• Journal of Distribution Research
• /
• v.17 no.1
• /
• pp.87-115
• /
• 2012

Introduction: In these days, a loyalty program is one of the most common marketing mechanisms (Lacey & Sneath, 2006; Nues & Dreze, 2006; Uncles et al., 20003). In recent years, Coalition Loyalty Program is more noticeable as one of progressed forms. In the past, loyalty program was operating independently by single product brand or single retail channel brand. Now, companies using Coalition Loyalty Program share their programs as one single service and companies to participate to this program continue to have benefits from their existing program as well as positive spillover effect from the other participating network companies. Instead of consumers to earn or spend points from single retail channel or brand, consumers will have more opportunities to utilize their points and be able to purchase other participating companies products. Issues that are related to form of loyalty programs are essentially connected with consumers' perceived view on convenience of using its program. This can be a problem for distribution companies' strategic marketing plan. Although Coalition Loyalty Program is popular corporate marketing strategy to most companies, only few researches have been published. However, compared to independent loyalty program, coalition loyalty program operated by third parties of partnership has following conditions: Companies cannot autonomously modify structures of program for individual companies' benefits, and there is no guarantee to operate and to participate its program continuously by signing a contract. Thus, it is important to conduct the study on how coalition loyalty program affects companies' success and its process as much as conducting the study on effects of independent program. This study will complement the lack of coalition loyalty program study. The purpose of this study is to find out how consumer loyalty affects affiliated brands, its cause and mechanism. The past study about loyalty program only provided the variation of performance analysis, but this study will specifically focus on causes of results. In order to do these, this study is designed and to verify three primary objects as following; First, based on opinions of Switching Barriers (Fornell, 1992; Ping, 1993; Jones, et at., 2000) about causes of loyalty of coalition brand, 'brand attractiveness' and 'brand switching cost' are antecedents and causes of change in 'brand loyalty' will be investigated. Second, influence of consumers' perception and attitude prior to joining coalition loyalty program, influence of program in retail brands, brand attractiveness and spillover effect of switching cost after joining coalition program will be verified. Finally, the study will apply 'prior brand preference' as a variable and will provide a relationship between effects of coalition loyalty program and prior preference level. Hypothesis Hypothesis 1. After joining coalition loyalty program, more preferred brand (compared to less preferred brand) will increase influence on brand attractiveness to brand loyalty. Hypothesis 2. After joining coalition loyalty program, less preferred brand (compared to more preferred brand) will increase influence on brand switching cost to brand loyalty. Hypothesis 3. (1)Brand attractiveness and (2)brand switching cost of more preferred brand (before joining the coalition loyalty program) will influence more positive effects from (1)program attractiveness and (2)program switching cost of coalition loyalty program (after joining) than less preferred brand. Hypothesis 4. After joining coalition loyalty program, (1)brand attractiveness and (2)brand switching cost of more preferred brand will receive more positive impacts from (1)program attractiveness and (2)program switching cost of coalition loyalty program than less preferred brand. Hypothesis 5. After joining coalition loyalty program, (1)brand attractiveness and (2)brand switching cost of more preferred brand will receive less impacts from (1)brand attractiveness and (2)brand switching cost of different brands (having different preference level), which joined simultaneously, than less preferred brand. Method : In order to validate hypotheses, this study will apply experimental method throughout virtual scenario of coalition loyalty program if consumers have used or available for the actual brands. The experiment is conducted twice to participants. In a first experiment, the study will provide six coalition brands which are already selected based on prior research. The survey asked each brand attractiveness, switching cost, and loyalty after they choose high preference brand and low preference brand. One hour break was provided prior to the second experiment. In a second experiment, virtual coalition loyalty program "SaveBag" was introduced to participants. Participants were informed that "SaveBag" will be new alliance with six coalition brands from the first experiment. Brand attractiveness and switching cost about coalition program were measured and brand attractiveness and switching cost of high preference brand and low preference brand were measured as same method of first experiment. Limitation and future research This study shows limitations of effects of coalition loyalty program by using virtual scenario instead of actual research. Thus, future study should compare and analyze CLP panel data to provide more in-depth information. In addition, this study only proved the effectiveness of coalition loyalty program. However, there are two types of loyalty program, which are Single and Coalition, and success of coalition loyalty program will be dependent on market brand power and prior customer attitude. Therefore, it will be interesting to compare effects of two programs in the future.

