Front Detection Research Articles

Image convolution-based experimental technique for flame front detection and dimension estimation: a case study on laminar-to-transition jet diffusion flame height measurement

A computationally-supported experimental technique is presented, to measure height of luminous flames, using convolution and density-based spatial clustering for image processing. The experimental setup employs a high-definition camera array to capture flame imagery from 0°, 45° and 90° visualization planes. The volumetric fuel flow was ranged from 350 to 1800 cc min−1 and images of the resulting flame structure were captured and measured. Results show that output measurements are affected by both the volumetric fuel flow and the visualization plane. Whilst the former is evidently the most significant factor, the latter was found to be relevant, since several flame features, particularly, flame tilt and flickering, are only perceivable through specific visualization planes; attributed to uneven flame structure due non-homogeneous thermal stress. The experimental technique proposed yields an overall statistical tolerance of 1.29 cm and an expanded uncertainty of 0.609 cm (∼11.5%). From these results, the test is considered successful and the proposed experimental technique is deemed to be on par with the already existing ones. The utilization of spatial density clustering of image gridded data has only been tested for this implementation; being severely constrained by the sample size and density variability of the data. Consequently, care should be exercised. Nonetheless, this approach was found to inherently recognize flame front edge, and mitigate variations of pixel value due change in flame intensity, normalizing image processing, hence, it is proposed as a viable alternative for flame feature/structure visualization and estimation, and further research is encouraged.

A Framework for Sea Breeze Front Detection from Coastal Regions of India Using Morphological Snake Algorithm

Sea breezes are the most common winds experienced by people living in coastal regions. In general, the sea breeze is a flow of winds that particularly takes place in coastal areas. Sea breezes cause irregular climatic conditions. The salty air coming from the sea breeze has got many adverse effects like deterioration. The collision of two powerful sea breezes fronts can cause severe thunderstorms across the coastal regions. Therefore, it is important to know the location of the sea breeze front to define the regions affected by sea breezes. In order to detect the sea breeze front from satellite images, it is important to segment the satellite images. Image segmentation which helps in extracting the objects of interest and make the image more meaningful for further processing. Then using contour detection, the outline of sea breeze, which is sea breeze front can easily be extracted. A proper methodology with user interface is proposed in this paper for detecting the sea breeze front from the satellite images.

The Interaction between In-Cylinder Turbulent Flow and Flame Front Propagation in an Optical SI Engine Measured by High-Speed PIV

The relationship between the flow field and flame propagation is essential in determining the dynamics and effects of turbulent flow in an optical SI engine. In this study, high turbulence flow at stable operations was achieved using 12,000 rpm engine speed, 60 kPa absolute intake pressure, 14.7 A/F, and 15 deg. BTDC spark timing. The turbulent flow field and flame propagation interplay were analyzed through the simultaneous high-speed PIV measurements of the in-cylinder flow and flame front propagation under firing conditions. The intensity of the seeder used was optimized by changing the crank angle. Successful simultaneous detection of the flame front and turbulent flow was demonstrated. Strong turbulence was produced at the flame front simultaneously with the flame movement. After ignition timing, the flame accelerated in the unburned region, and a vital turbulence region occurred.

Thermal Front Detection Using Satellite-Derived Sea Surface Temperature in the Northern Indian Ocean: Evaluation of Gradient-Based and Histogram-Based Methods

Thermal Front Detection Using Satellite-Derived Sea Surface Temperature in the Northern Indian Ocean: Evaluation of Gradient-Based and Histogram-Based Methods

An improved model for compressible multiphase flows based on Smoothed Particle Hydrodynamics with enhanced particle regeneration technique

