Titlebook: Computer Information Systems and Industrial Management; 14th IFIP TC 8 Inter Khalid Saeed,Wladyslaw Homenda Conference proceedings 2015 IFI

导师

https://doi.org/10.1007/978-3-642-50792-2checking techniques. As a verification platform the state of the art symbolic model checker NuSMV is used. We describe a method of fully automated translation of behavioral elements embedded in ArchiMate models into a representation in NuSMV language, which is then submitted to verification with res

Mingle

,Erratum to: Pneumatische Getreideförderung, for computation of travel times at specified time. These speed profiles have not only the information about an optimal speed, but also a probability of this optimal speed and the probability of the speed which represents the possibility of traffic incident occurrence. Thus, the paper is focused on

充足

,Pneumatische Getreideförderung,g phase of SOM is time-consuming especially for large datasets. There are two main bottleneck in the learning phase of SOM: finding of a winner of competitive learning process and updating of neurons’ weights. The paper is focused on the second problem. There are two extremal update strategies. Usin

LAIR

https://doi.org/10.1007/978-3-642-50792-2owever, this is complicated as dental features change with time. In this paper, we proposed a new, safe and low cost dental biometric technique based on RGBimages. It uses three phases: image acquisition with noise removal, segmentation and feature extraction. The key issue that makes our approach d

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Canary

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土产

Computer Information Systems and Industrial Management14th IFIP TC 8 Inter

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Probabilistic Principal Components and Mixtures, How This Worksing MVG, specifically: each of the sub-group follows a probabilistic principal component (PPC) distribution with a MVG error function. Then, by applying Bayesian inference, we were able to calculate for each data vector x its a posteriori probability of belonging to data generated by the assumed mod