scholarly journals Towards Detecting Red Palm Weevil Using Machine Learning and Fiber Optic Distributed Acoustic Sensing

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1592
Author(s):  
Biwei Wang ◽  
Yuan Mao ◽  
Islam Ashry ◽  
Yousef Al-Fehaid ◽  
Abdulmoneim Al-Shawaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Early Detection ◽  
Large Scale ◽  
Fiber Optic ◽  
Red Palm Weevil ◽  
Healthy Tree ◽  
Acoustic Sensing ◽  
Palm Weevil ◽  
Distributed Acoustic Sensing

Red palm weevil (RPW) is a detrimental pest, which has wiped out many palm tree farms worldwide. Early detection of RPW is challenging, especially in large-scale farms. Here, we introduce the combination of machine learning and fiber optic distributed acoustic sensing (DAS) techniques as a solution for the early detection of RPW in vast farms. Within the laboratory environment, we reconstructed the conditions of a farm that includes an infested tree with ∼12 day old weevil larvae and another healthy tree. Meanwhile, some noise sources are introduced, including wind and bird sounds around the trees. After training with the experimental time- and frequency-domain data provided by the fiber optic DAS system, a fully-connected artificial neural network (ANN) and a convolutional neural network (CNN) can efficiently recognize the healthy and infested trees with high classification accuracy values (99.9% by ANN with temporal data and 99.7% by CNN with spectral data, in reasonable noise conditions). This work paves the way for deploying the high efficiency and cost-effective fiber optic DAS to monitor RPW in open-air and large-scale farms containing thousands of trees.

scholarly journals Fiber Optic Train Monitoring with Distributed Acoustic Sensing: Conventional and Neural Network Data Analysis

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 450 ◽  
Author(s):  
Stefan Kowarik ◽  
Maria-Teresa Hussels ◽  
Sebastian Chruscicki ◽  
Sven Münzenberger ◽  
Andy Lämmerhirt ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Standard Deviation ◽  
Fiber Optic ◽  
Parameter Uncertainties ◽  
Spatial Direction ◽  
Acoustic Sensing ◽  
Analysis Methods ◽  
Distributed Acoustic Sensing ◽  
Deutsche Bahn

Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars. The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods. The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h. The analysis methods presented here will help to establish routines for near real-time train tracking and train integrity analysis.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

scholarly journals A Comparative Study on the Microbial Communities of Rhynchophorus ferrugineus (Red Palm Weevil)-Infected and Healthy Palm Trees

2021 ◽  
Author(s):  
N. Alshammari ◽  
Meshari Alazmi ◽  
Naimah A. Alanazi ◽  
Abdel Moneim E. Sulieman ◽  
Vajid N. Veettil ◽  
...  
Keyword(s):  
Microbial Communities ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Rhynchophorus Ferrugineus ◽  
Tree Trunk ◽  
Red Palm Weevil ◽  
Healthy Tree ◽  
Palm Trees ◽  
Infected Tree ◽  
Palm Weevil

AbstractSeveral studies have investigated palm trees’ microbiota infected with red palm weevil (RPW) (Rhynchophorus ferrugineus), the major pest of palm trees. This study compared the microbial communities of infected and uninfected palm trees in the Hail region, Northern Saudi Arabia, determined by high-throughput 16S rRNA gene sequencing by Illumina MiSeq. The results indicated that taxonomic diversity variation was higher for infected tree trunk than the healthy tree trunk. Soil samples from the vicinity of healthy and infected trees did not have a significant variation in bacterial diversity. Myxococcota, Acidobacteriota, and Firmicutes were the dominant phyla in RPW-infected tree trunk, and Pseudomonadaceae was the most prominent family. This study is the first report on the characterization of RPW-infected and healthy palm trees’ microbiome.

Preliminary evaluation of acoustic sensors for early detection of red palm weevil, Rhynchophorus ferrugineus incidence on oil palm and coconut in Malaysia

2021 ◽  
Author(s):  
Nur Ain Farhah Ros Saidon Khudri ◽  
Mohamed Mazmira Mohd Masri ◽  
Mohd Shawal Thakib Maidin ◽  
Noorhazwani Kamarudin ◽  
Mohamad Haris Hussain ◽  
...  
Keyword(s):  
Early Detection ◽  
Oil Palm ◽  
Preliminary Evaluation ◽  
Rhynchophorus Ferrugineus ◽  
Acoustic Sensors ◽  
Red Palm Weevil ◽  
Palm Weevil

Capturing Glacier-Wide Cryoseismicity With Distributed Acoustic Sensing

2021 ◽  
Author(s):  
Fabian Walter ◽  
Patrick Paitz ◽  
Andreas Fichtner ◽  
Pascal Edme ◽  
Wojciech Gajek ◽  
...  
Keyword(s):  
Fiber Optic ◽  
Flow Line ◽  
Ground Deformation ◽  
Sensor Coverage ◽  
Mountain Glacier ◽  
Acoustic Sensing ◽  
Alpine Glacier ◽  
Glacier Ice ◽  
Distributed Acoustic Sensing ◽  
Ice Sheet Dynamics

