Artificial Neural Network (ANN)-Bayesian Probability Framework (BPF) based method of dynamic force reconstruction under multi-source uncertainties
In view of the universal existence of multi-source uncertainty factors in engineering
structures, a novel method of dynamic force reconstruction is investigated based on Artificial …
structures, a novel method of dynamic force reconstruction is investigated based on Artificial …
Multi-fidelity cost-aware Bayesian optimization
Bayesian optimization (BO) is increasingly employed in critical applications such as
materials design and drug discovery. An increasingly popular strategy in BO is to forgo the …
materials design and drug discovery. An increasingly popular strategy in BO is to forgo the …
Fleet sizing and charging infrastructure design for electric autonomous mobility-on-demand systems with endogenous congestion and limited link space
Autonomous vehicles are to revolutionize the way urban mobility demands are served, and
they are most likely to be powered by electricity. To accurately quantify the benefits of …
they are most likely to be powered by electricity. To accurately quantify the benefits of …
[HTML][HTML] Prediction of uniaxial tensile flow using finite element-based indentation and optimized artificial neural networks
This study derives a uniaxial tensile flow from spherical indentation data using an artificial
neural network (ANN) combined with finite element (FE) analysis. The feasibility of the FE …
neural network (ANN) combined with finite element (FE) analysis. The feasibility of the FE …
IOHanalyzer: Detailed performance analyses for iterative optimization heuristics
Benchmarking and performance analysis play an important role in understanding the
behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic …
behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic …
[HTML][HTML] Distinguishing normal, neuropathic and myopathic EMG with an automated machine learning approach
Objective Distinguishing normal, neuropathic and myopathic electromyography (EMG)
traces can be challenging. We aimed to create an automated time series classification …
traces can be challenging. We aimed to create an automated time series classification …
[HTML][HTML] Machine learning for automated EEG-based biomarkers of cognitive impairment during deep brain stimulation screening in patients with Parkinson's disease
Objective A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that
cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was …
cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was …
The machine learning bazaar: Harnessing the ml ecosystem for effective system development
As machine learning is applied more widely, data scientists often struggle to find or create
end-to-end machine learning systems for specific tasks. The proliferation of libraries and …
end-to-end machine learning systems for specific tasks. The proliferation of libraries and …
High dimensional Bayesian optimization assisted by principal component analysis
Bayesian Optimization (BO) is a surrogate-assisted global optimization technique that has
been successfully applied in various fields, eg, automated machine learning and design …
been successfully applied in various fields, eg, automated machine learning and design …
Automated machine learning for EEG-based classification of Parkinson's disease patients
The treatment of Parkinson's Disease (PD) with Deep Brain Stimulation (DBS) can provide a
constant level of motor functioning. Several patients, however, may suffer from postoperative …
constant level of motor functioning. Several patients, however, may suffer from postoperative …