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In this paper, we use GA to improve the D-S evidence theory, and apply the improved D-S evidence theory to VIP intelligent recognition and recommendation system. In the VIP intelligent recognition and recommendation system of clothes, there are three main evidences: body size, personal preferences, and purchase records. So collision often happens inevitable. This requirement asks us to find out a suitable method to identify the VIPs needs. D-S evidence theory can improve the rate of identification, but has no idea about the collision. The improved D-S evidence theory based on genetic algorithm can deal with the collision evidence and improve the rate of the identification and the stability. As such we can provide VIP more suitable recommendation. The experiment results of clothes recommendation demonstrate the flexibility of the improved method.

Membrane protein interactions are vitally important for every process in a living cell. Information about these interactions can improve our understanding of diseases and provide the basis to revolutionize therapeutic treatments. However, current experimental techniques for large-scale detection of protein-protein interactions are biased against membrane proteins, it is necessary to develop novel tools to deal with this kind of bio-network. To realize this, we construct membrane protein interaction network based on gene regulatory network model. Three model forms, basic form, non-dimensionalization form, and more complex form, are proposed to understand the dynamic and collective control of developmental process and the characters of membrane protein interaction network, including small-world network, scale free distributing and robustness, and its significance for biology. Four simulation examples are presented to illustrate the usefulness and flexibility of the GRN model method for the study of membrane protein interaction network. The results show that the proposed approach holds a high potential to become a useful tool in prediction of membrane protein interactions. Moreover, it has biological significance and value for biology and pharmacology.

According to the characteristic of GPS system fo intelligent garment, this paper presents a strategy of GPS data processing by removing redundant information and shifting the combination codes. Its compressed result is analyzed by comparing with direct data compression and Huffman coding. The above-mentioned strategy takes into account both the speed and the effect of the compression, and has a good performance in the compression rate. The non-destructive GPS data processing method has a positive and practical significance in the intelligent garment for efficient monitoring.

Protein–protein interactions (PPIs) are vitally important for every process in a living cell. Information about these interactions can improve our understanding of diseases and provide the basis to revolutionize therapeutic treatments. However, since PPIs are involved with extremely complicated biological processes, it is necessary to develop novel tools to deal with this kind of network systems. To realize this, a bio-entity network approach is introduced to show the topology structure of dynamic and collective performances of PPI networks and analyze the variance of the protein node that plays an important role in the PPI network. Also, spectrum analysis is used to capture the discrete and stochastic feature of PPIs. The yeast protein interaction network is considered as a paradigm. It is demonstrated that the proposed approach can easily and clearly identify the hub-proteins that have the most impact on the PPI system concerned. It is expected that the bio-entity network approach as presented in this paper might become a useful tool in system biology. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

Inspired by the systemic emergence and self-evolution of the neuroendocrine-immune system, we have developed a bio-network architecture and built a novel bio-network platform for complex services on next-generation Internet. Since stability analysis is an important issue in complex networks, we discuss the evolutionary stability of the bio-network architecture based on energy design. We build a stability model of energy flow in the bio-network system. Considering that the stability model of energy flow is similar to the common market equilibrium model, we propose the measurement of energy entropy and global stability based on the Nash equilibrium game. The reinforcement learning of stability strategy based on the improved differential evolution algorithm is applied to solve the problem with global stability optimization. The simulation results of energy stability are provided to validate the proposed method.

Future Internet should be capable of extensibility, survivability, mobility, and adaptability to the changes of different users and network environments, so it is necessary to optimize the current Internet architecture and its applications. Inspired by the resemble features between the immune systems and future Internet, the authors introduce some key principles and mechanisms of the immune systems to design a bio-network architecture to address the challenges of future Internet. In the bio-network architecture, network resources are represented by various bioentities, while complex services and application can be emerged from the interactions among bio-entities. Also, they develop a bio-network simulation platform which has the capability of service emergence, evolution, and so forth. The simulation platform can be used to simulate some complex services and applications for Internet or distributed network. The simulators with different functions can be embedded in the simulation platform. As a demonstration, this chapter provides two immune network computation models to generate the emergent services through computer simulation experiments on the platform. The experimental results show that the bio-entities on the platform provide quickly services to the users’ requests with short response time. The interactions among bio-entities maintain the load balance of the bio-network and make the resources be utilized reasonably. With the advantages of adaptability, extensibility, and survivability, the bio-network architecture provides a novel way to design new intelligent Internet information services and applications.

In this paper, we focus on design of the efficient algorithms and tools for search and retrieval of documents on Internet. We propose an Internet information retrieval model based on mobile agent technique, fuzzy index algorithm, and genetic search methods. The whole work frame and the structure of the model are discussed. Mobile agents can be used to optimize network traffic for distributed applications. Efficient search of documents can be done with genetic algorithm that exploits the principles of temporal and spatial locality. Fuzzy index algorithm is used to intelligently evaluate the importance of the information searched on Internet. We have also implemented a software platform for experimenting with intelligent information retrieval on Internet using Java language. The experimental results demonstrate the effectiveness of our model.