• Magazine of the Korean Society of Agricultural Engineers
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• v.25 no.4
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• pp.50-60
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• 1983

305 samples of alluvial deposit in inland and harbour districts were selected and consolidation charateristics of the alluvium were put in order statiscally. The correlations between them were as follows. 1. The relationships between LL(liguid limit) and Cc (compression index) were explained as Cc=0. 03(LL-21. 7) in case of inland district soil and as Cc=0. 019(LL-19) in case of harbour district soil. As compared with formular proposed by Skernpton, the gradient of this linear line was slight steep. 2. The relationships between PI(plastic index) and Cc were explained as Cc=0. 063 PI-0. 52 in case of inland district soil and Cc=0. 043 PI-0. 31 in case of harbour district soil. 3. As void ratio and natural moisture content were increased, Cc was increased, and as wet density was increased, Cc was decreased with a gentle curve. 4. As LL and P1 increased, mv(coefficient of volume compressibility) was increased but if LL and P1 was increased beyond a certain extend, mv has a tendency of constant value, that is, mv show a tendency to take constant value in the very soft clay. and mv in P=2. 5kg/cm$^2$ was about l${\times}$ l0-$^1$cm$^2$/kg in case of land district soil and 6x 10-$^1$crn$^2$/kg in case of harbour district soil lower than that in P=0. 25kg/crn2. 5. Cv(coefficient of consolidation) was a tendency to decrease with a gentle curve as LL was increased, and Cv in P=0. 25kg/crn2 was about 3x l0-$^1$crn$^2$/min larger than that in P=2. 5kg/crn$^2$. 6. Relationships between Py(pre-consolidation pressure) which is included over consolidation soil and ∑r1h(effective over-burden pressure) were explained as Py=l. 12 ∑r'h in case of land district soil and as Py=l. l5∑r'h in case of harbour district soil. 7. Some of the properties show good correlations between them, practical and effective applications of these correlations are expected in the planning and excution of soil investigation and also in the evaluation of the results.

• Journal of Environmental Health Sciences
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• v.35 no.6
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• pp.517-525
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• 2009

The water quality of Lake Shihwa had been rapidly deteriorating since 1994 due to wastewater input from the watersheds, limited water circulation and the lack of a wastewater treatment policy. In 2000, the government decided to open the tidal embankment and make a comprehensive management plan to improve the water quality, especially inflowing stream water around Shihwa and Banwol industrial complex. However, the water quality and microbial community have not as yet been fully evaluated. The purpose of this study is to investigate the influent water quality around the industrial area based on chemical and biological analysis, and collected surface water sample from the Siheung Stream, up-stream to down-stream through the industrial complex, Samples were collected in July 2009. The results show that the downstream site near the industrial complex had higher concentrations of heavy metals (Cu, Mn, Fe, Mg, and Zn) and organic matter than upstream sites. A combination of DGGE (Denaturing Gradient Gel Electrophoresis) gels, lists of K-WQI (Korean Water Quality Index), cluster analysis, MDS (Multi-Dimensional Scaling) and PCA (Principal Component Analysis) has demonstrated clear clustering between Siheung stream 3 and 4 and with a high similarity and detected metal reducing bacteria (Shewanella spp.) and biodegrading bacteria (Acinetobacter spp.). These results suggest that use of both chemical and microbiological marker would be useful to fully evaluate the water quality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Korean Journal of Optics and Photonics
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• v.31 no.6
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• pp.247-258
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• 2020

In this paper, based on a stochastic parallel gradient descent (SPGD) algorithm we study phase control of a coherent-beam-combining system under turbulent atmospheric conditions. Based on the statistical theory of atmospheric turbulence, we carry out the analysis of the phase and wavefront distortion of a laser beam propagating through a turbulent atmospheric medium. We also conduct numerical simulations of a coherent-beam-combining system with 7- and 19-channel laser beams distorted by atmospheric turbulence. Through numerical simulations, we characterize the phase-control characteristics and efficiency of the coherent-beam-combining system under various degrees of atmospheric turbulence. It is verified that the SPGD algorithm is capable of realizing 7-channel coherent beam combining with a beam-combining efficiency of more than 90%, even under the turbulent atmospheric conditions up to cn2 of 10-13 m-2/3. In the case of 19-channel coherent beam combining, it is shown that the same turbulent atmospheric conditions result in a drastic reduction of the beam-combining efficiency down to 60%, due to the elevated impact of the corresponding refractive-index inhomogeneity. In addition, by putting together the number of iterations of the SPGD algorithm required for phase locking under atmospheric turbulence and the time intervals of atmospheric phenomena, which typically are of the order of ㎲, it is estimated that hundreds of MHz to a few GHz of computing bandwidth of SPGD-based phase control may be required for a coherent-beam-combining system to confront such turbulent atmospheric conditions. We expect the results of this paper to be useful for quantitatively analyzing and predicting the effects of atmospheric turbulence on the SPGD-based phase-control performance of a coherent-beam-combining system.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.334-343
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• 2021

