• The Journal of the Petrological Society of Korea
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• v.24 no.3
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• pp.165-180
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• 2015

Jurassic granite from Geochang was analysed with respect to the characteristics of the rock cleavage. The phases of distribution of microcracks were well evidenced from the enlarged photomicrographs(${\times}6.7$) of the thin section. In this study, the length - cumulative frequency diagrams were used for expressing the distribution characteristics of microcrack. The diagrams for the six directions were arranged in the magnitude of density(${\rho}$). These diagrams show an order of H2 < H1 < G2 < G1 < R2 < R1 from the related chart. Among six diagrams, the diagram for hardway 2(H2) occupies the lowermost region on the left. On the contrary, the diagram for rift 1(R1) occupies the uppermost region on the right. Curve patterns of the two diagrams change from uniform to exponential distribution type in accordance with the increased density. The overall distribution characteristics of the diagrams were well evidenced from the magnitude of the exponent(${\lambda}$) and length of line oa related to the exponential straight line. The magnitude of exponent governing the values of slope(${\theta}$) is inversely proportional to the values of microcrack parameters such as number(N), length(L) and density. On the contrary, length of line oa is directly proportional to the values of the above three parameters. Above microcrack parameters related to the order of arrangement of diagrams show an order of hardway(H1 + H2) < grain(G1 + G2) < rift(R1 + R2). The distribution characteristics of progressive variation are found among the six diagrams. The order of arrangement of the diagrams indicates a relative magnitude of the rock cleavage. Meanwhile, the parameters such as slope, exponent, density and length of line oa were arranged in an order of H2 < H1 < G2 < G1 < R2 < R1. The variation curves of a smooth quadratic function are shown from the related chart. From the correlation chart between density and the above parameters, a common regularity following power-law correlation function was derived. Finally, the analysis for the rock cleavage was conducted through the combination between the diagram and microcrack parameter. This type of combination contribute to the progressivity in evaluation for the rock cleavage.

• Korean journal of applied entomology
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• v.46 no.2
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• pp.213-219
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• 2007

The development of Schizaphis graminum (Rondani) was studied at various constant temperatures ranging from 15 to $32.5^{\circ}C$, with $65{\pm}5%$ RH, and a photoperiod of 16L:8D. Mortality of the $1_{st}-2_{nd}\;and\;the\;3_{rd}-4_{th}$ stage nymphs were similar at most temperature ranges while at high temperature of $32.5^{\circ}C$, more $3_{rd}-4_{th}$ stage individuals died. The total developmental time ranged from 13.8 days at $15^{\circ}C$ to 4.9 days at $30.0^{\circ}C$ suggesting that the higher the temperature, the faster the development. However, at higher end temperature of $32.5^{\circ}C$ the development took 6.4 days. The lower developmental threshold temperature and effective accumulative temperatures for the total immature stage were $6.8^{\circ}C$ and 105.9 day-degrees, respectively and the nonlinear shape of temperature related development was well described by the modified Sharpe and DeMichele model. The normalized cumulative frequency distributions of developmental period for each life stage were fitted to the three-parameter Weibull function. The attendance of shortened developmental times was apparent with $1_{st}-2_{nd}\;nymph,\;3_{rd}-4_{th}$ nymph, and total nymph stages in descending order. The coefficient of determination $r^2$ ranged between 0.80 and 0.87.

• Progress in Medical Physics
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• v.25 no.4
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• pp.271-280
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• 2014

Recently, digital breast tomosynthesis (DBT) has been investigated to overcome the limitation of conventional mammography for overlapping anatomical structures and high patient dose with cone-beam computed tomography (CBCT). However incomplete sampling due to limited angle leads to interference on the neighboring slices. Many studies have investigated to reduce artifacts such as interference. Moreover, appropriate filters for tomosynthesis have been researched to solve artifacts resulted from incomplete sampling. The primary purpose of this study is finding appropriate filter scheme with FBP reconstruction for DBT system to reduce artifacts. In this study, we investigated characteristics of various filter schemes with simulation and prototype digital breast tomosynthesis under same acquisition parameters and conditions. We evaluated artifacts and noise with profiles and COV (coefficinet of variation) to study characteristic of filter. As a result, the noise with parameter 0.25 of Spectral filter reduced by 10% in comparison to that with only Ramp-lak filter. Because unbalance of information reduced with decreasing B of Slice thickness filter, artifacts caused by incomplete sampling reduced. In conclusion, we confirmed basic characteristics of filter operations and improvement of image quality by appropriate filter scheme. The results of this study can be utilized as base in research and development of DBT system by providing information that is about noise and artifacts depend on various filter schemes.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.73-82
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• 2019

