• Structural Engineering and Mechanics
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• v.72 no.4
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• pp.469-477
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• 2019

The understanding of tool-rock interaction mechanism is of high essence for improving the rock breaking efficiency and optimizing the drilling parameters in mechanical rock breaking. In this study, the tool-rock interaction models of indentation and cutting are carried out by employing the discrete element method (DEM) to examine the rock failure modes of various brittleness rocks and critical indentation and cutting depths of the ductile to brittle failure mode transition. The results show that the cluster size and inter-cluster to intra-cluster bond strength ratio are the key factors which influence the UCS magnitude and the UCS to BTS ratio. The UCS to BTS strength ratio can be increased to a more realistic value using clustered rock model so that the characteristics of real rocks can be better represented. The critical indentation and cutting depth decrease with the brittleness of rock increases and the decreasing rate reduces dramatically against the brittleness value. This effort may lead to a better understanding of rock breaking mechanisms in mechanical excavation, and may contribute to the improvement in the design of rock excavation machines and the related parameters determination.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2737-2753
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• 2013

In this study, we propose a novel salient object detection strategy based on regional contrast and relative spatial compactness. Our algorithm consists of four basic steps. First, we learn color names offline using the probabilistic latent semantic analysis (PLSA) model to find the mapping between basic color names and pixel values. The color names can be used for image segmentation and region description. Second, image pixels are assigned to special color names according to their values, forming different color clusters. The saliency measure for every cluster is evaluated by its spatial compactness relative to other clusters rather than by the intra variance of the cluster alone. Third, every cluster is divided into local regions that are described with color name descriptors. The regional contrast is evaluated by computing the color distance between different regions in the entire image. Last, the final saliency map is constructed by incorporating the color cluster's spatial compactness measure and the corresponding regional contrast. Experiments show that our algorithm outperforms several existing salient object detection methods with higher precision and better recall rates when evaluated using public datasets.

• Journal of the Korean Home Economics Association
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• v.34 no.4
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• pp.295-308
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• 1996

The purpose of this study was to explore how family types clustered based on the intra system dynamics explained housewive's managerial behavior. The data were collected by means of questionnaire distributed to a stratified sample of 544 housewives in Seoul who lived with husband and children. The questionnaires included FACES Ⅱ and Ⅲ, Communication Scale, Managerial behavior Scale and Life Satisfaction Scale. Frequency, percentile, mean, correlation, factor analysis, cluster analysis, One-way ANOVA with Scheffe test, and multiple regression were used to analyze the data. This study had resulted in three major findings. The first was that families were clustered by four types, named structed-separated family, flexible-connected family, change oriented emashed, and rigid-disengaed family. The second finding was that a difference in managerial behavior was found among four types of family. Housewives whose family were more connected each other and adapted more easily to changing situations showed better managerial behavior. The last one was that the managerial behavior of housewives was better explained by family types than socio-demographic variables. The recommendations for future research and the better ways to lead effective managerial behavior were suggested.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.41.1-41.1
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• 2012

Interactions between the galactic interstellar medium (ISM) and the intra cluster medium (ICM) are believed to be one of the main processes affecting galaxy evolution in cluster environments. The aim of our research is to study the molecular gas properties of a galaxy under the ICM pressure in the cluster environment. It has been well known that cluster galaxies are deficient in atomic hydrogen gas (HI gas) compared to their field counterparts and now there is much evidence that low density ISM is being removed by ram pressure due to ICM wind. Meanwhile, no significant molecular gas deficiency of the cluster galaxy population has been found yet they show overall lower star formation rate than galaxies in the field, and it is still puzzling how the star formation could decrease without stripping of dense molecular gas. To address this issue, we probe the detailed molecular gas properties of NGC 4402, located near the cluster center, as part of a study of four spiral galaxies in the Virgo Cluster. NGC 4402 is well known undergoing ram pressure stripping with a truncated HI disk($D_{HI}/D_{opt}$ - 0.75 and only 36% of HI gas compare to field galaxies of a similar size) and a disturbed gas morphology. Comparing the high resolution 12CO and 13CO data of NGC 4402 from the Sub Millimeter Array (SMA) with existing other wavelength data, we probe the spatial distribution and a physical condition of molecular gas under strong ICM pressure. We discuss the star formation activity might have been altered and hence how the global color of NGC4402 would change in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3494-3510
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• 2019

Wireless sensor networks encounter energy saving as a major issue as the sensor nodes having no rechargeable batteries and also the resources are limited. Clustering of sensors play a pivotal role in energy saving of the deployed sensor nodes. However, in the cluster based wireless sensor network, the cluster heads tend to consume more energy for additional functions such as reception of data, aggregation and transmission of the received data to the base station. So, careful selection of cluster head and formation of cluster plays vital role in energy conservation and enhancement of lifetime of the wireless sensor networks. This study proposes a new mutation chemical reaction optimization (MCRO) which is an algorithm based energy efficient clustering protocol termed as MCRO-ECP, for wireless sensor networks. The proposed protocol is extensively developed with effective methods such as potential energy function and molecular structure encoding for cluster head selection and cluster formation. While developing potential functions for energy conservation, the following parameters are taken into account: neighbor node distance, base station distance, ratio of energy, intra-cluster distance, and CH node degree to make the MCRO-ECP protocol to be potential energy conserver. The proposed protocol is studied extensively and tested elaborately on NS2.35 Simulator under various senarios like varying the number of sensor nodes and CHs. A comparative study between the simulation results derived from the proposed MCRO-ECP protocol and the results of the already existing protocol, shows that MCRO-ECP protocol produces significantly better results in energy conservation, increase network life time, packets received by the BS and the convergence rate.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.80-90
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• 2002

