Noise Cancellation Method Based on TVF-EMD with Bayesian Parameter Optimization

To separate the noise and important signal features of the indoor carbon dioxide (CO2) concentration signal, we proposed a noise cancellation method, based on time-varying, filtering-based empirical mode decomposition (TVF-EMD) with Bayesian optimization (BO). The adaptive parameters of TVF-EMD, that is, bandwidth threshold ξ and B-spline order n, were determined by the BO algorithm, and the correlation coefficient for the kurtosis index (CCKur) constituted the objective function. Initially, the objective function CCKur was introduced to systematically identify anomalous signals while preserving signal feature extraction between the modes and the input signal. Subsequently, the proposed signal noise cancellation model based on TVF-EMD and the BO algorithm were employed, along with the Hurst exponent, to extract the sensitive mode. An examination of the optimization indices of the decomposed intrinsic mode functions (IMFs), namely CC, Kur, MI, EE, EEMI, and CCKur, revealed that the synthetic measurement index CCKur and objective function fitness were reasonable and effective. The proposed method exhibited better signal cancellation performance, compared to that of TVF-EMD with the default values, EMD, the moving average method, and the exponential smoothing method

Machine learning prediction of landslide deformation behaviour using acoustic emission and rainfall measurements

Knowledge of landslide displacement trends is important to understand risks and establish early warning trigger thresholds so that action can be taken to protect people and critical infrastructure. However, the availability of direct continuous displacement measurements is often limited due to relatively high costs. This has driven research to establish models that quantify relationships between landslide displacements and other measured parameters such as pore water pressures, rainfall and more recently acoustic emission (AE), so that displacement can be predicted, and hence made available at a lower cost. This paper describes an investigation of established machine learning models to predict displacements using time series measurements of AE and rainfall. Data from a case study site has been used to train models using measured displacements and then test to assess prediction accuracy. The LASSO-ELM model was shown to perform best. It was able to predict displacements to a mean absolute percentage error < 2.5% up to 60 days after the end of the training period, which is better than similar reported studies. Training a LASSO-ELM model using continuous high resolution AE measurements combined with rainfall data has potential to provide predicted displacement trends once direct measurement of displacement is no-longer available

Extracting knowledge on slope behaviour from acoustic emission measurements

Early warning systems for slope instability need to alert users of accelerating slope deformation behaviour to enable safety-critical decisions to be made. Field trials of acoustic emission (AE) monitoring of slopes have demonstrated conclusively that generated AE rates are proportional to slope deformation rates, and AE monitoring can be an effective approach to detect accelerating movements and communicate warnings to users. AE is becoming an accepted monitoring technology for geotechnical applications; however, challenges still exist to develop widely applicable interpretation strategies. In this paper, data from a field trial at Hollin Hill, North Yorkshire, UK and a large-scale experiment are used to develop strategies to extract knowledge on slope behaviour from AE measurements. Machine learning approaches for automated interpretation (warning trigger levels and quantifying rates of slope movement) are developed and demonstrated. A conceptual framework for extracting knowledge from AE measurements for use in decision-making is presented.</p

On image fusion of ground surface vibration for mapping and locating underground pipeline leakage: an experimental investigation

This paper is concerned with imaging techniques for mapping and locating underground pipeline leakage. Ground surface vibrations induced by the propagating axisymmetric wave can be measured by an array of acoustic/vibration sensors, with the extraction of magnitude information used to determine the position of leak source. A method of connected graph traversal is incorporated into the vibroacoustic technique to obtain the spatial image with better accuracy compared to the conventional magnitude contour plot. Measurements are made on a dedicated cast iron water pipe by an array of seven triaxial geophones. The spectral characteristics of the propagation of leak noise signals from underground water pipes to the ground surface are reported. Furthermore, it is demonstrated that suspicious leakage areas can be readily identified by extracting and fusing the feature patterns at low frequencies where leak noise dominates. The results agree well with the real leakage position in the underground pipeline

