Sensor-based material flow characterization (SBMC) promises to improve the performance of future-generation sorting plants by enabling new applications like automatic quality monitoring or process control. Prerequisite for this is the derivation of m...
Journal of chemical information and modeling
Nov 22, 2021
In recent years, the use of deep learning (neural network) potential energy surface (NNPES) in molecular dynamics simulation has experienced explosive growth as it can be as accurate as quantum chemistry methods while being as efficient as classical ...
Neural networks : the official journal of the International Neural Network Society
Mar 1, 2022
This paper investigates the problem of adaptive tracking control for a class of nonlinear multi-input and multi-output (MIMO) state-constrained systems with input delay and saturation. During the process of the control scheme, neural network is emplo...
This paper presents an artificial neural network-based adaptive control approach for a doubly-fed induction generator (DFIG) based wind energy conversion system (WECS). The control objectives are: (1) extraction of maximum available power from the wi...
Integrative measurement analysis of complex subjects, such as polymers is a major challenge to obtain comprehensive understanding of the properties. In this study, we describe analytical strategies to extract and selectively associate compositional i...
Reports on progress in physics. Physical Society (Great Britain)
Dec 7, 2021
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within ...
Deploying energy disaggregation models in the real-world is a challenging task. These models are usually deep neural networks and can be costly when running on a server or prohibitive when the target device has limited resources. Deep learning models...
Journal of chemical information and modeling
Feb 28, 2022
Some of the most common applications of machine learning (ML) algorithms dealing with small molecules usually fall within two distinct domains, namely, the prediction of molecular properties and the design of novel molecules with some desirable prope...
The human brain's neural networks are sparsely connected via tunable and probabilistic synapses, which may be essential for performing energy-efficient cognitive and intellectual functions. In this sense, the implementation of a flexible neural netwo...
The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, pa...