• The korean journal of orthodontics
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• v.36 no.5
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• pp.339-348
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• 2006

Objective: With development of the skeletal anchorage system, orthodontic mini-implant (OMI) assisted on masse sliding retraction has become part of general orthodontic treatment. But compared to the emphasis on successful anchorage preparation, the control of anterior teeth axis has not been emphasized enough. Methods: A 3-D finite element Base model of maxillary dental arch and a Lingual tipping model with lingually inclined anterior teeth were constructed. To evaluate factors influencing the axis of anterior teeth when OMI was used as anchorage, models were simulated with 2 mm or 5 mm retraction hooks and/or by the addition of 4 mm of compensating curve (CC) on the main archwire. The stress distribution on the roots and a 25000 times enlarged axis graph were evaluated. Results: Intrusive component of retraction force directed postero-superiorly from the 2 mm height hook did not reduce the lingual tipping of anterior teeth. When hook height was increased to 5 mm, lateral incisor showed crown-labial and root-lingual torque and uncontrolled tipping of the canine was increased.4 mm of CC added to the main archwire also induced crown-labial and root-lingual torque of the lateral incisor but uncontrolled tipping of the canine was decreased. Lingual tipping model showed very similar results compared with the Base model. Conclusion: The results of this study showed that height of the hook and compensating curve on the main archwire can influence the axis of anterior teeth. These data can be used as guidelines for clinical application.

• 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.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.867-879
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• 2020

As the era of space technology utilization is approaching, the launch of CAS (Compact Advanced Satellite) 500-1/2 satellites is scheduled during 2021 for acquisition of high-resolution images. Accordingly, the increase of image usability and processing efficiency has been emphasized as key design concepts of the CAS 500-1/2 ground station. In this regard, "CAS 500-1/2 Image Acquisition and Utilization Technology Development" project has been carried out to develop core technologies and processing systems for CAS 500-1/2 data collecting, processing, managing and distributing. In this paper, we introduce the results of the above project. We developed an operation system to generate precision images automatically with GCP (Ground Control Point) chip DB (Database) and DEM (Digital Elevation Model) DB over the entire Korean peninsula. We also developed the system to produce ortho-rectified images indexed to 1:5,000 map grids, and hence set a foundation for ARD (Analysis Ready Data)system. In addition, we linked various application software to the operation system and systematically produce mosaic images, DSM (Digital Surface Model)/DTM (Digital Terrain Model), spatial feature thematic map, and change detection thematic map. The major contribution of the developed system and technologies includes that precision images are to be automatically generated using GCP chip DB for the first time in Korea and the various utilization product technologies incorporated into the operation system of a satellite ground station. The developed operation system has been installed on Korea Land Observation Satellite Information Center of the NGII (National Geographic Information Institute). We expect the system to contribute greatly to the center's work and provide a standard for future ground station systems of earth observation satellites.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.3
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• pp.425-430
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• 2014

Foods contain various nutrients such as carbohydrates, protein, oil, vitamins, and minerals. Among them, carbohydrates, protein, and oil are the main constituents of foods. Usually, these constituents are analyzed by the Kjeldahl and Soxhlet method and so on. However, these analytical methods are complex, costly, and time-consuming. Thus, this study aimed to rapidly and effectively analyze carbohydrate, protein, and oil contents with near-infrared reflectance spectroscopy (NIRS). A total of 517 food samples were measured within the wavelength range of 400 to 2,500 nm. Exactly 412 food calibration samples and 162 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of carbohydrates, the most accurate equation was obtained under 1, 4, 5, 1 (1st derivative, 4 nm gap, 5 points smoothing, and 1 point second smoothing) math treatment conditions using the weighted MSC (multiplicative scatter correction) scatter correction method with MPLS (modified partial least square) regression. In the case of protein and oil, the best equation were obtained under 2, 5, 5, 3 and 1, 1, 1, 1 conditions, respectively, using standard MSC and standard normal variate only scatter correction methods with MPLS regression. Calibrations of these NIRS equations showed a very high coefficient of determination in calibration ($R^2$: carbohydrates, 0.971; protein, 0.974; oil, 0.937) and low standard error of calibration (carbohydrates, 4.066; protein, 1.080; oil, 1.890). Optimal equation conditions were applied to a validation set of 162 samples. Validation results of these NIRS equations showed a very high coefficient of determination in prediction ($r^2$: carbohydrates, 0.987; protein, 0.970; oil, 0.947) and low standard error of prediction (carbohydrates, 2.515; protein, 1.144; oil, 1.370). Therefore, these NIRS equations can be applicable for determination of carbohydrates, proteins, and oil contents in various foods.

