Related papers: Artificial Neural Network-Based Voltage Control of…

While Model Predictive Control (MPC) enforces safety via constraints, its real-time execution can exceed embedded compute budgets. We propose a Barrier-integrated Adaptive Neural Model Predictive Control (BAN-MPC) framework that synergizes…

High percentage penetrations of renewable energy generations introduce significant uncertainty into power systems. It requires grid operators to solve alternative current optimal power flow (AC-OPF) problems more frequently for economical…

This study proposes a machine learning-based Model Predictive Control (MPC) approach for controlling Air Handling Unit (AHU) systems by employing an Internet of Things (IoT) framework. The proposed framework utilizes an Artificial Neural…

In this paper, we compare the performance, stability and robustness of Artificial Neural Networks (ANN) and Boosted Decision Trees (BDT) using MiniBooNE Monte Carlo samples. These methods attempt to classify events given a number of…

Due to imprecision and uncertainties in predicting real world problems, artificial neural network (ANN) techniques have become increasingly useful for modeling and optimization. This paper presents an artificial neural network approach for…

A neural network-based energy management system (NN-EMS) has been proposed in this paper for islanded ac microgrids fed by multiple PV-battery based distributed generators (DG). The stochastic and unequal irradiation results in unequal PV…

In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty…

Power delivery network (PDN) design is a nontrivial, time-intensive, and iterative task. Correct PDN design must account for considerations related to power bumps, currents, blockages, and signal congestion distribution patterns. This work…

The development of next-generation autonomous control of fission systems, such as nuclear power plants, will require leveraging advancements in machine learning. For fission systems, accurate prediction of nuclear transport is important to…

Breast cancer is a common fatal disease for women. Early diagnosis and detection is necessary in order to improve the prognosis of breast cancer affected people. For predicting breast cancer, several automated systems are already developed…

The increase in capacity provided by coupled SDM systems is fundamentally limited by MDG and ASE noise. Therefore, monitoring MDG and optical SNR is essential for accurate performance evaluation and troubleshooting. Recent works show that…

Constant-power loads are notoriously known to destabilise power systems, such as DC microgrids, due to their negative incremental impedance. This paper equips distributed generation units with decentralised L1 adaptive controllers at the…

Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia. Researchers now increasingly target to improve performance and reduce energy consumption of such devices. Some recent efforts focus on…

Future Naval Microgrids (MGs) will include hybrid energy storage systems (ESS), including battery and supercapacitors to respond to emerging constant power loads (CPLs) and fluctuating pulsed power loads (PPLs). Voltage regulation of naval…

We develop a data-driven model, introducing recent advances in machine learning to reservoir simulation. We use a conventional reservoir modeling tool to generate training set and a special ensemble of artificial neural networks (ANNs) to…

Spiking Neural Networks (SNN) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge devices through asynchronous and sparse processing. However, being temporal models, SNNs depend…

We study learning based controllers as a replacement for model predictive controllers (MPC) for the control of autonomous vehicles. We concentrate for the experiments on the simple yet representative bicycle model. We compare training by…

Power electronic converters are fundamental building blocks of both AC and DC microgrids, enabling the integration of renewable energy sources, energy storage systems, electronic loads, and electric vehicles. In contrast, converter control…

DC microgrids are getting more and more applications due to simple converters, only voltage control and higher efficiencies compared to conventional AC grids. Droop control is a well know decentralized control strategy for power sharing…

In this work, we introduce a control variate approximation technique for low error approximate Deep Neural Network (DNN) accelerators. The control variate technique is used in Monte Carlo methods to achieve variance reduction. Our approach…