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Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

This research study proposes a classification system to identify and prevent occupational accidents in fiber storage warehouses at a pulp and paper facility. The present analysis is based on variables including pedestrian circulation, bobcat, trailer trucks, access, pedestrian circulation zones, and handrails. The proposed methodology defines and trains the system by using occupational accident event data collected at the facility. Three different predicting algorithms are used: J48 decision-making trees, Naive Bayes, and Bayesian nets. The results show that the J48 decision-making tree algorithm accurately identifies possible occupational accidents 90% of the time. It is concluded that identifying variables involved in occupational accidents allows generating a C4.5 (J48) decision-making tree that can be used as a support tool to prevent occupational accidents. [ABSTRACT FROM AUTHOR]

In this paper, we extend the notion of similarity matrix, which has been used to define similarity between nodes of two graphs, to the case of colored graphs, where the coloring is either on the nodes or on the edges of both graphs. The proposed method tries to find the optimal matching between the nodes or edges of both graphs but only performs the comparison when their colors are the same. The proposed cost function nevertheless uses the connectivity between all nodes and edges of both graphs. We then also show how to extend this to the notion of low rank similarity matrix, by defining it as a constrained optimization problem. [ABSTRACT FROM AUTHOR]

In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Modern information retrieval systems use keywords within documents as indexing terms for search of relevant documents. As Chinese is an ideographic character-based language, the words in the texts are not delimited by white spaces. Indexing of Chinese documents is impossible without a proper segmentation algorithm. Many Chinese segmentation algorithms have been proposed in the past. Traditional segmentation algorithms cannot operate without a large dictionary or a large corpus of training data. Nowadays, the Web has become the largest corpus that is ideal for Chinese segmentation. Although most search engines have problems in segmenting texts into proper words, they maintain huge databases of documents and frequencies of character sequences in the documents. Their databases are important potential resources for segmentation. In this paper, we propose a segmentation algorithm by mining Web data with the help of search engines. On the other hand, the Romanized pinyin of Chinese language indicates boundaries of words in the text. Our algorithm is the first to utilize the Romanized pinyin to segmentation. It is the first unified segmentation algorithm for the Chinese language from different geographical areas, and it is also domain independent because of the nature of the Web. Experiments have been conducted on the datasets of a recent Chinese segmentation competition. The results show that our algorithm outperforms the traditional algorithms in terms of precision and recall. Moreover, our algorithm can effectively deal with the problems of segmentation ambiguity, new word (unknown word) detection, and stop words. [ABSTRACT FROM AUTHOR]

Structural computing represents a form of hypertext, in which structure is foregrounded in the analysis, design and implementation of systems. Until now, the vast majority of structural computing work has focused on the implementation of frameworks. The applications implemented in these environments are often reimplementations of existing hypertext applications, intended to show the utility of the provided environments in terms of implementation, interoperability, or other metrics. Often, spatial hypertext applications are reimplemented, since they are seen as particularly good tests of structural computing infrastructures. This is because the demonds they pose are quite different from many other types of hypertext applications. However, structural computing researchers have failed to reanalyse and redesign these applications in a structurally-aware way. There are substantial improvements that result from rethinking such applications from a structure-first perspective. In this paper, we present such a reanalysis, and describe its implications for spatial hypertext within structural computing environments. [ABSTRACT FROM AUTHOR]