Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition

Hasegawa, Tatsuhito; Kondo, Kazuma

doi:10.1109/jiot.2022.3222221

Cited by 9 publications

(1 citation statement)

References 40 publications

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“…Based on 35 , 36 , well-regulated BO is essential to gain the best possible outcomes. To find the best Surrogate model for the BO, a comparative study 37 is conducted using the most popular surrogate models for calibration reasons, such as random forests (RF) 38 , deep ensembles (DE) 39 , Bayesian neural network (BNN) 40 , and Gaussian processes (GP) 41 . It is found that GPs can work well with BO-based design optimizations.…”

Section: Introductionmentioning

confidence: 99%

A physics-informed Bayesian optimization method for rapid development of electrical machines

Asef,

Vagg

2024

Sci Rep

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Advanced slot and winding designs are imperative to create future high performance electrical machines (EM). As a result, the development of methods to design and improve slot filling factor (SFF) has attracted considerable research. Recent developments in manufacturing processes, such as additive manufacturing and alternative materials, has also highlighted a need for novel high-fidelity design techniques to develop high performance complex geometries and topologies. This study therefore introduces a novel physics-informed machine learning (PIML) design optimization process for improving SFF in traction electrical machines used in electric vehicles. A maximum entropy sampling algorithm (MESA) is used to seed a physics-informed Bayesian optimization (PIBO) algorithm, where the target function and its approximations are produced by Gaussian processes (GP)s. The proposed PIBO-MESA is coupled with a 2D finite element model (FEM) to perform a GP-based surrogate and provide the first demonstration of the optimal combination of complex design variables for an electrical machine. Significant computational gains were achieved using the new PIBO-MESA approach, which is 45% faster than existing stochastic methods, such as the non-dominated sorting genetic algorithm II (NSGA-II). The FEM results confirm that the new design optimization process and keystone shaped wires lead to a higher SFF (i.e. by 20%) and electromagnetic improvements (e.g. maximum torque by 12%) with similar resistivity. The newly developed PIBO-MESA design optimization process therefore presents significant benefits in the design of high-performance electric machines, with reduced development time and costs.

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Section: Introductionmentioning

confidence: 99%