Copula-Based Uncertainty Quantification (Copula-UQ) for Multi-Sensor Data in Structural Health Monitoring.

The problem of uncertainty quantification (UQ) for multi-sensor data is one of the main concerns in structural health monitoring (SHM). One important task is multivariate joint probability density function (PDF) modelling. Copula-based statistical inference has attracted significant attention due to the fact that it decouples inferences on the univariate marginal PDF of each random variable and the statistical dependence structure (called copula) among the random variables. This paper proposes the Copula-UQ, composing multivariate joint PDF modelling, inference on model class selection and parameter identification, and probabilistic prediction using incomplete information, for multi-sensor data measured from a SHM system. Multivariate joint PDF is modeled based on the univariate marginal PDFs and the copula. Inference is made by combing the idea of the inference functions for margins and the maximum likelihood estimate. Prediction on the PDF of the target variable, using the complete (from normal sensors) or incomplete information (due to missing data caused by sensor fault issue) of the predictor variable, are made based on the multivariate joint PDF. One example using simulated data and one example using temperature data of a multi-sensor of a monitored bridge are presented to illustrate the capability of the Copula-UQ in joint PDF modelling and target variable prediction.

[The in silico elongation and analysis of the EST from Schistosoma japonicum].

OBJECTIVE: To construct a platform for in silico elongation and batch analysis of Schistosoma japonicum (Sj) ESTs, acquire the potential novel genes and research the expression profile of the genes. METHODS: On the basis of Linux operating system and local ESTs database of Sj, the BLAST and PHRAP softwares were used to construct a program to achieve the elongation of ESTs. Stand-alone BLAST search against the nr database helped analyze the elongated sequence. After finishing the batch analysis script, the platform was used to research the Sj gene expression profile and acquire the potential novel genes. RESULTS: The platform showed satisfactory efficiency and fidelity. 487 elongated sequences obtained from 552 and 307 elongated sequences showed high homology within the nr database downloaded from NCBI. Furthermore, 104 elongated sequences displayed significant homology but showed no homology before elongated. 27 potential novel genes were filtered out. CONCLUSION: An effective platform for Sj ESTs data mining was accomplished and further information on the potential novel genes was acquired.