0.8μm LDD CMOS Reliability Experiments and Analysis

In order to study the abnormal substrate current and reliability problem of LDDMOSFET observed in experiments, two dimensional numerical simulation for devices has been performed, and an optimum process for LDD is suggested.

SRXRF Experiments and Analytical Methods of Mineral Individual Fluid Inclusions

This paper focuses on the micro-beam and trace element non-destructive experiment and analytical method of mineral fluid inclusions by synchrotron radiation X-ray fluorescence （SRXRF） microprobe at Beijing Synchrotron Radiation Facility （BSRF）. The experimental instrument, measurement process and calculating method are introduced. A set of oil- and gas-containing typical mineral fluid inclusions taken from the Tazhong and Lunnan oilfields in the Tarim Basin were analyzed non-destructively. The trace element contents in the fluid inclusions may provide guidance for oil and gas exploration and development.

A novel pure data-selection framework for day-ahead wind power forecasting

Numerical weather prediction(NWP)data possess internal inaccuracies,such as low NWP wind speed corresponding to high actual wind power generation.This study is intended to reduce the negative effects of such inaccuracies by proposing a pure data-selection framework(PDF)to choose useful data prior to modeling,thus improving the accuracy of day-ahead wind power forecasting.Briefly,we convert an entire NWP training dataset into many small subsets and then select the best subset combination via a validation set to build a forecasting model.Although a small subset can increase selection flexibility,it can also produce billions of subset combinations,resulting in computational issues.To address this problem,we incorporated metamodeling and optimization steps into PDF.We then proposed a design and analysis of the computer experiments-based metamodeling algorithm and heuristic-exhaustive search optimization algorithm,respectively.Experimental results demonstrate that(1)it is necessary to select data before constructing a forecasting model;(2)using a smaller subset will likely increase selection flexibility,leading to a more accurate forecasting model;(3)PDF can generate a better training dataset than similarity-based data selection methods(e.g.,K-means and support vector classification);and(4)choosing data before building a forecasting model produces a more accurate forecasting model compared with using a machine learning method to construct a model directly.