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Extraction of Roads Using the archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network

SENSORS 2023年第21期23卷 8783-8783页

作者： Khan, Mohd Jawed Singh, Pankaj Pratap Pradhan, Biswajeet Alamri, Abdullah Lee, Chang-Wook Cent Inst Technol Dept Comp Sci & Engn Kokrajhar 783370 Assam India Univ Technol Sydney Ctr Adv Modelling & Geospatial Informat Syst Sch Civil & Environm Engn Sydney 2007 Australia Univ Kebangsaan Malaysia Inst Climate Change Bangi 43600 Malaysia King Saud Univ Coll Sci Dept Geol & Geophys Riyadh 21589 Saudi Arabia Kangwon Natl Univ Dept Sci Educ 1 Gangwondaehak Gil Chunchon 24341 South Korea

Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery. The issue of road extraction can be defined as a task that involves capturing contextual and complex elements while also preserving boundary information and producing high-resolution road segmentation maps for RS data. The objective of the proposed archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology is to tackle the aforementioned issues by enhancing the efficacy of image segmentation outcomes that exploit remote sensing imagery, coupled with archimedes optimization algorithm methods (AOA). The findings of this study demonstrate the enhanced road-extraction capabilities achieved by the ATP-QDCNNRE method when used with remote sensing imagery. The ATP-QDCNNRE method employs DL and a hyperparameter tuning process to generate high-resolution road segmentation maps. The basis of this approach lies in the QDCNN model, which incorporates quantum computing (QC) concepts and dilated convolutions to enhance the network's ability to capture both local and global contextual information. Dilated convolutions also enhance the receptive field while maintaining spatial resolution, allowing fine road features to be extracted. ATP-based hyperparameter modifications improve QDCNNRE road extraction. To evaluate the effectiveness of the ATP-QDCNNRE system, benchmark databases are used to assess its simulation results. The experimental results show that ATP-QDCNNRE performed with an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 of 90

关键词： road extraction remote sensing convolutional neural networks dilated convolutions quantum computing archimedes optimization algorithm artificial intelligence

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Optimal Design of C-Type Filter in Harmonics Polluted Distribution Systems

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ENERGIES 2022年第4期15卷 1587页

作者： Tamaskani, Rohollah Khodsuz, Masume Yazdani-Asrami, Mohammad Univ Sci & Technol Mazandaran Fac Elect & Comp Engn Behshahr *** Iran Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Lanark Scotland

This paper aims to find the optimal size of parameters of a C-type filter in a non-sinusoidal system using a new optimization method called the archimedes optimization algorithm (AOA). The inductance and capacitance values of the filter are acquired in which the power loss in the Thevenin resistor and the power loss characteristics of the load bus are minimized based on a new proposed objective function. Subject to technical and practical limitations of the IEEE 519 standard, an optimization problem is defined to achieve an optimal filter design that can increase the system power quality. The effectiveness of the proposed method is proved by comparison with the recent previously published methods. The results show the effectiveness of the proposed approach using the AOA in finding the minimum power losses and the harmonic content of frequency-dependent components. Eventually, the current study confirmed that the suggested objective function minimizes power losses of fundamental and harmonic order harmonics in non-sinusoidal systems.

关键词： C-type filter harmonic distortion non-sinusoidal network archimedes optimization algorithm

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Multi-Objective optimal scheduling of energy Hubs, integrating different solar generation technologies considering uncertainty

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 161卷

作者： Eladl, Abdelfattah A. El-Afifi, Magda I. Saadawi, Magdi M. Siano, Pierluigi Sedhom, Bishoy E. Mansoura Univ Fac Engn Elect Engn Dept Mansoura 35516 Egypt Nile Higher Inst Engn & Technol Mansoura Egypt Univ Salerno Dept Management & Innovat Syst I-84084 Fisciano SA Italy Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa

