• Korean Journal of Agricultural Science
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• v.51 no.1
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• pp.79-86
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• 2024

As the frequency and intensity of heavy rains increase, the vulnerability of agriculture to disasters also increases. Consequently, there is a need to improve flood and inundation predictions. To enhance the accuracy of inundation predictions, it is essential to monitor water level and discharge data within agricultural areas. This study was conducted to monitor water levels and rainfall in the Cheongju Sindae area from 2022 to 2023, and the data was utilized as input and validation data for agricultural inundation modeling. Four irrigation drainage canals were installed to a square-shaped concrete structure where the water level gauge is. It was then confirmed that the water level rises with rainfall. The flow velocities were monitored during periods of heavy rainfall. The rating curve, which estimates water level and flow velocity based on observations, was estimated using the software K-HQ. The resulting curve was presented with the Coefficient of Determination (R²). K-HQ was also used to calculate the equation for the rating curve, taking outliers into account at each data point. Outliers were extracted and the rating curve was recalculated. As the coefficient of determination of three out of four stations exceeded 0.95, the estimated rating curve may be considered reliable for discharge estimation. This study provides critical data for enhancing agricultural inundation modeling accuracy and drainage improvement projects.

• Journal of IKEEE
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• v.28 no.3
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• pp.352-359
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• 2024

This paper proposes a CNN-ViT hybrid model that automatically evaluates the aesthetic quality of images by combining local and global features. In this approach, CNN is used to extract local features such as color and object placement, while ViT is employed to analyze the aesthetic value of the image by reflecting global features. Color composition is derived by extracting the primary colors from the input image, creating a color palette, and then passing it through the CNN. The Rule of Thirds is quantified by calculating how closely objects in the image are positioned near the thirds intersection points. These values provide the model with critical information about the color balance and spatial harmony of the image. The model then analyzes the relationship between these factors to predict scores that align closely with human judgment. Experimental results on the AADB image database show that the proposed model achieved a Spearman's Rank Correlation Coefficient (SRCC) of 0.716, indicating more consistent rank predictions, and a Pearson Correlation Coefficient (LCC) of 0.72, which is 2~4% higher than existing models.

• Tuberculosis and Respiratory Diseases
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• v.51 no.6
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• pp.517-529
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• 2001

Background : Surfactant protein C(SP-C) is a hydrophobic 5,000 dalton molecule. SP-C has the primary roles in accelerating surface spreading of a surfactant phospholipid. The filter hybridization and solution hybridization assays are both rapid and sensitive and can be used to measure the RNAs complementary to any cloned DNA sequence. Methods : The authors measured the SP-C mRNA levels quantitatively using solution hybridization and filter hybridization assays to obtain a standard curve equation to quantify the mRNA of unknown samples comparatively. Results : 1. The minimum level of the specimens by solution hybridization was 3 pg for SP-C mRNA. 2. The standard curve equation of the solution hybridization assay between the counts per minute(Y) and the SP-C mRNA transcript input(X) was Y=6.46 X+244. The correlation coefficient was 0.99. 3. The minimum detection level of specimens by filter hybridization was 0.1 ng for SP-C mRNA. 4. The standard curve equation of the filter hybridization assay between the counts per minute(Y) and SP-C mRNA transcript input(X) is Y=2541.6 X+252.7. The correlation coefficient was 0.99. Conclusions : A comparison of CPM/filter in the linear range allowed an accurate and reproducible estimation of the SP-C mRNA copy number. Filter hybridization and solution hybridization assays are both rapid and sensitive and can be used to measure the RNAs complementary to any cloned DNA sequence. It is ideally suited to situations where accurate quantitation of multiple samples is required.

