• Journal of Korean Society of Environmental Engineers
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• v.34 no.4
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• pp.223-231
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• 2012

The purpose of this study is to investigate basically the mechanism of heat transfer by the resolution of complex fluid flow inside a sophisticated designed screw dryer for the treatment of sewage sludge by using numerical analysis and experimental study. By doing this, the result was quite helpful to obtain the design criteria for enhancing drying efficiency, thereby achieving the optimal design of a multiple screw type dryer for treating inorganic and organic sludge wastes. One notable design feature of the dryer was to bypass a certain of fraction of the hot combustion gases into the bottom of the screw cylinder, by the fluid flow induction, across the delicately designed holes on the screw surface to agitate internally the sticky sludges. This offers many benefits not only in the enhancement of thermal efficiency even for the high viscosity material but also greater flexibility in the application of system design and operation. However, one careful precaution was made in operation in that when distributing the hot flue gas over the lump of sludge for internal agitation not to make any pore blocking and to avoid too much pressure drop caused by inertial resistance across the lump of sludge. The optimal retention time for rotating the screw at 1 rpm in order to treat 200 kg/hr of sewage sludge was determined empirically about 100 minutes. The corresponding optimal heat source was found to be 150,000 kcal/hr. A series of numerical calculation is performed to resolve flow characteristics in order to assist in the system design as function of important system and operational variables. The numerical calculation is successfully evaluated against experimental temperature profile and flow field characteristics. In general, the calculation results are physically reasonable and consistent in parametric study. In further studies, more quantitative data analyses such as pressure drop across the type and loading of drying sludge will be made for the system evaluation in experiment and calculation.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.1-17
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• 2012

Personalized smart devices such as smartphones and smart pads are widely used. Unlike traditional feature phones, theses smart devices allow users to choose a variety of functions, which support not only daily experiences but also business operations. Actually, there exist a huge number of applications accessible by smart device users in online and mobile application markets. Users can choose apps that fit their own tastes and needs, which is impossible for conventional phone users. With the increase in app demand, the tastes and needs of app users are becoming more diverse. To meet these requirements, numerous apps with diverse functions are being released on the market, which leads to fierce competition. Unlike offline markets, online markets have a limitation in that purchasing decisions should be made without experiencing the items. Therefore, online customers rely more on item-related information that can be seen on the item page in which online markets commonly provide details about each item. Customers can feel confident about the quality of an item through the online information and decide whether to purchase it. The same is true of online app markets. To win the sales competition against other apps that perform similar functions, app developers need to focus on writing app descriptions to attract the attention of customers. If we can measure the effect of app descriptions on sales without regard to the app's price and quality, app descriptions that facilitate the sale of apps can be identified. This study intends to provide such a quantitative result for app developers who want to promote the sales of their apps. For this purpose, we collected app details including the descriptions written in Korean from one of the largest app markets in Korea, and then extracted keywords from the descriptions. Next, the impact of the keywords on sales performance was measured through our econometric model. Through this analysis, we were able to analyze the impact of each keyword itself, apart from that of the design or quality. The keywords, comprised of the attribute and evaluation of each app, are extracted by a morpheme analyzer. Our model with the keywords as its input variables was established to analyze their impact on sales performance. A regression analysis was conducted for each category in which apps are included. This analysis was required because we found the keywords, which are emphasized in app descriptions, different category-by-category. The analysis conducted not only for free apps but also for paid apps showed which keywords have more impact on sales performance for each type of app. In the analysis of paid apps in the education category, keywords such as 'search+easy' and 'words+abundant' showed higher effectiveness. In the same category, free apps whose keywords emphasize the quality of apps showed higher sales performance. One interesting fact is that keywords describing not only the app but also the need for the app have asignificant impact. Language learning apps, regardless of whether they are sold free or paid, showed higher sales performance by including the keywords 'foreign language study+important'. This result shows that motivation for the purchase affected sales. While item reviews are widely researched in online markets, item descriptions are not very actively studied. In the case of the mobile app markets, newly introduced apps may not have many item reviews because of the low quantity sold. In such cases, item descriptions can be regarded more important when customers make a decision about purchasing items. This study is the first trial to quantitatively analyze the relationship between an item description and its impact on sales performance. The results show that our research framework successfully provides a list of the most effective sales key terms with the estimates of their effectiveness. Although this study is performed for a specified type of item (i.e., mobile apps), our model can be applied to almost all of the items traded in online markets.

