• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.586-591
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• 2014

In this study, we introduce Radial Basis Function Neural Networks(RBFNNs) classifier using Artificial Bee Colony(ABC) algorithm in order to classify between precipitation event and non-precipitation event from given radar data. Input information data is rebuilt up through feature analysis of meteorological radar data used in Korea Meteorological Administration. In the condition phase of the proposed classifier, the values of fitness are obtained by using Fuzzy C-Mean clustering method, and the coefficients of polynomial function used in the conclusion phase are estimated by least square method. In the aggregation phase, the final output is obtained by using fuzzy inference method. The performance results of the proposed classifier are compared and analyzed by considering both QC(Quality control) data and CZ(corrected reflectivity) data being used in Korea Meteorological Administration.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.4
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• pp.261-269
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• 2019

This study investigates a method of designing pixel-type frequency selective surfaces(FSS) with flexibility while considering factors, such as polarization and incident angle. Among the various methods used to solve the discrete space problem when designing a pixel-type FSS, the binary particle swarm optimization(BPSO) algorithm is one of the most applicable techniques to determine the periodic structure pattern of an FSS. Therefore, a method of efficiently designing FSS with roll-off band pass characteristics using an improved BPSO algorithm is proposed. To solve the convergence problem in the fitness function design to induce particles in the desired solution, FSS with desired roll-off wave characteristics can be easily obtained by applying a fitness function using "slope" as an input parameter.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.721-732
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• 2018

Latent class analysis (LCA) is an important tool to explore unseen latent groups in multivariate categorical data. In practice, it is important to select a suitable set of variables because the inclusion of too many variables in the model makes the model complicated and reduces the accuracy of the parameter estimates. Dean and Raftery (Annals of the Institute of Statistical Mathematics, 62, 11-35, 2010) proposed a headlong search algorithm based on Bayesian information criteria values to choose meaningful variables for LCA. In this paper, we propose a new variable selection procedure for LCA by utilizing posterior probabilities obtained from each fitted model. We propose a new statistic to measure the adequacy of LCA and develop a variable selection procedure. The effectiveness of the proposed method is also presented through some numerical studies.

• Research in Plant Disease
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• v.27 no.4
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• pp.137-148
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• 2021

Plant RNA viruses are one of the most destructive pathogens that cause a significant loss in crop production worldwide. They have evolved with high genetic diversity and adaptability due to the short replication cycle and high mutation rate during genome replication, which are characteristics of RNA viruses. Plant RNA viruses exist as quasispecies with high genetic diversity; thereby, a rapid population transition with new fitness can occur due to selective pressure resulting from environmental changes. Plant resistance can act as selective pressure and affect the fitness of the virus, which may lead to the emergence of resistance-breaking variants. In this paper, we introduced the evolutionary perspectives of plant RNA viruses and the driving forces in their evolution. Based on this, we discussed the mechanism of the emergence of variant viruses that overcome plant resistance. In addition, strategies for deploying plant resistance to viral diseases and improving resistance durability were discussed.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.23-43
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• 2022

In order to apply AI (Artificial Intelligence) in various industries, interest in algorithm selection is increasing. Algorithm selection is largely determined by the experience of a data scientist. However, in the case of an inexperienced data scientist, an algorithm is selected through meta-learning based on dataset characteristics. However, since the selection process is a black box, it was not possible to know on what basis the existing algorithm recommendation was derived. Accordingly, this study uses k-means cluster analysis to classify types according to data set characteristics, and to explore suitable classification algorithms and methods for resolving class imbalance. As a result of this study, four types were derived, and an appropriate class imbalance resolution method and classification algorithm were recommended according to the data set type.

• Korean Journal of Environment and Ecology
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• v.18 no.4
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• pp.369-381
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• 2004

This study was achieved to present the way to restore the Seonheulgot pasture-grassland damaged by landuse and interference for a long time to evergreen broad-leaved forests as the native vegetation structure. As a result of analyzing ecological succession tendency of structure in survey area, we established the optimal restoration model. The total of survey sites were 26, and the classified plant community types were four types by M.I.P of dominant woody species. Finally we classified the four types based on diameter of dominant woody species in canopy layer. The six community types are as follows: Community I was runner-shrub forest, community II was evergreen broad-leaved shrub forest, and community III was evergreen broad-leaved forest of small diameter. Community IV and V were evergreen broad-leaved forest of middle diameter. Community Ⅵ was evergreen broad-leaved forest of large diameter. The number of constituent species was 24 in community I, 28 in community II as the shrub forest, 16 as the evergreen broad-leaved forest of small diameter, 29 in community III, 30 in community IV as the evergreen broad-leaved forest of middle diameter and 27 in community Ⅵ as the evergreen broad-leaved forest of large diameter. The range of Shannon's index of all communitys was from 0.8763 to 1.2630 and the Similarity index between the community composed of middle diameter woody species and large diameter woody species. The ecological succession of community I, II, and III were changed from pasture-grassland to broad-leaved forest and the structure of community IV, V, and Ⅵ was similar to evergreen broad-leaved forest in warm temperate region. We suggest the restoration planting model evergreen broad-leaved forest of in Seonheulgot pasture-grassland, as follows: The target restoration vegetation were Castanopsis cuspidata var. sievoldii community and Queycus glauca community. Castanopsis cuspidata var. sievoldii and Quercus glauca should be dominant woody species in canopy layer, the number of trees was 10 per 100$m^2$, and Castanopsis cuspidata var, sievoldii, Quercus glauca, Camellia japonica, and Eurya japonica should be dominant woody species in the understory layer, the number of trees was 14 per 100$m^2$.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.3-6
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• 2007