• Journal of Intelligence and Information Systems
• /
• v.19 no.4
• /
• pp.123-132
• /
• 2013

As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

• Journal of Intelligence and Information Systems
• /
• v.18 no.1
• /
• pp.1-21
• /
• 2012

In recent years, mobile phone has experienced an extremely fast evolution. It is equipped with high-quality color displays, high resolution cameras, and real-time accelerated 3D graphics. In addition, some other features are includes GPS sensor and Digital Compass, etc. This evolution advent significantly helps the application developers to use the power of smart-phones, to create a rich environment that offers a wide range of services and exciting possibilities. To date mobile AR in outdoor research there are many popular location-based AR services, such Layar and Wikitude. These systems have big limitation the AR contents hardly overlaid on the real target. Another research is context-based AR services using image recognition and tracking. The AR contents are precisely overlaid on the real target. But the real-time performance is restricted by the retrieval time and hardly implement in large scale area. In our work, we exploit to combine advantages of location-based AR with context-based AR. The system can easily find out surrounding landmarks first and then do the recognition and tracking with them. The proposed system mainly consists of two major parts-landmark browsing module and annotation module. In landmark browsing module, user can view an augmented virtual information (information media), such as text, picture and video on their smart-phone viewfinder, when they pointing out their smart-phone to a certain building or landmark. For this, landmark recognition technique is applied in this work. SURF point-based features are used in the matching process due to their robustness. To ensure the image retrieval and matching processes is fast enough for real time tracking, we exploit the contextual device (GPS and digital compass) information. This is necessary to select the nearest and pointed orientation landmarks from the database. The queried image is only matched with this selected data. Therefore, the speed for matching will be significantly increased. Secondly is the annotation module. Instead of viewing only the augmented information media, user can create virtual annotation based on linked data. Having to know a full knowledge about the landmark, are not necessary required. They can simply look for the appropriate topic by searching it with a keyword in linked data. With this, it helps the system to find out target URI in order to generate correct AR contents. On the other hand, in order to recognize target landmarks, images of selected building or landmark are captured from different angle and distance. This procedure looks like a similar processing of building a connection between the real building and the virtual information existed in the Linked Open Data. In our experiments, search range in the database is reduced by clustering images into groups according to their coordinates. A Grid-base clustering method and user location information are used to restrict the retrieval range. Comparing the existed research using cluster and GPS information the retrieval time is around 70~80ms. Experiment results show our approach the retrieval time reduces to around 18~20ms in average. Therefore the totally processing time is reduced from 490~540ms to 438~480ms. The performance improvement will be more obvious when the database growing. It demonstrates the proposed system is efficient and robust in many cases.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.25-38
• /
• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Korean Journal of Environmental Agriculture
• /
• v.27 no.2
• /
• pp.105-110
• /
• 2008

The main purpose of this work is the development of water quality level model using the data such as DO, EC, BOD, $COD_{Cr},\;NH_3-N,\;NO_3-N,\;PO_4-P$, T-N, T-P, and SS in 88 agricultural streams of the Han river watersheds. To grant water quality level for each parameters, it divided into 20% respectively in the order of water quality level. On the basis of the lowest water quality level, water quality of streams was assigned. As the result, number of stream corresponding to Level Ⅰ was 0, Level II was 1 stream, Level III was 3 streams, Level IV was 22 streams, and Level V was 62 streams. By standardized canonical discriminant function coefficient, $NO_3-N$ was the highest in 0.427 at the discriminant power. According to discriminant function for water quality level, it was equal to $-4.648+3.246{\times}[NO_3-N],\;-5.084+3.456{\times}[NO_3-N],\;-4.298+3.067{\times}[NO_3-N],\;and\;-7.369+4.396{\times}[NO_3-N]$ from Level II to Level V, respectively. As a result of test at real data of the Han river watersheds in 2007, the suitability of water quality level model was high to 88.4%.

• Korean Chemical Engineering Research
• /
• v.53 no.5
• /
• pp.620-626
• /
• 2015

