• Economic and Environmental Geology
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• v.54 no.2
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• pp.247-257
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• 2021

The study area has been contaminated with explosive materials and heavy metals for several decades. For the design of the pump and treat remediation method, groundwater flow before and during groundwater pumping in a contaminated aquifer system was simulated, calibrated, and predicted using a generalized multidimensional hydrological numerical model. A three-dimensional geologic formation model representing the geology, hydrogeology, and topography of the aquifer system was established. A steady-state numerical simulation with model calibration was performed to obtain initial steady-state spatial distributions of groundwater flow and groundwater table in the aquifer system before groundwater pumping, and its results were illustrated and analyzed. A series of transient-state numerical simulations were then performed during groundwater pumping with the four different pumping rates at a potential location of the pumping well. Its results are illustrated and analyzed to provide primary reference data for the pump and treat remediation method. The results of both steady-state and transient-state numerical simulations show that the spatial distribution and properties of the geologic media and the topography have significant effects on the groundwater flow and thus depression zone.

• Journal of Distribution Science
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• v.13 no.5
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• pp.53-60
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• 2015

Purpose - The purpose of this paper is to shed light on the importance of the slope and curvature of the volatility curve implied in option prices in the KOSPI 200 options index. A number of studies examine the implied volatility curve, however, these usually focus on cross-sectional characteristics such as the volatility smile. Contrary to previous studies, we focus on time-series characteristics; we investigate correlation dynamics among slope, curvature, and level of the implied volatility curve to capture market information embodied therein. Our study may provide useful implications for investors to utilize current market expectations in managing portfolios dynamically and efficiently. Research design, data, and methodology - For our empirical purpose, we gathered daily KOSPI200 index option prices executed at 2:50 pm in the Korean Exchange distribution market during the period of January 2, 2004 and January 31, 2012. In order to measure slope and curvature of the volatility curve, we use approximated delta distance; the slope is defined as the difference of implied volatilities between 15 delta call options and 15 delta put options; the curvature is defined as the difference between out-of-the-money (OTM) options and at-the-money (ATM) options. We use generalized method of moments (GMM) and the seemingly unrelated regression (SUR) method to verify correlations among level, slope, and curvature of the implied volatility curve with statistical support. Results - We find that slope as well as curvature is positively correlated with volatility level, implying that put option prices increase in a downward market. Further, we find that curvature and slope are positively correlated; however, the relation is weakened at deep moneyness. The results lead us to examine whether slope decreases monotonically as the delta increases, and it is verified with statistical significance that the deeper the moneyness, the lower the slope. It enables us to infer that when volatility surges above a certain level due to any tail risk, investors would rather take long positions in OTM call options, expecting market recovery in the near future. Conclusions - Our results are the evidence of the investor's increasing hedging demand for put options when downside market risks are expected. Adding to this, the slope and curvature of the volatility curve may provide important information regarding the timing of market recovery from a nosedive. For financial product distributors, using the dynamic relation among the three key indicators of the implied volatility curve might be helpful in enhancing profit and gaining trust and loyalty. However, it should be noted that our implications are limited since we do not provide rigorous evidence for the predictability power of volatility curves. Meaning, we need to verify whether the slope and curvature of the volatility curve have statistical significance in predicting the market trough. As one of the verifications, for instance, the performance of trading strategy based on information of slope and curvature could be tested. We reserve this for the future research.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.6
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• pp.171-178
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• 2010

Pipeline structure is one of core underground infrastructure which transports primary sources. Since the almost pipeline structures are placed underground and connected each other complexly, it is difficult to monitor their structural health condition continuously. In order to overcome this limitation of recent monitoring technique, recently, a Ubiquitous Sensor Network (USN) system based on on-line and real-time monitoring system is being developed by the authors' research group. In this study, real-time pipeline health monitoring (PHM) methodology is presented based on electromechanical impedance methods using USN. Two types of damages including loosened bolts and notches are artificially inflicted on the pipeline structures, PZT and MFC sensors that have piezoelectric characteristics are employed to detect these damages. For objective evaluation of pipeline conditions, Damage metric such as Root Mean Square Deviation (RMSD) value was computed from the impedance signals to quantify the level of the damage. Optimal threshold levels for decision making are estimated by generalized extreme value(GEV) based statistical method. Throughout a series of experimental studies, it was reviewed the effectiveness and robustness of proposed PHM system.

• Journal of Preventive Medicine and Public Health
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• v.49 no.5
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• pp.329-341
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• 2016

Objectives: The goal of this study was to investigate the short-term effect of ambient air pollution on emergency department (ED) visits in Seoul for asthma according to patients' prior history of allergic diseases. Methods: Data on ED visits from 2005 to 2009 were obtained from the Health Insurance Review and Assessment Service. To evaluate the risk of ED visits for asthma related to ambient air pollutants (carbon monoxide [CO], nitrogen dioxide [$NO_2$], ozone [$O_3$], sulfur dioxide [$SO_2$], and particulate matter with an aerodynamic diameter <$10{\mu}m$ [$PM_{10}$]), a generalized additive model with a Poisson distribution was used; a single-lag model and a cumulative-effect model (average concentration over the previous 1-7 days) were also explored. The percent increase and 95% confidence interval (CI) were calculated for each interquartile range (IQR) increment in the concentration of each air pollutant. Subgroup analyses were done by age, gender, the presence of allergic disease, and season. Results: A total of 33 751 asthma attack cases were observed during the study period. The strongest association was a 9.6% increase (95% CI, 6.9% to 12.3%) in the risk of ED visits for asthma per IQR increase in $O_3$ concentration. IQR changes in $NO_2$ and $PM_{10}$ concentrations were also significantly associated with ED visits in the cumulative lag 7 model. Among patients with a prior history of allergic rhinitis or atopic dermatitis, the risk of ED visits for asthma per IQR increase in $PM_{10}$ concentration was higher (3.9%; 95% CI, 1.2% to 6.7%) than in patients with no such history. Conclusions: Ambient air pollutants were positively associated with ED visits for asthma, especially among subjects with a prior history of allergic rhinitis or atopic dermatitis.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.