• Economic and Environmental Geology
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• v.54 no.1
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• pp.105-115
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• 2021

The geological CO2 sequestration in underground geological formation such as deep saline aquifers and depleted hydrocarbon reservoirs is one of the most promising options for reducing the atmospheric CO2 emissions. The process in geological CO2 sequestration involves injection of supercritical CO2 (scCO2) into porous media saturated with pore water and initiates CO2 flooding with immiscible displacement. The CO2 migration and distribution, and, consequently, the displacement efficiency is governed by the interaction of fluids. Especially, the viscous force and capillary force are controlled by geological formation conditions and injection conditions. This study aimed to estimate the effects of surfactant on interfacial tension between the immiscible fluids, scCO2 and porewater, under high pressure and high temperature conditions by using a pair of proxy fluids under standard conditions through pendant drop method. It also aimed to observe migration and distribution patterns of the immiscible fluids and estimate the effects of surfactant concentrations on the displacement efficiency of scCO2. Micromodel experiments were conducted by applying n-hexane and deionized water as proxy fluids for scCO2 and porewater. In order to quantitatively analyze the immiscible displacement phenomena by n-hexane injection in pore network, the images of migration and distribution pattern of the two fluids are acquired through a imaging system. The experimental results revealed that the addition of surfactants sharply reduces the interfacial tension between hexane and deionized water at low concentrations and approaches a constant value as the concentration increases. Also it was found that, by directly affecting the flow path of the flooding fluid at the pore scale in the porous medium, the surfactant showed the identical effect on the displacement efficiency of n-hexane at equilibrium state. The experimental observation results could provide important fundamental information on immiscible displacement of fluids in porous media and suggest the potential to improve the displacement efficiency of scCO2 by using surfactants.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• Korean Security Journal
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• no.50
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• pp.175-206
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• 2017

The current study described the history of Islamic extremism and the recent expansion of international Islamic extremism in Indonesia. For doing so, both content analysis of the existing written documents and qualitative interviews were conducted. For the content analysis, media reports and research articles were collected and utilized. For qualitative interviews, Indonesian students and workers in Korea, Korean spouses married to Indonesian, and Korean missionaries in Indonesia were contacted and interviewed. Qualitative interview was conducted between 30 minutes and 2 hours. On the spot, interviews were recorded and later transcribed into written documents. Due to the difficulty of identification of population and the uneasiness of accessability to th study subjects, convenient sampling and snowball sampling were used. According to the results, Islamic extremism in Indonesia had a deep historical root and generally shared similar historical experience with other muslim countries such as Afghanistan, Pakistan, Egypt, and Saudi Arabia where Islamic extremism was deeply rooted in. That is, Islamic extremism began as a reaction to the western imperialism, after independence, Islamic extremism elements were marginalized in the process of construction of the modern nation-state, and Islamic extremist movement was radicalized and became violent during the Soviet-Afghan War. In addition, after 9.11, Islamic extremism in Indonesia was connected to international Islamic extremism network and integrated into such global movement. Such a historical development of Indonesian Islamic extremism was quite organized and robust. Meanwhile, the eastward infiltration and expansion of international Islamic extremism such as IS and Al Qaeda was observed in Indonesia. Particularly, such a worrisome expansion was more clearly visible in the marginalized and underdeveloped countrysides in Indonesia. Such expansion in Indonesia could negatively affect on the security of South Korea. Geographically, Indonesia is proximate to South Korea. This geographical proximity could be a direct security threat to the Korean society, as if Islamic extremism in North Africa and Middle East becomes a direct security threat to Europe. Considering the presence of a large size of Indonesian immigrant workers and communities in South Korea, such a concern is very realistic. The arrest of an Indonesian Islamic extremism supporter in November, 2016, could be a harbinger of the coming trend of Islamic extremism expansion inside South Korea. The Indonesian Islamic community in South Korea could be a passage of Indonesian Islamic extremism into the South Korean society. In this context, it is timely and necessary to pay an attention to the recent trend of Islamic extremism expansion in Indonesia.

• Geotechnical Engineering
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• v.13 no.3
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• pp.101-122
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• 1997

A spiting reinforcement system is composed of a series of radially installed reinforcing spites along the perimeter of the tunnel opening ahead of excavation. The reinforcing spill network is extended into the in-situ soil mass both radially and longitudinally The sailing reinforcement system has been successfully used for the construction of underground openings to reinforce weak rock formations on several occasions. The application of this spiting reinforcement system is currently extended to soft ground tunneling in limited occasions because of lack of reliable analysis and design methods. A method of threetimensional limit equilibrium stability analysis of the smile-reinforced shallow tunnel in soft ground is presented. The shape of the potential failure wedge for the case of smile-reinforced shallow tunnel is assumed on the basis of the results of three dimensional finite element analyses. A criterion to differentiate the spill-reinforced shallow tunnel from the smile-reinforced deep tunnel is also formulated, where the tunnel depth, soil type, geometry of the tunnel and reinforcing spites, together with soil arching effects, are considered. To examine the suitability of the proposed method of threedimensional stability analysis in practice, overall stability of the spill-reinforced shallow tunnel at facing is evaluated, and the predicted safety factors are compared with results from twotimensional analyses. Using the proposed method of threetimensional limit equilibrium stability analysis of the smile-reinforced shallow tunnel in soft ground, a parametric study is also made to investigate the effects of various design parameters such as tunnel depth, smile length and wadial spill spacing. With slight modifications the analytical method of threeiimensional stability analysis proposed may also be extended for the analysis and design of steel pipe reinforced multi -step grouting technique frequently used as a supplementary reinforcing method in soft ground tunnel construction.

