A Random Subspace Based Approach for Stress Classification from Smartphone Data

Author(s):  
Ensar Arif Sagbas ◽  
Serdar Corukoglu ◽  
Serkan Balli
2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Andrzej T. Strzelczyk ◽  
Mike Stojakovic

ASME PVP Code stress linearization is needed for assessment of primary and primary-plus-secondary stresses. The linearization process is not precisely defined by the Code; as a result, it may be interpreted differently by analysts. The most comprehensive research on stress linearization is documented in the work of Hechmer and Hollinger [1998, “3D Stress Criteria Guidelines for Application,” WRC Bulletin 429.] Recently, nonmandatory recommendations on stress linearization have been provided in the Annex [Annex 5.A of Section VIII, Division 2, ASME PVP Code, 2010 ed., “Linearization of Stress Results for Stress Classification.”] In the work of Kalnins [2008, “Stress Classification Lines Straight Through Singularities” Proceedings of PVP2008-PVT, Paper No. PVP2008-61746] some linearization questions are discussed in two examples; the first is a plane-strain problem and the second is an axisymmetric analysis of primary-plus secondary stress at a cylindrical-shell/flat-head juncture. The paper concludes that for the second example, the linearized stresses produced by Abaqus [Abaqus Finite Element Program, Version 6.10-1, 2011, Simulia Inc.] diverge, therefore, these linearized stresses should not be used for stress evaluation. This paper revisits the axisymmetric analysis discussed by Kalnins and attempts to show that the linearization difficulties can be avoided. The paper explains the reason for the divergence; specifically, for axisymmetric models Abaqus inconsistently treats stress components, two stress components are calculated from assumed formulas and all other components are linearized. It is shown that when the axisymmetric structure from Kalnins [2008, “Stress Classification Lines Straight Through Singularities” Proceedings of PVP2008-PVT, Paper No. PVP2008-61746] is modeled with 3D elements, the linearization results are convergent. Furthermore, it is demonstrated that both axisymmetric and 3D modeling, produce the same and correct stress Tresca stress, if the stress is evaluated from all stress components being linearized. The stress evaluation, as discussed by Kalnins, is a primary-plus-secondary-stresses evaluation, for which the limit analysis described by Kalnins [2001, “Guidelines for Sizing of Vessels by Limit Analysis,” WRC Bulletin 464.] cannot be used. This paper shows how the original primary-plus-secondary-stresses problem can be converted into an equivalent primary-stress problem, for which limit analysis can be used; it is further shown how the limit analysis had been used for verification of the linearization results.


Author(s):  
Muhammad Syazani Hafiy Hilmy ◽  
Ani Liza Asnawi ◽  
Ahmad Zamani Jusoh ◽  
Khaizuran Abdullah ◽  
Siti Noorjannah Ibrahim ◽  
...  

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4132 ◽  
Author(s):  
Ku Ku Abd. Rahim ◽  
I. Elamvazuthi ◽  
Lila Izhar ◽  
Genci Capi

Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.


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