• Korean Journal of Environment and Ecology
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• v.32 no.3
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• pp.323-331
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• 2018

This study was conducted to examine the effects of constructing streetside urban forests on particulate matter (PM) content in pedestrian paths and open spaces created between the main streets and buildings in a high-rise, high-density urban area. The study site is a 70m-wide open space between Busan City Hall and Jungang-street in Busan, Korea. The results showed that the density of PM differences between the open space and the adjacent main street were small in regions without linear trees and shrub rows during both the weekdays and weekend. On the other hand, the areas with linear trees and shrub rows were found to have significantly higher concentrations of PM compared to the roadway. In particular, sections with linear trees and shrub rows had higher PM levels both on roads and in adjacent open space, indicating that the composition of linear trees and shrub rows increased the concentration of PM in the off-street open space in areas with wide space between the roadway and building. The impact was more significant in the open space than the roadway. This phenomenon can be explained by the fact that PM generated by vehicles flows through the roadside shrubs by rapid wind flow but does not disperse widely in the pedestrian paths where the wind flow was reduced. In this study, we found that the roadside tree and shrub walls slowed the flow of wind, causing vehicle-emitted PM to accumulate if a wide open space was created between the road and building, resulting in higher concentration of PM in the open space. We confirmed that the distance between the road and building was a critical factor for constructing linear trees and shrub rows to reduce PM generated by vehicle traffic.

• Journal of the Korean Institute of Landscape Architecture
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• v.36 no.5
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• pp.73-81
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• 2008

The purpose of this study is to create a database of the use of landscape greenery that surrounds commercial buildings in Seoul. The method of this study was: to review preceding studies and related laws, survey areas, measure trees, and analyze the results. The 20 representative sites were specifically investigated to measure the width, direction, and environment of planting conditions. To analyze the greens adjacent to the building, the greens were divided into three types: front greenery, side greenery, and rear greenery. The study surveyed the distance from trees to adjacent buildings, and their planting conditions. The results of this study are as follows. First, 45% of the front greenery and 30% of the rear greenery were not established, but 19 of the 20 side greens were. Second, 13 of the 44 green areas adjacent to commercial buildings were under 1m in width. Most side greenery was belt -shape and unrelated to the features of the site or building. Third, the average distance from trees to buildings was 0.76m, indicating that most trees were planted too close to the buildings. Fourth, of the 30 trees utilized, the species breakdown was: 8 evergreen trees, 15 deciduous trees, and 7 shrubs. For the most part, planting patterns were similar for all species. Fifth, most sites were ill-suited to tree growth, because crown shape, planting conditions, and light conditions, etc., had not been considered. Based on these results, it is suggested that more specific, subdivided standards for planting conditions should be established. For example, building plans should include a green area that is at least one meter in width. In addition, according to the location and type(closing/opening) of the greenery adjacent to the buildings, suitable management programs and supervision protocol should be adopted.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.38 no.2
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• pp.60-76
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• 2020

The purpose of this study was to examine the spatial and visual arrangement characteristics of Imsil Yi Ung-jae's old house's spatial and visual aspects in order to discover the value of landscape and traditional house garden. The results of this study are as follows. Dongchon-village in Dundeok-ri, where old house is located, is a typical form of with "Back to the mountain and facing the water(背山臨水)", and is located in the north of the three streams of water, forming a Jeonchaghugwan(前窄後寬). Dongchon Village, which has a traditional scenic spot between Danguidae(丹丘臺) and Samgyeseokmun(三溪石門), is understood to be the main street of Nojeokbong Peak and Gyegwanbong Peak, which is Ansan(案山), where the "A centipede flying in the sky(飛天蜈蚣形)". Yi Ung-jae's old house is the oldest existing high-priced house in the North Jeolla region and the closing price of a royal family of the Joseon Dynasty, which was arranged by Chunseongjeong(春城正), Yi Dam-son(李聃孫) in the mid-16C. The Japanese Invasion of Korea in 1592 and Japanese colonial era, the loyalty of the gate quarters, the filial piety of the gate quarters, and the faithfulness of the tablet(扁額) and Juryeons(柱聯) are enough to contribute to the rise of the value of a physical house. The men's quarters(Sarangchae), which are placed on a high-pocket or a layout without going against the sloping terrain, have the effect of making the distance as far as possible, enhancing its dignity and hierarchy as a royal building. In addition, the entrance to the main quarters(Anchae) through the four pillar gates(四柱門), the extensive support and the appropriation of the Chaewon(vegetable garden), and the official base for the Anchae are very unique compared to the general nobility. However, in the context of the postwar relationship, the shrine seeks to realize Confucian ideals while harmonizing with nature by arranging wide sponsorships around it. On the other hand, it is confirmed that there was a pond in the form of a circle in a square(方池圓島型) with a relatively large area, which is now disturbed and damaged. Written by the high priced planting species are sponsored pine trees, hackberry, persimmon trees, Japanese apricot flower, Ohmomiji, and plum tree in the side garden, as well as cotyledon trees in the outside garden. However, although flower bed(花階), which is built on the stone axis, is a place that clearly shows the expensive garden, it seems to have lost the texture of the plant due to the extremely high variety of species and the splendor that does not match the plant landscape of the flower world. Yi Ung-jae's old house is highly valuable as it is a portrait house of a prince of the blood in the mid-Joseon Dynasty. Based on these findings, this study proposed a plan to improve the management of high prices that could be met.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.101-116
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• 2015

