• Korean Chemical Engineering Research
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• v.61 no.3
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• pp.472-478
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• 2023

In this study, the acetaldehyde removal characteristics of activated carbon (AC) for air purifier filters were investigated using metal catalysts-impregnation and functional group-modification method. The AC with a high specific surface area(1700 m²/g) and micropores was prepared by KOH activation of coconut charcoal and the efficiency of catalyst and functional group immobilization was examined by varying the drying conditions within the pores after immersion. The physical properties of the prepared activated carbon were analyzed by BET, ICP, EA, and FT-IR, and the acetaldehyde adsorption performances were investigated using gas chromatography (GC) at various impregnation and modified conditions. As the concentration of impregnation solution increased, the amount of impregnated metal catalysts increased, while the specific surface area showed a decreasing trend. The adsorption tests of the metal catalyst-impregnated and functional group-modified activated carbons revealed that excellent adsorption performance in compositions MgO10@AC, CaO10@AC, EU10@AC, and H-U3N1@AC, respectively. The MgO10@AC, which showed the highest adsorption performance, had a breakthrough time of 533.8 minutes and adsorption capacity of 57.4 mg/g for acetaldehyde adsorption. It was found that the nano-sized MgO catalyst on the activated carbon improved the adsorption performance by interacting with carbonyl groups of acetaldehyde.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1085-1095
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• 2023

Time-series data generated from satellite data are crucial resources for change detection and monitoring across various fields. Existing research in time-series data generation primarily relies on single-image analysis to maintain data uniformity, with ongoing efforts to enhance spatial and temporal resolutions by utilizing diverse image sources. Despite the emphasized significance of time-series data, there is a notable absence of automated data collection and preprocessing for research purposes. In this paper, to address this limitation, we propose a system that automates the collection of satellite information in user-specified areas to generate time-series data. This research aims to collect data from various satellite sources in a specific region and convert them into time-series data, developing an automatic satellite image collection system for this purpose. By utilizing this system, users can collect and extract data for their specific regions of interest, making the data immediately usable. Experimental results have shown the feasibility of automatically acquiring freely available Landsat and Sentinel images from the web and incorporating manually inputted high-resolution satellite images. Comparisons between automatically collected and edited images based on high-resolution satellite data demonstrated minimal discrepancies, with no significant errors in the generated output.

• Health Policy and Management
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• v.34 no.1
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• pp.68-77
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• 2024

Background: Depressive disorders can be categorized into daily depression and clinical depression. The experience of depressive disorder can increase health care utilization due to decreased treatment compliance and somatization. On the other hand, the clinical depression group may also experience social prejudice associated with the illness, which can limit their access to health care utilization. In terms of the significance of health care utilization as a factor in individual and social issues, this study aims to compare the health care utilization of the clinical depression group with that of the non-depressed group and the daily depression group. Methods: The analysis utilized the inverse probability of treatment weighting based on the generalized propensity score. Results: As a result of the analysis, clinical depression and daily depression were higher among women, low-income groups, individuals with low education levels, and so forth. The clinical depression group was also higher among individuals who were not economically active, did not have private health insurance, or had multiple chronic diseases. The number of outpatient department visits in the depression group was significantly higher than in the non-depressed group. In addition, the number of outpatient department visits for the clinical depression group was significantly higher than that for the daily depression group. Outpatient medical expenses were higher in the depression group than in the non-depressed group, and there was no significant difference between the clinical depression group and the daily depression group. Conclusion: Health care utilization was higher in the depression group than the non-depressed group, it was also higher in the clinical depression group than the daily depression group.

• Tuberculosis and Respiratory Diseases
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• v.48 no.6
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• pp.909-921
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• 2000

Background : Defects in apoptotic signaling pathways play important role in tumor initiation, progression, metastasis and resistance to treatment. Several proteins which may promote tumorigenesis by inhibiting apoptosis were identified. The survivin protein is the member of inhibitor of apoptosis protein(IAPs) family which inhibits apoptosis. Unlike other IAPs, it is expressed in during the fetal period but not in adult differentiated tissues. Many reports have stated that survivin is selectively expressed in many cancer cell lines and cancer tissues. We performed immunohistochemical analysis for survivin expression in non-mall cell lung cancer to get evaluate its clinical implication. Methods : Twenty nine surgically resected lung cancers were examined. Immunohistochemical staining were performed by immuno-peroxidase technique using avidin-biotinylated horseradish pemxidase complex in the formalin-fixed, paraffin-embedded tissue $4{\mu}m$ section. Anti-survivin polyclonal antibody was used for primary antibody and anti-p53 monoclonal antibody was also used to analyze the correlation between survivin and p53 expression. The survivin expression scores were determined by as the sum of the stained area and intensity. Results : Immunohistochemical analysis showed cancer specific expression of survivin in 20 of 29 cases (69.0%). Western blot analysis also showed the selective survivin expression in tumor tissue. There was no correlation between survivin expression and clinicopathological parameters and prognosis. We analyzed the ∞π'elation between survivin expression and p53 expression, but found none. Conclusion: We confirmed the tumor specific expression of survival in non-small cell lung canær. But this expression was not correlated with clinical parameters as well as histology, tumor stage, recurrence, and survival rate. Also it was not statistically correlated with the expression of p53.