Recently, as the occurrence frequency of sudden floods due to climate change increased, the flood damage on riverside social infrastructures was extended so that there has been a threat of overflow. Therefore, a rapid prediction of potential flooding in riverside social infrastructure is necessary for administrators. However, most current flood forecasting models including hydraulic model have limitations which are the high accuracy of numerical results but longer simulation time. To alleviate such limitation, data driven models using artificial neural network have been widely used. However, there is a limitation that the existing models can not consider the time-series parameters. In this study the water surface elevation of the Hangang River bridge was predicted using the NARX model considering the time-series parameter. And the results of the ANN and RNN models are compared with the NARX model to determine the suitability of NARX model. Using the 10-year hydrological data from 2009 to 2018, 70% of the hydrological data were used for learning and 15% was used for testing and evaluation respectively. As a result of predicting the water surface elevation after 3 hours from the Hangang River bridge in 2018, the ANN, RNN and NARX models for RMSE were 0.20 m, 0.11 m, and 0.09 m, respectively, and 0.12 m, 0.06 m, and 0.05 m for MAE, and 1.56 m, 0.55 m and 0.10 m for peak errors respectively. By analyzing the error of the prediction results considering the time-series parameters, the NARX model is most suitable for predicting water surface elevation. This is because the NARX model can learn the trend of the time series data and also can derive the accurate prediction value even in the high water surface elevation prediction by using the hyperbolic tangent and Rectified Linear Unit function as an activation function. However, the NARX model has a limit to generate a vanishing gradient as the sequence length becomes longer. In the future, the accuracy of the water surface elevation prediction will be examined by using the LSTM model.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Korean Journal of Environment and Ecology
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• v.37 no.1
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• pp.86-93
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• 2023

This study investigated the ecological characteristics of Odontobutis interrupta at the Geumdang Stream from January to December 2021. The riverbed structure of the species habitat was rich in sand and mud. The water was deep, ranging from 21 to 124 cm, with an average of 48 cm. The stream velocity was slow at 0.24 (0.08-0.36) m/sec. The ratio of females to males was 1:0.98, and the total length of collected individuals ranged from 23 mm to 162 mm. The age according to the total length-frequency distribution as of May indicated that the group with a total length of 23-59 mm was one year old, the group with 60-99 mm was two years old, the group with 100-139 mm was three years old, and the group with 140-162 mm was four years old. As a secondary gender characteristic, the females genital papilla had a cylindrical shape, a hollow inside of the tip, and a longer diameter than males. The males had a cone shape with a pointed end. Sexually mature males had the nuptial color, with a black abdomen and whole body. Some females with a length ranging from 60 to 69 mm and all females 70 mm longer were sexually mature. Some males with a length ranging from 70 to 79 mm and all males 80 mm longer were sexually mature. The spawning season was from May to July, and the water temperature was between 19.6℃ to 29℃ during that period. The prosperous spawning season was June (26℃). The average number of eggs in the ovaries was 2,473 (883-4,955) per matured female, and the matured eggs were yellowish and spherical with a mean diameter of 1.42 (1.20-0.54) mm. The correlation between total length and weight was BW=0.0000006TL^3.21 with the constant a as 0.0000006 and parameter b as 3.21. The condition factor (K) was 1.67 (1.18-2.43) on average, and the slope was 0.116.

• Korean Journal of Environment and Ecology
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• v.38 no.2
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• pp.148-155
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• 2024

