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DAMAGE DETECTION AND LOCATION FOR IN AND OUT-OFPLANE CURVED BEAMS USING FUZZY LOGIC BASED ON FREQUENCY DIFFERENCE
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In this study, structures damage identification method based on changes in the dynamic characteristics
(frequencies) of the structure are examined, stiffness as well as mass matrices of the curved
(in and out-of-plane vibration) beam elements is formulated using Hamilton's principle. Each node
of both of them possesses seven degrees of freedom including the warping degree of freedom. The
curved beam element had been derived based on the Kang and Yoo’s thin-walled curved beam theory
in 1994. A computer program was developing to carry out free vibration analyses of the curved
beam as well as straight beam. Comparing with the frequencies for other researchers using the general
purpose program MATLAB. Fuzzy logic system (FLS) applied in two stages to calculate the
damage extent and location in simply in and out-of- plane curved beam, the damage deduce by reduction
in stiffness for three levels (20%, 40%, 60%). At the first stage the output faults of the fuzzy system represented by four levels of damage in curved beam (undamaged, slight, moderate, and severe), and at second stage indicate damage location at element with two defuzzification methods (centroid and middle of maximum). The results show that the frequency difference method is efficient to indicate and quantify
damage with accuracy about (99.5%) for slight and moderate damage about (100%) for severe damage. Consequently fuzzy logic performs well for detecting, locating and quantifying damage in curved beam.

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Publication Date
Sun Jun 30 2024
Journal Name
Iraqi Journal Of Science
Detection of Zn Water Pollution by a Biosensor Based on Alkaloids Derived from Iraqi Catharanthus Roseus
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     In this work, the detection of zinc (Zn) ions that cause water pollution is studied using the CSNPs- Linker-alkaloids compound that was prepared by linking extracted alkaloids from Iraqi Catharanthus roseus plant with Chitosan nanoparticles (CSNPs) using maleic anhydride. This compound is characterized by an X-ray diffractometer (XRD) which shows that it has an orthorhombic structure with crystallite size in the nano dimension. Zeta Potential results show that the CSNPs-Linker-alkaloids carried a positive charge of 54.4 mV, which means it possesses high stability.  The Fourier transform infrared spectroscopy (FTIR) shows a new distinct band at 1708.93 cm-1 due to C=O esterification. Scanning electron microscope (SEM) image

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Publication Date
Fri Oct 02 2009
Journal Name
Noise And Health
Expert system to predict effects of noise pollution on operators of power plant using neuro-fuzzy approach
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Ration power plants, to generate power, have become common worldwide. One such one is the steam power plant. In such plants, various moving parts of heavy machines generate a lot of noise. Operators are subjected to high levels of noise. High noise level exposure leads to psychological as well physiological problems; different kinds of ill effects. It results in deteriorated work efficiency, although the exact nature of work performance is still unknown. To predict work efficiency deterioration, neuro-fuzzy tools are being used in research. It has been established that a neuro-fuzzy computing system helps in identification and analysis of fuzzy models. The last decade has seen substantial growth in development of various neuro-fuzzy systems

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Publication Date
Wed Dec 30 2015
Journal Name
College Of Islamic Sciences
Weightings of Imam Ibn al-Mundhir               In his middle book in Sunan, unanimity and difference - the book of prayer
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The Imam Ibn al-Mundhir is likely in Shafi'i school. He has outlined some issues and disagreed with his doctrine. And that the vocabulary on which he relied on the weighting is correct and correct and I choose which I say. One of the most likely issues is the delay of Isha prayer to the darkness. And other issues simplified in the folds of research and study.

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Publication Date
Sat Apr 30 2022
Journal Name
Revue D'intelligence Artificielle
Performance Evaluation of SDN DDoS Attack Detection and Mitigation Based Random Forest and K-Nearest Neighbors Machine Learning Algorithms
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

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Publication Date
Wed Jun 01 2016
Journal Name
Nternational Journal Of Mathematics Trends And Technology (ijmtt)
Fuzzy Scheduling Problem on Two Machines
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Publication Date
Mon Mar 08 2021
Journal Name
Baghdad Science Journal
On Almost Quasi-Frobenius Fuzzy Rings
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In this paper, we introduce the concept of almost Quasi-Frobcnius fuzzy ring as a " " of Quasi-Frobenius ring. We give some properties about this concept with qoutient fuzzy ring. Also, we study the fuzzy external direct sum of fuzzy rings.

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Publication Date
Wed Jul 24 2024
Journal Name
Journal Of Plant Protection Research
Developing smart sprayer for weed control using an object detection algorithm (yolov5)
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Spraying pesticides is one of the most common procedures that is conducted to control pests. However, excessive use of these chemicals inversely affects the surrounding environments including the soil, plants, animals, and the operator itself. Therefore, researchers have been encouraged to...

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Publication Date
Wed Jun 16 2021
Journal Name
Cognitive Computation
Deep Transfer Learning for Improved Detection of Keratoconus using Corneal Topographic Maps
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Abstract <p>Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision b</p> ... Show More
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Publication Date
Tue May 01 2018
Journal Name
Journal Of Physics: Conference Series
Effect of radial magnetic field on peristaltic transport of Jeffrey fluid in curved channel with heat / mass transfer
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Publication Date
Wed Dec 01 2021
Journal Name
Baghdad Science Journal
Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images
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Although the number of stomach tumor patients reduced obviously during last decades in western countries, but this illness is still one of the main causes of death in developing countries. The aim of this research is to detect the area of a tumor in a stomach images based on fuzzy clustering. The proposed methodology consists of three stages. The stomach images are divided into four quarters and then features elicited from each quarter in the first stage by utilizing seven moments invariant. Fuzzy C-Mean clustering (FCM) was employed in the second stage for each quarter to collect the features of each quarter into clusters. Manhattan distance was calculated in the third stage among all clusters' centers in all quarters to disclosure of t

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