• Journal of Chest Surgery
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• v.31 no.5
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• pp.472-480
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• 1998

We reviewed the surgical results of intracardiac lateral tunnel Fontan procedure for the repair of functional single ventricles. Between 1990 and 1996, 104 patients underwent total cavopulmonary anastomosis. Patients' age and body weight averaged 35.9(range 10 to 173) months and 12.8(range 6.5 to 37.8) kg. Preoperative diagnoses included 18 tricuspid atresias and 53 double inlet ventricles with univentricular atrioventricular connection and 33 other complex lesions. Previous palliative operations were performed in 50 of these patients, including 37 systemic to pulmonary artery shunts, 13 pulmonary artery bandings, 15 surgical atrial septectomies, 2 arterial switch procedures, 2 resections of subaortic conus, 2 repairs of total anomalous pulmonary venous connection and 1 Damus-Stansel-Kaye procedure. In 19 patients bidirectional cavopulmonary shunt operation was performed before the Fontan procedure and in 1 patient a Kawashima procedure was required. Preoperative hemodynamics revealed a mean pulmonary artery pressure of 14.6(range 5 to 28) mmHg, a mean pulmonary vascular resistance of 2.2(range 0.4 to 6.9) wood-unit, a mean pulmonary to systemic flow ratio of 0.9(range 0.3 to 3.0), a mean ventricular end-diastolic pressure of 9.0 (range 3.0 to 21.0) mmHg, and a mean arterial oxygen saturation of 76.0(range 45.6 to 88.0)%. The operative procedure consisted of a longitudinal right atriotomy 2cm lateral to the terminal crest up to the right atrial auricle, followed by the creation of a lateral tunnel connecting the orifices of either the superior caval vein or the right atrial auricle to the inferior caval vein, using a Gore-Tex vascular graft with or without a fenestration. Concomitant procedures at the time of Fontan procedure included 22 pulmonary artery angioplasties, 21 atrial septectomies, 4 atrioventricular valve replacements or repairs, 4 corrections of anomalous pulmonary venous connection, and 3 permanent pacemaker implantations. In 31, a fenestration was created, and in 1 an adjustable communication was made in the lateral tunnel pathway. One lateral tunnel conversion was performed in a patient with recurrent intractable tachyarrhythmia 4 years after the initial atriopulmonary connection. Post-extubation hemodynamic data revealed a mean pulmonary artery pressure of 12.7(range 8 to 21) mmHg, a mean ventricular end-diastolic pressure of 7.6(range 4 to 12) mmHg, and a mean room-air arterial oxygen saturation of 89.9(range 68 to 100) %. The follow-up duration was, on average, 27(range 1 to 85) months. Post-Fontan complications included 11 prolonged pleural effusions, 8 arrhythmias, 9 chylothoraces, 5 of damage to the central nervous system, 5 infectious complications, and 4 of acute renal failure. Seven early(6.7%) and 5 late(4.8%) deaths occured. These results proved that the lateral tunnel Fontan procedure provided excellent hemodynamic improvements with acceptable mortality and morbidity for hearts with various types of functional single ventricle.

• Progress in Medical Physics
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• v.14 no.4
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• pp.211-217
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• 2003

