• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.85-97
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• 2010

Internet communities are a typical space for knowledge creation and use on the Internet as people discuss their common interests within the internet communities. When we define 'Knowledge Communities' as internet communities that are related to knowledge creation and use, they are categorized into 4 different types such as 'Search Engine,' 'Open Communities,' 'Specialty Communities,' and 'Activity Communities.' Each type of knowledge community does not remain the same, for example. Rather, it changes with time and is also affected by the external business environment. Therefore, it is critical to develop processes for practical use of such changeable knowledge communities. Yet there is little research regarding a strategic framework for knowledge communities as a source of knowledge creation and use. The purposes of this study are (1) to find factors that can affect knowledge creation and use for each type of knowledge community and (2) to develop a strategic framework for practical use of the knowledge communities. Based on previous research, we found 7 factors that have considerable impacts on knowledge creation and use. They were 'Fitness,' 'Reliability,' 'Systemicity,' 'Richness,' 'Similarity,' 'Feedback,' and 'Understanding.' We created 30 different questions from each type of knowledge community. The questions included common sense, IT, business and hobbies, and were uniformly selected from various knowledge communities. Instead of using survey, we used these questions to ask users of the 4 representative web sites such as Google from Search Engine, NAVER Knowledge iN from Open Communities, SLRClub from Specialty Communities, and Wikipedia from Activity Communities. These 4 representative web sites were selected based on popularity (i.e., the 4 most popular sites in Korea). They were also among the 4 most frequently mentioned sitesin previous research. The answers of the 30 knowledge questions were collected and evaluated by the 11 IT experts who have been working for IT companies more than 3 years. When evaluating, the 11 experts used the above 7 knowledge factors as criteria. Using a stepwise linear regression for the evaluation of the 7 knowledge factors, we found that each factors affects differently knowledge creation and use for each type of knowledge community. The results of the stepwise linear regression analysis showed the relationship between 'Understanding' and other knowledge factors. The relationship was different regarding the type of knowledge community. The results indicated that 'Understanding' was significantly related to 'Reliability' at 'Search Engine type', to 'Fitness' at 'Open Community type', to 'Reliability' and 'Similarity' at 'Specialty Community type', and to 'Richness' and 'Similarity' at 'Activity Community type'. A strategic framework was created from the results of this study and such framework can be useful for knowledge communities that are not stable with time. For the success of knowledge community, the results of this study suggest that it is essential to ensure there are factors that can influence knowledge communities. It is also vital to reinforce each factor has its unique influence on related knowledge community. Thus, these changeable knowledge communities should be transformed into an adequate type with proper business strategies and objectives. They also should be progressed into a type that covers varioustypes of knowledge communities. For example, DCInside started from a small specialty community focusing on digital camera hardware and camerawork and then was transformed to an open community focusing on social issues through well-known photo galleries. NAVER started from a typical search engine and now covers an open community and a special community through additional web services such as NAVER knowledge iN, NAVER Cafe, and NAVER Blog. NAVER is currently competing withan activity community such as Wikipedia through the NAVER encyclopedia that provides similar services with NAVER encyclopedia's users as Wikipedia does. Finally, the results of this study provide meaningfully practical guidance for practitioners in that which type of knowledge community is most appropriate to the fluctuated business environment as knowledge community itself evolves with time.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.99-112
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.1
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• pp.91-97
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• 2017

This study contains research on a bio-monitoring system (BMS) capable of detecting abnormal high water temperatures, the shell valve movements (SVMs) of Pacific oysters (Crassostrea gigas), which were measured at four different temperature (5, 10, 20 and $30^{\circ}C$) under laboratory conditions. All the Pacific oysters were kept under fasting conditions for 3 days to prevent the influence of food and excretions before the onset of the experiments. SVMs did not detect at $5^{\circ}C$. However, SVMs increased with an increase in temperature (at $10^{\circ}C$ : $6.31{\pm}2.18times/hr$ and at $20^{\circ}C$: $22.0{\pm}10.0times/hr$). At $30^{\circ}C$, SVMs were divided into two groups: those with no SVMs as at $5^{\circ}C$ and those with SVMs similar to conditions at $20^{\circ}C$($23.9{\pm}9.35times/hr$). This indicates oyster shells maintain a closed condition due to a decrease in metabolism at $30^{\circ}C$, although some Pacific oysters had active SVMs due to an increase in metabolism. If a BMS using the SVM status of Pacific oysters was installed to monitor abnormal high water around oyster farms, early warning levels and serious alerts might be made available more rapidly for SVMs of more than ca. 30 times/hr and closing conditions in a matter of hours, respectively. Therefore, a BMS using the SVMs of Pacific oysters might be an effective early warning system for abnormal high water temperatures.

