• Journal of Korean Society of Environmental Engineers
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• v.32 no.8
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• pp.739-746
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• 2010

The purposes of this study are 1) to develop an advanced chamber system within ${\pm}10%$ of air velocity at the particulate matter (PM) collection area, 2) to research theoretical characteristics of PM10 and PM2.5 samplers, 3) to assess the performance characteristics of PM10 and PM2.5 samplers through chamber experiments. The total six one-hour experiments were conducted using the cornstarch with an mass median aerodynamic diameter (MMAD) of $20\;{\mu}m$ and an geometric standard deviation of 2.0 at the two different air velocity conditions of 0.67 m/s and 2.15 m/s in the chamber. The aerosol samplers used in the present study are one APM PM10 and one PM2.5 samplers accordance with the US federal reference methods and specially designed three mini-volume aerosol samplers (two for PM10 and one for PM2.5). The overall results indicate that PM10 and PM2.5 mini-volume samplers need correction factors of 0.25 and 0.39 respectively when APM PM samplers considered as reference samplers and there is significant difference between two mini-volume aerosol samplers when a two-way analysis of variance is tested using the measured PM10 mass concentrations. The PM10 and PM2.5 samplers with the cutpoints and slopes (PM10: $10{\pm}0.5\;{\mu}m$ and $1.5{\pm}0.1$, PM2.5: $2.5{\pm}0.2\;{\mu}m$ and $1.3{\pm}0.03$) theoretically collect the ranges of 86~114% and 64~152% considering the cornstarch characteristics used in this research. Furthermore, the calculated mass concentrations of PM samplers are higher than the ideal mass concentrations when the airborne MMADs for the cornstarch used are smaller than the cutpoints of PM samplers and the PM samplers collected less PM in another case. The chamber experiment also showed that PM10 and PM2.5 samplers had the bigger collection ranges of 37~158% and 55~149% than the theocratical calculated mass concentration ranges and the relatively similar mass concentration ranges were measured at the air velocity of 2.15 m/s comparing with the 0.67 m/s.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.6
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• pp.421-428
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• 2014

For the analysis of water supply network, demand-driven and pressure-driven analysis methods have been proposed. Of the two methods, demand-driven analysis (DDA) can only be used in a normal operation condition to evaluate hydraulic status of a pipe network. Under abnormal conditions, i.e., unexpected pipe destruction, or abnormal low pressure conditions, pressure-driven analysis (PDA) method should be used to estimate the suppliable flowrate at each node in a network. In order to carry out the pressure-driven analysis, head-outflow relationship (HOR), which estimates flowrate at a certain pressure at each node, should be first determined. Most previous studies empirically suggested that each node possesses its own characteristic head-outflow relationship, which, therefore, requires verification by using actual field data for proper application in PDA modeling. In this study, a model pipe network was constructed, and various operation scenarios of normal and abnormal conditions, which cannot be realized in real pipe networks, were established. Using the model network, data on pressure and flowrate at each node were obtained at each operation condition. Using the data obtained, previously proposed HOR equations were evaluated. In addition, head-outflow relationship at each node was analyzed especially under multiple pipe destruction events. By analyzing the experimental data obtained from the model network, it was found that flowrate reduction corresponding to a certain pressure drop (by pipe destruction at one or multiple points on the network) followed intrinsic head-outflow relationship of each node. By comparing the experimentally obtained head-outflow relationship with various HOR equations proposed by previous studies, the one proposed by Wagner et al. showed the best agreement with the exponential parameter, m of 3.0.

• Journal of the Korean Society for Marine Environment & Energy
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• v.13 no.1
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• pp.18-29
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• 2010

Offshore subsurface storage of $CO_2$ is regarded as one of the most promising options to response severe climate change. Marine geological storage of $CO_2$ is to capture $CO_2$ from major point sources, to transport to the storage sites and to store $CO_2$ into the offshore subsurface geological structure such as the depleted gas reservoir and deep sea saline aquifer. Since 2005, we have developed relevant technologies for marine geological storage of $CO_2$. Those technologies include possible storage site surveys and basic designs for $CO_2$ transport and storage processes. To design a reliable $CO_2$ marine geological storage system, we devised a hypothetical scenario and used a numerical simulation tool to study its detailed processes. The process of transport $CO_2$ from the onshore capture sites to the offshore storage sites can be simulated with a thermodynamic equation of state. Before going to main calculation of process design, we compared and analyzed the relevant equation of states. To evaluate the predictive accuracies of the examined equation of states, we compare the results of numerical calculations with experimental reference data. Up to now, process design for this $CO_2$ marine geological storage has been carried out mainly on pure $CO_2$. Unfortunately the captured $CO_2$ mixture contains many impurities such as $N_2$, $O_2$, Ar, $H_{2}O$, $SO_{\chi}$, $H_{2}S$. A small amount of impurities can change the thermodynamic properties and then significantly affect the compression, purification and transport processes. This paper analyzes the major design parameters that are useful for constructing onshore and offshore $CO_2$ transport systems. On the basis of a parametric study of the hypothetical scenario, we suggest relevant variation ranges for the design parameters, particularly the flow rate, diameter, temperature, and pressure.

