• The Journal of Korean Society for Radiation Therapy
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• v.26 no.1
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• pp.137-147
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• 2014

Purpose : The purpose of this study is to verify the accuracy of dose delivery according to the patient's breathing cycle in Gated Volumetric Modulated Arc Therapy Materials and Methods : TrueBeam STxTM(Varian Medical System, Palo Alto, CA) was used in this experiment. The Computed tomography(CT) images that were acquired with RANDO Phantom(Alderson Research Laboratories Inc. Stamford. CT, USA), using Computerized treatment planning system(Eclipse 10.0, Varian, USA), were used to create VMAT plans using 10MV FFF with 1500 cGy/fx (case 1, 2, 3) and 220 cGy/fx(case 4, 5, 6) of doserate of 1200 MU/min. The regular respiratory period of 1.5, 2.5, 3.5 and 4.5 sec and the patients respiratory period of 2.2 and 3.5 sec were reproduced with the $QUASAR^{TM}$ Respiratory Motion Phantom(Modus Medical Devices Inc), and it was set up to deliver radiation at the phase mode between the ranges of 30 to 70%. The results were measured at respective respiratory conditions by a 2-Dimensional ion chamber array detector(I'mRT Matrixx, IBA Dosimetry, Germany) and a MultiCube Phantom(IBA Dosimetry, Germany), and the Gamma pass rate(3 mm, 3%) were compared by the IMRT analysis program(OmniPro I'mRT system software Version 1.7b, IBA Dosimetry, Germany) Results : The gamma pass rates of Case 1, 2, 3, 4, 5 and 6 were the results of 100.0, 97.6, 98.1, 96.3, 93.0, 94.8% at a regular respiratory period of 1.5 sec and 98.8, 99.5, 97.5, 99.5, 98.3, 99.6% at 2.5 sec, 99.6, 96.6, 97.5, 99.2, 97.8, 99.1% at 3.5 sec and 99.4, 96.3, 97.2, 99.0, 98.0, 99.3% at 4.5 sec, respectively. When a patient's respiration was reproduced, 97.7, 95.4, 96.2, 98.9, 96.2, 98.4% at average respiratory period of 2.2 sec, and 97.3, 97.5, 96.8, 100.0, 99.3, 99.8% at 3.5 sec, respectively. Conclusion : The experiment showed clinically reliable results of a Gamma pass rate of 95% or more when 2.5 sec or more of a regular breathing period and the patient's breathing were reproduced. While it showed the results of 93.0% and 94.8% at a regular breathing period of 1.5 sec of Case 5 and 6, it could be confirmed that the accurate dose delivery could be possible on the most respiratory conditions because based on the results of 100 patients's respiratory period analysis as no one sustained a respiration of 1.5 sec. But, pretreatment dose verification should be precede because we can't exclude the possibility of error occurrence due to extremely short respiratory period, also a training at the simulation and careful monitoring are necessary for a patient to maintain stable breathing. Consequently, more reliable and accurate treatments can be administered.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.4
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• pp.47-60
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• 2017

As the recent cultural contents area start-ups are creating remarkable outcomes such as investment attraction together with the reinforced institutional supports from the government, this study aimed to reverify the significance of researches related to correlation analysis between service of Business Incubating Center of Small & Medium Business Administration operated with no separation of business type, and corporate performance, in the aspect of Business Incubating Center in cultural contents area, and also to suggest the importance of establishing the business incubating system in the systematic and rational cultural contents area through the differentiated business incubating service by verifying the significant effects of the business validity of items on corporate performance, and then discovering services suitable for business incubating in cultural contents area, targeting Gyeonggi-do cultural contents area Business Incubating Center recently showing the biggest growth. Especially, contrary to the existing researches, in order to verify the characteristics of Gyeonggi-do Cultural Contents Business Incubating Center, the personal support service and marketing support service were included. It also aimed to understand the effects of the business validity of start-up items on corporate performance. Summarizing the results of this study, contrary to the results of the existing researches saying that spatial & additional support service, management support service, technical support service, personal support service, and marketing support service had significant effects on corporate performance, among the support service of Gyeonggi-do cultural contents area Business Incubating Center, the spatial & additional support service, personal support service, and marketing support service had significantly positive(+) effects on corporate performance while the management support service and technical support service had no significant effects on it. Comparing with the results of the researches on the support service of Business Incubating Center(BI) of Small & Medium Business Administration, the effects of the management support service and technical support service of Gyeonggi-do cultural contents area Business Incubating Center on corporate financial/non-financial performance were not huge. Also, in the results of analyzing the business validity of star-up items, the spatial & additional support service, management support service, and technical support service did not have significant effects on the business validity of start-up items while the personal support service and marketing support service had significantly positive(+) effects on it. In case when selecting companies, Gyeonggi-do Business Incubating Center emphasized the business validity of start-up items. However, the support service provided after the selection did not have huge effects on the business validity of start-up items. Lastly, in the results of analyzing the effects of the business validity of start-up items in Gyeonggi-do cultural contents area on corporate performance, among the success factors of business start-up, the business validity of start-up items was an important element having effects on corporate performance(financial/non-financial) in the cultural contents area.

