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Deep Learning Approach for Oil Pipeline Leakage Detection Using Image-Based Edge Detection Techniques
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Natural gas and oil are one of the mainstays of the global economy. However, many issues surround the pipelines that transport these resources, including aging infrastructure, environmental impacts, and vulnerability to sabotage operations. Such issues can result in leakages in these pipelines, requiring significant effort to detect and pinpoint their locations. The objective of this project is to develop and implement a method for detecting oil spills caused by leaking oil pipelines using aerial images captured by a drone equipped with a Raspberry Pi 4. Using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol, the acquired images and the global positioning system (GPS) coordinates of the images' acquisition are sent to the base station. Using deep learning approaches such as holistically-nested edge detection (HED) and extreme inception (Xception) networks, images are analyzed at the base station to identify contours using dense extreme inception networks for edge detection (DexiNed). This algorithm is capable of finding many contours in images. Moreover, the CIELAB color space (LAB) is employed to locate black-colored contours, which may indicate oil spills. The suggested method involves eliminating smaller contours to calculate the area of larger contours. If the contour's area exceeds a certain threshold, it is classified as a spill; otherwise, it is stored in a database for further review. In the experiments, spill sizes of 1m2, 2m2, and 3m2 were established at three separate test locations. The drone was operated at three different heights (5 m, 10 m, and 15 m) to capture the scenes. The results show that efficient detection can be achieved at a height of 10 meters using the DexiNed algorithm. Statistical comparison with other edge detection methods using basic metrics, such as perimage best threshold (OIS = 0.867), fixed contour threshold (ODS = 0.859), and average precision (AP = 0.905), validates the effectiveness of the DexiNed algorithm in generating thin edge maps and identifying oil slicks. © 2023 Lavoisier. All rights reserved.

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Publication Date
Sun Aug 13 2023
Journal Name
Arpn Journal Of Engineering And Applied Sciences
A NEW APPROACH FOR MODELLING THE VIBRATION OF BEAMS UNDER MOVING LOAD EFFECT
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In this paper, a new equivalent lumped parameter model is proposed for describing the vibration of beams under the moving load effect. Also, an analytical formula for calculating such vibration for low-speed loads is presented. Furthermore, a MATLAB/Simulink model is introduced to give a simple and accurate solution that can be used to design beams subjected to any moving loads, i.e., loads of any magnitude and speed. In general, the proposed Simulink model can be used much easier than the alternative FEM software, which is usually used in designing such beams. The obtained results from the analytical formula and the proposed Simulink model were compared with those obtained from Ansys R19.0, and very good agreement has been shown. I

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Publication Date
Thu Oct 22 2020
Journal Name
2020 4th International Symposium On Multidisciplinary Studies And Innovative Technologies (ismsit)
Artificial Intelligence in Smart Agriculture: Modified Evolutionary Optimization Approach for Plant Disease Identification
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Publication Date
Mon Dec 20 2021
Journal Name
Baghdad Science Journal
Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation
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Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

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Publication Date
Sun Apr 09 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Detection of Carbonic Anhydrase Enzyme in Sulcus Median in Hind Brain (Fourth Ventricle) in the Hind Brain of the Adult White Rat (Rutts rutts
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The method of heavy metals deposition which is based on cobalt in detection of Carbonic anhydrase enzyme in the Sulcus median in the hid brain (fourth ventricle) in the adult white rat (Rutts rutts). An essential amended in the method has been done by using cobalt chloride (CoCl2) instead of cobalt phosphate (Co3(PO4)2) in the reaction medium. Any efficacy of enzymic histochemical for carbonic anhydrase enzyme did not show in histological sections. The floor of the fourth ventricle of the brain is specific, clearly any histochemical reaction sediments have not been found in sulcus median of the floor of the fourth ventricle. The corresponding stain to green methyl which was observed clearly in sulcus median region. The ventral surface of

