• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.91-108
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• 2020

Korea's famous soccer players are steadily performing well in international leagues, which led to higher interests of Korean fans in the international leagues. Reflecting the growing social phenomenon of rising interests on international leagues by Korean fans, the study examined the overall consumer perception in the consumption of uniform by domestic soccer fans and compared the changes in perception following the transfers of the players. Among others, the paper examined the consumer perception and purchase factors of soccer fans shown in social media, focusing on periods before and after the recruitment of Heung-Min Son to English Premier League's Tottenham Football Club. To this end, the EPL uniform is the collection keyword the paper utilized and collected consumer postings from domestic website and social media via Python 3.7, and analyzed them using Ucinet 6, NodeXL 1.0.1, and SPSS 25.0 programs. The results of this study can be summarized as follows. First, the uniform of the club that consistently topped the league, has been gaining attention as a popular uniform, and the players' performance, and the players' position have been identified as key factors in the purchase and search of professional football uniforms. In the case of the club, the actual ranking and whether the league won are shown to be important factors in the purchase and search of professional soccer uniforms. The club's emblem and the sponsor logo that will be attached to the uniform are also factors of interest to consumers. In addition, in the decision making process of purchase of a uniform by professional soccer fan, uniform's form, marking, authenticity, and sponsors are found to be more important than price, design, size, and logo. The official online store has emerged as a major purchasing channel, followed by gifts for friends or requests from acquaintances when someone travels to the United Kingdom. Second, a classification of key control categories through the convergence of iteration correlation analysis and Clauset-Newman-Moore clustering algorithm shows differences in the classification of individual groups, but groups that include the EPL's club and player keywords are identified as the key topics in relation to professional football uniforms. Third, between 2002 and 2006, the central theme for professional football uniforms was World Cup and English Premier League, but from 2012 to 2015, the focus has shifted to more interest of domestic and international players in the English Premier League. The subject has changed to the uniform itself from this time on. In this context, the paper can confirm that the major issues regarding the uniforms of professional soccer players have changed since Ji-Sung Park's transfer to Manchester United, and Sung-Yong Ki, Chung-Yong Lee, and Heung-Min Son's good performances in these leagues. The paper also identified that the uniforms of the clubs to which the players have transferred to are of interest. Fourth, both male and female consumers are showing increasing interest in Son's league, the English Premier League, which Tottenham FC belongs to. In particular, the increasing interest in Son has shown a tendency to increase interest in football uniforms for female consumers. This study presents a variety of researches on sports consumption and has value as a consumer study by identifying unique consumption patterns. It is meaningful in that the accuracy of the interpretation has been enhanced by using a cluster analysis via convergence of iteration correlation analysis and Clauset-Newman-Moore clustering algorithm to identify the main topics. Based on the results of this study, the clubs will be able to maximize its profits and maintain good relationships with fans by identifying key drivers of consumer awareness and purchasing for professional soccer fans and establishing an effective marketing strategy.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Tuberculosis and Respiratory Diseases
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• v.46 no.2
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• pp.204-214
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• 1999

