• Communications for Statistical Applications and Methods
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• v.20 no.2
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• pp.97-105
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• 2013

Stratification (among other applications) is a popular technique used in survey practice to improve the accuracy of estimators. Its full potential benefit can be gained by the effective use of auxiliary variables in stratification related to survey variables. This paper focuses on the problem of stratum formation when multiple stratification variables are available. We first review a variance reduction strategy in the case of univariate stratification. We then discuss its use for multivariate situations in convenient and efficient ways using three methods: compromised measures of size, principal components analysis and a K-means clustering algorithm. We also consider three types of compromising factors to data when using these three methods. Finally, we compare their efficiency using data from MU281 Swedish municipality population.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.3
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• pp.451-467
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• 1997

Tabu search (TS) is a useful strategy that has been successfully applied to a number of complex combinatorial optimization problems. By guiding the search using flexible memory processes and accepting disimproved solutions at some iterations, TS helps alleviate the risk of being trapped at a local optimum. In this article, we propose TS-based heuristics for solving a class of clustering problems, and compare the relative performances of the TS-based heuristic and the simulated annealing (SA) algorithm. Computational experiments show that the TS-based heuristic with a long-term memory offers a higher possibility of finding a better solution, while the TS-based heuristic without a long-term memory performs better than the others in terms of the combined measure of solution quality and computing effort required.

• Proceedings of the KSRS Conference
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• v.1
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• pp.188-190
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• 2006

A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The local segmentor of the first stage performs regiongrowing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. This stage uses a sliding window strategy with boundary blocking to alleviate a computational problem in computer memory for an enormous data. The global segmentor of the second stage has not spatial constraints for merging to classify the segments resulting from the previous stage. The experimental results show that the new approach proposed in this study efficiently performs the segmentation for the images of very large size and an extensive number of bands

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.78.3-78.3
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• 2017

The positions of galaxies in comoving Cartesian space varies under different cosmological parameter choices, inducing a redshift-dependent scaling in the galaxy distribution. The shape of the two-point correlation of galaxies exhibits a significant redshift evolution when the galaxy sample is analyzed under a cosmology differing from the true, simulated one. In our previous works, we can made use of this geometrical distortion to constrain the values of cosmological parameters governing the expansion history of the universe. This current work is a continuation of our previous works as a strategy to constrain cosmological parameters using redshift-invariant physical quantities. We now aim to understand the redshift evolution of the full shape of the small scale, anisotropic galaxy clustering and give a firmer theoretical footing to our previous works.

• Journal of Information Technology Applications and Management
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• v.23 no.1
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• pp.33-43
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• 2016

Along with the spread of digital music and recent growth in the digital music industry, the demands for music recommender are increasing. These days, listeners have increasingly preferred to digital real-time streamlining and downloading to listen to music because it is convenient and affordable for the listeners to do that. We use Bayesian learning through weight of listener's prefered music site such as Melon, Billboard, Bugs Music, Soribada, and Gini. We reflect most popular current songs across all genres and styles for music recommender system using user profile. It is necessary for us to make the task of preprocessing of clustering the preference with weight of listener's preferred music site with popular music charts. We evaluated the proposed system on the data set of music sites to measure its performance. We reported some of the experimental result, which is better performance than the previous system.

• Journal of the Korean Society for Library and Information Science
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• v.35 no.4
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• pp.141-160
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• 2001

This study is to develop a new system for conducting the information retrieval automatically. The system in this study is programmed by Delphi 4.0(PASCAL) and consists of automatic indexing, clustering technique, establishing and expressing term hierarchic relation, and automatic information retrieval technique. Thus this browser system can automatically control all the processes of information searching such as representation, generation and extension of queries and construction of searching strategy and feedback searching.

• Journal of information and communication convergence engineering
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• v.17 no.4
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• pp.261-266
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• 2019

Differential evolution (DE) is an uncomplicated and serviceable developmental algorithm. Nevertheless, its execution depends on strategies and regulating structures. The combination of several strategies between subpopulations helps to stabilize the probing on DE. In this paper, we propose a unique k-mean soft island model DE(KSDE) algorithm which maintains population diversity through soft island model (SIM). A combination of various approaches, called KSDE, intended for migrating the subpopulation information through SIM is developed in this study. First, the population is divided into k subpopulations using the k-means clustering algorithm. Second, the mutation pattern is singled randomly from a strategy pool. Third, the subpopulation information is migrated using SIM. The performance of KSDE was analyzed using 13 benchmark indices and compared with those of high-technology DE variants. The results demonstrate the efficiency and suitability of the KSDE system, and confirm that KSDE is a cost-effective algorithm compared with four other DE algorithms.

