Estimation of Water Surface Flow Velocity in Coastal Video Imagery by Visual Tracking with Deep Learning

2020 ◽  
Vol 95 (sp1) ◽  
pp. 522
Author(s):  
Jinah Kim ◽  
Jaeil Kim
Keyword(s):  
Deep Learning ◽  
Flow Velocity ◽  
Visual Tracking ◽  
Water Surface ◽  
Surface Flow ◽  
Video Imagery

DOPPLER VS. SPINNER PLT SENSING FOR HYDROCARBON VELOCITY ESTIMATE BY DEEP-LEARNING APPROACH

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
◽  
Alberto Marsala ◽  
Virginie Schoepf ◽  
Linda Abbassi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Flow Velocity ◽  
Oil And Gas ◽  
Surface Flow ◽  
Sound Waves ◽  
Velocity Measurements ◽  
Data Set ◽  
Flowing Fluid ◽  
Flow Meters ◽  
Doppler Flow

Logging hydrocarbon production potential of wells has been at the forefront of enhancing oil and gas exploration and maximize productivity from oil and gas reservoirs. A major challenge is accurate downhole fluid phases flow velocity measurements in production logging (PLT) due to the criticality of mechanical spinner-based sensor devices. Ultrasonic Doppler-based sensors are more robust and deployable either in wireline or logging while drilling (LWD) conditions; however, due to the different sensing physics, the measurement results may vary. Ultrasonic Doppler flow meters utilize the Doppler effect that is a change in frequency of the sound waves that are reflected on a moving target. A common example is the change in pitch when a vehicle sounding a horn approaches and recedes from an observer. The frequency shift is in direct proportion of the relative velocity of the fluid with respect to the emitter-receiver and allows to infer the speed of the flowing fluid. Doppler flow meters offer many advantages over mechanical spinners such as the ability to measure without requiring calibration passes, the absence of mechanical moving parts, the sensors robustness to shocks and hits, easy installation and minimal affection by changes in temperature, density and viscosity of the fluid thus capability to work even in highly contaminated conditions such as tar, asphaltene deposits on equipment. Despite being widely used in surface flow metering, ultrasonic Doppler sensor applications to downhole environment have been so far very limited. We present in this work an innovative deep learning framework to estimate spinner phase velocities from Doppler based sensor velocities. Tests of the framework on a benchmark data set displayed strong estimation results, in particular outlining the ability to utilize Doppler-based sensors for downhole phase velocity measurements and allows the comparison of the estimates with previously recorded spinner velocity measurements. This allows for the real-time automated interpretative framework implementation and flow velocity estimations either in conventional wireline production logging technologies and potentially also in LWD conditions, when the well is flowing in underbalanced conditions.

EFEKTIVITAS TANAMAN GENJER DALAM MENURUNKAN BOD DAN COD LIMBAH CAIR TAHU HASIL PROSES ANAEROB

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Muhammad Irvan Nurliansyah
Keyword(s):  
Water Surface ◽  
Free Water ◽  
Surface Flow ◽  
Free Water Surface

ABSTRAK Limbah cair tahu merupakan limbah cair yang berasal dari proses pembuatan tahu. Limbah cair tahu mengandung senyawa organik yang tinggi. Pembuangan limbah cair tahu secara langsung ke badan air tanpa dilakukan pengolahan dapat mempengaruhi dan mencemari lingkungan. Suatu cara untuk menanggulangi permasalahan tersebut adalah melakukan pengolahan limbah cair tahu. Salah satu alternatif pengolahan limbah cair tahu yang dapat digunakan adalah fitoremediasi menggunakan tanaman genjer. Penelitian ini bertujuan untuk mengetahui efisiensi pengolahan dan efektivitas waktu tinggal pengolahan limbah cair tahu menggunakan tanaman genjer dalam menurunkan BOD dan COD effluen hasil proses pengolahan anaerob limbah cair tahu. Metode yang digunakan dalam penelitian ini adalah fitoremediasi menggunakan tanaman genjer pada sistem lahan basah buatan Free Water Surface flow dengan waktu tinggal 3 hari, 5 hari dan 7 hari. Hasil penelitian menunjukkan bahwa efisiensi pengolahan secara fitoremediasi pada hari ke 3 untuk BOD dan COD berturut-turut sebesar 21,28% dan 16,13%. Pada hari ke 5 efisiensi pengolahan yang diperoleh untuk BOD dan COD berturut-turut sebesar 52,60% dan 45,93% sedangkan efisiensi pengolahan pada hari ke 7 untuk BOD dan COD berturut-turut sebesar 76,42% dan 70,74%. Waktu tinggal efektif yang diperoleh pada penelitian ini adalah  7 hari dengan nilai BOD dan COD telah berada dibawah baku mutu yaitu berturut-turut sebesar 72,72 mg/l dan 213,33 mg/l.   Kata kunci : limbah cair tahu, fitoremediasi, tanaman genjer, efisiensi pengolahan, waktu tinggal

