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Earthquake Hazard Mitigation for Uncertain Building Systems Based on Adaptive Synergetic Control
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This study presents an adaptive control scheme based on synergetic control theory for suppressing the vibration of building structures due to earthquake. The control key for the proposed controller is based on a magneto-rheological (MR) damper, which supports the building. According to Lyapunov-based stability analysis, an adaptive synergetic control (ASC) strategy was established under variation of the stiffness and viscosity coefficients in the vibrated building. The control and adaptive laws of the ASC were developed to ensure the stability of the controlled structure. The proposed controller addresses the suppression problem of a single-degree-of-freedom (SDOF) building model, and an earthquake control scenario was conducted and simulated on the basis of earthquake acceleration data recorded from the El Centro Imperial Valley Earthquake. The effectiveness of the adaptive synergetic control was verified and assessed via numerical simulation, and a comparison study was conducted between the adaptive and classical versions of synergetic control (SC). The vibration suppression index was used to evaluate both controllers. The numerical simulation showed the capability of the proposed adaptive controller to stabilize and to suppress the vibration of a building subjected to earthquake. In addition, the adaptive controller successfully kept the estimated viscosity and stiffness coefficients bounded.

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Publication Date
Tue Jan 01 2013
Journal Name
International Journal Of Computer Applications
Content-based Image Retrieval (CBIR) using Hybrid Technique
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Image retrieval is used in searching for images from images database. In this paper, content – based image retrieval (CBIR) using four feature extraction techniques has been achieved. The four techniques are colored histogram features technique, properties features technique, gray level co- occurrence matrix (GLCM) statistical features technique and hybrid technique. The features are extracted from the data base images and query (test) images in order to find the similarity measure. The similarity-based matching is very important in CBIR, so, three types of similarity measure are used, normalized Mahalanobis distance, Euclidean distance and Manhattan distance. A comparison between them has been implemented. From the results, it is conclud

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Publication Date
Sat Jul 31 2021
Journal Name
Brain Sciences
Robust EEG Based Biomarkers to Detect Alzheimer’s Disease
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Biomarkers to detect Alzheimer’s disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reductio

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Scopus (50)
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Publication Date
Mon Jan 01 2018
Journal Name
International Journal Of Electronic Security And Digital Forensics
LSB based audio steganography preserving minimum sample SNR
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Publication Date
Tue Jun 23 2020
Journal Name
Baghdad Science Journal
Content Based Image Retrieval (CBIR) by Statistical Methods
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            An image retrieval system is a computer system for browsing, looking and recovering pictures from a huge database of advanced pictures. The objective of Content-Based Image Retrieval (CBIR) methods is essentially to extract, from large (image) databases, a specified number of images similar in visual and semantic content to a so-called query image. The researchers were developing a new mechanism to retrieval systems which is mainly based on two procedures. The first procedure relies on extract the statistical feature of both original, traditional image by using the histogram and statistical characteristics (mean, standard deviation). The second procedure relies on the T-

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Scopus (14)
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Publication Date
Mon Jan 01 2024
Journal Name
Journal Of Engineering
Face-based Gender Classification Using Deep Learning Model
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Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

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Scopus (7)
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Publication Date
Mon Jan 01 2024
Journal Name
Journal Of The College Of Languages (jcl)
Negation marker in Standard Persian : a typological analysis based on Givon’s functional model: عامل نفی در زبان فارسی معیار: بررسی رده‌شناختی براساس مدل نقش‌ گرایی گیون
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 Abstract:

Typological analysis about  the negation marker  in different languages is one of the fields of research that has attracted much attention.  In Persian language, this constituent has been analysed from different aspects. This study aimed to analyse  different aspects of negation marker  in  the adjectives,  the noun phrases and  the verb phrases based on typological analysis.  Many studies have been revealed that  the negation in adjectives has shown lexically and morphologically. In the noun phrases, /hich/  has used as a negative marker necessarily marking the verb phrase as negative too. In the verb phrases, negation occurs morphologically by the addition of the prefix /n

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Publication Date
Fri Nov 09 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Evaluation of Nurses' Practices toward the Control of Patients’ Complications at the Respiratory Care Unit in Baghdad Teaching Hospitals
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Objective(s): To evaluate nurses' practices who work in respiratory intensive care units to control the
complications of patients admitted at this unit and determine the relationship between nurses' sociodemographic
characteristics and their practices.
Methodology: A descriptive study was carried out at Respiratory Care Unit at Baghdad teaching hospitals that
started from February 22th, 2013 to August 30th, 2013. A purposive "non-probability" sample of (70) nurses who
work in Respiratory Care Unit was selected from Baghdad teaching hospitals. The data were collected through the
use of constructed questionnaire that consists of two parts; (l) Demographic data form that consists of 7items and
(2) nurses' practice form

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Publication Date
Thu Feb 01 2024
Journal Name
Baghdad Science Journal
Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model
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The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow

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Scopus (3)
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Publication Date
Mon May 23 2022
Journal Name
The International Journal Of Artificial Organs
Quantitative analysis and control of the torque profile of the upper limb using a kinetic model and motion measurements
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This paper investigates a new approach to the rapid control of an upper limb exoskeleton actuator. We used a mathematical model and motion measurements of a human arm to estimate joint torque as a means to control the exoskeleton’s actuator. The proposed arm model is based on a two-pendulum configuration and is used to obtain instantaneous joint torques which are then passed into control law to regulate the actuator torque. Nine subjects volunteered to take part in the experimental protocol, in which inertial measurement units (IMUs) and a digital goniometer were used to measure and estimate the torque profiles. To validate the control law, a Simscape model was developed to simulate the arm model and control law in which measurem

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Publication Date
Mon Oct 17 2016
Journal Name
Proceedings Of The Institution Of Mechanical Engineers, Part B: Journal Of Engineering Manufacture
Development and control of shaped metal deposition process using tungsten inert gas arc heat source in additive layered manufacturing
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Tungsten inert gas arc welding–based shaped metal deposition is a novel additive manufacturing technology which can be used for fabricating solid dense parts by melting a cold wire on a substrate in a layer-by-layer manner via continuous DC arc heat. The shaped metal deposition method would be an alternative way to traditional manufacturing methods, especially for complex featured and large-scale solid parts manufacturing, and it is particularly used for aerospace structural components, manufacturing, and repairing of die/molds and middle-sized dense parts. This article presents the designing, constructing, and controlling of an additive manufacturing system using tungsten inert gas plus wire–based shaped metal deposition metho

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Scopus (13)
Crossref (9)
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