Shinji Hirooka

Shinji Hirooka
Chiba University / HiSR

Ph.D.

About

18
Publications
3,464
Reads
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159
Citations
Citations since 2017
4 Research Items
114 Citations
20172018201920202021202220230510152025
20172018201920202021202220230510152025
20172018201920202021202220230510152025
20172018201920202021202220230510152025
Additional affiliations
December 2018 - present
HiSR Lab
Position
  • President
May 2017 - November 2018
Hmcomm Co. Ltd., Japan
Position
  • Chief Fellow
May 2014 - November 2014
National Central University
Position
  • PostDoc Position
Education
April 2008 - March 2014
Chiba University
Field of study
  • Ionospheric science, Deep learning

Publications

Publications (18)
Preprint
Full-text available
Automatic diagnosis of multiple cardiac abnormalities from reduced-lead electrocardiogram (ECG) data is challenging. One of the reasons for this is the difficulty of defining labels from standard 12-lead data. Reduced-lead ECG data usually do not have identical characteristics of cardiac abnormalities because of the noisy label problem. Thus, there...
Chapter
In recent years, many systems having a speech interface have grown. The speech interface includes spoken dialogue function and high performance of a spoken dialogue system has been required. The spoken dialogue system consists of a speech recognition module. In this study, we focus on the speech recognition module of the spoken dialogue system and...
Article
Recent studies have reported unusual behaviors of geomagnetic diurnal variation (GDV) in the vertical component prior to the 2011 off the Pacific coast of Tohoku earthquake (Mw 9.0). To make a better understanding of this phenomenon, time-spatial analysis of GDV has been applied in this study. Geomagnetic data of long term observations at 17 statio...
Article
The ionospheric anomalies possibly associated with large earthquakes have been reported by many researchers. In this paper, Total Electron Content (TEC) and tomography analyses have been applied to investigate the spatial and temporal distributions of ionospheric electron density prior to the 2011 Off the Pacific Coast of Tohoku earthquake (Mw9.0)....
Article
Full-text available
A numerical simulation has been done to evaluate the performance of the ionospheric tomography using the residual minimization training neural network (RMTNN) method. The results indicated that reconstruction with high-precision is possible when the standard deviation of the noise is about 2.5% or less of the average value of observed data (Slant T...
Article
An ionospheric anomaly prior to the 2007 Southern Sumatra earthquake (M8.5) was observed by GPS receivers around the Sumatra islands. In this paper, to investigate the three-dimensional structure of electron density in the iono-sphere, a tomographic approach (Residual Minimization Training Neural Network; RMTNN) has been used. Results of the tomogr...
Article
In this paper, neural network based tomography using GEONET data has been performed to investigate the fine structure possibly associated with the 2011 off the pacific coast of Tohoku Earthquake (Mw9.0). Although the possible ionospheric anomalies preceding large earthquakes have been reported by many researchers, a physical mechanism of the anomal...
Article
In this paper, we examine pre-earthquake ionospheric anomalies in time series and perform a statistical test by using total electron content (TEC) derived from global ionosphere maps (GIM) around the Japan area for the first time. The normalized GIM-TEC (GIM-TEC*), which is computed based on 15 days backward running mean of GIM-TEC, have been inves...
Article
Full-text available
Three-dimensional ionospheric tomography is effective for investigations of the dynamics of ionospheric phenomena. However, it is an ill-posed problem in the context of sparse data, and accurate electron density reconstruction is difficult. The Residual Minimization Training Neural Network (RMTNN) tomographic approach, a multilayer neural network t...
Conference Paper
Full-text available
In order to investigate the dynamics of ionospheric phenomena, perform the 3-D ionospheric tomography is effective. However, it is the ill-posed inverse problem and reconstruction is difficult because of the small number of data. The Residual Minimization Training Neural Network (RMTNN) tomographic approach proposed by Ma et al. [3] has an advantag...
Conference Paper
Full-text available
In this paper, neural network based ionospheric tomography was performed to investigate the detailed structure that may be associated with earthquakes. The 2007 Southern Sumatra earthquake (M8.5) is selected because significant decreases in the Total Electron Content (TEC) have been confirmed by GPS data analysis. With respect to the analyzed earth...
Article
Full-text available
An ionospheric anomaly prior to the 2007 Southern Sumatra earthquake (M8.5) was observed by GPS receivers around the Sumatra islands. In this paper, to investigate the three-dimensional structure of electron density in the ionosphere, a tomographic approach (Residual Minimization Training Neural Network; RMTNN) has been used. Results of the tomogra...
Article
Full-text available
A tomographic approach is used to investigate the fine structure of electron density in the ionosphere. In the present paper, the Residual Minimization Training Neural Network (RMTNN) method is selected as the ionospheric tomography with which to investigate the detailed structure that may be associated with earthquakes. The 2007 Southern Sumatra e...
Article
To detect electromagnetic (EM) waves emitted from the seismic active zone where the rock fracture is expected, the ELF/VLF electrode antenna system working in the deep sea has been developed. The minimum detectable signal amplitude at the input of the amplifier is about 7nV per root Hz, with the electrode span of 2m. Simultaneous observation of ELF...

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Projects

Project (1)
Project
Although a certain amount of precursory information is available in both earthquake catalogs and non-catalog observations, the earthquake forecast is still far from satisfactory. In most case, the precursory phenomena were studied individually. Against this background, we are trying to develop a new earthquake forecast model which combines catalog-based and non-catalog-based approaches.