scholarly journals Determination of Internal Elevation Fluctuation from CCTV Footage of Sanitary Sewers Using Deep Learning

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 503
Author(s):  
Hyon Wook Ji ◽  
Sung Soo Yoo ◽  
Dan Daehyun Koo ◽  
Jeong-Hee Kang
Keyword(s):  
Deep Learning ◽  
Water Level ◽  
Economic Feasibility ◽  
Sewer Pipe ◽  
Sewer Pipes ◽  
Flow Rates ◽  
Flow Disturbances ◽  
Learning Technique ◽  
Sanitary Sewers

The slope of sewer pipes is a major factor for transporting sewage at designed flow rates. However, the gradient inside the sewer pipe changes locally for various reasons after construction. This causes flow disturbances requiring investigation and appropriate maintenance. This study extracted the internal elevation fluctuation from closed-circuit television investigation footage, which is required for sanitary sewers. The principle that a change in water level in sewer pipes indirectly indicates a change in elevation was applied. The sewage area was detected using a convolutional neural network, a type of deep learning technique, and the water level was calculated using the geometric principles of circles and proportions. The training accuracy was 98%, and the water level accuracy compared to random sampling was 90.4%. Lateral connections, joints, and outliers were removed, and a smoothing method was applied to reduce data fluctuations. Because the target sewer pipes are 2.5 m concrete reinforced pipes, the joint elevation was determined every 2.5 m so that the internal slope of the sewer pipe would consist of 2.5 m linear slopes. The investigative method proposed in this study is effective with high economic feasibility and sufficient accuracy compared to the existing sensor-based methods of internal gradient investigation.

scholarly journals Investigating Soil Erosion Due to Defective Sewer

2021 ◽  
Author(s):  
S M Nazmuz Sakib
Keyword(s):  
Soil Erosion ◽  
Particle Sizes ◽  
Sewer Pipe ◽  
Infiltration Process ◽  
Sewer Pipes ◽  
Study Objective ◽  
Qualitative And Quantitative ◽  
Materials Used ◽  
Groundwater Infiltration

Cases of road cave-ins have been reportedly increasing globally and reports have associated this phenomenon to underground soil erosion due to defective sewer pipes. As the sewer pipes age, they may develop some defects which may lead to cracks and crevices that will lead to infiltration of the soils surrounding the pipe into the pipe, leading to the formation of cavities around the pipe. Therefore, this study investigated the factors behind the causes of underground soil erosion due to defective sewer pipes and proffered solutions for combating underground soil erosion due to defective sewer pipes. The study objective included; (a) establishing how the soil particle sizes affect the internal soil erosion due to defective sewer pipes, (b) determination of the effect of defect sizes on the internal soil erosion due to defective sewer pipes, (c) establishing the effect of the embedment material used on the internal soil erosion due to defective sewer pipes, (d) investigation of the type of soil erosion mechanism in the presence of a buried sewer pipe defect caused by the groundwater infiltration process. The methodology of the study involved reviewing and analyzing secondary qualitative and quantitative data. The findings established that the defect size of the pipe, the type and characteristics of the soil and the type of embedment materials used affected erosion of soil around a defective sewer pipe.

scholarly journals Measurement of Wastewater Discharge in Sewer Pipes Using Image Analysis

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1771
Author(s):  
Hyon Ji ◽  
Sung Yoo ◽  
Bong-Jae Lee ◽  
Dan Koo ◽  
Jeong-Hee Kang
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Water Level ◽  
Low Cost ◽  
Water Levels ◽  
Boundary Line ◽  
Sewer Pipe ◽  
Flow Meters ◽  
Submerged Area ◽  
Boundary Lines

Generally, the amount of wastewater in sewerage pipes is measured using sensor-based devices such as submerged area velocity flow meters or non-contact flow meters. However, these flow meters do not provide accurate measurements because of impurities, corrosion, and measurement instability due to high turbidity. However, cameras have advantages such as their low cost, easy service, and convenient operation compared to the sensors. Therefore, in this study, we examined the following three methods for measuring the flow rate by capturing images inside of a sewer pipe using a camera and analyzing the images to calculate the water level: direct visual inspection and recording, image processing, and deep learning. The MATLAB image processing toolbox was used for analysis. The image processing found the boundary line by adjusting the contrast of the image or removing noise; a network to find the boundary line between wastewater and sewer pipe was created after training the image segmentation results and placing them into three categories using deep learning. From the recognized water levels, geometrical features were used to identify the boundary lines, and flow velocities and flow rates were calculated from Manning’s equation. Using direct inspection and image-processing techniques, boundary lines in images were detected at rates of 12% and 53%, respectively. Although the deep-learning model required training, it demonstrated 100% water-level detection, thereby proving to be the most advantageous method. Moreover, there is enough potential to increase the accuracy of deep learning, and it can be a possible replacement for existing flow measurement sensors.

