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Recent Entries
K130915111026
Digital twin (DT) technology combined with artificial intelligence (AI) is becoming a key enabler for resilient and self-optimising electrical power networks that are increasingly digitalised. A digital twin is a continuously updated virtual representation of a physical grid asset or network that uses real-time sensor, phasor measurement unit (PMU), and Internet of Things (IoT) data to closely mirror its physical counterpart. Digital twins become predictive, prescriptive, and self-healing decision-support systems when machine learning (ML) and deep learning (DL) algorithms are integrated into this virtual-physical feedback loop. This review aims to bring together the recent literature (2020-2025) on the field of AIpowered digital twins under four thematic areas: (i) architectural foundations and enabling technologies, (ii) real-time monitoring and state estimation, (iii) predictive control and maintenance, and (iv) cybersecurity monitoring and threat mitigation. Tables summarise enabling technologies, predictive-control case studies, and case studies of cyber-threat countermeasures reported in the literature. The review also pinpoints open research challenges such as interoperability standards, federated and privacypreserving learning, explainability of AI-driven decisions on the grid, and quantum-assisted twin computation. It suggests a research roadmap for further work. The synthesis signals that while AI-powered digital twins can tangibly reduce unplanned downtime, improve forecasting precision, and boost cyberphysical attack resilience, standardisation, data governance, and trustworthy AI remain to be addressed to enable widespread adoption.
K130815111026
Modern times require smart solutions for crime detection to enhance public security levels because crime detection remains a fundamental challenge. Traditional surveillance relies on human monitoring but has weaknesses due to time consumption and human error. The automated crime detection system runs on machine learning and deep learning platforms using Convolutional Neural Networks and Recurrent Neural Networks, along with YOLO, Faster R-CNN, and the Azure Face API. The AI models perform two functions: detecting suspicious activities and identifying weapons and faces, and recognising patterns of criminal behaviour. The combination of local servers and cloud platforms powered by AI detects crimes precisely in real time through enhanced monitoring, improves accuracy, and prevents false alarms, while also enabling broader crime-prevention capabilities.
J130415100926
To meet sustainable development goals, global policies are strongly oriented toward valorising local materials such as agricultural waste. In this context, the present study aims to evaluate the mechanical behaviour of compressed earth blocks (CEB) with wood ash. To this end, we used wood ash as a binder to reinforce the base materials. The experimental program included seven formulations (0%, 2%, 4%, 6%, 8%, 10%, and 12%) of wood ash as a percentage of the total soil mass. The results showed that 4% wood ash in 90% clay soil achieved the best compressive strength, with a compaction stress of 4.30 MPa.
J130215100926
This paper compares the performance of conventional Pulse Width Modulation (PWM) with an optimised Neural Network (NN)-based modulation strategy for inverter control. Although PWM remains one of the most widely used modulation techniques because of its simplicity and reliability, its performance is inherently limited by fixed linear switching schemes. An NN-based controller offers a different approach by learning switching patterns from the system, enabling better overall performance. To investigate this, we developed and evaluated both modulation methods in the MATLAB/Simulink environment under identical nominal operating conditions. The results indicate that the proposed NN-based controller provides a higher output RMS voltage, improved output waveform quality and lower Total Harmonic Distortion (THD) than conventional PWM. Together, these improvements lead to a cleaner output waveform and more effective use of the available DC source, without requiring any hardware modifications. The present study is limited to simulation and does not account for practical hardware non-idealities. Even so, the results suggest that the neural network’s learning capability can improve performance under nonlinear operating conditions. Future work will focus on real-time implementation using DSP/FPGA platforms, further optimisation of the learning algorithm, and exploring hybrid modulation techniques for multilevel inverters.