• Journal of Intelligence and Information Systems
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• v.17 no.2
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• pp.97-107
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• 2011

Nowadays there are a lot of issues about copyright infringement in the Internet world because the digital content on the network can be copied and delivered easily. Indeed the copied version has same quality with the original one. So, copyright owners and content provider want a powerful solution to protect their content. The popular one of the solutions was DRM (digital rights management) that is based on encryption technology and rights control. However, DRM-free service was launched after Steve Jobs who is CEO of Apple proposed a new music service paradigm without DRM, and the DRM is disappeared at the online music market. Even though the online music service decided to not equip the DRM solution, copyright owners and content providers are still searching a solution to protect their content. A solution to replace the DRM technology is digital audio watermarking technology which can embed copyright information into the music. In this paper, the author proposed a new audio watermarking algorithm with two approaches. First, the watermark information is generated by two dimensional barcode which has error correction code. So, the information can be recovered by itself if the errors fall into the range of the error tolerance. The other one is to use chirp sequence of CDMA (code division multiple access). These make the algorithm robust to the several malicious attacks. There are many 2D barcodes. Especially, QR code which is one of the matrix barcodes can express the information and the expression is freer than that of the other matrix barcodes. QR code has the square patterns with double at the three corners and these indicate the boundary of the symbol. This feature of the QR code is proper to express the watermark information. That is, because the QR code is 2D barcodes, nonlinear code and matrix code, it can be modulated to the spread spectrum and can be used for the watermarking algorithm. The proposed algorithm assigns the different spread spectrum sequences to the individual users respectively. In the case that the assigned code sequences are orthogonal, we can identify the watermark information of the individual user from an audio content. The algorithm used the Walsh code as an orthogonal code. The watermark information is rearranged to the 1D sequence from 2D barcode and modulated by the Walsh code. The modulated watermark information is embedded into the DCT (discrete cosine transform) domain of the original audio content. For the performance evaluation, I used 3 audio samples, "Amazing Grace", "Oh! Carol" and "Take me home country roads", The attacks for the robustness test were MP3 compression, echo attack, and sub woofer boost. The MP3 compression was performed by a tool of Cool Edit Pro 2.0. The specification of MP3 was CBR(Constant Bit Rate) 128kbps, 44,100Hz, and stereo. The echo attack had the echo with initial volume 70%, decay 75%, and delay 100msec. The sub woofer boost attack was a modification attack of low frequency part in the Fourier coefficients. The test results showed the proposed algorithm is robust to the attacks. In the MP3 attack, the strength of the watermark information is not affected, and then the watermark can be detected from all of the sample audios. In the sub woofer boost attack, the watermark was detected when the strength is 0.3. Also, in the case of echo attack, the watermark can be identified if the strength is greater and equal than 0.5.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Radiation Oncology Journal
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• v.24 no.3
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• pp.185-191
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• 2006

[ $\underline{Purpose}$ ]: Standardization quality assurance (QA) program of CyberKnife for suitable circumstances in Korea has not been established. In this research, we investigated the development of QA program for CyberKnife and evaluation of the feasibility under applications. $\underline{Materials\;and\;Methods}$: Considering the feature of constitution for systems and the therapeutic methodology of CyberKnife, the list of quality control (QC) was established and divided dependent on the each period of operations. And then all these developed QC lists were categorized into three groups such as basic QC, delivery specific QC, and patient specific QC based on the each purpose of QA. In order to verify the validity of the established QA program, this QC lists was applied to two CyberKnife centers. The acceptable tolerance was based on the undertaking inspection list from the CyberKnife manufacturer and the QC results during last three years of two CyberKnife centers in Korea. The acquired measurement results were evaluated for the analysis of the current QA status and the verification of the propriety for the developed QA program. $\underline{Results}$: The current QA status of two CyberKnife centers was evaluated from the accuracy of all measurements in relation with application of the established QA program. Each measurement result was verified having a good agreement within the acceptable tolerance limit of the developed QA program. $\underline{Conclusion}$: It is considered that the developed QA program in this research could be established the standardization of QC methods for CyberKnife and confirmed the accuracy and stability for the image-guided stereotactic radiotherapy.

