• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.1
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• pp.11-36
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• 2021

The solar annual (Sa) and semiannual (Ssa) tides account for much of the non-uniform annual and seasonal variability observed in sea levels. These non-equilibrium tides depend on atmospheric variations, forced by changes in the Sun's distance and declination, as well as on hydrographic conditions. Here we employ tidal harmonic analyses to calculate Sa and Ssa harmonic constants for 21 Korean coastal tidal stations (TS), operated by the Korea Hydrographic and Oceanographic Agency. We used 19 year-long (1999 to 2017) 1 hr-interval sea level records from each site, and used two conventional harmonic analysis (HA) programs (Task2K and UTide). The stability of Sa harmonic constants was estimated with respect to starting date and record length of the data, and we examined the spatial distribution of the calculated Sa and Ssa harmonic constants. HA was performed on Incheon TS (ITS) records using 369-day subsets; the first start date was January 1, 1999, the subsequent data subset starting 24 hours later, and so on up until the final start date was December 27, 2017. Variations in the Sa constants produced by the two HA packages had similar magnitudes and start date sensitivity. Results from the two HA packages had a large difference in phase lag (about 78°) but relatively small amplitude (<1 cm) difference. The phase lag difference occurred in large part since Task2K excludes the perihelion astronomical variable. Sensitivity of the ITS Sa constants to data record length (i.e., 1, 2, 3, 5, 9, and 19 years) was also tested to determine the data length needed to yield stable Sa results. HA results revealed that 5 to 9 year sea level records could estimate Sa harmonic constants with relatively small error, while the best results are produced using 19 year-long records. As noted earlier, Sa amplitudes vary with regional hydrographic and atmospheric conditions. Sa amplitudes at the twenty one TS ranged from 15.0 to 18.6 cm, 10.7 to 17.5 cm, and 10.5 to 13.0 cm, along the west coast, south coast including Jejudo, and east coast including Ulleungdo, respectively. Except at Ulleungdo, it was found that the Ssa constituent contributes to produce asymmetric seasonal sea level variation and it delays (hastens) the highest (lowest) sea levels. Comparisons between monthly mean, air-pressure adjusted, and steric sea level variations revealed that year-to-year and asymmetric seasonal variations in sea levels were largely produced by steric sea level variation and inverted barometer effect.

• Journal of agriculture & life science
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• v.50 no.3
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• pp.1-11
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• 2016

With increasing interest, there have been studies on LiDAR(Light Detection And Ranging)-based DEM(Digital Elevation Model) to acquire three dimensional topographic information. For producing LiDAR DEM with better accuracy, Filtering process is crucial, where only surface reflected LiDAR points are left to construct DEM while non-surface reflected LiDAR points need to be removed from the raw LiDAR data. In particular, the changes of input values for filtering algorithm-constructing parameters are supposed to produce different products. Therefore, this study is aimed to contribute to better understanding the effects of the changes of the levels of GroundFilter Algrothm's Mean parameter(GFmn) embedded in FUSION software on the accuracy of the LiDAR DEM products, using LiDAR data collected for Hwacheon, Yangju, Gyeongsan and Jangheung watershed experimental area. The effect of GFmn level changes on the products' accuracy is estimated by measuring and comparing the residuals between the elevations at the same locations of a field and different GFmn level-produced LiDAR DEM sample points. In order to test whether there are any differences among the five GFmn levels; 1, 3, 5, 7 and 9, One-way ANOVA is conducted. In result of One-way ANOVA test, it is found that the change in GFmn level significantly affects the accuracy (F-value: 4.915, p<0.01). After finding significance of the GFmn level effect, Tukey HSD test is also conducted as a Post hoc test for grouping levels by the significant differences. In result, GFmn levels are divided into two subsets ('7, 5, 9, 3' vs. '1'). From the observation of the residuals of each individual level, it is possible to say that LiDAR DEM is generated most accurately when GFmn is given as 7. Through this study, the most desirable parameter value can be suggested to produce filtered LiDAR DEM data which can provide the most accurate elevation information.

• Korean Circulation Journal
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• v.54 no.4
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• pp.189-200
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• 2024

Background and Objectives: Concerns remain that early aspirin cessation may be associated with potential harm in subsets at high risk of ischemic events. This study aimed to assess the effects of P2Y12 inhibitor monotherapy after 3-month dual antiplatelet therapy (DAPT) vs. prolonged DAPT (12-month or longer) based on the ischemic risk stratification, the CHADS-P2A2RC, after percutaneous coronary intervention (PCI). Methods: This was a sub-study of the SMART-CHOICE trial. The effect of the randomized antiplatelet strategies was assessed across 3 CHADS-P2A2RC risk score categories. The primary outcome was a major adverse cardiac and cerebral event (MACCE), a composite of all-cause death, myocardial infarction, or stroke. Results: Up to 3 years, the high CHADS-P2A2RC risk score group had the highest incidence of MACCE (105 [12.1%], adjusted hazard ratio [HR], 2.927; 95% confidence interval [CI], 1.358-6.309; p=0.006) followed by moderate-risk (40 [1.4%], adjusted HR, 1.786; 95% CI, 0.868-3.674; p=0.115) and low-risk (9 [0.5%], reference). In secondary analyses, P2Y12 inhibitor monotherapy reduced the Bleeding Academic Research Consortium (BARC) types 2, 3, or 5 bleeding without increasing the risk of MACCE as compared with prolonged DAPT across the 3 CHADS-P2A2RC risk strata without significant interaction term (interaction p for MACCE=0.705 and interaction p for BARC types 2, 3, or 5 bleeding=0.055). Conclusions: The CHADS-P2A2RC risk score is valuable in discriminating high-ischemic-risk patients. Even in such patients with a high risk of ischemic events, P2Y12 inhibitor monotherapy was associated with a lower incidence of bleeding without increased risk of ischemic events compared with prolonged DAPT.