Determination of specific interfacial areas in swirled two-phase flow

1983 ◽  
Vol 48 (3) ◽  
pp. 842-853
Author(s):  
Kurt Winkler ◽  
František Kaštánek ◽  
Jan Kratochvíl
Keyword(s):  
Interfacial Area ◽  
Liquid Volume ◽  
Upward Flow ◽  
Two Phase ◽  
Flow Rates ◽  
Mist Flow ◽  
Interfacial Areas ◽  
Correlation Parameters ◽  
Flow Components

Specific gas-liquid interfacial area in flow tubes 70 mm in diameter of the length 725 and 1 450 mm resp. containing various swirl bodies were measured for concurrent upward flow in the ranges of average gas (air) velocities 11 to 35 ms-1 and liquid flow rates 13 to 80 m3 m-2 h-1 using the method of CO2 absorption into NaOH solutions. Two different flow regimes were observed: slug flow swirled annular-mist flow. In the latter case the determination was carried out separately for the film and spray flow components, respectively. The obtained specific areas range between 500 to 20 000 m3 m-2. Correlation parameters are energy dissipation criteria, related to the geometrical reactor volume and to the static liquid volume in the reactor.

Determination of trace concentrations of copper by FIA-FAAS after preconcentration on chelating sorbents

1989 ◽  
Vol 54 (7) ◽  
pp. 1785-1794 ◽  
Author(s):  
Vlastimil Kubáň ◽  
Josef Komárek ◽  
Zbyněk Zdráhal
Keyword(s):  
Sampling Frequency ◽  
Copper Determination ◽  
Flow Rates ◽  
Sample Injection ◽  
Injection Flow ◽  
Chelating Sorbents ◽  
Set Up ◽  
Trace Concentrations ◽  
Better Than

A FIA-FAAS apparatus containing a six-channel sorption equipment with five 3 x 26 mm microcolumns packed with Spheron Oxin 1 000, Ostsorb Oxin and Ostsorb DTTA was set up. Combined with sorption from 0.002M acetate buffer at pH 4.2 and desorption with 2M-HCl, copper can be determined at concentrations up to 100, 150 and 200 μg l-1, respectively. For sample and eluent flow rates of 5.0 and 4.0 ml min-1, respectively, and a sample injection time of 5 min, the limit of copper determination is LQ = 0.3 μg l-1, repeatability sr is better than 2% and recovery is R = 100 ± 2%. The enrichment factor is on the order of 102 and is a linear function of time (volume) of sample injection up to 5 min and of the sample injection flow rate up to 11 ml min-1 for Spheron Oxin 1 000 and Ostsorb DTTA. For times of sorption of 60 and 300 s, the sampling frequency is 70 and 35 samples/h, respectively. The parameters of the FIA-FAAS determination (acetylene-air flame) are comparable to or better than those achieved by ETA AAS. The method was applied to the determination of traces of copper in high-purity water.

ExypnoSteganos - A smarter approach to steganography

2021 ◽  
pp. 1-12
Author(s):  
Gaurav Sarraf ◽  
Anirudh Ramesh Srivatsa ◽  
MS Swetha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Compression ◽  
Building Block ◽  
Communication Techniques ◽  
Systems Security ◽  
Learning Technique ◽  
Critical Problems ◽  
Sporadic Cases ◽  
Compressed Data

With the ever-rising threat to security, multiple industries are always in search of safer communication techniques both in rest and transit. Multiple security institutions agree that any systems security can be modeled around three major concepts: Confidentiality, Availability, and Integrity. We try to reduce the holes in these concepts by developing a Deep Learning based Steganography technique. In our study, we have seen, data compression has to be at the heart of any sound steganography system. In this paper, we have shown that it is possible to compress and encode data efficiently to solve critical problems of steganography. The deep learning technique, which comprises an auto-encoder with Convolutional Neural Network as its building block, not only compresses the secret file but also learns how to hide the compressed data in the cover file efficiently. The proposed techniques can encode secret files of the same size as of cover, or in some sporadic cases, even larger files can be encoded. We have also shown that the same model architecture can theoretically be applied to any file type. Finally, we show that our proposed technique surreptitiously evades all popular steganalysis techniques.

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