Background: Resting pulmonary function tests(PFTs) are routinely used in the evaluation of pulmonary impairment/disability. But the significance of the cardiopulmonary exercise test(CPX) in the evaluation of pulmonary impairment is controvertible. Many experts believe that dyspnea, though a necessary part of the assessment, is not a reliable predictor of impairment. Nevertheless, oxygen requirements of an organism at rest are different from at activity or exercising, and a clear relationship between resting PFTs and exercise tolerance has not been established in patients with chronic pulmonary disease. As well, the relationship between resting PFTs and dyspnea is complex. To investigate the relationship of dyspnea, resting PFTs, and CPX, we evaluated the patients of stabilized chronic pulmonary disease with clinical dyspnea rating(baseline dyspnea index, BDI), resting PFTs, and CPX. Method: The 50 patients were divided into two groups: non-severe and severe group on basis of results of resting PFTs(by criteria of ATS), CPX(by criteria of ATS or Ortega), and dyspnea rating(by focal score of BDI). Groups were compared with respect to pulmonary function, indices of CPX, and dyspnea rating. Results: 1. According to the criteria of pulmonary impairment with resting PFTs, $VO_2$max, and focal score of BDI were significantly low in the severe group(p<0.01). According to the criteria of $VO_2$max(ml/kg/min) and $VO_2$max(%), the parameters of resting PFTs, except $FEV_1$ were not significantly different between non-severe and severe(p>0.05). According to focal score($FEV_1$(%), FVC(%), MW(%), $FEV_1/FVC$, and $VO_2$max were significantly lower in the severe group(p<0.01). However, in the more severe dyspneic group(focal score<5), only $VO_2$max(ml/kg/min) and $VO_2$max(%) were low(p<0.01). $FEV_1$(%) was correlated with $VO_2$max(%)(r=0.52;p<0.01), but not predictive of exercise performance. The focal score had the correlation with max WR(%) (r=0.55;p<0.01). Sensitivity and specificity analysis were utilized to compare the different criteria used to evaluate the severity of pulmonary impairment, revealed that the classification would be different according to the criteria used. And focal score for dyspnea showed similar sensitivity and specificity. Conclusion : According to these result, resting PFTs were not superior to rating of dyspnea in prediction of exercise performance in patients with chronic pulmonary diseases and less correlative with focal score for dyspnea than $VO_2$max and max WR. Therefore, if not contraindicated, CPX would be considered to evaluate the severity of pulmonary impairment in patients with chronic pulmonary diseases, including with severe resting PFTs. Current criteria used to evaluate the severity of impairment were insufficient in considering the degree of dyspnea, so new criteria, including the severity of dyspnea, may be necessary.

• 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.27 no.4
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• pp.1-22
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• 2021

Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, "The pasta was delicious, but the salad was not," the words "steak" and "salad," which are directly mentioned in the sentence, become the "target." So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, "Pizza and the salad were good, but the steak was disappointing." Although the aspect of this sentence is limited to "food," conflicting sentiments coexist. In addition, in the case of sentences such as "Shrimp was delicious, but the price was extravagant," although the target here is "shrimp," there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like "The food arrived too late and is cold now." there is no target (NULL), but it transmits a negative sentiment toward the aspect "service." Like this, failure to consider both aspects and targets - when sentiment or aspect is divided or when sentiment exists without a target - creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the model's performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to make efficient decisions. In addition, it is believed that the model can be fully learned and utilized by small businesses, those that do not have much data, given that they use a pre-training model and recorded a relatively high F1-score even with limited resources.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.281-290
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• 2020

With the spread of smartphones, services that want to use personalized data are increasing. In particular, healthcare-related services deal with a variety of data, and data visualization techniques are used to effectively show this. As data visualization techniques are used, interactions in visualization are also naturally emphasized. In the PC environment, since the interaction for data visualization is performed with a mouse, various filtering for data is provided. On the other hand, in the case of interaction in a mobile environment, the screen size is small and it is difficult to recognize whether or not the interaction is possible, so that only limited visualization provided by the app can be provided through a button touch method. In order to overcome the limitation of interaction in such a mobile environment, we intend to enable data visualization interactions through conversations with chatbots so that users can check individual data through various visualizations. To do this, it is necessary to convert the user's query into a query and retrieve the result data through the converted query in the database that is storing data periodically. There are many studies currently being done to convert natural language into queries, but research on converting user queries into queries based on visualization has not been done yet. Therefore, in this paper, we will focus on query generation in a situation where a data visualization technique has been determined in advance. Supported interactions are filtering on task x-axis values and comparison between two groups. The test scenario utilized data on the number of steps, and filtering for the x-axis period was shown as a bar graph, and a comparison between the two groups was shown as a line graph. In order to develop a natural language processing model that can receive requested information through visualization, about 15,800 training data were collected through a survey of 1,000 people. As a result of algorithm development and performance evaluation, about 89% accuracy in classification model and 99% accuracy in query generation model was obtained.

