Improving the estimation accuracy of multi-nuclide source term estimation method for severe nuclear accidents using temporal convolutional network optimized by Bayesian optimization and hyperband

2022 ◽  
Vol 242 ◽  
pp. 106787
Author(s):  
Yongsheng Ling ◽  
Tian Huang ◽  
Qi Yue ◽  
Qing Shan ◽  
Daqian Hei ◽  
...  
Keyword(s):  
Source Term ◽  
Estimation Method ◽  
Bayesian Optimization ◽  
Estimation Accuracy ◽  
Nuclear Accidents ◽  
Convolutional Network ◽  
Source Term Estimation

Source Term Estimation in Nuclear Accidents Using 4D Variational Data Assimilation for Heterologous Atmospheric Condition

2014 ◽  
Author(s):  
Yun Liu ◽  
Sheng Fang ◽  
Hong Li ◽  
Jingyuan Qu ◽  
Dong Fang
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Cost Function ◽  
Source Term ◽  
Atmospheric Stability ◽  
Estimation Method ◽  
Atmospheric Condition ◽  
Nuclear Accidents ◽  
Source Information ◽  
Source Term Estimation

The source information of the radionuclide release in nuclear accidents is a key issue of the nuclear emergency response. One way to estimate the source information is by inversing the radionuclide transportation process based on environment radiation monitoring data. The advantage of this method is that the required data are easy to obtain in accident. But it is vulnerable to large uncertainties in both data and transport model. To solve the problem, a source term estimation method based on four-dimensional variational (4DVAR) data assimilation technique was proposed for source term estimation in this study. The proposed method couples 4DVAR with the RIMPUFF air dispersion model. It formulates the inverse modelling of source term estimation as an optimization problem that is trying to find an optimal balance between real observation data and the background field. The advantage of this method is that the radionuclide transport in every time step is included in data assimilation and the result is global optimum in the whole assimilation period. The gradient for cost function is calculated by the backward integration of the adjoint model. Practical imperfectness of measurement were considered and integrated into the cost function. The proposed method was verified using numerical simulation for both homogeneous and heterologous atmospheric condition. The performance of the source term estimation method was also investigated with respect to different release profile, wind speed and atmospheric stability class. The simulation results demonstrate that the estimate matches the true release status well for both homogeneous and heterologous wind field. Also, the experimental results show that the proposed method has strong robustness to wind speed and atmospheric stability.

A source term estimation method for a nuclear accident using atmospheric dispersion models

2015 ◽  
Vol 58 (1/2) ◽  
pp. 39 ◽  
Author(s):  
Minsik Kim ◽  
Ryohji Ohba ◽  
Masamichi Oura ◽  
Shinsuke Kato ◽  
Masayuki Takigawa ◽  
...  
Keyword(s):  
Source Term ◽  
Atmospheric Dispersion ◽  
Estimation Method ◽  
Nuclear Accident ◽  
Dispersion Models ◽  
Atmospheric Dispersion Models ◽  
Source Term Estimation

scholarly journals Verification of Source Term Estimation Method against Measured Data for Decommissioning Waste from a CANDU Reactor

2011 ◽  
Vol 48 (7) ◽  
pp. 1087-1093 ◽  
Author(s):  
Dong-Keun CHO ◽  
Gwang-Min SUN ◽  
Jongwon CHOI ◽  
Ho-Yeon YANG ◽  
Tae-Won HWANG
Keyword(s):  
Source Term ◽  
Estimation Method ◽  
Measured Data ◽  
Source Term Estimation

scholarly journals Low-complexity beam-domain channel estimation and power allocation in hybrid architecture massive MIMO systems

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xiao Chen ◽  
Zaichen Zhang ◽  
Liang Wu ◽  
Jian Dang
Keyword(s):  
Channel Estimation ◽  
Power Allocation ◽  
Spectral Efficiency ◽  
Communication Systems ◽  
Mimo Systems ◽  
Beam Steering ◽  
Estimation Method ◽  
Low Complexity ◽  
Estimation Accuracy ◽  
Hybrid Architecture

Abstract In this journal, we investigate the beam-domain channel estimation and power allocation in hybrid architecture massive multiple-input and multiple-output (MIMO) communication systems. First, we propose a low-complexity channel estimation method, which utilizes the beam steering vectors achieved from the direction-of-arrival (DOA) estimation and beam gains estimated by low-overhead pilots. Based on the estimated beam information, a purely analog precoding strategy is also designed. Then, the optimal power allocation among multiple beams is derived to maximize spectral efficiency. Finally, simulation results show that the proposed schemes can achieve high channel estimation accuracy and spectral efficiency.

scholarly journals UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

2021 ◽  
Vol 13 (4) ◽  
pp. 803
Author(s):  
Lingchen Lin ◽  
Kunyong Yu ◽  
Xiong Yao ◽  
Yangbo Deng ◽  
Zhenbang Hao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Canopy ◽  
Estimation Method ◽  
Vertical Direction ◽  
Estimation Accuracy ◽  
3D Point Cloud ◽  
Area Index ◽  
Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

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