• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2709-2716
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• 2020

Compositions of large nuclear cores (e.g. boiling water reactors) are highly heterogeneous in terms of fuel composition, control rod insertions and flow regimes. For this reason, they usually lack high order of symmetry (e.g. 1/4, 1/8) making it difficult to estimate their neutronic parameters for large spaces of possible loading patterns. A detailed hyperparameter optimization technique (a combination of manual and Gaussian process search) is used to train and optimize deep neural networks for the prediction of three neutronic parameters for the Ringhals-1 BWR unit: power peaking factors (PPF), control rod bank level, and cycle length. Simulation data is generated based on half-symmetry using PARCS core simulator by shuffling a total of 196 assemblies. The results demonstrate a promising performance by the deep networks as acceptable mean absolute error values are found for the global maximum PPF (~0.2) and for the radially and axially averaged PPF (~0.05). The mean difference between targets and predictions for the control rod level is about 5% insertion depth. Lastly, cycle length labels are predicted with 82% accuracy. The results also demonstrate that 10,000 samples are adequate to capture about 80% of the high-dimensional space, with minor improvements found for larger number of samples. The promising findings of this work prove the ability of deep neural networks to resolve high dimensionality issues of large cores in the nuclear area.

• The Journal of Engineering Geology
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• v.24 no.3
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• pp.431-440
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• 2014

Since the establishment of the Groundwater Act in Korea in 1993, the national policy on groundwater has focused on the preservation and management of groundwater, which should be used only as a subsidiary water resource. However, population growth, increased water demand, climate change, and the need for uniform water distribution have brought changes to groundwater policy, and have led to the prioritization of development projects such as groundwater dams and river bank filtration. Population growth, changes to the water environment, and increased water risks have all played a role in triggering rapid growth within the water industry; the size of the investment in water resources will also continue to increase worldwide. Until now, private wells and bottled mineral water have led the groundwater industry in South Korea. However, a new area of the groundwater industry, which includes the health and medical sciences, employs groundwater properties derived from regional geology, and is growing. This requires the advancement of groundwater research and technical development connected with ICT (Information and Communication Technology) and medical science, and that the public development of groundwater and its various applications is expanded through locating groundwater in the core of the water industry cluster.

• Journal of Ecology and Environment
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• v.31 no.3
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• pp.233-241
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• 2008

Functional feeding groups (FFGs) of benthic macroinvertebrates are guilds of invertebrate taxa that obtain food in similar ways, regardless of their taxonomic affinities. They can represent a heterogeneous assemblage of benthic fauna and may indicate disturbances of their habitats. The proportion of different groups can change in response to disturbances that affect the food base of the system, thereby offering a means of assessing disruption of ecosystem functioning. In this study, we used benthic macroinvertebrate communities collected at 650 sites of 23 different water types in the province of Overijssel, The Netherlands. Physical and chemical environmental factors were measured at each sampling site. Each taxon was assigned to its corresponding FFG based on its food resources. A multilayer perceptron (MLP) using a backpropagation algorithm, a supervised artificial neural network, was applied to evaluate the influence of environmental variables to the FFGs of benthic macroinvertebrates through a sensitivity analysis. In the evaluation of input variables, the sensitivity analysis with partial derivatives demonstrates the relative importance of influential environmental variables on the FFG, showing that different variables influence the FFG in various ways. Collector-filterers and shredders were mainly influenced by $Ca^{2+}$ and width of the streams, and scrapers were influenced mostly with $Ca^{2+}$ and depth, and predators were by depth and pH. $Ca^{2+}$ and depth displayed relatively high influence on all four FFGs, while some variables such as pH, %gravel, %silt, and %bank affected specific groups. This approach can help to characterize community structure and to ecologically assess target ecosystems.