• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.45-69
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• 2016

Recently, sentiment analysis using open Internet data is actively performed for various purposes. As online Internet communication channels become popular, companies try to capture public sentiment of them from online open information sources. This research is conducted for the purpose of analyzing pulbic sentiment of Korean Top-10 companies using a multi-categorical sentiment lexicon. Whereas existing researches related to public sentiment measurement based on big data approach classify sentiment into dimensions, this research classifies public sentiment into multiple categories. Dimensional sentiment structure has been commonly applied in sentiment analysis of various applications, because it is academically proven, and has a clear advantage of capturing degree of sentiment and interrelation of each dimension. However, the dimensional structure is not effective when measuring public sentiment because human sentiment is too complex to be divided into few dimensions. In addition, special training is needed for ordinary people to express their feeling into dimensional structure. People do not divide their sentiment into dimensions, nor do they need psychological training when they feel. People would not express their feeling in the way of dimensional structure like positive/negative or active/passive; rather they express theirs in the way of categorical sentiment like sadness, rage, happiness and so on. That is, categorial approach of sentiment analysis is more natural than dimensional approach. Accordingly, this research suggests multi-categorical sentiment structure as an alternative way to measure social sentiment from the point of the public. Multi-categorical sentiment structure classifies sentiments following the way that ordinary people do although there are possibility to contain some subjectiveness. In this research, nine categories: 'Sadness', 'Anger', 'Happiness', 'Disgust', 'Surprise', 'Fear', 'Interest', 'Boredom' and 'Pain' are used as multi-categorical sentiment structure. To capture public sentiment of Korean Top-10 companies, Internet news data of the companies are collected over the past 25 months from a representative Korean portal site. Based on the sentiment words extracted from previous researches, we have created a sentiment lexicon, and analyzed the frequency of the words coming up within the news data. The frequency of each sentiment category was calculated as a ratio out of the total sentiment words to make ranks of distributions. Sentiment comparison among top-4 companies, which are 'Samsung', 'Hyundai', 'SK', and 'LG', were separately visualized. As a next step, the research tested hypothesis to prove the usefulness of the multi-categorical sentiment lexicon. It tested how effective categorial sentiment can be used as relative comparison index in cross sectional and time series analysis. To test the effectiveness of the sentiment lexicon as cross sectional comparison index, pair-wise t-test and Duncan test were conducted. Two pairs of companies, 'Samsung' and 'Hanjin', 'SK' and 'Hanjin' were chosen to compare whether each categorical sentiment is significantly different in pair-wise t-test. Since category 'Sadness' has the largest vocabularies, it is chosen to figure out whether the subgroups of the companies are significantly different in Duncan test. It is proved that five sentiment categories of Samsung and Hanjin and four sentiment categories of SK and Hanjin are different significantly. In category 'Sadness', it has been figured out that there were six subgroups that are significantly different. To test the effectiveness of the sentiment lexicon as time series comparison index, 'nut rage' incident of Hanjin is selected as an example case. Term frequency of sentiment words of the month when the incident happened and term frequency of the one month before the event are compared. Sentiment categories was redivided into positive/negative sentiment, and it is tried to figure out whether the event actually has some negative impact on public sentiment of the company. The difference in each category was visualized, moreover the variation of word list of sentiment 'Rage' was shown to be more concrete. As a result, there was huge before-and-after difference of sentiment that ordinary people feel to the company. Both hypotheses have turned out to be statistically significant, and therefore sentiment analysis in business area using multi-categorical sentiment lexicons has persuasive power. This research implies that categorical sentiment analysis can be used as an alternative method to supplement dimensional sentiment analysis when figuring out public sentiment in business environment.

• Journal of the Korean GEO-environmental Society
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• v.19 no.10
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• pp.13-19
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• 2018

Following the 2016 Gyeongju earthquake, the Pohang Earthquake occurred in 2017, and the south-east region in Korea is under the threat of an earthquake. Especially, in the Pohang Earthquake, the liquefaction phenomenon occurred in the sedimentation area of the coast, and preparation of countermeasures is very important. The soil liquefaction can affect the underground facilities directly as well as various structures on the ground. Therefore, it is necessary to identify the liquefaction risk of facilities and the structures against the possible earthquakes and to prepare countermeasures to minimize them. In this study, we investigated the seismic liquefaction risk about the electric power utility tunnels in the southeast area where the earthquake occurred in Korea recently. In the analysis of seismic liquefaction risk, the earthquake with return period 1000 years and liquefaction potential index are used. The liquefaction risk analysis was conducted in two stages. In the first stage, the liquefaction risk was analyzed by calculating the liquefaction potential index using the ground survey data of the location of electric power utility tunnels in the southeast region. At that time, the seismic amplification in soil layer was considered by soil amplification factor according to the soil classification. In the second stage, the liquefaction risk analysis based on the site response analyses inputted 3 earthquake records were performed for the locations determined to be dangerous from the first step analysis, and the final liquefaction potential index was recalculated. In the analysis, the site investigation data were used from the National Geotechnical Information DB Center. Finally, it can be found that the proposed two stage assessments for liquefaction risk that the macro assessment of liquefaction risk for the underground facilities including the electric power utility tunnel in Korea is carried out at the first stage, and the second risk assessment is performed again with site response analysis for the dangerous regions of the first stage assessment is reasonable and effective.

