• Journal of IKEEE
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• v.17 no.3
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• pp.339-345
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• 2013

In this paper, the probabilistic exclusion based orthogonal matching pursuit (PEOMP) algorithm for the sparse signal reconstruction is proposed. Some of recent greedy algorithms such as CoSaMP, gOMP, BAOMP improved the reconstruction performance by deleting unsuitable atoms at each iteration. They still often fail to converge to the solution because the support set could not escape from a local minimum. PEOMP helps to escape by excluding a random atom in the support set according to a well-chosen probability function. Experimental results show that PEOMP outperforms several OMP based algorithms and the $l_1$ optimization method in terms of exact reconstruction probability.

• Honam Mathematical Journal
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• v.32 no.1
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• pp.113-129
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• 2010

Inclusion and exclusion is used in many papers to count certain objects exactly or asymptotically. Also it is used to derive the Bonferroni inequalities in probabilistic area [6]. Inclusion and exclusion on finitely many types of properties is first used in R. Meyer [7] in probability form and first used in the paper of McKay, Palmer, Read and Robinson [8] as a form of counting version of inclusion and exclusion on two types of properties. In this paper, we provide a proof for inclusion and exclusion on finitely many types of properties in counting version. As an example, the asymptotic number of general cubic graphs via inclusion and exclusion formula is given for this generalization.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.239-246
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• 2017

There are various transversal structures (small dams or drop structures) in median and small streams in Korea. Most of them are concrete structures and it is so hard to exclude low-level water. Unless drainage valves and/or gates would not be installed near bottom of bed, sediment from upstream should be deposited and also contaminants attached to the sediments would devastatingly threaten the water quality and ecosystem. One of countermeasures for such problem is the bypass pipe installed underneath the transversal structure. However, there is still issued whether it would be workable if the gravels and/or stones would roll into and be not excluded. Therefore, in this study, the conditions to exclude the rip stone which enter into the bypass pipe was reviewed. Based on sediment transport phenomenon, the behavior of stones was investigated with the concepts from the critical shear stress of sediment and d'Alembert principle. As final results, the basis condition (${\tau}_c{^*}$) was derived using the Lagrangian description since the stones are in the moving state, not in the stationary state. From hydraulic experiments the relative velocity could be obtained. In order to minimize the scale effect, the extra wide channel of 5.0 m wide and 1.0 m high was constructed and the experimental stones were fully spherical ones. Experimental results showed that the ratio of flow velocity to spherical particle velocity was measured between 0.5 and 0.7, and this result was substituted into the suggested equation to identify the critical condition wether the stones were excluded. Regimes about the exclusion of stone in bypass pipe were divided into three types according to particle Reynolds number ($Re_p$) and dimensionless critical shear force (${\tau}_c{^*}$) - exclusion section, probabilistic exclusion section, no exclusion section. Results from this study would be useful and essential information for bypass pipe design in transveral structures.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.145-161
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• 2014

Recently, many large organizations have multiple data sources (MDS') distributed over different branches of an interstate company. Local patterns analysis has become an effective strategy for MDS mining in national and international organizations. It consists of mining different datasets in order to obtain frequent patterns, which are forwarded to a centralized place for global pattern analysis. Various synthesizing models [2,3,4,5,6,7,8,26] have been proposed to build global patterns from the forwarded patterns. It is desired that the synthesized rules from such forwarded patterns must closely match with the mono-mining results (i.e., the results that would be obtained if all of the databases are put together and mining has been done). When the pattern is present in the site, but fails to satisfy the minimum support threshold value, it is not allowed to take part in the pattern synthesizing process. Therefore, this process can lose some interesting patterns, which can help the decider to make the right decision. In such situations we propose the application of a probabilistic model in the synthesizing process. An adequate choice for a probabilistic model can improve the quality of patterns that have been discovered. In this paper, we perform a comprehensive study on various probabilistic models that can be applied in the synthesizing process and we choose and improve one of them that works to ameliorate the synthesizing results. Finally, some experiments are presented in public database in order to improve the efficiency of our proposed synthesizing method.