In the current study, an improved numerical model is proposed in the compressible fields based on Smoothed Particle Hydrodynamics (SPH), which is comprised of MUSCL interpolation in multiphase flow, enhanced particle regeneration technique (PRT) and the particle shifting technique (PST) in compressible flows. The PRT is specially proposed to deal with compressible problems, in which the volume of particles have large variation during the whole simulation process. Different to the conventional PRT [1], an interface control (IC) method is proposed to deal with the mass conservation problem which may result in the unphysical movement of interface when modeling multifluids. The multiphase MUSCL interpolation aims at dealing with over-dissipation problem that exists in the Godunov-type SPH which may result in the wrong detection of wave front. To avoid large discontinuity between different fluids, two kinds of extrapolation schemes (constant extrapolation and isentropic extrapolation) are discussed and compared at the inspiration of the ghost fluid method (GFM). The proposed MUSCL-based compressible SPH model is validated and discussed in several challenging test cases, such as multiphase shock tube like problem, the shock wave impacting on multifluids interface problem and Richtmyer-Meshkov instability etc., in which good agreements are obtained.

Real-Time Georeferencing of Fire Front Aerial Images Using Iterative Ray-Tracing and the Bearings-Range Extended Kalman Filter.

Although Aerial Vehicle images are a viable tool for observing large-scale patterns of fires and their impacts, its application is limited by the complex optical georeferencing procedure due to the lack of distinctive visual features in forest environments. For this reason, an exploratory study on rough and flat terrains was conducted to use and validate the Iterative Ray-Tracing method in combination with a Bearings-Range Extended Kalman Filter as a real-time forest fire georeferencing and filtering algorithm on images captured by an aerial vehicle. The Iterative Ray-Tracing method requires a vehicle equipped with a Global Positioning System (GPS), an Inertial Measurement Unit (IMU), a calibrated camera, and a Digital Elevation Map (DEM). The proposed method receives the real-time input of the GPS, IMU, and the image coordinates of the pixels to georeference (computed by a companion algorithm of fire front detection) and outputs the geographical coordinates corresponding to those pixels. The Unscented Transform B is proposed to characterize the Iterative Ray-Tracing uncertainty. A Bearings-Range filter measurement model is introduced in a sequential filtering architecture to reduce the noise in the measurements, assuming static targets. A performance comparison is done between the Bearings-Only and the Bearings-Range observation models, and between the Extended and Cubature Kalman Filters. In simulation studies with ground truth, without filtering we obtained a georeferencing Root Mean Squared Errors (RMSE) of 30.7 and 43.4 m for the rough and flat terrains respectively, while filtering with the proposed Bearings-Range Extended Kalman Filter showed the best results by reducing the previous RMSE to 11.7 and 19.8 m, respectively. In addition, the comparison of both filter algorithms showed a good performance of Bearings-Range filter which was slightly faster. Indeed, these experiments based on the real data conducted to results demonstrated the applicability of the proposed methodology for the real-time georeferencing forest fires.

Automated detection and classification of synoptic-scale fronts from atmospheric data grids

Abstract. Automatic determination of fronts from atmospheric data is an important task for weather prediction as well as for research of synoptic-scale phenomena. In this paper we introduce a deep neural network to detect and classify fronts from multi-level ERA5 reanalysis data. Model training and prediction is evaluated using two different regions covering Europe and North America with data from two weather services. We apply label deformation within our loss function, which removes the need for skeleton operations or other complicated post-processing steps as used in other work, to create the final output. We obtain good prediction scores with a critical success index higher than 66.9 % and an object detection rate of more than 77.3 %. Frontal climatologies of our network are highly correlated (greater than 77.2 %) to climatologies created from weather service data. Comparison with a well-established baseline method based on thermodynamic criteria shows a better performance of our network classification. Evaluated cross sections further show that the surface front data of the weather services as well as our network classification are physically plausible. Finally, we investigate the link between fronts and extreme precipitation events to showcase possible applications of the proposed method. This demonstrates the usefulness of our new method for scientific investigations.

