Aiming at the problem of real-time and low accuracy of automatic recognition of human abnormal behaviour in a public area surveillance video, a simple multi-scale human anomaly behaviour detection algorithm based on video was proposed. Firstly, the binary image sequence of human body in surveillance video is acquired by background modelling method based on visual background extraction (ViBe). Then, the simple multi-scale algorithm is constructed by combining the aspect ratio, motion trajectory and video continuous interframe motion acceleration of the minimum circumscribed rectangle of the binarised image. The human target behaviour is judged, and then the normal behaviour of the human body - standing, walking, jogging, and abnormal behaviour - shouting for help, falling, punching, wandering, and sudden running are identified. The experimental results show that the human body moving target recognition by ViBe combined with simple multi-scale algorithm for abnormal behaviour detection has good real-time performance and high accuracy.

The equipment lock has been an important tool for the power company to protect the electricity metering equipment. However, the conventional equipment lock has two potential problems: vandalism and counterfeiting. To fulfill the control and track the potential illegal behavior, the human labor and paper are required to proceed with related operations, resulting in the consumption of a large amount of human resources and maintenance costs. This study focused on the design of RFID technology applied to the traditional equipment lock, which, through the mobile and electronic technology, strengthens the management/operating convenience of the lock and provides the solutions for anti-counterfeiting and spoilage detection so that the national energy can be properly protected and fairly distributed.

It is very important that construct a common library from the software development process in a standard analysis pattern. Reusing analysis knowledge in the same domain and the common library can help to build up high-quality applications in limited developing time. System analysis patterns enable a software application to model a specific problem by representing some domain classes and their relationships as modeling components. In this paper, a conceptual model was developed that emphasized the role of analysis pattern and domain library for application with ontology including domain model and dynamic characteristics of classes in tree maps. The result shows two conclusions were derived: first, less development and requirement changes the process costs with common library in ontology and we easy to search and reuse the library components. The second, it is more highly user satisfaction due to the system flexibility and more patients were paid for requirement changes.

In recent year, because of the professional teamwork, to improve the qualification percentage of products, to accelerate the acknowledgement of product defects and to find out the solution, the LCD driver IC packaging factories have to establish an analysis mode for quality problems of product for more effective and quicker acquisition of needed information and to improve the customer’s satisfaction for information system. The past information system used neural network to improve the yield rate of production. In this research employs the star schema of data warehousing as the base of line analysis, and uses decision tree in data mining to establish a quality analysis system for the defects found in the production processes of package factories in order to provide an interface for problem analysis, enabling quick judgment and control over the cause of problem to shorten the time solving the quality problem. The result of research shows that the use of decision tree algorithm reducing the numbers of defected inner leads and chips has been improved, and using decision tree algorithm is more suitable than using neural network in quality problem classification and analysis of the LCD driver IC packaging industry.

First, we classify the selected customers into clusters using RFM model to identify high-profit, gold customers. Subsequently, we carry out data mining using association rules algorithm. We measure the similarity, difference and modified difference of mined association rules based on three rules, i.e. emerging pattern rule, unexpected change rule, and added/perished rule. In the meantime, we use rule matching threshold to derive all types of rules and explore the rules with significant change based on the degree of change measured. In this paper, we employ data mining tools and effectively discover the current spending pattern of customers and trends of behavioral change, which allow management to detect in a large database potential changes of customer preference, and provide as early as possible products and services desired by the customers to expand the clientele base and prevent customer attrition.

This paper aims to explain a specific intelligent computer integrated manufacturing system by integrating the following five major domains: computer integrated manufacturing, data warehouse, online analytical processing, data mining and artificial intelligence. The data mining system makes use of the decision tree algorithm and classification model in exploring the meaningful information, which is useful in the process of decision making. Subsequently, the rules discovered by the data mining system are expressed through the rule based knowledge presentation method of the expert system. The intelligent CIM system is applied to semiconductor packing factories and also point at the great fluctuation of dynamic random access memory prices. The contribution can increase business competitiveness, reduce production cost, and promote the rate of available promise for order.