• Archives of design research
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• v.19 no.2 s.64
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• pp.261-272
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• 2006

As we cannot think of our lives without a nation, it is closely related to almost every part of our daily lives. The role of government is becoming more important in the complex modern society as an essential element of national authority even though the government has indirect and secondary characteristics in its functional performance. Therefore, the government has to be efficient in planning and executing its policies, and it needs to be representative and fair as part of a national authoritative community. In the 21st century when symbolic and cultural importance of images are becoming more important, it is crucial for the government organizations to have an integrated identity design system that can satisfy both of these requirements of the government. However, the C.I.(Corporate Identity) of each Korean administrative branch has been developed separately and sporadically, which resulted in lack of consistency as part of the government. Shape and material of their C.I.s that follow short term design trend and popularity also lack uniqueness which can be distinguished from those of any private corporation. This may show that our government lacks systematic administrative capability, since image of a feature represents its characteristics and reality, and their recognition and evaluation from others become identity of the feature. In this perspective, the purpose of this thesis is to suggest an identity design system that has certain rules and regularity with wide variety of possible alterations for the central administration in Korea. In order to represent this visually, identity design system with both integrity and variety of possible alteration is created based on traditional Korean culture, especially the concept of Umyang-ohaeng and Samjae.

• Cartoon and Animation Studies
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• s.39
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• pp.261-295
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• 2015

Cyber singer appeared in the late 1990s has disappeared briefly appeared. although a few attempts in the 2000s, it did not show significant successes. cyber singer was born thanks to the technical development of the IT industry and the emergence of an idol training system in the music industry. It was developed by Vocaloid 'Seeyou' starting from 'Adam'. cyber singer that differenatiated typical digital characters in a cartoon or game may be subject to idolize to the music as a medium. They also feature forming a plurality of fandom. therefore, such attempts and repeated failures, this could be considered a fashion, but it flew content creation and ongoing attempts to take advantage of the new media, such as Vocaloid can see that there are expectations for a true Cyber-born singer. Early-Cyber singer is made only resemble human appearance, but 'Sciart' and 'Seeyou' has been evolving to becoming more like the human capabilities. in this paper, stylized cyber singer had disappeared in the past in the process of developing the technology to evolve into own artificial life does not end in failure cases, gradually led to a change in public perceptions of the image look looking machine was an attempt in that sense. With the direction of the evolution of the mechanical function to obtain a human, fun and human exchanges and mutual feelings. And it is equipped with an artificial life form that evolved with it only in appearance and function. in order to support this logic, I refer to the study of the coevolution of man and machine at every Bruce Mazlish. And, I have analyzed the evolution of cyber singer Bruce research from the perspective of the development process since the late 1990s, the planning of the eight singers who have appeared and design of the cyber character and important voices to be evaluated as a singer (vocal). The machine has been evolving coevolution with humans. cyber singer ambivalent development targets are recognized, but strive to become the new artificial creatures of horror idea of human desire and death continues. therefore, the new Cyber-organisms are likely to be the same style as 'Seeyou'. because, cartoon forms and whirring voice may not be in the form of a signifier is the real human desires, but this is because the contemporary public's desire to be desired and the technical development of this type can be created at the point where the cross-signifier.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.117-129
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• 2015

