5730613
A Rule-Based Approach for Fuzzy Overhaul Scheduling
Decisions on an overhaul schedule are often required by a dredging company in Shanghai because the fleet of dredges require regularly maintenance due to their heavy duty work. The decision on an overhaul schedule attempts to determine how the overhaul project completion time can be minimized. However, there are a number of constraints where jobs in the overhaul project have precedence relations, and human resources are limited. In addition, job duration times are fuzzy, and jobs may have several performance modes. Because of the complexity of computation, any exact approaches may be not viable. This paper proposes a fuzzy rule-based heuristic approach using the information available in the system. The approach developed here provides a fast scheduling result in such a complex decision, aiding readjustment of production in time. In addition, the approach also helps the company to save significant costs annually in decisions on overhaul projects.
26508095
Computer-aided system to solve uncertainty in project management
In project management, uncertainty often exists in relation to the estimation of activity durations. Fuzzy set theory is an effective way of solving the uncertainty caused by a lack of sufficient precise information. To schedule a project under conditions of limited resources, a genetic algorithm combined with a tabu mechanism is developed in order to search effectively and efficiently for a global-optimal or near-optimal solution. The fuzzy algorithm developed has been integrated into a computer-aided system. An experiment has demonstrated that the system is beneficial for solving such NP-hard problems when the project size increases.
26572934
Intelligent techniques for global e-business Web portal development
This paper presents a series of intelligent techniques that facilitates the automatic development of multilingual Web portal for e-business operating as global enterprise. It aims to provide an efficient and effective framework for selecting and organizing knowledge from voluminous linguistically diverse Web contents. To achieve this, a concept-based approach that incorporates text mining and Web intelligence using neural network and fuzzy techniques is proposed. First, a concept-based taxonomy of themes, which will act as the hierarchical backbone of the Web portal, is automatically generated. Second, a concept-based multilingual Web crawler is developed to intelligently harvest relevant multilingual documents from the Web. Finally, a concept-based multilingual text categorization technique is proposed to organize multilingual documents by concepts. As such, correlated multilingual Web documents can be gathered/filtered/organised based on their semantic content to facilitate high-performance multilingual information access.
14622679
A multilingual text mining approach to web cross-lingual text retrieval
Abstract To enable concept-based cross-lingual text retrieval (CLTR) using multilingual text mining, our approach will first discover the multilingual concept–term relationships from linguistically diverse textual data relevant to a domain. Second, the multilingual concept–term relationships, in turn, are used to discover the conceptual content of the multilingual text, which is either a document containing potentially relevant information or a query expressing an information need. When language-independent concepts hidden beneath both document and query are revealed, concept-based matching is made possible. Hence, concept-based CLTR is facilitated. This approach is employed for developing a multi-agent system to facilitate concept-based CLTR on the Web.
30777974
Identifying Parallel Web Documents by Filenames
Parallel Web documents are the crucial lexical basis for constructing robust multilingual Web-based linguistic knowledge resources. To identify parallel Web documents efficiently and effectively, this paper develops a new automatic approach based on filenames using the commonly used parallel document naming practice on the Web. The approach involves three procedures for identifying common file descriptor, language flag, and language flag-pair respectively among all file names examined. To examine how these three procedures can be used to get the best result, five methods are developed by incorporating these procedures in different ways. An experimental study on a Hong Kong government Web site is conducted to evaluate the performance of these five methods in terms of recall and precision. The experimental result shows that the method combining the procedures of the file descriptor alignment and the language flag-pair alignment outperforms other methods, with a 95.3% of precision rate and a 91.0% of recall rate.
19978728
Fuzzy Utilities Comparison in Multicriteria Analysis
This paper presents a new approach for comparing fuzzy utilities in fuzzy multicriteria analysis. The approach developed combines the merit of two prominent concepts individually used in the existing methods: the fuzzy reference set and the degree of dominance. The decisive information of the fuzzy utilities being compared is sensibly used. The computation involved is simple, and the underlying concepts are logically sound and comprehensible. The comparative study conducted on benchmark cases in the literature shows that the approach compares favorably with other methods examined.
7363085
An Empirical Study On The Generation Of Zero Anaphors In Chinese
In this paper, we describe the creation of rules for generating Chinese zero anaphors through a sequence of experiments in a stepwise enhanced manner. In the experiments, we basically examined the occurence of zero anaphors in a real text and the ones generated by the algorithms employing the rules, assuming the same semantic and discourse structures as the text. The factors of locality, syntactic constraints, discourse structure and salience of objects were considered in the rules. The results of the experiment show that 93% of the zero anaphors in the text can be correctly generated by an algorithm using a rule involving all the above factors.
10821054
A Kernel-Based Two-Stage One-Class Support Vector Machines Algorithm
One-class SVM is a kernel-based method that utilizes the kernel trick for data clustering. However it is only able to detect one cluster of non-convex shape in the input space. In this study, we propose an iterative two-stage one-class SVM to cluster data into several groups. In the first stage, one-class SVM is used to find an optimal weight vector for each cluster in the feature space, while in the second stage the weight vector is used to refine the clustering result. A mechanism is provided to control the optimal hyperplane to work against outliers. Experimental results have shown that our method compares favorably with other kernel based clustering algorithms, such as KKM and KFCM on several synthetic data sets and UCI real data sets.