• Journal of the Korean Society of Food Science and Nutrition
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• v.35 no.4
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• pp.464-469
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• 2006

This study was conducted to evaluate a knowledge on food components of muscle and adductor muscle of pearl oyster (Pinctada fucata martensii) as pearl processing byproducts. The concentrations of mercury and chromium as heavy metal were not detected in both pearl oyster muscle and adductor muscle, and those of cadmium and lead were 0.06 ppm and 0.11 ppm in only pearl oyster muscle, respectively. Thus, the heavy metal levels of pearl processing byproducts were below the reported safety limits. The volatile basic nitrogen (VBN) content and pH of pearl oyster muscle were 11.6 mg/100g and 6.31 and those of abductor muscle were 8.6 mg/100 g and 6.33, respectively. It was concluded that pearl oyster muscle and adductor muscle might not invoke health risk in using food resource. The contents of crude protein (16.5%) and total amino acid (15,691 mg/100 g) of adductor muscle were higher than those of muscle (11.2% and 10,131 mg/100 g) and oyster (12.1% and 11,213 mg/100 g) as a control. The contents of calcium and phosphorus were 95.4 mg/100 g and 116.0 mg/100 g in muscle, 75.2 mg/100g and 148.1 mg/100 g in adductor muscle, respectively. The calcium level based on phosphorus was a good ratio for absorbing calcium. The free amino acid contents and taste values were 635.5 mg/100 g and 40.2 in muscle, and 734.9 mg/100 g and 24.1 in adductor muscle, respectively, but that (882.8 mg/100 g and 40.2) of oyster was higher than those of pearl processing byproducts. Based on the results of physicochemical and nutritional properties, pearl oyster muscle and adductor muscle can be utilized as a food resource.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).

• Journal of Korean Medicine for Obesity Research
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• v.14 no.2
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• pp.47-54
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• 2014

Objectives: This study is to confirm the effect of combined extract and individual extract of Samjunghwan (SJH) in anti-oxidative and anti-obesity effect. Methods: Combined ethanol extract of readily made SJH and individual ethanol extract of Atractylodes japonica, Cortex lycii radicis, and Morus alba Linne was combined after the extraction. To evaluate the anti-oxidative effect of SJH, total phenol compound and 2,2-Diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging ability were conducted. Real-time quantitative-polymerase chain reaction analysis of transcription factor peroxisome proliferator-activated receptror ${\gamma}$ ($PPAR{\gamma}$), adenosine monophosphate-activated protein kinase (AMPK)-${\alpha}1$, tumor necrosis factor-${\alpha}$ ($TNF{\alpha}$) and 3-hydroxy-3-methylglutaryl CoA reducatase (HMG-CoA reductase) were done with 3T3-L1 cells to investigate the ant-obesity effect. Also, cell viability analysis were done to see to toxicity of SJH. Results: Individual extract of SJH showed significant decrease in $TNF{\alpha}$ and AMPK transcription while $PPAR{\gamma}$ showed significant increase. Combined extract and individual extract of SJH both showed decrease in HMG-CoA reductase. DPPH free radical scavenging ability and total phenol compound was analogous between two groups. Conclusions: Individual extract of SJH appears to be more effective in anti-oxidation and anti-obesity effect compared to combined extract of SJH.

