• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.469-470
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• 2007

We used the non-reversible hierarchical scheme and the fixed channel assignment scheme for the strategy of call processing at hierarchical cellular system. We analyzed the performance of system according to the number of channel assigned at macrocell out of the usable channel at global system. Microcell is assigned the channel excepted the channel assigned at macrocell. The more the channel was aigned at macrocell, the more decreased the blocking probability became. The blocking probability of microcell had similar trend.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.471-472
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• 2007

At this paper, we use non reversible hierarchical scheme and fixed channel assignment scheme at the hierarchical cellular system with macrocell and microcell for the call process strategy Using this strategy, we analyze the system performance in accordance with the various dwell time at each cell. In this simulation result, the more the dwell time is small at microcell, the more the blocking probability and the handover failure probability is low.

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.349-359
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• 2015

We study a semiparametric Bayesian approach to small area estimation under a nested error linear regression model with area level covariate subject to measurement error. Consideration is given to radial basis functions for the regression spline and knots on a grid of equally spaced sample quantiles of covariate with measurement errors in the nested error linear regression model setup. We conduct a hierarchical Bayesian structural measurement error model for small areas and prove the propriety of the joint posterior based on a given hierarchical Bayesian framework since some priors are defined non-informative improper priors that uses Markov Chain Monte Carlo methods to fit it. Our methodology is illustrated using numerical examples to compare possible models based on model adequacy criteria; in addition, analysis is conducted based on real data.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.79-86
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• 2019

For decades, in-memory data structures have been designed for DRAM-based main memory that provides symmetric read/write performances and has no limited write endurance. However, such data structures provide sub-optimal performance for NVM as it has different characteristics to DRAM. With this motivation, we rethink a conventional red-black tree in terms of its efficacy under NVM settings. The original red-black tree constantly rebalances sub-trees so as to export fast access time over dataset, but it inevitably increases the write traffic, adversely affecting the performance for NVM with a long write latency and limited endurance. To resolve this problem, we present a variant of the red-black tree called a hierarchical balanced binary search tree. The proposed structure maintains multiple keys in a single node so as to amortize the rebalancing cost. The performance study reveals that the proposed hierarchical binary search tree effectively reduces the write traffic by effectively reaping the high capacity of NVM.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.138-144
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• 2019

In a modern society, people are concerned seriously about their travel destinations depending on time, economic problem. In this paper, we propose an non-hierarchical clustering based hybrid recommendation using context knowledge. The proposed method is personalized way of recommended knowledge about preferred travel places according to the user's location, place, and weather. Based on 14 attributes from the data collected through the survey, users with similar characteristics are grouped using a non-hierarchical clustering based hybrid recommendation. This makes more accurate recommendation by weighting implicit and explicit data. The users can be recommended a preferred travel destination without spending unnecessary time. The performance evaluation uses accuracy, recall, F-measure. The evaluation result was shown 0.636 accuracy, 0.723 recall, and 0.676 F-measure.

• KIISE Transactions on Computing Practices
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• v.21 no.6
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• pp.436-441
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• 2015

There are many types of clustering algorithms such as centroid, hierarchical, or density-based methods. Each algorithm has unique data grouping principles, which creates different varieties of clusters. Ordering Points To Identify the Clustering Structure (OPTICS) is a well-known density-based algorithm to analyze arbitrary shaped and varying density clusters, but the obtained clusters only correlate loosely. Hierarchical agglomerative clustering (HAC) reveals a hierarchical structure of clusters, but is unable to clearly find non-convex shaped clusters. In this paper, we provide a novel hierarchy generation framework and application which can aid users by combining the advantages of the two clustering methods.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.227-236
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• 2005

Hierarchical Prefix Delegation (HPD) protocol refers to a type of solution to problems inherent in non-optimal routing which occurs with Network Mobility (NEMO) basic solution. However, because HPD cannot improve the micro-mobility Problems, Problem surfaces each time Mobile Network Node (MNN) changes the attachment point; as happens also in a Mobile IPv6 (MIPv6) protocol in sen야ng Binding Update (BU) messages to Home Agent (HA) / Correspondent Nodes(CNs) By applying Hierarchical Mobile IPv6 protocol concept to HPD, this study Proposes an algorithm for effectively handling micro-mobility problems which occur with HPD in a nested NEMO environment. By sending BU only to nearby Mobility Anchor Point(MAP) during MNN location change within a MAP's domain, the proposed protocol will alleviate service disruption delays and signaling loads during the handover process, overcoming the limitations of HPD.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.195-200
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• 2002

ART (Adaptive Resonance Theory [1]) neural network and its variations perform non-hierarchical clustering by unsupervised learning. We propose a scheme "arboART" for hierarchical clustering by using several ART1.5-SSS networks. It classifies multidimensional vectors as a cluster tree, and finds features of clusters. The Basic idea of arboART is to use the prototype formed in an ART network as an input to other ART network that has looser distance criteria (Ishihara, et al., [2,3]). By sending prototype vectors made by ART to one after another, many small categories are combined into larger and more generalized categories. We can draw a dendrogram using classification records of sample and categories. We have confirmed its ability using standard test data commonly used in pattern recognition community. The clustering result is better than traditional computing methods, on separation of outliers, smaller error (diameter) of clusters and causes no chaining. This methodology is applied to Kansei evaluation experiment data analysis.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.147-155
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• 2006

Hierarchical Prefix Delegation (HPD) protocol refers to a type of solution to problems inherent in non-optimal routing which occurs with Network Mobility (NEMO) basic solution. However, because HPD cannot improve the micro-mobility problems, problem surfaces each time Mobile Network Node (MNN) changes the attachment point; as happens also in a Mobile IPv6 (MIPv6) protocol in sending Binding Update (BU) messages to Home Agent (HA) / Correspondent Nodes(CNs). By applying Hierarchical Mobile IPv6 protocol concept to HPD, this study proposes an algorithm for effectively handling micro-mobility problems which occur with HPD in a nested NEMO environment. By sending BU only to nearby Mobility Anchor Point(MAP) during MNN location change within a MAP's domain, the proposed protocol will alleviate service disruption delays and signaling loads during the handover process, overcoming the limitations of HPD.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11C
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• pp.1551-1563
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• 2004

This paper proposes a hierarchical approach to shape model ASM using wavelet transform. Local structure model fitting in the ASM plays an important role in model-based pose and shape analysis. The proposed algorithm can robustly find good solutions in complex images by using wavelet decomposition. we also proposed effective method that estimates and corrects object's movement by using Wavelet transform-based hierarchical motion estimation scheme for ASM-based, real-time video tracking. The proposed algorithm has been tested for various sequences containing human motion to demonstrate the improved performance of the proposed object tracking.