• Journal of Korea Soil Environment Society
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• v.3 no.3
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• pp.93-107
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• 1998

Because of its stable quantity and quality, groundwater has long been a reliable source of drinking water for domestic users. Rapid economic growth and rising standards of living have in recent years put severe demands on drinking water supplies in Korea. Groundwaters that are currently being used for natural mineral water were hydrochemically evaluated and investigated in order to maintain their quality to satisfy strict health standards. There exist 15 natural mineral water plants in the Okcheon metamorphic belt. Characteristics of groundwaters are different from those of other areas in that electrical conductivity, hardness, contents of Ca, Mg and $HCO_3$are relatively high. The content of major cations is in the order of Ca>Mg, Na>K, whereas that of major anions shows the order of $HCO_3$>$SO_4$>Cl>F. The fact that the Ca-Mg-HCO$_3$type is mostly predominant among water types reflects that dissolution of carbonates that are abundantly present in the metamorphic rocks plays an important part in groundwater chemistry. Representative correlation coefficients between chemical species show Mg-$HCO_3$(0.92), Ca-$HCO_3$(0.88), Ca-Mg(0.80), Ca-Cl(0.78), Mg-$SO_4$(0.78), Ca-$SO_4$(0.71), possibly due to the effect by dissolution of carbonates, gypsum or anhydrite. Determinative coefficients between some chemical species represent a good relationship, especially for EC-(K+Na+Ca), Ca-$HCO_3$, Ca-Mg, indiacting that they are similar in chemical behaviors. According to saturation index, most chemical species are undersaturated with respect to major minerals, except for some silica phases. Groundwater is slightly undersaturated with respect to calcite and dolomite, whereas it is still greatly undersaturated with respect to gypsum, anhydrite and fluorite, Based on the Phase equilibrium in the systems $NA_2$O-$Al_2$$O_3$-$SiO_2$-$H_2$O and $K_2$O-$Al_2$$O_3$-$SiO_2$-$H_2$O, it is clear that groundwater is in equilibrium with kaolinite, evolved from the stability area of gibbsite during water-rock interaction. It is expected that chemical evolution of groundwater continue to proceed with increasing pH by reaction of feldspars, with calcite much less reactive.

• Nuclear Medicine and Molecular Imaging
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• v.43 no.5
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• pp.459-467
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• 2009

Purpose: The maximum likelihood-expectation maximization (ML-EM) is the statistical reconstruction algorithm derived from probabilistic model of the emission and detection processes. Although the ML-EM has many advantages in accuracy and utility, the use of the ML-EM is limited due to the computational burden of iterating processing on a CPU (central processing unit). In this study, we developed a parallel computing technique on GPU (graphic processing unit) for ML-EM algorithm. Materials and Methods: Using Geforce 9800 GTX+ graphic card and CUDA (compute unified device architecture) the projection and backprojection in ML-EM algorithm were parallelized by NVIDIA's technology. The time delay on computations for projection, errors between measured and estimated data and backprojection in an iteration were measured. Total time included the latency in data transmission between RAM and GPU memory. Results: The total computation time of the CPU- and GPU-based ML-EM with 32 iterations were 3.83 and 0.26 see, respectively. In this case, the computing speed was improved about 15 times on GPU. When the number of iterations increased into 1024, the CPU- and GPU-based computing took totally 18 min and 8 see, respectively. The improvement was about 135 times and was caused by delay on CPU-based computing after certain iterations. On the other hand, the GPU-based computation provided very small variation on time delay per iteration due to use of shared memory. Conclusion: The GPU-based parallel computation for ML-EM improved significantly the computing speed and stability. The developed GPU-based ML-EM algorithm could be easily modified for some other imaging geometries.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.