• Radiation Oncology Journal
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• v.10 no.1
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• pp.59-67
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• 1992

From March 1979 through December 1986, 124 patients with early stage carcinoma of the uterine cervix received curative radiation therapy. According to FIGO classification, 35 patients were stage IB and 89 were stge II A. In stage IB, five year locoregional control, five year disease free survival, and five year overall survival was $79.0\%$, $76.4\%$ and $81.8\%$, respectively. In stage II A, five year locoregional control, five year disease free survival, and five year overall survival were $78.0\%$, $66.8\%$, and $72.1\%$, respectively. To identify prognostic factors, pretreatment parameters including age, ECOG performance status, number of pregnancies, history of diabetes mellitus and hypertension, histology, size and shape of primary tumor, CT findings and blood parameters were retrospectively analyzed in terms of locoregional control, disease free survival and overall survival using univariate analysis and multivariate analysis. In univariate analysis, tumor size on physicai examination and rectal invasion on CT significantly affected locoregional control, disease free survival and overall survival. Parametrial involvement on CT was a significant prognostic factor on locoregional control and disease free survival. Hemoglobin level affected disease free survival and overall survival. Histology and age were significant prognostic factors on locoregional control. In multivariate analysis excluding CT finding, tumor size on physical examination was a significant factor in terms of locoregioal control and overall survival. Hemoglobin level was significant in terms of disease free survival. In multivariate analysis including CT, histology was a prognostic factor on locoregional control and disease free survival. Hemoglobin level and rectal invasion on CT were significant factors on locoregional control.

• The Journal of Korean Academy of Prosthodontics
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• v.50 no.1
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• pp.61-66
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• 2012

Purpose: This study was to compare the cumulative survival rate (CSR) of Br${\aa}$nemark machined surface implants and TiUnite$^{TM}$ imlants and to analyze association between risk factors and the CSR of the implants. Materials and methods: A retrospective study design was used to collect long-term follow-up clinical data from dental records of 156 patients treated with 541 Br${\aa}$nemark machined and TiUnite$^{TM}$ implants at Korea University Guro hospital in South Korea from 1993 through 2008. Machined implant and TiUnite$^{TM}$ implant were compared by CSR. Exposure variables such as gender, systemic disease, location, implant length, diameter, prosthesis type, opposing occlusion type, date of implant placement, type of edentulous space, abutment type, existence of splinting with natural teeth, and existence of cantilever were collected. Life table analysis was undertaken to examine the CSR. Cox regression method was conducted to assess the association between potential risk factors and overall CSR (${\alpha}$=.05). Results: Patient ages ranged from 16 to 75 years old (mean age, 51 years old). Implants were more frequently placed in men than women (94 men versus 63 women). Since 1993, 264 Br${\aa}$nemark machined implants were inserted in 79 patients and since 2001, 277 TiUnite$^{TM}$ implants were inserted in 77 patients. A total survival rate of 86.07% was observed in Br${\aa}$nemark and Nobel Biocare TiUnite$^{TM}$ during 15 years. A survival rate of machined implant during 15 years was 82.89% and that of TiUnite$^{TM}$ implant during 5 years was 98.74%. The implant CSR revealed lower rates association with several risk factors such as, systemic disease, other accompanied surgery, implant location, and Kennedy classification. Conclusion: Clinical performance of Br${\aa}$nemark machined and TiUnite$^{TM}$ implant demonstrated a high level of predictability. In this study, TiUnite$^{TM}$ implant was more successful than machined implant. The implant CSR was associated with several risk factors.

