• 제목/요약/키워드: Statistical energy partitioning

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Bayesian analysis of random partition models with Laplace distribution

  • Kyung, Minjung
    • Communications for Statistical Applications and Methods
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    • 제24권5호
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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).

통계적 여과기법에서 훼손 허용도를 위한 퍼지 로직을 사용한 적응형 전역 키 풀 분할 기법 (Adaptive Partitioning of the Global Key Pool Method using Fuzzy Logic for Resilience in Statistical En-Route Filtering)

  • 김상률;조대호
    • 한국시뮬레이션학회논문지
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    • 제16권4호
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    • pp.57-65
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    • 2007
  • 많은 센서 네트워크 응용에서, 센서 노드들은 개방된 환경에 배포되므로 노드의 암호 키 완전히 훼손하는 물리 공격에 취약하다. 위조 감지 보고서는 훼손된 노드를 통하여 네트워크에 주입될 수 있으며, 이는 거짓 경보를 울릴 수 있을 뿐만 아니라 전지로 동작하는 네트워크의 제한된 에너지 자원을 고갈시킬 수 있다. Fan Ye 등은 이에 대한 대안으로 전송과정에서 허위 보고서를 검증할 수 있는 통계적 여과 기법을 제안하였다. 이 기법에서 허위 보고서에 대한 검증이 가능한 인증키의 노출 정도인 훼손 허용도를 나타내는 분할 값은 전역 키 풀이 나눠진 구획들의 수로 소비 에너지와 서로 대치되는 관계에 있어 그 결정이 매우 중요하다. 전체 구획들의 인증키가 노출될 경우 허위 보고서를 더 이상 검증을 할 수 없고 각 구획들의 노출되지 않은 나머지 인증키들은 인증키로써의 기능도 잃게 된다. 본 논문에서는 전역 키 풀 분할에 퍼지 규칙 시스템을 사용해 다수의 구획들로 나누는 퍼지 기반의 적응형 분할 기법을 제안한다. 퍼지 로직은 훼손된 구획의 수, 노드의 밀도와 잔여 에너지양을 고려하여 분할 값을 결정한다. 이 퍼지 기반의 분할 값은 충분한 훼손 허용도를 제공하면서 에너지를 보존할 수 있다.

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Photodissociation Dynamics of Cyanamide at 212 nm

  • Kwon, Chan-Ho;Lee, Ji-Hye;Kim, Hong-Lae
    • Bulletin of the Korean Chemical Society
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    • 제28권9호
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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.

비겹침 구형 모델을 이용한 세공 박막 내 수소 기체의 분산 및 확산 특성 (Partitioning and Diffusion Properties of Hydrogen Gases In Porous Membranes Using the Nonoverlapping Sphere Model)

  • 서승혁;하기룡
    • 한국수소및신에너지학회논문집
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    • 제9권3호
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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.

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Power peaking factor prediction using ANFIS method

  • Ali, Nur Syazwani Mohd;Hamzah, Khaidzir;Idris, Faridah;Basri, Nor Afifah;Sarkawi, Muhammad Syahir;Sazali, Muhammad Arif;Rabir, Hairie;Minhat, Mohamad Sabri;Zainal, Jasman
    • Nuclear Engineering and Technology
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    • 제54권2호
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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.