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Planning under uncertainty is an area of interest in artificial intelligence. We present a novel approach based on tree search and graph machine learning for the scheduling problem known as Disjunctive Temporal Networks with Uncertainty…
As required by Industry 4.0, companies will move towards flexible and individual manufacturing. To succeed in this transition, convergence of 5G and time-sensitive networks (TSN) is the most promising technology and has thus attracted…
This paper presents a model predictive control (MPC)-based online real-time adaptive control scheme for emergency voltage control in power systems. Despite tremendous success in various applications, real-time implementation of MPC for…
The convergence of IT and OT technologies results in the need for efficient network management solutions for automotive and industrial automation environments. However, configuring real-time Ethernet networks while maintaining the desired…
Optimal dispatch of energy storage systems (ESSs) in distribution networks involves jointly improving operating economy and voltage security under time-varying conditions and possible topology changes. To support fast online decision…
Restructuring of the power market introduced demand uncertainty in transmission expansion planning (TEP), which in turn also requires an accurate estimation of demand not served (DNS). Unfortunately, the graph theory based minimum-cut…
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment. While previous work has made significant…
Deterministic routing has emerged as a promising technology for future non-terrestrial networks (NTNs), offering the potential to enhance service performance and optimize resource utilization. However, the dynamic nature of network topology…
Sharing entanglement among multiple users remains a central challenge for scalable quantum networks. Recent work proposed an on-demand entanglement packet architecture in which a controller uses a Time Division Multiple Access (TDMA)…
Accurate traffic flow forecasting is a crucial research topic in transportation management. However, it is a challenging problem due to rapidly changing traffic conditions, high nonlinearity of traffic flow, and complex spatial and temporal…
A novel hybrid deep neural network architecture is designed to capture the spatial-temporal features of unsteady flows around moving boundaries directly from high-dimensional unsteady flow fields data. The hybrid deep neural network is…
Dynamic contingency screening is a challenging task in dynamic security assessment, when traditional numerical approaches are computationally intensive and often not able to repeatedly solve full AC power flow for all possible contingencies…
To improve the application-level communication performance, scheduling of coflows, a collection of parallel flows sharing the same objective, is prevalent in modern data center networks (DCNs). Meanwhile, a hybrid-switched DCN design…
Due to the proficiency of self-attention mechanisms (SAMs) in capturing dependencies in sequence modeling, several existing dynamic graph neural networks (DGNNs) utilize Transformer architectures with various encoding designs to capture…
Reliable evaluations of geotechnical hazards like landslides and debris flow require accurate simulation of granular flow dynamics. Traditional numerical methods can simulate the complex behaviors of such flows that involve solid-like to…
Optimal transmission switching (OTS) improves optimal power flow (OPF) by selectively opening transmission lines, but its mixed-integer formulation increases computational complexity, especially on large grids. To deal with this, we propose…
Deep Reinforcement Learning (DRL) has shown outstanding performance on inducing effective action policies that maximize expected long-term return on many complex tasks. Much of DRL work has been focused on sequences of events with discrete…
In non-terrestrial networks (NTN), high-speed satellite orbital motion, limited pilot signaling resources, and spatiotemporally heterogeneous traffic make accurate channel and traffic state characterization particularly challenging. In this…
Time-Sensitive Networking (TSN) is increasingly adopted in industrial systems to meet strict latency, jitter, and reliability requirements. However, evaluating TSN's fault tolerance under realistic failure conditions remains challenging.…
A permanently increasing number of on-board automotive control systems requires new approaches to their digital mapping that improves functionality in terms of adaptability and robustness as well as enables their easier on-line software…