• The Journal of Korean Academy of Prosthodontics
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• v.46 no.2
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• pp.125-136
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• 2008

Statement of problem: Since the concept of osseointegration in dental implants was introduced by $Br{{\aa}}nemark$ et al, high long-term success rates have been achieved. Though the use of dental implants have increased dramatically, there are few studies on domestic implants with clinical and objective long-term data. Purpose: The aim of this retrospective study was to provide long-term data on the $Neoplan^{(R)}$ implant, which features a sandblasted and acid-etched surface and external connection. Material and methods: 96 $Neoplan^{(R)}$ implants placed in 25 patients in Yonsei University Hospital were examined to determine the effect of the factors on marginal bone loss, through clinical and radiographic results during 18 to 57 month period. Results: 1. Out of a total of 96 implants placed in 25 patients, two fixtures were lost, resulting in 97.9% of cumulative survival rate. 2. Throughout the study period, the survival rates were 96.8% in the maxilla and 98.5% in the mandible. The survival rates were 97.6% in the posterior regions and 100% in the anterior regions. 3. The mean bone loss for the first year after prosthesis placement and the mean annual bone loss after the first year for men were significantly higher than that of women (P<0.05). 4. The group of partial edentulism with no posterior teeth distal to the implant prosthesis showed significantly more bone loss compared to the group of partial edentulism with presence of posterior teeth distal to the implant prosthesis in terms of mean bone loss for the first year and after the first year (P<0.05). 5. The mean annual bone loss after the first year was more pronounced in posterior regions compared to anterior regions (P<0.05). 6. No significant difference in marginal bone loss was found in the following factors: jaws, type of prostheses, type of opposing dentition, and submerged /non-submerged implants (P<0.05). Conclusion: On the basis of these results, the factors influencing marginal bone loss were gender, type of edentulism, and location in the arch, while the factors such as arch, type of prostheses, type of opposing dentition, submerged / non- submerged implants had no significant effect on bone loss. In the present study, the cumulative survival rate of the $Neoplan^{(R)}$ implant with a sandblasted and acid-etched surface was 97.9% up to a maximum 57-month period. Further long-term investigations for this type of implant system and evaluation of other various domestic implant systems are needed in future studies.

• Journal of Genetic Medicine
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• v.8 no.2
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• pp.89-99
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• 2011

Over the years Korean health care system has improved in delivery of quality care to the general population for many areas of the health problems. The system is now being recognized in the world as the most cost effective one. It is covered by the uniform national health insurance policy for which most people in Korea are mandatory policy holders. Genetic counseling service, however, which is well recognized as an integral part of clinical genetics service deals with diagnosis and management of genetic condition as well as genetic information presentation and family support, is yet to be delivered in comprehensive way for the patients and families in need. Two major obstacles in providing genetic counseling service in korean health care system are identified; One is the lack of recognition for the need for genetic counseling service as necessary service by the national health insurance. Genetic counseling consumes a significant time in delivery and the current very low-fee schedule for physician service makes it very difficult to provide meaningful service. Second is the critical shortage of qualified professionals in the field of medical genetics and genetic counseling who can provide the service of genetic counseling in clinical setting. However, recognition and understanding of the fact that the scope and role of genetic counseling is expanding in post genomic era of personalized medicine for delivery of quality health care, will lead to the efforts to overcome obstacles in providing genetic counseling service in korean health care system. Only concerted efforts from health care policy makers of government on clinical genetics service and genetic counseling for establishing adequate reimbursement coverage and professional communities for developing educational program and certification process for professional genetic counselors, are necessary for the delivery of much needed clinical genetic counseling service in Korea.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.

