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Journal Articles

AMR-Net: Convolutional neural networks for multi-resolution steady flow prediction

Asahi, Yuichi; Hatayama, Sora*; Shimokawabe, Takashi*; Onodera, Naoyuki; Hasegawa, Yuta; Idomura, Yasuhiro

Proceedings of 2021 IEEE International Conference on Cluster Computing (IEEE Cluster 2021) (Internet), p.686 - 691, 2021/10

We develop a convolutional neural network model to predict the multi-resolution steady flow. Based on the state-of-the-art image-to-image translation model pix2pixHD, our model can predict the high resolution flow field from the set of patched signed distance functions. By patching the high resolution data, the memory requirements in our model is suppressed compared to pix2pixHD.

Journal Articles

Multi-resolution steady flow prediction with convolutional neural networks

Asahi, Yuichi; Hatayama, Sora*; Shimokawabe, Takashi*; Onodera, Naoyuki; Hasegawa, Yuta; Idomura, Yasuhiro

Keisan Kogaku Koenkai Rombunshu (CD-ROM), 26, 4 Pages, 2021/05

We develop a convolutional neural network model to predict the multi-resolution steady flow. Based on the state-of-the-art image-to-image translation model Pix2PixHD, our model can predict the high resolution flow field from the signed distance function. By patching the high resolution data, the memory requirements in our model is suppressed compared to Pix2PixHD.

Journal Articles

A Learning data collection using a simulator for point cloud based identification system

Tanifuji, Yuta; Kawabata, Kuniaki

Proceedings of International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2020) (Internet), p.246 - 249, 2020/02

Journal Articles

Development of a GUI-based operation system for building a 3D point cloud classifier

Tanifuji, Yuta; Kawabata, Kuniaki; Hanari, Toshihide

Proceedings of 2019 IEEE Region Ten Conference (TENCON 2019) (Internet), p.36 - 40, 2019/10

Journal Articles

A Structure discrimination method by deep learning with point cloud data

Tanifuji, Yuta; Kawabata, Kuniaki

Proceedings of International Topical Workshop on Fukushima Decommissioning Research (FDR 2019) (Internet), 4 Pages, 2019/05

Oral presentation

Integrating deep learning-based object detection and optical character recognition for automatic extraction of link information from piping and instrumentation diagrams

Dong, F.*; Chen, S.*; Demachi, Kazuyuki*; Hashidate, Ryuta; Takaya, Shigeru

no journal, , 

Piping and Instrumentation Diagrams contain information about the piping and process equipment together with the instrumentation and control devices, which is essential to the design and management of Nuclear Power Plants. There are abundant complex objects on P&IDs, with imbalanced distribution of these objects and their linked information across different diagrams. Therefore, the content of P&IDs is generally extracted and analyzed manually, which is time consuming and error prone. To efficiently address these issues, we integrate state-of-the-art deep learning-based object detection and Optical Character Recognition models to automatically extract link information from P&IDs. Besides, we propose a novel image pre-processing approach using sliding windows to detect low resolution small objects. The performance of the proposed approach was experimentally evaluated, and the experimental results demonstrate it capable to extract link information from P&IDs of NPPs.

Oral presentation

Predicting plume concentrations in the urban area using a deep learning model

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.

Oral presentation

Development of a deep learning model for predicting plume concentrations in the urban area

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.

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