Optimization of sensing-pad functionalizing strategy toward separative extended-gate FET biosensors for PSA detection

In this study, separative extended-gate AlGaAs/GaAs high electron mobility transistor (HEMT) biosensor is proposed for prostate-specific antigen (PSA) detection. Zinc oxide nanotetrapods (T-ZnO) with a four-leg structure is introduced onto the sensing pad to form a three-dimensional (3D) and concave detection front. Compared with common plane front, the elaborately-designed-3D and concave detection front can offer more biological modification sites and decrease the Debye volume, resultantly improving both the detection scope and sensitivity. Anti-PSA probes can be fixed at any sites of T-ZnO via a chemical bio-functionalization method, which facilitates better detection performance by comparison with a physical modification scheme. On the other hand, the T-ZnO nanostructures with the four-leg configuration are capable of releasing the stress and erosion effect of the solution on the plane Au film, contributing to a great improvement in the reliability of the biosensors. The optimized biosensors with chemical bio-functionalized T-ZnO detection front demonstrate good linear current/voltage response to label-free PSA target in the concentration range from 5 fg/ml~5 ng/ml and a sensitivity variation ~ 1.3% dec−1.

Pixelwise Distance Regression for Glacier Calving Front Detection and Segmentation

Glacier calving front position (CFP) is an important glaciological variable. Traditionally, delineating the CFPs has been carried out manually, which was subjective, tedious and expensive. Automating this process is crucial for continuously monitoring the evolution and status of glaciers. Recently, deep learning approaches have been investigated for this application. However, the current methods get challenged by a severe class-imbalance problem. In this work, we propose to mitigate the class-imbalance between the calving front class and the non-calving front class by reformulating the segmentation problem into a pixel-wise regression task. A Convolutional Neural Network gets optimized to predict the distance values to the glacier front for each pixel in the image. The resulting distance map localizes the CFP and is further post-processed to extract the calving front line. We propose three post-processing methods, one method based on statistical thresholding, a second method based on conditional random fields (CRF), and finally the use of a second U-Net. The experimental results confirm that our approach significantly outperforms the state-of-the-art methods and produces accurate delineation. The Second U-Net obtains the best performance results, resulting in an average improvement of about 21% dice coefficient enhancement.

LSENet: Location and Seasonality Enhanced Network for Multiclass Ocean Front Detection

Ocean fronts can cause the accumulation of nutrients and affect the propagation of underwater sound, so high-precision ocean front detection is of great significance to the marine fishery and national defense fields. However, the current ocean front detection methods either have low detection accuracy or most can only detect the occurrence of ocean front by binary classification, rarely considering the differences of the characteristics of multiple ocean fronts in different sea areas. In order to solve the above problems, we propose a semantic segmentation network called location and seasonality enhanced network (LSENet) for multi-class ocean fronts detection at pixel level. In this network, we first design a channel supervision unit structure, which integrates the seasonal characteristics of the ocean front itself and the contextual information to improve the detection accuracy. We also introduce a location attention mechanism to adaptively assign attention weights to the fronts according to their frequently occurred sea area, which can further improve the accuracy of multi-class ocean front detection. Compared with other semantic segmentation methods and current representative ocean front detection method, the experimental results demonstrate convincingly that our method is more effective.

A Deep Learning Method for Ocean Front Extraction in Remote Sensing Imagery

Oceanfront is a typical and important sea surface feature that is reported to be associated with marine ecosystems and can be used as a reference for locating fishing grounds. Frontal zone extraction is often performed using a gradient threshold to classify image pixels and the result can sometimes contain too many spikes and become chaotic, leading to a negative effect for visual interpretation. And most conventional methods that focus on extracting the ridges of fronts struggle with false fronts due to imperfect data. Also, choosing appropriate thresholds for them is another dilemma, which sometimes leads to too many frontal ridges in unwanted areas or too little than needed in the region of interest. To meet the needs for visual interpretation and automatic front detection in significant frontal areas, a novel method based on deep learning is proposed in this letter. In this method, a deep learning model with U-Net architecture was designed to detect and locate significant frontal zones in grayscale sea surface temperature (SST) images. Then, an area threshold was adopted to filter the output of the model to improve the result. To demonstrate the effect of the proposed method, it was applied to an example SST image. The results show that the proposed method can not only merge messy fronts but also capture the overall patterns of frontal zones and work with conventional methods to get a better frontal ridge extraction result.

