• Korean Journal of Construction Engineering and Management
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• v.10 no.1
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• pp.156-166
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• 2009

Residential land development projects are tending upwards recently. However, an indiscreet residential land development has tended to damage environment by destroying existing green lands and trees of target lands and generating many cut slopes with transformation of its topography. There are Prior Environmental Review(PER) for district designation and Environmental Impact Assessment(EIA) before approval on development plans. PER is implemented after developing a residential land development plan and EIA is implemented after completing a detail design. As the result, many of residential land development projects are passive to reduce potential environmental problems on the designated sites. Object of this study is to construct an evaluation system on alternatives in the early step of site designation for implementing residential land development projects with environment-friendly and sustainable way. For this, alternative evaluation model is constructed by using Fuzzy Inference and Analytic Hierarchy Process(AHP) method based on Environmental Evaluation Factors of residential land development project, which are proposed in the precedent research. If a decision maker evaluates environment damage by ten-point method, the point is transformed Environmental Performance(EP) by Fuzzy Inference, and then, applying weight that is already calculated by AHP method, Total Environmental Performance(TEP) is calculated. After all, an alternative with the highest TEP is selected as the best one. Using this evaluation system, more than two alternatives of residential land development project site, which can hold location appropriateness in the early under undecided land use plan, can be evaluated quantitatively. As environmental damages, which can be generated by implementing a residential land development project, can be detected in the early step, environmental damages can be removed or reduced at the source.

• Journal of Korean Society of Forest Science
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• v.98 no.6
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• pp.646-651
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• 2009

In this study, it was intended to prepare a stem volume table (with or without bark) and a stand yield table for Juglans mandshurica, plantations in Chungju, located in Chungcheongbuk-do, Korea. For the calculation of stem volume, we applied Kozak's growth model, which showed the best fitness index (97%). With this model, it was able to prepare the first yield table for Juglans mandshurica in Korea. Site index model, an indicator of forest productivity, was derived by using the Chapman-Richard model, in which the basic stand age was set to 30 years. The resulted site index ranged between 16 and 22. Based on the yield table of Juglans mandshurica resulted from this study, the volume for a 70-year-old stand with a midium site index class was estimated to be $238m^3/ha$, which is $100m^3/ha$ higher than the volume estimated from the yield table of Quercus acutissima. The yield table of oak trees has been used in the estimation of most broadleaf stands in Korea. However, the result of this study indicated that it is necessary to generate a stand yield table for each broadleaf species. The annual $CO_2$ removals of 30-year-old Juglans mandshurica plantations in the ChungJu region was estimated to be $5.84tCO_2/ha$. The stem volume and stand yield table of Juglans mandshurica plantation resulted from this study would provide a good information in decision making for forest management in ChungJu region.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.151-161
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• 2016

As part of a policy to address climate change and pollution problem, the government introduced a green credit card scheme in order to motivate pro-environmental behaviors in July 2011. It is important to present the specific ways to facilitate pro-environmental behaviors using the consumer behavior pattern data. This study was a result of data from total fifty seven thousands customer purchasing history data of green credit card to be created for the 3 months from January to March 2015. As the analysis process is put in to operation the analysis of the purchasing customer's profile firstly, and the second come into association analysis to consider the buying associations for green products purchasing networks, the third estimate the useful parameters to affect the customer's pro-environmental behavior and customer characteristics. It shows that royal customers are from 30 to 40 years old and their incomes are from 30 million won to 40 million won. Especially, they live in Daegu, Gyeonggi, and Seoul.

• Journal of Korean Society of Forest Science
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• v.104 no.2
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• pp.248-253
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• 2015

This study applied the portfolio approach as a means to provide decision-making information for the establishment of the optimal production plan for non-timber products. The target items of non-timber forest product were Chestnut, Jujube, Walnut and Astringent Persimmon. The data used in this study were the annual report of forestry production cost survey which contains the annual production, annual gross income, and annual product cost from 2008 to 2013. These data were used to calculate the expected return of non-timber forest product. The objective function in the portfolio models was to minimize the expected return volatility, called risk and the constrain was to achieve the minimum expected return rate. Results indicated that the production ratio of the nuts and fruits in 2013 was 7% for Chestnut, 20% for Jujube, 5% for Walnut and 68% for Astringent Persimmon. Furthermore, portfolio presented that the production ratio was 10% for Chestnut, 9% for Jujube, 3% for Walnut and 78% for Astringent Persimmon in the near future. The cause was analyzed due to maintain stable production and income of Astringent Persimmon and Chestnut. Meanwhile, the revenue of Walnuts and Jujube was in great variation with relatively higher revenues.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.7-12
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• 2012

Recently the medical field to efficiently process the vast amounts of information to decision trees, neural networks, Bayesian Networks, including the application method of various data mining techniques are investigated. In addition, the basic personal information or patient history, family history, in addition to information such as MRI, HRCT images and additional information to collect and leverage in the diagnosis of disease, improved diagnostic accuracy is to promote a common status. But in real world situations that affect the results much because of the variable exists for a particular data mining techniques to obtain information through the enemy can be seen fairly limited. Medical images were taken as well as a minor can not give a positive impact on the diagnosis, but the proportion increased subjective judgments by the automated system is to deal with difficult issues. As a result of a complex reality, the situation is more advantageous to deal with the relative probability of the multivariate model based on Bayesian network, or TAN in the K2 search algorithm improves due to expansion model has been proposed. At this point, depending on the type of search algorithm applied significantly influenced the performance characteristics of the extended Bayesian network, the performance and suitability of each technique for evaluation of the facts is required. In this paper, we extend the Bayesian network for diagnosis of diseases using the same data were carried out, K2, TAN and changes in search algorithms such as classification accuracy was measured. In the 10-fold cross-validation experiment was performed to compare the performance evaluation based on the analysis and the onset of high-risk classification for patients with HRCT images could be possible to identify high-risk data.

