• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.

• Journal of the Korean Geotechnical Society
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• v.25 no.12
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• pp.87-105
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• 2009

Nonlinear dynamic deformation characteristics, expressed in terms of normalized shear modulus reduction curve (G/$G_{max}-\log\gamma$, G/$G_{max}$ curve) and damping curve (D-$\log\gamma$), are important input parameters with shear wave velocity profile ($V_s$-profile) in the seismic analysis of (new or existing) fill dam. In this paper, the reasonable and economical methods to evaluate the nonlinear dynamic deformation characteristics for core zone and rockfill zone respectively are presented. For the core zone, 111 G/$G_{max}$ curves and 98 damping curves which meet the requirements of core material were compiled and representative curves and ranges were proposed for the three ranges of confining pressure (0~100 kPa, 100 kPa~200 kPa, more than 200 kPa). The reliability of the proposed curves for the core zone were verified by comparing with the resonant column test results of two kinds of core materials. For the rockfill zone, 135 G/$G_{max}$ curves and 65 damping curves were compiled from the test results of gravelly materials using large scale testing equipments. The representative curves and ranges for G/$G_{max}$ were proposed for the three ranges of confining pressure (0~50 kPa, 50 kPa~100 kPa, more than 100 kPa) and those for damping were proposed independently of confining pressure. The reliability of the proposed curves for the rockfill zone were verified by comparing with the large scale triaxial test results of rockfill materials in the B-dam which is being constructed.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.4
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• pp.327-351
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• 2009

Laboratory tests are performed in this paper to investigate the brittle failure characteristics of over-stressed rocks taken in deep depth. Also, numerical simulation performed using that the so-called CWFS(Cohesion Weakening Frictional Strengthening) model is known to predict brittle failure phenomenon reasonably well. The most typical rock types of Korean peninsula - granite and gneiss - were used for testing. Results of uniaxial compression tests showed that the crack initiation stress was about 41 % to 42% of the uniaxial compressive strength regardless of rock types, where as, the crack damage stress of granite was about 75%, and that of gneiss was about 97%. Through the damage-controlled test, strength parameters of each rock were obtained as a function of damage degree. After the peak, the crack damage stress and the maximum stress were decreased, The cohesion was decreased and the friction angle was increased with increase of rock damage. Before reaching the peak, the elastic modulus was slightly increased, while decreased after the peak. Poisson's ratio was increased as the damage of rock proceeds. Comparison of uniaxial compression tests and damage-controlled tests shows the crack initiation stress estimated from the damage-controlled test fluctuated within the range of crack initiation stress obtained from the uniaxial compression test; the crack damage stress was less than that estimated from the uniaxial compression test. In order to predict the critical depth that brittle failure occurs, numerical simulations using the CWFS model were performed for an example site. Material parameters obtained from the laboratory tests mentioned above were used for CWFS simulation. Comparison between the critical depth predicted from the numerical simulation using the CWFS model and that predicted by using the damage index proposed by Martin et al.(l999), showed that critical depth cannot be reasonably predicted by the currently used damage index except for circular tunnels. A modified damage index was proposed by the author which takes the shape of tunnels other than circular into account.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4B
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• pp.337-346
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• 2010

