• The Korean Journal of Air & Space Law and Policy
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• v.30 no.1
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• pp.31-64
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• 2015

The Chicago Convention and Annexes have become the basis of aviation safety regulations for every contracting state. Generally, aviation safety regulations refer to the SARPs provided in the Annexes of the Chicago Convention. In order to properly reflect international aviation safety regulations, constant studies of the aviation fields are of paramount importance. Treaties duly concluded and promulgated under the Constitution and the generally recognized rules of international law shall have the same effect as the domestic laws of the Republic of Korea. Each contracting state to the Chicago Convention should meet ICAO SARPs about AOC and FAOC. According to ICAO SARPs, Civil Aviation Authorities shall issue AOC to air carriers of the state, but don't require to issue for foreign air carrier. However some contracting states of the Chicago Convention issue FAOC and/or Operations Specifications for the foreign operators. This FAOC is being expanded from USA to the other contracting states. Foreign operators have doubly burden to implement AOC of the ICAO SARPs because FAOC is an additional requirement other than that prescribed by the ICAO SARPs In Article 33, the Chicago Convention stipulates that each contracting state shall recognize the validity of the certificates of airworthiness and licenses issued by other contracting states as long as they are equal to or above the minimum standards of the ICAO. In ICAO Annex 6, each contracting state shall recognize as valid an air operator certificate issued by another contracting state, provided that the requirements under which the certificate was issued are at least equal to the applicable Standards specified in this Annex. States shall establish a programme with procedures for the surveillance of operations in their territory by a foreign operator and for taking appropriate action when necessary to preserve safety. Consequently, it is submitted that the unilateral action of the states issuing the FAOC to the foreign air carriers of other states is against the Convention. Hence, I make some proposals on the FAOC as an example of comprehensive problem solving after comparative study with ICAO SARPs and the contracting state's regulations. Some issues must be improved and I have made amendment proposals to meet ICAO SARPs and to strengthen aviation development. Operators should be approved by FAOC at most 190 if all states require FAOC. Hence, it is highly recommended to eliminate the FAOC or reduce the restrictions it imposes. In certain compliance-related issues, delayed process shall not be permitted to flight operations. In addition, it is necessary for the ICAO to provide more unified and standardized guidelines in order to avoid confusion or bias regarding the arbitrary expansion of the FAOC. For all the issue mentioned above, I have studied the ICAO SARPs and some state's regulation regarding FAOC, and suggested some proposals on the FAOC as an example of comprehensive problem solving. I hope that this paper is 1) to help understanding about the international issue, 2) to help the improvement of korean aviation regulations, 3) to help compliance with international standards and to contribute to the promotion of aviation safety, in addition.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Tuberculosis and Respiratory Diseases
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• v.42 no.3
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• pp.361-369
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• 1995

Background: A number of different weaning techniques can be employed such as spontaneous breathing trial, Intermittent mandatory ventilation(IMV) or Pressure support ventilation(PSV). However, the conclusive data indicating the superiority of one technique over another have not been published. Usually, a conventional spontaneous breathing trial is undertaken by supplying humidified $O_2$ through T-shaped adaptor connected to endotracheal tube or tracheostomy tube. In Korea, T-tube trial is not popular because the high-flow oxygen system is not always available. Also, the timing of extubation is not conclusive and depends on clinical experiences. It is known that to withdraw the endotracheal tube after weaning is far better than to go through any period. The tube produces varying degrees of resistance depending on its internal diameter and the flow rates encountered. The purpose of present study is to