Chun-Ho Wu

Chaotic hybrid algorithm and its application in circle detection

An evolutionary circle detection method based on a novel Chaotic Hybrid Algorithm (CHA) is proposed. The method combines the strengths of particle swarm optimization, genetic algorithms and chaotic dynamics, and involves the standard velocity and position updating rules of PSO with the ideas of GA selection, crossover and mutation. In addition, the notion of species is introduced into the proposed CHA to enhance its performance in solving multimodal problems. The effectiveness of the Species based Chaotic Hybrid Algorithm (SCHA) is proven through simulations and benchmarking, and finally, it is successfully applied to solve circle detection problems.

Using a fuzzy association rule mining approach to identify the financial data association

Highlights? We study Hong Kong Stock Exchange which was the second largest stock market in Asia up to 2010. ? We examine association of Hang Seng Index with other economics indices. ? Increasing RMB-HK exchange rate and export value will highly increase the variation of Hang Seng Index. ? Increasing RMB-HK exchange rate, export value and GDP will also highly increase the variation of Hang Seng Index. ? The minded rules reveal interesting patterns between different economical indices and the individual stock market price. In the rapidly changing financial market, investors always have difficulty in deciding the right time to trade. In order to enhance investment profitability, investors desire a decision support system. The proposed artificial intelligence methodology provides investors with the ability to learn the association among different parameters. After the associations are extracted, investors can apply the rules in their decision support systems. In this work, the model is built with the ultimate goal of predicting the level of the Hang Seng Index in Hong Kong. The movement of Hang Seng Index, which is associated with other economics indices including the gross domestic product (GDP) index, the consumer price index (CPI), the interest rate, and the export value of goods from Hong Kong, is learnt by the proposed method. The case study shows that the proposed method is a feasible way to provide decision support for investors who may not be able to identify the hidden rules between the Hang Seng Index and other economics indices.

A novel 3D model retrieval approach using combined shape distribution

With the rapid development of 3D digital shape information, content-based 3D model retrieval has become an important research field. 3D models are likely to be as prevalent as other multimedia data types in the future. There is a pressing need for effective content-based 3D model retrieval methods. In this paper, a novel combined shape distribution (CSD) descriptor is proposed for 3D model retrieval based on principal plane analysis and group integration. Firstly, based on principal plane analysis, the second principal plane is obtained by using sequential quadratic programming. Secondly, two novel 3D shape descriptors are proposed by introducing the plane normal vectors to other shape distributions. Thirdly, since the histogram of the proposed descriptors can be classified as belonging to one of three types: positive, negative, or crossed with each principal plane, further improvements to the descriptors are presented by integrating these three types of histograms. Finally, a CSD descriptor based on the synthesis of the above descriptors is proposed. Several retrieval performance measures and visual experimental results show that the new methods achieved good retrieval performance.

An improved species based genetic algorithm and its application in multiple template matching for embroidered pattern inspection

This paper describes an improved genetic algorithm (GA) using the notion of species in order to solve an embroidery inspection problem. This inspection problem is actually a multiple template matching problem which can be formulated as a multimodal optimization problem. In many cases, the run time of the multiple template matching problem is dominated by repeating the similarity calculations and moving the templates over the source image. To cope with this problem, the proposed species based genetic algorithm (SbGA) is capable to determine its neighborhood best values for solving multimodal optimization problems. The SbGA has been statistically tested and compared with other genetic algorithms on a number of benchmark functions. After proving its effectiveness, it is integrated with multi-template matching method, namely SbGA-MTM method to solve the embroidery inspection problem. Furthermore, the notion of bounded partial correlation (BPC) is also adopted as an acceleration strategy, which enhances the overall efficiency. Experimental results indicate that the SbGA-MTM method is proven to solve the inspection problem efficiently and effectively. With the proposed method, the embroidered patterns can be identified and checked automatically.

Chaotic species based particle swarm optimization algorithms and its application in PCB components detection

An improved particle swarm optimizer using the notion of chaos and species is proposed for solving a template matching problem which is formulated as a multimodal optimization problem. Template matching is one of the image comparison techniques. This technique is widely applied to determine the existence, location and alignment of a component within a captured image in the printed circuit board (PCB) industry where 100% quality assurance is always required. In this research, an efficient auto detection method using a multiple templates matching technique for PCB components detection is described. The new approach using chaotic species based particle swarm optimization (SPSO) is applied to the multi-template matching (MTM) process. To test its performance, the proposed Chaotic SPSO based MTM algorithm is compared with other approaches by using real captured PCB images. The Chaotic SPSO based MTM method is proven to be superior to other methods in both efficiency and effectiveness.