&lt;p&gt;Over the past 1-2 decades, seismological measurements have provided new and unique insights into glacier and ice sheet dynamics. At the same time, sensor coverage is typically limited in harsh glacial environments with littile or no access. Turning kilometer-long fiber optic cables placed on the Earth&amp;#8217;s surface into thousands of seismic sensors, Distributed Acoustic Sensing (DAS) may overcome the limitation of sensor coverage in the cryosphere.&lt;/p&gt;&lt;p&gt;First DAS applications on the Greenland and Antarctic ice sheets and on Alpine glacier ice have highlighted the technique&amp;#8217;s superiority. Signals of natural and man-made seismic sources can be resolved with an unrivaled level of detail. This offers glaciologists new perspectives to interpret their seismograms in terms of ice structure, basal boundary conditions and source locations. However, previous studies employed only relatively small network scales with a point-like borehole deployment or &lt; 1 km cable aperture at the ice surface.&lt;/p&gt;&lt;p&gt;Here we present a DAS installation, which aims to cover the majority of an Alpine glacier catchment: For one month in summer 2020 we deployed a 9 km long fiber optic cable on Rhonegletscher, Switzerland, and gathered continuous DAS data. The cable followed the glacier&amp;#8217;s central flow line starting in the lowest kilometer of the ablation zone and extending well into the accumulation area. Even for a relatively small mountain glacier such as Rhonegletscher, cable deployment was a considerable logistical challenge. However, initial data analysis illustrates the benefit compared to conventional cryoseismological instrumentation: DAS measurements capture ground deformation over many octaves, including typical high-frequency englacial sources (10s to 100s of Hz) related to crevasse formation and basal sliding as well as long period signals (10s to 100s of seconds) of ice deformation. Depending on the presence of a snow cover, DAS records contain strong environmental noise (wind, meltwater flow, precipitation) and thus exhibit lower signal-to-noise ratios compared to conventional on-ice seismic installations. This is nevertheless outweighed by the advantage of monitoring ground unrest and ice deformation of nearly an entire glacier. We present a first compilation of signal and noise records and discuss future directions to leverage DAS data sets in glaciological research.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Investigation of the Effects of Surrounding Media on the Distributed Acoustic Sensing of Helically-Wound Fiber-Optic Cable with Application to the New Afton Deposit, British Columbia

2020 ◽  
Author(s):  
Sepidehalsadat Hendi ◽  
Mostafa Gorjian ◽  
Gilles Bellefleur ◽  
Christopher D. Hawkes ◽  
Don White
Keyword(s):  
British Columbia ◽  
Numerical Modeling ◽  
Analytical Model ◽  
Field Data ◽  
Fiber Optic ◽  
Incident Angle ◽  
Acoustic Sensing ◽  
Distributed Acoustic Sensing ◽  
Straight Fiber

Abstract. Fiber optic sensing technology has recently become popular for oil and gas, mining, geotechnical engineering, and hydrogeology applications. With a successful track record in many applications, distributed acoustic sensing using straight fiber optic cables has become a method of choice for seismic studies. However, distributed acoustic sensing using straight fiber optic cables is not able to detect off-axial strain, hence a helically wound cable design was introduced to overcome this limitation. The helically wound cable field data in New Afton deposit showed that the quality of the data is tightly dependent on the incident angle (the angle between the ray and normal vector of the surface) and surrounding media. We introduce a new analytical two-dimensional approach to determine the dynamic strain of a helically wound cable in terms of incident angle in response to elastic plane waves propagating through multilayered media. The method can be used to quickly and efficiently assess the effects of various materials surrounding a helically wound cable. Results from the proposed analytical model are compared with results from numerical modeling obtained with COMSOL Multiphysics, for scenarios corresponding to a real installation of helically wound cable deployed underground at the New Afton mine in British Columbia, Canada. Results from the analytical model are consistent with numerical modeling results. Our modeling results demonstrate the effects of cement quality, and casing installment on the quality of the helically-wound cable response. Numerical modeling results and field data suggest that, even if reasonably effective coupling achieved, the soft nature of the rocks in these intervals would result in low fiber strains for the HWC. The proposed numerical modeling workflow would be applied for more complicated scenarios (e.g., non-linear material constitutive behaviour, and the effects of pore fluids). The results of this paper can be used as a guideline for analyzing the effect of surrounding media and incident angle on the response of helically wound cable, optimizing the installation of helically wound cable in various conditions, and to validate boundary conditions of 3-D numerical model built for analyzing complex scenarios.

scholarly journals Detection of Leak-Induced Pipeline Vibrations Using Fiber—Optic Distributed Acoustic Sensing

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2841 ◽  
Author(s):  
Pavol Stajanca ◽  
Sebastian Chruscicki ◽  
Tobias Homann ◽  
Stefan Seifert ◽  
Dirk Schmidt ◽  
...  
Keyword(s):  
Fiber Optic ◽  
Broadband Noise ◽  
Gas Pipeline ◽  
Accelerometer Data ◽  
Acoustic Sensing ◽  
System Sensitivity ◽  
Spectral Integration ◽  
Detection Approach ◽  
Distributed Acoustic Sensing ◽  
Pipeline Vibration

In the presented work, the potential of fiber-optic distributed acoustic sensing (DAS) for detection of small gas pipeline leaks (<1%) is investigated. Helical wrapping of the sensing fiber directly around the pipeline is used to increase the system sensitivity for detection of weak leak-induced vibrations. DAS measurements are supplemented with reference accelerometer data to facilitate analysis and interpretation of recorded vibration signals. The results reveal that a DAS system using direct fiber application approach is capable of detecting pipeline natural vibrations excited by the broadband noise generated by the leaking medium. In the performed experiment, pipeline vibration modes with acceleration magnitudes down to single μg were detected. Simple leak detection approach based on spectral integration of time-averaged DAS signals in frequency domain was proposed. Potential benefits and limitations of the presented monitoring approach were discussed with respect to its practical applicability. We demonstrated that the approached is potentially capable of detection and localization of gas pipeline leaks with leak rates down to 0.1% of the pipeline flow volume and might be of interest for monitoring of short- and medium-length gas pipelines.

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