A novel polymer membrane for extraction applications

In this study, a new type of Aliquat 336/PVC membrane has been made for extraction experiments. This new membrane is capable of holding more Aliquat 336 than previously developed extraction membranes, hence overcoming a major problem that has confronted many researchers for a long time. The new membrane has been used to investigate the rate of extraction for the Cd(II) ion in 2.0 M HCl solution and the effect of membrane thickness on the rate of extraction. The experimental results have shown this new membrane has a promising future in relevant industrial applications. A new method is also used in this study to qualitatively identify the oily substance on the surface of membrane after the extraction experiment was completed. This oily substance has been found to be Aliquat 336.

Improved membranes for the extraction of heavy metals

This work presents a series of experimental tests on new practical approaches in membrane design to improve extraction capacity and rate. We chose an extraction system involving Aliquat 336 as the extractant and Cd(II) as the metal ion to be extracted to demonstrate these new approaches. The core element in the new membrane assembly was the extractant loaded sintered glass filter. This membrane assembly provided a large interface area between the extractant and the aqueous solution containing metal ions. By recycling the aqueous solution through the membrane assembly, the extraction rate was significantly improved. The membrane assembly also offered good extraction capacity.

Linkage measures of the construction sector using the hypothetical extraction method

The hypothetical extraction method (HEM) is used to extract a sector hypothetically from an economic system and examine the influence of this extraction on other sectors in the economy. Linkage measures based on the HEM become increasingly prominent. However, little construction linkage research applies the HEM. Using the recently published Organisation for Economic Co-operation and Development input-output database at constant prices, this research applies the HEM to the construction sector in order to explore the role of this sector in national economies and the quantitative interdependence between the construction sector and the remaining sectors. The output differences before and after the hypothetical extraction reflect the linkages of the construction sector. Empirical results show a declining trend of the total, backward and forward linkages, which confirms the decreasing role of the construction sector with economic maturity over the examined period from a new angle. Analytical results reveal that the unique nature of the construction sector and multifold external factors are the main reasons for the linkage difference between countries. Moreover, hypothesis-testing results consider statistically that the extraction structures employed in this research are appropriate to analyse the linkages of the construction sector.

Knowledge extraction from a mixed transfer function artificial neural network

One of the main problems with Artificial Neural Networks (ANNs) is that their results are not intuitively clear. For example, commonly used hidden neurons with sigmoid activation function can approximate any continuous function, including linear functions, but the coefficients (weights) of this approximation are rather meaningless. To address this problem, current paper presents a novel kind of a neural network that uses transfer functions of various complexities in contrast to mono-transfer functions used in sigmoid and hyperbolic tangent networks. The presence of transfer functions of various complexities in a Mixed Transfer Functions Artificial Neural Network (MTFANN) allow easy conversion of the full model into user-friendly equation format (similar to that of linear regression) without any pruning or simplification of the model. At the same time, MTFANN maintains similar generalization ability to mono-transfer function networks in a global optimization context. The performance and knowledge extraction of MTFANN were evaluated on a realistic simulation of the Puma 560 robot arm and compared to sigmoid, hyperbolic tangent, linear and sinusoidal networks.

Fault detection in a cold forging process through feature extraction with a neural network

This paper investigates the application of neural networks to the recognition of lubrication defects typical to an industrial cold forging process employed by fastener manufacturers. The accurate recognition of lubrication errors, such as coating not being applied properly or damaged during material handling, is very important to the quality of the final product in fastener manufacture. Lubrication errors lead to increased forging loads and premature tool failure, as well as to increased defect sorting and the re-processing of the coated rod. The lubrication coating provides a barrier between the work material and the die during the drawing operation; moreover it needs be sufficiently robust to remain on the wire during the transfer to the cold forging operation. In the cold forging operation the wire undergoes multi-stage deformation without the application of any additional lubrication. Four types of lubrication errors, typical to production of fasteners, were introduced to a set of sample rods, which were subsequently drawn under laboratory conditions. The drawing force was measured, from which a limited set of features was extracted. The neural network based model learned from these features is able to recognize all types of lubrication errors to a high accuracy. The overall accuracy of the neural network model is around 98% with almost uniform distribution of errors between all four errors and the normal condition.

Knowledge extraction from a mixed transfer function artificial neural network

One of the big problems with Artificial Neural Networks (ANN) is that their results are not intuitively clear. For example, if we use the traditional neurons, with a sigmoid activation function, we can approximate any function, including linear functions, but the coefficients (weights) in this approximation will be rather meaningless. To resolve this problem, this paper presents a novel kind of ANN with different transfer functions mixed together. The aim of such a network is to i) obtain a better generalization than current networks ii) to obtain knowledge from the networks without a sophisticated knowledge extraction algorithm iii) to increase the understanding and acceptance of ANNs. Transfer Complexity Ratio is defined to make a sense of the weights associated with the network. The paper begins with a review of the knowledge extraction from ANNs and then presents a Mixed Transfer Function Artificial Neural Network (MTFANN). A MTFANN contains different transfer functions mixed together rather than mono-transfer functions. This mixed presence has helped to obtain high level knowledge and similar generalization comparatively to monotransfer function nets in a global optimization context.

