• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1014-1034
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• 2015

Location information of sensor nodes plays a critical role in many wireless sensor network (WSN) applications and protocols. Although many localization algorithms have been proposed in recent years, they usually target at dense networks and perform poorly in sparse networks. In this paper, we propose two component-based localization algorithms that can localize many more nodes in sparse networks than the state-of-the-art solution. We first develop the Basic Common nodes-based Localization Algorithm, namely BCLA, which uses both common nodes and measured distances between adjacent components to merge components. BCLA outperforms CALL, the state-of-the-art component-based localization algorithm that uses only distance measurements to merge components. In order to further improve the performance of BCLA, we further exploit the angular information among nodes to merge components, and propose the Component-based Localization with Angle and Distance information algorithm, namely CLAD. We prove the merging conditions for BCLA and CLAD, and evaluate their performance through extensive simulations. Simulations results show that, CLAD can locate more than 90 percent of nodes in a sparse network with average node degree 7.5, while CALL can locate only 78 percent of nodes in the same scenario.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.529-531
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• 2001

We present an expectation-maximization algorithm for principal component analysis via orthogonalization. The algorithm finds actual principal components, whereas previously proposed EM algorithms can only find principal subspace. New algorithm is simple and more efficient thant probabilistic PCA specially in noiseless cases. Conventional PCA needs computation of inverse of the covariance matrices, which makes the algorithm prohibitively expensive when the dimensions of data space is large. This EM algorithm is very powerful for high dimensional data when only a few principal components are needed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1091-1110
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• 2016

Hybrid deployment under current cloud data centers is a combination of online and offline services, which improves the utilization of the cluster resources. However, the performance of the cluster is often affected by the online services in the hybrid deployment environment. To improve the response time of online service (e.g. search engine), an effective scheduling algorithm based on Storm is proposed. At the component level, the algorithm dispatches the component with more influence to the optimal performance node. Inside the component, a reasonable resource allocation strategy is used. By searching the compressed index first and then filtering the complete index, the execution speed of the component is improved with similar accuracy. Experiments show that our algorithm can guarantee search accuracy of 95.94%, while increasing the response speed by 68.03%.

• Journal of the Korea Society of Computer and Information
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• v.21 no.3
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• pp.57-64
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• 2016

This paper deals with the PCB assembly time minimization problem that the PAP (pick-and-placement) machine pickup the K-weighted group of N-components, loading, and place into the PCB placement location. This problem considers the rotational turret velocity according to component weight group and moving velocity of distance in two component placement locations in PCB. This paper suggest heavy-weight component group first pick-and-place strategy that the feeder sequence fit to the placement location Hamiltonean cycle sequence. This algorithm applies the quadratic assignment problem (QAP) that considers feeder sequence and location sequence, and the linear assignment problem (LAP) that considers only feeder sequence. The proposed algorithm shorten the assembly time than iATMA for QAP, and same result as iATMA that shorten the assembly time than ATMA.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.87-93
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• 2007

In the component based software development, it is very important to interface between modules of component. Almost of existing method, Connectors are deal with all communication channels between two or more components/interfaces by RPC(Remote procedure call) and event call. But these process has limits when component send a lot of request call to other component through connector. That is, we need more efficient interface method that connector can process multi request call. In this paper, I propose interaction scheduling algorithm using message queue in the connector. For this purpose, I use message buffer which operate to save and load message temporarily.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.1025-1036
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• 2003

Variable selection algorithm for principal component analysis using penalty function is proposed. We use the fact that usual principal component problem can be expressed as a maximization problem with appropriate constraints and we will add penalty function to this maximization problem. Simulated annealing algorithm is used in searching for optimal solutions with penalty functions. Comparisons between several well-known penalty functions through simulation reveals that the HARD penalty function should be suggested as the best one in several aspects. Illustrations with real and simulated examples are provided.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 1997.10a
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• pp.151-158
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• 1997

The main concern of numerical dynamic analysis of large structures is to find an acceptable solution with fewer mode shapes and less computational efforts. component mode method utilizes substructure technique to reduce the degrss of freedom but have a disadvantage to not consider the dynamic characteristics of loads. Ritz Vector method consider the load characteristics but requires many integrations and errors are accumulated. In this study, to prove the effectiveness of component mode method, Lanczos algorithm are introduced. To prove the effectiveness of this method, example structures areanalyzed and the results are compared with SAP90.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.65-71
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• 2004

We improved the MLLR speaker adaptation algorithm with reduction of the order of HMM parameters using PCA(Principle Component Analysis) or ICA(Independent Component Analysis). To find a smaller set of variables with less redundancy, we adapt PCA(principal component analysis) and ICA(independent component analysis) that would give as good a representation as possible, minimize the correlations between data elements, and remove the axis with less covariance or higher-order statistical independencies. Ordinary MLLR algorithm needs more than 30 seconds adaptation data to represent higher word recognition rate of SD(Speaker Dependent) models than of SI(Speaker Independent) models, whereas proposed algorithm needs just more than 10 seconds adaptation data. 10 components for ICA and PCA represent similar performance with 36 components for ordinary MLLR framework. So, compared with ordinary MLLR algorithm, the amount of total computation requested in speaker adaptation is reduced by about 1/167 in proposed MLLR algorithm.

• Computational Structural Engineering
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• v.9 no.2
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• pp.115-120
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• 1996

The main concern of numerical dynamic analysis of large structures is to find an acceptable solution with fewer mode shapes and less computational efforts. Component mode method utilizes substructure technique to reduce the degree of freedom but have a disadvantage to not consider the dynamic characteristics of loads. Ritz Vector method consider the load characteristics but requires many integrations and errors are accumulated. In this study, to improve the effectiveness of component mode method, Lanczos algorithm is introduced. To prove the effectiveness of this method, example structure are analyzed and the results are compared with SAP90.

• Smart Structures and Systems
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• v.31 no.2
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• pp.155-169
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• 2023

The quasi-static component of the moving vehicle-induced dynamic response is promising in damage detection as it is sensitive to bridge damage but insensitive to environmental changes. However, accurate extraction of quasi-static component from the dynamic response is challenging especially when the vehicle velocity is high. This paper proposes an adaptive quasi-static component extraction method based on the modified variational mode decomposition (VMD) algorithm. Firstly the analytical solutions of the frequency components caused by road surface roughness, high-frequency dynamic components controlled by bridge natural frequency and quasi-static components in the vehicle-induced bridge response are derived. Then a modified VMD algorithm based on particle swarm algorithm (PSO) and mutual information entropy (MIE) criterion is proposed to adaptively extract the quasi-static components from the vehicle-induced bridge dynamic response. Numerical simulations and real bridge tests are conducted to demonstrate the feasibility of the proposed extraction method. The results indicate that the improved VMD algorithm could extract the quasi-static component of the vehicle-induced bridge dynamic response with high accuracy in the presence of the road surface roughness and measurement noise.