For a few decades, operators of energy systems have sought to achieve appropriate frameworks due to energy crises and rapid growth in energy requirements. In this regard, this study presents a multi-objective optimization model for an energy hub (EH) designed to manage a diverse energy portfolio. The EH receives electricity, natural gas, hydrogen, seawater, and solar energy as inputs, aiming to satisfy electricity, heating, and freshwater demands at the output port while considering a limited available area. The model incorporates the selection of the optimal solar energy technology (photovoltaics, parabolic dish, or parabolic trough collector) through a comprehensive evaluation encompassing technical, economic, and environmental aspects. To achieve optimal scheduling of the EH's production units, the model factors in forecasts of solar energy availability alongside electrical, heat, and water load demands. The evaluation of the EH's performance is conducted through a multiobjective framework considering social welfare, CO2 emissions, voltage stability margin (VSM), a newly proposed simplified fast temperature stability index (SFTSI), and a similarly novel simplified fast pressure stability index (SFPSI). The optimization problem is formulated within a MATLAB environment and solved using a multiobjective archimedes optimization algorithm across five distinct case studies, each characterized by a varying designated area for solar energy generation. The effectiveness of the proposed model and optimization technique is validated through test systems, with the obtained results demonstrating significant improvements compared to a baseline scenario. These improvements include a 36.18% reduction in CO2 emissions, a 14.22% increase in total social welfare, and reductions in the average values of VSM, SFTSI, and SFPSI when incorporating all solar energy technologies.

关键词： Energy hubs Energy management Solar energy CO 2 emissions archimedes optimization algorithm

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Impact of loading capability on optimal location of renewable energy systems distribution networks

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AIN SHAMS ENGINEERING JOURNAL 2024年第1期15卷

作者： Hemeida, Ashraf Mohamed Bakry, Omima Alkhalaf, Salem Mikhaylov, Alexey Zobaa, Ahmed F. Senjyu, Tomonobu Mikhailef, Saad Dardeer, Mostafa Aswan Univ Fac Energy Engn Elect Engn Dept Aswan 81528 Egypt Aswan Univ Fac Engn Elect Engn Dept Aswan 81542 Egypt Qassim Univ Coll Sci & Arts Ar Rass Dept Comp Ar Rass 52571 Saudi Arabia Financial Univ Govt Russian Federat Moscow Russia Brunel Univ London Elect & Elect Engn Dept Uxbridge England Univ Ryukyus Fac Engn Dept Elect & Elect Engn Nishihara 9030213 Japan Swinburne Univ Technol Sch Sci Comp & Engn Technol Hawthorn Vic 3122 Australia

A distribution system's network reconfiguration is the process of altering the open/closed status of sec-tionalizing and tie switches to change the topological structure of distribution feeders. For the last two decades, numerous heuristic search evolutionary algorithms have been used to tackle the problem of net-work reconfiguration for time-varying loads, which is a very difficult and highly non-linear efficiency challenge. This research aims to offer an ideal solution for addressing network reconfiguration difficulties in terms of a system for power distribution, to decrease energy losses, and increase the voltage profile. A hybrid Genetic archimedes optimization technique (GAAOA) has also been developed to size and allocate three types of DGs, wind turbine, fuel cell and PV considering load variation. This approach is quite useful and may be used in many situations. This technique is evaluated for loss reduction and voltage profile on a typical 33-bus radial distribution system and a 69-bus radial distribution system. The system has been simulated using MATLAB software. The findings suggest that this approach is effective and acceptable for real-time usage.(c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Distribution systems Hybrid optimization Techniques Distributed Generators Different load levels Genetic algorithms archimedes optimization algorithm

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Assessment of Converter Performance in Hybrid AC-DC Power System under Optimal Power Flow with Minimum Number of DC Link Control Variables

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ENERGIES 2023年第15期16卷 5800页

作者： Patel, Chintan Malakar, Tanmoy Sreejith, S. Natl Inst Technol Silchar Silchar 788010 India

