The primary objective of water distribution systems (WDSs) is to provide a safe and high-quality water supply while ensuring operational efficiency. However, inherent characteristics of WDSs—such as nonlinear dynamics, complex network structures, and varying demand patterns—pose significant challenges.…
The inherent nature of uncertainty in the inputs of data-driven models can lead to incorrect outputs. Such outcomes are difficult to ascertain due to the lack of reference data during the deployment, which hinders their acceptance in practical applications. This highlights the need to evaluate the degree…
This work investigates the economic feasibility of using a battery energy storage system (BESS) for grid reinforcement in low-voltage (LV) power grids. We study the combined benefit of BESS when, on the one hand, it provides voltage stabilization as a grid service and, on the other hand, participates…
Masked language modeling (MLM) and masked image modeling (MIM) pretraining paradigms have achieved remarkable success in both natural language processing (NLP) and computer vision (CV). However, extending MIM to large-scale outdoor point cloud data presents significant challenges due to the inherent…
New York, NY: Institute of Electrical and Electronics Engineers (IEEE), 2025-07-31
Tremendous scenarios have to be considered for autonomous driving, leading to extremely large amount of point cloud data which need to be labeled for model training. Manually labeling such data is labor-intensive and highly expensive. In this paper, we propose CLF3D, a simple and effective coarse-labeling…
New York, NY: Institute of Electrical and Electronics Engineers (IEEE), 2025-06-18
Reliable process monitoring is crucial for ensuring efficient and fault-tolerant industrial operations. Therefore, accurate process models and reliable state prediction are required to detect and proactively prevent faults. However, the effectiveness of state prediction heavily depends on the accuracy…
Regularization is an important tool for the generalization of ANN models. Due to the lack of constraints, it cannot guarantee that the model will work in a real environment with input data distribution changes. Inspired by neuroplasticity, this paper introduces a bounded regularization method that can…
Large-scale LiDAR-based point cloud semantic segmentation is a critical challenge for autonomous driving perception. Most state-of-the-art LiDAR semantic segmentation methods rely on complex operators, such as sparse 3D convolutions or KdTree structures, which hinder their deployment on modern embedded…
New York, NY: Institute of Electrical and Electronics Engineers (IEEE), 2025-02-26
Contamination poses a significant risk to public health by degrading water quality in water distribution systems (WDSs). As one of the key tasks of a response strategy to contamination incidents in a WDS, pipe system flushing has been widely implemented in practice. However, due to the complexity of…
Most existing approaches to ensuring water quality in water distribution systems (WDSs) are deterministic, i.e., they do not consider uncertainties, although they may have significant impacts on the water quality. It is well recognized that water demand represents a predominant uncertainty in a WDS.…
Lithium-ion battery energy storage systems are rapidly gaining widespread adoption in power systems across the globe. This trend is primarily driven by their recognition as a key enabler for reducing carbon emissions, advancing digitalization, and making electricity grids more accessible to a broader…
Microgrids have been attracted increasingly attention due to renewable energy integration. Multiple microgrids are often interconnected for benefiting each other. A significant challenge in operating such power systems lies in the uncertainties including intermittence in renewable energy sources and…
A grid blackout is an intractable problem with serious economic consequences in many developing countries. Although it has been proven that microgrids (MGs) are capable of solving this problem, the uncertainties regarding when and for how long blackouts occur lead to extreme difficulties in the design…
Pressure reducing valves (PRVs) are essentially used to reduce operational pressures in water distribution systems (WDSs) to minimize water leakage. However, water age in a WDS is an important variable describing the water quality and should be kept as low as possible. Therefore, the aim of this study…
Dordrecht [u.a.]: Springer Science + Business Media B.V, 2024-04-08
This paper introduces a learning-based Nonlinear Model Predictive Control (NMPC) method that combines NMPC with a Reinforcement Learning (RL) algorithm to achieve automatic parameter tuning of the NMPC optimizer, resulting in better control performance. In this study, two learning-based models were designed…
Renewable energy resources and energy efficiency measures are effective means of curtailing CO2 emissions. Solar and wind technologies have been mostly developed to meet the energy demand of off-grid remote areas or locations without grid connections. However, it is well-known that the power generation…
Model predictive control (MPC) heavily relies on the accuracy of the system model. Nevertheless, process models naturally contain random parameters. To derive a reliable solution, it is necessary to design a stochastic MPC. This work studies the chance constrained MPC of systems described by parabolic…
The interest in installing photovoltaic (PV)-based microgrids (MGs) has increased significantly in the last few years due to the urgent need for reducing greenhouse gas emissions and improving the reliability as well as the quality of power supply, particularly in developing countries. However, the critical…
In this paper, we present an auto-tuning method for Linear Quadratic Regulator (LQR) based on correlation analysis. Unlike previous studies which focused on LQR tuning strategies exclusively by evaluating the control performance, we propose to explore the explicit relationship between the model and weighting…