相关论文: Leveraging Digital Twin and Machine Learning Techn…
Increased connectivity and remote reprogrammability/reconfigurability features of embedded devices in current-day power systems (including interconnections between information technology -- IT -- and operational technology -- OT --…
Digital Twins (DT) facilitate monitoring and reasoning processes in cyber-physical systems. They have progressively gained popularity over the past years because of intense research activity and industrial advancements. Cognitive Twins is a…
The increasing digitization of smart grids has made addressing cybersecurity issues crucial in order to secure the power supply. Anomaly detection has emerged as a key technology for cybersecurity in smart grids, enabling the detection of…
Deep neural networks (DNNs) are often coupled with physics-based models or data-driven surrogate models to perform fault detection and health monitoring of systems in the low data regime. These models serve as digital twins to generate…
In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple functions can be…
Emerging technologies and applications make the network unprecedentedly complex and heterogeneous, leading physical network practices to be costly and risky. The digital twin network (DTN) can ease these burdens by virtually enabling users…
Motivated by climate change, increasing industrialization and energy reliability concerns, the smart grid is set to revolutionize traditional power systems. Moreover, the exponential annual rise in number of grid-connected users and…
Cyber-physical microgrids are vulnerable to stealth attacks that can degrade their stability and operability by performing low-magnitude manipulations in a coordinated manner. This paper formulates the interactions between CSAs and…
Traditional statistical optimization-based state estimation (DSSE) algorithms rely on detailed grid parameters and mathematical assumptions of all possible uncertainties. Furthermore, random data missing due to communication failures,…
Three-dimensional integrated circuit (3D IC) pack-aging and heterogeneous integration have emerged as central pillars of contemporary semiconductor scaling. Yet, the multi-physics coupling inherent to stacked architectures manifesting as…
Optimizing modern wireless networks is exceptionally challenging due to their high dynamism and complexity. While the agentic artificial intelligence (AI) powered by reinforcement learning (RL) offers a promising solution, its practical…
Telecom networks scale with growing users and data-intensive applications, generating heavy traffic that causes congestion, reducing throughput, increasing delay, and raising computational costs. Traditional routing protocols act only after…
The advancement of smart grid technologies necessitates the integration of cutting-edge computational methods to enhance predictive energy optimization. This study proposes a multi-faceted approach by incorporating (1) Deep Reinforcement…
Smart grid is an alternative solution of the conventional power grid which harnesses the power of the information technology to save the energy and meet today's environment requirements. Due to the inherent vulnerabilities in the…
The maturity and commercial roll-out of 5G networks and its deployment for private networks makes 5G a key enabler for various vertical industries and applications, including robotics. Providing ultra-low latency, high data rates, and…
In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications. In the recent years, numerous smart grid applications, such as defect detection and load forecasting, have…
The growing complexity of next-generation networks exacerbates the modeling and algorithmic flaws of conventional network optimization methodology. In this paper, we propose a mobile network digital twin (MNDT) architecture for 6G networks.…
Network digital twin (NDT) models are virtual models that replicate the behavior of physical communication networks and are considered a key technology component to enable novel features and capabilities in future 6G networks. In this work,…
The concept of a digital twin has exploded in popularity over the past decade, yet confusion around its plurality of definitions, its novelty as a new technology, and its practical applicability still exists, all despite numerous reviews,…
The concept of creating a virtual copy of a complete Cyber-Physical System opens up numerous possibilities, including real-time assessments of the physical environment and continuous learning from the system to provide reliable and precise…