52 research outputs found
EEMD-MUSIC-Based Analysis for Natural Frequencies Identification of Structures Using Artificial and Natural Excitations
This paper presents a new EEMD-MUSIC- (ensemble empirical mode decomposition-multiple signal classification-) based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations and also considering several factors as nonstationary effects, close modal frequencies, and noisy environments, which are common situations where several techniques reported in literature fail. The EEMD and MUSIC methods are used to decompose the vibration signal into a set of IMFs (intrinsic mode functions) and to identify the natural frequencies of a structure, respectively. The effectiveness of the proposed methodology has been validated and tested with synthetic signals and under real operating conditions. The experiments are focused on extracting the natural frequencies of a truss-type scaled structure and of a bridge used for both highway traffic and pedestrians. Results show the proposed methodology as a suitable solution for natural frequencies identification of structures from free and ambient vibration signals
To which world regions does the valenceâdominance model of social perception apply?
Over the past 10 years, Oosterhof and Todorovâs valenceâdominance model has emerged as the most prominent account of
how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social
judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether
these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorovâs methodology across
11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorovâs original analysis strategy,
the valenceâdominance model generalized across regions. When we used an alternative methodology to allow for correlated
dimensions, we observed much less generalization. Collectively, these results suggest that, while the valenceâdominance
model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed
when we use different extraction methods and correlate and rotate the dimension reduction solution.C.L. was supported by the Vienna Science and Technology Fund (WWTF VRG13-007);
L.M.D. was supported by ERC 647910 (KINSHIP); D.I.B. and N.I. received funding from
CONICET, Argentina; L.K., F.K. and Ă. Putz were supported by the European Social
Fund (EFOP-3.6.1.-16-2016-00004; âComprehensive Development for Implementing
Smart Specialization Strategies at the University of PĂŠcsâ). K.U. and E. Vergauwe were
supported by a grant from the Swiss National Science Foundation (PZ00P1_154911 to E.
Vergauwe). T.G. is supported by the Social Sciences and Humanities Research Council
of Canada (SSHRC). M.A.V. was supported by grants 2016-T1/SOC-1395 (Comunidad
de Madrid) and PSI2017-85159-P (AEI/FEDER UE). K.B. was supported by a grant
from the National Science Centre, Poland (number 2015/19/D/HS6/00641). J. Bonick
and J.W.L. were supported by the Joep Lange Institute. G.B. was supported by the Slovak
Research and Development Agency (APVV-17-0418). H.I.J. and E.S. were supported
by a French National Research Agency âInvestissements dâAvenirâ programme grant
(ANR-15-IDEX-02). T.D.G. was supported by an Australian Government Research
Training Program Scholarship. The Raipur Group is thankful to: (1) the University
Grants Commission, New Delhi, India for the research grants received through its
SAP-DRS (Phase-III) scheme sanctioned to the School of Studies in Life Science;
and (2) the Center for Translational Chronobiology at the School of Studies in Life
Science, PRSU, Raipur, India for providing logistical support. K. Ask was supported by
a small grant from the Department of Psychology, University of Gothenburg. Y.Q. was
supported by grants from the Beijing Natural Science Foundation (5184035) and CAS
Key Laboratory of Behavioral Science, Institute of Psychology. N.A.C. was supported
by the National Science Foundation Graduate Research Fellowship (R010138018). We
acknowledge the following research assistants: J. Muriithi and J. Ngugi (United States
International University Africa); E. Adamo, D. Cafaro, V. Ciambrone, F. Dolce and E.
Tolomeo (Magna GrĂŚcia University of Catanzaro); E. De Stefano (University of Padova);
S. A. Escobar Abadia (University of Lincoln); L. E. Grimstad (Norwegian School of
Economics (NHH)); L. C. Zamora (Franklin and Marshall College); R. E. Liang and R.
C. Lo (Universiti Tunku Abdul Rahman); A. Short and L. Allen (Massey University, New
Zealand), A. AteĹ, E. GĂźneĹ and S. Can Ăzdemir (BoÄaziçi University); I. Pedersen and T.
