•Bayesian framework was developed for modal identification using seismic response.•Uncertainty of modal parameters could be quantified based on the Bayesian framework.•Fast algorithm was developed to ...carry out modal identification efficiently.•Both the closely-spaced modes and well-separated mode could be well identified.•The method was illustrated by synthetic data, shake table test data and field data.
Modal identification aims at identifying the modal parameters that mainly include natural frequencies, damping ratios and mode shapes. They provide baseline modal properties of objective structures and play an important role in the seismic design, structural health monitoring, model updating, etc. In existing monitoring systems, the input and output responses are usually recorded simultaneously, which allows the identification of the modal parameters using earthquake records in a short period of time. The Bayesian method can properly account for the uncertainty in accordance with probability logic for modal identification. This paper proposes a novel Bayesian method for modal identification using collected data during earthquakes with known input. The probability density function of modal parameters based on the Fast Fourier Transform of measured data is derived analytically. A fast algorithm has been developed to efficiently optimize the modal parameters, where the cases of closely-spaced modes and well-separated mode are applicable, even for a large number of measured degrees of freedom. A synthetic example and shaking table test were used to illustrate the efficiency and accuracy of the proposed method. Finally, this method was applied to a seven-story building - Van Nuys Hotel to investigate its dynamic characteristics using seismic response data.
Person identification is a problem that has received substantial attention, particularly in security domains. Gait recognition is one of the most convenient approaches enabling person identification ...at a distance without the need for high-quality images. There are several review studies addressing person identification such as the utilization of facial images, silhouette images, and wearable sensor. Despite skeleton-based person identification gaining popularity while overcoming the challenges of traditional approaches, existing survey studies lack the comprehensive review of skeleton-based approaches to gait identification. We present a detailed review of the human pose estimation and gait analysis that makes the skeleton-based approaches possible. The study covers various types of related datasets, tools, methodologies, and evaluation metrics with associated challenges, limitations, and application domains. Detailed comparisons are presented for each of these aspects with recommendations for potential research and alternatives. A common trend throughout this paper is the positive impact that deep learning techniques are beginning to have on topics such as human pose estimation and gait identification. The survey outcomes might be useful for the related research community and other stakeholders in terms of performance analysis of existing methodologies, potential research gaps, application domains, and possible contributions in the future.
N6-adenosine methylation (m6A) of mRNA is an essential process in most eukaryotes, but its role and the status of factors accompanying this modification are still poorly understood.
Using combined ...methods of genetics, proteomics and RNA biochemistry, we identified a core set of mRNA m6A writer proteins in Arabidopsis thaliana.
The components required for m6A in Arabidopsis included MTA, MTB, FIP37, VIRILIZER and the E3 ubiquitin ligase HAKAI. Downregulation of these proteins led to reduced relative m6A levels and shared pleiotropic phenotypes, which included aberrant vascular formation in the root, indicating that correct m6A methylation plays a role in developmental decisions during pattern formation.
The conservation of these proteins amongst eukaryotes and the demonstration of a role in writing m6A for the E3 ubiquitin ligase HAKAI is likely to be of considerable relevance beyond the plant sciences.
In this paper, we present a novel multiple input multiple output (MIMO) linear parameter varying (LPV) state-space refinement system identification algorithm that uses tensor networks. Its novelty ...mainly lies in representing the LPV sub-Markov parameters, data and state-revealing matrix condensely and in exact manner using specific tensor networks. These representations circumvent the 'curse-of-dimensionality' as they inherit the properties of tensor trains. The proposed algorithm is 'curse-of-dimensionality'-free in memory and computation and has conditioning guarantees. Its performance is illustrated using simulation cases and additionally compared with existing methods.
An accurate seismic response simulation of civil structures requires accounting for the nonlinear soil response behavior. This, in turn, requires understanding the nonlinear material behavior of in ...situ soils under earthquake excitations. System identification methods applied to data recorded during earthquakes provide an opportunity to identify the nonlinear material properties of in situ soils. In this study, we use a Bayesian inference framework for nonlinear model updating to estimate the nonlinear soil properties from recorded downhole array data. For this purpose, a one-dimensional finite element model of the geotechnical site with nonlinear soil material constitutive model is updated to estimate the parameters of the soil model as well as the input excitations, including incident, bedrock, or within motions. The seismic inversion method is first verified by using several synthetic case studies. It is then validated by using measurements from a centrifuge test and with data recorded at the Lotung experimental site in Taiwan. The site inversion method is then applied to the Benicia-Martinez geotechnical array in California, using the seismic data recorded during the 2014 South Napa earthquake. The results show the promising application of the proposed seismic inversion approach using Bayesian model updating to identify the nonlinear material parameters of in situ soil by using recorded downhole array data.
This study focuses on the parameter identification problems of pseudo-linear systems. The main goal is to present recursive least squares (RLS) estimation methods based on the auxiliary model ...identification idea and the decomposition technique. First, an auxiliary model-based RLS algorithm is given as a comparison. Second, to improve the computation efficiency, a decomposition-based RLS algorithm is presented. Then for the system identification with missing data, an interval-varying RLS algorithm is derived for estimating the system parameters. Furthermore, this study uses the decomposition technique to reduce the computational cost in the interval-varying RLS algorithm and introduces the forgetting factors to track the time-varying parameters. The simulation results show that the proposed algorithms can work well.
