• Journal of Korean Society of Forest Science
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• v.77 no.4
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• pp.429-444
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• 1988

Considering the recent trent toward the development of multiple-use of forest trees, investigations for comprehensive information on these young stands of Hinoki cypress are necessary for rational forest management. From this point of view, 83 sample trees were selected and cut down from 23-ear old stands of Hinoki cypress at Changsung-gun, Chonnam-do. Various stem growth factors of felled trees were measured and canonical correlaton analysis, principal component analysis and factor analysis were applied to investigate the stem growth characteristics, relationships among stem growth factors, and to get potential information and comprehensive information. The results are as follows ; Canonical correlation coefficient between stem volume and quality growth factor was 0.9877. Coefficient of canonical variates showed that DBH among diameter growth factors and height among height growth factors had important effects on stem volume. From the analysis of relationship between stem-volume and canonical variates, which were linearly combined DBH with height as one set, DBH had greater influence on volume growth than height. The 1st-2nd principal components here adopted to fit the effective value of 85% from the pincipal component analysis for 12 stem growth factors. The result showed that the 1st-2nd principal component had cumulative contribution rate of 88.10%. The 1st and the 2nd principal components were interpreted as "size factor" and "shape factor", respectively. From summed proportion of the efficient principal component fur each variate, information of variates except crown diameter, clear length and form height explained more than 87%. Two common factors were set by the eigen value obtained from SMC (squared multiple correlation) of diagonal elements of canonical matrix. There were 2 latent factors, $f_1$ and $f_2$. The former way interpreted as nature of diameter growth system. In inherent phenomenon of 12 growth factor, communalities except clear length and crown diameter had great explanatory poorer of 78.62-98.30%. Eighty three sample trees could he classified into 5 stem types as follows ; medium type within a radius of ${\pm}1$ standard deviation of factor scores, uniformity type in diameter and height growth in the 1st quadrant, slim type in the 2nd quadrant, dwarfish type in the 3rd quadrant, and fall-holed type in the 4 th quadrant.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.171-183
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• 2012

In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.185-196
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.

• The korean journal of orthodontics
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• v.32 no.4 s.93
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• pp.241-255
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• 2002

This study was performed to evaluate horizontal and vertical characteristics according to lateral cephalometry of adult Korean skeletal Class II patients using a selected horizontal and vortical reference planes of Koreans. 60 males and 60 females consisting of freshman of Yonsei University from 1996 to 1997 and patients with history of orthognatic surgery at the Dental Hospital of Yonsei University with a skeletal Class II profile were chosen and compared with 70 males and 70 females with normal occlusion. The skeletal Class R group had the following conditions : 1. Profile composed of a retrognathic mandible or protrusive maxilla; 2. Class II molar and canine key; 3. ANB-greater than $4^{\circ}; 4$. Wits appraisal-greater than 1.0mm; Cephalometric analysis consisted of 22 skeletal, 25 soft tissue, 12 dental measurements. The results were as follows. 1. There was no considerable vortical measurement difference between the skeletal Class II malocclusion group and the normal occlusion group in skeletal analysis. But, some variations were found between the two groups in soft tissue analysis. 2. Mandibular length of the skeletal Class II malocclusion group was smaller than that of the normal occlusion group. Mandible was more posteriorly positioned in the Class II malocclusion group than in the normal occlusion group. 3. The length and antero-posterior position of the maxilla were not different between the Class II malocclusion and the normal occlusion group. 4. The antero-posterior position of the nose, upper lip and maxillary soft tissue, and nasolabial angle were not different between the two groups. 5. Mandibular soft tissue of the Class H malocclusion group was more posteriorly positioned than that of the normal. 6. The vertical measurements of the incisors(U1-HP, L1-HP) were bigger in the Class II malocclusion group than in the normal, but those of the molars(U6-HP, U6-MP) showed no significant difference between the two groups. 7 Classifying the skeletal Class II malocclusion group according to the antero-posterior position of both jaws, normally positioned maxilla and retruded mandible was 43.3%, both normally positioned maxilla and mandible 28.3%, both retruded maxilla and mandible 20.0%..

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.5
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• pp.757-764
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• 2016

The purpose of this study was to investigate the salt and sodium content of lunch meals served at middle schools in Gyeongsangbuk-do. Four middle schools were investigated for their salt and sodium content and salt content contributing rate per meal. Average salt content of every lunch meal was 4.41 g, and average sodium content was 1,736.02 mg. During the investigation period, the lowest sodium content was 943.19 mg while the highest was 2,827.56 mg. Samples were classified into 16 food items and investigated for salt and sodium content. Total average salinity was 1.06%. Kimchi was the saltiest, followed by fresh-vegetables, boiled-vegetables, stir-fried foods, pan-fried foods, and hard-boiled foods. Total average salt content was 0.74 g, and most salty dishes were single dish rice noodles, followed by stews, steamed foods, broths, soups, kimchies, stir-fried foods, roasted foods, and hard-boiled foods. Samples were classified into seven menu groups, including cooked rice, single dish rice noodles, soup stew, main dish, side dish, kimchi, and desserts. Contributing rate of total average salt content was high in single dish rice noodles (40.56%), soup stew (23.23%), kimchies (20.30%), and main dish (18.13%). These results can be useful for establishing a database for sodium contents of meals in middle schools. 'SamSam foodservice' should be initiated in school foodservice to reduce sodium intake.

