• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.2
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• pp.109-120
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• 2017

A discounted cost model for preventive maintenance of armor units of rubble-mound breakwaters is mathematically derived by combining the deterioration model based on a discrete-time stochastic process of shock occurrence with the cost model of renewal process together. The discounted cost model of condition-based maintenance proposed in this paper can take into account the nonlinearity of cumulative damage process as well as the discounting effect of cost. By comparing the present results with the previous other results, the verification is carried out satisfactorily. In addition, it is known from the sensitivity analysis on variables related to the model that the more often preventive maintenance should be implemented, the more crucial the level of importance of system is. However, the tendency is shown in reverse as the interest rate is increased. Meanwhile, the present model has been applied to the armor units of rubble-mound breakwaters. The parameters of damage intensity function have been estimated through the time-dependent prediction of the expected cumulative damage level obtained from the sample path method. In particular, it is confirmed that the shock occurrences can be considered to be a discrete-time stochastic process by investigating the effects of uncertainty of the shock occurrences on the expected cumulative damage level with homogeneous Poisson process and doubly stochastic Poisson process that are the continuous-time stochastic processes. It can be also seen that the stochastic process of cumulative damage would depend directly on the design conditions, thus the preventive maintenance would be varied due to those. Finally, the optimal periods and scale for the preventive maintenance of armor units of rubble-mound breakwaters can be quantitatively determined with the failure limits, the levels of importance of structure, and the interest rates.

• Analytical Science and Technology
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• v.25 no.2
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• pp.121-126
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• 2012

It is essential to evaluate the quality of Hanji record paper without damaging the record paper by previous destructive methods. The samples were Hanji record paper produced in the 1900s. Near-infrared (NIR) spectrometer was used as a non destructive method for evaluating the quality of record papers. Fourier transform (FT) spectrometer was used with 12,500 to 4,000 $cm^{-1}$ wavenumber range for quantitative analysis and it has high accuracy and good signal-to-noise ratio. The acidity and moisture content of Hanji record paper were measured by integrating sphere as diffuse reflectance type. The acidity (pH) of chemical factors as a quality evaluated factor of Hanji was correlated to NIR spectrum. The NIR spectrum was pretreated to obtain the coefficients of optimum correlation. Multiplicative scatter correction (MSC) and First derivative of Savitzky-Golay were used as pretreated methods. The coefficients of optimum correlation were calculated by PLSR (partial least square regression). The correlation coefficients ($R^2$) of acidity had 0.92 on NIR spectra without pretreatment. Also the standard error of prediction (SEP) of pH was 0.24. And then the NIR spectra with pretreatment would have better correlation coefficient ($R^2$ = 0.98) and 0.19 as SEP on pH. For moisture contents, the linearity correlation without pretreatment was higher than the case with pretreatment (MSC, $1^{st}$ derivative). As the best result, the $R^2$ was 0.99 and SEP was 0.45. This indicates that it is highly proper to evaluate the quality of Hanji record papers speedily with integrated sphere and FT NIR analyzer as a non-destructive method.

• 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 the Korea Institute of Information and Communication Engineering
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• v.4 no.4
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• pp.875-884
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• 2000

Sea fog/stratus is very difficult to detect because of the characteristics of air-sea interaction and locality ,and the scantiness of the observed data from the oceans such as ships or ocean buoys. The aim of our study develops new algorism for sea fog detection by using Geostational Meteorological Satellite-5(GMS-5) and suggests the technics of its continuous detection. In this study, atmospheric synoptic patterns on sea fog day of May, 1999 are classified; cold air advection type(OOUTC, May 10, 1999) and warm air advection type(OOUTC, May 12, 1999), respectively, and we collected two case days in order to analyze variations of water vapor at Osan observation station during May 9-10, 1999.So as to detect daytime sea fog/stratus(OOUTC, May 10, 1999), composite image, visible accumulated histogram method and surface albedo method are used. The characteristic value during day showed A(min) .20% and DA < 10% when visible accumulated histogram method was applied. And the sea fog region which is detected is similar in composite image analysis and surface albedo method. Inland observation which visibility and relative humidity is beneath 1Km and 80%, respectively, at OOUTC, May 10,1999; Poryoung for visble accumulated histogram method and Poryoung, Mokp'o and Kangnung for surface albedo method. In case of nighttime sea fog(18UTC, May 10, 1999), IR accumulated histogram method and Maximum brightness temperature method are used, respectively. Maxium brightness temperature method dectected sea fog better than IR accumulated histogram method with the charateristic value that is T_max < T_max_trs, and then T_max is beneath 700hPa temperature of GDAPS(Global Data Assimilation and Prediction System). Sea fog region which is detected by Maxium brighness temperature method was similar to the result of National Oceanic and Atmosheric Administratio/Advanced Very High Resolution Radiometer (NOAA/AVHRR) DCD(Dual Channel Difference), but usually visibility and relative humidity are not agreed well in inland.