• Journal of The Korean Association For Science Education
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• v.40 no.2
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• pp.177-189
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• 2020

In this study, we implemented an education program to improve the responsive teaching ability of pre-service science teachers, and analyzed the responsive teaching practices revealed during the program process. Through this, we derived the types and characteristics of responsive teaching practice, identified factors that made it difficult for pre-service teachers to practice, and obtained empirical data on under what conditions the responsive teaching capacity of pre-service teachers was developed. For this purpose, a practice-based teacher education program was designed and carried out for 14 pre-service teachers who had no experience in responsive teaching. The program consists of four steps; observation of class, practice through rehearsal, application in practicum, and post-reflection on educational practice. In particular, qualitative analysis was conducted on the types of responsive teaching and their detrimental factors revealed during application in practicum. As a result of the analysis, four types were derived; discriminator type, communicator type, guide type, and facilitator type. Each type was identified as having a common responsive teaching step element. The education program implemented in this study was effective for pre-service teachers to recognize the importance of student-participation class and the educational effect of responsive teaching. However, three barriers that prevented pre-service teachers from responsive teaching practice were also analyzed. First was the pressure to achieve specific learning goals within a given class time. Second was the rigid belief of the fixed curriculum. Third was the obsession that the teacher should lead the class. Based on these results, it was suggested that in order to improve the responsive teaching ability of pre-service teachers, it is necessary to support the recognition of breaking out of the thinking the time constraint, the flexibility of the curriculum, and the role of teacher as a class supporter.

• Particle and aerosol research
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• v.14 no.4
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• pp.153-169
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• 2018

To identify a variety of organic compounds in the ambient aerosols, the two-dimensional gas chromatography-time of flight mass spectrometry (GCxGC) system (2DGC) has been applied. While 2DGC provides more peaks, the amount of the generated data becomes huge. A two-step approach has been proposed to efficiently interpret the organic aerosol analysis data. The two-dimensional 2DGC data were divided into 6 chemical groups depending on their volatility and polarity. Using these classification standards, all the peaks were subject to both qualitative and quantitative analyses and then classified into 8 classes. The aerosol samples collected in Seoul in summer 2013 and winter 2014 were used as the test case. It was found that some chemical classes such as furanone showed seasonal variation in the high polarity-volatile organic compounds (HP-VOC) group. Also, for some chemical classes, qualitative and quantitative analyses showed different trends. Limitations of the proposed method are discussed.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Korean Society of Environmental Engineers
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• v.34 no.8
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• pp.533-540
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• 2012

The concentration of organic compounds was analyzed at each step of BAC (biological activated carbon) process though BDOC (biodegradable dissolved organic carbon) total/rapid/slow. Further, bacteria communities and biomass concentrations measured DGGE (denaturing gradirnt gel electrophoresis) and ATP (adenosine triphosphate) methods were analyzed. The bed volume of steady state is different based on assessment of organic compounds removal. Bed volumes at steady state in DOC, $BDOC_{rapid}$ and $BDOC_{total/slow}$ removal were around 27,500, 15,000 and 32,000, respectively. A biomass didn't change after the bed volume reached 22,500 according to analyzing HPC (heterotrophic plate count) and ATP concentration of bacteria. The concentration of HPC and ATP were $3.3{\times}10^8$ cells/g and $2.14{\mu}g/g$, respectively. The number of the DGGE band were only 5 at the bed volume 8,916, but increased up to 11 at the bed volume 49,632. As operation time increase, bacterial group were more diversity. Four bacteria species including Pseudomonas fluorescens, the uncultured bacterium similar to Acinetobacteria, uncultured Novosphingobium sp. and Flavobacterium frigidarium have detected from the early stages and Proteobacteria group were dominantly detected.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.12
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• pp.667-675
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• 2016

