Abstract Modern Building Automation Systems (BASs), as the brain that enable the
smartness of a smart building, often require increased connectivity both among system …

The demand for carbon neutrality leads to the transition from traditional synchronous
generator-based power systems to power electronics-enabled power systems. The …

It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an
unknown and stochastic environment under hard constraints that require the system state …

The robustness of deep neural networks has received significant interest recently, especially
when being deployed in safety-critical systems, as it is important to analyze how sensitive …

Neural networks (NNs) playing the role of controllers have demonstrated impressive
empirical performance on challenging control problems. However, the potential adoption of …

Smart solutions increasingly involve the use of sensor data to represent the physical world in
the digital world and apply intelligence to such representation. The main approach is a …

Building heating, ventilation, and air conditioning (HVAC) systems account for nearly half of
building energy consumption and 20% of total energy consumption in the US. Their …

Data quantity and quality are crucial factors for data-driven learning methods. In some target
problem domains, there are not many data samples available, which could significantly …

Cyber-physical systems are starting to adopt neural network (NN) models for a variety of
smart sensing applications. While several efforts seek better NN architectures for system …

Reinforcement learning is challenging in delayed scenarios, a common real-world situation
where observations and interactions occur with delays. State-of-the-art (SOTA) state …