• Journal of the Korean Society for Marine Environment & Energy
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• v.12 no.3
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• pp.217-226
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• 2009

Marine geological storage of $CO_2$ is regarded as one of the most promising options to response climate change. Marine geological storage of $CO_2$ is to capture $CO_2$ from major point sources, to transport to the storage sites and to store $CO_2$ into the marine geological structure such as deep sea saline aquifer. Up to now, process design for this $CO_2$ marine geological storage has been carried out mainly on pure $CO_2$. Unfortunately the captured $CO_2$ mixture contains many impurities such as $N_2$, $O_2$, Ar, $H_2O$, $SO_x$, $H_2S$. A small amount of impurities can change the thermodynamic properties and then significantly affect the compression, purification and transport processes. In order to design a reliable $CO_2$ marine geological storage system, it is necessary to perform numerical process simulation using thermodynamic equation of state. The purpose of the present paper is to compare and analyse the relevant equations of state including PR, PRBM, RKS and SRK equation of state for $CO_2-N_2$ mixture. To evaluate the predictive accuracy of the equation of the state, we compared numerical calculation results with reference experimental data. In addition, optimum binary parameter to consider the interaction of $CO_2$ and $N_2$ molecules was suggested based on the mean absolute percent error. In conclusion, we suggest the most reliable equation of state and relevant binary parameter in designing the $CO_2-N_2$ mixture marine geological storage process.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.1
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• pp.123-134
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• 2014

Identification of methods to optimize the growth of a plant community, including the capacity of the soil to further sequester carbon, is important in urban design and planning. In this study, to construct and manage an urban park to mitigate carbon emissions, soil organic carbon of varying biomass, different park construction times, and a range of vegetation types were analyzed by measuring aboveground and belowground carbon in Seoseoul Lake Park and Yangjae Citizen's Forest. The urban parks were constructed during different periods; Seoseoul Lake Park was constructed in 2009, whereas Yangjae Citizen's Forest was constructed in 1986. To identify the differences in soil organic carbon in various plant communities and soil types, above and belowground carbon were measured based on biomass, as well as the physical and chemical features of the soil. Allometric equations were used to measure biomass. Soil total organic carbon (TOC) and chemical properties such as pH, cation exchange capacity (CEC), total nitrogen (TN), and soil microbes were analyzed. The analysis results show that the biomass of the Yangjae Citizen's Forest was higher than that of the Seoseoul Lake Park, indicating that older park has higher biomass. On the other hand, TOC was lower in the Yangjae Citizen's Forest than in the Seoseoul Lake Park; air pollution and acid rain probably changed the acidity of the soil in the Yangjae Citizen's Forest. Furthermore, TOC was higher in mono-layered plantation area compared to that in multi-layered plantation area. Improving the soil texture would, in the long term, result in better vegetation growth. To improve the soil texture of an urban park, park management, including pH control by using lime fertilization, soil compaction control, and leaving litter for soil nutrition is necessary.

• Journal of Korean Society of Forest Science
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• v.86 no.2
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• pp.241-250
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• 1997

The study was conducted to make a map for selecting righteous tree species for each site by digital terrain analysis. We set an algorithmic value for each tree species' characteristics with distribution pattern analysis, and the soil types were digitized from data indicated on soil map. Mean altitude, slope, aspect and micro-topography were estimated from the digital map for each block which had been calculated by regression equations with altitude. The results obtained from the study could be summarized as follows 1. We could develope a method to select righteous tree species for a given site with concern of soil, forest condition and topographic factors on Muju-Gun in Chonbuk province(2,500ha) by the terrain analysis and multi-variate digital map with a personal computer. 2. The brown forest soils were major soil types for the study area, and 29 tree species were occurred with Pinus densiflora as a dominant species. The differences in site condition and soil properties resulted in site quality differences for each tree species. 3. We tried to figure out the accuracy of a basic program(DTM.BAS) enterprised for this study with comparing the mean altitude and aspect calculated from the topographic terrain analysis map and those from surveyed data. The differences between the values were less than 5% which could be accepted as a statistically allowable value for altitude, as well as the values for aspect showed no differences between both the mean altitude and aspect. The result may indicate that the program can be used further in efficiency. 4. From the righteous-site selection map, the 2nd group(R, $B_1$) took the largest area with 46% followed by non-forest area (L) with 23%, the 5th group with 7% and the 4th group with 5%, respectively. The other groups occupied less than 6%. 5. We suggested four types of management tools by silvicultural tree species with considering soil type and topographic conditions.

