scholarly journals Properties of coherent structures over Paris: a study based on an automated classification method for Doppler lidar observations

2021 ◽  
Author(s):  
Ioannis Cheliotis ◽  
Elsa Dieudonné ◽  
Hervé Delbarre ◽  
Anton Sokolov ◽  
Egor Dmitriev ◽  
...  
Keyword(s):  
Coherent Structures ◽  
Training Dataset ◽  
Process Time ◽  
Automated Classification ◽  
Large Fluctuations ◽  
Wind Lidar ◽  
Lidar Observations ◽  
Doppler Wind Lidar

AbstractThe studies related to the coherent structures in the atmosphere, using Doppler wind lidar observations, so far relied on the manual detection and classification of the structures in the lidar images, making this process time-consuming. We developed an automated classification based on texture analysis parameters and the quadratic discriminant analysis algorithm for the detection of medium-to-large fluctuations and coherent structures recorded by single Doppler wind lidar quasi-horizontal scans. The algorithm classified a training dataset of 150 cases into four types of patterns, namely streaks (narrow stripes), rolls (wide stripes), thermals (enclosed areas) and “others” (impossible to classify), with 91% accuracy. Subsequently, we applied the trained algorithm to a dataset of 4577 lidar scans recorded in Paris, atop a 75 m tower for a 2-month period (September-October 2014). The current study assesses the quality of the classification by examining the physical properties of the classified cases. The results show a realistic classification of the data: with rolls and thermals cases mostly classified concurrently with a well-developed atmospheric boundary layer and the streaks cases associated with nocturnal low-level jets (nllj) events. Furthermore, rolls and streaks cases were mostly observed under moderate or high wind conditions. The detailed analysis of a four-day period reveals the transition between the types. The analysis of the space spectra in the direction transverse to the mean wind, during these four days, revealed streaks spacing of 200 to 400 m, and rolls sizes, as observed in the lower level of the mixed layer, of approximately 1 km.

scholarly journals Airborne Doppler Wind Lidar Observations of the Tropical Cyclone Boundary Layer

2018 ◽  
Vol 10 (6) ◽  
pp. 825 ◽  
Author(s):  
Jun Zhang ◽  
Robert Atlas ◽  
G. Emmitt ◽  
Lisa Bucci ◽  
Kelly Ryan
Keyword(s):  
Boundary Layer ◽  
Tropical Cyclone ◽  
Wind Lidar ◽  
Tropical Cyclone Boundary Layer ◽  
Lidar Observations ◽  
Doppler Wind Lidar

scholarly journals Semantic Indexing of 19th-Century Greek Literature Using 21st-Century Linguistic Resources

2021 ◽  
Vol 13 (16) ◽  
pp. 8878
Author(s):  
Dimitris Dimitriadis ◽  
Sofia Zapounidou ◽  
Grigorios Tsoumakas
Keyword(s):  
19Th Century ◽  
21St Century ◽  
Sequence Length ◽  
Automated Classification ◽  
Greek Literature ◽  
Linguistic Resources ◽  
Domain Expertise ◽  
The 19Th Century

Manual classification of works of literature with genre/form concepts is a time-consuming task requiring domain expertise. Building automated systems based on language understanding can help humans to achieve this work faster and more consistently. Towards this direction, we present a case study on automatic classification of Greek literature books of the 19th century. The main challenges in this problem are the limited number of literature books and resources of that age and the quality of the source text. We propose an automated classification system based on the Bidirectional Encoder Representations from Transformers (BERT) model trained on books from the 20th and 21st century. We also dealt with BERT’s constraint on the maximum sequence length of the input, leveraging the TextRank algorithm to construct representative sentences or phrases from each book. The results show that BERT trained on recent literature books correctly classifies most of the books of the 19th century despite the disparity between the two collections. Additionally, the TextRank algorithm improves the performance of BERT.

scholarly journals Triple Doppler wind lidar observations during the mountain terrain atmospheric modeling and observations field campaign

2016 ◽  
Vol 10 (2) ◽  
pp. 026015 ◽  
Author(s):  
Yansen Wang ◽  
Christopher M. Hocut ◽  
Sebastian W. Hoch ◽  
Edward Creegan ◽  
Harindra J. S. Fernando ◽  
...  
Keyword(s):  
Atmospheric Modeling ◽  
Field Campaign ◽  
Mountain Terrain ◽  
Wind Lidar ◽  
Lidar Observations ◽  
Doppler Wind Lidar

Application of Doppler wind lidar observations to hurricane analysis and prediction

2017 ◽  
Author(s):  
Robert M. Atlas ◽  
George D. Emmitt ◽  
Lisa Bucci ◽  
Kelly Ryan ◽  
Jun A. Zhang
Keyword(s):  
Wind Lidar ◽  
Lidar Observations ◽  
Doppler Wind Lidar

scholarly journals Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Tien-Thinh Le ◽  
Van-Hai Nguyen ◽  
Minh Vuong Le
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Training Dataset ◽  
Error Measurement ◽  
Testing Data ◽  
Infrastructure Inspection ◽  
Classification Of Images ◽  
Deep Learning Model