Since fuzzy knowledge representation can facilitate interaction between an expert system and its users, the effective construction of a fuzzy knowledge base is important. Fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, and can thus be helpful in building a prototype fuzzy knowledge base. We define that a fuzzy sequence is an ordered list of frequent fuzzy grids, and the length of a fuzzy sequence is the number of frequent fuzzy grids in the frequent fuzzy sequence. Frequent fuzzy grids and frequent fuzzy sequences can be determined by comparing individual fuzzy supports with the user-specified minimum fuzzy support. A fuzzy sequential pattern is just a frequent fuzzy sequence, but it is not contained in any other frequent fuzzy sequence. In this paper, an effective algorithm called the Fuzzy Grids Based Sequential Patterns Mining Algorithm (FGBSPMA) is proposed to generate fuzzy sequential patterns. A numerical example is used to show an analysis of the user visit to websites, demonstrating the usefulness of the proposed algorithm.

Abstract Sequential patterns refer to the frequently occurring patterns related to time or other sequences, and have been widely applied to solving decision problems. For example, they can help managers determine which items were bought after some items had been bought. However, since fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, they are helpful in building a prototype fuzzy knowledge base in a business. Moreover, each fuzzy sequential pattern consisting of several fuzzy sets described by the natural language is well suited for the thinking of human subjects and will help to increase the flexibility for users in making decisions. Additionally, since the comprehensibility of fuzzy representation by human users is a criterion in designing a fuzzy system, the simple fuzzy partition method is preferable. In this method, each attribute is partitioned by its various fuzzy sets with pre‐specified membership functions. The advantage of the simple fuzzy partition method is that the linguistic interpretation of each fuzzy set is easily obtained. The main aim of this paper is exactly to propose a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method. Two numerical examples are utilized to demonstrate the usefulness of the proposed method.

For each decision problem, there is a competence set, proposed by Yu (1990), consisting of ideas, knowledge, information, and skills required for solving the problem. Thus, it is reasonable that we view a set of useful patterns discovered from a relational database by data mining techniques as a needed competence set for solving one problem. Significantly, when decision makers have not acquired the competence set, they may lack confidence in making decisions. In order to effectively acquire a needed competence set to cope with the corresponding problem, it is necessary to find appropriate learning sequences for acquiring those useful patterns, the so-called competence set expansion. This paper thus proposes an effective method consisting of two phases to generate learning sequences. The first phase finds a competence set consisting of useful patterns by using a proposed data mining technique. The other phase expands that competence set with minimum learning cost by the minimum spanning table method (Feng and Yu (1998)). From a numerical example, we can see that it is possible to help decision makers to solve the decision problems by use of the data mining technique and the competence set expansion, enabling them to make better decisions.

We propose the Fuzzy Grid Based Sequential Pattern Mining Algorithm (FGBSPMA) to generate all fuzzy sequential patterns from relational databases. In FGBSPMA, each quantitative attribute is viewed as a linguistic variable, and can be divided into many candidate 1-dim fuzzy grids. FGBSPMA consists of two phases: one is to generate all the large 1-fuzzy sequences, the other is to generate all the fuzzy sequential patterns. FGBSPMA is an efficient fuzzy sequential pattern mining algorithm, because FGBSPMA scans the database only once and applies proper operations on rows of tables to generate large fuzzy sequences and fuzzy sequential patterns. An example is given to illustrate a detailed process for mining the fuzzy sequential patterns from a specified relation. From this example, we show the efficiency and usefulness of FGBSPMA.

This paper presents a prototype of logistics flows and an information extraction system between customers and vendors using the World Wide Web (WWW) and XML with SAP R/3 programming technology (ABAP/4 Language). Three issues are considered: (I) to resolve the SAP SAPLPD performance issue existing in multi-byte character print with SAPWIN by developing an HTML layout set for the report printout; (2) to support business process improvement for customer services, logistics, planning and manufacturing through an XML data interface embedded in the software solutions and; (3) to provide a reliable and scalable information technology infrastructure which could fulfill the requirements of data transparency and data consistency. We present an open architecture of the data extraction method. It can be used to realise data transparency and data consistency for a supply chain. Enterprise within a multidatabase can improve the performance of information flows. It can solve the data printing problem in many types of layout set to meet the demands of the enterprise.

We develop a heuristic algorithm for the reliability-oriented file assignment problem (HROFA), which uses a careful reduction method to reduce the problem space. Based on some numerical results, the HROFA algorithm obtains the exact solution in most cases and the computation time is improved significantly. When it fails to give an exact solution, the deviation from the exact solution is very small.