• Korea journal of population studies
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• v.15 no.2
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• pp.15-59
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• 1992

After world war II Japanese life expectancy has been improved remarkably, and reached the highest level in the world around late 1970's. The life expectancy of Korean has also shown tremendous improvement in recent years with about 20 year's gap from the Japanese. The reason of rapid improvement of life expectancy can be explained by changes in the structure of cause of death due to health system, living standard, social welfare, health behavior of individuals and so on. Korean in Japan is placed under different situations from both Korean in Korea and Japanese in these regards, and expected to show different picture of cause of death pattern. The objective of this study is the comparision of changing patterns of cause of death of three population groups, Korean in Japan, Korean in Korea and Japanese, and to investigate the reasons which effect to the structural difference of mortality cause with special emphasis on health ecological aspects. One of the major limitations of the Korean causes of death statistics is the under-registration which ranges about 10% of the total events, and inaccuracy of the exact cause of death. Some 20% of registered deaths were unable to classify by ICD. However, it is concluded that the Korean data are evaluated as sufficient to stand for over-viewing of trends of cause of death pattern. The evaluation is done by comparing data from registration and field survey over the same population sample. Population data of Korean in Japan differ between two sources of data; census and foreigner's registration. Correction is done by life table method under the assumption that age-specific mortality pattern would accord with that of the Japanese. The crude death rate was lowest among Korean in Japan, 5.7 deaths per 1,000 population in 1965. The crude death rates of Korean in Japan and Japanese are increasing recently influenced by age structure while Korean in Korea still shows decreasing tendency. The adjusted death rate is lowest among Japanese, followed by Korean in Japan, and Korean in Korea. The leading causes of death of Korean in Korea until 1960's was infectious diseases including pneumonia and tuberculosis. The causes of death structure changed gradually to accidents, neoplasm, hypertensive disease, cerebro-vascular disease in order. The main difference in cause of death between Korean and Japanese if high rate of liver diseases and diabetes for both Korean in Japan and Korea. A special feature of cause of death among Korean in Korea is remakably high rate of hypertensive disease, which is assumed to be caused by physicians tendency in choosing diagnostic categories. The low ischemic heart disease and high vasculo-cerebral disease are the distinctive characteristic of the three population groups compared to western countries. Specific causes of death were selected for detailed sex, age and ethnic group comparisons based on their high death rates. Cancer is the cause of death which showed most dramatical increase in all three population groups. In Korea 20.1% of all death were caused by cancer in 1990 compared with 10.5% in 1981. Cancer of the liver is the leading cause of cancer death among Korean in Japan for both sexes, followed by cancer of the lung and cancer of the stomach, while that of Korean in Korea is cancer of the stomach, followed by cancer of the liver and cancer of the lung for male. Causes of infant mortality were examined among the three population groups since 1980 on yearly bases. For both Japanese and Korean in Japan, leading cause of death ranks as conditions originating in the perinatal period, congenital anomalies, accidents and other violent causes. Trends since 1980 for these two population groups in the leading cause of infant mortality showed no changes. On the contrary, significant changes in leading cause of death structure in Korea were observed : the ranking of leading cause of death in 1981 were congenital asnomalies, pneumonia bronchitis, infectious disease, heart disease, conditions originating in the perinatal period, accident and other violent causes ; in 1990 the ranking shifted to congenital anomalies, accident, pneumonia bronchities, conditions originating in the perinatal period, infectious disease. The mortality rate by congenital anomalies in Korea continuously grew than any other causes. Larger increase ocurred during the 1990's

• Journal of Nutrition and Health
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• v.42 no.5
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• pp.423-433
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• 2009

