A data-driven visual simulation of fire phenomena

Cha, Moohyun; Lee, Jaikyung; Choi, Byung-Il; Lee, Hyokwang; Han, Soonhung

doi:10.1145/1599301.1599375

Cited by 7 publications

(3 citation statements)

References 1 publication

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“…물리 기반 유체 시뮬레이션은 물 [1,2], 불 [3,4,5], 연기 [6,7,8], 불똥 [9,10,11], 거품 [12,13], 버블 [14,15], 미스트 [16,17 [22,23].…”

Section: 서론unclassified

Refinement of Projection Map Based on Artificial Neural Networks to Represent Noise-Reduced Foam Effects

Kim¹

2021

J Korea Comput Graph Soc

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In this paper, we propose an artificial neural network framework that can represent the foam effects expressed in liquid simulation in detail without noise. The position and advection of foam particles are calculated using the existing screen projection method, and the noise problem that appears in this process is solved through an proposed artificial neural network. The important thing in the screen projection approach is the projection map, but noise occurs in the projection map in the process of projecting momentum into the discretized screen space, and we efficiently solve this problem by using an artificial neural network-based denoising network. When the foam generating area is selected through the projection map, 2D is inversely transformed into 3D space to generate foam particles. We solve the existing denoising network problem in which small-scaled foam particles disappear. In addition, by integrating the proposed algorithm with the screen-space projection framework, all the advantages of this approach can be accommodated. As a result, it shows through various experiments whether it is possible to stably represent not only the clean foam effects but also the foam particles lost due to the denoising process.

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“…물리 기반 유체 시뮬레이션은 물 [1,2], 불 [3,4,5], 연기 [6,7,8], 불똥 [9,10,11], 거품 [12,13], 버블 [14,15], 미스트 [16,17 [22,23].…”

Section: 서론unclassified

Refinement of Projection Map Based on Artificial Neural Networks to Represent Noise-Reduced Foam Effects

Kim¹

2021

J Korea Comput Graph Soc

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“…Diversos autores tiveram como objetivo o desenvolvimento e implementac ¸ão de ambientes em RV focados no treino e avaliac ¸ão de bombeiros, dando foco ao desempenho individual [7] [2] [8] [9] e em equipa [10], de modo a melhorar e a treinar a capacidade destes profissionais de forma segura e repetitiva. Existem igualmente ambientes relacionados com simulac ¸ões de desastres relacionados com incêndios, como, por exemplo o estudo de Aizhun Ren et al [11], em que os resultados mostraram que o sistema construído em RV pode ser usado para evacuac ¸ão e avaliac ¸ão de seguranc ¸a do edifício onde ocorre o incêndio, salientando assim o possível uso de RV para o treino relacionado com situac ¸ões de perigo.…”

Section: Estado Da Arteunclassified

Developing Training Applications for Hydrogen Emergency Response Training

Pinto

Peixoto

Gonçalves

et al. 2019

2019 International Conference on Graphics and Interaction (ICGI)

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Virtual Reality (VR) has been evolving over the years, becoming more and more accessible, in a wide area of applications. One of these areas where VR can have a major impact is training and certification. Hydrogen vehicles are becoming a reality and first responders still lack proper tools and resources to train emergency responses for the purpose. VR can play here a crucial role in ensuring a proper hydrogen emergency response training due to the advantages associated with VR training programs such as resource optimization, repeatability, and replicability. This paper proposes using VR for hydrogen emergency response training by developing a solution composed of three components: tutorial mode, training mode, and certification mode. A usability study is further conducted to evaluate its usability and user satisfaction. The results show that the use of this application regards usability and user satisfaction were extremely positive.

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“…Physics-based fluid simulation has been used to realize various visual special effects to simulate water [ 1 , 2 ], fire [ 3 – 5 ], smoke [ 6 – 8 ], fire-flake [ 9 – 11 ], foam [ 12 , 13 ], bubble [ 14 , 15 ], and mist (or spray) [ 16 , 17 ]. When expressing water, the associated secondary effects such as foam, bubble, and splash are caused by oscillating movements, and various approaches have been proposed to efficiently model these characteristics [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Efficient learning representation of noise-reduced foam effects with convolutional denoising networks

Kim

2022

PLoS ONE

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This study proposes a neural network framework for modeling the foam effects found in liquid simulation without noise. The position and advection of the foam particles are calculated using the existing screen projection method, and the noise problem that occurs in this process is prevented by using the neural network. A significant problem in the screen projection approach is the noise generated in the projection map during the projecting of momentum onto the discretized screen space. We efficiently solve this problem by utilizing a denoising neural network. Following the selection of the foam generation area using a projection map, the foam particles are generated through the inverse transformation of the 2D space into 3D space. This solves the problem of small-sized foam dissipation that occurs in conventional denoising networks. Furthermore, by integrating the proposed algorithm with the screen-space projection framework, it is able to maintain all the advantages of this approach. In conclusion, the denoising process and clean foam effects enable the proposed network to model the foam effects stably.

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A data-driven visual simulation of fire phenomena

Cited by 7 publications

References 1 publication

Refinement of Projection Map Based on Artificial Neural Networks to Represent Noise-Reduced Foam Effects

Refinement of Projection Map Based on Artificial Neural Networks to Represent Noise-Reduced Foam Effects

Developing Training Applications for Hydrogen Emergency Response Training

Efficient learning representation of noise-reduced foam effects with convolutional denoising networks

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