$Ho^{3+}$ doped $SrAl_2O_4$ upconversion phosphor powders were synthesized by spray pyrolysis, and the crystallographic properties and luminescence characteristics were examined by varying activator concentrations and heattreatment temperatures. The effect of organic additives on the crystal structure and luminescent properties was also investigated. $SrAl_2O_4:Ho^{3+}$ powders showed intensive green emission due to the $^5F_4/^5S_2{\rightarrow}^5I_8$ transition of $Ho^{3+}$. The optimal $Ho^{3+}$ concentration in order to achieve the highest luminescence was 0.1%. Over this concentration, emission intensities were largely diminished via a concentration quenching due to dipole-dipole interaction between activator ions. According to the dependence of emission intensity on the pumping power of a laser diode, it was clear that the upconversion of $SrAl_2O_4:Ho^{3+}$ occurred via the ground state absorption-excited state absorption processes involving two near-IR photons. Synthesized powders were monoclinic as a major phase, having some hexagonal phase. The increase of heat-treatment temperatures from $1000^{\circ}C$ to $1350^{\circ}C$ led to crystallinity enhancement of monoclinic phase, reducing hexagonal phase. The hexagonal phase, however, did not disappear even at $1350^{\circ}C$. When both citric acid (CA) and ethylene glycol (EG) were added to the spray solution, the resulting powders had pure monoclinic phase without forming hexagonal phase, and led to largely enhancement of crystallinity. Also, N,N-Dimethylformamide (DMF) addition to the spray solution containing both CA and EG made it possible to effectively reduce the surface area of $SrAl_2O_4:Ho^{3+}$ powders. Consequently, the $SrAl_2O_4:Ho^{3+}$ powders prepared by using the spray solution containing CA/EG/DMF mixture as the organic additives showed about 168% improved luminescence compared to the phosphor prepared without organic additives. It was concluded that both the increased crystallinity of high-purity monoclinic phase and the decrease of surface area were attributed to the large enhancement of upconversion luminescence.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.58 no.4
• /
• pp.367-375
• /
• 2013

The rice bran, a by-product of rice milling process, is well known for various functional components, such as tocopherol, tocotrienol, ${\gamma}$-oryzanol, carrying antioxidant activities. This study was conducted to investigate the antioxidant components and antioxidant activities in rice bran of different Korean rice cultivars. The 8 isomers of vitamin E, ${\gamma}$-oryzanol, flavonoids, and polyphenolics in rice bran from 16 Korean premium and high quality rice cultivars were quantified. DPPH and ABTS radical scavenging activities and reducing power of the ethanol extracts of rice bran were measured. 'Hopum' showed the highest total vitamin E content, $221.47{\mu}g/g$ among the cultivars, and 'Hanseol' showed the lowest content. The rice bran showed different compositions of ${\alpha}-$, ${\beta}-$, ${\gamma}-$, ${\delta}-$ tocopherol and tocotrienol among rice cultivars. The antioxidant contents were also different by cultivar; the ${\gamma}$-oryzanol contents ranged from 1.99 mg/g (Unkwang) to 4.30 mg/g (Chilbo), the polyphenol contents ranged from 427.22 mg gallic acid eq./100 g (Odaebyeo) to 775.80 mg gallic acid eq./100 g (Hopum). 'Hopum' also had the highest DPPH and ABTS free radical scavenging activities, 9.82% and 187.5 AEAC mg/100 g, respectively. In vitro, the rice bran extracts from 'Hopum' had significantly higher antioxidant activities than that of other cultivars.

• Journal of Korean Academy of Nursing
• /
• v.13 no.1
• /
• pp.7-21
• /
• 1983

For the flexible and rational distribution of limited existing health resources based on measurements of individual risk, the socalled Risk Approach is being proposed by the World Health Organization as a managerial tool in maternal and child health care program. This approach, in principle, puts us under the necessity of developing a technique by which we will be able to measure the degree of risk or to discriminate the future outcomes of pregnancy on the basis of prior information obtainable at prenatal care delivery settings. Numerous recent studies have focussed on the identification of relevant risk factors as the Prior infer mation and on defining the adverse outcomes of pregnancy to be dicriminated, and also have tried on how to develope scoring system of risk factors for the quantitative assessment of the factors as the determinant of pregnancy outcomes. Once the scoring system is established the technique of classifying the patients into with normal and with adverse outcomes will be easily de veloped. The scoring system should be developed to meet the following four basic requirements. 1) Easy to construct 2) Easy to use 3) To be theoretically sound 4) To be valid In searching for a feasible methodology which will meet these requirements, the author has attempted to apply the“Likelihood Method”, one of the well known principles in statistical analysis, to develop such scoring system according to the process as follows. Step 1. Classify the patients into four groups: Group $A_1$: With adverse outcomes on fetal (neonatal) side only. Group $A_2$: With adverse outcomes on maternal side only. Group $A_3$: With adverse outcome on both maternal and fetal (neonatal) sides. Group B: With normal outcomes. Step 2. Construct the marginal tabulation on the distribution of risk factors for each group. Step 3. For the calculation of risk score, take logarithmic transformation of relative proport-ions of the distribution and round them off to integers. Step 4. Test the validity of the score chart. h total of 2, 282 maternity records registered during the period of January 1, 1982-December 31, 1982 at Ewha Womans University Hospital were used for this study and the“Questionnaire for Maternity Record for Prenatal and Intrapartum High Risk Screening”developed by the Korean Institute for Population and Health was used to rearrange the information on the records into an easy analytic form. The findings of the study are summarized as follows. 1) The risk score chart constructed on the basis of“Likelihood Method”ispresented in Table 4 in the main text. 2) From the analysis of the risk score chart it was observed that a total of 24 risk factors could be identified as having significant predicting power for the discrimination of pregnancy outcomes into four groups as defined above. They are: (1) age (2) marital status (3) age at first pregnancy (4) medical insurance (5) number of pregnancies (6) history of Cesarean sections (7). number of living child (8) history of premature infants (9) history of over weighted new born (10) history of congenital anomalies (11) history of multiple pregnancies (12) history of abnormal presentation (13) history of obstetric abnormalities (14) past illness (15) hemoglobin level (16) blood pressure (17) heart status (18) general appearance (19) edema status (20) result of abdominal examination (21) cervix status (22) pelvis status (23) chief complaints (24) Reasons for examination 3) The validity of the score chart turned out to be as follows: a) Sensitivity: Group $A_1$: 0.75 Group $A_2$: 0.78 Group $A_3$: 0.92 All combined : 0.85 b) Specificity : 0.68 4) The diagnosabilities of the“score chart”for a set of hypothetical prevalence of adverse outcomes were calculated as follows (the sensitivity“for all combined”was used). Hypothetidal Prevalence : 5％ 10％ 20％ 30％ 40％ 50％ 60％ Diagnosability : 12% 23% 40% 53% 64% 75% 80%.

