• Clinical and Experimental Pediatrics
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• v.46 no.12
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• pp.1253-1259
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• 2003

Backgroud : Seizures in the neonate are relatively common and their clinical features are different from those in children and adults. The study aimed to provide the clinical profiles of neonatal seizure in our hospital. Methods : A total of 41 newborns with seizures were enrolled in this study over a period of three years. They were evaluated with special reference to risk factors, neurologic examinations, laboratory data, neuroimaging studies, EEG findings, seizure types, response to treatment, and prognosis, etc. Results : The average age at onset of seizures was $6.1{\pm}4.6days$ and the majority of patients(42%) had multifocal clonic seizure and 24% had subtle seizure. Factors that are known to increase risk of neonatal seizures include abnormal delivery history, birth asphyxia, and electrolyte imbalance, etc. However, they remain obscure in about 20% of cases. More than 50 percent showed abnormal lesions on neuroimaging studies such as brain hemorrhage, periventricular leukomalacia, brain infarction, cortical dysplasia, hydrocephalus, etc. and 17 out of 32 patients showed abnormal electroencephalographic patterns. Phenobarbital was tried as a first line antiepileptic drug and phenytoin was added if it failed to control seizures. The treatments were terminated in the majority of patients during the hospital stay. The overall prognosis was relatively good except for those with abnormal EEG background or congenital central nervous system malformations. Conclusion : Neonatal seizures may permanently disrupt brain development. Better understanding of their clinical profiles and appropriate management may lead to a reduction in neurological disability in later childhood.

• The Korean Journal of Pesticide Science
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• v.7 no.4
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• pp.264-270
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• 2003

To control effectively and safely Phytophthora root rot caused by Phytophthora capsici on tomato in hydroponic culture, tank-mixing method was considered with two pesticides, metalaxyl copper oxychloride 50% WP and dimethomorph dithianon 38% WP. Forty days after transplanting of tomato seedlings, 4 mL of sporangia of P. capsici (about 25 sporangi/mL) per plot was inoculated around tomato plant roots, and at 5 days after inoculation, the pesticides tank-mixed at three dilution levels, 12,500, 25,000 and 50,000, were drenched 1, 2 or 3 times per plot on the culture cube every 15 days for metalaxyl copper oxychloride 50% WP and every 10 days for dimethomorph dithianon 38% WP. During the drenching period, the residue levels of metalaxyl and dimethomorph in hydroponic culture solution were similar to the initial levels but the level of dithianon was drastically decreased from one day after tank-mixing. In tomato drenched with metalaxyl copper oxychloride 50% WP, metalaxyl was detected $0.02\sim0.04$ mg/kg in all diluted plots. Dimethomorph was detected $0.012\sim0.021$, $0.001\sim0.006$ and $0.001\sim0.003$ mg/kg in 12,500, 25,000 and 50,000 times diluted plots, respectively, while dithianon was detected 0.005, 0.003 mg/kg in 12,500 and 50,000 times diluted plots, respectively. The detection levels of three pesticides were far below compared with the levels of Korean MRLs. Incidences of Phytophthora root rot were not found in all the plots, but phytotoxic responses were recognized in the 12,500 times diluted plots of both pesticides. Based on the above results, the drenching of the culture solution tank-mixed with these pesticides could be recommended as a very safe and effective method to control Phytophthora root rot in tomato in hydroponic culture.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.32 no.2
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• pp.42-54
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• 2014

This study was conducted to identify landscaping elements such as location, situation and feng shui included in the spatiality of Jangseong Pilmaseowon and to interpret aesthetic features of visual-perceptual spatial composition according to its arrangement. As it is shown in 'Pilamseowon', 'Pilbongseowon', and 'Gimhaseoseowon' appearing in antique maps, the awareness considering 'Pilam' as 'Pilbong' and 'Gimhaseo' was revealed. Mountain Pilamsan[Mountain Munpilsan] which is the location of seowon and Pilam(Brush-shaped rock) is the core of establishment of location identity of Pilamseowon and the symbol of Haseo Kim In-hu, which shows that they are deeply related to Ingeoljiryeong(人傑地靈: 'a place derives reflected glory from an illustrious human') based on connection. Pilamseowon shows locational characteristics of living in stream(溪居) facing panoramic 'jeungsan field' without Ansan(案山). Based on the teachings of Neo-Confucianism, Village Maekdong which is the birth place of Haseo, Pilam, seowon geomancy considering the Danbonghamseo-type(丹鳳含書形) geographical shape, formative reflection, Pilmaseowon and structures revealed in building naming more clearly show symbolic landscaping features resulting from 'theory of 'Heaven-Man Unity'(天人合一)' representing the union of nature and haman, than other seowons. The maximization of centrality through connected yards constructed with the 'jeondang hujae(前堂後齋)' arrangement in the order of Whakyeon-lu, Chenogjeol-dang, Jindeak-jae or Sungui-jae, and Woodong-sa is a unique feature of spatial frame of Pilmaseowon. In addition, it reveals the centrality reinforced with 'the move of inner center through arrangement of Kyeongjang-kag and Kyesengbi inside 'YuSik(遊息)' space and religious space' and the landscaping arrangement of Pilmaseowon from installation and device for reinforcement of territoriality. Moreover, it was found that orders and aesthetic features based on Neo-Confucianism were logically realized in the formation of Pilmaseowon with visual and compositional landscaping arrangement such as 'reinforcement of view centrality through composition of windows and doors', 'securement of visual transparency through framing and duplication', and 'realization of hierarchy through height of jaesil toenmaru'. The meaning system and spatial or visual aesthetic features of Pilmaseowon newly arranged and interpreted through landscaping recomposition is not a coincidental but inevitable result. It is another resource basis and an element that can improve the internal exuberance of Pilamseowon. This landscaping reading study is expected to improve the understanding of landscapes of Pilmaseowon and elevate the sensibility of unrevealed cultural landscapes.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.31 no.1
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• pp.48-58
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• 2013