evaluate the effectiveness of weaning and extubation following a 60 minutes spontaneous breathing trial with simple oxygen supply through the endotracheal tube. Methods: We analyzed the result of weaning and extubation following a 60 minutes spontaneous breathing trial with simple oxygen supply through the endotracheal tube in 18 subjects from June, 1993 to June, 1994. They consisted of 9 males and 9 females. The duration of mechanical ventilation was from 38 hours to 341 hours(mean: $105.9{\pm}83.4$ hours). In all cases, the cause of ventilator dependency should be identified and precipitating factors should be corrected. The weaning trial was done when the patient became alert and arterial $O_2$ tension was adequate($PaO_2$ > 55mmHg) with an inspired oxygen fraction of 40%. We conducted a careful physical examination when the patient was breathing spontaneously through the endotracheal tube. Failure of weaning trial was signaled by cyanosis, sweating, paradoxical respiration, intercostal recession. Weaning failure was defined as the need for mechanical ventilation within 48 hours. Results: In 19 weaning trials of 18 patients, successful weaning and extubation was possible in 16/19(84.2 %). During the trial of spontaneous breathing for 60 minutes through the endotracheal tube, the patients who could wean developed slight increase in respiratory rates but significant changes of arterial blood gas values were not noted. But, the patients who failed weaning trial showed the marked increase in respiratory rates without significant changes of arterial blood gas values. Conclusion: The result of present study indicates that weaning from mechanical ventilation following a 60 minutes spontaneous breathing with $O_2$ supply through the endotracheal tube is a simple and effective method. Extubation can be done at the same time of successful weaning except for endobronchial toilet or airway protection.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.2
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• pp.131-143
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• 2015

Purpose : By comparing a CT fusion plan based on MRI with a plan based on only MRI without CT, we intended to study usefulness of a plan based on only MRI. And furthermore, we intended to realize a realtime MR-IGRT by MRI image without CT scan during the course of simulation, treatment planning, and radiation treatment. Materials and Methods : BBB CT (Brilliance Big Bore CT, 16slice, Philips), Viewray MRIdian system (Viewray, USA) were used for CT & MR simulation and Treatment plan of 11 patients (1 Head and Neck, 5 Breast, 1 Lung, 3 Liver, 1 Prostate). When scanning for treatment, Free Breathing was enacted for Head&Neck, Breast, Prostate and Inhalation Breathing Holding for Lung and Liver. Considering the difference of size between CT and Viewray, the patient's position and devices were in the same condition. Using Viewray MRIdian system, two treatment plans were established. The one was CT fusion treatment plan based on MR image. Another was MR treatment plan including electron density that [ICRU 46] recommend for Lung, Air and Bone. For Head&Neck, Breast and Prostate, IMRT was established and for Lung and Liver, Gating treatment plan was established. PTV's Homogeneity Index(HI) and Conformity Index(CI) were use to estimate the treatment plan. And DVH and dose difference of each PTV and OAR were compared to estimate the treatment plan. Results : Between the two treatment plan, each difference of PTV's HI value is 0.089% (Head&Neck), 0.26% (Breast), 0.67% (Lung), 0.2% (Liver), 0.4% (Prostate) and in case of CI, 0.043% (Head&Neck), 0.84% (Breast), 0.68% (Lung), 0.46% (Liver), 0.3% (Prostate). As showed above, it is on Head&Neck that HI and CI's difference value is smallest. Each difference of average dose on PTV is 0.07 Gy (Head&Neck), 0.29 Gy (Breast), 0.18 Gy (Lung), 0.3 Gy (Liver), 0.18 Gy (Prostate). And by percentage, it is 0.06% (Head&Neck), 0.7% (Breast), 0.29% (Lung), 0.69% (Liver), 0.44% (Prostate). Likewise, All is under 1%. In Head&Neck, average dose difference of each OAR is 0.01~0.12 Gy, 0.04~0.06 Gy in Breast, 0.01~0.21 Gy in Lung, 0.06~0.27 Gy in Liver and 0.02~0.23 Gy in Prostate. Conclusion : PTV's HI, CI dose difference on the Treatment plan using MR image is under 1% and OAR's dose difference is maximum 0.89 Gy as heterogeneous tissue increases when comparing with that fused CT image. Besides, It characterizes excellent contrast in soft tissue. So, radiation therapy using only MR image without CT scan is useful in the part like Head&Neck, partial breast and prostate cancer which has a little difference of heterogeneity.