This paper is devoted to the development of a deep learning- (DL-) based model to detect crack fractures on concrete surfaces. The developed model for the classification of images was based on a DL Convolutional Neural Network (CNN). To train and validate the CNN model, a database containing 40,000 images of concrete surfaces (with and without cracks) was collected from the available literature. Several conditions on the concrete surfaces were taken into account such as illumination and surface finish (i.e., exposed, plastering, and paint). Various error measurement criteria such as accuracy, precision, recall, specificity, and F1-score were employed for accessing the quality of the developed model. Results showed that for the training dataset (50% of the database), the precision, recall, specificity, F1-score, and accuracy were 99.5%, 99.8%, 99.5%, 99.7%, and 99.7%, respectively. On the other hand, for the validating dataset, the precision, recall, specificity, F1-score, and accuracy are 96.5%, 98.8%, 96.6%, 97.7%, and 97.7%, respectively. Thus, the developed CNN model may be considered valid because it performs the classification of cracks well using the testing data. It is also confirmed that the developed DL-based model was robust and efficient, as it can take into account different conditions on the concrete surfaces. The CNN model developed in this study was compared with other works in the literature, showing that the CNN model could improve the accuracy of image classification, in comparison with previously published results. Finally, in further work, such model could be combined with Unmanned Aerial Vehicles (UAVs) to increase the productivity of concrete infrastructure inspection.

scholarly journals Turbulence characteristics and coherent structures in the convective boundary layer analyzed using Doppler wind lidars during FESSTVaL@MOL 2020

2021 ◽  
Author(s):  
Noviana Dewani ◽  
Mirjana Sakradzija ◽  
Linda Schlemmer ◽  
Jürg Schmidli
Keyword(s):  
Boundary Layer ◽  
Field Experiment ◽  
Coherent Structures ◽  
Convective Boundary Layer ◽  
Elevation Angle ◽  
Convective Boundary ◽  
Boundary Layer Turbulence ◽  
Meteorological Observatory ◽  
Wind Lidar ◽  
Doppler Wind Lidar

<p>Doppler wind lidars are used to measure boundary layer turbulence, which is an important process to transfer heat and moisture within the boundary layer. Turbulence measurements using Doppler wind lidars were conducted during FESSTVaL@MOL field experiment from June to August 2020. The FESSTVaL@MOL 2020 is a part of the FESSTVaL (Field Experiment on sub-mesoscale spatio-temporal variability in Lindenberg) measurement campaign conducted at the boundary layer site Falkenberg, a part of the Lindenberg Meteorological Observatory – Richard-Aßmann-Observatorium (MOL-RAO). One Doppler wind lidar has been operated in vertical stare mode to characterize turbulence in the convective boundary layer during the summer. Two other Doppler wind lidars have been operated in low elevation angle PPI scan mode and one Doppler wind lidar has been operated in RHI scan mode. These three scanning configurations are used to investigate the dominant coherent structures near the surface.</p><p>The retrieved wind data from vertical stare mode are categorized into cloud-topped boundary layer and cloud-free boundary layer days. We will analyze the intensity of the turbulence using vertical velocity variance and dissipation rate of the turbulent kinetic energy and the source of turbulence using a skewness profile for both categories. These profiles will be combined with low elevation angle PPI scan mode to categorize the coherent structures near the surface by their intensity and origin. Besides, we will present the overview of the preliminary study about the evolution of mixing layer height before and after cold-pool passage from several cases during FESSTVaL@MOL 2020 using vertical stare and RHI scan data.</p>

Classification of impedance cardiography dZ/dt complex subtypes using pattern recognition artificial neural networks

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sara Benouar ◽  
Abdelakram Hafid ◽  
Malika Kedir-Talha ◽  
Fernando Seoane
Keyword(s):  
Pattern Recognition ◽  
Impedance Cardiography ◽  
Divide And Conquer ◽  
Training Dataset ◽  
Automated Classification ◽  
Hemodynamic Parameters ◽  
Average Accuracy ◽  
Characteristic Points ◽  
Artificial Neural

Abstract In impedance cardiography (ICG), the detection of dZ/dt signal (ICG) characteristic points, especially the X point, is a crucial step for the calculation of hemodynamic parameters such as stroke volume (SV) and cardiac output (CO). Unfortunately, for beat-to-beat calculations, the accuracy of the detection is affected by the variability of the ICG complex subtypes. Thus, in this work, automated classification of ICG complexes is proposed to support the detection of ICG characteristic points and the extraction of hemodynamic parameters according to several existing subtypes. A novel pattern recognition artificial neural network (PRANN) approach was implemented, and a divide-and-conquer strategy was used to identify the five different waveforms of the ICG complex waveform with output nodes no greater than 3. The PRANN was trained, tested and validated using a dataset from four volunteers from a measurement of eight electrodes. Once the training was satisfactory, the deployed network was validated on two other datasets that were completely different from the training dataset. As an additional performance validation of the PRANN, each dataset included four volunteers for a total of eight volunteers. The results show an average accuracy of 96% in classifying ICG complex subtypes with only a decrease in the accuracy to 83 and 80% on the validation datasets. This work indicates that the PRANN is a promising method for automated classification of ICG subtypes, facilitating the investigation of the extraction of hemodynamic parameters from beat-to-beat dZ/dt complexes.

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