As the Internet becomes ubiquitous, a large volume of information is posted on the Internet with exponential growth every day. Accordingly, it is not unusual that investors in stock markets gather and compile firm-specific or market-wide information through online searches. Importantly, it becomes easier for investors to acquire value-relevant information for their investment decision with the help of powerful search tools on the Internet. Our study examines whether or not the Internet helps investors assess a firm's value better by using firm-level data over long periods spanning from January 2004 to December 2013. To this end, we construct weekly-based search volume for information technology (IT) services firms on the Internet. We limit our focus to IT firms since they are often equipped with intangible assets and relatively less recognized to the public which makes them hard-to measure. To obtain the information on those firms, investors are more likely to consult the Internet and use the information to appreciate the firms more accurately and eventually improve their investment decisions. Prior studies have shown that changes in search volumes can reflect the various aspects of the complex human behaviors and forecast near-term values of economic indicators, including automobile sales, unemployment claims, and etc. Moreover, search volume of firm names or stock ticker symbols has been used as a direct proxy of individual investors' attention in financial markets since, different from indirect measures such as turnover and extreme returns, they can reveal and quantify the interest of investors in an objective way. Following this line of research, this study aims to gauge whether the information retrieved from the Internet is value relevant in assessing a firm. We also use search volume for analysis but, distinguished from prior studies, explore its impact on return comovements with market returns. Given that a firm's returns tend to comove with market returns excessively when investors are less informed about the firm, we empirically test the value of information by examining the association between Internet searches and the extent to which a firm's returns comove. Our results show that Internet searches are negatively associated with return comovements as expected. When sample is split by the size of firms, the impact of Internet searches on return comovements is shown to be greater for large firms than small ones. Interestingly, we find a greater impact of Internet searches on return comovements for years from 2009 to 2013 than earlier years possibly due to more aggressive and informative exploit of Internet searches in obtaining financial information. We also complement our analyses by examining the association between return volatility and Internet search volumes. If Internet searches capture investors' attention associated with a change in firm-specific fundamentals such as new product releases, stock splits and so on, a firm's return volatility is likely to increase while search results can provide value-relevant information to investors. Our results suggest that in general, an increase in the volume of Internet searches is not positively associated with return volatility. However, we find a positive association between Internet searches and return volatility when the sample is limited to larger firms. A stronger result from larger firms implies that investors still pay less attention to the information obtained from Internet searches for small firms while the information is value relevant in assessing stock values. However, we do find any systematic differences in the magnitude of Internet searches impact on return volatility by time periods. Taken together, our results shed new light on the value of information searched from the Internet in assessing stock values. Given the informational role of the Internet in stock markets, we believe the results would guide investors to exploit Internet search tools to be better informed, as a result improving their investment decisions.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Restorative Dentistry and Endodontics
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• v.34 no.3
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• pp.177-183
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• 2009

The aim of this study was to evaluate the marginal and internal gaps in CEREC3 CAD/CAM inlays of three different preparation designs. CEREC3 Inlays of three different preparation designs (n=10) were fabricated according to Group I-conventional functional cusp capping/shoulder preparation, Group II-horizontal reduction of cusps and Group III-complete reduction of cusps/shoulder preparation. After cementation of inlays. the bucco-lingual cross section was performed through the center of tooth. Cross section images of 20 magnifications were obtained through the stereomicroscope. The gaps were measured using the Leica application suite software at each reference point. Statistical analysis was performed using one-way ANOVA and Tukey's test (${\alpha}<0.05$). The marginal gaps ranged from 80.0 to $97.8{\mu}m$ for Group I, 42.0 to $194.8{\mu}m$ for Group II, 51.0 to $80.2{\mu}m$ for Group III. The internal gaps ranged from 90.5 to $304.1{\mu}m$ for Group I, 80.0 to $274.8{\mu}m$ for Group II, 79.7 to $296.7{\mu}m$ for Group III. The gaps of each group were the smallest on the margin and the largest on the horizontal wall. For the CEREC3 CAD/CAM inlays, the simplified designs (groups II and III) did not demonstrate superior results compared to the traditional cusp capping design (group I).

• Journal of radiological science and technology
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• v.33 no.4
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• pp.327-334
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• 2010

Exposure during childhood results in higher risk for certain detrimental cancers than exposure during adulthood. We measured entrance skin dose (ESD) under 7-year children undergoing chest imaging and compared the relationship between ESD and age, height, weight, chest thickness. Though it is important to measure chest thickness for setting up the exposure condition of chest examination, it is difficult to measure chest thickness of children. We set up exposure parameters according to age because chest thickness of children has correlation with age. In the exposure parameters, for chest A-P examination under 2 year-children, tube voltage (kVp) in hospital A was higher than that in hospital B while tube current (mAs) was higher in hospital B, thus the ESD values were about 1.7 times higher in hospital B. However, for chest P-A examination over 4 year-children, the tube voltage was 7 kVp higher in hospital B, the tube current were same in all two systems, and focus to image receptor distance (FID) in hospital B (180 cm) was longer than that in hospital A (130 cm), thus the ESD values were 1.4 times higher in hospital A. For same ages, the ESD values for chest A-P examinations were higher than those for chest P-A examinations. Comparing ESD according to age, ESD values were $154{\mu}Gy$, $194{\mu}Gy$ and $138{\mu}Gy$ for children under 1 year, 1 to under 4 years and 4 to under 7 years of age, respectively. These values were lower than reference level ($200{\mu}Gy$) recommended in JART (japan association of radiological technologists), however these were higher than reference values recommended by EC (european commission), NRPB (national radiological protection board) and NIFDS (national institute of food & drug safety evaluation). In conclusion, the values of ESD were affected by exposure parameters from radiographer's past experience more than x-ray system. ESD values for older children were not always higher than those for younger children. Therefore we need to establish our own DRLs (diagnostic reference levels) according to age of the children in order to optimize pediatric patient protection.