• The Mathematical Education
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• v.29 no.1
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• pp.21-34
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• 1990

This is a study on an instruction of estimation for error correction in the calculation with a computer or a calculator. The aim of this study is to survey a new aspect of calaulation teaching and the teaching strategy of estimation and finally to frame a new curriculum model of estimation instruction. This research required a year and the outcomes of the research can be listed as follows: 1. Social utilities of estimation were made clear, and a new trend of calculation teaching related to estimation instruction was shown. 2. The definition of estimation was given and actual examples of conducting an estimation among pupils in lower grades were given for them to have abundant experiences. 3. The ways of finding estimating values in fraction and decimal fraction were presented for the pupils to be able to conduct an estimation. 4. The following contents were given as a basic strategy for estimation. 1) Front-end strategy 2) Clustering strategy 3) Rounding strategy 4) Compatible numbers strategy 5) Special numbers strategy 5. In an instuction of estimation the meaning, method. and process of calculation and calculating algorithm were reviewed for the cultivation of children's creativity through promoting their basic skill, mathematical thinking and problem-solving ability. 6. The following contents were also covered as an estimation strategy for measurement 1) Calculating the sense of quantity on the size of unit. 2) Estimating the total quantity by frequent repetition of unit quantity. 3) Estimating the length and the volume by weighing. 4) Estimating unknown quantity based on the quatity already known. 5) Estimating the area by means of equivalent area transformation. 7. The ways of instructing mental computation were presented. 8. Reviews were made on the curricular and the textbook contents concerning estimation instructions both in Korea and Japan. and a new model of curriculum was devised with reference to estimation instruction data of the United States.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.664-677
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• 2011

In order to control interference and improve spectrum efficiency in the femtocell and macrocell overlaid system (FMOS), we propose a joint frequency bandwidth dynamic division, clustering and power control algorithm (JFCPA) for orthogonal-frequency-division-multiple access-based downlink FMOS. The overall system bandwidth is divided into three bands, and the macro-cellular coverage is divided into two areas according to the intensity of the interference from the macro base station to the femtocells, which are dynamically determined by using the JFCPA. A cluster is taken as the unit for frequency reuse among femtocells. We map the problem of clustering to the MAX k-CUT problem with the aim of eliminating the inter-femtocell collision interference, which is solved by a graph-based heuristic algorithm. Frequency bandwidth sharing or splitting between the femtocell tier and the macrocell tier is determined by a step-migration-algorithm-based power control. Simulations conducted to demonstrate the effectiveness of our proposed algorithm showed the frequency-reuse probability of the FMOS reuse band above 97.6% and at least 70% of the frequency bandwidth available for the macrocell tier, which means that the co-tier and the cross-tier interference were effectively controlled. Thus, high spectrum efficiency was achieved. The simulation results also clarified that the planning of frequency resource allocation in FMOS should take into account both the spatial density of femtocells and the interference suffered by them. Statistical results from our simulations also provide guidelines for actual FMOS planning.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.862-870
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• 2011

In this paper, we introduce a new topology of Radial Basis Function-based Polynomial Neural Networks (RPNN) that is based on a genetically optimized multi-layer perceptron with Radial Polynomial Neurons (RPNs). This study offers a comprehensive design methodology involving mechanisms of optimization algorithms, especially Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization (PSO) algorithms. In contrast to the typical architectures encountered in Polynomial Neural Networks (PNNs), our main objective is to develop a design strategy of RPNNs as follows : (a) The architecture of the proposed network consists of Radial Polynomial Neurons (RPNs). In here, the RPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Fuzzy C-Means (FCM) clustering method. The RPN dwells on the concepts of a collection of radial basis function and the function-based nonlinear (polynomial) processing. (b) The PSO-based design procedure being applied at each layer of RPNN leads to the selection of preferred nodes of the network (RPNs) whose local characteristics (such as the number of input variables, a collection of the specific subset of input variables, the order of the polynomial, and the number of clusters as well as a fuzzification coefficient in the FCM clustering) can be easily adjusted. The performance of the RPNN is quantified through the experimentation where we use a number of modeling benchmarks - NOx emission process data of gas turbine power plant and learning machine data(Automobile Miles Per Gallon Data) already experimented with in fuzzy or neurofuzzy modeling. A comparative analysis reveals that the proposed RPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.