Deep learning assisted visual tracking of evader-UAV

2021 ◽  
Author(s):  
Athanasios Tsoukalas ◽  
Daitao Xing ◽  
Nikolaos Evangeliou ◽  
Nikolaos Giakoumidis ◽  
Anthony Tzes
Keyword(s):  
Deep Learning ◽  
Visual Tracking

Denoising of river surface photogrammetric DEMs using deep learning

2021 ◽  
Author(s):  
Radosław Szostak ◽  
Przemysław Wachniew ◽  
Mirosław Zimnoch ◽  
Paweł Ćwiąkała ◽  
Edyta Puniach ◽  
...  
Keyword(s):  
Higher Education ◽  
Deep Learning ◽  
Water Level ◽  
Water Surface ◽  
Research University ◽  
Static Characteristic ◽  
Water Levels ◽  
Training Dataset ◽  
Observation Data ◽  
Characteristic Points

<p>Unmanned Aerial Vehicles (UAVs) can be an excellent tool for environmental measurements due to their ability to reach inaccessible places and fast data acquisition over large areas. In particular drones may have a potential application in hydrology, as they can be used to create photogrammetric digital elevation models (DEM) of the terrain allowing to obtain high resolution spatial distribution of water level in the river to be fed into hydrological models. Nevertheless, photogrammetric algorithms generate distortions on the DEM at the water bodies. This is due to light penetration below the water surface and the lack of static characteristic points on water surface that can be distinguished by the photogrammetric algorithm. The correction of these disturbances could be achieved by applying deep learning methods. For this purpose, it is necessary to build a training dataset containing DEMs before and after water surfaces denoising. A method has been developed to prepare such a dataset. It is divided into several stages. In the first step a photogrammetric surveys and geodetic water level measurements are performed. The second one includes generation of DEMs and orthomosaics using photogrammetric software. Finally in the last one the interpolation of the measured water levels is done to obtain a plane of the water surface and apply it to the DEMs to correct the distortion. The resulting dataset was used to train deep learning model based on convolutional neural networks. The proposed method has been validated on observation data representing part of Kocinka river catchment located in the central Poland.</p><p>This research has been partly supported by the Ministry of Science and Higher Education Project “Initiative for Excellence – Research University” and Ministry of Science and Higher Education subsidy, project no. 16.16.220.842-B02 / 16.16.150.545.</p>

scholarly journals A NEW FULLY-AUTOMATIC PROCEDURE FOR THE IDENTIFICATION AND THE COUPLING OF THE OVERTOPPING WAVES

2018 ◽  
pp. 36 ◽  
Author(s):  
Sara Mizar Formentin ◽  
Barbara Zanuttigh
Keyword(s):  
Flow Velocity ◽  
Water Surface ◽  
Automatic Identification ◽  
Practical Relevance ◽  
Threshold Values ◽  
Flow Conditions ◽  
Fully Automatic ◽  
The Individual ◽  
Coupling Algorithm ◽  
Submerged Conditions

This contribution presents a new procedure for the automatic identification of the individual overtopping events. The procedure is based on a zero-down-crossing analysis of the water-surface-elevation signals and, based on two threshold values, can be applied to any structure crest level, i.e. to emerged, zero-freeboard, over-washed and submerged conditions. The results of the procedure are characterized by a level of accuracy comparable to the human-supervised analysis of the wave signals. The procedure includes a second algorithm for the coupling of the overtopping events registered at two consecutive gauges. This coupling algorithm offers a series of original applications of practical relevance, a.o. the possibility to estimate the wave celerities, i.e. the velocities of propagation of the single waves, which could be used as an approximation of the flow velocity in shallow water and broken flow conditions.

