By Patrícia Macedo, Luis M. Camarinha-Matos (auth.), Luis M. Camarinha-Matos, Pedro Pereira, Luis Ribeiro (eds.)
This e-book constitutes the refereed court cases of the 1st IFIP WG 5.5/SOCOLNET Doctoral convention on Computing, electric and business platforms, DoCEIS 2010, held in Costa de Caparica, Portugal, in February 2010.
The sixty two revised complete papers have been conscientiously chosen from a number of submissions. They conceal a large spectrum of issues starting from collaborative firm networks to microelectronics.The papers are geared up in topical sections on firm networks and strategic alignment; details structures; collaborative networks aid; possibility evaluate and selection aid; evolvable manufacturing unit automation; cooperative robots; robots and manipulation; Petri nets dependent modeling; telecommunications; sensorial conception; sign processing; power platforms; devoted strength structures; electric equipment; electronics layout and try out; and digital circuits.
Read or Download Emerging Trends in Technological Innovation: First IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2010, Costa de Caparica, Portugal, February 22-24, 2010. Proceedings PDF
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Extra info for Emerging Trends in Technological Innovation: First IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2010, Costa de Caparica, Portugal, February 22-24, 2010. Proceedings
To illustrate the suitability and efficiency of the method, we apply it to an astronomical data base. Keywords: Euler method for differential equation, clustering, network mapping, genetic algorithm, computational astronomy. 1 Introduction Determining the interrelationships of objects belonging to either a given data set (clustering) or networks (network mapping) is a central problem in many fields, including data mining (web mining, unsupervised classification) , computational biology (quantitative trait loci, analysis of gene expression profiles  and proteinprotein interaction networks ), and computational astronomy (clustering profiles of variable stars, determining types of planets by luminosity clustering ).
As the majority of users are accustomed to expressing their information needs in terms of keywords, the TSTS method allows users to ask questions using a traditional ‘Google like’ interface. However, the TSTS uses semantic information concerning the application domain to obtain results that are not possible to be found when traditional search methods are applied. The TSTS method is based on the TST similarity measure that allows assessing the distance between different concepts in a semantic, spatiotemporal database.
The ET-DRN algorithm also allows for the merging of classes based on the value of the Kullback-Leibler divergence, thus increasing its usability and making it independent of any apriori information about the data base. We believe this method can be applied to any kind of database. Since it would be easy to transform into a parallel processing platform, the ET-DRN algorithm can be applied to very large databases, such as biological databases and future astronomical databases from European Space Agency (108) .