AI-enabled frequency synchronization control considering FDI attack using metaheuristic algorithm

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

Language: Английский

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AI-based approach for detecting FDI attacks in load frequency control for centralized multi-area power systems

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110060 - 110060

Published: Feb. 5, 2025

Language: Английский

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Cybersecurity in microgrids: A review on advanced techniques and practical implementation of resilient energy systems

Energy Strategy Reviews, Journal Year: 2025, Volume and Issue: 58, P. 101654 - 101654

Published: Feb. 5, 2025

Language: Английский

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Trajectory planning and tracking control in autonomous driving system: Leveraging machine learning and advanced control algorithms

Engineering Science and Technology an International Journal, Journal Year: 2025, Volume and Issue: 64, P. 101950 - 101950

Published: Feb. 18, 2025

Language: Английский

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Verification of SPI Protocol Using Universal Verification Methodology for Modern IoT and Wearable Devices

Electronics, Journal Year: 2025, Volume and Issue: 14(5), P. 837 - 837

Published: Feb. 20, 2025

The Serial Peripheral Interface (SPI) protocol plays a crucial role in wearable and IoT devices, enabling high-speed communication between microcontrollers peripherals such as sensors, displays, connectivity modules. With the increasing complexity of modern devices system-on-chip (SoC) designs, robust verification methods are essential to ensure functionality reliability. This paper utilizes Universal Verification Methodology (UVM) develop scalable reusable testbench for SPI verification. process encompasses test planning, simulation, emulation, top-level validate multi-slave coordination error-handling scenarios. results demonstrate critical importance UVM ensuring performance dependability advanced electronics, contributing reliable integration future devices. demonstrated functional coverage 83.33% 100% assertion coverage, confirming our approach’s robustness. Analysis uncovered bins revealed that specific edge cases, timing violations arbitration conflicts, require additional scenarios full Furthermore, successfully identified handled fault conditions, clock jitter, bus contention, framing errors, operation real-world deployments. These findings highlight effectiveness UVM-based improving reliability robustness low-power, resource-constrained embedded systems.

Language: Английский

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Detecting and Mitigating SQL Injection in .NET Applications Using AI-Based Anomaly Detection

Published: April 11, 2025

SQL Injection (SQLi) persists as a major threat to .NET applications since attackers can inject harmful code into databases for database manipulation purposes. The presence of this vulnerability leads hackers gaining access unauthorized data and causing system integrity failure while resulting in lost which threatens organizations utilizing these applications. Signature-based detection systems demonstrate limited effectiveness when it comes detecting contemporary or innovative SQLi attacks that create new patterns. Artificial Intelligence through anomaly technology provides capable defensive solution overcome particular challenge. normal behavior patterns queries inside become manageable AI machine learning algorithms detect abnormal signal attack vulnerabilities. research introduces specific AI-based designed application environments. Our method begins with collecting query logs then performing preprocessing before extracting important features are used train model between valid hostile queries. process relies on an RNN autoencoder understands sequences thus identifying anomalous related injection. Experimental testing shows the proposed reaches high precision alongside minimal false alarms recognized well unrecognized attacks. security position becomes more robust implementation protecting against current future

Language: Английский

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Distributed computing in multi-agent systems: a survey of decentralized machine learning approaches

Computing, Journal Year: 2024, Volume and Issue: 107(1)

Published: Nov. 19, 2024

Language: Английский

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Demand side management through energy efficiency measures for the sustainable energy future of Pakistan

Heliyon, Journal Year: 2024, Volume and Issue: 10(15), P. e34798 - e34798

Published: July 21, 2024

Pakistan is facing energy crises due to localized shortages, market manipulation, infrastructure disruption, rising demand, governance issues, climate and geopolitical events. In this situation Demand Side Management (DSM) a promising solution overcome the problem of crises. DSM strategy helps manage consumer demand through conservation rather than addition new power capacity. study, Low Emissions Analysis Platform (LEAP) develops an model for period 2022-2050. Three scenarios has been constructed namely Baseline (BAS), Green Energy Policy (GEP), Efficiency (ENE) predict future production, carbon emissions investment cost which covers capital, operational maintenance costs. The results suggest that targets should be achieved implementation ENE scenario. Predicted production consumption under scenario are substantially less those BAS country can meet its 635.83,000 GWh with 747.15,000 production. Non-renewable sources produce 171.27,000 GWh, whilst renewable 575.88,000 GWh. According scenario, by 2050, CO

Language: Английский

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Electric Mobility Challenges and Approaches for Sustainable Green Power Synergy in Smart Cities

Arabian Journal for Science and Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 10, 2024

Language: Английский

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Load frequency control progress: A comprehensive review on recent development and challenges of modern power systems

Energy Strategy Reviews, Journal Year: 2024, Volume and Issue: 57, P. 101604 - 101604

Published: Dec. 19, 2024

Language: Английский

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