63 citations · 76 across the 6 of their papers we have counts for
6 papers
A simple DNN regression for the chemical composition in essential oil
Yuki Harada, Shuichi Maeda, Masato Kiyama +1
Although experimental design and methodological surveys for mono-molecular activity/property has been extensively investigated, those for chemical composition have received little…
Extension of Convolutional Neural Network along Temporal and Vertical Directions for Precipitation Downscaling
Takeyoshi Nagasato, Kei Ishida, Ali Ercan +4
Deep learning has been utilized for the statistical downscaling of climate data. Specifically, a two-dimensional (2D) convolutional neural network (CNN) has been successfully appli…
Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modeling
Kei Ishida, Ali Ercan, Takeyoshi Nagasato +2
An architecture consisting of a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory (LSTM) network, which is referred as CNN…
Development of Quantized DNN Library for Exact Hardware Emulation
Masato Kiyama, Motoki Amagasaki, Masahiro Iida
Quantization is used to speed up execution time and save power when runnning Deep neural networks (DNNs) on edge devices like AI chips. To investigate the effect of quantization, w…
Capabilities of Deep Learning Models on Learning Physical Relationships: Case of Rainfall-Runoff Modeling with LSTM
Kazuki Yokoo, Kei Ishida, Ali Ercan +4
This study investigates the relationships which deep learning methods can identify between the input and output data. As a case study, rainfall-runoff modeling in a snow-dominated…
Multi-Time-Scale Input Approaches for Hourly-Scale Rainfall-Runoff Modeling based on Recurrent Neural Networks
Kei Ishida, Masato Kiyama, Ali Ercan +2
This study proposes two straightforward yet effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neur…