• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.5
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• pp.79-96
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• 2021

Green infrastructure planning represents landscape planning measures to reduce particulate matter. This study aimed to derive factors that may be used in planning green infrastructure for particulate matter reduction using text mining techniques. A range of analyses were carried out by focusing on keywords such as 'particulate matter reduction plan' and 'green infrastructure planning elements'. The analyses included Term Frequency-Inverse Document Frequency (TF-IDF) analysis, centrality analysis, related word analysis, and topic modeling analysis. These analyses were carried out via text mining by collecting information on previous related research, policy reports, and laws. Initially, TF-IDF analysis results were used to classify major keywords relating to particulate matter and green infrastructure into three groups: (1) environmental issues (e.g., particulate matter, environment, carbon, and atmosphere), target spaces (e.g., urban, park, and local green space), and application methods (e.g., analysis, planning, evaluation, development, ecological aspect, policy management, technology, and resilience). Second, the centrality analysis results were found to be similar to those of TF-IDF; it was confirmed that the central connectors to the major keywords were 'Green New Deal' and 'Vacant land'. The results from the analysis of related words verified that planning green infrastructure for particulate matter reduction required planning forests and ventilation corridors. Additionally, moisture must be considered for microclimate control. It was also confirmed that utilizing vacant space, establishing mixed forests, introducing particulate matter reduction technology, and understanding the system may be important for the effective planning of green infrastructure. Topic analysis was used to classify the planning elements of green infrastructure based on ecological, technological, and social functions. The planning elements of ecological function were classified into morphological (e.g., urban forest, green space, wall greening) and functional aspects (e.g., climate control, carbon storage and absorption, provision of habitats, and biodiversity for wildlife). The planning elements of technical function were classified into various themes, including the disaster prevention functions of green infrastructure, buffer effects, stormwater management, water purification, and energy reduction. The planning elements of the social function were classified into themes such as community function, improving the health of users, and scenery improvement. These results suggest that green infrastructure planning for particulate matter reduction requires approaches related to key concepts, such as resilience and sustainability. In particular, there is a need to apply green infrastructure planning elements in order to reduce exposure to particulate matter.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.882-889
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• 2022

In this study, to understand the factors influencing the flow change the Tsushima Warm Current (TWC), the correlation between the volume transport the TWC, El Niño Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO) was analyzed. A calculation of the monthly volume transport of TWC for 25 years (1993-2018) revealed that the seasonal fluctuation cycle was the largest in summer and smallest in winter. Power spectrum analysis to determine the periodicity of the TWC volume transport, Oceanic Niño Undex (ONI), and PDO indicated that the TWC volume transport peaked at a one year cycle, but ONI and PDO showed no clear cycle. Further, to understand the correlation between the TWC transport volume and ONI and PDO, the coherence estimation method was used for analysis. The coherence of ONI and PDO had a high mutual contribution in long-period fluctuations of three years or more but had low mutual contribution in short-period fluctuations within one year. However, the coherence value between the two factors of the TWC volume transport and PDO was 0.7 in the 0.8-1.2 year cycle, which had a high mutual contribution. Meanwhile, the TWC volume transport and PDO have an inverse correlation between period I (1993-2002) and period III (2010-2018). When the TWC maximum transport volume (2.2 Sv or more) was high, the PDO index showed a negative value below -1.0, and the PDO index showed a positive value when the TWC maximum transport volume was (below 2.2 Sv). Therefore, using long-term PDO index data, changes in the TWC transport volume and water temperature in the East Sea coastal area could be predicted.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.885-894
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• 2006

The purpose of this study is focused on the development of the effective classification technique using ultra multiband of hyperspectral image. This study suggests the classification technique using canonical correlation analysis, one of multivariate statistical analysis in hyperspectral image classification. High accuracy of classification result is expected for this classification technique as the number of bands increase. This technique is compared with Maximum Likelihood Classification(MLC). The hyperspectral image is the EO1-hyperion image acquired on September 2, 2001, and the number of bands for the experiment were chosen at 30, considering the band scope except the thermal band of Landsat TM. We chose the comparing base map as Ground Truth Data. We evaluate the accuracy by comparing this base map with the classification result image and performing overlay analysis visually. The result showed us that in MLC's case, it can't classify except water, and in case of water, it only classifies big lakes. But Canonical Correlation Classification (CCC) classifies the golf lawn exactly, and it classifies the highway line in the urban area well. In case of water, the ponds that are in golf ground area, the ponds in university, and pools are also classified well. As a result, although the training areas are selected without any trial and error, it was possible to get the exact classification result. Also, the ability to distinguish golf lawn from other vegetations in classification classes, and the ability to classify water was better than MLC technique. Conclusively, this CCC technique for hyperspectral image will be very useful for estimating harvest and detecting surface water. In advance, it will do an important role in the construction of GIS database using the spectral high resolution image, hyperspectral data.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• 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.

