• Wind and Structures
• /
• v.7 no.3
• /
• pp.159-172
• /
• 2004

The study of how buildings affect wind flow is an important part of the research being conducted on urban climate and urban air quality. NJU-UCFM, a standard $k-{\varepsilon}$ turbulence closure model, is presented and is used to simulate how the following affect wind flow characteristics: (1) an isolated building, (2) urban canyons, (3) an irregular shaped building cluster, and (4) a real urban neighborhood. The numerical results are compared with previous researchers' results and with wind tunnel experiment results. It is demonstrated that the geometries and the distribution of urban buildings affect airflow greatly, and some examples of this include a changing of the vortices behind buildings and a "channeling effect". Although the mean air flows are well simulated by the standard $k-{\varepsilon}$ models, it is important to pay attention to certain discrepancies when results from the standard $k-{\varepsilon}$ models are used in design or policy decisions: The standard $k-{\varepsilon}$ model may overestimate the turbulence energy near the frontal side of buildings, may underestimate the range of high turbulence energy in urban areas, and may omit some important information (such as the reverse air flows above the building roofs). In ideal inflow conditions, the effects of the heights of buildings may be underestimated, when compared with field observations.

• The Bulletin of The Korean Astronomical Society
• /
• v.41 no.2
• /
• pp.63.2-63.2
• /
• 2016

One of the well-known problems in the lambda cold dark matter (${\Lambda}CDM$) models is a missing satellite problem. The slope of the mass function of low mass galaxies predicted by ${\Lambda}CDM$ models is much steeper than that based on the luminosity function of dwarf galaxies in the local universe. This implies that the model prediction is an overestimate of low mass galaxies, or that the current census of dwarf galaxies in the local universe may be an underestimate of dwarf galaxies. Previous studies of galaxy luminosity functions to address this problem are based mostly on the sample of galaxies brighter than Mv ~ -10 in the nearby galaxies. In this study we try to search for ultra-faint galaxies (UFDs), which are much fainter than those in the previous studies. We use multi-field HST ACS images of M60 in the archive. M60 is a giant elliptical galaxy located in the east part of the Virgo cluster, and hosts a large population of globular clusters and UCDs. Little is known about the dwarf galaxies in this galaxy. UFDs are much fainter, much smaller, and have lower surface brightness than normal dwarf galaxies so HST images of massive galaxies are an ideal resource. We present preliminary results of this search.

• The Bulletin of The Korean Astronomical Society
• /
• v.35 no.2
• /
• pp.31.2-31.2
• /
• 2010

We have performed a spectroscopic study of globular cluster (GC) system associated with the Virgo cD galaxy M87 using the Subaru/FOCAS MOS mode. We derive ages, metallicities and abundance ratios from the GC spectra using Simple Stellar Population (SSP) models. The metallicity distribution function (MDF) obtained empirically based on Milky Way GCs is consistent with the MDF derived from SSP models. A comparison with a meta-analysis using literature data sample of 15 other GC systems shows good agreement with our results. The properties of GCs acquired from the spectra will be used to test the recent theoretical prediction of a significant inflection along the colour-metallicity relations (Yoon et al. 2006). If confirmed, the non-linearity of the relations would shed new light on the interpretation of the GC colour bimodality. The robustness of our results is being tested against the choice of a SSP model, measurement errors and sample selection towards the goal of better understanding the formation history of GCs and host galaxy.

• Health Policy and Management
• /
• v.12 no.2
• /
• pp.1-22
• /
• 2002

Since the early 1980s scholars have applied latent structure and other type of finite mixture models from various academic fields. Although the merits of finite mixture model are well documented, the attempt to apply the mixture model to medical service has been relatively rare. The researchers aim to try to fill this gap by introducing finite mixture model and segmenting inpatients DB from one general hospital. In section 2 finite mixture models are compared with clustering, chi-square analysis, and discriminant analysis based on Wedel and Kamakura(2000)'s segmentation methodology schemata. The mixture model shows the optimal segments number and fuzzy classification for each observation by EM(expectation-maximization algorism). The finite mixture model is to unfix the sample, to Identify the groups, and to estimate the parameters of the density function underlying the observed data within each group. In section 3 and 4 we illustrate results of segmenting 4510 patients data including menial and ratio scales. And then, we show AHP can be identify the attractiveness of each segment, in which the decision maker can select the best target segment.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.17 no.4
• /
• pp.95-103
• /
• 2008

Diamond core drills are applied to drill difficult-to-cut materials. This paper proposes basic understanding of ceramic drilling mechanics and characteristics of main factors affecting tool life, tool wear, cutting force, and chipping thickness. In contrast to conventional drilling, the core drilling process make deep grooves on the workpiece. One difficulty of it is the evacuation of chips from the drilled groove. As the drilling depth increases, an increased amount of chips tend to cluster together and clog the groove. Eventually severe wear develops and diamond grits are separated from the drill body. To relieve the clogging problem and to evacuate chips from the groove easily, the helical drilling process is applied for the core drilling process. To analyze drilling characteristics and derive optimal drilling conditions, tool life, tool wear, cutting force, and chipping thickness are quantified through the monitoring system and the Taguchi method. Mathematical models for the tool life and chipping thickness are derived from the response surface method. Optimal drilling database has been constructed through the experimental models.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.23 no.2
• /
• pp.1-16
• /
• 1995

