Artykuł stanowi kontynuację cyklu badań związanych z wykorzystaniem algebraicznych metod identyfikacji słów kluczowych w dokumentach tekstowych. Jego celem jest teoretyczna analiza i empiryczna weryfikacja przydatności użycia metod identyfikacji słów kluczowych opartej na dekompozycji SVD w naukowych tekstach polskojęzycznych.
EN
The article is a continuation of the cycle of studies related to the use of algebraic methods for keywords identification in text documents. Its purpose is to theoretical analysis and empirical verification of the suitability of the use of methods for keywords identification based on SVD decomposition of scientific in Polish texts.
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The possibilities of application the mathematical method - Singular Value Decomposition in wibroacoustical analysis of building objects on sacral objects example were shown in the paper. Singular Value Decomposition (SVD) is a technique used in the reduction of matrix sizes and analysis of independencies of variables. Application of this tool in acoustic problems of sacral interiors gave the possibility of analysis of proposed index method of acoustic assessment of sacral objects. The dependencies between partial indices were obtained from SVD as well as the formulae which can approximately assess global acoustic quality of sacral interior. The verification of index method with SVD was performed for six real roman-catholic churches. The approximate global index and partial indices can be used for acoustic assessment of real interior where acoustic adaptation is needed as well as for designed sacral rooms. The proposed indices are calculated from simulation research on created geometrical model. The inverse problem was formulated in the paper, where at the assumed global index, partial indices determining individual properties - are looked for.
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With the tools of modern metrology we can measure almost all variables in the phenomenon field of a working machine, and many of the measured quantities can be symptoms of machine conditions. On this basis, we can form a symptom observation matrix (SOM) intended for condition monitoring and wear trend (fault) identification. On the other hand, we know that contemporary complex machines may have many modes of failure, called faults. The paper presents a method of the extraction of the information about faults from the symptom observation matrix by means of singular value decomposition (SVD), in the form of generalized fault symptoms. As the readings of the symptoms can be unstable, the moving average of the SOM is applied with success. An attempt to assess the diagnostic contribution of a primary symptom is made, and also an approach to assess the symptom limit value and to connect the SVD methodology with neural nets is considered. Finally, a condition forecasting problem is discussed and an application of grey system theory (GST) to symptom prognosis is presented. These possibilities are illustrated by processing data taken directly from the machine vibration condition monitoring area.
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