Automated operational modal analysis of an end-supported pontoon bridge using covariance-driven stochastic subspace identification and a density-based hierarchical clustering algorithm

2021 ◽  
pp. 3041-3048
Author(s):  
K.A. Kvåle ◽  
O. Øiseth
Keyword(s):  
Modal Analysis ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Operational Modal Analysis ◽  
Subspace Identification ◽  
Stochastic Subspace Identification ◽  
Hierarchical Clustering Algorithm

scholarly journals Operational Modal Analysis for Estimating the Dynamic Properties of a Stadium Structure during a Football Game

2007 ◽  
Vol 14 (4) ◽  
pp. 283-303 ◽  
Author(s):  
Bart Peeters ◽  
Herman Van der Auweraer ◽  
Frederik Vanhollebeke ◽  
Patrick Guillaume
Keyword(s):  
Modal Analysis ◽  
Dynamic Properties ◽  
Estimation Method ◽  
Operational Modal Analysis ◽  
Primary Data ◽  
Subspace Identification ◽  
Time Lags ◽  
Data Set ◽  
Stochastic Subspace Identification ◽  
Football Game

During a football game, the ambient vibrations at the roof of a football stadium were recorded. A very large data set consisting of 4 hours of data, sampled at 80 Hz, is available. By a data reduction procedure, the complete data set could be analysed at once in a very short time. The data set was also split in shorter segments corresponding to certain events before, during and after the game to investigate the influence of varying operational conditions on the dynamic properties.As the structural vibrations were caused by unmeasurable wind and crowd excitation, Operational Modal Analysis has to be applied to find the dynamic characteristics of the structure. The new operational PolyMAX parameter estimation method is used and compared with Stochastic Subspace Identification. Stochastic Subspace Identification requires the correlations between the responses as primary data, whereas PolyMAX operates on spectra or half spectra (i.e. the Fourier transform of the positive time lags of the correlation functions). The main advantage of PolyMAX is that it yields extremely clear stabilisation diagrams, making an automation of the parameter identification process rather straightforward. This enables a continuous monitoring of the dynamic properties of a structure.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

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