Shiuan Wan

A modified base-isolated design strategy is proposed and shown to effectively protect precision machinery system against extreme earthquakes. Preliminary presumed range of design parameter in base-isolated machinery systems could be narrowed down via entropy-based classification (EBC) by finding an appropriate cutting point for a proper design range. The designs for the modified parametric ranges through EBC effectively raise isolator's energy-absorbing ability. However, in this pervious work, when all the possible cases are considered to modify the designs of the base-isolated system, the examined parametric ranges might be a little large. If only one cutting point from EBC is used, some uncertainties in a large parametric range could make imprecise design strategies. Accordingly, modified EBC (MEBC) is introduced in this study for finding more cutting points. Simply speaking, MEBC is the use of EBC more than once. However, some additional limitations must be redefined for EBC to reduce the influence of uncertainty data. A case study is practically presented in this study. Designs for a lead rubber bearing's base-isolated machinery model located at Central Science Park in Taiwan are made. Ten excitations of Chi-Chi earthquake from different stations nearby the park are considered. In the results, they are verified that appropriate design strategies from MEBC could raise isolator's energy-absorbing ability and lower machinery's maximum deformation. Copyright © 2013 John Wiley & Sons, Ltd.

Remote Sensing (RS) data can assist in the classification of landscapes to identify landslides. Recognizing the relationship between landform/landscape and landslide areas is, however, complex. Soil properties, geomorphological, and groundwater conditions govern the instability of slopes. Previous study of Wan ( 2009 Wan, S. 2009. A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map. Engineering Geology, 108: 237–251. [Crossref], [Web of Science ®] [Google Scholar] ; A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map. Engineering Geology, 108, 237–251) used the maximum-likelihood classifier to classify the multi-category landslide image data. Unfortunately, the classification does not consider the geomorphologic condition. Accordingly, a Landslide Expert System was developed to modify these problems. The system uses multi-date SPOT image data to develop the landslide database. The threshold slope which becomes vulnerable to landslides is obtained by the K-means method. Then, an innovative Data Mining technique – Discrete Rough Sets (DRS) – is applied to obtain the core variables and their relevant thresholds. Finally, the Expert Knowledge Translation Platform (EKTP) is used to create the rules for classification. This study used a new approach called ‘Rough Set Tree’ to demonstrate the performance of the approach. The classification of landslide vulnerable areas, bare land, rock, streams, and water-body is greatly improved.

The analysis, measurement, and computation of remote sensing images often require an enhanced supervised classification technique to develop an efficient spatial decision support system. Rice is a crop of global importance, which has drawn a great interest in using remote sensing techniques for evaluating its production. Ancillary information is widely used to improve the classification accuracy of satellite images. However, few of these studies questioned the importance and strategies of using this ancillary information. The enhanced decision support system in our study has two stages. In the first stage, the images are obtained from the remote sensing technique and the ancillary information is employed to increase the accuracy of classification. In the second stage, it is decided to construct an efficiently supervised classifier, which is used to evaluate the ancillary information. Back-propagation neural network (BPN) with extended delta bar delta (EDBD) algorithm is incorporated into our decision support classifier system. This classifier renders two crucial contributions: (1) the EDBD algorithm accelerates the convergence speed of the learning process and (2) the relative importance (RI) on each band of ancillary information is evaluated rationally.

A parallel study of regression and semi-theoretical equations is presented to forecast the drop force and the drop length of a free-falling nappe for an aerated straight-drop spillway. The ratio between the critical velocity and the exit velocity at the drop wall was introduced to establish semi-theoretical equations. Regression techniques were applied by using the dimensionless channel bed slope and the drop number to describe the drop characteristics. There were 64 groups of test data to calibrate the parameters of equations. Another 36 groups of test data were used to verify the developed model. The simulation results indicate that the semi-theoretical method provides reliable estimates for the drop force, and that the regression method can offer accurate predictions for the drop length.