Keyword extraction for text categorization

Text categorization (TC) is one of the main applications of machine learning. Many methods have been proposed, such as Rocchio method, Naive bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct a classifier. A new coming text document's category can be predicted. However, these methods do not give the description of each category. In the machine learning field, there are many concept learning algorithms, such as, ID3 and CN2. This paper proposes a more robust algorithm to induce concepts from training examples, which is based on enumeration of all possible keywords combinations. Experimental results show that the rules produced by our approach have more precision and simplicity than that of other methods.

Acoustic features extraction for emotion recognition

In the last decade, the efforts of spoken language processing have achieved significant advances, however, the work with emotional recognition has not progressed so far, and can only achieve 50% to 60% in accuracy. This is because a majority of researchers in this field have focused on the synthesis of emotional speech rather than focusing on automating human emotion recognition. Many research groups have focused on how to improve the performance of the classifier they used for emotion recognition, and few work has been done on data pre-processing, such as the extraction and selection of a set of specifying acoustic features instead of using all the possible ones they had in hand. To work with well-selected acoustic features does not mean to delay the whole job, but this will save much time and resources by removing the irrelative information and reducing the high-dimension data calculation. In this paper, we developed an automatic feature selector based on a RF2TREE algorithm and the traditional C4.5 algorithm. RF2TREE applied here helped us to solve the problems that did not have enough data examples. The ensemble learning technique was applied to enlarge the original data set by building a bagged random forest to generate many virtual examples, and then the new data set was used to train a single decision tree, which selects the most efficient features to represent the speech signals for the emotion recognition. Finally, the output of the selector was a set of specifying acoustic features, produced by RF2TREE and a single decision tree.

OWDEAH: Online Web Data Extraction based on Access History

Web data extraction systems are the kernel of information mediators between users and heterogeneous Web data resources. How to extract structured data from semi-structured documents has been a problem of active research. Supervised and unsupervised methods have been devised to learn extraction rules from training sets. However, trying to prepare training sets (especially to annotate them for supervised methods), is very time-consuming. We propose a framework for Web data extraction, which logged usersrsquo access history and exploit them to assist automatic training set generation. We cluster accessed Web documents according to their structural details; define criteria to measure the importance of sub-structures; and then generate extraction rules. We also propose a method to adjust the rules according to historical data. Our experiments confirm the viability of our proposal.

Extraction of compounds associated with water repellency in sandy soils of different origin

After an initial evaluation of several solvents, the efficiency of Soxhlet extractions with isopropanol/ammonia (s.g. 0.88) (70 : 30 v : v; 24 h) in extracting compounds associated with water repellency in sandy soils was examined using a range of repellent and wettable control soils (n = 15 and 4) from Australia, Greece, Portugal, The Netherlands, and the UK. Extraction efficiency and the role of the extracts in causing soil water repellency was examined by determining extract mass, sample organic carbon content and water repellency (after drying at 20°C and 105°C) pre- and post-extraction, and amounts of aliphatic C–H removed using DRIFT, and by assessing the ability of extracts to cause repellency in acid-washed sand (AWS).

Key findings are: (i) none of organic carbon content, amount of aliphatic C–H, or amount of material extracted give any significant correlation with repellency for this diverse range of soils; (ii) sample drying at 105°C is not necessarily useful before extraction, but may provide additional information on extraction effectiveness when used after extraction; (iii) the extraction removed repellency completely from 13 of the 15 repellent samples; (iv) extracts from all repellent and wettable control soils were capable of inducing repellency in AWS. The findings suggest that compounds responsible for repellency represent only a fraction of the extract composition and that their presence does not necessarily always cause repellency.

The application of rough set and Kohonen network to feature selection for object extraction

Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, images understanding and machine learning. The paper describes an application of rough sets method to feature selection and reduction in texture images recognition. The proposed methods include continuous data discretization based on Kohonen neural network and maximum covariance, and rough set algorithms for feature selection and reduction. The experiments on trees extraction from aerial images show that the methods presented in this paper are practical and effective.

Automatic pattern-taxonomy extraction for web mining

In this paper, we propose a model for discovering frequent sequential patterns, phrases, which can be used as profile descriptors of documents. It is indubitable that we can obtain numerous phrases using data mining algorithms. However, it is difficult to use these phrases effectively for answering what users want. Therefore, we present a pattern taxonomy extraction model which performs the task of extracting descriptive frequent sequential patterns by pruning the meaningless ones. The model then is extended and tested by applying it to the information filtering system. The results of the experiment show that pattern-based methods outperform the keyword-based methods. The results also indicate that removal of meaningless patterns not only reduces the cost of computation but also improves the effectiveness of the system.