The results of this research to study forms, structure, changes, symbolic meanings of 16 Hahoe sceneries through analyses of "Hahwaesipyukgyeong" and "Hahwaenakgangsanghaildaedo" are as belows. The coherence of headword is not discovered in 16 Hahoe sceneries, but based on various variables and sense dependence, endemicity with original natural scenes, human's life and phenomena of riverside village are spread in 3km viewing areas within 200m from Gyeonamjeongsa(謙巖精舍) and Okyeonjeongsa(玉淵精舍). As the viewing points of Gyeonam and Okyeonjeongsa are symmetrically facing and separately independent, while viewing angles do not intersect at Wonjijeongsa (遠志精舍) and Binyeonjeongsa(賓淵精舍) because of Buyongdae(芙蓉臺), and crating each independent viewing area, we can see 16 Hahoe sceneries are perfect views by supplementing Gyeonam and Okyeon Jeongsa, as well as points of views from Wonji and Binyeonjeongsa. Meanwhile, as the view point of 16 Hahoe sceneries, Gyeomam, Okyeon, Binyeon, and Wonji Jeongsa are clearly described, and 12 natural sceneries, which are Hwasan(花山), Ipam(立巖), Maam(馬巖), Jando(棧道), Bangi(盤磯), Hoengju(橫舟), and Honggyo(虹橋), among landscape elements of 16 Hahoe sceneries that can be expressed on canvas in the Haoedo are realistically described, there is high possibility that Haoedo is the 'Mental Stroll about Nature(臥遊) of 16 Hahoe sceneries. The belted forest surrounding the village in the painting is assumed to be an erosion control forest, and considering row-expressed trees, the south belted forest may be a different broad-leaved forest from current Mansongjeong(萬松亭) pine forest. In 16 Hahoe sceneries, there is Neo-confucianism tendency, which connects the nature and human life, and moreover prioritize human life than the nature. Especially as seen in the 'Choljae(拙齋)', the pen name of 16 Hahoe sceneries' author park, the 16 Hahoe scenery poet suggests 'Beauty of Jolbak(拙撲美)' based on the simple life that upright classical scholars pursued as the basic emotion. The thinking system shown in the poet is interpreted as Neo-confucianism category including one's sense and emotion depended on natural features or phenomena. Ultimately, 16 Hahoe sceneries are landscape that reflects moral world views of Confucianism scholars who wanted to express ideal thoughts based on natural features and phenomena in reality at Jeongsa in Buyongdae and Hahoe Village.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.109-130
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• 2011

One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.53-74
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• 2018

Since South Korea has an economic structure that has a characteristic which market-dependent on overseas, the tourism industry is considered as a very important industry for the national economy, such as improving the country's balance of payments or providing income and employment increases. Accordingly, the necessity of more accurate forecasting on the demand in the tourism industry has been raised to promote its industry. In the related research, economic variables such as exchange rate and income have been used as variables influencing tourism demand. As information technology has been widely used, some researchers have also analyzed the effect of media on tourism demand. It has shown that the media has a considerable influence on traveler's decision making, such as choosing an outbound destination. Furthermore, with the recent availability of online information searches to obtain the latest information and two-way communication in social media, it is possible to obtain up-to-date information on travel more quickly than before. The information in online media such as blogs can naturally create the Word-of-Mouth effect by sharing useful information, which is called eWOM. Like all other service industries, the tourism industry is characterized by difficulty in evaluating its values before it is experienced directly. And furthermore, most of the travelers tend to search for more information in advance from various sources to reduce the perceived risk to the destination, so they can also be influenced by online media such as online news. In this study, we suggested that the number of online media posting, which causes the effects of Word-of-Mouth, may have an effect on the number of outbound travelers. We divided online media into public media and private media according to their characteristics and selected online news as public media and blog as private media, one of the most popular social media in tourist information. Based on the previous studies about the eWOM effects on online news and blog, we analyzed a relationship between the volume of eWOM and the outbound tourism demand through the panel model. To this end, we collected data on the number of national outbound travelers from 2007 to 2015 provided by the Korea Tourism Organization. According to statistics, the highest number of outbound tourism demand in Korea are China, Japan, Thailand, Hong Kong and the Philippines, which are selected as a dependent variable in this study. In order to measure the volume of eWOM, we collected online news and blog postings for the same period as the number of outbound travelers in Naver, which is the largest portal site in South Korea. In this study, a panel model was established to analyze the effect of online media on the demand of Korean outbound travelers and to identify that there was a significant difference in the influence of online media by each time and countries. The results of this study can be summarized as follows. First, the impact of the online news and blog eWOM on the number of outbound travelers was significant. We found that the number of online news and blog posting have an influence on the number of outbound travelers, especially the experimental result suggests that both the month that includes the departure date and the three months before the departure were found to have an effect. It is shown that online news and blog are online media that have a significant influence on outbound tourism demand. Next, we found that the increased volume of eWOM in online news has a negative effect on departure, while the increase in a blog has a positive effect. The result with the country-specific models would be the same. This paper shows that online media can be used as a new variable in tourism demand by examining the influence of the eWOM effect of the online media. Also, we found that both social media and news media have an important role in predicting and managing the Korean tourism demand and that the influence of those two media appears different depending on the country.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.221-241
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• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.7
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• pp.699-705
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• 2010

