• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1353-1359
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• 2010

Basic objective in cluster analysis is to discover natural groupings of items. In general, clustering is conducted based on some similarity (or dissimilarity) matrix or the original input data. Various measures of similarities between objects are developed. In this paper, we consider a clustering of huge categorical real data set which shows the aspects of time-location-activity of Korean people. Some useful similarity measure for the data set, are developed and adopted for the categorical variables. Hierarchical and nonhierarchical clustering method are applied for the considered data set which is huge and consists of many categorical variables.

• Journal of KIISE:Information Networking
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• v.32 no.2
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• pp.190-202
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• 2005

Mobile nodes are change their point of attachment dynamically such as networks deployed in aircraft, boats, trains, and cars. These mobile nodes move together and share the same nobility properties. Therefore, this paper addresses an efficient location management scheme based on collaboration of mobile nodes to a mobile network. We propose a scheme that combines multicast routing with Mobile IPv6 to support mobile network in the internet. This paper also tests the usefulness of our proposed method using analytical models and compares the results with our scheme and HMIPv6(Hierarchical Mobile IPv6) and Network Mobility(NEMO).

• Journal of Internet Computing and Services
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• v.11 no.1
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• pp.15-20
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• 2010

In this paper, we propose a novel approach to reduce spatial query access latency and energy consumption by leveraging results from nearby peers in wireless broadcast environments. We propose a three-tier Hierarchical Location-Based Sequential access index, called HLBS, which provides selective tuning (pruning and searching entries) without pointers using a linear accessing structure based on the location of each data object. The HLBS saves search cost and index overhead, since the small index size with a sequential index structure results in low access latency overhead and facilitates efficient searches for sequential-access media (wireless channels with data broadcast). Comprehensive experiments illustrate that the proposed scheme is more efficient than the previous techniques in terms of energy consumption.

• Structural Engineering and Mechanics
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• v.70 no.4
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• pp.457-466
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• 2019

This study develops a damage detection method based on neural networks. The performance of the method is numerically and experimentally verified using a three-story shear building model. The framework is mainly composed of two hierarchical stages to identify damage location and extent using artificial neural network (ANN). The normalized damage signature index, that is a normalized ratio of the changes in the natural frequency and mode shape caused by the damage, is used to identify the damage location. The modal parameters extracted from the numerically developed structure for multiple damage scenarios are used to train the ANN. The positive alarm from the first stage of damage detection activates the second stage of ANN to assess the damage extent. The difference in mode shape vectors between the intact and damaged structures is used to determine the extent of the related damage. The entire procedure is verified using laboratory experiments. The damage is artificially modeled by replacing the column element with a narrow section, and a stochastic subspace identification method is used to identify the modal parameters. The results verify that the proposed method can accurately detect the damage location and extent.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.2
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• pp.82-90
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• 2015

Location selection of distribution centers is a crucial task for logistics operators and key decision makers of an organization. This is a multi-criteria decision making (MCDM) process which includes both quantitative and qualitative criteria. In order to propose an optimized location selection model, this research suggests a hierarchical group of evaluation criteria : 5 major criteria with 15 sub-criteria. The MCDM approach presented in this research, by integrating Grey Relational Analysis (GRA) with Analytic Hierarchy Process (AHP), tends to rectify the overall quality and uncertainty of the values of evaluation criteria. An example of a location selection case in Korea is illustrated in this study to show the effectiveness of this method.

• Journal of Korea Multimedia Society
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• v.19 no.1
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• pp.30-40
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• 2016

Recently, the competition among global IT companies for the market occupancy of the IoT(Internet of Things) is fierce. Internet of Things are all the things and people around the world connected to the Internet, and it is becoming more and more intelligent. In addition, for the purpose of providing users with a customized services to variety of context-awareness, IoT platform and related research have been active area. In this paper, we analyze third party instant messengers of Windows 8 Style UI and propose a digital forensic methodology. And, we are well aware of the Android-based map and navigation applications. What we want to show is GPS information analysis by using the R. In addition, we propose a structured data analysis applying the hierarchical clustering model using GPS data in the digital forensics modules. The proposed model is expected to help support the IOT services and efficient criminal investigation process.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.803-814
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• 2016

Recent times have seen an exponential increase in the amount of spatial data, which is in many cases associated with temporal data. Recent advances in computer technology and computation of hierarchical Bayesian models have enabled to analyze complex spatio-temporal data. Our work aims at modeling data of daily average nitrogen dioxide (NO2) levels obtained from 25 air monitoring sites in Seoul between 2003 and 2010. We considered an independent Gaussian process model and an auto-regressive model and carried out estimation within a hierarchical Bayesian framework with Markov chain Monte Carlo techniques. A Gaussian predictive process approximation has shown the better prediction performance rather than a Hierarchical auto-regressive model for the illustrative NO2 concentration levels at any unmonitored location.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.862-869
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• 2013

In order to support a cell-independent traffic asymmetry, the conventional TDD system cannot avoid crossed time slot (CTS) interference. Moreover, the TDD/FDD hierarchical overlay cellular systems is taken into account as a generally accepted cell model in a heterogeneous radio environment. In this paper, we propose an interference resolving radio resource allocation technique in a TDD-OFDMA cellular system that overlays a FDD-CDMA cell. In our proposed scheme, we exploit under-used FDD-CDMA uplink resource by TDD mobile abiding by a region based time slot(TS) allocation which in turn mitigates CTS interference considerably. It is demonstrated that combined with under-used resource utilization scheme based on mobile's location, the proposed technique can reduce CTS interference considerably and support the asymmetric traffic in TDD system.

• Communications for Statistical Applications and Methods
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• v.25 no.3
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• pp.263-274
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• 2018

One of the common issues in large dataset analyses is to detect and construct homogeneous groups of objects in those datasets. This is typically done by some form of clustering technique. In this study, we present a divisive hierarchical clustering method for two monothetic characteristics of histogram data. Unlike classical data points, a histogram has internal variation of itself as well as location information. However, to find the optimal bipartition, existing divisive monothetic clustering methods for histogram data consider only location information as a monothetic characteristic and they cannot distinguish histograms with the same location but different internal variations. Thus, a divisive clustering method considering both location and internal variation of histograms is proposed in this study. The method has an advantage in interpreting clustering outcomes by providing binary questions for each split. The proposed clustering method is verified through a simulation study and applied to a large U.S. house property value dataset.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.55-61
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• 2005

In this paper we propose a distributed location management scheme to reduce the bottleneck problem of HLR in Large Mobile Networks (LMN). Using analytical modeling and numerical simulation, we show that replicating location information is both appropriate and efficient for small mobile networks. Then, we extend the scheme in a hierarchical environment to reduce the overhead traffic and scale to LMN. In numerical results, we show the superiority of our scheme compared to the current IS-95 standard scheme in IMT-2000 networks.