State-of-charge (SOC) is a crucial battery quantity that needs constant monitoring to ensure cell longevity and safe operation. However, SOC is not an observable quantity and cannot be practically measured outside of laboratory environments. Hence, machine learning (ML) has been employed to map correlated observable signals such as voltage, current and temperature to SOC values. In recent studies, deep learning (DL) has been a prominent ML approach outperforming many existing methods for SOC estimation. However, yielding optimal performance from DL models relies heavily on appropriate selection of hyperparameters. At present, researchers relied on established heuristics to select hyperparameters through manual tuning or exhaustive search methods such as grid search (GS) and random search (RS). This results in lengthy development time in addition to less accurate and inefficient models. This study proposes a systematic and automated approach to hyperparameter selection with a Bayesian o
Yardley, PA (PRWEB) June 08, 2022 AXENDIA, Inc. today announced the release of a new research report, “The Value of Computational Modeling & Simulation in
Spontaneous heating in the active goaf area during normal mining processes poses increased threats to mine productivity and safety, as evidenced in events induced by spontaneous combustion of coal. To control and mitigate this engineering problem, there is a need to gain critical knowledge of spontaneous combustion in the longwall goaf area, which can be achieved through a combination of field tests and numerical modeling. This paper introduces the spontaneous combustion management system widely used in Australia and presents Computational Fluid Dynamics (CFD) models for the simulation of gas flow dynamics in the goaf area, based on the site conditions of an underground coal mine where coal seam gas is predominantly comprised of carbon dioxide. The models were validated with gas monitoring data and used to conduct parametric studies for proactive goaf inertisation optimization. Qualitative and quantitative analysis of simulation results indicated that better goaf inertisation could be
Gender-bias has been the long standing hurdle to the modernness and advancement of practicing science. This is particularly true for research being done in the field of computational physiology and metabolism. As per recent reports by United Nations Educational Scientific and Cultural Organization (UNESCO), women are under represented in the Science, Technology, Engineering and Mathematics (STEM) fields, with total estimates being close to only 30% worldwide. On the path to defeating stereotypes, women researchers are working towards answering some of the most important questions in Science technology. Therefore, this article collection shall focus on highlighting the quintessential contribution of women researchers to the field of