• Journal of Distribution Science
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• v.9 no.4
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• pp.5-13
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• 2011

This study provides analysis of the effectiveness of domestic marketing strategies of the Korean coffee shop "Caffe Bene". It bases its evaluation on statistical outputs of 'choice attributes,' "market segmentation," demographic characteristics," and "satisfaction differences." The results are summarized in four points. First, five choice attributes were extracted from factor analysis: price, atmosphere, comfort, taste, and location; these are related to coffee shop selection behavior. Based on these five factors, cluster analysis was conducted, with statistical results classifying customers into three major groups: atmosphere oriented; comfort oriented; and taste oriented. Second, discriminant analysis tested cluster analysis and showed two discriminant functions: location and atmosphere. Third, cross-tabulation analysis based on demographic characteristics showed distinctive demographic characteristics within the three groups. Atmosphere oriented group, early-20s, as women of all ages was found to be 'walking down the street 'and 'through acquaintances' in many cases, as the cognitive path, and mostly found the store through 'outdoor advertising', and 'introduction'. Comfort oriented group was mainly women who are students in their early twenties or professionals, and appeared as a group to be very loyal because of high recommendation to other customers compared to other groups. Taste oriented group, unlike the other group, was mainly late-20s' college graduates, and was confirmed, as low loyalty, with lower recommendation activity. Fourth, to analyze satisfaction differences, one-way ANOVA was conducted. It shows that groups which show high satisfaction in the five main factors also show high menu satisfaction and high overall satisfaction. This results show that segmented marketing strategies are necessary because customers are considering price, atmosphere, comfort, taste, location when they choose coffee shop and demographics show different attributes based on segmented groups. For example, atmosphere oriented group is satisfied with shop interior and comfort while dissatisfied with price because most of the customers in this group are early 20s and do not have great financial capability. Thus, price discounting marketing strategies based on individual situations through CRM system is critical. Comfort oriented group shows high satisfaction level about location and shop comfort. Also, in this group, there are many early 20s female customers, students, and self-employed people. This group customers show high word of mouth tendency, hence providing positive brand image to the customers would be important. In case of taste oriented group, while the scores of taste and location are high, word of mouth score is low. This group is mainly composed of educated and professional many late 20s customers, therefore, menu differentiation, increasing quality of coffee taste and price discrimination is critical to increase customers' satisfaction. However, it is hard to generalize the results of study to other coffee shop brand, because this study have researched only one domestic coffee shop, Caffe Bene. Thus if future study expand the scope of locations, brands, and occupations, the results of the study would provide more generalizable results. Finally, research of customer satisfactions of menu, trust, loyalty, and switching cost would be critical in the future study.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.75-95
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• 2020

With the growth of social networks, various forms of SNS have emerged. Based on various motivations for use such as interactivity, information exchange, and entertainment, SNS users are also on the fast-growing trend. Facebook is the main SNS channel, and companies have started using Facebook pages as a public relations channel. To this end, in the early stages of operation, companies began to secure a number of fans, and as a result, the number of corporate Facebook fans has recently increased to as many as millions. from a corporate perspective, Facebook is attracting attention because it makes it easier for you to meet the customers you want. Facebook provides an efficient advertising platform based on the numerous data it has. Advertising targeting can be conducted using their demographic characteristics, behavior, or contact information. It is optimized for advertisements that can expose information to a desired target, so that results can be obtained more effectively. it rethink and communicate corporate brand image to customers through contents. The study was conducted through Facebook advertising data, and could be of great help to business people working in the online advertising industry. For this reason, the independent variables used in the research were selected based on the characteristics of the content that the actual business is concerned with. Recently, the company's Facebook page operation goal is to go beyond securing the number of fan pages, branding to promote its brand, and further aiming to communicate with major customers. the main figures for this assessment are Facebook's 'OK', 'Attachment', 'Share', and 'Number of Click' which are the dependent variables of this study. in order to measure the outcome of the target, the consumer's response is set as a key measurable key performance indicator (KPI), and a strategy is set and executed to achieve this. Here, KPI uses Facebook's