A Gaussian Process-Based emulator for modeling pedestrian-level wind field

Weerasuriya, Asiri Umenga; Zhang, Xuelin; Lu, Bin; Tse, Kam Tim; Liu, C.H.

doi:10.1016/j.buildenv.2020.107500

Cited by 30 publications

(7 citation statements)

References 85 publications

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“…Random Forests/Ensemble Trees [76]: Random Forest is a widely used feature selection algorithm, it automatically calculates the importance of each feature, so no separate programming is required. This helps us choose a smaller subset of features.…”

Section: Based In Algorithmsmentioning

confidence: 99%

Human Activity Recognition Data Analysis: History, Evolutions, and New Trends

Ariza-Colpas

Vicario

Carrascal

et al. 2022

Sensors

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The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems—ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities.

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Section: Based In Algorithmsmentioning

confidence: 99%

Human Activity Recognition Data Analysis: History, Evolutions, and New Trends

Ariza-Colpas

Vicario

Carrascal

et al. 2022

Sensors

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“…In the study by , it took 4 weeks to obtain the training data set for a given urban setting using a high-efficiency PALM model. For applications concerning generic building geometries, there might be available pre-trained models (Weerasuriya et al, 2021), and these models can be recommended to be more computationally effective.…”

Section: Applicability Of Unsteady-state Simulations In Plw Comfort A...mentioning

confidence: 99%

Recent advances in modeling turbulent wind flow at pedestrian-level in the built environment

Zhong

Liu

Zhao

et al. 2022

ARIN

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Pressing problems in urban ventilation and thermal comfort affecting pedestrians related to current urban development and densification are increasingly dealt with from the perspective of climate change adaptation strategies. In recent research efforts, the prime objective is to accurately assess pedestrian-level wind (PLW) environments by using different simulation approaches that have reasonable computational time. This review aims to provide insights into the most recent PLW studies that use both established and data-driven simulation approaches during the last 5 years, covering 215 articles using computational fluid dynamics (CFD) and typical data-driven models. We observe that steady-state Reynolds-averaged Navier-Stokes (SRANS) simulations are still the most dominantly used approach. Due to the model uncertainty embedded in the SRANS approach, a sensitivity test is recommended as a remedial measure for using SRANS. Another noted thriving trend is conducting unsteady-state simulations using high-efficiency methods. Specifically, both the massively parallelized large-eddy simulation (LES) and hybrid LES-RANS offer high computational efficiency and accuracy. While data-driven models are in general believed to be more computationally efficient in predicting PLW dynamics, they in fact still call for substantial computational resources and efforts if the time for development, training and validation of a data-driven model is taken into account. The synthesized understanding of these modeling approaches is expected to facilitate the choosing of proper simulation approaches for PLW environment studies, to ultimately serving urban planning and building designs with respect to pedestrian comfort and urban ventilation assessment.

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“…In the final step, the Pareto optimal design points were processed using decision-making techniques, such as the Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) and Shannon's entropy, to determine the optimum values. Similarly, different approaches can be used to develop the surrogate model, such as an artificial neural network (ANN) [55] and the Gaussian process (GP) [162], as shown in Fig. 15(d) and (e), respectively.…”

Section: Surrogate Model-based Frameworkmentioning

confidence: 99%

“…Thus, these frameworks significantly reduce the overall computational costs and speed up the optimization process. For example, the GP-based framework for optimizing the PLW environment around an isolated building is more than 400 times faster than its CFD counterpart [162].…”

Section: Surrogate Model-based Frameworkmentioning

confidence: 99%