Modeling Bridge Condition Levels in the United States

The objectives of this paper are to （I） quantify the effects of age and other key factors on bridge deterioration rates, and （2） provide bridge managers with strategic forecasting tools. A model for forecasting substructure conditionisestimated from the National Bridge Inventory that includes the effects of bridge material, design load, structural type, operating rating, average daily traffic, water, and the state where the bridge is located. Bridge age is the quantitative independent variable. The relationship between age and substructure condition is a fourth-order polynomial. Some of the key findings are： （I） a bridge substructure is expected to lose from 0.52 to 0.11 rating points per decade as it ages from 10 to 70 years; （2） levels of deterioration increase significantly as the material changes from concrete, to steel, to timber; （3） slab bridges have lower levels of deterioration than other structures; （4） bridges that span water have lower condition ratings; （5） bridges with higher operating ratingshave higher condition ratings; and （6） substructure condition ratings vary significantly among states.

Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.

Hybrid ARIMA/RBF Framework for Prediction BUX Index

In this paper, we construct and implement a new architecture and learning method of customized hybrid RBF neural network for high frequency time series data forecasting. The hybridization is carried out using two running approaches. In the first one, the ARCH (Autoregressive Conditionally Heteroscedastic)-GARCH (Generalized ARCH) methodology is applied. The second modeling approach is based on RBF (Radial Basic Function) neural network using Gaussian activation function with cloud concept. The use of both methods is useful, because there is no knowledge about the relationship between the inputs into the system and its output. Both approaches are merged into one framework to predict the final forecast values. The question arises whether non-linear methods like neural networks can help modeling any non-linearities being inherent within the estimated statistical model. We also test the customized version of the RBF combined with the machine learning method based on SVM learning system. The proposed novel approach is applied to high frequency data of the BUX stock index time series. Our results show that the proposed approach achieves better forecast accuracy on the validation dataset than most available techniques.

Machining Error Control by Integrating Multivariate Statistical Process Control and Stream of Variations Methodology

For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control （MSPC） and stream of variations （SoV） is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error （OSFE） is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.