• Journal of Korean Academy of Nursing
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• v.26 no.1
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• pp.223-247
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• 1996

This study was done to provide information which would lead to nursing care of the elderly being more holistically through an understanding of the phenomena of powerlessness based on the lived experience of powerlessness by the elderly, the meaning the elderly give to such phenomena, and what essence of powerlessness is. The methodology used in this study was Max Van Manen's phenomenological method based on the philosophy of Merleu-Ponty and a concerted approach was realized through the 11 steps suggested in the Van Manen's method. Data collection was done from March 2, 1995 to December 30, 1995. The subjects for this study were four elderly persons who lived with their families and who were over 60 years of age. Data were collected about the lived experience of the elderly, this researcher's experience of powerlessness, the linguistic meaning of powerlessness, idioms of the word or a feeling of powerlessness, and descriptions of powerlessness in the elderly as they appeared in the literature, are works, and phenomenological literature. All data were used to provide insights into the phenomena of powerlessness. Data about the experience of powerlessness by the elderly were collected through open interviews, participation, and observation. In the analysis of the theme of this study, the aspects of the theme, powerlessness in the elderly were clarified, thereby abstracting and finding meaningful statements by the elderly about their feeling of powerlessness, and then those significant statements were expressed as linguistic transformations. The summarized findings from the study are as follows : 1. Five meanings of powerlessness in the elderly were defined. 〈weakness〉, 〈dependence〉, 〈frustration〉, 〈worthlessness〉 and 〈giving up〉. 2. 〈Weakness〉 means that the elderly experience, not only their aging but also, their becoming weak and the loss of physical function frequently caused by diseases. 〈Dependence〉 means that the elderly experience dependence without any influence from the surroundings and that elderly patients who are hospitalized lose their autonomy, follow entirely their doctor's prescriptions, use aid equipment and directions, and depend only on those things. 〈Frustration〉 means that the elderly experience the loss of their roles from the past, there by feeling that there is no work for them to do anymore and therefore feel unable to do anything. 〈Worthlessness〉 means that the elderly experience the feeling of losing their social roles from the past, having no financial ability, thereby being a burden to their children or the people around them, and therefore regarding themselves useless. 〈Giving up〉 means that the elderly experience the feeling of closeness to death in the final stage of their lifetime, lose hope to be healed from their disease, and recognize the incontrollability of their own body. 3. From a general view of the meaning of the theme the powerlessness in the elderly-the most essential meaning of the theme is the 〈sense of loss〉. For the elderly are experiencing a sense of loss in the situation of being elderly and therefore being often hospitalized. Brief definitions of the five phenomena could be 〈weakness〉 meaning the loss of physical strength, 〈dependence〉 the loss of mentality caused by disease and hospitalization, 〈frustration〉 and 〈worthlessness〉 the loss of social performance caused by the loss of social functions from the past, and lastly 〈giving up〉 the loss of the controllability of such situations of aging and suffering disease. In light of the discussion above, it is understandable that the hospitalized elderly experience powerlessness not only as it related to their diseases but also to their normal aging, and this related to other characteristics of being elderly means that the 〈sense of loss〉 is the very essence of their powerlessness. 4. While most cases are of the normal elderly experiencing powerlessness in relation to their social network, cases of elderly who are hospitalized are of those experiencing powerlessness in relation to the loss of their physical desire. 5. The findings discussed above can serve as guidelines for nurses who take care of the ill elderly who are hospitalized and that can provide cues to appropriate nursing service, recognizing that the subjective experience of the objective age of the elderly is so important. Nurses can provide highly qualitative nursing service, based on their deep understanding of the suffering of the elderly due to feelings of powerlessness.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.101-114
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• 2023

In the wartime, aircraft carrying out a mission to strike the enemy deep in the depth are exposed to the risk of being shoot down. As a key combat force in mordern warfare, it takes a lot of time, effot and national budget to train military flight personnel who operate high-tech weapon systems. Therefore, this study studied the path problem of predicting the route of emergency escape from enemy territory to the target point to avoid obstacles, and through this, the possibility of safe recovery of emergency escape military flight personnel was increased. based problem, transforming the problem into a TSP, VRP, and Dijkstra algorithm, and approaching it with an optimization technique. However, if this problem is approached in a network problem, it is difficult to reflect the dynamic factors and uncertainties of the battlefield environment that military flight personnel in distress will face. So, MDP suitable for modeling dynamic environments was applied and studied. In addition, GIS was used to obtain topographic information data, and in the process of designing the reward structure of MDP, topographic information was reflected in more detail so that the model could be more realistic than previous studies. In this study, value iteration algorithms and deterministic methods were used to derive a path that allows the military flight personnel in distress to move to the shortest distance while making the most of the topographical advantages. In addition, it was intended to add the reality of the model by adding actual topographic information and obstacles that the military flight personnel in distress can meet in the process of escape and escape. Through this, it was possible to predict through which route the military flight personnel would escape and escape in the actual situation. The model presented in this study can be applied to various operational situations through redesign of the reward structure. In actual situations, decision support based on scientific techniques that reflect various factors in predicting the escape route of the military flight personnel in distress and conducting combat search and rescue operations will be possible.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.