ad numbers 'reach', 'exposure', 'like', 'share', 'comment', 'clicks', and 'CPC' depending on the situation. in order to achieve the corresponding figures, the consideration of content production must be prior, and in this study, the independent variables were organized by dividing into three considerations for content production into three. The effects of content material, content structure, and message styles on Facebook's user behavior were analyzed using regression analysis. Content materials are related to the content's difficulty, company relevance, and daily involvement. According to existing research, it was very important how the content would attract users' interest. Content could be divided into informative content and interesting content. Informational content is content related to the brand, and information exchange with users is important. Interesting content is defined as posts that are not related to brands related to interesting movies or anecdotes. Based on this, this study started with the assumption that the difficulty, company relevance, and daily involvement have an effect on the dependent variable. In addition, previous studies have found that content types affect Facebook user activity. I think it depends on the combination of photos and text used in the content. Based on this study, the actual photos were used and the hashtag and independent variables were also examined. Finally, we focused on the advertising message. In the previous studies, the effect of advertising messages on users was different depending on whether they were narrative or non-narrative, and furthermore, the influence on message intimacy was different. In this study, we conducted research on the behavior that Facebook users' behavior would be different depending on the language and formality. For dependent variables, 'OK' and 'Full Click Count' are set by every user's action on the content. In this study, we defined each independent variable in the existing study literature and analyzed the effect on the dependent variable, and found that 'good' factors such as 'self association', 'actual use', and 'hidden' are important. Could. Material difficulties', 'actual participation' and 'large scale * difficulties'. In addition, variables such as 'Self Connect', 'Actual Engagement' and 'Sexual Sexual Attention' have been shown to have a significant impact on 'Full Click'. It is expected that through research results, it is possible to contribute to the operation and production strategy of company Facebook operators and content creators by presenting a content strategy optimized for the purpose of the content. In this study, we defined each independent variable in the existing research literature and analyzed its effect on the dependent variable, and we could see that factors on 'good' were significant such as 'self-association', 'reality use', 'concernal material difficulty', 'real-life involvement' and 'massive*difficulty'. In addition, variables such as 'self-connection', 'real-life involvement' and 'formative*attention' were shown to have significant effects for 'full-click'. Through the research results, it is expected that by presenting an optimized content strategy for content purposes, it can contribute to the operation and production strategy of corporate Facebook operators and content producers.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.5
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• pp.100-108
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• 2012

The purpose of the study was to evaluate the influence of shading and ventilation on Mean Radiant Temperature(MRT) of the outdoor space at a summer outdoor. The Wind Speed(WS), Air Temperature(AT) and Globe Temperature(GT) were recorded every minute from $1^{st}$ of May to the $30^{th}$ of September 2011 at a height of 1.2m above in four experimental plots with different shading and ventilating conditions, with a measuring system consisting of a vane type anemometer(Barini Design's BDTH), Resistance Temperature Detector(RTD, Pt-100), standard black globe(${\O}$ 150mm) and data acquisition systems(National Instrument's Labview and Compfile Techs' Moacon). To implement four different ventilating and shading conditions, three hexahedral steel frames, and one natural plot were established in the open grass field. Two of the steel frames had a dimension of $3m(W){\times}3m(L){\times}1.5m(H)$ and every vertical side covered with transparent polyethylene film to prevent lateral ventilation(Ventilation Blocking Plot: VP), and an additional shading curtain was applied on the top side of a frame(Shading and Ventilation Blocking Plot: SVP). The third was $1.5m(W){\times}1.5m(L){\times}1.5m(H)$, only the top side of which was covered by the shading curtain without the lateral film(Shading Plot: SP). The last plot was natural condition without any kind of shading and wind blocking material(Natural Open Plot: NP). Based on the 13,262 records of 44 sunny days, the time serial difference of AT and GT for 24 hour were analyzed and compared, and statistical analysis was done based on the 7,172 records of daytime period from 7 A.M. to 8 P.M., while the relation between the MRT and solar radiation and wind speed was analyzed based on the records of the hottest period from 11 A.M. to 4 P.M.. The major findings were as follows: 1. The peak AT was $40.8^{\circ}C$ at VP and $35.6^{\circ}C$ at SP showing the difference about $5^{\circ}C$, but the difference of average AT was very small within${\pm}1^{\circ}C$. 