• Current Research on Agriculture and Life Sciences
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• v.4
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• pp.87-94
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• 1986

This study was performed in order to contemplate their correlations between physical and mechanical properties of the marine clays which were collected from main harbors in Korea. The results obtained are as follows: 1. Most of the soils in experimental districts consist of CH. CL. and ML. and they are considered to be still proceeding. 2. The equations of the relationship between compression index and liquid limit are as, follows: CH : $C_c=0.0137$ (LL-22.60) CL : $C_c=0.0123$ (LL-14.64) 3. The relationship between compression index and initial void ratio appears that the higher the plasticity, the easier the slope of the regression line. The equations are as follows : CH : $C_c=0.431$ ($e_o-0.504$) CH : $C_c=0.471$ ($e_o-0.235$) ML : $C_c=0.641$ ($e_o-0.393$) 4. The equations of the relationship between compression index and natural water content are as follows: CH : $C_c=0.0133$ ($W_n-28.27$) CL : $C_c=0.0225$ ($W_n-23.56$) ML : $C_c=0.0106$ ($W_n-16.42$) 5. The relationship between initial void ratio and natural water content, and compression index is highly positive correlation and the equations are as follows : CH : $C_c=0.301$ ($e_o+0.017W_n-1.05$) CL : $C_c=0.141$ ($e_o+0.0567W_n-1.054$) ML : $C_c=0.421$ ($e_o+0.0214W_n-1.121$) 6. The equations of the relationship between initial void ratio and liquid limit, and compression index are as follows : CH : $C_c=0.36$ ($e_o+0.08LL-0.819$) CL : $C_c=0.269$ ($e_o+0.026LL-0.929$) 7. The cohesion of marine clays is no concerned with the increment of depth. The equations of relationship between cohesion and unconfined compression strength are as follows. CH : qu=1.896C+0.0107 CL : qu=1.849C+0.04.

• Journal of Environmental Impact Assessment
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• v.28 no.5
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• pp.427-439
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• 2019

Natural environmental ecology ofthe environmental impact assessment(EIA)is very much lacking in quantitative evaluation. Thus, this study attempted to evaluate quantitative assessment for ecosystem service in the site of Eco-delta project in Busan. As a part of climate change adaptation, this study evaluated and compared with the value for carbon fixation and habitat quality using the InVEST model before and after development with three alternatives of land-use change. Carbon fixation showed 216,674.48 Mg of C (year 2000), and 203,474.25 Mg of C (year 2015)reducing about 6.1%, and in the future of year 2030 the value was dropped to 120,490.84 Mg of C which is 40% lower than year 2015. Alternative 3 of land use planning was the best in terms of carbon fixation showing 6,811.31 Mg of C. Habitat quality also changed from 0.57 (year 2000), 0.35 (year 2015), and 0.21 (year 2030) with continued degradation as development goes further. Alternative 3 also was the highest with 0.21(Alternative 1 : 0.20, Alternative 2 : 0.18). In conclusion,this study illustrated that quantitative method forland use change in the process of EIA can helpdecision making for stakeholders anddevelopers with serving the best scenario forlow impact of carbon. Also it can help better for land use plan, greenhouse gas and natural environmental assets in EIA. This study could be able to use in the environmental policy with numerical data of ecosystem and prediction. Supplemented with detailed analysis and accessibility of basic data, this method will make it possible for wide application in the ecosystem evaluation.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.968-974
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• 2010

We assessed the feasibility of discrete wavelet transform (DWT) applied for the spectral processing to enhance the estimation performance quality of soil organic matters using visible-near infrared spectra and mapped their distribution via block Kriging model. Continuum-removal and $1^{st}$ derivative transform as well as Haar and Daubechies DWT were used to enhance spectral variation in terms of soil organic matter contents and those spectra were put into the PLSR (Partial Least Squares Regression) model. Estimation results using raw reflectance and transformed spectra showed similar quality with $R^2$ > 0.6 and RPD> 1.5. These values mean the approximation prediction on soil organic matter contents. The poor performance of estimation using DWT spectra might be caused by coarser approximation of DWT which not enough to express spectral variation based on soil organic matter contents. The distribution maps of soil organic matter were drawn via a spatial information model, Kriging. Organic contents of soil samples made Gaussian distribution centered at around 20 g $kg^{-1}$ and the values in the map were distributed with similar patterns. The estimated organic matter contents had similar distribution to the measured values even though some parts of estimated value map showed slightly higher. If the estimation quality is improved more, estimation model and mapping using spectroscopy may be applied in global soil mapping, soil classification, and remote sensing data analysis as a rapid and cost-effective method.