Climate change has a huge impact on various parts of the world. This study quantified and analyzed the effects on hydrological behavior caused by climate, vegetation canopy and land use change of Soyanggang dam watershed (2,694.4 $km^2$) using the semi-distributed model SWAT (Soil Water Assessment Tool). For the 1997-2006 daily dam inflow data, the model was calibrated with the Nash-Sutcliffe model efficiencies between the range of 0.45 and 0.91. For the future climate change projection, three GCMs of MIROC3.2hires, ECHAM5-OM, and HadCM3 were used. The A2, A1B and B1 emission scenarios of IPCC (Intergovernmental Panel on Climate Change) were adopted. The data was corrected for each bias and downscaled by Change Factor (CF) method using 30 years (1977-2006, baseline period) weather data and 20C3M (20th Century Climate Coupled Model). Three periods of data; 2010-2039 (2020s), 2040-2069 (2050s), 2070-2099 (2080s) were prepared for future evaluation. The future annual temperature and precipitation were predicted to change from +2.0 to $+6.3^{\circ}C$ and from -20.4 to 32.3% respectively. Seasonal temperature change increased in all scenarios except for winter period of HadCM3. The precipitation of winter and spring increased while it decreased for summer and fall for all GCMs. Future land use and vegetation canopy condition were predicted by CA-Markov technique and MODIS LAI versus temperature regression respectively. The future hydrological evaluation showed that the annual evapotranspiration increases up to 30.1%, and the groundwater recharge and soil moisture decreases up to 55.4% and 32.4% respectively compared to 2000 condition. Dam inflow was predicted to change from -38.6 to 29.5%. For all scenarios, the fall dam inflow, soil moisture and groundwater recharge were predicted to decrease. The seasonal vapotranspiration was predicted to increase up to 64.2% for all seasons except for HadCM3 winter.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1B
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• pp.11-22
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• 2009

Long-term rainfall-runoff modeling is a key element in the Earth's hydrological cycle, and associated with many different aspects such as dam design, drought management, river management flow, reservoir management for water supply, water right permission or coordinate, water quality prediction. In this regard, hydrologists have used the hydrologic models for design criteria, water resources assessment, planning and management as a main tool. Most of rainfall-runoff studies, however, were not carefully performed in terms of considering reservoir effects. In particular, the downstream where is severely affected by reservoir was poorly dealt in modeling rainfall-runoff process. Moreover, the effects can considerably affect overall the rainfallrunoff process. An objective of this study, thus, is to evaluate the impact of reservoir operation on rainfall-runoff process. The proposed approach is applied to Anseong watershed, where is in a mixed rural/urban setting of the area and in Korea, and has been experienced by flood damage due to heavy rainfall. It has been greatly paid attention to the agricultural reservoirs in terms of flood protection in Korea. To further investigate the reservoir effects, a comprehensive assessment for the results are discussed. Results of simulations that included reservoir in the model showed the effect of storage appeared in spring and autumn when rainfall was not concentrated. In periods of heavy rainfall, however, downstream runoff increased in simulations that do not consider reservoir factor. Flow duration curve showed that changes in streamflow depending upon the presence or absence of reservoir factor were particularly noticeable in ninety-five day flow and low flow.

• 재활복지
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• v.14 no.3
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• pp.225-256
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• 2010