This study investigated the ecological characteristics of Odontobutis platycephala at Jaho stream from January to December 2022. The riverbed structure of the species' habitat was rich in cobble and pebble. The water was deep, ranging from 22 to 153 cm, with an average of 64 cm, and the stream velocity was rapid at 0.89 (0.42-1.46) m/sec. The ratio of females to males was 1:1.02, and the total length of collected individuals ranged from 38 to 156 mm. The age according to the total length frequency distribution as of May indicated that the group with a total length of 38-69 mm was one year old, the group with 60-99 mm was two years old, the group with 100-139 mm was three years old, and the group 140-156 mm was four years or older. As a secondary gender characteristic, the genital papilla was cylindrical in females and cone-shaped with a pointed tip in males. Some females with a length ranging from 60 to 69 mm and all females 70 mm or longer were sexually mature. Some males with a length ranging from 70 to 79 mm and all males 80 mm or longer were sexually mature. The spawning season was from May to July, and the water temperature was between 17 ℃ and 28 ℃ during that period. The prosperous spawning season was June (24 ℃). The average number of eggs in the ovaries was 988 (284-2,722) per mature female, and the mature eggs were yellowish and spherical with a mean diameter of 1.46 (1.19-1.71) mm. The correlation between total length and body weight is BW=0.00000006TL^3.12 with the constant a as 0.00000006 and the parameter b as 3.12. The mean condition factor (K) was 1.44 (0.96-2.26), and the slope was negative at -0.0007

• Korean Journal of Mineralogy and Petrology
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• v.33 no.2
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• pp.99-114
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• 2020

The characteristics of the Brazilian tensile strengths(σ_t) parallel to the rock cleavages in Jurassic granite from Geochang were analysed. The evaluation for the six directions of rock cleavages was performed using the parameter values on microcrack length and the above strength. The strength values of the five test specimens belonging to each direction were classified into five groups. The strength values of these five groups increase in order of group A < B < C < D < E. The close dependence between the above microcrack and strength was derived. The analysis results of this study are summarized as follows. First, the chart showing the variation and characteristics of strength among the three rock cleavages were made. In the above chart, the strength values of six directions belonging to each group were arranged in order of rift(R1 and R2), grain(G1 and G2) and hardway(H1 and H2). The strength distribution lines of the five groups concentrate in the direction of R1. And the widths among the above five lines indicating strength difference(Δσ_t) are the most narrowest in R1 direction. From the related chart, the variation characteristics among the two directions forming each rock cleavage were derived. G2(2)-test specimen shows higher value and lower value of the difference in strength compared to the case of G1(1)-test specimen. These kinds of phenomena are the same as the case between the test specimen H2(2) and H1(1). The strength characteristics of the above test specimens (2) suggest lower microcrack density value and higher degree of uniformity in the distribution of microcracks arrayed parallel to the loading direction compared to those of test specimens (1). The six strength values belonging to each group were arranged in increasing order in the above chart. The strength values of the test specimens belonging to both group D and E appear in order of R1 < R2 < G1 < H1 < G2 < H2. Therefore, the strength values of group D and E can be indicator values for evaluating the six directions of rock cleavages. Second, the correlation chart between slope angle(θ) and strength difference(Δσ_t) were made. The values of the above two parameters were obtained from the five strength distribution lines connecting between the two directions. From the chart related to rift plane(G1-H1, R'), grain plane(R1-H2, G') and hardway plane(R2-G2, H'), the slope values of linear functions increase in order of R'(0.391) < G'(0.470) < H'(0.485). Among three planes, the charts related to hardway plane show the highest distribution density among the five groups. From the related chart for rift(R1-R2, R), grain(G1-G2, G) and hardway(H1-H2, H), the slope values of linear functions increase in order of rift(0.407) < hardway(0.453) < grain(0.460). Among three rock cleavages, the charts related to rift show the highest frequency of groups belonging to the lower region. Taken together, the width of distribution of the slope angle among the three planes and three rock cleavages increase in order of H' < G < R' < R < G' < H. Third, the correlation analysis among the parameters related to microcrack length and the tensile strengths was performed. These parameters may include frequency(N), total length(Lt), mean length(Lm), median length(Lmed) and density(ρ). The correlation charts among individual parameters on the above microcrack(X) and corresponding five levels of tensile strengths for the five groups(Y) were made. From the five kinds of correlation charts, the values of correlation coefficients(R²) increase along with the five levels of strengths. The mean values of the five correlation coefficients from each chart increase in order of 0.22(N) < 0.34(Lt) < 0.38(ρ) < 0.57(Lmed) < 0.58(Lm). Fourth, the correlation chart among the corresponding maximum strength for group E(X) and the above five parameters(Y) were made. From the related chart, the values of correlation coefficient increase in order of 0.61(N) < 0.81(Lt) < 0.87(ρ) < 0.93(Lm) < 0.96(Lmed). The two parameters that have the highest correlations are median length with maximum strength. Through the above correlation analysis between microcrack and strength, the credibility for the results from this study can be enhanced.