사람들이 어떠한 행동을 할 때는 특정 의도를 가지고 있기 때문에 상황에 맞는 적합한 서비스를 제공하기 위해서는 사용자가 현재 하고 있는 행동에 대한 의도를 파악해야한다. 이를 위해 의도와 행동사이의 연관성을 이용하여 사용자의 의도에 따른 행동의 모델을 만든다. 일상생활에서 사람들이 하는 행동은 작은 단위 행동들의 연속(sequence)으로 이루어지므로, 사용자의 단위행동의 순서를 분석한다면 의도에 따른 행동 모델을 만들기가 용이해진다. 하지만, 이런 단위 행동 분석 방법의 문제점은 같은 의도를 가진 행동이 완벽하게 동일한 단위 행동의 순서로 일어나지는 않는다는 점이다. 시스템은 동일한 동작 순서로 일어나지 않는 행동들을 서로 다른 의도를 가진 행동으로 이해하게 된다. 따라서 이 문제점을 해결할 수 있는 사용자 의도 파악 기법이 필요하다. 본 논문에서는 과거의 사용자의 행동 정보를 기반으로 행동들의 유사성을 판별하였고, 그 결과를 이용하여 행동의 의도를 파악하는 방법을 사용한다. 이를 위해, 과거 사용자가 한 행동들을 단위 시간 별로 나누어 단위 행동의 순서로 만들고, 이를 K-평균 군집화 방법(K-means)으로 군집들의 순서로 나타내었다. 이 변경된 사용자 행동 정보를 사용하여 은닉 마코프 모델을 학습 시키고, 이렇게 만들어진 은닉 마코프 모델은 현재 사용자가 행한 행동이 어떤 행동인지를 예측하여 사용자의 의도를 파악한다.

• Asia Marketing Journal
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• v.3 no.3
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• pp.1-24
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• 2001

본 연구는 자원의존이론, 거래비용이론, 관계계약이론 등의 선행연구를 검토하여 관계지향성의 원인과 성과를 밝히고자 하였다. 먼저 구매기업과 공급기업간의 행동적 관계지향요인과 관계지향성 분석을 실시하였다. 행동적 관계지향요인으로는 정보교환, 조화노력, 행동적 규범, 업무결속을 들고, 관계지향성 분석을 위해 군집분석을 실시하였다. 연구표본에는 C지역의 전문건설업체를 대상으로 500부의 설문지를 배포, 그 중 185부를 회수하여, 적합한 설문 140부가 사용되었다. 군집분석결과 관계지향성 집단은 정보교환, 조화 노력, 협동규범 수용, 업무결속이 모두 높게 나타났고, 비관계지향성 집단은 정보교환, 조화노력, 협동규범 수용, 업무결속이 모두 낮게 나타나 기업간의 관계지향성을 의미있는 2개의 집단으로 나눌 수 있었다. 둘째, 기업간 관계지향성의 영향요인으로 환경의 역동성, 대안의 이용가능성, 공급의 중요성, 상호호혜전략, 신뢰성을 검토하였다. 본 연구에서 선정한 관계지향성에 대한 영향요인들이 관계지향집단과 비관계지향집단으로 분류하는데 얼마나 유용하게 이용될 수 있는가를 알아보기 위해 판별분석을 실시하였다. 관계지향집단과 비관계지향집단의 분류에 환경의 역동성, 대안의 이용가능성, 공급의 중요성은 영향이 없는 것으로 나타났으며, 호혜전략, 신뢰성은 의미가 있는 것으로 나타났다. 셋째, 기업간 관계지향성의 성과로서 실현경쟁우위와 관계유지의도를 검토하였다. 관계지향성이 성과에 어떠한 영향을 미치는가를 검증하기 위해 MANOVA(multivariatee analysis of variance)분석을 실시하였다. 실증 결과, 관계 정도가 높은 집단이 관계 정도가 낮은 집단보다 실현 경쟁우위수준을 높게 지각하고 관계를 유지하려는 의도가 높은 것으로 나타났다.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.9
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• pp.283-288
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• 2015

Scientists gain benefits from on-demand scalable resource provisioning, and various computing environments by using cloud computing resources for their applications. However, many cloud computing service providers offer their cloud resources according to their own policies. The descriptions of resource specification are diverse among vendors. Subsequently, it becomes difficult to find suitable cloud resources according to the characteristics of an application. Due to limited understanding of resource availability, scientists tend to choose resources used in previous experiments or over-performed resources without considering the characteristics of their applications. The need for standardized notations on diverse cloud resources without the constraints of complicated specification given by providers leads to active studies on intercloud to support interoperability in hybrid cloud environments. However, projects related to intercloud studies are limited as they are short of expertise in application characteristics. We define an intercloud resource classification and propose semantic resource recommendation based on statistical analysis to provide semantic cloud resource services for an application in hybrid cloud computing environments. The scheme proves benefits on resource availability and cost-efficiency with choosing semantically similar cloud resources using cluster analysis while considering application characteristics.

• Fire Science and Engineering
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• v.30 no.6
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• pp.130-136
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• 2016

A university needs to analyze and improve its curricula with the perspective of the consumer to develop a syllabus for the training of industry-demand customized human resources. Accordingly, this paper surveyed the demand of fire-related industry workers to evaluate the major fitness of the curriculum of fire risk prediction and assessment and carried out descriptive statistical analysis, factor analysis, cluster analysis, and one-way ANOVA based on the results. According to the analysis, fire-related industry workers reported that the curriculum of fire risk prediction and assessment is suitable for majors. In addition, they were greatly aware of the necessity of basic major and common major subjects among subjects of fire risk prediction and assessment. The results of this analysis will provide the basic data to improve the curriculum continuously in the future.