• 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.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.35-52
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• 2019

As public services are provided in various forms, including e-government, the level of public demand for public service quality is increasing. Although continuous measurement and improvement of the quality of public services is needed to improve the quality of public services, traditional surveys are costly and time-consuming and have limitations. Therefore, there is a need for an analytical technique that can measure the quality of public services quickly and accurately at any time based on the data generated from public services. In this study, we analyzed the quality of public services based on data using process mining techniques for civil licensing services in N city. It is because the N city's building license complaint service can secure data necessary for analysis and can be spread to other institutions through public service quality management. This study conducted process mining on a total of 3678 building license complaint services in N city for two years from January 2014, and identified process maps and departments with high frequency and long processing time. According to the analysis results, there was a case where a department was crowded or relatively few at a certain point in time. In addition, there was a reasonable doubt that the increase in the number of complaints would increase the time required to complete the complaints. According to the analysis results, the time required to complete the complaint was varied from the same day to a year and 146 days. The cumulative frequency of the top four departments of the Sewage Treatment Division, the Waterworks Division, the Urban Design Division, and the Green Growth Division exceeded 50% and the cumulative frequency of the top nine departments exceeded 70%. Higher departments were limited and there was a great deal of unbalanced load among departments. Most complaint services have a variety of different patterns of processes. Research shows that the number of 'complementary' decisions has the greatest impact on the length of a complaint. This is interpreted as a lengthy period until the completion of the entire complaint is required because the 'complement' decision requires a physical period in which the complainant supplements and submits the documents again. In order to solve these problems, it is possible to drastically reduce the overall processing time of the complaints by preparing thoroughly before the filing of the complaints or in the preparation of the complaints, or the 'complementary' decision of other complaints. By clarifying and disclosing the cause and solution of one of the important data in the system, it helps the complainant to prepare in advance and convinces that the documents prepared by the public information will be passed. The transparency of complaints can be sufficiently predictable. Documents prepared by pre-disclosed information are likely to be processed without problems, which not only shortens the processing period but also improves work efficiency by eliminating the need for renegotiation or multiple tasks from the point of view of the processor. The results of this study can be used to find departments with high burdens of civil complaints at certain points of time and to flexibly manage the workforce allocation between departments. In addition, as a result of analyzing the pattern of the departments participating in the consultation by the characteristics of the complaints, it is possible to use it for automation or recommendation when requesting the consultation department. In addition, by using various data generated during the complaint process and using machine learning techniques, the pattern of the complaint process can be found. It can be used for automation / intelligence of civil complaint processing by making this algorithm and applying it to the system. This study is expected to be used to suggest future public service quality improvement through process mining analysis on civil service.

• The Korean Journal of Nuclear Medicine
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• v.39 no.3
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• pp.182-190
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• 2005

Purpose: There are differences between Standard Uptake Value (SUV) of CT attenuation corrected PET and that of $^{137}Cs$. Since various causes lead to difference of SUV, it is important to know what is the cause of these difference. Since only the X-ray CT and $^{137}Cs$ transmission data are used for the attenuation correction, in Philips GEMINI PET/CT scanner, proper transformation of these data into usable attenuation coefficients for 511 keV photon has to be ascertained. The aim of this study was to evaluate the accuracy in the CT measurement and compare the CT and $^{137}Cs$-based attenuation correction in this scanner. Methods: For all the experiments, CT was set to 40 keV (120 kVp) and 50 mAs. To evaluate the accuracy of the CT measurement, CT performance phantom was scanned and Hounsfield units (HU) for those regions were compared to the true values. For the comparison of CT and $^{137}Cs$-based attenuation corrections, transmission scans of the elliptical lung-spine-body phantom and electron density CT phantom composed of various components, such as water, bone, brain and adipose, were performed using CT and $^{137}Cs$. Transformed attenuation coefficients from these data were compared to each other and true 511 keV attenuation coefficient acquired using $^{68}Ge$ and ECAT EXACT 47 scanner. In addition, CT and $^{137}Cs$-derived attenuation coefficients and SUV values for $^{18}F$-FDG measured from the regions with normal and pathological uptake in patients' data were also compared. Results: HU of all the regions in CT performance phantom measured using GEMINI PET/CT were equivalent to the known true values. CT based attenuation coefficients were lower than those of $^{68}Ge$ about 10% in bony region of NEMA ECT phantom. Attenuation coefficients derived from $^{137}Cs$ data was slightly higher than those from CT data also in the images of electron density CT phantom and patients' body with electron density. However, the SUV values in attenuation corrected images using $^{137}Cs$ were lower than images corrected using CT. Percent difference between SUV values was about 15%. Conclusion: Although the HU measured using this scanner was accurate, accuracy in the conversion from CT data into the 511 keV attenuation coefficients was limited in the bony region. Discrepancy in the transformed attenuation coefficients and SUV values between CT and $^{137}Cs$-based data shown in this study suggests that further optimization of various parameters in data acquisition and processing would be necessary for this scanner.