• Korean Journal of Health Education and Promotion
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• v.14 no.1
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• pp.23-45
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• 1997

This study was conducted from March 1, 1996 through June 30, in order to provide basic data for devising a policy for school health especially students health promotion and for developing of an education program. Middle school students were 1000, high school students were 2000 and a total of 3000 students were selected randomly among the boys/girls/middle/high schools which are in Seoul, Pusan, Taegu, Pohang, Suwon, Kyungsan, Milyang and a town or subcountry. The summary and conclusion are as follows. 1. In general characteristics of respondents, 51.8% were girl students, 33.7% were middle school students, 66.3% were high school students. 37.2% were living in a medium and small city, 89.1% were middle classes, 43.6% were having no religion, 27.3% were buddhists. 2. As a result of analyzing, exercise, nutrition, personal behavior, knowledge of health education and behavior level which are the factors promotion, exercise level were 3.61 of the perfect 9(40.1/100), nutrition level were 3.71(41.1/100), personal hygiene were 6.89(76.6/100), health education level were 5.1(58.9/100), all of the them are low level. 3. Judging from characteristics, in case of exercise behavior level, It was far higher in boy students than in girl students, in middle school students than in high school students. It was respectively higher than other groups in the second graders of middle school, in the first graders of high school, in the residents who live in a big city, in the high classes in the buddhists. 4. The students level against disease was average 9.11 of the perfect score 14(65.1/100). The level of disease consciousness was high in girl students by characteristics, in the second graders of high school by grades, in high school students than middle school students. 5. In health status, 55.4% were healthy, 7.9% were unhealthy. It was respectively higher than the other groups in boy students, in middle school students, in the residents who live in a big city, in high classes of life level, in buddihists, in higher education level of parents. 6. Judging from the factors of health status and health promotion and the degree of significance, there's a significant differences between exercise and dietary life as P〈0.001, in personal hygiene as P〈0.05, in health education an P〈0.01. 7. Knowledge on disease, health promotion behavior level were average 19.42 ± 4.01 of the perfect score 50(38.8/100) this score was too low. As for characteristics, the level between variables was statistically significant in the higher life level, in the higher parents education level, in the happier family. 8. Judging from health status, knowledge on disease, health behavior level, knowledge and health promotion behavior level significantly in the better health status, in the better school record. 9. As a result of the multiplex regression analyzing the factors which were under influence on health status, the variables like exercise, school record level, the degree of family happiness, nutrition, grades, the members of family influenced much and its persuasive power was 10.2%. The factors which are under the influence on the health promotion were exercise, satisfied degree of education, health status, the degree of family happiness, knowledge on disease, the usage of physical training, sex, the number of the family members, mother's education level. It’s explained power was 21.3%. promotion were high We should develop a text book and an education program to study exercise, nutrition(dietary life), personal hygiene, knowledge on disease and health systematically. As far as health education irrespective city and locality without considering the entrance exam for high school and university we should execute it continuously. To do this, it’s important to cultivate and secure qualified men of ability who can teach things related health promotion and the related subject, that is, health or health promotion subject should be established in middle and high school curriculum necessarily.

• Management & Information Systems Review
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• v.35 no.1
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• pp.189-206
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• 2016

It was required to attach the documents on the details of external audit including the number of the participants in external audit, audited parts and audit times under the Article 7-2 on the audit report to the accounting audit report from 2014 in accordance with the amendment to the Act on External Audit of Stock Companies. This study aim to calculate the audit fee based on the estimated cost of service calculation of the government contribution agencies by reflecting the implementation of the revised external audit. This study calculated the audit fee for the target company (a listed company assumed to have no internal control risks and relevant audit risks for unqualified opinion in the previous year, 100 billion won of total amount of asset, manufacturing company in the previous year and preliminary client request) by putting together four items of expenditure including employment costs, expenditure, general management expenses and profit in accordance with the calculation system of cost of service under the State Contract Act. Then, it used the data collected from the documents on the details of the revised external audit after requesting estimation on the target company with the estimated cost to Big-4 accounting firms to identify the participants and times of the accounting audit. The employment costs applied 150% of participation rate of the base price of employment costs for the academic research service cost in 2014, the expenditure used the average value of accounting firms of corporate business management analysis of the Bank of Korea (2013), the general management expenses applied 5% of the general management rate of service business under Article 7-1 of the Enforcement Rule of the Act on Contracts to which the State is a Party and the profit applied 10% of profit rate of service business under Article 7-2 of the Enforcement Rule of the Act on Contracts to which the State is a Party. Based on the calculation of the estimated costs by applying the above, the audit fee was estimated at 50,617,769won. Although the result is not the optimal audit fee, it may be used as a basic scale to compare the audit fees of companies without criteria. Also, such amendment to the Act on External Audit of Stock Companies may improve independence of auditors and transparency of the accounting system rather than previous announcing only the total audit times.