Applying Chaotic Particle Swarm Optimization to the Template Matching Problem

An improved particle swarm optimization algorithm, CSPSO (Chaotic Species-based particle swarm optimization), is proposed for solving the template matching problem. Template matching is one of the image comparison techniques widely applied to component existence checking in the printed circuit board (PCB) and electronics assembly industries. The proposed approach adopts the special nonlinear characteristic and ergodicity of chaos to enrich the search ability of the species-based particle swarm optimization (SPSO). To test its performance, the proposed CSPSO-based approach is compared with SPSO-based approach using two experimental studies. The CSPSO-based approach is proven to be superior to the original SPSO-based one in term of efficiency.

Network Topology Management Optimization of Wireless Sensor Network (WSN)

Network topology management is one of the critical concerns when designing a Wireless Sensor Network (WSN). In this research, four basic factors including the total production cost, sensing coverage, network connectivity and fault tolerance are considered. A mathematical model is proposed to optimize four optimization metrics corresponding to the four design factors. This approach attaches a weighting coefficient to each optimization metric to adjust their importance in the optimization model. To solve the proposed model, an Ant Colony Optimization (ACO) based metaheuristics method, called MAX–MIN Ant System (MMAS) is used. In the experiment, Greedy algorithm (Greedy) and Genetic Algorithm (GA) are also adopted to solve the proposed model. The results indicate that MMAS shows a satisfactory performance on solving the proposed model, which there is an improvement on the number of sensor nodes comparing to the result of Greedy, and a better fitness value than the result of GA.

Design of a wireless sensor network monitoring system for biological and pharmaceutical products

Biological and pharmaceutical (B&P) products are essential in modern life. Currently, the demand and requirements of these products are dramatically growing; product quality and integrity then become crucial. As a key factor for ensuring quality and integrity, the current product status monitoring system needs to be enhanced in a more sophisticated and comprehensive way. This research describes the design of a wireless sensor network (WSN) monitoring system for B&P products supply chain management. In this study, the system architecture is presented based on the proposed framework. The framework consists of three processes: the first one is to generate a WSN with full coverage, the second one is to determine the optimal position of the sink, and the last process is to add additional relay nodes for providing fault tolerance to the network. Finally, a feasibility test on B&P product storage in a hospital is conducted. The proposed system provides a systematic approach for the deployment of sensor nodes and relay nodes. It also provides a relatively simple and rapid method for industries in general to follow and implement when designing a WSN system.

Franchising decision support system for formulating a center positioning strategy

Purpose – The purpose of this paper is to propose a franchising decision support system (FDSS) for future development planning by center positioning strategy formulation under a franchising business model. Design/methodology/approach – The system makes use of data collected from the franchising business and external environment analysis for decision making in center positioning problems. The fuzzy logic approach is integrated into the system for analyzing the geographical market dynamics including profitability and competitiveness in the district concerned. To demonstrate the application of the proposed FDSS, a case study is conducted in a Hong Kong-based franchising private education center, i.e. Dr I-Kids Education Center. Findings – The tailor made FDSS helps to facilitate the business operations of the franchising education center and develops a district positioning model for the centers located in the 18 districts of Hong Kong. The findings provide a solid foundation for marketing strategy and expans...

A case study on multimedia e-tailing services: can watermarks help build customer relationships?

Digital watermarking is widely discussed as a promising technique for digital good protection sold in the internet. Copyright information can be embedded directly into media files. However, few researchers have studied any possible contribution brought by watermarks in e-customer relationship building. Through a survey of 185 e-customers who had purchased multimedia products online, the hypothesised model was validated using LISREL. All hypotheses are supported. This study has contributed to both theory and practice by building a model to realise the e-customer relationship building in buying multimedia product and suggesting watermarks can help relationship building and retain e-customer to obtain multimedia products from legal channel. Two schemes have been suggested which are suitable for applying watermarking techniques in the e-market.