2. The difference of the peak GT was $12^{\circ}C$ showing $52.5^{\circ}C$ at VP and $40.6^{\circ}C$ at SP, while the gap of average GT between the two plots was $6^{\circ}C$. Comparing all four plots including NP and SVP, it can be said that the shading decrease $6^{\circ}C$ GT while the wind blocking increase $3^{\circ}C$ GT. 3. According to the calculated MRT, the shading has a cooling effect in reducing a maximum of $13^{\circ}C$ and average $9^{\circ}C$ MRT, while the wind blocking has heating effect of increasing average $3^{\circ}C$ MRT. In other words, the MRT of the shaded area with natural ventilation could be cooler than the wind blocking the sunny site to about $16^{\circ}C$ MRT maximum. 4. The regression and correlation tests showed that the shading is more important than the ventilation in reducing the MRT, while both of them do an important role in improving the outdoor thermal comfort. In summary, the results of this study showed that the shade is the first and the ventilation is the second important factor in terms of improving outdoor thermal comfort in summer daylight hours. Therefore, it can be apparently said that the more shade by the forest, shading trees etc., the more effective in conditioning the microclimate of an outdoor space reducing the useless or even harmful heat energy for human activities. Furthermore, the delicately designed wind corridor or outdoor ventilation system can improve even the thermal environment of urban area.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.125-148
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• 2018

Recently, as the demand for big data analysis increases, cases of analyzing unstructured data and using the results are also increasing. Among the various types of unstructured data, text is used as a means of communicating information in almost all fields. In addition, many analysts are interested in the amount of data is very large and relatively easy to collect compared to other unstructured and structured data. Among the various text analysis applications, document classification which classifies documents into predetermined categories, topic modeling which extracts major topics from a large number of documents, sentimental analysis or opinion mining that identifies emotions or opinions contained in texts, and Text Summarization which summarize the main contents from one document or several documents have been actively studied. Especially, the text summarization technique is actively applied in the business through the news summary service, the privacy policy summary service, ect. In addition, much research has been done in academia in accordance with the extraction approach which provides the main elements of the document selectively and the abstraction approach which extracts the elements of the document and composes new sentences by combining them. However, the technique of evaluating the quality of automatically summarized documents has not made much progress compared to the technique of automatic text summarization. Most of existing studies dealing with the quality evaluation of summarization were carried out manual summarization of document, using them as reference documents, and measuring the similarity between the automatic summary and reference document. Specifically, automatic summarization is performed through various techniques from full text, and comparison with reference document, which is an ideal summary document, is performed for measuring the quality of automatic summarization. Reference documents are provided in two major ways, the most common way is manual summarization, in which a person creates an ideal summary by hand. Since this method requires human intervention in the process of preparing the summary, it takes a lot of time and cost to write the summary, and there is a limitation that the evaluation result may be different depending on the subject of the summarizer. Therefore, in order to overcome these limitations, attempts have been made to measure the quality of summary documents without human intervention. On the other hand, as a representative attempt to overcome these limitations, a method has been recently devised to reduce the size of the full text and to measure the similarity of the reduced full text and the automatic summary. In this method, the more frequent term in the full text appears in the summary, the better the quality of the summary. However, since summarization essentially means minimizing a lot of content while minimizing content omissions, it is unreasonable to say that a "good summary" based on only frequency always means a "good summary" in its essential meaning. In order to overcome the limitations of this previous study of summarization evaluation, this study proposes an automatic quality evaluation for text summarization method based on the essential meaning of summarization. Specifically, the concept of succinctness is defined as an element indicating how few duplicated contents among the sentences of the summary, and completeness is defined as an element that indicating how few of the contents are not included in the summary. In this paper, we propose a method for automatic quality evaluation of text summarization based on the concepts of succinctness and completeness. In order to evaluate the practical applicability of the proposed methodology, 29,671 sentences were extracted from TripAdvisor 's hotel reviews, summarized the reviews by each hotel and presented the results of the experiments conducted on evaluation of the quality of summaries in accordance to the proposed methodology. It also provides a way to integrate the completeness and succinctness in the trade-off relationship into the F-Score, and propose a method to perform the optimal summarization by changing the threshold of the sentence similarity.