• Journal of Intelligence and Information Systems
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• v.16 no.1
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• pp.93-116
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• 2010

Ensemble approach is applied to the detection modeling of illegal cash accommodation (ICA) that is the well-known type of fraudulent usages of credit cards in far east nations and has not been addressed in the academic literatures. The performance of fraud detection model (FDM) suffers from the imbalanced data problem, which can be remedied to some extent using an ensemble of many classifiers. It is generally accepted that ensembles of classifiers produce better accuracy than a single classifier provided there is diversity in the ensemble. Furthermore, recent researches reveal that it may be better to ensemble some selected classifiers instead of all of the classifiers at hand. For the effective detection of ICA, we adopt ensemble size reduction technique that prunes the ensemble of all classifiers using accuracy and diversity measures. The diversity in ensemble manifests itself as disagreement or ambiguity among members. Data imbalance intrinsic to FDM affects our approach for ICA detection in two ways. First, we suggest the training procedure with over-sampling methods to obtain diverse training data sets. Second, we use some variants of accuracy and diversity measures that focus on fraud class. We also dynamically calculate the diversity measure-Forward Addition and Backward Elimination. In our experiments, Neural Networks, Decision Trees and Logit Regressions are the base models as the ensemble members and the performance of homogeneous ensembles are compared with that of heterogeneous ensembles. The experimental results show that the reduced size ensemble is as accurate on average over the data-sets tested as the non-pruned version, which provides benefits in terms of its application efficiency and reduced complexity of the ensemble.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.625-634
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• 2002

In this paper, we conducted various experiments with Bayesian networks in order to analyze clinical data of infertility patients. With these experiments, we tried to find out inter-dependencies among important factors playing the key role in clinical pregnancy, and to compare 3 different kinds of Bayesian network classifiers (including NBN, BAN, GBN) in terms of classification performance. As a result of experiments, we found the fact that the most important features playing the key role in clinical pregnancy (Clin) are indication (IND), stimulation, age of female partner (FA), number of ova (ICT), and use of Wallace (ETM), and then discovered inter-dependencies among these features. And we made sure that BAN and GBN, which are more general Bayesian network classifiers permitting inter-dependencies among features, show higher performance than NBN. By comparing Bayesian classifiers based on probabilistic representation and reasoning with other classifiers such as decision trees and k-nearest neighbor methods, we found that the former show higher performance than the latter due to inherent characteristics of clinical domain. finally, we suggested a feature reduction method in which all features except only some ones within Markov blanket of the class node are removed, and investigated by experiments whether such feature reduction can increase the performance of Bayesian classifiers.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.35-44
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• 2022

In this study, we analyze and forecast quantum computer technology trends. Previous research has been mainly focused on application fields centered on technology for quantum computer technology trends analysis. Therefore, this paper analyzes important quantum computer technologies and performs future signal detection and prediction, for a more market driven technical analysis and prediction. As analyzing words used in news articles to identify rapidly changing market changes and public interest. This paper extends conference presentation of Cha & Chang (2022). The research is conducted by collecting domestic news articles from 2019 to 2021. First, we organize the main keywords through text mining. Next, we explore future quantum computer technologies through analysis of Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), and Key Emergence Map (KEM). Finally, the relationship between future technologies and supply and demand is identified through random forests, decision trees, and correlation analysis. As results of the study, the interest in artificial intelligence was the highest in frequency analysis, keyword diffusion and visibility analysis. In terms of cyber-security, the rate of mention in news articles is getting overwhelmingly higher than that of other technologies. Quantum communication, resistant cryptography, and augmented reality also showed a high rate of increase in interest. These results show that the expectation is high for applying trend technology in the market. The results of this study can be applied to identifying areas of interest in the quantum computer market and establishing a response system related to technology investment.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.65-77
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• 2022

Due to the recent development in electronic financial services, transactions of electronic prepayment are rapidly growing, leading to growing fraud attempts. This paper proposes a methodology that can effectively detect fraud transactions in electronic prepayment by machine learning algorithms, including support vector machines, decision trees, and artificial neural networks. Actual transaction data of electronic prepayment services were collected and preprocessed to extract the most relevant variables from raw data. Two different approaches were explored in the paper. One is a transaction-based approach, and the other is a user ID-based approach. For the transaction-based approach, the first model is primarily based on raw data features, while the second model uses extra features in addition to the first model. The user ID-based approach also used feature engineering to extract and transform the most relevant features. Overall, the user ID-based approach showed a better performance than the transaction-based approach, where the artificial neural networks showed the best performance. The proposed method could be used to reduce the damage caused by financial accidents by detecting and blocking fraud attempts.

• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.572-577
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• 2009

Large corpus-based concatenating Text-to-Speech (TTS) systems can generate natural synthetic speech without additional signal processing. To prune the redundant speech segments in a large speech segment DB, we can utilize a decision-tree based triphone clustering algorithm widely used in speech recognition area. But, the conventional methods have problems in representing the acoustic transitional characteristics of the phones and in applying context questions with hierarchic priority. In this paper, we propose a new clustering algorithm to downsize the speech DB. Firstly, three 13th order MFCC vectors from first, medial, and final frame of a phone are combined into a 39 dimensional vector to represent the transitional characteristics of a phone. And then the hierarchically grouped three question sets are used to construct the triphone trees. For the performance test, we used DTW algorithm to calculate the acoustic similarity between the target triphone and the triphone from the tree search result. Experimental results show that the proposed method can reduce the size of speech DB by 23% and select better phones with higher acoustic similarity. Therefore the proposed method can be applied to make a small sized TTS.