Fuzzy controller design by using neural network techniques

This paper investigates the relationship between the piecewise linear fuzzy controller (PLFC), in which the membership functions for fuzzy variables and the associated inference rules are all in piecewise linear forms, and a Gaussian potential function network based controller (GPFNC), in which the network output is a weighted summation of hidden responses from a series of Gaussian potential function units (GPFU's). Systematic procedures are proposed for transformation from a PLFC to its GPFNC counterpart, and vice versa. Based on these transformation principles, a series of systematic and feasible steps is presented for the design of an optimized PLFC (PLFC*) by using neural network techniques. In the design procedures, the simplified PLFC is used as the initial controller structure, then a GPFNC, which gives the approximate control response to the initially given PLFC, is found for further optimization. A neutralization process is used to demonstrate the feasibility and the potential applicability of these intelligent controllers on the regulation of highly nonlinear chemical processes     

Exploring latent browsing graph for question answering recommendation

In this paper, we develop a framework of Question Answering Pages (referred to as QA pages) recommendation. Our proposed framework consists of the two modules: the off-line module to determine the importance of QA pages and the on-line module for on-line QA page recommendation. In the off-line module, we claim that the importance of QA pages could be discovered from user click streams. If the QA pages are of higher importance, many users will click and spend their time on these QA pages. Moreover, the relevant relationships among QA pages are captured by the browsing behavior on these QA pages. As such, we exploit user click streams to model the browsing behavior among QA pages as QA browsing graph structures. The importance of QA pages is derived from our proposed QA browsing graph structures. However, we observe that the QA browsing graph is sparse and that most of the QA pages do not link to other QA pages. This is referred to as a sparsity problem. To overcome this problem, we utilize the latent browsing relations among QA pages to build a QA Latent Browsing Graph. In light of QA latent browsing graph, the importance score of QA pages (referred to as Latent Browsing Rank) and the relevance score of QA pages (referred to as Latent Browsing Recommendation Rank) are proposed. These scores demonstrate the use of a QA latent browsing graph not only to determine the importance of QA pages but also to recommend QA pages. We conducted extensive empirical experiments on Yahoo! Asia Knowledge Plus to evaluate our proposed framework.     

An empirical study of design-of-experiment data mining for yield-loss diagnosis for semiconductor manufacturing

To maintain competitive advantages, semiconductor industry has strived for continuous technology migrations and quick response to yield excursion. As wafer fabrication has been increasingly complicated in nano technologies, many factors including recipe, process, tool, and chamber with the multicollinearity affect the yield that are hard to detect and interpret. Although design of experiment (DOE) is a cost effective approach to consider multiple factors simultaneously, it is difficult to follow the design to conduct experiments in real settings. Alternatively, data mining has been widely applied to extract potential useful patterns for manufacturing intelligence. However, because hundreds of factors must be considered simultaneously to accurately characterize the yield performance of newly released technology and tools for diagnosis, data mining requires tremendous time for analysis and often generates too many patterns that are hard to be interpreted by domain experts. To address the needs in real settings, this study aims to develop a retrospective DOE data mining that matches potential designs with a huge amount of data automatically collected in semiconductor manufacturing to enable effective and meaningful knowledge extraction from the data. DOE can detect high-order interactions and show how interconnected factors respond to a wide range of values. To validate the proposed approach, an empirical study was conducted in a semiconductor manufacturing company in Taiwan and the results demonstrated its practical viability.     

Optimization of membrane gas separation systems using genetic algorithm

Genetic algorithm is applied for the optimization of the membrane gas separation systems. Air separation for enriched oxygen production is the selected system for investigation. Optimizations for single and triple objective functions are studied. The optimization problem involves the selection of the optimal system configurations from three alternatives, including continuous membrane column (CMC), single stripper permeator (SSP), and two stripper in series permeator (TSSP), as well as the optimal operating conditions. Models of the three configurations and the genetic algorithm procedure are computerized. The objective functions discussed are the Rony separation index, power consumption per unit equivalent pure oxygen, and the membrane area. Both high-pressure and low-pressure (vacuum) operation modes are optimized and the effects of different oxygen product purity and feed rate are analyzed. For single objective function optimization, the solutions obtained using genetic algorithm are slightly inferior in one case but superior in other cases compared to those by pure mathematical optimization methods. For triple objective function optimization, the Pareto plots presenting multiple trade-off solutions are generated. In general, compared to high-pressure operation mode, the product recovery and power consumption for low-pressure operation mode are lower. For almost all the cases studied, CMC configuration with its high flexibility appears in the optimal solutions.     

Recovery of nitrotoluenes from wastewater by solvent extraction enhanced with salting-out effect

Toluene extraction enhanced by salting-out effect was employed to recover dinitrotoluene isomers and 2,4,6-trinitrotoluene (2,4,6-TNT) from wastewater of toluene nitration processes (e.g. dinitration or trinitration). The batchwise experiments were conducted to elucidate the influence of various operating variables on the extracting performance, including concentrations and species of inorganic salts, such as NaCl, KCl, Na(2)SO(4), K(2)SO(4) and MgSO4, acidity of wastewater, volume ratios of solvent versus wastewater and extraction stages in existence of inorganic salts. It was found that recovery of total organic compounds (TOC) was significantly elevated with increasing concentrations of salts, whose promoting effects were in the following order: NaCl>Na(2)SO(4)>K(2)SO(4)>MgSO4>KCl on the weight basis of wastewater. Besides, high volume ratio of toluene/wastewater (ca. 2.0) was more suitable for recovery of TOC from wastewater with or without addition of NaCl, of which extractable priority was as follows: 2,6-DNT>2,4-DNT>2,4,6-TNT. It is remarkable that TOC in wastewater would be almost completely recovered by sequential four stages toluene extraction, promoted continuously by salting-out effect.     

A multi-model approach to determine early manufacturing parameters for small-data-set prediction

Constructing an accurate prediction model from a small training data set is an important but difficult task in the field of forecasting. This is because when the data size is small, the incomplete data may mean that the model produced cannot sufficiently represent the true data structure or cause the model training to be overfitted. To address this issue, this paper presents an approach that combines multiple prediction models to extract data information in multiple facets. In the multi-model approach, a compromise weight method is proposed to determine the relative reliability of each of the prediction model. The methods used include multiple regression, artificial neural network, and support vector machines for regression. A thin-film transistor liquid crystal display manufacturing case study is used to illustrate the details of this research. The empirical results not only show that the proposed multi-model can reduce the manufacturing variation and increase the production yield, but also can propose a robust and reliable parameter interval to the online engineers in the early manufacturing stage.