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Publication Date
Sun Mar 06 2011
Journal Name
Baghdad Science Journal
Detection of Extraintestinal Pathogenic Escherichia coli among Normal Stool Flora of Young, Healthy, Unmarried Males & Females as Predisposing Factor to Extraintestinal Infections:A Comparison Study
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In this study we surveyed the dominant normal stool flora of randomly selected healthy, young (18-23 years old), unmarried (doctrinal) Iraqi college students (males and females) for the carriage of extraintestinal pathogenic E. coli (ExPEC). ExPEC virulence was detected phenotypically by mannose resistant hemagglutination of human red blood cells (MRHA) and mannose sensitive (MS) agglutination of Bakers' yeast (Saccharomyces cerevisceae). From 88 college students, 264 E. coli isolates were obtained (3 isolates per person): 123 from 41 females and 141 from 47 males. Of these isolates, 56% (149/264) caused MS agglutination of yeast cells and 4.16% (11/264) showed MRHA. Eighty two percent (9/11) of the isolates with MRHA also caused MS agglu

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Publication Date
Fri Jan 01 2021
Journal Name
Journal Of Economics And Administrative Sciences
Formulating a strategy for the Northern Oil Company(Case Study)
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                  The research aims to present a proposed strategy for the North Oil Company, and the proposed strategy took into account the surrounding environmental conditions and adopted in its formulation on the basis and scientific steps that are comprehensive and realistic, as it covered the main activities of the company (production and exploration activities, refining and refining activities, export and transport of oil, research and development activity, financial activity, information technology, human resources) and the (David) model has been adopted in the environmental analysis of the factors that have been diagnosed according to a

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Publication Date
Thu Apr 21 2022
Journal Name
Journal Of Petroleum Research And Studies
Smart Well Modelling for As Reservoir in AG Oil Field
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Intelligent or smart completion wells vary from conventional wells. They have downhole flow control devices like Inflow Control Devices (ICD) and Interval Control Valves (ICV) to enhance reservoir management and control, optimizing hydrocarbon output and recovery. However, to explain their adoption and increase their economic return, a high level of justification is necessary. Smart horizontal wells also necessitate optimizing the number of valves, nozzles, and compartment length. A three-dimensional geological model of the As reservoir in AG oil field was used to see the influence of these factors on cumulative oil production and NPV. After creating the dynamic model for the As reservoir using the program Petrel (2017.4), we

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Publication Date
Mon Jun 30 2025
Journal Name
Journal Of Economics And Administrative Sciences
Formulating a strategy for the Northern Oil Company(Case Study)
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يهدف البحث الى تقديم استراتيجية مقترحة لشركة نفط الشمال ، وأخذت الاستراتيجية المقترحة بنظر الاعتبار الظروف البيئية المحيطة واعتمدت في صياغتها على اسس وخطوات علمية تتسم بالشمولية والواقعية ، اذ انها غطت الانشطة الرئيسية في الشركة (نشاط الانتاج والاستكشاف , نشاط التكرير والتصفية , التصدير ونقل النفط , نشاط البحث والتطوير , النشاط المالي , تقنية المعلومات , الموارد البشرية ) وقد اعتمد نموذج (David) في التحليل البيئي

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Publication Date
Fri Dec 31 2021
Journal Name
Iraqi Geological Journal
Construction of Comprehensive Geological Model for an Iraqi Oil Reservoir
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The paper generates a geological model of a giant Middle East oil reservoir, the model constructed based on the field data of 161 wells. The main aim of the paper was to recognize the value of the reservoir to investigate the feasibility of working on the reservoir modeling prior to the final decision of the investment for further development of this oilfield. Well log, deviation survey, 2D/3D interpreted seismic structural maps, facies, and core test were utilized to construct the developed geological model based on comprehensive interpretation and correlation processes using the PETREL platform. The geological model mainly aims to estimate stock-tank oil initially in place of the reservoir. In addition, three scenarios were applie

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Publication Date
Sat Apr 03 2021
Journal Name
Lubricants
UV-Visible Spectrophotometer for Distinguishing Oxidation Time of Engine Oil
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Samples of gasoline engine oil (SAE 5W20) that had been exposed to various oxidation times were inspected with a UV-Visible (UV-Vis) spectrophotometer to select the best wavelengths and wavelength ranges for distinguishing oxidation times. Engine oil samples were subjected to different thermal oxidation periods of 0, 24, 48, 72, 96, 120, and 144 hours, resulting in a range of total base number (TBN) levels. Each wavelength (190.5 – 849.5 nm) and selected wavelength ranges were evaluated to determine the wavelength or wavelength ranges that could best distinguish among all oxidation times. The best wavelengths and wavelength ranges were analyzed with linear regression to determine the best wavelength or range to predict oxidation t

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