• Korean Journal of Environmental Biology
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• v.19 no.4
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• pp.261-269
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• 2001

The purpose of present study was to introduce a multimetric approach, so called the Index of Biological Integrity (IBI) as a tool for evaluations of water environments. We used 11 metric systems for the IBI to evaluate stream conditions, based on the fish community, and modified 5 original metric attributes suggested by Karr (1981). Overall IBI values in Kap Stream averaged 36 (n = 5) and ranged 17${\sim}$49, indicating a 'fair condition' according to the modified criteria of Karr (1981) and U.S. EPA (1993). However, there were distinct differences in the IBI values among 5 study sites. The IBI values at sites 1, 2, and 3 were 49, 45, and 41, which indicated 'good${\sim}$excellent', 'good', and 'fair' condition, respectively, while values at sites 4 and 5 were 17 and 29, which indicated 'very poor' and 'poor', respectively. The minimum IBI at site 4 was probably due to continuous inputs of wastewater from wastewater disposal plants. The condition at site 4 resulted in predominance of tolerant species (50%), omnivore species (50%), and high abnormalies (43%). In the mean time, the IBI value at site 5, located near 5km downstream from the site 4, increased compared to that of site 4, and this seemed to be a result of recovery of water quality as the polluted water goes downward. We believe that present bioassessment methodology of IBI applied in this study may be used as a key tool to set up specific goals for stream restoration plans and dentify recovery levels of lotic ecosystems after restoration activities(i.e., prevention of point-source pollutant input, restoration of physical habitats, construction of riparian vegetation) as well as a biological measure diagnosing current stream conditions.

• Journal of the Korean earth science society
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• v.22 no.5
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• pp.360-376
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• 2001

This qualitative research investigated in-service science teachers' perceptions about cooperative learning and their perceived barriers in implementing cooperative learning in their classrooms. The underlying premise for cooperative learning is founded in constructivist epistemology. Cooperative learning (CL) is presented as an alternative frame to the current educational system which emphasizes content memorization and individual student performance through competition. An in-depth interview was conducted with 18 in-service science teachers who enrolled in the first-class teacher certification program during 2001 summer vacation. These secondary school teachers's interview data were analyzed and categorized into three areas: teachers' definition of cooperative learning, issues with implementing cooperative learning in classrooms, and teachers' and students' responses towards cooperative learning. Each of these areas are further subdivided into 10 themes: teachers' perceived meaning of cooperative learning, the importance of talk in learning, when to use cooperative learning, how to end a cooperative class, how to group students for cooperative learning, obstacles to implementing cooperative learning, students' reactions to cooperative learning, teachers' reasons for choosing (not choosing) student-centered approaches to learning/teaching, characteristics of teachers who use cooperative learning methods, and teachers' reasons for resisting cooperative learning. Detailed descriptions of the teachers' responses and discussion on each category are provided. For the development and implementation of CL in more classrooms, there should be changes and supports in the following five areas: (1) teachers have to examine their pedagogical beliefs toward constructivist perspectives, (2) teacher (re)education programs have to provide teachers with cooperative learning opportunities in methods courses, (3) students' understanding of their changed roles (4) supports in light of curriculum materials and instructional resources, (5) supports in terms of facilities and administrators. It's important to remember that cooperative learning is not a panacea for all instructional problems. It's only one way of teaching and learning, useful for specific kinds of teaching goals and especially relevant for classrooms with a wide mix of student academic skills. Suggestions for further research are also provided.

• Journal of Nutrition and Health
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• v.51 no.4
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• pp.340-356
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• 2018

Purpose: This study was conducted to develop a nutrition quotient (NQ) to assess overall dietary quality and food behaviors of Korean adults. Methods: The NQ was developed in three steps: item generation, item reduction, and validation. Candidate items of the NQ checklist were derived from a systematic literature review, expert in-depth interviews, statistical analyses of the Korea National Health and Nutrition Examination Survey (2010 ~ 2013) data, and national nutrition policies and recommendations. A total of 368 adults (19 ~ 64 years) participated in a one-day dietary record survey and responded to 43 items in the food behavior checklist. Pearson's correlation coefficients between responses to the checklist items and nutritional intake status of the adults were calculated. Item reduction was performed, and 24 items were selected for a nationwide survey. A total of 1,053 nationwide adult subjects completed the checklist questionnaires. Exploratory and confirmatory factor analyses were performed to develop a final NQ model. Results: The 21 checklist items were used as final items for NQ. Checklist items were composed of four factors: nutrition balance (seven items), food diversity (three items), moderation for the amount of food intake (six items), and dietary behavior (five items). The four-factor structure accounted for 41.8% of the total variance. Indicator tests of the NQ model suggested an adequate model fit (GRI = 0.9693, adjusted GFI = 0.9617, RMR = 0.0054, SRMR = 0.0897, p < 0.05), and item loadings were significant for all subscales. Standardized path coefficients were used as weights of the items. The NQ and four-factor scores were calculated according to the obtained weights of the questionnaire items. Conclusion: NQ for adults would be a useful tool for assessing adult dietary quality and food behavior. Further investigations of adult NQ are needed to reflect changes in their food behavior, environment, and prevalence of chronic diseases.