Objective: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods: We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results: The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869-0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815-0.948] and 0.899 [95% CI, 0.820-0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant (p > 0.05 for all). Conclusion: Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.73-85
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• 2024

Predicting water quality of rivers and reservoirs is necessary for the management of water resources. Artificial Neural Networks (ANNs) have been used in many studies to predict water quality with high accuracy. Previous studies have used Gradient Descent (GD)-based optimizers as an optimizer, an operator of ANN that searches parameters. However, GD-based optimizers have the disadvantages of the possibility of local optimal convergence and absence of a solution storage and comparison structure. This study developed improved optimizers to overcome the disadvantages of GD-based optimizers. Proposed optimizers are optimizers that combine adaptive moments (Adam) and Nesterov-accelerated adaptive moments (Nadam), which have low learning errors among GD-based optimizers, with Harmony Search (HS) or Novel Self-adaptive Harmony Search (NSHS). To evaluate the performance of Long Short-Term Memory (LSTM) using improved optimizers, the water quality data from the Dasan water quality monitoring station were used for training and prediction. Comparing the learning results, Mean Squared Error (MSE) of LSTM using Nadam combined with NSHS (NadamNSHS) was the lowest at 0.002921. In addition, the prediction rankings according to MSE and R² for the four water quality indices for each optimizer were compared. Comparing the average of ranking for each optimizer, it was confirmed that LSTM using NadamNSHS was the highest at 2.25.

• Korean Journal of Environment and Ecology
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• v.32 no.3
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• pp.313-322
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• 2018

To investigate the changing patterns of sub-alpine forest vegetation due to climate change requires accumulation of contiguous reference data and continuous monitoring. Furthermore, it is crucial to monitor short-term ecological change of lower level vegetation to understand the trend of long-term vegetation change. Therefore, this study carried out a vegetation survey and tree diameter measurement in 36 plots of Mt. Jiri inhabited by Abies koreana species from 2015 to 2017 to examine the short-term dynamics of Abies koreana seedling and the change of vegetation distribution according to altitude. We analyzed the importance value and MIV (mean importance value) of major species by each stratum as well as the importance value and species diversity index of major species and the change of seedling population by altitude. The results showed that Abies koreana had the highest importance value on tree layer, Rhododendron schlippenbachii on shrub layer and Tripterygium regelii on herb layer. MIV was high in the order of Abies koreana, Rhododendron schlippenbachii and Acer pseudosieboldianum. Regarding the species composition and species diversity index (H') along the altitudinal gradient, Sasa borealis showed high MI and low H' in the elevation less than 1,500 m, and IV of Tripterygium regelii and H' of herb layer were high in the elevation of 1,700 - 1,800 m. Abies koreana seedling decreased by 22.4% from 1,250 n/ha in 2015 to 970 n/ha in 2017 (p <0.05) throughout the investigated area. The decline rate along seedling and sapling height were 22.9% in less than 10 cm, 3.4% in 10-30 cm, 8.9% in 30-50 cm, 39.3% in 50-100 cm, and 55.1% more than 100 cm. Few of A. koreana seedlings appeared due to the dominance of Sasa borealis in the elevation of 1,500 m or less and due to the dominance and high species diversity of Tripterygium regelii in the elevation of 1,700-1,800 m. On the other hand, many of A. koreana seedlings appeared in the elevation of 1,600-1,700 m due to no distribution of S. borealis and T. regelii species in that altitude range. Therefore, we concluded that those seedlings and saplings of A. koreana could be more stable in the altitude of 1,600-1,700 m.