In this study, three different biological activated carbons (BACs) were prepared from activated carbons made of each coal (F400, Calgon), coconut (Samchully) and wood(Pica, Picabiol) which were run for two and half years in the pilot plant. The attached bio-film microorganisms in and on the BACs were isolated and identified. The results showed that nine different bacteria species (Chryseomonas luteola, Stenotrophomonas maltophilia, Pseudomonas vesicularis, Aeromonas hydrophila, Spingomonas paucimobilis, Agrobacterium radiobacter, Pseudomonas fluorescens, Spirillum spp., and Pasteurella haemolytica) were isolated and identified, the dominant species was Pseudomonas sp. that had occupied 56.5%. More specifically, it was observed that the populations of the microorganisms deceased in the order: Pasteurella haemolytica (18.9%) > Chryseomonas luteola (4.0%) > Agrobacterium radiobacter (3.5%) > Aeromonas hydrophila (2.0%) in and on the BACs. After isolating of 9 species of biofilm microorganisms, the growth curve for the biomass was investigated. During 24~96 hours, the biomass has the highest concentration, and activity of the biomass was the best to uptake geosmin as carbon resources. The operation temperatures for investigating the biodegradation of geosmin were set at $4^{\circ}C$ and $25^{\circ}C$. Pseudomonas vesicularis, Pseudomonas fluorescens, Agrobacterium radiobacter and Stenotrophomonas maltophilia played a maior role in removing the target compound as geosmin. However, geosmin was not biodegraded well by Chryseomonas luteola, Spingomonas paucimobilis, and Spirillum spp.. It is also interesting to evaluate kinetics of biodegradability of geosmin. The first-order rate constants for biodegradability of geosmin at $4^{\circ}C$ and $25^{\circ}C$ were $0.00006{\sim}0.0002\;hr^{-1}$ and $0.0043{\sim}0.0046\;hr^{-1}$ respectively. Higher water temperature produced better geosmin removal rates. When concentrations of geosmin increased from 10 to 10,000 ng/L, the rate constants for biodegradability of geosmin increased from 0.0003 to $0.0882\;hr^{-1}$. As described earlier, higher geosmin concentration in the reactor produced higher rate constant.

• Korean Chemical Engineering Research
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• v.48 no.3
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• pp.391-396
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• 2010

In this research the characteristics of ammonia removal from malodorous waste-air were investigated under various operating condition of biofiilter packed with equal volume of rubber media and compost for the efficient removal of ammonia, representative source of malodor frequently generated at compost manufacturing factory and publicly owned facilities. Then the optimum conditions were constructed to treat waste-air containing ammonia with biofilter. Biofilter was run for 30 days(experimental frequency of 2 times/day makes 60 experimental times.) with the ammonia loading from $2.18g-N/m^3/h$ to $70g-N/m^3/h$ at $30^{\circ}C$. The ammonia removal efficiency reached almost 100% for I through IV stage of run to degrade up to the ammonia loading of $17g-N/m^3/h$. However the removal efficiency dropped to 80% when ammonia loading increased to $35g-N/m^3/h$, which makes the elimination capacity of ammonia $28g-N/m^3/h$ for V stage of run. However, the removal efficiency remained 80% and the maximum elimination capacity reached $55g-N/m^3/h$ when ammonia loading was doubled $70g-N/m^3/h$ for VI stage of run. Thus the maximum elimination capacity exceeded $1,200g-N/m^3/day$(i.e., $50g-N/m^3/h$) of the experiment of biofilter packed with rock wool inoculated with night soil sludge by Kim et al.. However, the critical loading did not exceed $810g-N/m^3/day$ (i.e., $33.75g-N/m^3/h$) of the biofilter experiment by Kim et al.. The reason to exceed the maximum elimination capacity of Kim et al. may be attributed to that the rubber media used as biofilter packing material provide the better environment for the fixation of nitrifying and denitrification bacteria to its surface coated with coconut based-activated carbon powder and well-developed inner-pores, respectively.