Automation model of sewerage rehabilitation planning.

The major steps of sewerage rehabilitation include inspection of sewerage, assessment of structural conditions, computation of structural condition grades, and determination of rehabilitation methods and materials. Conventionally, sewerage rehabilitation planning relies on experts with professional background that is tedious and time-consuming. This paper proposes an automation model of planning optimal sewerage rehabilitation strategies for the sewer system by integrating image process, clustering technology, optimization, and visualization display. Firstly, image processing techniques, such as wavelet transformation and co-occurrence features extraction, were employed to extract various characteristics of structural failures from CCTV inspection images. Secondly, a classification neural network was established to automatically interpret the structural conditions by comparing the extracted features with the typical failures in a databank. Then, to achieve optimal rehabilitation efficiency, a genetic algorithm was used to determine appropriate rehabilitation methods and substitution materials for the pipe sections with a risk of mal-function and even collapse. Finally, the result from the automation model can be visualized in a geographic information system in which essential information of the sewer system and sewerage rehabilitation plans are graphically displayed. For demonstration, the automation model of optimal sewerage rehabilitation planning was applied to a sewer system in east Taichung, Chinese Taiwan.     

Two-level optimal output feedback stabilizer design

Design of two-level system stabilizers is considered using an optimal reduced-order model whose state variables are torque angles and speeds. The reduced-order model retains their physical meaning and is used to design a two-level linear feedback controller that takes into account the realities and constraints of the electrical power systems. The two-level control strategy is used, and a global control signal is generated from the output variables to minimize the effect of interaction. The effectiveness of this controller is evaluated, and an example, the multimachine system, is given to illustrate the advantages of the proposed method. The responses of the system with the two-level scheme and optimal reduced order scheme are included for comparative analyses     

膜分离技术大行其道

食品与膜分离技术 膜分离技术是以滤膜作为选择性障碍层，允许某些成分透过而保留混合物中的其它成分。他在食品工业上，主要应用于制造过程中的前处理，去除杂质、除菌、浓缩、澄清、分离，甚至污水处理等。在分离过程中，不涉及相变化及化学变化，具有高效能、节省能源、避免环境污染等特点。膜过滤技术包括逆渗透、     

Kinetics of AuSn4 migration in lead-free solders

The relatively fast diffusion of Au atoms in eutectic PbSn matrix is considered one of the contributing factors to the Au embrittlement problem. In this study, we further investigated the Au embrittlement problem in high-Sn solders. Experimentally, Sn3.5Ag (wt.%) spheres with 500-μm diameter were soldered over the Au/Ni soldering pads. It was found that some of the AuSn₄ needles that formed after reflow inside the solder migrated back to the solder/pad interface during thermal aging. However, the migration kinetics in high-Sn solders was slower compared to that in eutectic PbSn. The difference in migration kinetics of AuSn₄ in eutectic PbSn and SnAg was ascribed to the difference in the magnitudes of the Au flux and the Ni flux. In eutectic PbSn, the Au flux was much greater than that of the Ni flux, and the Au and Ni flux were in the same order of magnitude in eutectic SnAg. The relative magnitude of the Au and Ni flux changed in eutectic PbSn and SnAg because the homologous temperatures of PbSn and SnAg were different.     

Application of artificial neural networks to the analysis of two‐dimensional fluorescence spectra in recombinant E coli fermentation processes

A two‐dimensional (2D) spectrofluorometer was used to monitor various fermentation processes with recombinant E coli for the production of 5‐aminolevulinic acid (ALA). The whole fluorescence spectral data obtained during a process were analyzed using artificial neural networks, ie self‐organizing map (SOM) and feedforward backpropagation neural network (BPNN). The SOM‐based classification of the whole spectral data has made it possible to qualitatively associate some process parameters with the normalized weights and variances, and to select some useful combinations of excitation and emission wavelengths. Based on the classified fluorescence spectra a supervised BPNN algorithm was used to predict some of the process parameters. It was also shown that the BPNN models could elucidate some sections of the process's performance, eg forecasting the process's performance. Copyright © 2005 Society of Chemical Industry     

《石油沥青纸胎油毡》国家标准的修订

《石油沥青纸胎油毡》国家标准进行了第三次修订。本文介绍了修订的理由、修订的内容与依据，并与国内外同类产品标准进行了比较。