Folate and vitamin $B_{12}$ are essential cofactors for homocysteine (Hcy) metabolism. Homocysteinemia has been related with cardiovascular and neurodegenerative disease. We examined the effect of folate and/or vitamin $B_{12}$ deficiency on biomarkers of one carbon metabolism in blood, liver and brain, and analyzed the correlation between vitamin biomarkers in mild and moderate homocysteinemia. In this study, Sprague-Dawley male rats (5 groups, n = 10) were fed folatesufficient diet (FS), folate-deficient diet (FD) with 0 or 3 g homocystine (FSH and FDH), and folate-/vitamin $B_{12}$-deficient diet with 3 g homocystine (FDHCD) for 8 weeks. The FDH diet induced mild homocysteinemia (plasma Hcy 17.41 ${\pm}$ 1.94 nmol/mL) and the FDHCD diet induced moderate homocysteinemia (plasma Hcy 44.13 ${\pm}$ 2.65 nmol/mL), respectively. Although liver and brain folate levels were significantly lower compared with those values of rats fed FS or FSH (p < 0.001, p < 0.01 respectively), there were no significant differences in folate levels in liver and brain among the rats fed FD, FDH and FDHCD diet. However, rats fed FDHCD showed higher plasma folate levels (126.5 ${\pm}$ 9.6 nmol/L) compared with rats fed FD and FDH (21.1 ${\pm}$ 1.4 nmol/L, 22.0 ${\pm}$ 2.2 nmol/L)(p < 0.001), which is the feature of "ethyl-folate trap"by vitamin $B_{12}$ deficiency. Plasma Hcy was correlated with hepatic folate (r = -0.641, p < 0.01) but not with plasma folate or brain folate in this experimental condition. However, as we eliminated FDHCD group during correlation test, plasma Hcy was correlated with plasma folate (r = -0.581, p < 0.01), hepatic folate (r = -0.684, p < 0.01) and brain folate (r = -0.321, p < 0.05). Hepatic S-adenosylmethionine (SAM) level was lower in rats fed FD, FDH and FDHCD than in rats fed FS and FSH (p < 0.001, p < 0.001 respectively) and hepatic S-adenosylhomocysteine (SAH) level was significantly higher in those groups. The SAH level in brain was also significantly increased in rats fed FDHCD (p < 0.05). However, brain SAM level was not affected by folate and/or vitamin $B_{12}$ deficiency. This result suggests that dietary folate- and vitamin B12-deficiency may inhibit methylation in brain by increasing SAH rather than decreasing SAM level, which may be closely associated with impaired cognitive function in nutritional homocysteinemia.

• Journal of Internet Computing and Services
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• v.16 no.1
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• pp.67-74
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• 2015

In recent years, frequent itemset mining for considering the importance of each item has been intensively studied as one of important issues in the data mining field. According to strategies utilizing the item importance, itemset mining approaches for discovering itemsets based on the item importance are classified as follows: weighted frequent itemset mining, frequent itemset mining using transactional weights, and utility itemset mining. In this paper, we perform empirical analysis with respect to frequent itemset mining algorithms based on transactional weights. The mining algorithms compute transactional weights by utilizing the weight for each item in large databases. In addition, these algorithms discover weighted frequent itemsets on the basis of the item frequency and weight of each transaction. Consequently, we can see the importance of a certain transaction through the database analysis because the weight for the transaction has higher value if it contains many items with high values. We not only analyze the advantages and disadvantages but also compare the performance of the most famous algorithms in the frequent itemset mining field based on the transactional weights. As a representative of the frequent itemset mining using transactional weights, WIS introduces the concept and strategies of transactional weights. In addition, there are various other state-of-the-art algorithms, WIT-FWIs, WIT-FWIs-MODIFY, and WIT-FWIs-DIFF, for extracting itemsets with the weight information. To efficiently conduct processes for mining weighted frequent itemsets, three algorithms use the special Lattice-like data structure, called WIT-tree. The algorithms do not need to an additional database scanning operation after the construction of WIT-tree is finished since each node of WIT-tree has item information such as item and transaction IDs. In particular, the traditional algorithms conduct a number of database scanning operations to mine weighted itemsets, whereas the algorithms based on WIT-tree solve the overhead problem that can occur in the mining processes by reading databases only one time. Additionally, the algorithms use the technique for generating each new itemset of length N+1 on the basis of two different itemsets of length N. To discover new weighted itemsets, WIT-FWIs performs the itemset combination processes by using the information of transactions that contain all the itemsets. WIT-FWIs-MODIFY has a unique feature decreasing operations for calculating the frequency of the new itemset. WIT-FWIs-DIFF utilizes a technique using the difference of two itemsets. To compare and analyze the performance of the algorithms in various environments, we use real datasets of two types (i.e., dense and sparse) in terms of the runtime and maximum memory usage. Moreover, a scalability test is conducted to evaluate the stability for each algorithm when the size of a database is changed. As a result, WIT-FWIs and WIT-FWIs-MODIFY show the best performance in the dense dataset, and in sparse dataset, WIT-FWI-DIFF has mining efficiency better than the other algorithms. Compared to the algorithms using WIT-tree, WIS based on the Apriori technique has the worst efficiency because it requires a large number of computations more than the others on average.

