• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.5
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• pp.431-443
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• 2014

In this paper case studies of artificial ground freezing, which have not been applied in Korea, have been investigated for the water cut-off in a subsea tunnel under high water pressure and the most commonly used cooling mediums of brine and liquid nitrogen are examined. Since sea water with pressure has the lower freezing point than pure water, the lower temperature cooling medium is required in the application of subsea tunnel. Also, the cooling medium must have refrigeration safety and is able to reduce executing time. Brine freezing system can reuse cooling medium and is safer than liquid nitrogen freezing. But it takes more time to freeze ground and needs complex circulation plants. On the other hand, liquid nitrogen freezing system can't recycle cooling medium and may cause breathing problems or asphyxiation through oxygen deficiency. But, freezing with liquid nitrogen is fast and requires simple refrigeration equipment. Principal elements of design for ground freezing in subsea tunnel have been extracted and these elements are needed further research.

• Fisheries and Aquatic Sciences
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• v.13 no.2
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• pp.167-181
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• 2010

To predict changes in the marine environment of the Beolgyo Stream Estuary in Jeonnam Province, South Korea, where cohesive tidal flats cover a broad area and a large bridge is under construction, this study conducted numerical simulations involving tidal flow and cohesive sediment transport. A wetting and drying (WAD) technique for tidal flats from the Princeton Ocean Model (POM) was applied to a large-scale-grid hydrodynamic module capable of evaluating the flow resistance of structures. Derivation of the eddy viscosity coefficient for wakes created by structures was accomplished through the explicit use of shear velocity and Chezy's average velocity. Furthermore, various field observations, including of tide, tidal flow, suspended sediment concentrations, bottom sediments, and water depth, were performed to verify the model and obtain input data for it. In particular, geologic parameters related to the evaluation of settling velocity and critical shear stresses for erosion and deposition were observed, and numerical tests for the representation of suspended sediment concentrations were performed to determine proper values for the empirical coefficients in the sediment transport module. According to the simulation results, the velocity variation was particularly prominent around the piers in the tidal channel. Erosion occurred mainly along the tidal channels near the piers, where bridge structures reduced the flow cross section, creating strong flow. In contrast, in the rear area of the structure, where the flow was relatively weak due to the formation of eddies, deposition and moderated erosion were predicted. In estuaries and coastal waters, changes in the flow environment caused by artificial structures can produce changes in the sedimentary environment, which in turn can affect the local marine ecosystem. The numerical model proposed in this study will enable systematic prediction of changes to flow and sedimentary environments caused by the construction of artificial structures.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.681-690
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• 2017

This study estimated the daily maximum snow depth using the Artificial Neural Network (ANN) model in Korean Peninsula. First, the optimal ANN model structure was determined through the trial-and-error approach. As a result, daily precipitation, daily mean temperature, and daily minimum temperature were chosen as the input data of the ANN. The number of hidden layer was set to 1 and the number of nodes in the hidden layer was set to 10. In case of using the observed value as the input data of the ANN model, the cross validation correlation coefficient was 0.87, which is higher than that of the case in which the daily maximum snow depth was spatially interpolated using the Ordinary Kriging method (0.40). In order to investigate the performance of the ANN model for estimating the daily maximum snow depth of the ungauged area, the input data of the ANN model was spatially interpolated using Ordinary Kriging. In this case, the correlation coefficient of 0.49 was obtained. The performance of the ANN model in mountainous areas above 200m above sea level was found to be somewhat lower than that in the rest of the study area. This result of this study implies that the ANN model can be used effectively for the accurate and immediate estimation of the maximum snow depth over the whole country.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.527-536
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• 2017

With the recent development of smart grid industry, the necessity for efficient EMS(Energy Management System) has been increased. In particular, in order to reduce electric load and energy cost, sophisticated electric load forecasting and efficient smart grid operation strategy are required. In this paper, for more accurate electric load forecasting, we extend the data collected at demand time into high time resolution and construct an artificial neural network-based forecasting model appropriate for the high time resolution data. Furthermore, to improve the accuracy of electric load forecasting, time series data of sequence form are transformed into continuous data of two-dimensional space to solve that problem that machine learning methods cannot reflect the periodicity of time series data. In addition, to consider external factors such as temperature and humidity in accordance with the time resolution, we estimate their value at the time resolution using linear interpolation method. Finally, we apply the PCA(Principal Component Analysis) algorithm to the feature vector composed of external factors to remove data which have little correlation with the power data. Finally, we perform the evaluation of our model through 5-fold cross-validation. The results show that forecasting based on higher time resolution improve the accuracy and the best error rate of 3.71% was achieved at the 3-min resolution.

