• Tuberculosis and Respiratory Diseases
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• v.54 no.1
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• pp.57-70
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• 2003

Background : Small cell lung cancer represents approximately 20% of all carcinomas of the lung, and is recognized as having a poor long term outcome compared to non-small cell lung cancer. Therefore, this study investigated the prognostic factors in small cell lung cancer patients in order to improved the survival rate by using the proper therapeutic methods. Material and method : The clinical data from 394 patients who diagnosed with small cell lung cancer and treated from 1993 to 2001 at the Kosin University Gospel Hospital, were analyzed. Result : There were 314 male patients (79.7%), and 80 female patients (20.3%). The number of those with limited disease was 177 (44.9%), and the number of those with extensive disease was 217 (55.1%). Overall, 366 out of 394 enrolled patients had died. The median survival time was 215 days (95% CI : 192-237days). The disease stage, Karnofsky performance state, 5% body weight loss for the recent 3 months, chemotherapy regimens, and the additive chest radiotherapy were identified as being statistically significant factors for the survival time. The median survival times of the supportive care group, one anticancer therapy, and two or more treatment groups were 17 days, 211 days, and 419 day, respectively (p<0.001). These data emphasize the importance of anticancer treatment to improve survival time for patients. The group of concurrent chemoradiotherapy (30 patients) showed significantly longer survival time than the group given sequential chemoradiotherapy (55 patients) (528 days versus 373 days, p=0.0237). The favorable prognostic factors of laboratory study were groups of leukocyte =8,000/mm3, ALP=200 U/L, LDH=450 IU/L, NSE=15 ng/mL, s-GOT=40 IU/L. In extensive disease, there was no difference according to the number of metastatic site. However, the median survival time of patients with ipsilateral pleural effusion had longer than patients having other metastatic sites. According to the survey periods, three groups were divided into 1993-1995, 1996-1998, and 1999-2001. The median survival time was significantly prolonged after 1999 in comparison to previous groups (177 days, 194 days, 289 days, p=0.001, 0.002, respectively). Conclusion: Disease stage and 5% body weight loss for recent 3 months at diagnostic state were significant prognostic factors. In addition, the performance status, serum ALP, LDH, NSE, CEA levels also appear to be prognostic factors. The survival time of those patients with small cell lung cancer has been prologned in recent years. It was suggested that the used of the EP (etoposied and cisplatin) chemotherapy method and concurrent chemoradiotherapy for patients with a limited stage contributed to the improved survival time.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.153-169
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• 2012

Exhibitions have played a key role of effective marketing activity which directly informs services and products to current and potential customers. Through participating in exhibitions, exhibitors have got the opportunity to make face-to-face contact so that they can secure the market share and improve their corporate images. According to this economic importance of exhibitions, show organizers try to adopt a new IT technology for improving their performance, and researchers have also studied services which can improve the satisfaction of visitors through analyzing visit patterns of visitors. Especially, as smart technologies make them monitor activities of visitors in real-time, they have considered booth recommender systems which infer preference of visitors and recommender proper service to them like on-line environment. However, while there are many studies which can improve their performance in the side of new technological development, they have not considered the choice factor of visitors for booth recommender systems. That is, studies for factors which can influence the development direction and effective diffusion of these systems are insufficient. Most of prior studies for the acceptance of new technologies and the continuous intention of use have adopted Technology Acceptance Model (TAM) and Extended Technology Acceptance Model (ETAM). Booth recommender systems may not be new technology because they are similar with commercial recommender systems such as book recommender systems, in the smart exhibition environment, they can be considered new technology. However, for considering the smart exhibition environment beyond TAM, measurements for the intention of reuse should focus on how booth recommender systems can provide correct information to visitors. In this study, through literature reviews, we draw factors which can influence the satisfaction and reuse intention of visitors for booth recommender systems, and design a model to forecast adaptation of visitors for booth recommendation in the exhibition environment. For these purposes, we conduct a survey for visitors who attended DMC Culture Open in November 2011 and experienced booth recommender systems using own smart phone, and examine hypothesis by regression analysis. As a result, factors which can influence the satisfaction of visitors for booth recommender systems are the effectiveness, perceived ease of use, argument quality, serendipity, and so on. Moreover, the satisfaction for booth recommender systems has a positive relationship with the development of reuse intention. For these results, we have some insights for booth recommender systems in the smart exhibition environment. First, this study gives shape to important factors which are considered when they establish strategies which induce visitors to consistently use booth recommender systems. Recently, although show organizers try to improve their performances using new IT technologies, their visitors have not felt the satisfaction from these efforts. At this point, this study can help them to provide services which can improve the satisfaction of visitors and make them last relationship with visitors. On the other hands, this study suggests that they managers along the using time of booth recommender systems. For example, in the early stage of the adoption, they should focus on the argument quality, perceived ease of use, and serendipity, so that improve the acceptance of booth recommender systems. After these stages, they should bridge the differences between expectation and perception for booth recommender systems, and lead continuous uses of visitors. However, this study has some limitations. We only use four factors which can influence the satisfaction of visitors. Therefore, we should development our model to consider important additional factors. And the exhibition in our experiments has small number of booths so that visitors may not need to booth recommender systems. In the future study, we will conduct experiments in the exhibition environment which has a larger scale.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• Korean Journal of Veterinary Service
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• v.23 no.4
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• pp.321-333
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• 2000

