Cf F. Cheung

A quantitative correlation coefficient mining method for business intelligence in small and medium enterprises of trading business

Business intelligence based on data mining has been one of the popular and indispensable tools for identifying business opportunity in sales and marketing of new products. The traditional data mining methods based on association rules may be inadequate in completely uncovering the hidden patterns of sales based on transaction records. This paper presents a qualitative correlation coefficient mining method which is capable of uncovering hidden patterns of sales and market. Hence, a prototype business intelligence system (BIS) named correlation coefficient sales data mining system (CCSDMS) has been developed and successfully trial implemented in a selected reference site. A series of experiments have been conducted to evaluate the performance of the proposed system. The results generated by the BIS are compared with a well known market available data mining system. The proposed quantitative correlation coefficient mining method is found to possess higher accuracy, better computational effectiveness and higher predictive power. With the new approach, associations for product relations and customer periodic demands are revealed and this can help to leverage organizational marketing capital to enhance quality and speed of promotions as well as awareness of product relations.

Knowledge-based extraction of intellectual capital-related information from unstructured data

Nowadays, there is an increasing demand for the identification of an organizations intellectual capital (IC) for decision support and providing important managerial insights in knowledge-intensive industries. In traditional approaches, identification of an organizations IC is usually done manually through interviews, surveys, workshops, etc. These methods are labor and time intensive and the quality of the results is highly dependent on, among other things, the experience of the investigators. This paper presents a Knowledge-based Intellectual Capital Extraction (KBICE) algorithm which incorporates the technologies of computational linguistics and artificial intelligence (AI) for automatic processing of unstructured data and extraction of important IC-related information. The performance of KBICE was assessed through a series of experiments conducted by using publicly available financial reports from the banking industry as the testing batch and encouraging results have been obtained. The results showed that, through the use of hybrid intelligent matching strategies, it is possible to extract commonly referred IC-related information from unstructured data automatically. IC information analyst can rely on this method as an additional mean to identify and extract the commonly sought IC information from financial reports in a fast, systematic and reliable manner.

An unstructured information management system (UIMS) for emergency management

Emergency management has drawn the attention of governments worldwide. It is an emerging realm for governments and organizations to respond to sudden catastrophic events rapidly and effectively during a crisis, e.g. social unrest, natural disaster, unforeseen event, and so on. There is a lot of information and knowledge involved in emergency management. The key question is how to search for the appropriate information and knowledge quickly and accurately from the massive information and knowledge based on the nature, characteristics, status and situations of the unexpected emergency in order to support the intelligent decision-making process in handling emergencies. In this paper, an unstructured information management system (UIMS) is presented for emergency management. A concept relationship model (CRM) and a dynamic knowledge flow model (DKFM) of the emergency incidence have been built to organize and represent emergency knowledge. The models can support decision-makers to have better understanding of the dependence and degree of correlation between different concepts about the emergency in order to make the appropriate decisions. The system is trial implemented in a city emergency management system and its performance is evaluated and discussed.

Measuring optical freeform surfaces using a coupled reference data method

Flat optical freeform surfaces usually possess non-rotational symmetry with a small curvature and lack of strong features for surface alignment. Due to the lack of strong features and small curvature, it is difficult to align the design and measured surfaces for characterizing the surface quality of flat optical freeform surfaces with sub-micrometre form accuracy. The traditional least squares method (LSM) generally produces large errors as there is a lack of strong features as reference for the alignment of the design and measured surfaces. This paper proposes a novel and practical method named the coupled reference data method (CRDM) to evaluate flat optical freeform surfaces with high efficiency and precision in the nanometre scale. The method couples reference data to the workpiece of the freeform surface designed model and the concerning reference features are machined together with the workpiece. By aligning the reference data, the proposed CRDM carries out fast surface matching. This makes good preparation for the next matching optimization which is conducted by the least-squares and minimax zone method. After the precise surface matching, the flat optical freeform surface can be evaluated by 3D form error topography and parameters. As compared with a traditional freeform measurement method such as LSM, it is interesting to note that the accuracy and the stability of the measurement can be significantly enhanced by the CRDM.

Development of a virtual machining and inspection system for ultra-precision diamond turning

Abstract The conventional approach to establish optimal processing conditions in ultraprecision diamond turning is empirical in nature. The achievement of a super mirror finish and submicrometre form accuracy in many current industrial applications still depends much on the experience and skills of the machine operators through trial cutting tests when new materials or new machine tools are used. In this paper, a virtual machining and inspection system (VMIS) is presented for ultra-precision diamond turning. The development of the VMIS aims at creating a virtual manufacturing environment for the optimization of process parameters and evaluating the quality of machined surface through various modelbased simulation modules. The VMIS is validated through a series of practical cutting tests conducted on a two-axis computer numerical control ultra-precision turning machine. The experimental results agree well with the simulated data.

A pilot study on a knowledge-based case library to support suicide risk assessment

This article describes the system development processes for an electronic case library, built to facilitate and support the suicide prevention service provided by a social service organization in Hong Kong. It is a knowledge management system in which both ‘rule-based reasoning’ (RBR) and ‘case-based reasoning’ (CBR) are adopted. Initial test results, potentials and limitations of this knowledge management approach are discussed.