• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.3
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• pp.18-32
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• 2024

This study aims to analyze the summer human thermal environment in Jeju City's outdoor parking lots by measuring microclimate data and comparing pavement and vegetation albedoes and elements through computer simulations. In measured cases, results due to albedo showed no significance, but there was a significant difference between sunny and shaded areas by trees. The sunny area had a PET (physiological equivalent temperature) in the 'very hot' level, while the shaded area exhibited a 2-step lower 'warm' level. UTCI (universal thermal climate index) also showed that the sunny area was in the 'very strong heat stress' level, whereas the shaded area was 1-step lower in the 'strong heat stress' level, confirming the role of trees in reducing incoming solar radiant energy. Simulation results, using the measured albedoes, closely resembled the measured results. Regarding vegetation, scenarios with a wide canopy, high leaf density, and narrow planting spacing were effective in mitigating the human thermal environment, and the differences due to tree height varied across scenarios. The scenario with the lowest PET value was H9W9L3D8 (tree height 9m, canopy width 9m, leaf area index 3.0, planting spacing 8m), indicating a 0.7-step decrease compared to the current landscaping scenario. Thus, it was confirmed that, among landscaping elements, trees have a significant impact on the summer human thermal environment compared to ground pavement.

• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.425-436
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• 2023

In Korea, the U.S. Tier-4 Final emission standards have been applied to agricultural machinery since 2015. This study was conducted to analyze the emission characteristics of agricultural tractors during plow tillage operations using PEMS (portable emissions measurement systems). The tractor working speed was set as M2 (5.95 km/h) and M3 (7.60 km/h), which was the most used gear stage during plow tillage operation. An engine idling test was conducted before the plow tillage operation was conducted because the level of emissions differed depending on the temperature of the engine (cold and hot states). The estimated level of emissions for the regular area (660 m²), which was the typical area of cultivation, was based on an implement width of 2.15 m and distance from the work area of 2.2 m. As a result, average emission of CO (carbon monoxide), THC (total hydrocarbons), NOx (nitric oxides), and PM (particulate matter) were approximately 6.17×10-2, 3.36×10^-4, 2.01×10^-4, and 6.85×10^-6 g/s, respectively. Based on the regular area, the total emission of CO, THC, NOx, and PM was 2.62, 3.76×10^-2, 1.63, and 2.59×10^-4 g, respectively. The results of total emission during plow tillage were compared to Tier 4 emission regulation limits. Tier 4 emission regulation limits means maximum value of the emission per consumption power (g/kWh), calculated as ratio of the emission and consumption power. Therefore, the total emission was converted to the emission per power using the rated power of the tractor. The emission per power was found to be satisfied below Tier 4 emission regulation limits for each emission gas. It is necessary to measure data by applying various test modes in the future and utilize them to calculate emission because the emission depends on various variables such as measurement environment and test mode.

• Korean Security Journal
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• no.48
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• pp.147-176
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• 2016

Korean Peninsula is surrounded by ocean on three sides. Because of this geographical quality over 97% of export and import volumes are exchange by sea. Foreign ship and international passenger vessels carries foreign tourist and globalization and internationalization increases this trends. Leisure population grows with national income increase and interest of ocean. And accidents and incidents rates are also increases. Korea Coast Guard's jurisdiction area is 4.5 times bigger than our country. The length of coastline is 14,963km including islands. One patrol vessel is responsible for 24,068km and one coast guard substation is responsible for 94km. Efficient patrol activities can not be provided. This research focus on this problem. Analyze the status and trends of maritime crime and suggest efficient patrol activities. To deal with increasing maritime crime rate this study suggest to use geographical profile method which developed early 1900s in USA. This geographical profile analyse the spatial characteristic and mapping this result. With this result potential crime zone can be predicted. One of the result is hot spot management which gives data about habitual crime zone. In Korea National Police Agency adopt this method in 2008 and apply on patrol and crime prevention activity by analysis of different criteria. Korea National Police Agency analyse the crime rate with crime type, crime zone and potential crime zone, and hourly, regionally criteria. Korea Coast Guard need to adopt this method and apply on maritime to make maritime crime map, which shows type of crime with regional, periodical result. With this geographical profiling we can set a Criminal Point which shows the place where the crime often occurs. The Criminal Points are set with the data of numerous rates such as homicide, robbery, burglary, missing, collision which happened in ocean. Set this crime as the major crime and manage the data more thoroughly. I expect to enhance the reinforcement of marine crime using this Criminal Points. Because this points will give us efficient way to prevent the maritime crime by placing the patrol vessel where they needed most.