• The Journal of Korean Academy of Prosthodontics
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• v.51 no.4
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• pp.261-268
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• 2013

Purpose: The purpose of this study was to investigate the effect of geometrically different wax pattern shapes and sizes on the castability of Grade2 Cp-Ti (commercially pure titanium). Materials and methods: Total of 40 mesh wax pattern ($61mm{\times}24mm$, 207 grids), ($61mm{\times}17mm$, 138 grids) was cast in this experiment. Depending on the geometrical shape of the wax pattern, 8 groups was organized; Flat, Semicircular, Horse-shoe and V-shape, each consisting 5 samples. Runner-bar sprue was used in all patterns. The number of completely cast grid in wax pattern served as a measure for the castability of comercially pure titanium. Results: The mean value of square count in each group was as followed; 133.20 squares in group SS (96.52%), 132.40 squares in group SH (95.94%), 132.00 squares in group SF (95.65%), 127.60 squares in SV (91.43%), 198.60 squares in group LF (95.94%), 197.80 squares in group LV (95.56%), 196.40 squares in group LS (94.88%), and 188.00 squares in group LH (90.82%). Conclusion: Within the limitations of this study the results indicate that there were no sttistically significant difference in castability of titanium regarding wax pattern shape (P>.05). However, Small size wax patterns were showing the noticeable castability more than Large size pattern.

• Information Systems Review
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• v.15 no.2
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• pp.1-19
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• 2013

Smart working sparked by iPhone3 opens a revolution in smart ways of working at any time, regardless of location and environment. Also, It provide real-time information processing and analysis, rapid decision-making and the productivity of businesses, including through the timely response and the opportunity to increase the efficiency. As a result, every company are developing mobile information systems. But company data is accessed from the outside, it has problems to solve like security, hacking and information leakage. Also, Mobile devices such as smart phones belonging to the privately-owned asset can't be always controlled to archive company security policy. In the meantime, public smart phones owned by company was always applied security policy. But it can't not apply to privately-owned smart phones. Thus, this paper is focused to archive company security policy, but also enable the individual's free to use of smart phones when we use mobile information systems. So, when we use smart phone as individual purpose, the normal operation of all smart phone functions. But, when we use smart phone as company purpose like mobile information systems, the smart phone functions are blocked like screen capture, Wi-Fi, camera to protect company data. In this study, we suggest the design and implementation of real time control and management of mobile device using MDM(Mobile Device Management) solution. As a result, we can archive company security policy and individual using of smart phone and it is the optimal solution in the BYOD(Bring Your Own Device) era.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.2
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• pp.121-134
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• 2016

The current insurance market is facing a real problem that the high cost of insurance spent in maintaining a non- face-to-face sales channels face of the channel facing growing contribution to the reduction of side. As a result, the productivity issue facing designers of representative organizations in the organization channel will be referred to an urgent problem. As a result of improved organizational productivity architect that is the goal of this study to demonstrate what a performance improvement factor of insurance agents. Personality factors and individual insurance agents individual-environmental suitability and job satisfaction, consider the impact on turnover intention year of the results architects extroversion, sincerity, openness, it won a chronic, emotional gender, personality representing the honesty factor is organizational commitment and job satisfaction It has had a significant impact on. In other words, this is a lively and extroverted nature of the actuary, the more harmonious interpersonal relationships and higher emotional empathy with others can raise the extent that has a strong sense of belonging and attachment to their company's commitment. Whereas personality factors were not significant influence turnover intention has. This can be made to represent the need for screening of agents introduced from the introduction stage. Depending on the personality factors of organizational commitment, personal planners also occurs because of the differences and job satisfaction. Whereas turnover of agents is the result of empirical factors that are affected by other agents than to individual character generated by the character of the individual agents. Compliance boss, job suitability, individuals representing a fellow fitness, tissue compatibility environmental compliance is having a significant impact on both the degree of organizational commitment, job satisfaction and turnover intention. In other words, the boss or colleague, values and personality, working method, as fits well the concerns and pursuing goals are similar, and their job aptitude higher the suitability of the organization is about to have a sense of belonging and attachment to the company commitment can do. This is the result of a demonstration that the work environment of the actuary agents productivity gains and loyalty depends on the insurance company, which currently belongs.