scholarly journals Discharge Estimation Using Tsallis and Shannon Entropy Theory in Natural Channels

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1786
Author(s):  
Jitendra Kumar Vyas ◽  
Muthiah Perumal ◽  
Tommaso Moramarco
Keyword(s):  
Flow Velocity ◽  
Surface Flow ◽  
Tsallis Entropy ◽  
Accurate Estimation ◽  
Mean Flow ◽  
Flow Area ◽  
Cross Sectional ◽  
The Cross ◽  
Discharge Estimation ◽  
Mean Flow Velocity

Streamflow measurements during high floods is a challenge for which the World Meteorological Organization fosters the development of innovative technologies for achieving an accurate estimation of the discharge. The use of non-contact sensors for monitoring surface flow velocities is of interest to turn these observed values into a cross-sectional mean flow velocity, and subsequently, into discharge if bathymetry is given. In this context, several techniques are available for the estimation of mean flow velocity, starting from observed surface velocities. Among them, the entropy-based methodology for river discharge assessment is often applied by leveraging the theoretical entropic principles of Shannon and Tsallis, both of which link the maximum flow velocity measured at a vertical of the flow area, named the y-axis, and the cross-sectional mean flow velocity at a river site. This study investigates the performance of the two different entropic approaches in estimating the mean flow velocity, starting from the maximum surface flow velocity sampled at the y-axis. A velocity dataset consisting of 70 events of measurements collected at two gauged stations with different geometric and hydraulic characteristics on the Po and Tiber Rivers in Italy was used for the analysis. The comparative evaluation of the velocity distribution observed at the y-axis of all 70 events of measurement was closely reproduced using both the Shannon and Tsallis entropy approaches. Accurate values in terms of the cross-sectional mean flow velocity and discharge were obtained with average errors not exceeding 10%, demonstrating that the Shannon and Tsallis entropy concepts were equally efficient for discharge estimation in any flow conditions.

Deep learning assisted robust visual tracking with adaptive particle filtering

2018 ◽  
Vol 60 ◽  
pp. 183-192 ◽  
Author(s):  
Xiaoyan Qian ◽  
Lei Han ◽  
Yuedong Wang ◽  
Meng Ding
Keyword(s):  
Deep Learning ◽  
Visual Tracking ◽  
Particle Filtering

Development of a Surface Flow Sensor for Measuring Turbulent Drag Force

2019 ◽  
Author(s):  
Il Doh ◽  
Il-Bum Kwon ◽  
Jiho Chang ◽  
Sejong Chun
Keyword(s):  
Wind Tunnel ◽  
Flow Velocity ◽  
Drag Force ◽  
Mass Flow ◽  
Surface Flow ◽  
Flow Sensor ◽  
Constant Current ◽  
Thermal Mass ◽  
Turbulent Drag ◽  
Mass Flow Sensor

Abstract A surface flow sensor is needed if turbulent drag force is to be measured over a vehicle, such as a car, a ship, and an airplane. In case of automobile industry, there are no automobile manufacturers which measure surface flow velocity over a car for wind tunnel testing. Instead, they rely on particle image velocimetry (PIV), pressure sensitive paint (PSP), laser Doppler anemometry (LDA), pitot tubes, and tufts to get information regarding the turbulent drag force. Surface flow sensors have not devised yet. This study aims at developing a surface flow sensor for measuring turbulent drag force over a rigid body in a wind tunnel. Two sensing schemes were designed for the fiber-optic distributed sensor and the thermal mass flow sensor. These concepts are introduced in this paper. As the first attempt, a thermal mass flow sensor has been fabricated. It was flush-mounted on the surface of a test section in the wind tunnel to measure the surface flow velocity. The thermal mass flow sensor was operated by either constant current or constant resistance modes. Resistance ratio was changed as the electric current was increased by the constant current mode, while power ratio was saturated as the resistance was increased by the constant resistance mode. Either the resistance ratio or the power ratio was changed with the flow velocity measured by a Pitot tube, located at the center of test section.

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