• Structural Engineering and Mechanics
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• v.37 no.1
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• pp.17-37
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• 2011

This paper presents a new Internet-based computational framework for the realistic simulation of multi-scale response of structural systems. Two levels of parallel processing are involved in this frame work: multiple local distributed computing environments connected by the Internet to form a cluster-to-cluster distributed computing environment. To utilize such a computing environment for a realistic simulation, the simulation task of a structural system has been separated into a simulation of a simplified global model in association with several detailed component models using various scales. These related multi-scale simulation tasks are distributed amongst clusters and connected to form a multi-level hierarchy. The Internet is used to coordinate geographically distributed simulation tasks. This paper also presents the development of a software framework that can support the multi-level hierarchical simulation approach, in a cluster-to-cluster distributed computing environment. The architectural design of the program also allows the integration of several multi-scale models to be clients and servers under a single platform. Such integration can combine geographically distributed computing resources to produce realistic simulations of structural systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4644-4661
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• 2015

Wireless sensor networks are often organized in the form of clusters leading to the new framework of WSN called cluster or hierarchical WSN where each cluster head is responsible for its own cluster and its members. These hierarchical WSN are prone to various routing layer attacks such as Black hole, Gray hole, Sybil, Wormhole, Flooding etc. These routing layer attacks try to spoof, falsify or drop the packets during the packet routing process. They may even flood the network with unwanted data packets. If one cluster head is captured and made malicious, the entire cluster member nodes beneath the cluster get affected. On the other hand if the cluster member nodes are malicious, due to the broadcast wireless communication between all the source nodes it can disrupt the entire cluster functions. Thereby a scheme which can detect both the malicious cluster member and cluster head is the current need. Abnormal energy consumption of nodes is used to identify the malicious activity. To serve this purpose a learning based energy prediction algorithm is proposed. Thus a two level energy prediction based intrusion detection scheme to detect the malicious cluster head and cluster member is proposed and simulations were carried out using NS2-Mannasim framework. Simulation results achieved good detection ratio and less false positive.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.6
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• pp.619-625
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• 2009

FastSLAM which uses the Rao-Blackwellized particle filter is one of the famous solutions to SLAM (Simultaneous Localization and Mapping) problem that estimates concurrently a robot's pose and surrounding environment. However, the particle depletion problem arises from the loss of the particle diversity in the resampling process of FastSLAM. Then, the performance of FastSLAM degenerates over the time. In this work, DIR (Density Information-based Resampling) technique is proposed to solve the particle depletion problem. First, the cluster is constructed based on the density of each particle, and the density of each cluster is computed. After that, the number of particles to be reserved in each cluster is determined using a linear method based on the distance between the highest density cluster and each cluster. Finally, the resampling process is performed by rejecting the particles which are not selected to be reserved in each cluster. The performance of the DIR proposed to solve the particle depletion problem in FastSLAM was verified in computer simulations, which significantly reduced both the RMS position error and the feature error.

• Bulletin of the Korean Chemical Society
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• v.31 no.10
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• pp.2806-2808
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• 2010

The structure of 3-aminophenol $(CO_2)_1$ cluster was computationally studied both in the ground and the lowest singlet excited electronic states. The ground state structure and binding energy of the cluster was investigated using the second-order M$\ddoot{o}$ller-Plesset perturbation theory (MP2) at the complete basis set (CBS) limit. The excited state geometry of the cluster was obtained at the second-order approximate coupled cluster (CC2) level with cc-pVDZ basis set, and the $S_0-S_1$ absorption spectrum was simulated by calculating Franck-Condon overlap integral. The ground state geometry of the global minimum with a very high binding energy of 4.3 kcal/mol was found for the cluster, due to the interaction between amino group and $CO_2$ in addition to the strong $\pi-\pi$ interaction between the aromatic ring and $CO_2$. The excited state geometry shows a very big shift in the position of $CO_2$ compared to the ground state geometry, which results in low intensity and broad envelope in the Franck-Condon simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.7
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• pp.1293-1310
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• 2011

A simple modification of well known Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is proposed to exploit cooperative diversity. Instead of selecting a single cluster-head, we propose M cluster-heads in each cluster to obtain a diversity of order M. The cluster-heads gather data from all the sensor nodes within the cluster using same technique as LEACH. Cluster-heads transmit gathered data cooperatively towards the destination or higher order cluster-head. We propose a code combining based cooperative diversity protocol which is similar to coded cooperation that maximizes the performance of the proposed cooperative LEACH protocol. The implementation of the proposed cooperative strategy is analyzed. We develop the upper bounds on bit error rate (BER) and frame error rate (FER) for our proposal. Space time block codes (STBC) are also a suitable candidate for our proposal. In this paper, we argue that the STBC performs worse than the code combining cooperation.

• The KIPS Transactions:PartA
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• v.8A no.4
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• pp.359-366
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• 2001

This paper overviews the design of SANique$^{TM}$ -a shred file system for Linux cluster based on SAN environment. SANique$^{TM}$ has the capability of transferring user data from network-attached SAN disks to client applcations directly without the control of centralized file server system. The paper also presents the characteristics of each SANique$^{TM}$ subsystem: CFM(Cluster File Manager), CVM(Cluster Volume Manager), CLM(Cluster Lock Manager), CBM(Cluster Buffer Manager) and CRM(Cluster Recovery Manager). Under the SANique$^{TM}$ design layout, then, the syndrome of '||'&'||'quot;split-brain'||'&'||'quot; in shared file system environments is described and defined. The work first generalizes and illustrates possible situations in each of which a shared file system environment may split into two or more pieces of separate brain. Finally, the work describes the SANique$^{TM}$ approach to the given "split-brain"problem using SAN disk named "split-brain" and develops the overall recovery procedure of shared file systems.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.1-6
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• 2024

Energy efficiency in wireless sensor networks (WSNs) is a critical issue because batteries are used for operation and communication. In terms of scalability, energy efficiency, data integration, and resilience, WSN-cluster-based routing algorithms often outperform routing algorithms without clustering. Low-energy adaptive clustering hierarchy (LEACH) is a cluster-based routing protocol with a high transmission efficiency to the base station. In this paper, we propose an energy consumption model for LEACH and compare it with the existing LEACH, advanced LEACH (ALEACH), and power-efficient gathering in sensor information systems (PEGASIS) algorithms in terms of network lifetime. The energy consumption model comprises energy-sensitive cluster formation and a cluster head selection technique. The setup and steady-state phases of the proposed model are discussed based on the cluster head selection. The simulation results demonstrated that a low-energy-consumption network was introduced, modeled, and validated for LEACH.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.431-434
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• 2003

In this paper we introduce a modified objective function for fuzzy c-means clustering with logistic regression model in the presence of noise cluster. The logistic regression model is commonly used to describe the effect of one or several explanatory variables on a binary response variable. In real application there is very often no sharp boundary between clusters so that fuzzy clustering is often better suited for the data.

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.3
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• pp.4-19
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• 1991

In this paper, we propose a fuzzy neural network pattern classifier utilizing fuzzy information. This system works without any a priori information about the number of clusters or cluster centers. It classifies each input according to the distance between the weights and the normalized input using Bezdek's [1] fuzzy membership value equation. This model returns the correct membership value for each input vector and find several cluster centers. Some experimental studies of comparison with other algorithms will be presented for sample data sets.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.282-287
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• 2002

In this paper, we introduce the degrees of fuzzy net-convergences in L-fuzzy topologies using complete MV-algebras. We investigate the relationships among the degrees of fuzzy convergent, fuzzy cluster and fuzzy adherent points. We study the properties of net-convergences.