Recently, due to the development of smart devices and social media, vast amounts of information with the various forms were accumulated. Particularly, considerable research efforts are being directed towards analyzing unstructured big data to resolve various social problems. Accordingly, focus of data-driven decision-making is being moved from structured data analysis to unstructured one. Also, in the field of recommendation system, which is the typical area of data-driven decision-making, the need of using unstructured data has been steadily increased to improve system performance. Approaches to improve the performance of recommendation systems can be found in two aspects- improving algorithms and acquiring useful data with high quality. Traditionally, most efforts to improve the performance of recommendation system were made by the former approach, while the latter approach has not attracted much attention relatively. In this sense, efforts to utilize unstructured data from variable sources are very timely and necessary. Particularly, as the interests of users are directly connected with their needs, identifying the interests of the user through unstructured big data analysis can be a crew for improving performance of recommendation systems. In this sense, this study proposes the methodology of improving recommendation system by measuring interests of the user. Specially, this study proposes the method to quantify interests of the user by analyzing user's internet usage patterns, and to predict user's repurchase based upon the discovered preferences. There are two important modules in this study. The first module predicts repurchase probability of each category through analyzing users' purchase history. We include the first module to our research scope for comparing the accuracy of traditional purchase-based prediction model to our new model presented in the second module. This procedure extracts purchase history of users. The core part of our methodology is in the second module. This module extracts users' interests by analyzing news articles the users have read. The second module constructs a correspondence matrix between topics and news articles by performing topic modeling on real world news articles. And then, the module analyzes users' news access patterns and then constructs a correspondence matrix between articles and users. After that, by merging the results of the previous processes in the second module, we can obtain a correspondence matrix between users and topics. This matrix describes users' interests in a structured manner. Finally, by using the matrix, the second module builds a model for predicting repurchase probability of each category. In this paper, we also provide experimental results of our performance evaluation. The outline of data used our experiments is as follows. We acquired web transaction data of 5,000 panels from a company that is specialized to analyzing ranks of internet sites. At first we extracted 15,000 URLs of news articles published from July 2012 to June 2013 from the original data and we crawled main contents of the news articles. After that we selected 2,615 users who have read at least one of the extracted news articles. Among the 2,615 users, we discovered that the number of target users who purchase at least one items from our target shopping mall 'G' is 359. In the experiments, we analyzed purchase history and news access records of the 359 internet users. From the performance evaluation, we found that our prediction model using both users' interests and purchase history outperforms a prediction model using only users' purchase history from a view point of misclassification ratio. In detail, our model outperformed the traditional one in appliance, beauty, computer, culture, digital, fashion, and sports categories when artificial neural network based models were used. Similarly, our model outperformed the traditional one in beauty, computer, digital, fashion, food, and furniture categories when decision tree based models were used although the improvement is very small.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.4
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• pp.45-56
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• 2016