Roos (Ă
bo Akademi University); N. Paetz (Escuela de ComunicaciĂłn MĂłnica Herrera);
J. Green (University of Gothenburg); M. Krainz (University of Vienna, Austria); and B.
Todorova (University of Vienna, Austria). The funders had no role in study design, data
collection and analysis, decision to publish or preparation of the manuscript.https://www.nature.com/nathumbehav/am2023BiochemistryGeneticsMicrobiology and Plant Patholog
Hardware-software system for simulating and analyzing earthquakes applied to civil structures
The occurrence of recent strong earthquakes, the incessant worldwide movements of tectonic plates and the continuous ambient vibrations caused by traffic and wind have increased the interest of researchers in improving the capacity of energy dissipation to avoid damages to civil structures. Experimental testing of structural systems is essential for the understanding of physical behaviors and the building of appropriate analytic models in order to expose difficulties that may not have been considered in analytical studies. This paper presents a hardware-software system for exciting, monitoring and analyzing simultaneously a structure under earthquake signals and other types of signals in real-time. Effectiveness of the proposed system has been validated by experimental case studies and has been found to be a useful tool in the analysis of earthquake effects on structures
Entropy Wavelet-Based Method to Increase Efficiency in Highway Bridge Damage Identification
Highway bridges are crucial civil constructions for the transport infrastructure, which require proper attention from the corresponding institutions of each country and constant financing for their adequate maintenance; this is important because different types of damage can be generated within these structures, caused by natural disasters, among other sources, and the heavy loads they transport every day. Therefore, the development of simple, efficient, and low-cost methods is of vital importance, allowing us to identify damage in a timely manner and avoid bridges collapsing. As reported in a previous work, the wavelet energy accumulation method (WEAM) and its corresponding application in the Rio Papaloapan Bridge (RPB) represented an important advance within the field. Despite identifying damage in bridges with precision and at a low cost, there are several aspects to improve in that method. Therefore, in this work, that method was improved, eliminating several steps, and meaningfully reducing the computational burden by implementing an algorithm based on the Shannon entropy, thus giving way to the new entropy wavelet-based method (EWM). This new method was applied directly with regard to the real-life RPB, in both its healthy and damaged conditions. Also, its corresponding numerical model based on the finite element method in its healthy condition and different damage scenarios were carried out. The results indicate that the new EWM retains the advantages of WEAM, and it allows for damage identification to be completed more efficiently, increasing the precision by approximately 0.11%, and significantly reducing the computing time required to obtain results by 5.67 times
Determination of system frequencies in mechanical systems during shutdown transient
415-421This
study proposed a methodology for system frequency detection of mechanical
systems with variable load conditions. Vibration signals from shutting-down
transient were analyzed in combined time-frequency domain using wavelet and FFT
analyses. Effectiveness of proposed methodology was validated by experimental
case study and found that this methodology can be applied to any rotating
mechanical system
DWT-based methodology for detection of seismic precursors on electric field signals in Mexico
This paper presents an analysis of atmospheric electric field signals which were taken on an important seismic activity period from 2012 to 2015 to study its relationship with seismic events. For this purpose, several measurements were acquired every second by using a triaxial electric field monitoring system. Furthermore, the discrete wavelet transform (DWT) was applied to electric field signals with seismic events of magnitudes greater than Mw >Â 5.5, which occurred in Mexico with different focal mechanisms. The analysed epochs consist of 24Â h of observations for a data-set corresponding to 55 different earthquakes (EQs). The time series were processed 12Â h before and 12Â h after each seismic event. The proposed methodology proves to be an efficient tool to detect signals with relations between electric field and seismic activity. The methodology presented herein shows important anomalies on different time instants according to the focal mechanism. Finally, a statistical post-processing algorithm was performed in order to quantify the data dispersion as a measure of seismic activity. It is found that the variance increases before, during, and after the seismic event about the coefficients D1 to D7 obtained using the DWT
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