Computer Information Systems and Industrial Management: 14th by Khalid Saeed, Wladyslaw Homenda

By Khalid Saeed, Wladyslaw Homenda

This e-book constitutes the complaints of the 14th IFIP TC eight overseas convention on machine info structures and commercial administration, CISIM 2015, held in Warsaw, Poland, in September 2015.
The forty seven papers offered during this quantity have been conscientiously reviewed and chosen from approximately eighty submissions. the most subject matters lined are biometrics, safeguard structures, multimedia, category and clustering with purposes, and business management.

 

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This problem is usually solved using methods like one class classification, novelty or anomaly detection, and outlier identification, see, for example, [2,8,12,14]. In the following we will be concerned only with the last item. We will consider only one machine being in good condition. Our novel contributions are related to a modelling of multidimensional diagnostic data using probabilistic approach. Our proposal is to combine three statistical models into one common model, which yields so called probabilities a posteriori (posteriors).

X15 ] of both samples are ordered according to increasing values of ZWE corresponding to their respective x vectors. The B500 set is supposed to be the learning sample and the Bres set the test sample for the constructed probabilistic model. Our first goal is to obtain for the data set B500 a decomposition into two Gaussian sub-samples numerated as j = 1 and j = 2. A second goal is to assert the connection of the derived sub-samples with the load variable ZWE. A third goal is to obtain an affirmation that the obtained decomposition (un-mixing of the data set B500 into two component sets from which it is composed) fits adequately to the gathered data.

Within this approach, feature descriptors are assigned to a given music excerpt in order to perform automatic annotation of a given piece. Thus, the adequate selection of parameters, the algorithm optimization in terms of signal processing and data exploration techniques serve as key technologies that provide effective music tagging automatically. An example of a set of descriptors (191 in total) based on MPEG 7 standard, mel cepstral and dedicated parameters before optimization is given in Table 1 [7][10][28][29].

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