The Principal component analysis (PCA) is a well-known data analysis method that is useful in linear feature extraction and data compression. The PCA is a linear transformation that applies an orthogonal rotation to the original data, so as to maximize the retained variance. PCA is a classical technique for obtaining an optimal overall mapping of linearly dependent patterns of correlation between variables (e.g. neurons). PCA provides, in the mean-squared error sense, an optimal linear mapping of the signals which are spread across a group of variables. These signals are concentrated into the first few components, while the noise, i.e. variance which is uncorrelated across variables, is sequestered in the remaining components. PCA has been used extensively to resolve temporal patterns in neurophysiological recordings. Because the retinal signal is stochastic process, PCA can be used to identify the retinal spikes. With excised rabbit eye, retina was isolated. A piece of retina was attached with the ganglion cell side to the surface of the microelectrode array (MEA). The MEA consisted of glass plate with 60 substrate integrated and insulated golden connection lanes terminating in an 8${\times}$8 array (spacing 200 $\mu$m, electrode diameter 30 $\mu$m) in the center of the plate. The MEA 60 system was used for the recording of retinal ganglion cell activity. The action potentials of each channel were sorted by off­line analysis tool. Spikes were detected with a threshold criterion and sorted according to their principal component composition. The first (PC1) and second principal component values (PC2) were calculated using all the waveforms of the each channel and all n time points in the waveform, where several clusters could be separated clearly in two dimension. We verified that PCA-based waveform detection was effective as an initial approach for spike sorting method.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.347-358
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• 2019

Lambertian cloud model (Lambertian Cloud Model) is the simplified cloud model which is used to effectively retrieve the vertical ozone distribution of the atmosphere where the clouds exist. By using the Lambertian cloud model, the optical characteristics of clouds required for radiative transfer simulation are parametrized by Optical Centroid Cloud Pressure (OCCP) and Effective Cloud Fraction (ECF), and the accuracy of each parameter greatly affects the radiation simulation accuracy. However, it is very difficult to generalize the vertical ozone error due to the OCCP error because it varies depending on the radiation environment and algorithm setting. In addition, it is also difficult to analyze the effect of OCCP error because it is mixed with other errors that occur in the vertical ozone calculation process. This study analyzed the ozone retrieval error due to OCCP error using two methods. First, we simulated the impact of OCCP error on ozone retrieval based on Optimal Estimation. Using LIDORT radiation model, the radiation error due to the OCCP error is calculated. In order to convert the radiation error to the ozone calculation error, the radiation error is assigned to the conversion equation of the optimal estimation method. The results show that when the OCCP error occurs by 100 hPa, the total ozone is overestimated by 2.7%. Second, a case analysis is carried out to find the ozone retrieval error due to OCCP error. For the case analysis, the ozone retrieval error is simulated assuming OCCP error and compared with the ozone error in the case of PROFOZ 2005-2006, an OMI ozone profile product. In order to define the ozone error in the case, we assumed an ideal assumption. Considering albedo, and the horizontal change of ozone for satisfying the assumption, the 49 cases are selected. As a result, 27 out of 49 cases(about 55%)showed a correlation of 0.5 or more. This result show that the error of OCCP has a significant influence on the accuracy of ozone profile calculation.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.25-41
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• 2011

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al., 2005; Kim, 2003).The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified, a terrible economic loss for investors or financial decision makers may happen. Therefore, it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt, 1999; Drish, 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk, i.e. bankruptcy probability.

• The Journal of Korean Academy of Prosthodontics
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• v.47 no.1
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• pp.61-69
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• 2009

Statement of problem: Porcelain veneers have become a popular treatment modality for aesthetic anterior prosthesis. Fitting porcelain veneers in the mouth usually involve a try-in appointment, which frequently results in salivary contamination of fitting surfaces. Purpose: An in vitro study was carried out to investigate the effect of silane treatment timing and saliva contamination on the resin bond strength to porcelain veneer surface. Material and methods: Cylindrical test specimens (n=360) and rectangular test specimens (n=5) were prepared for shear bond test and contact angle analysis. Whole cylindrical specimens divided into 20 groups, each of which received a different surface treatment and/or storage condition. The composite resin cement stubs were light-polymerized onto porcelain adherends. The shear bond strengths of cemented stubs were measured after dry storage and thermocycling (3,000 cycles) between 5 and $55^{\circ}C$. The silane and their reactions were chemically monitored by using Fourier Transform Infrared Spectroscopy analysis (FTIR) and contact angle analysis. One-way analysis of variance (ANOVA) and Dunnett's multiple comparison were used to analyze the data. Results: FT-IR analysis showed that salivary contamination and silane treatment timing did not affect the surface interactions of silane. Observed water contact angles were lower on the saliva contaminated porcelain surface and the addition of 37% phosphoric acid for 20 seconds on saliva contaminated porcelain increased the degree of contact angle. Silane applied to the porcelain, a few days before cementation, resulted in increasing the bond strength after thermocycling. Conclusion: Within the limitation of this study, it can be concluded that it would be better to protect porcelain prosthesis before saliva contamination with silane treatment and to clean the contaminated surface by use of phosphoric acid.