This study aims to provide the basic data of the rehabilitation program for the schoolchild with intellectual disability by designing new framework of the features of postural control for the schoolchild with intellectual disability. For this, the study investigated what sensations the schoolchild are using to maintain posture by selectively or synthetically applying vision, vestibular sensation and somato-sensation, and how the coordinative sensory system of the schoolchild is responding to any sway referenced sensory stimulus. The study intended to prove the limitation of motor system in estimating the postural stability by providing the cognitive motor task, and provided the features of postural control of the schoolchild with intellectual disability by measuring the onset times and orders of muscle contraction of neuron-muscle when there is a postural control taking place due to the exterior disturbance. Furthermore, by comparatively analyzing the difference between the normal schoolchild and the intellectually disabled schoolchild, this study provided an optimal direction for treatment planning when the rehabilitation program is applied in the postural control ability training program for the schoolchild with intellectual disability. Taking gender and age into consideration, 52 schoolchild including 26 normal schoolchild and 26 intellectually disabled schoolchild were selected. To measure the features of postural control, CTSIB test, and postural control strategy test were conducted. The result of experiment is as followed. First, the schoolchild with intellectual disability showed different feature in using sensory system to control posture. The normal schoolchild tended to depend on somato-sensory or vision, and showed a stable postural control toward a sway referenced stimulus on somato-sensory system. The schoolchild with intellectual disability tended to use somato-sensory or vision, and showed a very instable postural control toward a sway referenced vision or a sway referenced stimulus on somato-sensory system. In sensory analysis, the schoolchild with intellectual disability showed lower level of proficiency in somato-sensation percentile, vision percentile and vestibular sensation percentile compare to the normal schoolchild. Second, as for the onset times and orders of muscle contraction for strategies of postural control when there is an exterior physical stimulus, the schoolchild with intellectual disability showed a relatively delayed onset time of muscle control, and it was specially greater when the perturbation is from backward. As for the onset orders of muscle contraction, it started from muscles near coax then moved to the muscles near ankle joint, and the numbers and kinds of muscles involved were greater than the normal schoolchild. The normal schoolchild showed a fast muscle contracting reaction from every direction after the perturbation stimulus, and the contraction started from the muscles near the ankle joint and expanded to the muscles near coax. From the results of the experiments, the special feature of the postural control of the schoolchild with intellectual disability is that they have a higher dependence on vision in sensory system, and there was no appropriate integration of swayed sensation observed in upper level of central nerve system. In the motor system, the onset time of muscle contraction for postural control was delayed, and it proceeded in reversed order of the normal schoolchild. Therefore, when use the clinical physical therapy to improve the postural control ability, various sensations should be provided and should train the schoolchild to efficiently use the provided sensations and use the sensory experience recorded in upper level of central nerve system to improve postural control ability. At the same time, a treatment program that can improve the processing ability of central nerve system through meaningful activities with organizing and planning adapting reaction should be provided. Also, a proprioceptive motor control training program that can induce faster muscle contraction reaction and more efficient onset orders from muscularskeletal system is need to be provided as well.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.337-345
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• 2023

Estimating the rice heading date is one of the most crucial agricultural tasks related to productivity. However, due to abnormal climates around the world, it is becoming increasingly challenging to estimate the rice heading date. Therefore, a more objective classification method for estimating the rice heading date is needed than the existing methods. This study, we aimed to classify the rice heading stage from various images using a CNN classification model. We collected top-view images taken from a drone and a phenotyping tower, as well as slanted-view images captured with a RGB camera. The collected images underwent preprocessing to prepare them as input data for the CNN model. The CNN architectures employed were ResNet50, InceptionV3, and VGG19, which are commonly used in image classification models. The accuracy of the models all showed an accuracy of 0.98 or higher regardless of each architecture and type of image. We also used Grad-CAM to visually check which features of the image the model looked at and classified. Then verified our model accurately measure the rice heading date in paddy fields. The rice heading date was estimated to be approximately one day apart on average in the four paddy fields. This method suggests that the water head can be estimated automatically and quantitatively when estimating the rice heading date from various paddy field monitoring images.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.655-667
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• 2023

Algal bloom outbreaks are frequently reported around the world, and serious water pollution problems arise every year in Korea. It is necessary to protect the aquatic ecosystem through continuous management and rapid response. Many studies using satellite images are being conducted to estimate the concentration of chlorophyll-a (Chl-a), an indicator of algal bloom occurrence. However, machine learning models have recently been used because it is difficult to accurately calculate Chl-a due to the spectral characteristics and atmospheric correction errors that change depending on the water system. It is necessary to consider the factors affecting algal bloom as well as the satellite spectral index. Therefore, this study constructed a dataset by considering water quality, hydrological and meteorological factors, and sentinel-2 images in combination. Representative ensemble models random forest and extreme gradient boosting (XGBoost) were used to predict the concentration of Chl-a in eight weirs located on the Nakdong river over the past five years. R-squared score (R²), root mean square errors (RMSE), and mean absolute errors (MAE) were used as model evaluation indicators, and it was confirmed that R² of XGBoost was 0.80, RMSE was 6.612, and MAE was 4.457. Shapley additive expansion analysis showed that water quality factors, suspended solids, biochemical oxygen demand, dissolved oxygen, and the band ratio using red edge bands were of high importance in both models. Various input data were confirmed to help improve model performance, and it seems that it can be applied to domestic and international algal bloom detection.