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

導(dǎo)師

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 Getreidef?rderung, 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

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,Pneumatische Getreidef?rderung,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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Customary

土產(chǎn)

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