• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.65-75
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• 2017

The Detection Model is the model to find the result of a certain purpose using artificial intelligent, data mining, intelligent algorithms In Cyber Security, it usually uses to detect intrusion, malwares, cyber incident, and attacks etc. There are an amount of unlabeled data that are collected in a real environment such as security data. Since the most of data are not defined the class labels, it is difficult to know type of data. Therefore, the label determination process is required to detect and analysis with accuracy. In this paper, we proposed a KDFL(K-means and D-S Fusion based Labeling) method using D-S inference and k-means(unsupervised) algorithms to decide label of data records by fusion, and a detection model architecture using a proposed labeling method. A proposed method has shown better performance on detection rate, accuracy, F1-measure index than other methods. In addition, since it has shown the improved results in error rate, we have verified good performance of our proposed method.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.7-13
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• 2018

In the current study, we proposed an optimized brain-computer interface (BCI) which employed blind source separation (BBS) approach to remove noises. Thus motor imagery (MI) signal and steady state visual evoked potential (SSVEP) signal were easily to be detected due to enhancement in signal-to-noise ratio (SNR). Moreover, a combination between MI and SSVEP which is typically can increase the number of commands being generated in the current BCI. To reduce the computational time as well as to bring the BCI closer to real-world applications, the current system utilizes a single-channel EEG signal. In addition, a convolutional neural network (CNN) was used as the multi-class classification model. We evaluated the performance in term of accuracy between a non-BBS+BCI and BBS+BCI. Results show that the accuracy of the BBS+BCI is achieved $16.15{\pm}5.12%$ higher than that in the non-BBS+BCI by using BBS than non-used on. Overall, the proposed BCI system demonstrate a feasibility to be applied for multi-dimensional control applications with a comparable accuracy.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1C
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• pp.65-76
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• 2010

Recent earthquake events revealed that severe seismic damages were concentrated mostly at sites composed of soil sediments rather than firm rock. This indicates that the site effects inducing the amplification of earthquake ground motion are associated mainly with the spatial distribution and dynamic properties of the soils overlying bedrock. In this study, an integrated GIS-based information system for geotechnical data was constructed to establish a regional counterplan against ground motions at a representative metropolitan area, Seoul, in Korea. To implement the GIS-based geotechnical information system for the Seoul area, existing geotechnical investigation data were collected in and around the study area and additionally a walkover site survey was carried out to acquire surface geo-knowledge data. For practical application of the geotechnical information system used to estimate the site effects at the area of interest, seismic zoning maps of geotechnical earthquake engineering parameters, such as the depth to bedrock and the site period, were created and presented as regional synthetic strategy for earthquake-induced hazards prediction. In addition, seismic zonation of site classification was also performed to determine the site amplification coefficients for seismic design at any site and administrative sub-unit in the Seoul area. Based on the case study on seismic zonations for Seoul, it was verified that the GIS-based geotechnical information system was very useful for the regional prediction of seismic hazards and also the decision support for seismic hazard mitigation particularly at the metropolitan area.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.668-676
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• 2016

This study reviewed the planning characteristics of contemporary Japanese middle school architecture on which related studies are insufficient, aiming to obtain new ideas for planning Korean middle school facilities. Fourteen case schools built after 1990s were selected and analyzed. They were divided into learning-living space and other major spaces. The planning characteristics of the case schools are summarized as follows 1) The case schools were classified into two categories, departmentalized classroom type (D type) and usual with variation type (UV type) by school system. These categories can also be the classification standard for basic architectural characteristics in learning and living space of case schools. 2) D type case schools have departmentalized classrooms, home base, media space and teacher's space for learning-living space. D type case schools are divided into 'attached-to-classroom type' and 'separate type' depending on the adjacency of the home base and departmentalized classroom. 3) UV type case schools have multipurpose space around the classroom for learning-living space and can be divided into two types, i.e., 'directly adjacent' and 'separate', depending on the connectivity to classroom of multipurpose room. 4) Specialized classrooms are designed to have the openness to the public and the own characteristics of school subjects strengthened and show the spatial differentiation with connected ancillary spaces. 5) Libraries are designed as complex zones grouped with computer labs, audio visual rooms and multipurpose halls not as a single room and as open plan not with a closed wall. 6) The gymnasium is the basic sports facility with a martial arts room and outdoor pool, which are for after-school activities as well as physical education class. 7) The terrace, balcony and outdoor stairs are frequently used architectural vocabularies as diverse outdoor spaces with a variety of functions.

• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.159-173
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• 2017

In order to provide technical financial support to small and medium-sized venture companies based on technology, the government implemented the TCB evaluation, which is a kind of technology rating evaluation, from the Kibo and a qualified private TCB. In this paper, we briefly review the current state of TCB evaluation and available indicators related to technology evaluation accumulated in the Korea Credit Information Services (TDB), and then use indicators that have a significant effect on the technology rating score. Multiple regression techniques will be explored. And the relative importance and classification accuracy of the indicators were calculated by applying the key indicators as independent features applied to the generalized boosting model, which is a representative machine learning classifier, as the class influence and the fitness of each model. As a result of the analysis, it was analyzed that the relative importance between the two models was not significantly different. However, GBM model had more weight on the InnoBiz certification, R&D department, patent registration and venture confirmation indicators than regression model.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.375-384
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• 2021

Deep learning techniques have been proven to have high performance in image processing and are applied in various fields. The most widely used methods for validating a deep learning model include a holdout verification method, a k-fold cross verification method, and a bootstrap method. These legacy methods consider the balance of the ratio between classes in the process of dividing the data set, but do not consider the ratio of various features that exist within the same class. If these features are not considered, verification results may be biased toward some features. Therefore, we propose a deep learning model validation method based on data feature coverage for image classification by improving the legacy methods. The proposed technique proposes a data feature coverage that can be measured numerically how much the training data set for training and validation of the deep learning model and the evaluation data set reflects the features of the entire data set. In this method, the data set can be divided by ensuring coverage to include all features of the entire data set, and the evaluation result of the model can be analyzed in units of feature clusters. As a result, by providing feature cluster information for the evaluation result of the trained model, feature information of data that affects the trained model can be provided.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.250-257
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• 2020

It is difficult for farmers to select new crops for cultivation to increase income. So we conducted land suitability assessment of grapes with soil and climate information related to crop growth. At first, land suitabilities for grapes were classified into three categories (most suitable, suitable, low productive & not suitable areas) according to soil and climate conditions, respectively. In details, land suitability with respect to soil was assessed by soil morphological and physical properties including soil texture, drainage class, available soil depth, slope and gravel content, whereas one in accordance with climate was evaluated by average annual temperature, temperature during the growing season, temperature during maturation, the lowest temperature, chilling requirement and precipitation during the growing season. Secondly, we combined both soil and climate classification results using a most-limiting characteristic method. Maps showing the suitable land for grapes cultivation were drawn. The results indicate that the most suitable area of cultivation for grapes in south Korea was 3.43% and suitable (possible) area was 10.61%. This study may help to preserve land and increase the productivity through providing valuable information regarding where more suitable areas for grapes are located.

• The Journal of Sustainable Design and Educational Environment Research
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• v.14 no.3
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• pp.9-17
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• 2015

In this paper, there are several analysis on G-SEED, Building Energy Efficiency Rating System, Energy Performance Index, Energy Saving Plan about how they are applied by classification and planning standard. The analysis result found out that G-SEED has low select percentage by having difficulties to managing and additional cost when the each class is selected. And also, Building Energy Efficiency Rating System in school is planed in comparably simple design and similar size and also mostly uses high efficient machines, which was in high lever comparing to the system in facilities in other uses. In the case of EPI, there are differences on acquiring grades by each region. Especially, Gyung-gi region has a low grade on architecture part comparing to other parts, which seems to acquire more grades by strengthen insulation performance. By the result from the three standards, many facilities has only formal plan to pass the required standard without considering specialities of each buildings, which has a tendency to have a pattern to have a minimum criteria. However, School has a symbolic building which has a obligation to be the base of the aim for growing green energy buildings and green education for students. Therefore, planning with understanding of specialities of the facility, having various and rational evaluation standards from the planning of the building is necessary.

• Korean Journal of Ecology and Environment
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• v.57 no.2
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• pp.83-91
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• 2024

This study utilizes association rule learning and clustering analysis to explore the co-occurrence and relationships within ecosystems, focusing on the endangered brackish-water snail Clithon retropictum, classified as Class II endangered wildlife in Korea. The goal is to analyze co-occurrence patterns between brackish-water snails and other species to better understand their roles within the ecosystem. By examining co-occurrence patterns and relationships among species in large datasets, association rule learning aids in identifying significant relationships. Meanwhile, K-means and hierarchical clustering analyses are employed to assess ecological similarities and differences among species, facilitating their classification based on ecological characteristics. The findings reveal a significant level of relationship and co-occurrence between brackish-water snails and other species. This research underscores the importance of understanding these relationships for the conservation of endangered species like C. retropictum and for developing effective ecosystem management strategies. By emphasizing the role of a data-driven approach, this study contributes to advancing our knowledge on biodiversity conservation and ecosystem health, proposing new directions for future research in ecosystem management and conservation strategies.