• Radiation Oncology Journal
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• v.10 no.2
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• pp.219-225
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• 1992

Fifty patients with carcinoma of the uterine cervix received curative radiotherapy by external irradiation of the whole pelvis and intracavitary radiation at the Department of Therapeutic Radiology, Kangnam St. Mary's Hospital from September, 1983 to October, 1986. External beam whole pelvic irradiation was done first up to 4500-5940 cGy in 5 weeks to 6.5 weeks, followed by an intracavitary radiation. Total dose of radiation to point A varied from 6500 cGy to 11344 cGy (average 6764 cGy). Of the 50 patients, one patient was lost to follow up and follow up period of the remaining 49 patients ranged from 3 months to 93 months (median 32 months). According to FIGO classification, 6 ($12.2\%$) were in stage Ib, 6 ($12.2\%$) in stage IIa, 25 ($51\%$) in stage IIb, 7 ($14\%$) in stage III, and 5 ($10.2\%$) in stage IV. Age of the patients ranged from 33 to 76 years (median 60 years). Pathologically, fourty six ($94\%$) patients had squamous cell carcinoma, 2 ($4\%$) had adenocarcinoma, and 1 ($2\%$) had adenosquamous cell carcinoma. Overall response rate was $84\%$. 5-year survival rate was $49\%$ for entire group ($75\%$ for stage Ib, $83\%$ for Stage IIa, $42.5\%$ for stage IIb, $25\%$ for stage III, $40\%$ for stage IV). Complications were observed in 11 ($22.4\%$) patients, who revealed rectal complications with most common frequency. Others were self limiting trifle ones such as wet desquamation, fatigue, mild leukopenia, etc. The correlation of the survival rate with various factors (age, dose, Hb level, pelvic lymph node status, performance status, local recurrence) was evaluated but showed no statistical significance except the age and local recurrence in this series; survival of patients less than 50 years of age was worse than that of the older, and the presence of local recurrence had worse prognosis (p<0.05).

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.6 no.1
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• pp.74-89
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• 1995

Selective mutism is a childhood condition defined by persisten failure to speak in specific social situation when speaking is expected, dispite preserved ability to comprehend spoken language and speak. Present study is to investigate clinical characteristics, treatment method and outcome of 23 children who were diagnosed as selective mutism by DSM-IV criteria at the child psychiatry ouptatient department of SNUH. The results were as follows : 1) The Sex ratio was 1: 4.8, female dominant Mear age of onset was 33 years old and mean age of first referral was 7.7 years old. 2) 22% of subjects had perinatal problem such as low birth weight, preterm birth, 26% of the subjects have history of delayed language development. There are subjects who had been separated with mam caretaker before 3 years old(26%) and who experienced physical or psychological trauma before 3 years old(26%). A few subjects had enurests(30%) and encoprests(4%). 3) Many subjects(65%) had symbiotic relationship with their mother. These families consist of dominant, verbally aggressive mother and passive father. Parents of 39% of all subjects were judged to have definite psychopathology(social phobic, depression, hysterical trait or alcohol problem) 26% of all subject, were reported physically abused. 4) The personality trait of the subjects were frequently described as follows(in order of frequency) ; Shy(100%), anxious(83%), stubborn(83%)m rigid and tense posture(78%), immature(65%) overdependent(65%), irritable(52%), manipulative(39%), depressive(39%). 5) The mean performance IQ of 16 subjects by KEDI-WISC was 88.3 Among them, the subjects with IQ below 69 were seven and those with IQ above 70 were nine. When comparing these two group(Mental retardation group vs Normal IQ group), we could find some difference in language development, personality trait, family dynamics and treatment outcome. 6) Among several treatment methods for selective mutism, play therapy was the most frequently used method(65%). Other commonly used treatment methods were pharmacotherapy(21%), behavioral therapy(8%), combined therapy(play therapy+pharmacotherapy+family therapy+behavioral therapy)(12%), 7) Regarding the outcome of treatment 8.6% was evaluated as Excellent, 30.4% as Good, 52% as Fair, 8.7% as Poor at the tinic of treatment. At follow up interview 21.7% was evaluated Excellent, 13% as Good, 21.7% as Fair, 34.8% as Poor. 8) We classified all subjects by Havden's 4 subtype. Symbiotic mutism was most common(65%) and other subtypes are Speech phobic mutism(8.6), Reactive mutism(13%) and Passive-aggressive mutism(30%).