• Journal of Global Scholars of Marketing Science
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• v.18 no.1
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• pp.59-80
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• 2008

Recently, e-sports are growing with potentiality as a new industry with conspicuous profit model. But studies that dealing with e-sports are not enough. Hence, proposes of this paper are both to establish basic model that is for the design of e-sport marketing strategy and to contribute toward future studies which are related to e-sports. Recently, the researches to explain sports-sponsorship through the identification theory have been discovered. Many researches say that somewhat proper identification is a requirement for most sponsors to improve the their images which is essential to sponsorship activity. Consequently, the research for sponsorship associated with identification in the e-sports, not in the physical sports is the core sector of this study. We extracted the variables from online's major characteristics and existing sport sponsorship researches. First, because e-sports mean the tournaments or leagues in the use of online game, the main event of the game is likely to call it online game. Online media's attributes are distinguished from those of offline. Especially, interactivity, anonymity, and expandibility as a e-sport game attributes are able to be mentioned. So, these inherent online attributes are examined on the relationship with flow. Second, in physical sports games, Fisher(1998) revealed that team similarity and team attractivity were positively related to team identification. Wann(1996) said that the result of former game influenced the evaluation of the next game, then in turn has an effect on the identification of team supporters. Considering these results in the e-sports side, e-sports gamer' attractivity, similarity, and match result seem to be important precedent variables of the identification with a gamer. So, these e-sport gamer attributes are examined on the relationship with both flow and identification with a gamer. Csikszentmihalyi(1988) defined the term flow as feeling status for him to be making current positive experience optimally. Hoffman and Novak(1996) also said that if a user experienced the flow he would visit a website without any reward. Therefore flow might be positively associated with user's identification with a gamer. And, Swanson(2003) disclosed that team identification influenced the positive results of sponsorship, which included attitude toward sponsors, sponsor patronage, and satisfaction with sponsors. That is, identification with a gamer expect to be connected with corporation identification significantly. According to the above, we can design the following research model. All variables used in this study(interactivity, anonymity, expandibility, attractivity, similarity, match result, flow, identification with a gamer, and identification with a sponsor) definitely were defined operationally underlying precedent researches. Sample collection was carried out to the person who has an experience to have enjoyed e-sports during June 2006. Much portion of samples is men because much more men than women enjoy e-sports in general. Two-step approach was used to test the hypotheses. First, confirmatory factor analysis was committed to guarantee the validity and reliability of variables. The results showed that all variables had not only intensive and discriminant validity, but also reliability. Then, research model was examined with fully structural equation using LISREL 8.3 version. The fitness of the suggested model mostly was at the acceptable level. Shortly speaking about the results, first of all, in e-sports game attributes, only interactivity which is called a basic feature in online situation affected flow positively. Secondly, in e-sports gamer's attributes, similarity with a gamer and match result influenced flow positively, but there was no significant effect in the relationship between the attractivity of a gamer and flow. And as expected, similarity had an effect on identification with a gamer significantly. But unexpectedly attractivity and match result did not influence identification with a gamer significantly. Just the same as the fact verified in the many precedent researches, flow greatly influenced identification with a gamer, and identification with a gamer continually had an influence on the identification with a sponsor significantly. There are some implications in these results. If the sponsor of e-sports supports the pro-game player who absolutely should have the superior ability to others and is similar to the user enjoying e-sports, many amateur gamers will feel much of the flow and identification with a pro-gamer, and then after all, feel the identification with a sponsor. Such identification with a sponsor leads people enjoying e-sports to have purchasing intention for products produced by the sponsor and to make a positive word-of-mouth for those products or the sponsor. For the future studies, we recommend a few ideas. Based on the results of this study, it is necessary to find new variables relating to the e-sports, which is not mentioned in this study. For this work to be possible, qualitative research seems to be needed to consider the inherent e-sport attributes. Finally, to generalize the results related to e-sports, a wide range of generations not a specific generation should be researched.