• Magazine of the Korean Society of Agricultural Engineers
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• v.17 no.1
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• pp.3642-3654
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• 1975

The purpose of this thesis is to derive a unit hydrograph which may be applied to the ungaged watershed area from the relations between directly measurable unitgraph properties such as peak discharge(qp), time to peak discharge (Tp), and lag time (Lg) and watershed characteristics such as river length(L) from the given station to the upstream limits of the watershed area in km, river length from station to centroid of gravity of the watershed area in km (Lca), and main stream slope in meter per km (S). Other procedure based on routing a time-area diagram through catchment storage named Instantaneous Unit Hydrograph(IUH). Dimensionless unitgraph also analysed in brief. The basic data (1969 to 1973) used in these studies are 9 recording level gages and rating curves, 41 rain gages and pluviographs, and 40 observed unitgraphs through the 9 sub watersheds in Nak Oong River basin. The results summarized in these studies are as follows; 1. Time in hour from start of rise to peak rate (Tp) generally occured at the position of 0.3Tb (time base of hydrograph) with some indication of higher values for larger watershed. The base flow is comparelatively higher than the other small watershed area. 2. Te losses from rainfall were divided into initial loss and continuing loss. Initial loss may be defined as that portion of storm rainfall which is intercepted by vegetation, held in deppression storage or infiltrated at a high rate early in the storm and continuing loss is defined as the loss which continues at a constant rate throughout the duration of the storm after the initial loss has been satisfied. Tis continuing loss approximates the nearly constant rate of infiltration (${\Phi}$-index method). The loss rate from this analysis was estimated 50 Per cent to the rainfall excess approximately during the surface runoff occured. 3. Stream slope seems approximate, as is usual, to consider the mainstreamonly, not giving any specific consideration to tributary. It is desirable to develop a single measure of slope that is representative of the who1e stream. The mean slope of channel increment in 1 meter per 200 meters and 1 meter per 1400 meters were defined at Gazang and Jindong respectively. It is considered that the slopes are low slightly in the light of other river studies. Flood concentration rate might slightly be low in the Nak Dong river basin. 4. It found that the watershed lag (Lg, hrs) could be expressed by Lg=0.253 (L.Lca)0.4171 The product L.Lca is a measure of the size and shape of the watershed. For the logarithms, the correlation coefficient for Lg was 0.97 which defined that Lg is closely related with the watershed characteristics, L and Lca. 5. Expression for basin might be expected to take form containing theslope as {{{{ { L}_{g }=0.545 {( { L. { L}_{ca } } over { SQRT {s} } ) }^{0.346 } }}}} For the logarithms, the correlation coefficient for Lg was 0.97 which defined that Lg is closely related with the basin characteristics too. It should be needed to take care of analysis which relating to the mean slopes 6. Peak discharge per unit area of unitgraph for standard duration tr, ㎥/sec/$\textrm{km}^2$, was given by qp=10-0.52-0.0184Lg with a indication of lower values for watershed contrary to the higher lag time. For the logarithms, the correlation coefficient qp was 0.998 which defined high sign ificance. The peak discharge of the unitgraph for an area could therefore be expected to take the from Qp=qp. A(㎥/sec). 7. Using the unitgraph parameter Lg, the base length of the unitgraph, in days, was adopted as {{{{ {T}_{b } =0.73+2.073( { { L}_{g } } over {24 } )}}}} with high significant correlation coefficient, 0.92. The constant of the above equation are fixed by the procedure used to separate base flow from direct runoff. 8. The width W75 of the unitgraph at discharge equal to 75 per cent of the peak discharge, in hours and the width W50 at discharge equal to 50 Per cent of the peak discharge in hours, can be estimated from {{{{ { W}_{75 }= { 1.61} over { { q}_{b } ^{1.05 } } }}}} and {{{{ { W}_{50 }= { 2.5} over { { q}_{b } ^{1.05 } } }}}} respectively. This provides supplementary guide for sketching the unitgraph. 9. Above equations define the three factors necessary to construct the unitgraph for duration tr. For the duration tR, the lag is LgR=Lg+0.2(tR-tr) and this modified lag, LgRis used in qp and Tb It the tr happens to be equal to or close to tR, further assume qpR=qp. 10. Triangular hydrograph is a dimensionless unitgraph prepared from the 40 unitgraphs. The equation is shown as {{{{ { q}_{p } = { K.A.Q} over { { T}_{p } } }}}} or {{{{ { q}_{p } = { 0.21A.Q} over { { T}_{p } } }}}} The constant 0.21 is defined to Nak Dong River basin. 11. The base length of the time-area diagram for the IUH routing is {{{{C=0.9 {( { L. { L}_{ca } } over { SQRT { s} } ) }^{1/3 } }}}}. Correlation coefficient for C was 0.983 which defined a high significance. The base length of the T-AD was set to equal the time from the midpoint of rain fall excess to the point of contraflexure. The constant K, derived in this studies is K=8.32+0.0213 {{{{ { L} over { SQRT { s} } }}}} with correlation coefficient, 0.964. 12. In the light of the results analysed in these studies, average errors in the peak discharge of the Synthetic unitgraph, Triangular unitgraph, and IUH were estimated as 2.2, 7.7 and 6.4 per cent respectively to the peak of observed average unitgraph. Each ordinate of the Synthetic unitgraph was approached closely to the observed one.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.