• Journal of Korean Society of Forest Science
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• v.109 no.2
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• pp.115-123
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• 2020

The vertical distribution of vegetation can be classified according to the altitudinal gradient and the distribution of species along this gradient. The purpose of this study was to analyze the vegetation structure, species composition, dimensional density, and change according to altitude. These data illustrate the distribution of coniferous forest by altitude. By order of importance, the vegetation structure of this mixed forest consisted of Abies nephrolepis (12.2), Pinus koraiensis (10.86), and Acer komarovii (8.11). As a result of species composition according to the altitude, A. nephrolepis and Maianthemum bifolium increased in importance with increasing altitude. Tripterygium regelii emerged between 1,400 m and 1,600 m, which indicates that forest gaps were frequent at that elevation. The species diversity index was the highest from 1,400-1,500 m and coincided with the presence of forest gaps. The changes in A. nephrolepis of evergreen conifers increased significantly from 402 ± 5.4 ha.^-1 to 528 ± 11.6 ha.^-1 for two years, and decreased from 57 ± 1.3 ha.^-1 to 56 ± 1.6 ha.^-1 for P. koraiensis. The density of A. nephrolepis and P. koraiensis seedlings significantly increased at 1,500-1,600 m. The results of this study can be used as a basis to identify the mast seeding year with the increase or decrease of seedlings. In addition to documenting the evergreen conifer population of the Seorak Mountain, these results can be built upon for future monitoring of seedlings mortality.

• Journal of Korean Society of Forest Science
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• v.109 no.1
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• pp.1-22
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• 2020

In order to evaluate the vertical distribution and distributional pattern of vascular plants in the Samga district of Sobaeksan National Park, vascular plants were surveyed along a hiking trail from the Samga Tour Support Center to the top of a mountain. The elevation range was divided into 11 sections with 100 m intervals from 400 m to 1439 m above sea level.A total of 375 taxa were listed, comprising 92 families, 235 genera, 332 species, 3 subspecies, 37 varieties, and 3 forms. The pattern of species richness along the elevational gradient showed a reverse hump-shaped trend. The species distribution pattern was positively correlated with the soil exchangeable cations Ca²⁺ and Mg²⁺, soil pH, available phosphate, and the warmth index. Furthermore, slope, soil moisture content, and soil exchangeable cations were significantly correlated with species distribution. DCA grouped herb species into two groups. Stands of each section were sequentially arranged from 400 m to 1500 m along an altitudinal gradient. Soil moisture content, soil pH, soil K²⁺ and Na²⁺, available phosphate, and slope were significantly correlated with stand distribution. This study provides important data that could be useful for conservation and the sustainable use of biodiversity in the study area. In order to understand the ecological and environmental characteristics and distribution of plant species, it will be necessary to continuously develop relative studies with continuous monitoring.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6852-6859
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• 2015

The purpose of this study was to determine the growth characteristics and distribution pattern of a brackish water clam Corbicula japonica in Seomjin River. Field samples were taken from 14 stations with salinity gradients during spring. Salinity at the bottom layer ranged from 1.0 psu to 32.9 psu, with low salinities in the upper area of the river. In particular, salinity at St.11 was decreased drastically to be ca. 15.0 psu, indicating an intermediate salinity zone. The distribution pattern of C. japonica was related to the salinity gradient, with the highest densities of $2,102ind.m^{-2}$ at Station 13, followed by $1,507ind.m^{-2}$ at Station 11. Here, we focused on the growth characteristics of collected C. japonica collected at two stations with different salinity values. The relationship between shell length and total weight was highly correlated ($R^2=0.91$, P<0.001) at Station 13 compared to that at Station 11 ($R^2=0.72$, P<0.001). On the other hands, the degree of correlation between shell length and shell height (SH) or shell width (SW) at Station 11 (SH: $R^2=0.91$, P<0.001; SW: $R^2=0.69$, P<0.001) was higher than that at Station 13 (SH: $R^2=0.64$, P<0.001; SW: $R^2=0.48$, P<0.001). In addition, fatness index of C. japonica at Station 13 was significantly (P < 0.001) higher than that at St. 11 (t-test value=-22.8, p<0.001). This implies that C. japonica at Station 13 might have enhanced their somatic growth, whereas C. japonica at Station 11 might have this kind of defense mechanism their internal organization against the salinity stress. Ecologically, this kind of defense mechanism of C. japonica against salinity flucuation may play an important role in their survival strategy.