In this study, an analysis has been conducted on the evaluation criteria of current G-SEED(Green Standard for Energy and Environmental Design) and on the 78 buildings, certified by G-SEED, for 3 years from November, 2012 to November, 2015. Based on the results of this analysis, four issues are driven and proposed hereinafter. Issue 1 : Nowadays, the psychological proportion of landscape architecture in building is getting greater than ever so that it shows reliable reduction of carbon dioxide. Therefore, so far as the eight kinds of buildings are concerned, the evaluation items of G-SEED must include those of landscape architecture mandatorily through its enlargement. Issue 2 : It is undesirable factor that inhibits precise evaluation on landscaping area to let other areas appraise landscape architecture because it requires outstanding professionalism. So, G-SEED should not only ensure landscaping professionalism for the correct evaluation but also let landscape area participate in assessing other areas. Issue 3 : Many previous researches turned out that landscape planting technique has excellent effect on saving energy and reducing temperature of buildings. Thus, landscape planting technique of landscape area is required to be one of the evaluation items of energy sector. Issue 4 : Tree management also has to be newly included as one of the evaluation factor for the maintenance relating to the landscape architecture. G-SEED, enacted and enforced by the Green Building Creation Support Act in 2013, surely is effective system to reduce carbon dioxide in buildings. This is a special Act in its nature that is superior to Construction Law and must be observed by all means to construct buildings. Under the umbrella of this legal system, various of researches and products are contributing to creating new jobs in construction area. However, it is a well-known fact that landscape architecture area has shown less interest on this Act than that of construction area. In conclusion, it is necessary that landscape industry should conduct continuous researches on G-SEED and pay more attention to the Act enough to harvest related products and enlarge its work area.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.3
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• pp.163-179
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• 2003

Multifunctionality of agriculture which is not traded on the market now has been an important international issue in that it environmental and public benefits. We carried out to modify and to update the function of upland farming on flood prevention and fostering water resources. Economic values of environmental benefits were evaluated by replacement cost methods. Models to evaluate the function of preventing flood were selected as: (1)precipitation(flood-inducing) - runoff(A), (2) soil depth ${\times}$ soil air phase, (3) precipitation (flood-inducing) - runoff(B), (4) soil depth ${\times}$ effective porosity of soil. Models to estimate the function of fostering water resources were (1) saturated hydraulic conductivity (Ks) ${\times}$ duration of saturation(days) ${\times}$ (1-ratio of water flow directly into river), (2) precipitation ${\times}$ ratio of water fostered by rain resources ${\times}$ (area of upland/total land area), and (3) soil water retention quantity(under standing crop or tree) - SWRQ(in bare soil). Function of preventing flood was $883Mg\;ha^{-1}$ of water per year and 645 million Mg for the whole upland area. Function of fostering water resources was $94.1Mg\;ha^{-1}$ of water per year and 69 million Mg for the whole upland area. The value of flood-preventing function evaluated by replacement cost methods was estimated 1,428 billion won per year as compared to the cost for dam construction. The value of water resource fostering were estimated 8.6 billion won in the price of living water.

• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.341-354
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• 2021

A large part of Gapyeong is occupied by Korean pine (Pinus koraiensis) and Japanese larch (Larix kaempferi) plantations. Abies holophylla stands are scattered throughout Gapyeong, but little information on their distribution is available. This study explored the potential of succession from planted species to native A. holophylla in plantations. Trees were inventoried and regeneration of A. holoplhylla and stand management history were examined in Korean pine, Japanese larch, and A. holophylla-dominated stands. The importance percentage of A. holophylla was the highest among species with a range of 36.1% to 79.1% in all stands and the density of A. holophylla in understory (DBH <2 cm or <1.3 m height) ranged from 50 to 5,820 trees ha^-1. Non-metric multidimensional scaling classified stands into four types, AN, AP, AM, and P. The AN type showed a reverse J-shape DBH distribution, which was similar to that in natural A. holophylla stands. Both AP and AM types included Korean pine plantations with A. holophylla seed trees within stands. For AP, A. holophylla competed with planted species in overstory and deciduous broadleaved species in understory. The AM type was once thinned from below, thus stem density in the mid DBH classes was lower than upper or lower DBH classes. The P type consisted of plantations without A. holophylla seed trees. However, understory regeneration of A. holophylla was abundant through seed supply from A. holophylla in adjacent stands. Plantations with A. holophylla seed trees within or in adjacent stands showed vigorous natural regeneration of A. holophylla, highlighting the potential for succession from planted species to native A. holophylla in the Gapyeong area. Further studies can help develop techniques to restore plantations to native species-dominated natural stands using ecological succession.