• Journal of Korean Society of Forest Science
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• v.106 no.2
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• pp.219-229
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• 2017

Forest road is an essential infrastructure for forest management such as the composition and management of forest resources, timber and forest byproduct production & transportation. It has recently been utilized forest recreation and forest sports as well as also forest pest control, forest fire prevention and evolution. When you build a forest road, the economic function in the forest is activated, so that it can result in the ripple effect of induced employment, value-added creation and production inducement. The purpose of this study is to analyze the impact caused by forest road construction occurring as the overall economy. For analysis it was applied to inter industry analysis method that is a method for analyzing the quantitative cross-correlation. The data were used in the Input-Output Tables In 2014, the Bank of Korea. When you build a forest road, economic effect due to the construction of the forest road is generated and economic effects are also generated due to the increase in the production of forest products after the construction of the forest road. Therefore, we will analyze the economic impact of the two effects. The estimated economic value of forest products, which is the economic effect of forest product cultivation, was calculated through some assumptions and the economic ripple effect was analyzed. The forest road construction sector is defined as land clearing and reclamation, and irrigation project construction and the forestry forest products sector is defined as the sum of raw timber, edible forest products and misc. forest products. In total, 32 sectors were classified, and except for the two sectors defined as forest road construction and forestry forest products, the remaining sectors were integrated according to the classification system of 30 integrated classifications of the Bank of Korea. As a result, the production inducement coefficient for forest construction was analyzed to be 2.767 and the production inducement coefficient for forestry forest products was analyzed to be 1.565. This means that 2,767 times the production of forest road construction investment is induced in the whole industry and the production of 1.562 times the amount of forestry forest products is caused by the whole industry as the production of forestry forest products increases. The value added inducement coefficient for forest road construction was 0.977 and the value added inducement coefficient for forestry forest products was 0.985. Forest road are essential infrastructure for forestry development and should be continuously invested because they are essential elements of timber production and forest byproduct production with functions such as forest management, forest recreation, forest sports, and town connection.

• Journal of the Korean Geotechnical Society
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• v.30 no.5
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• pp.47-54
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• 2014

Although shear wave velocity ($V_s$) is an important design factor in seismic design, the measurement is not usually made in typical field investigation due to time and economic limitations. In the present study, an investigation was made to predict sand $V_s$ based on the standard penetration test (SPT) results by using artificial neural network (ANN) model. A total of 650 dataset composed of SPT-N value ($N_{60}$), water content, fine content, specific gravity for input data and $V_s$ for output data was used to build and train the ANN model. The sensitivity analysis was then performed for the trained ANN to examine the effect of the input variables on the $V_s$. Also, the ANN model was compared with seven existing empirical models on the performance. The sensitivity analysis results revealed that the effect of the SPT-N value on $V_s$ is significantly greater compared to other input variables. Also, when compared with the empirical models using Nash-Sutcliffe Model Efficiency Coefficient (NSE) and Root Mean Square Error (RMSE), the ANN model was found to exhibit the highest prediction capability.

• The Journal of Engineering Geology
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• v.23 no.2
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• pp.137-147
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• 2013

For the effective management of groundwater resources, it is necessary to predict groundwater level fluctuations in response to rainfall events. In the present study, time series models using artificial neural networks (ANNs) and support vector machines (SVMs) have been developed and applied to groundwater level data from the Gasan, Shingwang, and Cheongseong stations of the National Groundwater Monitoring Network. We designed four types of model according to input structure and compared their performances. The results show that the rainfall input model is not effective, especially for the prediction of groundwater recession behavior; however, the rainfall-groundwater input model is effective for the entire prediction stage, yielding a high model accuracy. Recursive prediction models were also effective, yielding correlation coefficients of 0.75-0.95 with observed values. The prediction errors were highest for Shingwang station, where the cross-correlation coefficient is lowest among the stations. Overall, the model performance of SVM models was slightly higher than that of ANN models for all cases. Assessment of the model parameter uncertainty of the recursive prediction models, using the ratio of errors in the validation stage to that in the calibration stage, showed that the range of the ratio is much narrower for the SVM models than for the ANN models, which implies that the SVM models are more stable and effective for the present case studies.