• Korean Journal of Applied Biomechanics
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• v.15 no.1
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• pp.29-44
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• 2005

The purpose of this study is to reveal the effects of two different kicks, the drop kick and the punt kick, into the kicking motion, through the kinetic comparative analysis of the kicking motion, which is conducted when one kicks a soccer goal. To grasp kinetic changing factors, which is performed by individual's each body segment, I connected kicking motions, which were analyzed by a two dimension co-ordination, into the personal computer to concrete the digits of it and smoothed by 10Hz. Using the smoothed data, I found a needed kinematical data by inputting an analytical program into the computer. The result of comparative analysis of two kicking motions can be summarized as below. 1. There was not a big difference between the time of the loading phase and the time of the swing phase, which can affect the exact impact and the angle of balls aviation direction. 2. The two kicks were not affected the timing and the velocity of the kicking leg's segment. 3. In the goal kick motion, the maximum velocity timing of the kicking leg's lower segment showed the following orders: the thigh(-0.06sec), the lower leg(-0.05sec), the foot(-0.018sec) in the drop kick, and the thigh(-0.06sec), the lower leg(-0.05sec), the foot(-0.015sec) in the punt kick. It showed that whipping motion increases the velocity of the foot at the time of impact. 4. At the time of impact, there was not a significant difference in the supporting leg's knee and ankle. When one does the punt kick, the subject spreads out his hip joint more at the time of impact. 5. When the impact performed, kicking leg's every segment was similar. Because the height of the ball is higher in the punt kick than in the drop kick, the subject has to stretch the knees more when he kicks a ball, so there is a significant affect on the angle and the distance of the ball's flying. 6. When one performs the drop kick, the stride is 0.02m shorter than the punt kick, and the ratio of height of the drop kick is 0.05 smaller than the punt kick. This difference greatly affects the center of the ball, the supporting leg's location, and the location of the center of gravity with the center of the ball at the time of impact. 7. Right before the moment of the impact, the center of gravity was located from the center of the ball, the height of the drop kick was 0.67m ratio of height was 0.37, and the height of the punt kick was 0.65m ratio of height was 0.36. The drop kick was located more to the back 0.21m ratio of height was 0.12, the punt kick was located more to the back 0.28m ratio of height was 0.16. 8. There was not a significant difference in the absolute angle of incidence and the maximum distance, but the absolute velocity of incidence showed a significant difference. This difference is caused from that whether players have the time to perform of not; the drop kick is used when the players have time to perform, and punt kick is used when the players launch a shifting attack. 9. The surface reaction force of the supporting leg had some relation with the approaching angle. Vertical reaction force (Fz) showed some differences in the two movements(p<0.05). The maximum force of the right and left surface reaction force (Fx) didn't have much differences (p<0.05), but it showed the tendency that the maximum force occurs before the peak force of the front and back surface (Fy) occurs.

• Journal of Distribution Research
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• v.17 no.5
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• pp.59-83
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• 2012

Agriculture is a primary industry that influenced by the weather or meterological factors more than other industry. Global warming and worldwide climate changes, and unusual weather phenomena are fatal in agricultural industry and human life. Therefore, many previous studies have been made to find the relationship between weather and the productivity of agriculture. Meterological factors also influence on the distribution of agricultural product. For example, price of agricultural product is determined in the market, and also influenced by the weather of the market. However, there is only a few study was made to find this link. The objective of this study is to investigate the effects of meterological factors on the distribution of agricultural products, focusing on the distribution of chinese cabbages. Chinese cabbage is a main ingredient of Kimchi, and basic essential vegetable in Korean dinner table. However, the production of chinese cabbages is influenced by weather and very fluctuating so that the variation of its price is so unstable. Therefore, both consumers and farmers do not feel comfortable at the unstable price of chinese cabbages. In this study, we analyze the real transaction data of chinese cabbage in wholesale markets and meterological factors depending on the variety and geography. We collect and analyze data of meterological factors such as temperatures, humidity, cloudiness, rainfall, snowfall, wind speed, insolation, sunshine duration in producing and consuming region of chinese cabbages. The result of this study shows that the meterological factors such as temperature and humidity significantly influence on the volume and price of chinese cabbage transaction in wholesale market. Especially, the weather of consuming region has greater correlation effects on transaction than that of producing region in all types of chinese cabbages. Among the whole agricultural lifecycle of chinese cabbages, 'seeding - harvest - shipment - wholesale', meterological factors such as temperature and rainfall in shipment and wholesale period are significantly correlated with transaction volume and price of crops. Based on the result of correlation analysis, we make a regression analysis to verify the meterological factors' effects on the volume and price of chines cabbage transaction in wholesale market. The results of stepwise regression analysis are shown in