• Tuberculosis and Respiratory Diseases
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• v.53 no.5
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• pp.497-509
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• 2002

Background : The mechanisms through which cellular activation results in intracellular mycobacterial killing is only partially understood. However, in vitro studies of human immunity to Mycobacterium tuberculosis have been largely modeled on the work reported by Crowle, which is complicated by several factors. The whole blood culture is simple and allows the simultaneous analysis of the relationship between bacterial killing and the effect of effector cells and humoral factors. In this study, we attempted to determine the extent to which M. tuberculosis is killed in a human whole blood culture and to explore the role of the host and microbial factor in this process. Methods : The PPD positive subject were compared to the umbilical cord blood and patients with tuberculosis, diabetes and lung cancer. The culture is performed using heparinized whole blood diluted with a culture medium and infected with a low number of M. avium or M. tuberculosis $H_{37}Ra$ for 4 days by rotating the culture in a $37^{\circ}C$, 5% $CO_2$ incubator. In some experiments, methlprednisolone- or pentoxifyline were used to inhibit the immune response. To assess the role of the T-cell subsets, CD4+, CD8+ T-cells or both were removed from the blood using magnetic beads. The ${\Delta}$ log killing ratio was defined using a CFU assay as the difference in the log number of viable organisms in the completed culture compared to the inoculum. Results : 1. A trend was noted toward the improved killing of mycobacteria in PPD+ subjects comparing to the umbilical cord blood but there was no specific difference in the patients with tuberculosis, diabetes and lung cancer. 2. Methylprednisolone and pentoxifyline adversely affected the killing in the PPD+ subjects umbilical cord blood and patients with tuberculosis. 3. The deletion of CD4+ or CD8+ T-lymphocytes adversely affected the killing of M. avium and M. tuberculosis $H_{37}Ra$ by PPD+ subjects. Deletion of both cell types had an additive effect, particularly in M. tuberculosis $H_{37}Ra$. 4. A significantly improved mycobacterial killing was noted after chemotherapy in patients with tuberculosis and the ${\Delta}$ logKR continuously decreased in a 3 and 4 days of whole blood culture. Conclusion : The in vitro bactericidal assay by human whole blood culture model was settled using a CFU assay. However, the host immunity to M. tuberculosis was not apparent in the human whole blood culture bactericidal assay, and patients with tuberculosis showed markedly improved bacterial killing after anti-tuberculous chemotherapy compared to before. The simplicity of a whole blood culture facilitates its inclusion in a clinical trial and it may have a potential role as a surrogate marker in a TB vaccine trial.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Tuberculosis and Respiratory Diseases
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• v.50 no.4
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• pp.437-449
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• 2001

Background : In the severe community-acquired pneumonia, it has been known that the immune status is occasionally suppressed. This study was performed to identify the immunologic markers related with the prognostic factors in severe community-acquired pneumonia. Methods : 23 patients with severe community-acquired pneumonia were involved in this study, and divided into survivor (16) and nonsurvivor (7) groups. In this study, the medical history, laboratory tests(complete blood counts, routine chemistry profile, immunoglobulins, complements, lymphocyte subsets, cytokines, sputum and blood culture, urine analysis), and chest radiographs were scrutinized. Results : 1) Both groups had lymphopenia(total lymphocyte count $995.6{\pm}505.7/mm^3$ in the survivor and $624.0{\pm}287.6/mm^3$ in the nonsurvivor group). 2) The T-lymphocyte count of the nonsurvivor group($295.9{\pm}203.0/mm^3$) was lower than the survivor group($723.6{\pm}406.5/mm^3$) (p<0.05). 3) The total serum protein(albumin) was $6.0{\pm}1.0(2.7{\pm}0.7)\;g/d{\ell}$ in the survivor and $5.2{\pm}1.5(2.3{\pm}0.8)g/d{\ell}$ in the nonsurvivor group. The BUN of the nonsurvivor group($41.7{\pm}30.0mg/d{\ell}$) was higher than that of the survivor group($18.9{\pm}9.8mg/d{\ell}$)(p<0.05). The creatinine concentration was higher in the nonsurvivor group($1.8{\pm}1.0mg/d{\ell}$) than that in the survivor group($1.0{\pm}0.3mg/d{\ell}$)(p<0.05). 4) The immunoglobulin G level was higher in the survivor group($1433.0{\pm}729.5mg/d{\ell}$) than in the nonsurvivor group($849.1{\pm}373.1mg/d{\ell}$) (p<0.05). 5) The complement $C_3$ level was $108.0{\pm}37.9mg/d{\ell}$ in the survivor group and $88.0{\pm}32.1mg/d{\ell}$ in the nonsurvivor group. 6) A cytokine study showed an insignificant difference in both groups. 7) Chronic liver disease, DM, and COPD were major underlying diseases in both groups. Conclusion : These results suggest that decreased a T-lymphocyte count and immunoglobulin G level, and an increased BUN and creatinine level may be associated with the poor prognosis of severe community-acquired pneumonia.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.