• Journal of Korea Port Economic Association
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• v.24 no.2
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• pp.193-208
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• 2008

This study aims to analyse the ripple effect of Physical distribution service industry on national economy using input-output tables and present the results as data for political plans in this field. For the analysis, it uses input-output tables developed and published by Sank of Korea in 1998, 2000, and 2003. To sum up the results, production inducement effects are 1757 for railroad transport, 1688 for road transport and 1617 for loading. Import inducement effects of assistant services, loading, storage, warehouse and other transport-related services are low while the effects of water and air transport are high as follows: 0.679 and 0.558 respectively. Then, added-value inducement effects are presented as follows: 0.841 for railway transport, 0.828 for road transport, 0.962 for transport assistant service, 0.939 for loading, 0.938 for storage and warehouse, and 0.942 for other transport-related services. Sensitivity dispersion index of road transport is high while that of water transport, storage and warehouse is low. And influence coefficient of railway and road transport is high while that of water and air transport is low. In respect to the employment structure of Physical distribution service industry, 744,000 are employed for road transport industry, which is the largest number, 19,000 for air transport and 20,000 for assistant services, which is the least number.

• Journal of Advanced Navigation Technology
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• v.20 no.4
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• pp.344-351
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• 2016

In the present study, a dual-band multiple-input-multiple-output (MIMO) antenna covering WLAN frequency bands of 2.4 GHz (2.4 ~ 2.484 GHz) and 5 GHz (5.15 ~ 5.825 GHz) is newly presented to avoid use of decoupling structure for increasing isolation. The antenna consists of two L-shaped slots with n-shaped slots etched on the floating ground plane surrounded by open ended L-shaped slots which are placed in the left and right corner of PCB respectively. The proposed antenna is designed and fabricated on one side of FR4 substrate with dielectric constant of 4.3, thickness of 1.6 mm, and size of $50{\times}50mm2$. It has been observed that the measured impedance bandwidths ($S_{11}{\leq}-10dB$) are 0.3 GHz (2.28 ~ 2.58 GHz) in 2.4 GHz frequency band and 0.89 GHz (5.11 ~ 6 GHz) in 5 GHz frequency band respectively. In addition, It has been observed that the whole efficiency are more than 80 % in the whole operating frequency band and envelope correlation coefficient of the antenna is less than 0.05 as a very small value in spite of nothing of the decoupling structure.

• Ecology and Resilient Infrastructure
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• v.7 no.2
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• pp.114-125
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• 2020

Despite the various artificial neural networks that have been developed, most of the discharge models in previous studies have been developed using deep neural networks. This study aimed to develop a discharge model using a convolution neural network (CNN), which was used to solve classification problems. Furthermore, the applicability of CNN was evaluated. The photographs (pictures or images) for input data to CNN could not clearly show the characteristics of the study area as well as precipitation. Hence, the model employed in this study had to use numerical images. To solve the problem, the CN of NRCS was used to generate images as input data for the model. The generated images showed a good possibility of applicability as input data. Moreover, a new application of CN, which had been used only for discharge prediction, was proposed in this study. As a result of CNN training, the model was trained and generalized stably. Comparison between the actual and predicted values had an R² of 0.79, which was relatively high. The model showed good performance in terms of the Pearson correlation coefficient (0.84), the Nash-Sutcliffe efficiency (NSE) (0.63), and the root mean square error (24.54 ㎥/s).

• Journal of Korean Society of Environmental Engineers
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• v.22 no.4
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• pp.785-796
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• 2000

In this study, the possibility of application of TUNVEN model was investigated through the validation and calibration processes. In order to validate and calibrate the TUNVEN model developed in USA to obtain prediction of the quasi-steady state longitudinal air velocities and the pollutants concentrations by solving the coupled one-dimensional steady state tunnel aerodynamic and advection equations. The major input parameters such as the concentration data for CO and $NO_x$, meteorological data and traffic volume in Hawngryung tunnel were measured. Prior to preparing the input parameters, the sensitivity analysis was conducted to identify the input parameters which need to be most accurately estimated in TUNVEN program. In order to establish the relationships between the model values and the measured values, the linear regression analysis was applied. In linear regression analysis, the model values were taken as independent parameter(X) and the measured values were taken as dependent parameter(Y) for four cases of data sef. From the results of linear regression analysis, the correlation coefficient(r) for four cases were calculated more than 0.91 and the values of slope and interception were analyzed as 0.5~2.2 and 0.01~2.3 respectively. From the above results, we concluded that the suitability of TUNVEN model was identified in prediction the longitudinal pollutant concentrations in tunnel.