This paper presents a strategy to evaluate the performances of converter stations under the optimized operating points of hybrid AC-DC power systems with a reduced number of DC link variables. Compared to previous works reported with five DC-side control variables (CVs), the uniqueness of the presented optimal power flow (OPF) formulation lies within the selection of only two DC-side control variables (CVs), such as the inverter voltage and current in the DC link, apart from the conventional AC-side variables. Previous research has mainly been focused on optimizing hybrid power system performance through OPF-based formulations, but has mostly ignored the associated converter performances. Hence, in this study, converter performance, in terms of ripple and harmonics in DC voltage and AC current and the utilization of the converter infrastructure, is evaluated. The minimization of active power loss is taken as an objective function, and the problem is solved for a modified IEEE 30 bus system using a recently developed and very efficient archimedes optimization algorithm (AOA). Case studies are performed to assess the efficacy of the presented OPF model in power systems, as well as converter performance. Furthermore, the results are extended to assess the applicability of the proposed model to the allocation of photovoltaic (PV)-type distributed generations (DGs) in hybrid AC-DC systems. The average improvement in power loss is found to be around 7.5% compared to the reported results. Furthermore, an approximate 10% improvement in converter power factor and an approximate 50% reduction in ripple factor are achieved.

关键词： optimal power flow AC-DC power system HVDC link archimedes optimization algorithm

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Leveraging Game Theory to Design Incentive-Compatible Time-Varying electricity pricing with Demand-Side management

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ENERGY CONVERSION AND MANAGEMENT-X 2024年 24卷

作者： El-Afifi, Magda I. Sedhom, Bishoy E. Eladl, Abdelfattah A. Sanjeevikumar, Padmanaban Fawzy, Samaa Mansoura Univ Fac Engn Dept Elect Engn Mansoura 35516 Egypt Nile Higher Inst Engn & Technol Mansoura Egypt Univ South Eastern Norway Dept Elect Engn IT & Cybernet Porsgrunn Norway

This study investigates a novel approach to improve energy efficiency through a Demand Response (DR) program with a Game Theory (GT)-based Time-of-Use (ToU) pricing model. While traditional DR programs encourage consumption shifts towards off-peak periods, they utilize a flat pricing structure. This means all users pay the same regardless of their individual load contribution during peak times, where prices fluctuate based on demand exceeding generation capacity. The proposed GT-based ToU model addresses this by establishing dynamic onpeak and shoulder-peak hour rates tailored to each user's consumption profile. This personalized pricing incentivizes a more targeted shift away from peak hours, potentially leading to further efficiency gains. The model's effectiveness is evaluated against the existing ToU system and the current day-ahead real-time pricing scheme. Additionally, the study acknowledges the potential for increased demand during off-peak hours due to load shifting. To address this, the influence of two optimization algorithms, Genetic algorithm (GA) and archimedes optimization algorithm (AOA), on user electricity bills and peak-to-average ratio following load scheduling is examined. The research concludes by demonstrating the superiority of the GT-based ToU model and highlighting AOA's superior performance compared to GA in optimizing these factors.

关键词： Demand response program Pricing scheme archimedes optimization algorithm Game theory Meta-heuristic optimization

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Demand side management strategy for smart building using multi-objective hybrid optimization technique

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RESULTS IN ENGINEERING 2024年 22卷

作者： El-Afifi, Magda I. Sedhom, Bishoy E. Eladl, Abdelfattah A. Elgamal, Mohamed Siano, Pierluigi Mansoura Univ Fac Engn Dept Elect Engn Mansoura 35516 Egypt Nile Higher Inst Engn & Technol Mansoura Egypt Ural Fed Univ Ural Power Engn Inst Dept Automated Elect Syst Ekaterinburg 620002 Russia Univ Salerno Dept Management & Innovat Syst I-84084 Fisciano SA Italy Univ Johannesburg Dept Elect & Elect Engn Sci ZA-2006 Johannesburg South Africa