• Korean Journal of Environment and Ecology
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• v.35 no.5
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• pp.480-489
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• 2021

Climate change poses great threats to wildlife populations by decreasing their number and destroying their habitats, jeopardizing biodiversity conservation. Asiatic salamander (Hynobiidae) species are particularly vulnerable to climate change due to their small home range and limited dispersal ability. Thus, this study used one salamander species, the Korean clawed salamander (Onychodactylus koreanus), as a model species and examined their habitat characteristics and current distribution in South Korea to predict its spatial distribution under climate change. As a result, we found that altitude was the most important environmental factor for their spatial distribution and that they showed a dense distribution in high-altitude forest regions such as Gangwon and Gyeongsanbuk provinces. The spatial distribution range and habitat characteristics predicted in the species distribution models were sufficiently in accordance with previous studies on the species. By modeling their distribution changes under two different climate change scenarios, we predicted that the distribution range of the Korean clawed salamander population would decrease by 62.96% under the RCP4.5 scenario and by 98.52% under the RCP8.5 scenario, indicating a sharp reduction due to climate change. The model's AUC value was the highest in the present (0.837), followed by RCP4.5 (0.832) and RCP8.5 (0.807). Our study provides a basic reference for implementing conservation plans for amphibians under climate change. Additional research using various analysis techniques reflecting habitat characteristics and minute habitat factors for the whole life cycle of Korean-tailed salamanders help identify major environmental factors that affect species reduction.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1631-1645
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• 2021

This research aims to provide the characteristics of the world's first active lidar sensor Atmospheric Laser Doppler Instrument (ALADIN) wind data and Geostationary Korea Multi Purpose Satellite 2A (GK2A) Atmospheric Motion Vector (AMV) data by comparing two wind data. As a result of comparing the data from September 2019 to August 1, 2020, The total number of collocated data for the AMV (using IR channel) and Mie channel ALADIN data is 177,681 which gives the Root Mean Square Error (RMSE) of 3.73 m/s and the correlation coefficient is 0.98. For a more detailed analysis, Comparison result considering altitude and latitude, the Normalized Root Mean Squared Error (NRMSE) is 0.2-0.3 at most latitude bands. However, the upper and middle layers in the lower latitudes and the lower layer in the southern hemispheric are larger than 0.4 at specific latitudes. These results are the same for the water vapor channel and the visible channel regardless of the season, and the channel-specific and seasonal characteristics do not appear prominently. Furthermore, as a result of analyzing the distribution of clouds in the latitude band with a large difference between the two wind data, Cirrus or cumulus clouds, which can lower the accuracy of height assignment of AMV, are distributed more than at other latitude bands. Accordingly, it is suggested that ALADIN wind data in the southern hemisphere and low latitude band, where the error of the AMV is large, can have a positive effect on the numerical forecast model.

• Journal of Korean Society of Forest Science
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• v.110 no.4
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• pp.504-515
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• 2021

To examine the relationship between environmental factors influencing the habitat of Aconitum austrokoreense Koidz., this study employed the MexEnt model to evaluate 21 environmental factors. Fourteen environmental factors having an AUC of at least 0.6 were found to be the age of stand, growing stock, altitude, topography, topographic wetness index, solar radiation, soil texture, mean temperature in January, mean temperature in April, mean annual temperature, mean rainfall in January, mean rainfall in August, and mean annual rainfall. Based on the response curves of the 14 descriptive factors, Aconitum austrokoreense Koidz. on the Baekun Mountain were deemed more suitable for sites at an altitude of 600 m or lower, and habitats were not significantly affected by the inclination angle. The preferred conditions were high stand density, sites close to valleys, and distribution in the northwestern direction. Under the five-age class system, the species were more likely to be observed for lower classes. The preferred solar radiation in this study was 1.2 MJ/m². The species were less likely to be observed when the topographic wetness index fell below the reference value of 4.5, and were more likely observed above 7.5 (reference of threshold). Soil analysis showed that Aconitum austrokoreense Koidz. was more likely to thrive in sandy loam than clay. Suitable conditions were a mean January temperature of - 4.4℃ to -2.5℃, mean April temperature of 8.8℃-10.0℃, and mean annual temperature of 9.6℃-11.0℃. Aconitum austrokoreense Koidz. was first observed in sites with a mean annual rainfall of 1,670- 1,720 mm, and a mean August rainfall of at least 350 mm. Therefore, sites with increasing rainfall of up to 390 mm were preferred. The area of potential habitats having distributive significance of 75% or higher was 202 ha, or 1.8% of the area covered in this study.