46527427
Form design of product image using grey relational analysis and neural network models
This paper presents a new approach to determining the best design combination of product form elements for matching a given product image represented by a word pair. A grey relational analysis (GRA) model is used to examine the relationship between product form elements and product image, thus identifying the most influential elements of product form for a given product image. A grey prediction (GP) model and a neural network (NN) model are used individually and in conjunction with the GRA model, in order to predict and suggest the best form design combination. An experimental study on the form design of mobile phones is conducted to evaluate the performance of these models. Based on expert surveys, the concept of Kansei Engineering is used to extract and evaluate the experimental samples, and a morphological analysis is used to extract form elements from these sample mobile phones. The evaluation result shows that all the NN-based models outperform the GP-based models, suggesting that the NN model should be used to help product designers determine the best combination of form elements for achieving a desirable product image. The GRA model can be incorporated into the NN model to help designers focus on the most influential elements in form design of mobile phones.
6763775
A Metaheuristic Approach to Fuzzy Project Scheduling
In practice, projects may contain many activities. To schedule such projects, under constraints of limited resource and precedence relations, it becomes an NP hard problem. Any exact algorithms will have difficulty solving such problems. In addition, many activities of a project are quite often imprecise and vague due to lack of sufficient information. Fuzzy set theory is the best way to describe such data. In this study, a fuzzy simulated annealing approach is developed to handle resource-constrained project scheduling with fuzzy data.
32278799
Personalized Multilingual Web Content Mining
Personalized multilingual Web content mining is particularly important for user who wants to keep track of global knowledge that is relevant to his/her personal domain of interest over the multilingual WWW. This paper presents a novel concept-based approach to personal multilingual Web content mining by constructing a personal multilingual Web space using self-organising maps. Multilingual linguistic knowledge required to define the multilingual Web space is made available by encoding all multilingual concept-term relationships using a multilingual concept map. With this map as the linguistic knowledge base, a concept-based multilingual text miner is developed to reveal the conceptual content of multilingual Web documents and to form concept categories of multilingual Web documents on a concept-based browsing interface. To construct the personal multilingual Web space, a concept-based user profile is generated from a user’s bookmark file for highlighting the user’s topics of information interests on the browsing interface. As such, personal multilingual Web mining activities ranging from explorative browsing to user-oriented concept-focused information filtering are facilitated.
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.
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.
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.
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.
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.