This study proposes a home energy management system that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a smart house. This system's goal is to optimize the energy of household appliances in order to effectively regulate load demand, with the end result being a reduction in the peak-to-average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. Load scheduling based on both a next-day and realtime basis is what is used to meet the load demand requested by energy customers. In addition to providing a fitness criterion, utilizing a multi-objective hybrid optimization technique makes it easier to achieve an equitable distribution of workload between on-peak and off-peak hours. Moreover, the idea of developing coordination among home appliances in order to achieve real-time rescheduling is now being studied as a concept. Because of the inherent parallels between the two problems, the real-time rescheduling issue is framed as a knapsack problem and is solved using a dynamic programming strategy. The performance of the suggested methodology is evaluated in this study in relation to real-time pricing (RTP), time-of-use pricing (ToU), and crucial peak pricing (CPP). The simulation findings, which were assessed using a confidence interval that was set at 95 %, provide proof of the relevance that has been shown to be associated with the proposed optimization method. During scheduling RTP signal showcases a minimum PAR of 2.22 and a cost reduction of 24.06 % for HAG compared to the unscheduled case. Under the TOU tariff, HAG manages to reduce PAR by 46.14 % and cost by 20.44 %. Similarly, in the case of CPP, HAG outperforms by reducing PAR by up to 29.5 % and cost by up to 31.47 %.

关键词： Smart homes archimedes optimization algorithm Genetic algorithm Day-ahead and real-time scheduling Demand side management

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Optimal directed acyclic graph federated learning model for energy-efficient IoT communication networks

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Nalinipriya, G. Lydia, E. Laxmi Sree, S. Rama Nikolenko, Denis Potluri, Sirisha Ramesh, Janjhyam Venkata Naga Jayachandran, Sheela Saveetha Engn Coll Dept Informat Technol Chennai 602105 Tamilnadu India Siddhartha Acad Higher Educ Deemed Univ VR Siddhartha Engn Coll A Dept Informat Technol Vijayawada India Aditya Univ Dept Comp Sci & Engn Surampalem Andhra Pradesh India Sechenov First Moscow State Med Univ Dept Prosthet Dent Moscow Russia Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Hyderabad 500043 Telangana India Graph Era Hill Univ Dept Comp Sci & Engn Dehra Dun Uttarakhand India VIT AP Univ Sch Comp Sci & Engn SCOPE Amaravathi Andhra Pradesh India

Federated learning (FL) stimulates distributed on-device computation systems to process an optimum technique efficiency by communicating local process upgrades and global method distribution from aggregation averaging procedure. On-device FL is a standard application in wireless environments, with several mobile devices participating as nodes in the FL network. Managing extensive multi-dimensional process upgrades and resource-constrained computations in large-scale heterogeneous IoT cellular networks can be challenging. This article introduces a Lifetime Maximization using Optimal Directed Acyclic Graph Federated Learning in IoT Communication Networks (LM-ODAGFL) technique. The proposed LM-ODAGFL technique utilizes FL and metaheuristic optimization algorithms for energy-effective IoT networks. The Direct Acyclic Graph (DAG) model addresses device asynchrony in FL while minimizing additional resource usage. The archimedes optimization algorithm (AOA) is designed to optimize the DAG model by reducing both user energy consumption and the training loss of the FL model. The performance validation of the LM-ODAGFL technique is performed by utilizing a series of experimentations. The obtained results of the LM-ODAGFL model demonstrate superior performance by consuming significantly less energy than SDAGFL and ESDAGFL, with values ranging from 0.373 to 0.485 kJ per round on the FMNIST-Clustered dataset and 16.27 to 20.34 kJ per round on the Poets dataset, compared to 0.000 to 1.442 kJ and 0.00 to 63.89 kJ respectively.

关键词： Internet of things Federated learning archimedes optimization algorithm Lifetime maximization Energy consumption

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