Clustering is a useful technique for grouping data points such that points within a single group/cluster have similar characteristics. Many clustering algorithms have been developed and used in engineering applications including pattern recognition and image processing etc. Recently, it has drawn increasing attention as one of important techniques in data mining. However, clustering algorithms such as K-means and Fuzzy C-means suffer from difficulties. Those are the needs to determine the number of clusters apriori and the clustering results depending on the initial set of clusters which fails to gain desirable results. In this paper, we propose a new clustering algorithm, which solves mentioned problems. In our method we use evolutionary algorithm to solve the local optima problem that clustering converges to an undesirable state starting with an inappropriate set of clusters. We also adopt a new measure that represents how well data are clustered. The measure is determined in terms of both intra-cluster dispersion and inter-cluster separability. Using the measure, in our method the number of clusters is automatically determined as the result of optimization process. And also, we combine heuristic that is problem-specific knowledge with a evolutionary algorithm to speed evolutionary algorithm search. We have experimented our algorithm with several sets of multi-dimensional data and it has been shown that one algorithm outperforms the existing algorithms.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.393-400
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• 2011

In a large scale survey, cluster sampling design in which a set of observation units called clusters are selected is often used to satisfy practical restrictions on time and cost. Especially, a two stage cluster sampling design is preferred when a strong intra-class correlation exists among observation units. The sample Primary Sampling Unit(PSU) and Secondary Sampling Unit(SSU) size for a two stage cluster sample is determined by the survey cost and precision of the estimator calculated. For this study, we derive the optimal sample PSU and SSU size when the population SSU size across the PSU are di erent by extending the result obtained under the assumption that all PSU have the same number of SSU. The results on the sample size are then applied to the $4^{th}$ Korea Hospital Discharge results and is compared to the conventional method. We also propose the optimal sample SSU (discharged patients) size for the $7^{th}$ Korea Hospital Discharge Survey.

• Communications for Statistical Applications and Methods
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• v.23 no.5
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• pp.433-444
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• 2016

When we observe binary responses in a cluster (such as rat lab-subjects), they are usually correlated to each other. In clustered binomial counts, the independence assumption is violated and we encounter an extra-variation. In the presence of extra-variation, the ordinary statistical analyses of binomial data are inappropriate to apply. In testing the homogeneity of proportions between several treatment groups, the classical Pearson chi-squared test has a severe flaw in the control of Type I error rates. We focus on modifying the chi-squared statistic by incorporating variance inflation factors. We suggest a method to adjust data in terms of dispersion estimate based on a quasi-likelihood model. We explain the testing procedure via an illustrative example as well as compare the performance of a modified chi-squared test with competitive statistics through a Monte Carlo study.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.6
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• pp.1068-1073
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• 1987

Traditional circuit partitioning algorithm using the cluster development method, which is suitable for such applications as single chip floor planning or multiple layer PCB system placement, where the clusters are formed so that inter-cluster nets are localized within the I/O connector pins, may not be appropriate for the functiona block placement in truly 3-D electronic modules. 3-D hybrid IC is one such example where the inter-layer routing as well as the intra-layer routing can be maximally incorporated to reduce the overall circuit size, cooling requirements and to improve the speed performance. In this paper, we propose a new algorithm called MBE(Minimum Box Embedding) for the layer assignment of each functional block in 3-D hybrid IC design. The sequence of MBE is as follows` i) force-directed relaxation in 3-D space, ii) exhaustive search for the optimal orientation of the slicing plane and iii) layer assignment. The algorithm is first explaines for a 2-D reduced problem, and then extended for 3-D applications. An example result for a circuit consisting of 80 blocks has been shown.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.86-96
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• 2015

In mobile communication, mobile services [MSs] (e.g., phone calls, short/multimedia messages, and Internet data) incur a cost to both mobile users (MUs) and mobile service providers (MSPs). The proposed model MobPrice consists of dynamic data pricing schemes for mobile communication in order to achieve optimal usage of MSs at minimal prices. MobPrice inspires MUs to subscribe MSs with flexibility of data sharing and intra-peer exchanges, thereby reducing overall cost. The main contributions of MobPrice are three-fold. First, it proposes a novel k-level data-pricing (kDP) scheme for MSs. Second, it extends the kDP scheme with the notion of service-sharing-based pricing schemes to a collaborative peer-to-peer data-pricing (pDP) scheme and a cluster-based data-pricing (cDP) scheme to incorporate the notion of 'cluster' (made up of two or more MUs) in mobile communication. Third, our performance study shows that the proposed schemes are indeed effective in maximizing MS subscriptions and minimizing MS's price/user.