• Journal of Life Science
• /
• v.30 no.3
• /
• pp.298-303
• /
• 2020

This study aimed to investigate possible applications of Rodgersia podophylla in the food and cosmetic industry. Ethanol extracts of leaves (RP-L), branches (RP-B), and root (RP-R) were prepared, and their antimicrobial, antioxidant, and anti-diabetic activities were evaluated. The polyphenol content in the RP-R, RP-L, and RP-B extracts was 79.6, 30.4, and 16.9 mg/g, respectively. An antimicrobial activity assay showed that the RP-L and RP-R extracts exhibited strong growth inhibition of pathogenic and food spoilage Gram-positive bacteria. Furthermore, the RP-R extract inhibited the growth of the Gramnegative E. coli and P. vulgaris bacteria. All extracts showed strong scavenging activity for DPPH, ABTS, nitrite, and reducing power determined by A 700 nm. In particular, the RC₅₀s of the RP-R extract for the DPPH anion and ABTS cation were 23.0-29.7 and 15.0-18.2 ㎍/ml, respectively, which are comparable to those of vitamin C (9.8 and 8.0 ㎍/ml, respectively). An activity assay of α-glucosidase and β-amylase suggested a high potential for the RP-R extract as an anti-diabetic agent. Its inhibition levels of α-glucosidase and β-amylase at 0.5 mg/ml were 6.9 and 48.5%, respectively. This is the first report of the antimicrobial and anti-diabetic activities of R. podophylla. Our results suggest that RP-L and RP-R extracts could be developed as novel cosmeceutical and functional food resources.

• Cartoon and Animation Studies
• /
• s.37
• /
• pp.393-411
• /
• 2014

The volume of world market for serious game in year 2015 is expected to be about 9.6 trillion, and the volume of educational serious game market is expected to surpass half of the whole serious game market. In Korea, the development of game for educational purpose has dominated around the education enterprises since late 90s. In 2008, 'Serious Game Forum' was founded led by the Ministry of Culture, Sports, and Tourism with experts from many fields in the society and there were progressing of making policies and plans for potential development of the serious game industry, but the effects were not successful than expected. In 2012, the Ministry of Education, Science, and Technology announced commercialization policy of digital textbook by 2015 and the serious game for educational purpose got attention again. Then, the serious game market became more vigorous with the dispersion of smart devices.13) As a result, the serious games on the smart devices or interlocking between the online and smart devices became an important issue in development rather than the online only serious games. Math field has international competitive power through export in the educational serious game market which takes more than half of the serious game market. Therefore, developing serious game for math education is a good area to raise competitiveness in domestic and international game industries. Moreover, it has no received preferences from students and parents although it has high potential for positive change of individuals and society. The reason is that students recognize it as educational content rather than a game and they avoid it, while parents recognize it as game but not an education. This phenomenon happens because the game elements and educational elements are not properly mixed but focused only on education or emphasized only the fun factors of game when it was developed. Therefore, the purpose of this research is to suggest a direction of developing serious games effectively combining with elementary math for elementary students to get interested in math while playing games. The research will analyze the current elementary math textbooks and find contents which may be combined with the game genre that elementary students enjoy playing these days. This research received advice from serious game developers and math education expert group to reflect the inclination of elementary school students, and respond to the demands from parents and educational institutions, and suggested a direction of developing serious games for effective math education.