• Korean Journal of Plant Taxonomy
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• v.33 no.3
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• pp.279-293
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• 2003

Somatic chromosomes about 13 taxa of Korean Euphorbia L. was investigated to estimate its taxonomic significance. Somatic chromosome numbers of treated taxa were 2n= 12, 20, 22, 28, 40, 42, 56, therefore basic chromosome numbers of those were x=6, 7, 10, 11. The chromosome numbers of E. pallasii Turcz. (2n=20), E. hylonoma Hand.-Mazz (2n=20.), E. fauriei H. L$\acute{e}$v. & Vaniot ex H. L$\acute{e}$v (2n=28) and E. jolkini Boiss. (2n=28) were determined for the first time in this study. The chromosome numbers of four taxa were same as previous ones; E. sieboldiana Moor. & Decne. (2n=20), E. ebracteolata Hayata (2n=20), E. humifusa Willd. ex Schlecht. (2n=22). But those of six taxa were different; E. esula L (2n= 16, 20, 60, 64 vs 2n=20), E. helioscopia L. (2n=12, 42 vs 2n=42), E. lucorum Rupr. (2n=28, 40 vs 2n=56), E. pekinensis Rupr. in Maxim. (2n=24 vs 2n=28, 56), E. maculata L. (2n=28, 42 vs 2n=12), E. supina Raf. (n=7 vs 2n=40). E. ebracteolata, E. pallasii and E. hylonoma were distingushcd from the other taxa by the chromosome numbers, size and satellites, E. maculata, E. humifusa, E. supina had the different basic and somatic chromosome numbers in spite of the similar morphological. anatomical and palynological chracters. The chromosomal character of Korean Euphorbia was supported the Ma and Hu's systems, and as above results, it was found to be a good character in delimiting above sections and estimating relationships for some species.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.39 no.3
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• pp.195-203
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• 2013

In this study, antioxidative effects of the extracts of different species and flowering periods of Inula britannica were investigated. According to the free radical (1,1-diphenyl-2-picrylhydrazyl, DPPH) scavenging activity of the extracts, The I. britannica var. chinensis flower extract (500 ${\mu}g/mL$) was measured in a 79.89% free radical scavenging activity, but the flower extracts of similar species (I. britannica var. linariaefolia Regel, I. britannica var. ramosa, I. salicina var. asiatica) did not show any effect on the free radical scavenging activity. The effects of the free radical scavenging activity of I. britannica var. chinensis flower extracts were exhibited in the order of full bloom (93.68%), bud (43.28%), and fallen blossom (14.11%). Next, we established optimum condition of extract solvent, temperature, extraction time. The extract from ethanol at $60^{\circ}C$ showed the most free radical scavenging activity among other conditions and extraction time not relevant in free radical scavenging activity. The protective effects of the extract of I. britannica var. chinensis flower on the photohemolysis of human erythrocytes by using rose bengal were increased in a concentration-dependent manner (5 ~ 50 ${\mu}g/mL$). In particular, the extract in 50 ${\mu}g/mL$ concentration exhibited better protective activity (${\tau}_{50}$ = 116.1 min) than (+)-${\alpha}$-tocopherol (${\tau}_{50}$ = 73.44 min), which is a known lipophilic antioxidant. Principle component of I. britannica var. chinensis flower was identified as quercetin of flavonoids by high-performance liquid chromatography (HPLC). These results indicate that the extract of I. britannica var. chinensis flower can function as antioxidants in biological systems, particularly skin exposed to UV radiation by scavenging free radical and $^1O_2$, and protect cellular membranes against ROS. It is concluded that the antioxidative effects of the extract of I. britannica var. chinensis flower could be applicable to functional cosmetics.

