• Fashion & Textile Research Journal
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• v.15 no.4
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• pp.523-532
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• 2013

This study investigates risk reduction behavior with risk perception in outdoor wear purchase situations. Data were collected via a questionnaire from 400 consumers between the ages of 20 to 50 residing in Seoul and Kyonggi-do. Data analysis were conducted with SPSS 20 program on the reliability test, factor analysis, cluster analysis, t-test, ANOVA, and Duncan's multiple range test. Factor analyses were employed for the participation motivation of mountain climbing activities, risk reduction behavior and risk perception. Five factors were for the participation motivation of mountain climbing activities (health and fitness, external ostentation, achievement and excitement, improvement of climbing skills, and society). Five factors were for risk perception (fashionability loss and social risk, time and convenience loss, economic risk, performance risk, and psychological risk). Five factors were for the risk reduction behavior (interpersonal information sources use, marketer-dominated information sources use, professional information sources use, pre-purchase deliberation/observation/experience, and brand dependence). Three clusters were identified based on the motivation of outdoor activities (the affiliation/display, the health/internal informativeness and low motivation). The participation motivation for mountain climbing activities were varied. Manufacturers should increase efforts to develop products with good qualities at a reasonable cost as well as establish new marketing strategies since the risk of product performance and economic efficiency in the purchase of outdoor wear was a significant consumer perception.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.198-204
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• 2012

Generally, the clustering of sensor nodes in WSN is a useful mechanism that helps to cope with scalability problem and, if combined with network data aggregation, may increase the energy efficiency of the network. The Hierarchical clustering routing algorithm is a typical algorithm for enhancing overall energy efficiency of network, which selects cluster-head in order to send the aggregated data arriving from the node in cluster to a base station. In this paper, we propose the improved-LEACH that uses comparably simple and light-weighted policy to select cluster-head nodes, which results in reduction of the clustering overhead and overall power consumption of network. By using fine-grained power model, the simulation results show that i-LEACH can reduce clustering overhead compared with the well-known previous works such as LEACH. As result, i-LEACH algorithm and LEACH algorithm was compared, network power-consumption of i-LEACH algorithm was improved than LEACH algorithm with 25%, and network-traffic was improved 16%.

• Korean Journal of Environmental Agriculture
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• v.28 no.4
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• pp.333-339
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• 2009

Various cultural practices have been promoted as management options for enhancing soil quality and health. The use of soil management methods can cause changes in fertility by affecting soil chemical properties. This study aimed to evaluate interactions between soil chemical properties and soil management methods in protected cultivation, and to classify soil management methods that similarly affect soil chemical properties. Water-logging and irrigation reduced soil pH and available $P_2O_5$ content. Application of animal manures has a positive effect on levels of organic matter, Av.$P_2O_5$, K, Zn, and Cu. The electrical conductivites tened to be low in the application of organic amendments, including rice and wood residues. Deeper plowing caused a reduction in Ca content. Practicing soil nutrient-considering fertilization and fertigation did not exert an influence on nutrient element contents. In a cluster analysis of the soil management methods according to major nutrients, low similarities were found with deeper plowing and crop rotation with rice in comparison with other practices. In a cluster analysis by minor nutrient characteristics, crop rotation and application of animal manures and rice residues were linked at a high Ward's distance, while other practices were found to be relatively low distinct. Each soil management method has a similar or different effect on soil chemical properties. These results suggest the necessity of establishing limits and standards according to the effects of soil management methods on soil chemical properties for economic soil practices.

• The Korean Journal of Food And Nutrition
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• v.31 no.4
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• pp.565-570
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• 2018

The objective of this study is to analyze the characteristics of persons with a high sugar-intake ratio (%E) and factors affecting sugar-intake ratio with the Korea National Health and Nutrition Examination Survey ($6^{th}$ KNHANES). The sugar-intake ratio is the calories of sugar from processed food divided by the daily total intake of calories. In this research we used two statistical methods, the cluster analysis and one-way analysis of variance (ANOVA). Cluster analysis was used to classify groups of sugar-intake ratios. For analysis of factors affecting the sugar-intake ratio, we applied the ANOVA. Korean have about a 3.89% sugar-intake ratio from processed food per day. The demographic characteristics of people with higher sugar-intake ratios were found to be more women than men, younger men with less education, more people in the household, smaller height, weight waistline and body mass index (BMI). Also office worker, lower drinking frequency, not getting a hypertension, diabetes, hyperlipidemia, lower breakfast and dinnner frequency, not experiencing nutritional education, and not using nutritional labeling. For reducing intake sugar in what are called health-hazardable nutrients in the food sanitation act, it is necessary to educate the people with high sugar ratio who were identified in this study.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.135-140
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• 2007

Feature selection is a preprocessing technique commonly used on high dimensional data. Feature selection studies how to select a subset or list of attributes that are used to construct models describing data. Feature selection methods attempt to explore data's intrinsic properties by employing statistics or information theory. The recent developments have involved approaches like correlation method, dimensionality reduction and mutual information technique. This feature selection have become the focus of much research in areas of applications with massive and complex data sets. In this paper, we provide a feature selection method considering data characteristics and generalization capability. It provides a computational approach for feature selection based on fuzzy cluster analysis of its attribute values and its performance measures. And we apply it to the system for classifying computer virus and compared with heuristic method using the contrast concept. Experimental result shows the proposed approach can give a feature ranking, select the features, and improve the system performance.

• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.29-35
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• 2008

For efficient message passing of Parallel programs, it is required to schedule the involved two processes at the same time which are executed on different nodes, that is called 'co-scheduling' However, each node of cluster systems is built on top of general purpose multitasking OS. which autonomously manages local Processes. Thus it is not so easy to co-schedule two (or more) processes in such computing environment. Our work proposes a co-scheduling scheme for MPI-based parallel programs which exploits message exchange information between two parties. We implement the scheme on Linux cluster which requires slight kernel hacking and MPI library modification. The experiment with NPB parallel suite shows that our scheme results in 33-56% reduction in the execution time compared to the typical scheduling case. and especially better Performance in more communication-bound applications.

• Bulletin of the Korean Chemical Society
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• v.31 no.4
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• pp.934-940
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• 2010

The reduction of [$Os_3(CO)_{11}P(OMe)_3$] and subsequent ionic coupling of the reduced species with $[Ru({\eta}^5-C_5H_5)(CH_3CN)_3]^+$ resulted in the formation of [$Os_3(CO)_{11}P(OMe)_3(Ru({\eta}^5-C_5H_5))_2$] which can be converted to spiked tetrahedral cluster, [$HOs_3(CO)_{11}P(OMe)_3Ru_2({\eta}^5-C_5H_5)(C_5H_4)$] via the intramolecular hydrogen transfer. Due to the unavailability of a suitable single crystal, the PW91/SDD and LDA/SDD density functional methods were used to predict possible structures and the available spectroscopic information (IR, NMR) of [$Os_3(CO)_{11}P(OMe)_3(Ru({\eta}^5-C_5H_5))_2$]. The most probable geometry found by constrained search is the isomer (a2) in which the phosphite, $P(OMe)_3$, occupies an axial position on one of the two osmium atoms that is edge bridged by the $Ru(CO)_2({\eta}^5-C_5H_5)$ unit. By using the most probably geometry, the predicted infrared frequencies and $^1H$, $^{13}C$ and $^{31}P$ NMR chemical shifts of the compound are in the same range as the experimental values. For this type of complex, the LDA/SDD method is appropriate for IR predictions whereas the OPBE/IGLO-II method is appropriate for NMR predictions. The activation energy and reaction energy of the intramolecular hydrogen transfer coupled with the structural change of the transition metal framework were estimated at the PW91/SDD level to be 110.32 and -0.14 kcal/mol respectively.

• KIISE Transactions on Computing Practices
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• v.21 no.9
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• pp.587-595
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• 2015

Several fields of science have traditionally demanded large-scale workflow support, which requires thousands of central processing unit (CPU) cores. In order to support such large-scale scientific workflows, large-capacity cluster systems such as supercomputers are widely used. However, as users require a diversity of software packages and configurations, a system administrator has some trouble in making a service environment in real time. In this paper, we present a container-based cluster management platform and introduce an implementation case to minimize performance reduction and dynamically provide a distributed computing environment desired by users. This paper offers the following contributions. First, a container-based virtualization technology is assimilated with a resource and job management system to expand applicability to support large-scale scientific workflows. Second, an implementation case in which docker and HTCondor are interlocked is introduced. Lastly, docker and native performance comparison results using two widely known benchmark tools and Monte-Carlo simulation implemented using various programming languages are presented.

• Journal of the Society of Disaster Information
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• v.19 no.1
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• pp.184-194
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• 2023

Purpose: The purpose of this study is to identify the characteristics of C02 emissions by road before establishing a policy to reduce greenhouse gas emissions. Method: As for the analysis method, the traffic volume and speed of the road were estimated using the traffic Assignment model targeting 27 arterial road axes in Incheon Metropolitan City. And, after estimating CO2 emissions by road axis by applying this, the characteristics of each group were analyzed through cluster analysis. Result: As a result of cluster analysis using total CO2 emissions, CO2 emissions by truck vehicles, and the ratio of truck vehicle emissions to total carbon dioxide emissions, four clusters were classified. When examining the characteristics of each road included in each group, it was analyzed that the characteristics of each group appeared according to the level of impact by CO2 emissions and truck vehicles. Conclusion: It is judged that it is necessary to establish a plan in consideration of CO2 emission characteristics for road CO2 management for greenhouse gas reduction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.61-68
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• 2012

Recommender Systems have gained much popularity among researchers and is applied in a number of applications. The exponential growth of users and products poses some key challenges for recommender systems. Recommender Systems mostly suffer from scalability and accuracy. The accuracy of Recommender system is somehow inversely proportional to its scalability. In this paper we proposed a Context Aware Hybrid Recommender System using matrix reduction for Hybrid model and clustering technique for predication of item features. In our approach we used user item-feature rating, User Demographic information and context information i.e. specific time and day to improve scalability and accuracy. Our Algorithm produce better results because we reduce the dimension of items features matrix by using different reduction techniques and use user demographic information, construct context aware hybrid user model, cluster the similar user offline, find the nearest neighbors, predict the item features and recommend the Top N- items.