Automatic classification of landslide kinematics using acoustic emission measurements and machine learning

Founded on understanding of a slope’s likely failure mechanism, an early warning system for instability should alert users of accelerating slope deformation behaviour to enable safety-critical decisions to be made. Acoustic emission (AE) monitoring of active waveguides (i.e. a steel tube with granular internal/external backfill installed through a slope) is becoming an accepted monitoring technology for soil slope stability applications; however, challenges still exist to develop widely applicable AE interpretation strategies. The objective of this study was to develop and demonstrate the use of machine learning (ML) approaches to automatically classify landslide kinematics using AE measurements, based on the standard landslide velocity scale. Datasets from large-scale slope failure simulation experiments were used to train and test the ML models. In addition, an example field application using data from a reactivated landslide at Hollin Hill, North Yorkshire, UK, is presented. The results show that ML can automatically classify landslide kinematics using AE measurements with the accuracy of more than 90%. The combination of two AE features, AE rate and AE rate gradient, enable both velocity and acceleration classifications. A conceptual framework is presented for how this automatic approach would be used for landslide early warning in the field, with considerations given to potentially limited site-specific training data

Identification and Characterization of Auxin/IAA Biosynthesis Pathway in the Rice Blast Fungus Magnaporthe oryzae

The rice blast fungus Magnaporthe oryzae has been known to produce the phytohormone auxin/IAA from its hyphae and conidia, but the detailed biological function and biosynthesis pathway is largely unknown. By sequence homology, we identified a complete indole-3-pyruvic acid (IPA)-based IAA biosynthesis pathway in M. oryzae, consisting of the tryptophan aminotransferase (MoTam1) and the indole-3-pyruvate decarboxylase (MoIpd1). In comparison to the wild type, IAA production was significantly reduced in the motam1&Delta; mutant, and further reduced in the moipd1&Delta; mutant. Correspondingly, mycelial growth, conidiation, and pathogenicity were defective in the motam1&Delta; and the moipd1&Delta; mutants to various degrees. Targeted metabolomics analysis further confirmed the presence of a functional IPA pathway, catalyzed by MoIpd1, which contributes to IAA/auxin production in M. oryzae. Furthermore, the well-established IAA biosynthesis inhibitor, yucasin, suppressed mycelial growth, conidiation, and pathogenicity in M. oryzae. Overall, this study identified an IPA-dependent IAA synthesis pathway crucial for M. oryzae mycelial growth and pathogenic development

A live attenuated vaccine prevents replication and transmission of H7N9 highly pathogenic influenza viruses in mammals

Abstract H7N9 influenza viruses emerged in 2013 and have caused severe disease and deaths in humans in China. Some H7N9 viruses circulating in chickens have mutated to highly pathogenic viruses that have caused several disease outbreaks in chickens. Studies have shown that when the H7N9 highly pathogenic viruses replicate in ferrets or humans, they easily acquire certain mammalian-adapting mutations and become highly lethal in mice and highly transmissible in ferrets by respiratory droplet, creating the potential for human-to-human transmission. Therefore, the development of effective control measures is a top priority for H7N9 pandemic preparedness. In this study, we evaluated the protective efficacy of a cold-adapted, live attenuated H7N9 vaccine (H7N9/AAca) against two heterologous H7N9 highly pathogenic viruses in mice and guinea pigs. Our results showed that one dose of the H7N9/AAca vaccine prevented disease and death in mice challenged with two different H7N9 highly pathogenic viruses, but did not prevent replication of the challenge viruses; after two doses of H7N9/AAca, the mice were completely protected from challenge with A/chicken/Hunan/S1220/2017(H7N9) virus, and very low viral titers were detected in mice challenged with H7N9 virus CK/SD008-PB2/627 K. More importantly, we found that one dose of H7N9/AAca could efficiently prevent transmission of CK/SD008-PB2/627 K in guinea pigs. Our study suggests that H7N9/AAca has the potential to be an effective H7N9 vaccine and should be evaluated in humans