• Journal of Nutrition and Health
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• v.51 no.2
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• pp.177-185
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• 2018

Purpose: This study analyzed the perception and importance of country-of-origin labeling at restaurants in 500 college students in Jeju surveyed from April 15 to May 5, 2016 with the aim of providing basic data. A total of 465 questionnaires out of 500 were used as base data for this study. Methods: The data were analyzed using descriptive analysis, ${\chi}^2-test$, and t-test using the SPSS Win program (version 21.0). Results: Regarding food safety-related dietary behaviors, average score was 3.65 points (out of 5), and 'put the food in a refrigerator or freezer immediately (4.07)' showed the highest score, whereas 'cool rapidly hot food prior to putting it in the refrigerator (3.08)' showed the lowest score. Regarding the awareness of country-of-origin labeling at restaurants, 67.5% of subjects were aware of it. With regard to dietary behavior of food safety, the high group showed a higher score than the low group (p < 0.001). Regarding reliability of the system, 4.9% of subjects indicated 'very reliable' and 45.4% 'somewhat reliable'. For perception of subject's country-of-origin labeling, the average score was 3.77 (out of 5). Regarding checking country-of-origin labeling at restaurants, 68.0% of subjects checked country-of-origin labeling, and the high group in the safety-related dietary behavior score ranking showed a higher rate (79.3%) than the low group (57.1%) (p < 0.001). With regard to importance by item, 'honest country-of-origin labeling of restaurants' showed the highest score at 4.27 (out of 5). Conclusion: It is necessary to provide continuing education for college students in order to enhance their perception of country-of-origin labeling at restaurants. Moreover, a systematic and appropriate support and control system by the government and local government needs to be developed in order to improve country-of-origin labeling at restaurants.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.5
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• pp.848-852
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• 2012

Vegetation can make not only to lower the urban ambient air temperature (UAAT) by crop evapotranspiration (ET) and increasing solar radiation albedo, but also to reduce the urban air pollution by $CO_2$ uptake and $O_2$ emission in addition to the reducing ozone concentrations by aid of lower the UAAT. To evaluate the effect of vegetation on urban heat island mitigation (UHIM), the climate change of 6 cities during 30 years are analysed, and the amount of ET, $CO_2$ uptake, $O_2$ emission and ozone concentrations are estimated in Korea. The most hot season is the last part of July and the first part of August, and the highest average UAAT of a period of ten days was $35.03^{\circ}C$ during 30 years (1979 - 2008). The mean values of maximum ET of rice and soybean in urban area during urban heat island phenomena were 6.86 and $6.00mm\;day^{-1}$, respectively. The effect of rice and soybean cultivation on lowering the UAAT was assessed to be 10.5 and $3.0^{\circ}C$ in Suwon, respectively, whereas the differences between the UAAT and canopy temperature at urban paddy and upland in Ansung were 2.6 and $2.2^{\circ}C$. On the other hand, the urban-garden in Suwon city had resulted in lowering the UAAT and the surface temperature of buildings to 2.0 and $14.5^{\circ}C$, respectively. Furthermore, the amounts of $CO_2$ uptake by rice and soybean were estimated to be 20.27 and $15.54kg\;CO_2\;10a^{-1}day^{-1}$, respectively. The amounts of $O_2$ emission by rice and soybean were also assessed to be 14.74 and $11.30kg\;O_2\;10a^{-1}day^{-1}$, respectively. As other cleaning effect of air pollution, the ozone concentrations could be also estimated to reduce 21.0, 8.8, and 4.0 ppb through rice-, soybean cultivation, and urban gardening during most highest temperature period in summer, respectively.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.1
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• pp.105-116
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• 2017

Murals in villages revitalize communities and spaces, and are economically efficient. Central and local governments are therefore actively undertaking Mural Village Projects but there are some issues and concerns about the projects for the provision of uniformed landscapes for the regions that are the result of a short-term completion of projects, and instead of cohesion, cause destruction of place identities. In addition, the problem of sustainable landscape management that is the result of low community involvement can be pointed out because the murals are products of government-led projects. The study covered the context of landscape and space change processes from a critical perspective, and focused on Ihwa-dong Mural Village, which is considered the first mural village in Korea and has begun to undergo drastic changes due to attention received from media. The purposes of this study are as follows. First, the study provides data about difference of place identity perception and landscape preference between residents and tourists in Ihwa-dong Mural Village. Second, this paper evaluates the current Mural Village Projects and finds alternative directions to improve the projects by using these data. This paper analyzed tourist hot spots in Ihwa-dong Mural Village by using SNS analysis, a field study and focus group interviews. The difference of place identity perception and landscape preference was examined among three groups: residents, new residents who are invited by Mural Village Projects, and tourists. This study showed that many tourists are focused on landscape areas that were not intentionally constructed projects. In addition, the locations of preferred landscapes and stores overlapped. Meanwhile, using qualitative data analysis, it was found that residents perceived the area as being an under-privileged location, while the murals, a non-daily landscape, largely affected place identity perception of new residents and tourists. For landscape preference, tourists preferred outdoor rest areas, while new residents and residents preferred less. Additionally, new residents and tourists preferred an area's night view while residents made no mention of this. Related to the direction of the projects, three groups showed their dependence on the government. This empirical study is significant from a participatory design perspective and in analyzing the issues for mural villages' landscapes, which are spreading across the nation and proceeding without criticism in urban regeneration. Implications for urban planners and suggestions for the future projects are given.