• Korean Journal of Plant Taxonomy
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• v.45 no.4
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• pp.353-361
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• 2015

Spatio-temporal variation in fruit set in orchids would affect long-term population viability and will influence genetic diversity over many generations. The aim of this study was to examine the breeding system of the nectariferous terrestrial orchid Epipactis thunbergii, to specifically determine levels of fruit set in terms of time and space under natural conditions. We examined pollination under natural conditions and conducted hand pollination experiments during a 2-year survey in four populations located along 1.5 km of coastal line in Jinguiri (rual village) [Jeollanam-do (province), southern Korea]. We found that, over a 2-year period, levels of percentage of fruit set were similar within patches of the four populations. By contrast, we detected significant differences in the percentage of fruit set among patches. We also found that plants with larger inflorescence size produced significantly more fruits than plants with fewer flowers. Over a 2-year period, the percentage of fruit set for E. thunbergii was similar but low (14.1%) compared to that averaged for eighty-four rewarding species (37.1%). However, an increase in fruit set was achieved by hand-pollinations: artificial self-pollination (90.5-95.2%), artificial geitonogamy (94.7-95.0%), and cross-pollination (artificial xenogamy, 91.3-91.4%). No emasculated flowers produced fruits and no automatic pollination was found in E. thunbergii. Our findings suggest that E. thunbergii is a self-compatible terrestrial orchid that depends on pollinators (insects) to achieve fruit set in natural habitats, and that local environmental conditions were similar over a period of 2 years in the study area. Our results also highlight the cryptic variation of fruit production in time, but more pronounced variability in space.

• Economic and Environmental Geology
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• v.55 no.3
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• pp.309-316
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• 2022

In order to minimize the human and time consumption required for rock classification, research on rock classification using artificial intelligence (AI) has recently developed. In this study, basic volcanic rocks were subdivided by using polarizing microscope thin section images. A convolutional neural network (CNN) model based on Tensorflow and Keras libraries was self-producted for rock classification. A total of 720 images of olivine basalt, basaltic andesite, olivine tholeiite, trachytic olivine basalt reference specimens were mounted with open nicol, cross nicol, and adding gypsum plates, and trained at the training : test = 7 : 3 ratio. As a result of machine learning, the classification accuracy was over 80-90%. When we confirmed the classification accuracy of each AI model, it is expected that the rock classification method of this model will not be much different from the rock classification process of a geologist. Furthermore, if not only this model but also models that subdivide more diverse rock types are produced and integrated, the AI model that satisfies both the speed of data classification and the accessibility of non-experts can be developed, thereby providing a new framework for basic petrology research.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.571-585
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• 2022

As the threat of natural disasters such as droughts, floods, forest fires, and landslides increases due to climate change, social demand for high-resolution soil moisture retrieval, such as Synthetic Aperture Radar (SAR), is also increasing. However, the domestic environment has a high proportion of mountainous topography, making it challenging to retrieve soil moisture from SAR data. This study evaluated the usability of Sentinel-1 SAR, which is applied with the Artificial Neural Network (ANN) technique, to retrieve soil moisture. It was confirmed that the backscattering coefficient obtained from Sentinel-1 significantly correlated with soil moisture behavior, and the possibility of stand-alone use to correct vegetation effects without using auxiliary data observed from other satellites or observatories. However, there was a large difference in the characteristics of each site and topographic group. In particular, when the model learned on the mountain and at flat land cross-applied, the soil moisture could not be properly simulated. In addition, when the number of learning points was increased to solve this problem, the soil moisture retrieval model was smoothed. As a result, the overall correlation coefficient of all sites improved, but errors at individual sites gradually increased. Therefore, systematic research must be conducted in order to widely apply high-resolution SAR soil moisture data. It is expected that it can be effectively used in various fields if the scope of learning sites and application targets are specifically limited.

• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Proceedings of the Korean Aquaculture Society Conference
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• 2003.10a
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• pp.28-28
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• 2003

Viable interspecific hybrids between yellowtail flounder (Pleuronectes ferrugineus, Store.) and winter flounder (Pleuronectes americanus, Walbaum) were produced by artificial insemination of yellowtail flounder eggs with winter flounder sperm. However, mean fertilization rate, hatching success and early survival up to 3 weeks post hatch were significantly lower than those of parental pure cross controls (P<0.01). Overall, cytogenetic traits (karyological analysis and estimation of cellular DNA contents using flow cytometry) of hybrid flounder were intermediate between the two parental species. Microsatellite assay was used to distinguish the parental genomes in the hybrids; in most cases, one allele was specific to each of the parents. Morphometrics assessed by body proportions indicated that hybrids generally displayed a morphology intermediate between the maternal and paternal species. Interspecific hybrids exhibited abnormal and retarded gonad development in both sexes based on histological analysis of gonads from adult fish. The sterility of the hybrids presents a significant advantage for their use in aquaculture, as potential escapees would not be capable of reproducing in the wild and contaminating natural stocks.

• Wind and Structures
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• v.6 no.6
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• pp.437-450
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• 2003

In this paper, the structural random response due to the turbulent boundary layer excitation is investigated. Using the mode shapes and natural frequencies of an undamped structural operator, a fully analytical model has been assembled. The auto and cross-spectral densities of kinematic quantities are so determined through exact analytical expansions. In order to reduce the computational costs associated with the needed number of modes, it has been tested an innovative methodology based on a scaling procedure. In fact, by using a reduced spatial domain and defining accordingly an augmented artificial damping, it is possible to get the same energy response with reduced computational costs. The item to be checked was the power spectral density of the displacement response for a flexural simply supported beam; the very simple structure was selected just to highlight the main characteristics of the technique. In principle, it can be applied successfully to any quantity derived from the modal operators. The criterion and the rule of scaling the domain are also presented, investigated and discussed. The obtained results are encouraging and they allow thinking successfully to the definition of procedure that could represent a bridge between modal and energy methods.