Ocean Front Detection with Glider and Satellite-Derived SST Data in the Southern California Current System

This study proposes a method to detect ocean fronts from in situ temperature and density glider measurements. This method is applied to data collected along the CalCOFI Line 90, south of the California Current System (CCS), over the 2006–2013 period. It is based on image-processing techniques commonly applied to sea surface temperature (SST) satellite data. Front detection results using glider data are consistent with those obtained in other studies carried out in the CCS. SST images of the Multi-scale Ultra-high Resolution (MUR) dataset were also used to compare the probability of occurrence or front frequency (FF) obtained with the two datasets. Glider and MUR temperatures are highly correlated. Along Line 90, frontal frequency exhibited the same maxima near the transition zone (~130 km offshore) as derived from MUR and glider datasets. However, marked differences were found in the bimonthly FF probability with high (low) front frequency in spring-summer for glider (MUR) data. Methodological differences explaining these contrasting results are investigated. Thermohaline-compensated fronts are more abundant towards the oceanic zone, although most fronts are detected using both temperature and density criteria, indicating a significant contribution of temperature to density in this region.

Machine Learning-Based Front Detection in Central Europe

Extreme weather phenomena such as wind gusts, heavy precipitation, hail, thunderstorms, tornadoes, and many others usually occur when there is a change in air mass and the passing of a weather front over a certain region. The climatology of weather fronts is difficult, since they are usually drawn onto maps manually by forecasters; therefore, the data concerning them are limited and the process itself is very subjective in nature. In this article, we propose an objective method for determining the position of weather fronts based on the random forest machine learning technique, digitized fronts from the DWD database, and ERA5 meteorological reanalysis. Several aspects leading to the improvement of scores are presented, such as adding new fields or dates to the training database or using the gradients of fields.

Ocean Temperature and Color Frontal Zones in the Gulf of Mexico: Where, When, and Why

AbstractThe spatial and temporal patterns of ocean frontal zones in the Gulf of Mexico (GoM) are studied using ocean color and sea surface temperature (SST) imagery collected by the Moderate Resolution Imaging Spectroradiometer/Aqua (MODIS/A) over an 18‐year (2002–2019) period. A gradient‐based front detection algorithm is applied to each daily image to detect frontal features, from which a comprehensive data set of ocean frontal zones (both climatology and time series) is established. Prominent seasonality is found in both ocean color and SST frontal zones while their seasonal variations differ. Major persistent frontal zones are mainly distributed in coastal waters within the 130‐m isobath, and they are often associated with wind forcing, wind‐driven Ekman transport, upwelling/downwelling, river discharge, ocean currents, and topography. The seasonal and interannual variations in frontal distributions are also revealed in the dominant spatial and temporal modes derived from an empirical orthogonal function (EOF) decomposition analysis. These findings may have significant implications on fisheries and ocean ecology research. For example, both ocean color and SST frontal features in certain months are found to coincide with a well‐known fishing ground in the eastern GoM, and surface macroalgae features are found to align well with the identified ocean frontal features.

Asymmetry of daily mean temperature series over China and its frontal mechanism

AbstractWe analyse the European Center for Medium‐Range Weather Forecasts reanalysis temperature time series over China and find that daily mean temperature at 850 hPa pressure level warms gradually and cools rapidly, which is known as the asymmetry property. Previous studies pointed out that front events contribute to this asymmetry property, but only presented indirect evidences. Here, we confirm this conjecture with more convincing and direct evidences. The time series of front events are obtained over China by an improved objective front detection algorithm. The high Pearson correlation between time series of monthly temperature asymmetry measure and time series of monthly front events over some specific regions indicates they are closely related to each other and front events indeed contribute to temperature asymmetry. We discover that the North China Plain is a representative region where front events contribute to temperature asymmetry significantly. Further diagnostic analysis by temperature tendency equation shows that the asymmetry of daily mean temperature series is due to the asymmetry between the frequency and intensity (mainly the intensity of meridional nonlinear advection term) of cold and warm fronts in North China Plain.