In order to know the depletive changes of sulfamethazine residues in senlm and practical organs of rats orally administered with sulfamethazine sodium(SMS), the concentration of sulfamethazine was measured in serum and tissue(kidney, liver, spleen, testis, and skeletal muscle) of rats with using high performance liquid chromatography(HPLC). SMS was orally administrated to sprague-dawley male rats(body weight, 200~300g) with using sonde at the rate of 20mg/100g body weight(recommended therapeutic dose) on once a day for 3 days. There were investigated the depletive changes of the sulfamethazine in serum, kidney, liver, spleen, testis and skeletal muscle of rat at the time 8 hours, 1st, 2nd, 3rd, 4th, 5th and 6th day after administration SMS, respectively. The results obtained were summarized as follows; 1. After oral administration of the SMS, the mean concentrations of sulfamethazine in serum according to the time lapsed were showed 215.53$\pm$42.99ppm at the 8 hours after withdrawal of medicated sulfamethazine. And gradually according to the time lapsed, the concentrations of sulfamethazine residues in serum were significantly (p<.05) decreased 25.87$\pm$5.18ppm at 1st day, 2.30$\pm$0.61ppm at 3rd day and 0.11$\pm$0.02ppm at 6th day respectively. 2. The mean concentrations of sulfamethazine in kidney, liver, spleen, muscle and testis according to the time lapsed after administration SMS were showed 83.82$\pm$12.16, 81.77$\pm$12.88, 36.96$\pm$5.35, 35.96$\pm$TEX>$\pm$1.39 and 27.89$\pm$1.92 ppm at the 8 hours, respectively. And gradually according to the time lapsed, the concentrations of sulfamethazine residues in the each of samples were significantly(p<.05) decreased such as 7.15$\pm$0.26, 5.62$\pm$0.72, 2.43$\pm$0.29, 1.99$\pm$0.14 and 3.11$\pm$0.48 ppm at 1st day, 0.52$\pm$0.04, 1.32$\pm$0.22, 0.13$\pm$0.03, 0.15$\pm$0.06 and 0.26$\pm$0.11ppm at 3rd day, and 0.03$\pm$0.01, 0.11$\pm$0.03, 0.02$\pm$0.01, 0.009$\pm$0.001 and 0.02$\pm$0.01 ppm at 6th day, respectively. 3. After oral administration of the SMS to rats, the residual concentrations of sulfamethazine in skeletal muscle were significantly (p<.05) decreased 35.96$\pm$1.39 to 0.009$\pm$$\pm$0.001 ppm between 8 hours and 6th day, respectively From the 4th day, the residual concentrations of sulfamethazine were showed 0.10$\pm$0.04 ppm below 0.1 ppm at the permitted limit concentration of muscle in Korea. In conclusion, this study could be suggested the relationship between administrated period, doses of sulfonamides and residual aspects of serum and practical organs, and the importance of observing ceasing period of antibiotic drugs before forwarding livestocks to slaughter.

• The Korean Journal of Air & Space Law and Policy
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• v.27 no.2
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• pp.67-106
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• 2012

Today air traffic at the airport is complicated including a significant increase in the volume of air transport, so aviation accidents are constantly occurring. Therefore, we should newly recognize importance of the Air Traffic Safety, the core values of the Air Traffic. The location of airport that is the basic infrastructure of the air traffic and the security of safety for facilities and equipments are more important than what you can. From this dimension, I analyze the step-by-step safety factors that are taken into account in the airport development projects from the construction or improvement of the airport within the current laws and institutions and give my opinion on the enhancement of safety in the design and construction of airport. The safety of air traffic, as well as airport, depends on location, development, design, construction, inspection and management of the airport including airport facilities because we have to carry out the national responsibility that prevents the risk of large social overhead capital for many and unspecified persons in modern society through legislation regarding intervention of specialists and locational criteria for aviation safety from the planning stage of airport development. In addition, well-defined installation standards of airports and air navigation facilities, the key points of the airport development phase, can ensure the safety of the airport and airport facilities. Of course, the installation standards of airport and air navigation facilities are based on the global standard due to the nature of air traffic. However, to prevent the chaos for the safety standards in design, construction, inspection of them and to ensure the aviation safety, the safety standards must be further subdivided in the course of domestic legislation. The criteria for installation of the Air Navigation facilities is regulated most specifically. However, to ensure the safety of the operation for Air Navigation Facilities, performance system proved suitable for the Safety of Air Navigation Facilities must change over from arbitrary restrictions to mandatory restrictions and be applied for foreign producers as well as domestic producers. Of course, negligence of pilots and defective aircraft maintenance lead to a large portion of the aviation accidents. However, I think that air traffic accidents can be reduced if the airport or airport facility is perfect enough to ensure the safety. Therefore, legal and institutional supplement to prioritize the aviation safety from the stage of airport development may be necessary.