• The Journal of Korean Academy of Prosthodontics
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• v.41 no.2
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• pp.182-206
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• 2003

Statement of problem : Successful osseointegration of endosseous threaded implants is dependent on many factors. These may include the surface characteristics and gross geometry of implants, the quality and quantity of bone where implants are placed, and the magnitude and direction of stress in functional occlusion. Therefore clinical quantitative measurement of primary stability at placement and functional state of implant may play a role in prediction of possible clinical symptoms and the renovation of implant geometry, types and surface characteristic according to each patients conditions. Ultimately, it may increase success rate of implants. Purpose : Many available non-invasive techniques used for the clinical measurement of implant stability and osseointegration include percussion, radiography, the $Periotest^{(R)}$, Dental Fine $Tester^{(R)}$ and so on. There is, however, relatively little research undertaken to standardize quantitative measurement of stability of implant and osseointegration due to the various clinical applications performed by each individual operator. Therefore, in order to develop non-invasive experimental method to measure stability of implant quantitatively, the resonance frequency analyzer to measure the natural frequency of specific substance was developed in the procedure of this study. Material & method : To test the stability of the resonance frequency analyzer developed in this study, following methods and materials were used : 1) In-vitro study: the implant was placed in both epoxy resin of which physical properties are similar to the bone stiffness of human and fresh cow rib bone specimen. Then the resonance frequency values of them were measured and analyzed. In an attempt to test the reliability of the data gathered with the resonance frequency analyzer, comparative analysis with the data from the Periotest was conducted. 2) In-vivo study: the implants were inserted into the tibiae of 10 New Zealand rabbits and the resonance frequency value of them with connected abutments at healing time are measured immediately after insertion and gauged every 4 weeks for 16 weeks. Results : Results from these studies were such as follows : The same length implants placed in Hot Melt showed the repetitive resonance frequency values. As the length of abutment increased, the resonance frequency value changed significantly (p<0.01). As the thickness of transducer increased in order of 0.5, 1.0 and 2.0 mm, the resonance frequency value significantly increased (p<0.05). The implants placed in PL-2 and epoxy resin with different exposure degree resulted in the increase of resonance frequency value as the exposure degree of implants and the length of abutment decreased. In comparative experiment based on physical properties, as the thickness of transducer increased, the resonance frequency value increased significantly(p<0.01). As the stiffness of substances where implants were placed increased, and the effective length of implants decreased, the resonance frequencies value increased significantly (p<0.05). In the experiment with cow rib bone specimen, the increase of the length of abutment resulted in significant difference between the results from resonance frequency analyzer and the $Periotest^{(R)}$. There was no difference with significant meaning in the comparison based on the direction of measurement between the resonance frequency value and the $Periotest^{(R)}$ value (p<0.05). In-vivo experiment resulted in repetitive patternes of resonance frequency. As the time elapsed, the resonance frequency value increased significantly with the exception of 4th and 8th week (p<0.05). Conclusion : The development of resonance frequency analyzer is an attempt to standardize the quantitative measurement of stability of implant and osseointegration and compensate for the reliability of data from other non-invasive measuring devices It is considered that further research is needed to improve the efficiency of clinical application of resonance frequency analyzer. In addition, further investigation is warranted on the standardized quantitative analysis of the stability of implant.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.79-98
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• 2018

The purpose of this study is to suggest a model analysing spatio-temporal characteristics of the civil complaints for the officially assessed land price based on big data mining. Specifically, in this study, the underlying reasons for the civil complaints were found from the spatio-temporal perspectives, rather than the institutional factors, and a model was suggested monitoring a trend of the occurrence of such complaints. The official documents of 6,481 civil complaints for the officially assessed land price in the district of Jung-gu of Incheon Metropolitan City over the period from 2006 to 2015 along with their temporal and spatial poperties were collected and used for the analysis. Frequencies of major key words were examined by using a text mining method. Correlations among mafor key words were studied through the social network analysis. By calculating term frequency(TF) and term frequency-inverse document frequency(TF-IDF), which correspond to the weighted value of key words, I identified the major key words for the occurrence of the civil complaint for the officially assessed land price. Then the spatio-temporal characteristics of the civil complaints were examined by analysing hot spot based on the statistics of Getis-Ord $Gi^*$. It was found that the characteristic of civil complaints for the officially assessed land price were changing, forming a cluster that is linked spatio-temporally. Using text mining and social network analysis method, we could find out that the occurrence reason of civil complaints for the officially assessed land price could be identified quantitatively based on natural language. TF and TF-IDF, the weighted averages of key words, can be used as main explanatory variables to analyze spatio-temporal characteristics of civil complaints for the officially assessed land price since these statistics are different over time across different regions.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.