Characterization of fronts in the Western Mediterranean with a special focus on the North Balearic Front

We focus on the characterization of thermohaline fronts in the Western Mediterranean, with a particular focus on the North Balearic Front (NBF), separating the Atlantic Waters that spread into the Algerian Basin from the saltier and colder waters of the Liguro-Provençal area. We use a simple gradient-based method of front detection applied to a 20-year (from June 1993 to June 2013) reanalysis of the Mediterranean. Statistics of daily frontal indices are used to identify areas of recurrent fronts, i.e., frontal zones. Comparisons with data from glider transects and remotely sensed sea surface temperature and altimetry data have been used to validate our approach from daily to seasonal and interannual time scales. Our method yielded two co-existing frontal zones in the area of the NBF. One with an almost permanent haline surface frontal zone extending southeastward from the Balearic Islands to Sardinia, representing the northern limit of the fresher Atlantic Water that spreads via instabilities of the Algerian Current and associated Algerian Eddies. Between years, its position shifts by about 1° of latitude, possibly due to processes associated with both the deep water formation in the Provençal Basin and the spreading of southern Algerian Eddies. The second frontal zone is seasonal and thermally driven extending off the northeast of Menorca to the northwest of Corsica. The Pyrenees Front, a sharp thermal front off Cap de Creus which marks the boundary between the warm surface waters of the Balearic Sea and the cooler waters in the Gulf of Lion in late summer, appears to facilitate the formation of the aforementioned seasonal thermal front through the northeastward advection of its waters toward the West Corsican Current. The divergent eastward extensions of the two frontal zones, and their differences in nature, structure, and spatio-temporal variability, call for a revisit of the NBF appellation and a clarification of its dynamics.

Detection of Chlorophyll-a Front and Its Relation to Skipjack Tuna Catches in Makassar Strait

The purpose of this study is to detect chlorophyll-a front by using the Single Image Edge Detection (SIED) method and analyze the influence of the closest distance of chlorophyll-a front to the skipjack tuna catches.

Analysis of the ‘dynamics of railway vehicles’ research field development using scientometric approach

The paper demonstrates the possibilities of a scientometric approach using CiteSpace II software analysis of the field of railway dynamics with detection of research front, main clusters of cross-cited papers, their evolution and pivotal points (see terms in Sections 2.1 and 2.2). The initial database contained 5126 Web Of Science records published between 1975 and 2019 (45 years). The linguistic analysis determined the evolution of burst terms and grouped the papers in 14 clusters that corresponded with the view of the specialists in the field. ‘Railway vehicle’, ‘Curved track’ (hunting stability and derailment risk), ‘Unconventional railway truck’, ‘Railway turnout (crossing)’ and ‘Polygonal wear’ clusters were analysed in more detail with a determination of top-cited papers in each cluster and papers that determined connections between clusters (pivotal papers). Two pivotal papers were verified with their authors.

Research Current Situation and Front Detection of Dance Movement Therapy

Research Current Situation and Front Detection of Dance Movement Therapy

Ocean Fronts detection over the Bay of Bengal using changepoint algorithms – A non-parametric approach

Oceanic fronts are regions over the oceans where a significant change in the characteristics of the water masses is observed. Advanced Very High Resolution Radiometer (AVHRR) satellite imagery over the Bay of Bengal shows regions that are populated by frontal structures. Over the Bay of Bengal, some of the strongest gradients in temperature and salinity are observed. In recent years, there has been a tremendous growth in the availability of satellite imagery and the necessity of automated fast detection of the frontal features is needed for services like potential fishing zones over open oceans. In this article, an algorithm to infer oceanic fronts over the Bay of Bengal is described using changepoint analysis. The changepoint algorithm is combined in a novel way with a contextual median filter to detect frontal features in AVHRR imagery. The changepoint analysis is a non-parametric technique that does not put thresholds on the gradients of brightness temperatures of the satellite imagery. In the open oceans, the gradients of temperature and salinity are not sharp and changepoint analysis is found to be a useful complementary technique to the existing front detecting methods when combined with contextual median filters.