Nanoscale zero-valent iron (nZVI) has been effectively applied for environmental remediation due to its ability to reduce various toxic compounds. However, quantification of nZVI reactivity has not yet been standardized. Here, we adapted colorimetric assays for determining reductive activity of nZVIs. A modified indophenol method was suggested to determine reducing activity of nZVI. The method was originally developed to determine aqueous ammonia concentration, but it was further modified to quantify phenol and aniline. The assay focused on analysis of reduction products rather than its mother compounds, which gave more accurate quantification of reductive activity. The suggested color assay showed superior selectivity toward reduction products, phenol or aniline, in the presence of mother compounds, 4-chlorophenol or nitrobenzene. Reaction conditions, such as reagent concentration and reaction time, were optimized to maximize sensitivity. Additionally, pretreatment step using $Na_2CO_3$ was suggested to eliminate the interference of residual iron ions. Monometallic nZVI and bimetallic Ni/Fe were investigated with the reaction. The substrates showed graduated reactivity, and thus, reduction potency and kinetics of different materials and reaction mechanism was distinguished. The colorimetric assay based on modified indophenol reaction can be promises to be a useful and simple tool in various nZVI related research topics.

• Journal of Advanced Marine Engineering and Technology
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• v.8 no.1
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• pp.17-36
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• 1984

Since 1973, the competition on the development of fuel saving type internal combustion engines has become severe by the two times oil shock, and new type engines are reported every several months. Whenever these new type engines are developed, new designs are required and they will be offered in the market after performing the endurance test for a long time. But the engine market is faced with a heavy burden of finance, as the developing of a new engine requires tremendous expenses. For this reason, the computer simulation method has been lately developed to cope with it. The computer simulation method can be available to perform the reasonable research works by the theoretical analysis before carrying out practical experiments. With these processes, the developing expenses are cut down and the period of development is curtailed. The object of this study is the development of simulation computer program for the small naturally aspirated four-stroke diesel engine which is intended to product by the original design of our country. The process of simulation is firstly investigated for the ideal engine cycle, and secondly for the real engine cycle. In the ideal engine cycle, each step of the cycle is simulated by the energy balance according to the first law of thermodynamics, and then the engine performance is calculated. In the real cycle imulation program, the injection rate, the preparation rate and the combustion rate of fuel and the heat transfer through the wall of combustion chamber are considered. In this case, the injection rate is supposed as constant through the crank angle interval of injection and the combustion rate is calculated by the Whitehouse-Way equation and the heat transfer is calculated by the Annand's equation. The simulated values are compared with measured values of the YANMAR NS90(C) engine and Mitsubishi 4D30 engine, and the following conclusions are drawn. 1. The heat loss by the exhaust gas is well agree with each other in the lower load, but the measured value is greater than the calculated value in the higher load. The maximum error rate is about 15% in the full load. 2. The calculated quantity of heat transfer to the cooling water is greater than the measured value. The maximum error rate is about 11.8%. 3. The mean effective pressure, the fuel consumption, the power and the torque are well agree with each other. The maximum error is occurred in the fuel consumption, and its error rate is about 7%. From the above remarks, it may be concluded that the prediction of the engine performance is possibly by using the developed program, although the program needs to reform by adding the simulation of intake and exhaust process and assumping more reliable mechanical efficiency, volumetric efficiency, preparation rate and combustion rate.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.4 no.3 s.8
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• pp.61-72
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• 2005

This paper is designed to report the results of response manual development in relation to the freeway Incident Management System(FIMS) development as part of Intelligent Transportation Systems Research and Development program. The central core of the FIMS is an integration of the component parts and the modular, but integrated system for freeway management. The whole approach has been component-orientated, with a secondary emphasis being placed on the traffic characteristics at the sites. The first task taken during the process was the selection of the required actions for each step within the Incident Management System. After through review and analysis of existing incident response procedures and manuals, the incident response manual led to the utilization of different technologies and actions in relation to the specific needs and character of the incidents. FIMS also provides Integrated Incident Management according to the verified incident information provided by the each components The deployment of containment and mitigation strategies for incidents will be automatic or manual depending on the configuration of the system. It is anticipated that, over a period of time, operators will be able to response the incident using integrated and organized Procedures and action items.

• Journal of the Korea Concrete Institute
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• v.26 no.2
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• pp.219-227
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• 2014

The purpose of this study is to investigate water purification properties of porous concrete by using effective microorganisms through the long-term continuous flow test. To solve the problems such as desorption of conventional microorganisms, in this study, tertiary treatment of the effective microorganisms identified by 16S rDNA sequence analysis was adopted per each step in the manufacturing process of porous concrete. And concentration for optimum continuous flow test and operation conditions through basic experiments according to retention time were investigated. Based on the experimental results, the porous concrete applying effective microorganisms showed no toxicity on the biological water quality and exhibited excellent removal efficiency than normal porous concrete. Therefore, contaminated water quality would be improved by treatment performance investigation of contaminants through long-term continuous flow test. If problems are complemented during the experiment process, it is expected to be able to reduce the non-point pollution sources flowing into river.