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Meigo, Shinichiro; Nakashima, Hiroshi; Takada, Hiroshi; Kasugai, Yoshimi; Ino, Takashi*; Maekawa, Fujio; Hastings, J.*; Watanabe, Noboru; Oyama, Yukio; Ikeda, Yujiro
JAERI-Data/Code 2001-014, 23 Pages, 2001/03
JAERI-Data-Code-2001-014.pdf:1.55MB
no abstracts in English
Al conductorTakahashi, Yoshikazu; Sugimoto, Makoto; Isono, Takaaki; Oshikiri, Masayuki*; Hosono, Fumikazu*; *; *; Hanawa, Hiromi*; Seki, Shuichi*; Wakabayashi, Hiroshi*; et al.
JAERI-M 93-072, 55 Pages, 1993/03
no abstracts in English
Nuclear Instruments and Methods in Physics Research A, 249, p.137 - 140, 1986/00
Times Cited Count：1 Percentile：41.14(Instruments & Instrumentation)no abstracts in English
*;
JAERI-M 9516, 23 Pages, 1981/06
no abstracts in English
*; ; ; ;
JAERI-M 7810, 61 Pages, 1978/08
no abstracts in English
IEEE(Inst.Electr.Electron.Eng.)Trans.Instrum.Meas., IM-21(3), p.249 - 255, 1972/08
no abstracts in English
Asahi, Yuichi; Onodera, Naoyuki; Hasegawa, Yuta; Idomura, Yasuhiro
no journal, ,
We have developed a convolutional neural network (CNN) model to predict the plume concentrations in the urbanarea under uniform flow condition. By combining the Transformer or Multilayer Perceptron (MLP) layers with CNN model, our model can predict the plume concentrations from the building shapes, release points of plumeand time series data at observation stations.
Asahi, Yuichi; Onodera, Naoyuki; Hasegawa, Yuta; Idomura, Yasuhiro
no journal, ,
We have developed a convolutional neural network (CNN) model to predict the plume concentrations in the urban area under uniform flow condition. By combining the Transformer or Multilayer Perceptron (MLP) layers with CNN model, our model can predict the plume concentrations from the building shapes, release points of plume and time series data at observation stations. It is also shown that the exactly same model can be applied to predict the source location, which also gives reasonable prediction accuracy.