• Journal of the Korean Society for Marine Environment & Energy
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• v.15 no.4
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• pp.337-348
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• 2012

The Yellow Sea Ecoregion Conservation Project is a joint international project which is carried out under the purposes of conserving the habitats in the Yellow Sea Ecoregion and biodiversity from various threats that damage ecosystems, informing the importance and value of the Yellow Sea Ecoregion, and promoting the understanding and interests of Korea, China and Japan. Subsequent to the Yellow Sea Ecoregion Planning Programme which had been performed during the period from 2002 to 2006, the Yellow Sea Ecoregion Support Project has been performed over 7 years in total from 2007 to 2014. Panasonic is sponsoring the financing of the project, and the organizations in charge of the project by country are Korea Institute of Ocean Science & Technology for Korea and World Wide Fund for Nature branches for China and Japan. While the Yellow Sea Ecoregion Planning Programme was focused on the biological assessment and the selection of potential priority area by scientific review, the Yellow Sea Ecoregion Support Project is oriented toward practical activities targeting more diversified stakeholder. Especially, this project plans to support direct conservation activities in the region and participation and cooperation from local residents. The project plan is comprised of 3 phases. During the first period from 2008 to 2009, small grant projects were operated targeting 16 institutions of Korea and China, and for the second period from 2010 to 2012, one place each was selected as demonstration site for habitat conservation in Korea and China respectively and supported for three years to introduce the conservation method based on international standards such as the management of marine protected areas, ecosystem-based management and community-based management and simultaneously to develop habitat conservation activities suitable for national and regional characteristics. During the period from 2013 to 2014 which is the last phase, the project plans to distribute the performance of small grant projects and demonstration site activities through a series of forums among stakeholder. Through the activities described above, the recognition of general public on the conservation of the Yellow Sea Ecoregion was changed positively, and community-based management began to be reflected in the policies for the marine protected areas of central and local government.

• Journal of the Korean Society of Marine Environment & Safety
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• v.17 no.4
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• pp.315-322
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• 2011

The importance of aquafarming is increasing all over the world, however the coastal environment in the semi-closed inner bay has been aggravated due to the long term production and the high stocking density. For the sustainable aquafarming, there is a requirement for a eco-friendly fishery management by the estimation of ecological carrying capacity. The model development and application is still in the initial step, because it has to consider the whole ecosystem and all culture activities. As an alternative, there is a requirement for ecological indicator to assess the ecological performance. This study tried the estimation of ecological carrying capacity using ecological indicator. The production and the facility of the oyster farms was 4,935M/T, $49ind./m^3$ in Geoje-Hansan Bay(2008). Filtration pressure indicator was 0.203 which could provide a guidance on the present level of culture development. According to the environmental characteristics and the present oyster farms in Geoje-Hansan Bay, the newly assessed filtration pressure for the acceptable ecological carrying capacity was 0.102. Consequently, ecological carrying capacity in Geoje-Hansan Bay was 2,480M/T, $25ind./m^3$ and this represents the level of culture that can be introduced into Geoje-Hansan Bay without leading to significant changes to ecological process, species, populations or communities. Our study utilized the ecological indicator to estimate ecological carrying capacity of oyster farming for sustainable productivity and this could be the scientific basis for the eco-friendly fishery management.

• Analytical Science and Technology
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• v.28 no.6
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• pp.409-416
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• 2015

ICP-AES has been used in many laboratories due to the advantages of wide calibration range and multi-element analysis, but it may give erroneous results and suffer from spectral interference due to the large number of emission lines associated with each element. In this study, certified reference materials (CRMs) and field samples were analyzed by ICP-AES and HG-AAS according to the official Korean testing method for soil pollution to investigate analytical problems. The applicability of HG-ICP-AES was also tested as an alternative method. HG-AAS showed good accuracies (90.8~106.3%) in all CRMs, while ICP-AES deviated from the desired range in CRMs with low arsenic and high Fe/Al. The accuracy in CRM030 was estimated as below 39% at the wavelength of 193.696 nm by ICP-AES. Significant partial overlaps and sloping background interferences were observed near to 193.696 nm with the presence of 50 mg/L Fe and Al. Most CRMs were quantified with few or no interferences of Fe and Al at 188.980 nm. ICP-AES properly assessed low and high level arsenic for field samples, at 188.980 nm and 193.696 nm, respectively. The importance of the choice of measurement wavelengths corresponding to relative arsenic level should be noted. Because interferences were affected by the sample matrix, operation conditions and instrument figures, the analysts were required to consider spectral interferences and compare the analytical performance of the recommended wavelengths. HG-ICP-AES was evaluated as a suitable alternative method for ICP-AES due to improvement of the detection limit, wide calibration ranges, and reduced spectral interferences by HG.