• Korean Journal of Medicinal Crop Science
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• v.5 no.4
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• pp.266-275
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• 1997

Anemarrhena asphodeloides BUNGE is one of important medicinal crops, which has been collected or/and cultivated for its rhizomes. The main medicinal ingredient of the A. asphodeloides Bunge rhizomes is a saponin, which is known to have medical values for diaphoresis, sedatives and biuresis. However, studies on cultural methods and breeding works on this plant have not been made in detail. To increase productivity and to improve quality of crops, it is important to collect cultivated and wild lines, to classify them based on morphological and genetic characteristics, and to select superior pure lines at first. Therefore, total 20 A. asphodeloides lines collected were cultivated at the fieldof Chungnam Provincial Administration of Rural Development in 1995 to study the agronomic characteristics and to classify them based on morphological characteristics. Characteristics related with reproductive organ such as the number of spikes per plant and peduncle length showed greater variations than vegetative organrelated characteristics such as leaf length and rhizome length based on the coefficient of variation. Vigorous shoot growth resulted in better development of reproductive organs such as peduncle length and number of spikes per plant. However, the development of spikes was negatively correlated with chlorophyll content. Characteristics of underground parts were more significantly correlated with spike characteristics than aerial part characteristics. A. asphodeloides tested in this study were classified into 2 groups (group A and group B) based on centroid linkage cluster analysis. Group A showed more vigorous shoot growth with more leaves and spikes per plant, longer peduncle length, thicker peduncle diameter and higher shoot dry weight than group B. Group A could be further classified into 2 sub-groups based on leaf size, spike length and peduncle diameter, while group B also could be classified based on number of leaves, number of spikes and shoot dry weight.

• Journal of the Korean Society of Earth Science Education
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• v.15 no.2
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• pp.285-298
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• 2022

This study is to investigate the effects and characteristics of science and engineering integrated lessons on elementary students' creative engineering problem solving propensity (CEPSP). The science and engineering integrated lessons used in this study was a 10 lesson-hours STEM program, co-developed by University of Minnesota and Purdue University. The program was implemented in the 6th grade science class of H Elementary School located in P Metropolitan city. The main data of this study are the pre-post CEPSP result and interview with 5 students collected before and after the research. The CEPSP result was analyzed by a paired-sample t-test and hierarchical cluster analysis. As a result of the t-test, it was found that overall, the program has a positive effect on the students' CEPSP score. As a result of cluster analysis, it was confirmed that studnets' CEPSP could be classified into two groups (lower and higher score cluster). Five students whose, CEPSP score has significantly improved after the lessons were interviewed to find out what the characteristics of the program that contribute the significant change are. As a result of conducting centroid analysis of the interview transcription and the hybrid analysis method, it was found that the meaningful experiences that the five students commonly shared were 'problem solving through collaboration' and 'through repeated experiments (redesign)', problem solving' and 'utilization of scientific knowledge'. As minor reactions, 'choice of the best experimental method' and 'difference between science and engineering' appeared.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.2
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• pp.150-156
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• 2008

Many papers have shown that the hierarchical clustering method takes good-performance, but is limited because of its quadratic time complexity. In contrast, with a large number of variables, K-means reduces a time complexity. Think of the factor of simplify, high-quality and high-efficiency, we combine the two approaches providing a new system named CONDOR system with hierarchical structure based on document clustering using K-means algorithm. Evaluated the performance on different hierarchy depth and initial uncertain centroid number based on variational relative document amount correspond to given queries. Comparing with regular method that the initial centroids have been established in advance, our method performance has been improved a lot.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.587-592
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• 2003

Many real world control problems have continuous states and actions. When the state space is continuous, the reinforcement learning problems involve very large state space and suffer from memory and time for learning all individual state-action values. These problems need function approximators that reason action about new state from previously experienced states. We introduce Fuzzy Q-Map that is a function approximators for 1 - step Q-learning and is based on fuzzy clustering. Fuzzy Q-Map groups similar states and chooses an action and refers Q value according to membership degree. The centroid and Q value of winner cluster is updated using membership degree and TD(Temporal Difference) error. We applied Fuzzy Q-Map to the mountain car problem and acquired accelerated learning speed.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.169-178
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• 2021

Recently, a bike-sharing system (BSS) has become popular as a convenient "last mile" transportation. Rebalancing of bikes is a critical issue to manage BSS because the rents and returns of bikes are not balanced by stations and periods. For efficient and effective rebalancing, accurate traffic prediction is important. Recently, cluster-based traffic prediction has been utilized to enhance the accuracy of prediction at the station-level and the clustering step is very important in this approach. In this paper, we propose a k-means based clustering algorithm that overcomes the drawbacks of the existing clustering methods for BSS; indeterministic and hardly converged. By employing the centroid initialization and using the temporal proportion of the rents and returns of stations as an input for clustering, the proposed algorithm can be deterministic and fast.