• Korean Journal of Social Welfare
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• v.56 no.3
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• pp.159-181
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• 2004

This study examines the realities and causes of youth and new college graduate unemployment, and seeks some assignments for mitigating youth and new college graduate unemployment. An analysis of the realities and causes of youth and new college graduate unemployment is summarized as follows. First, youth unemployment rate, which rapidly increased after the IMF economic crisis, slowly decreased after 2000, but was still somewhat higher in 2002 than that before the IMF. Second, new college graduate unemployment rate, which rapidly increased after the IMF economic crisis, slowly decreased after 2000 and became a similar level to that before the IMF economic crisis, but the number of the unemployed new college graduates highly increased after the IMF. Third, an analysis of the causes of youth unemployment shows that economic growth and the employment elasticity of economic growth negatively affect the unemployment rate, and the rate of entrance into colleges positively affects the unemployment rate. Fourth, an analysis of the causes of new college graduate unemployment demonstrates that economic growth and the employment elasticity of economic growth negatively affect the unemployment rate, and the increase rate of new college graduates, the college graduate/youth population ratio, and the time trend positively affect the unemployment rate. These results suggest several implications for mitigating the unemployment rate of the youth and new college graduates. First, in order to increase labor demand, emphasis must be placed on preparing economic conditions which can raise economic growth rate and on fostering industries and occupations which have high employment elasticity. Second, in the aspect of labor supply, it is necessary to adjust the number of new college graduates corresponding to labor demands in industries. Third, in order to redress the mismatch between the demand and the supply of the youth labor market, attention should be paid to remedying educational systems such as the activation of vocational education and training in middle and high schools and the reformation of college education to match the education and training provided in colleges and the skills requirements of the world of work, and preparing a unified program to support the youth unemployed systematically and synthetically.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Economic and Environmental Geology
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• v.37 no.3
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• pp.303-309
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• 2004

Pyrochlores were known as promising materials for the immobilization of radioactive actinide. Accordingly, we synthesized pyrochlores with Gd$_2$Ti$_2$$O_7$ and Gd$_2$Zr$_2$$O_7$compositions by sintering method, and studied its properties and phase relations in Gd-Ti-O and Gd-Zr-O system. The mixed powders were pressed into pellets under 200-400 kgf/cm$^2$ at room temperature. and then sintered at 1000-1$600^{\circ}C$ for 0.5-40 hours. The synthesized samples were analyzed and were identified with XRD and SEM/EDS analyses. The optimal synthetic conditions of pyrochlores with Gd$_2$Ti$_2$$O_7$composition were at 140$0^{\circ}C$/0.5hrs, 130$0^{\circ}C$/3hrs and 120$0^{\circ}C$/20hrs. Its chemical composition was $Gd_{2.0-2.1}$$Ti_{1.9-2.0}$$O_7$ and similar to the stoichiometric composition without any relationship in temperature and atmosphere. The optimal synthetic conditions of pyrochlores with $Gd_{2}$$Zr_{2}$$O_7$composition were at 155$0^{\circ}C$/40hrs and 1$600^{\circ}C$/30hrs. The compositions of pyrochlore synthesized from these optimal conditions were irregular with $Gd_{1.5-2.4}$$Zr_{1.7-2.4}$$O_7$. Such heterogeneity indicates that the reaction rate of pyrochlore with Gd$_2$Zr$_2$$O_7$composition is very low, and then its equilibrium state could not be attained even for 40 hours which was the longest sintering time in this research.

• Journal of Animal Science and Technology
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• v.50 no.3
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• pp.391-400
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• 2008

Odor management is significantly concerned with sustainable livestock production because odor nuisance is a primary cause for complaint to neighbors. This study was conducted to measure the concentration of odorous compounds, odor intensity, and odor offensiveness at unit process in animal waste treatment facility combined composting and methane fermentation process by an instrumental analysis and direct olfactory method. Ammonia, sulfur-containing compounds, and volatile fatty acid were analyzed at each process units and boundary area in summer and winter, respectively. Higher concentration of odorants occurred in the summer than in the winter due to high ambient temperature. The maximum concentration of odorants was detected in composting pile when mixed manure was being turned followed by inlet, curing, outlet, and screen & packing process. Highest concentration of detected odorous compounds was ammonia ranging from 3.4 to 224.7 ppm. Among the sulfur-containing compounds measured, hydrogen sulfide was a maximum level of 2.3 ppm and most of them exceeded reported odor detection thresholds. Acetic acid was the largest proportion of VFA generated, reaching a maximum of 51 to 89%, followed by propionic and butyric acid at 1.9 to 35% and 1.8 to 15%, respectively. Malodor assessment by a human panel appeared a similar tendency in instrumental analysis data. Odor quotient for predicting major odor-causing compounds was calculated by dividing concentrations measured in process units by odor detection thresholds. In the composting process, hydrogen sulfide, ammonia, dimethyl sulfide, and methyl mercaptan were deeply associated with odor-causing compounds, while the major malodor compounds in the inlet process were methyl mercaptan, hydrogen sulfide, and butyric acid.