based signal processing array thus constituting a sensing panel capable of recording oral suction patterns in an underwater environment.

关键词： bidirectional pressure sensing nanogenerators self-powered sensor array ferroelectret invasive species detection

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Optimal Management in Island Microgrids with V2G Devices Using Differential Evolution Algorithm 16

Optimal Management in Island Microgrids with V2G Devices Usi...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Rahimi, Mohammad Reza Noroozian, Reza Kamali, Sadegh University of Zanjan University Blvd Department of Electrical Engineering Zanjan*** Iran K. N Toosi University of Technology Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331533946

The advancement of power systems to meet energy demands and expand supply through renewable energy sources (RESs) is an effective approach. Additionally, replacing conventional automobiles with Electric Vehicles (EVs), which emit fewer pollutants and have charge-discharge capabilities, is highly effective in addressing load demand. This study introduces a microgrid (MG) operating in island mode to optimize energy management and minimize the costs associated with fossil fuel energy production and environmental pollution. Demand-side management (DSM) is a critical strategy for optimizing energy usage in future networks. It helps reduce peak load by reshaping the supplier load profile and adjusting consumers' energy consumption. The charge-discharge capability of EVs can further enhance energy management. Their flexibility and high mobility allow them to be positioned anywhere on the grid. This study employed the Differential Evolution Algorithm to analyze the impact of energy sources and vehicle-to-grid (V2G) technology on peak load reduction and the significant decrease in the objective function value. © 2025 IEEE.

关键词： Demand side management

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Exploratory Data Analytics of Ota Transmission Sub-Station Electricity Load Patterns: Insights for Forecasting and Policy Direction

Exploratory Data Analytics of Ota Transmission Sub-Station E...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Oyebisi, Rafiu O. Olusanya, Olamide O. Bells University of Technology Department of Electrical/Electronics and Computer Engineering Ogun State Ota Nigeria Bells University of Technology Department of Computer Engineering Ogun State Ota Nigeria

ISBN: (纸本)9798350358155

Electricity supply and planning play a vital role in supporting economic development and improving the quality of life for communities. Effective decision-making in this domain requires a thorough understanding of electricity load patterns and consumption trends. Exploratory Data Analysis offers valuable opportunities to gain insights from electricity load data, as a use case of data science. In this study, 33KV feeders' peak load electricity measurements are acquired from Ota transmission stations, spanning four years (2019 to 2022). This data provides a rich source of information on seasonal variations in electricity consumption toward understanding demand patterns for optimizing resource allocation and infrastructure planning. Data analytics was therefore implemented on the historical peak load measurements to gather actionable insights for industry practitioners. Experimental results returns mean load value as a range from 4.24 to 20.16, indicating variations in electricity demand across the areas. The median values, ranging from 4.00 to 18.00, shows load measurements tend to center around a relatively stable level. Standard deviations value, between 1.27 and 2.61, illustrate the degree of variability in load measurements. The maximum load value discovered as 23.5 signifies the highest demand observed during the four-year study period, while the minimum load value of 1.0 signifies periods of low electricity consumption. The results also highlight variations in total load, emphasizing the importance of location-specific policies and infrastructure planning. For instance, AMJE exhibits the highest total load of 967.5, followed by Sango with 850.5 and IDI-IROKO with 689.5. Positive correlations are observed between SANGO and SUMO, indicating a tendency for these locations to experience similar load patterns. Whereas, negative correlations observed between IDI-IROKO and SUMO indicate contrasting load patterns between these locations. These correlations provide valua

关键词： Decision making

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