With the increasing domain and widespread use of wireless devices in recent years (mobile phones, Internet of Things, Wi-Fi), the electromagnetic spectrum has become extremely crowded. To counter ...security threats posed by rogue or unknown transmitters, we must identify RF transmitters not only by the data content of the transmissions but also based on the intrinsic physical characteristics of the transmitters. RF waveforms represent a particular challenge because of the extremely high data rates involved and the potentially large number of transmitters sharing a channel in a given location. These factors outline the need for rapid fingerprinting and identification methods that go beyond the traditional hand-engineered approaches. In this paper, we investigate the use of machine learning strategies to the classification and identification problem. We evaluate four different strategies: conventional deep neural nets, convolutional neural nets, support vector machines, and deep neural nets with multi-stage training. The latter was by far the most accurate, achieving 100% classification accuracy of 12 transmitters, and showing remarkable potential for scalability to large transmitter populations.
This paper addresses the problem of recursive identification of Wiener nonlinear systems whose linear subsystems are observable state-space models. The maximum likelihood principle and the recursive ...identification technique are employed to develop a recursive maximum likelihood identification algorithm which estimates the unknown parameters and the system states interactively. In comparison with the developed recursive maximum likelihood algorithm, a recursive generalized least squares algorithm is also proposed for identification of such Wiener systems. The performance of the developed algorithms is validated by two illustrative examples.
•CT images of the head offer a reliable and rapid means of personal identification.•Identification can be achieved with a single standardized CT image of the head.•This single, standardized image is ...oriented parallel to the orbitomeatal plane.•Osseous landmarks are used as reference points for reformation of the image.•Reformation of this image has an excellent inter- and intra-rater reliability.
The aim of this study was to assess the reproducibility of a standardized image for personal identification (SIPI), used in the comparative analysis of paranasal sinuses, and test the effect of inaccurate reformation of the SIPI on suitability for comparative identification.
Five raters with different professional backgrounds independently reformatted SIPIs from ten post-mortem head CTs. Inter-rater, intra-rater agreement as well angular deviations between reformatted SIPI images and reference SIPI images were calculated. Second, raters assessed the suitability of 70 accurately and inaccurately reformatted SIPIs for identification with a 4-point Likert scale. Inter-rater agreement as well as levels of significance regarding image suitability were calculated.
Inter-rater agreement regarding reproducibility of SIPI reformation was excellent (inter-rater correlation coefficient (ICC) 0.9995, intra-rater ICC 0.9983). Deviation between the angular dimensions of the reformatted SIPIs and the reference SIPIs was ≤1° in 94% of all 300 measurements. Inter-rater agreement regarding the effect of inaccurate SIPI reformation on suitability for comparative identification was fair (ICC 0.6809). There was no statistically significant difference between raters’ evaluation of image suitability (p=0.9755).
This study shows that the standardized image for personal identification can be accurately reformatted by different raters with varying professional backgrounds. In addition, raters agree that inaccurately reformatted SIPIs are still suitable for comparative identification in the majority of cases.
Phantom dark energy (w<−1) can produce amplified cosmic acceleration at late times, thus increasing the value of H0 favored by CMB data and releasing the tension with local measurements of H0. We ...show that the best fit value of H0 in the context of the CMB power spectrum is degenerate with a constant equation-of-state parameter w, in accordance with the approximate effective linear equation H0+30.93w−36.47=0 (H0 in km sec−1 Mpc−1). This equation is derived by assuming that both Ω0mh2 and dA=∫0zrecdz/H(z) remain constant (for an invariant CMB spectrum) and equal to their best fit Planck/ΛCDM values as H0, Ω0m, and w vary. For w=−1, this linear degeneracy equation leads to the best fit H0=67.4 km sec−1 Mpc−1 as expected. For w=−1.22, the corresponding predicted CMB best fit Hubble constant is H0=74 km sec−1 Mpc−1, which is identical with the value obtained by local-distance ladder measurements, while the best fit matter density parameter is predicted to decrease, since Ω0mh2 is fixed. We verify the above H0−w degeneracy equation by fitting a wCDM model with fixed values of w to the Planck TT spectrum, showing also that the quality of fit (χ2) is similar to that of ΛCDM. However, when including SnIa, baryon acoustic oscillation, or growth data, the quality of fit becomes worse than ΛCDM when w<−1. Finally, we generalize the H0−w(z) degeneracy equation for the parametrization w(z)=w0+w1z/(1+z) and identify analytically the full w0−w1 parameter region (straight line) that leads to a best fit H0=74 km sec−1 Mpc−1 in the context of the Planck CMB spectrum. This exploitation of H0−w(z) degeneracy can lead to immediate identification of all parameter values of a given w(z) parametrization that can potentially resolve the H0 tension.
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