This study was carried out to suggest method which can be used to improve competitive power of theme parks. The characteristics of Hybrid Conjoint Analysis were described and its usefulness for identification of specific types of service theme parks have to provide was tested "Lotte World, " "Seoul land," and "farmland" were selected as study areas, and .7 attributes with 3 levels were utilized for analyses. Master design with 81 profiles was constructed to meet the requirement of ′Compromise Plan 1,′and data was collected by in-personal interviews on the study areas. Respondents were grouped by cluster analysis, and their characteristics were analyzed by discriminant analysis. Then, part-worth of each attribute . was estimated by stagewise estimation model Calibrated model of each group did not show part-worths of attributes clearly because both main effects and 2-way interaction effects were included in the models. Therefore, calibrated models′ coefficients were used to calculate utilities of all possible combinations of attributes levels. The results showed that managers of theme parks have several options for providing a new service: the combination of attribute levels with the highest utility is they however, they can choose the other combinations with next highest utlities is they can not afford it. Several suggestions were described to cope with the problems when Hybrid Conjoint Analysis is applied to landscape architectural study.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.6
• /
• pp.2824-2837
• /
• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• Journal of Korea Multimedia Society
• /
• v.25 no.6
• /
• pp.886-893
• /
• 2022

Outlier detection refers to the task of detecting data that deviate significantly from the normal data distribution. Most outlier detection methods compute an outlier score which indicates the degree to which a data sample deviates from normal. However, setting a threshold for an outlier score to determine if a data sample is outlier or normal is not trivial. In this paper, we propose a binary prediction method for outlier detection based on spectral clustering and one-class SVM ensemble. Given training data consisting of normal data samples, a clustering method is performed to find clusters in the training data, and the ensemble of one-class SVM models trained on each cluster finds the boundaries of the normal data. We show how to obtain a threshold for transforming outlier scores computed from the ensemble of one-class SVM models into binary predictive values. Experimental results with high dimensional text data show that the proposed method can be effectively applied to high dimensional data, especially when the normal training data consists of different shapes and densities of clusters.

• The Bulletin of The Korean Astronomical Society
• /
• v.45 no.1
• /
• pp.31.3-32
• /
• 2020

The surface brightness fluctuation (SBF) is one of the most crucial distance indicators for unresolved stellar systems at large distances. Here, we present an evolutionary population synthesis model of the surface brightness fluctuation (SBF) for normal and He-enriched simple stellar populations (SSPs). Our SBF model for the normal-He population agrees well with other existing models, but the He-rich populations bring about a substantial change in the SBF of SSPs. Our normal-He SBF model well reproduces the observed SBFs of the Milky Way globular clusters, but the SBFs of early-type galaxies in the Virgo Cluster are placed between the normal-He and He-rich SBF models. We show that the SBF-based distance estimation would be affected by up to a 10-20% level in I- and near-IR bands at given colors. Finally, we propose that when combined with independent metallicity and age indicators such as Mg2 and H��, the UV and optical SBFs can readily detect underlying He-rich populations in unresolved stellar systems. Given the degree of the SBF variation resulting from the population difference, we suggest that the distance measurement before the proper in-depth analysis of stellar populations should be done with great caution.

• Journal of Korea Port Economic Association
• /
• v.37 no.1
• /
• pp.31-70
• /
• 2021

The purpose of this paper is to measure the clustering change and analyze empirical results, and choose the clustering ports for Busan, Incheon, and Gwangyang ports by using Hierarchical clustering(single, complete, average, and centroid), Silhouette, and 2SCE[the Second Stage(Type II) cross-efficiency] matrix clustering models on Asian container ports over the period 2009-2018. The models have chosen number of cranes, depth, birth length, and total area as inputs and container TEU as output. The main empirical results are as follows. First, ranking order according to the efficiency increasing ratio during the 10 years analysis shows Silhouette(0.4052 up), Hierarchical clustering(0.3097 up), and 2SCE(0.1057 up). Second, according to empirical verification of the Silhouette and 2SCE models, 3 Korean ports should be clustered with ports like Busan Port[ Dubai, Hong Kong, and Tanjung Priok], and Incheon Port and Gwangyang Port are required to cluster with most ports. Third, in terms of the ASEAN, it would be good to cluster like Busan (Singapore), Incheon Port (Tanjung Priok, Tanjung Perak, Manila, Tanjung Pelpas, Leam Chanbang, and Bangkok), and Gwangyang Port(Tanjung Priok, Tanjung Perak, Port Kang, Tanjung Pelpas, Leam Chanbang, and Bangkok). Third, Wilcoxon's signed-ranks test of models shows that all P values are significant at an average level of 0.852. It means that the average efficiency figures and ranking orders of the models are matched each other. The policy implication is that port policy makers and port operation managers should select benchmarking ports by introducing the models used in this study into the clustering of ports, compare and analyze the port development and operation plans of their ports, and introduce and implement the parts which required benchmarking quickly.