• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.457-480
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• 2017

We develop a random partition procedure based on a Dirichlet process prior with Laplace distribution. Gibbs sampling of a Laplace mixture of linear mixed regressions with a Dirichlet process is implemented as a random partition model when the number of clusters is unknown. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities, unlike its counterparts. A full Gibbs-sampling algorithm is developed for an efficient Markov chain Monte Carlo posterior computation. The proposed method is illustrated with simulated data and one real data of the energy efficiency of Tsanas and Xifara (Energy and Buildings, 49, 560-567, 2012).

• Journal of the Korea Society for Simulation
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• v.16 no.4
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• pp.57-65
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• 2007

In many sensor network applications, sensor nodes are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the node's cryptographic keys. False sensing report can be injected through compromised nodes, which can lead to not only false alarms but also the depletion of limited energy resource in battery powered networks. Fan Ye et al. proposed that statistical en-route filtering scheme(SEF) can do verify the false report during the forwarding process. In this scheme, the choice of a partition value represents a trade off between resilience and energy where the partition value is the total number of partitions which global key pool is divided. If every partition are compromised by an adversary, SEF disables the filtering capability. Also, when an adversary has compromised a very small portion of keys in every partition, the remaining uncompromised keys which take a large portion of the total cannot be used to filter false reports. We propose a fuzzy-based adaptive partitioning method in which a global key pool is adaptively divided into multiple partitions by a fuzzy rule-based system. The fuzzy logic determines a partition value by considering the number of compromised partitions, the energy and density of all nodes. The fuzzy based partition value can conserve energy, while it provides sufficient resilience.

• Bulletin of the Korean Chemical Society
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• v.28 no.9
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• pp.1485-1488
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• 2007

Photodissociation dynamics of cyanamide (NH2CN) at 212 nm has been investigated by measuring rotationally resolved laser induced fluorescence spectra of CN fragments exclusively produced in the ground electronic state. From the spectra, rotational population distributions of CN as well as translational energy releases in the products were obtained. The measured average rotational energies of CN were 12.4 ± 0.5 and 11.6 ± 0.5 kJ/ mol for v'' = 0 and v'' = 1, respectively and the center of mass average translational energy release among products was 41.8 ± 6.4 kJ/mol. The observed energy partitioning was well represented by statistical prior calculations, from which it was suggested that the dissociation takes place on the ground electronic surface after rapid internal conversion.

• Transactions of the Korean hydrogen and new energy society
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• v.9 no.3
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• pp.119-125
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• 1998

The modified statistical-mechanical theory for dense fluid mixtures of rigid spheres has been applied to rigid sphere fluids in the nonoverlapping pore model. The resulting expressions for the partition coefficient and diffusivity illustrate the influence of steric hindrance on the thermodynamic and transport properties in such systems. The open membrane model without the size-exclusion and shielding effects shows considerable overestimation of the diffusion flux when the effective mean pore radii of the order of $20{\AA}$ or less are involved. Theoretical predictions investigated here were also compared with experimental data for hydrogen gases in inorganic porous membranes and it was observed a qualitative agreement in the low pressure limit.

• Bulletin of the Korean Chemical Society
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• v.29 no.9
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• pp.1685-1688
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• 2008

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.608-616
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• 2022

Power peaking factors (PPF) is an important parameter for safe and efficient reactor operation. There are several methods to calculate the PPF at TRIGA research reactors such as MCNP and TRIGLAV codes. However, these methods are time-consuming and required high specifications of a computer system. To overcome these limitations, artificial intelligence was introduced for parameter prediction. Previous studies applied the neural network method to predict the PPF, but the publications using the ANFIS method are not well developed yet. In this paper, the prediction of PPF using the ANFIS was conducted. Two input variables, control rod position, and neutron flux were collected while the PPF was calculated using TRIGLAV code as the data output. These input-output datasets were used for ANFIS model generation, training, and testing. In this study, four ANFIS model with two types of input space partitioning methods shows good predictive performances with R² values in the range of 96%-97%, reveals the strong relationship between the predicted and actual PPF values. The RMSE calculated also near zero. From this statistical analysis, it is proven that the ANFIS could predict the PPF accurately and can be used as an alternative method to develop a real-time monitoring system at TRIGA research reactors.