يتطلب نظام الحماية الكاثودية للتيار القسري (ICCP) قياسات كميات منخفضة للغاية من الخصائص الكهربائية. استخدم العمل التجريبي الحالي وحدة مستشعر Adafruit INA219 للحصول على  قيم الجهد والتيار وقدرة  الحمل الافتراضي الذي يستهلك طاقة منخفضة جدًا تحاكي نظام ICCP. تكمن المشكلة الرئيسية في تكييف مستشعر INA219 مع بيئة LabVIEW بسبب عدم وجود مكتبة المستشعر هذا. تم تخصيص هذا العمل لتكييف وحدة استشعار Adafruit INA219 في بيئة LabVIEW من خلال إنشاء

An impressed current cathodic protection system (ICCP) requires measurements of extremely low-level quantities of its electrical characteristics. The current experimental work utilized the Adafruit INA219 sensor module for acquiring the values for voltage, current, and power of a default load, which consumes quite low power and simulates an ICCP system. The main problem is the adaptation of the INA219 sensor to the LabVIEW environment due to the absence of the library of this sensor. This work is devoted to the adaptation of the Adafruit INA219 sensor module in the LabVIEW environment through creating, developing, and successfully testing a Sub VI to be ready for employment in an ICCP system. The sensor output was monitored with an Arduino

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

This study employs evolutionary optimization and Artificial Intelligence algorithms to determine an individual’s age using a single-faced image as the basis for the identification process. Additionally, we used the WIKI dataset, widely considered the most comprehensive collection of facial images to date, including descriptions of age and gender attributes. However, estimating age from facial images is a recent topic of study, even though much research has been undertaken on establishing chronological age from facial photographs. Retrained artificial neural networks are used for classification after applying reprocessing and optimization techniques to achieve this goal. It is possible that the difficulty of determining age could be reduce

Visualization of water flow around different bluff bodies at different Reynolds number ranging (1505 - 2492) was realized by designing and building a test rig which contains an open channel capable to ensure water velocity range (4-8cm/s) in this channel. Hydrogen bubbles generated from the ionized water using DC power supply are visualized by a light source and photographed by a digital camera. Flow pattern around a circular disk of (3.6cm) diameter and (3mm) thickness, a sphere of (3.8cm) diameter and a cylinder of
(3.2cm) diameter and (10cm) length are studied qualitatively. Parameters of the vortex ring generated in the wake region of the disk and the separation angle of water stream lines from the surface of the sphere are plott

Combining ultrasonic irradiation and the Fenton process as a sono-Fenton process, the chemical oxygen demand (COD) in refinery wastewater was successfully eliminated using response surface methodology (RSM) with central composite design (CCD). The impact of two main influential operational parameters (iron dosage and reaction time) on the COD removal from wastewater generated by an Iraqi petroleum refinery facility was explored. Removal of 85.81% was attained under the optimal conditions of 21 minutes and 0.289 mM of  concentration. Additionally, the results revealed that the concentration of has the highest effect on the COD elimination, followed by reaction time. The high R² value (96.40%) validated the strong fit of the mo

Design and Construction system for recording Finger print by laser, and separted the signal to noise by holographic element, was done. For safety, total reflection lighting ensures hat aser earns an not enter An operators eyes. Holographic diffraction grating was used instead of computer program to contrast images.