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Article
Online Tracking Control System For Robot Manipulator Using Adaptive Fuzzy Wavelet Network
نظام سيطرة تتبع آني لحركة ﺫراع آلي باستخدام شبكة المويجه المضببه المتكيفة

Authors: Yhya R. Kuraz يحيى رجب محمد --- Waleed Ameen Mahmoud Al-Jawher وليد أمين محمود
Journal: AL Rafdain Engineering Journal مجلة هندسة الرافدين ISSN: 18130526 Year: 2008 Volume: 16 Issue: 3 Pages: 1-10
Publisher: Mosul University جامعة الموصل

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Abstract

AbstractIt is well known that Wavelet Networks (WN) are powerful tools for handling problems of large dimensions. The integration of Wavelet Network and Fuzzy Logic (FL) enable a tool condition monitoring system to have a high monitoring success rate and fast training feed over a wide range of cutting conditions in drilling applications. To overcome offline learning and to perform efficient tracking behavior, an Auto Tuning Adaptive Fuzzy Wavelet Network (ATAFWN) controller is proposed. It was shown that such structure don’t need offline learning to govern the system in stable regions. It can be handle also a wide range of parameter changes in comparison with the conventional controller as well as such controller is simple to configure since it doesn’t need a process model and can be easily adapted to the existing controller and plants.Keywords: online controller, fuzzy logic, wavelet network, fuzzy wavelet network.

الخلاصةمما لا شك فيه أن تقنية شبكة المويجه استطاعت التغلب على مشكلة الأبعاد الكبيرة للاشاره. وكان لدمج تقنية شبكة المويجه مع تقنية المنطق المضبب الأثر الواضح في تحسين مراقبة الأنظمة وسرعة في التدريب للانظمه المعقدة منقوصة المعلومات. للتغلب على مشكلة التدريب المسبق ولزيادة كفاءة سلوك النظام، تم اقتراح مسيطر توليف ذاتي لشبكة المويجة المضببة المتكيفة. وأظهرت النتائج أن هذا المسيطر لا يحتاج إلى تدريب مسبق لجعل النظام مستقرا. كما وأظهرت النتائج أن المسيطر المقترح استطاع الصمود بوجه التغيرات الطارئه على متغيرات النظام، بالاضافه إلى كل ما سبق فان المسيطر المقترح يعتبر سهل التنصيب خاصة وانه لا يحتاج لمعرفه مسبقة بالنظام المراد السيطرة عليه.


Article
Design and Implementation of Adaptive Wavelet Network PID Controller for AQM in the TCP Network

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Abstract

Abstract – Internet represents a shared resource wherein users contend for the finitenetwork bandwidth. Contention among independent user demands can result incongestion, which, in turn, leads to long queuing delays, packet losses or both.Congestion control regulates the rate at which traffic sources inject packets into anetwork to ensure high bandwidth utilization while avoiding network congestion. In thecurrent Internet, there are two mechanisms which deal with congestion; the end-to-endmechanism which is achieved by the Transmission Control Protocol (TCP) and theintermediate nodes algorithms such as Active Queue Management (AQM) in routers.In this paper, a combined model of TCP and AQM (TCP/AQM) is formulated andfirst simulated without a controller. The results show that it is unable to track the desiredqueue size. So, to get better tracking performance, an adaptive PID controller based onwavelet network (AWNPID) is used as AQM in the router queue. The non-adaptive PIDcontroller is also demonstrated, and its weakness to the network dynamic changes iscompared to the robustness of adaptive controller (AWNPID). The analytical results forlinearized TCP/AQM model are presented in MATLAB version 7.0.


Article
INFECTED REGION RECOGNITION IN HUMAN BODY MEMBERS BASED ON WAVENET WITH MINIMUM DISTANCE

Author: Hassan J. Hassan
Journal: IRAQI JOURNAL OF COMPUTERS,COMMUNICATION AND CONTROL & SYSTEMS ENGINEERING المجلة العراقية لهندسة الحاسبات والاتصالات والسيطرة والنظم ISSN: 18119212 Year: 2007 Volume: 7 Issue: 2 Pages: 95-107
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

Abstract: Image identification plays a great role in industrial, remote sensing, medical and military applications. It is concerned with the generation of a signature to the image. This work proposes a dynamic program (use Neural Network) to classify the texture of human member image then identify whether the member is infected or not. The program has the ability of determining which part of that member is infected depending on the comparison between the healthy member image stored in advance with a test image. The first step is to make approximation to the image using wavelet network (Wavenet) technique. Through this technique we shall get an approximated image with reduced data. In addition, we shall get implicit information to that image. The second step is to subdivide the resultant image from the first step into 16 equally subparts then deal with each subpart as a unique image. Finally, in the third step, the minimum distance (Mahalanobias Distance) approach is employed for subpart identification. All programs are written using MATLAB VER. 6.5 package.

الخلاصة :إن تحليل نسيج الصورة لغرض التعرف عليها وتشخيصها يلعب دورا كبيرا في مجالات شتى, منها الصناعة والاستشعار من بعد إضافة إلى التطبيقات الطبية والعسكرية.هذا البحث يقترح برنامج ديناميكي يعتمد على شبكة المويجة (Wavenet) لتصنيف نسيج الصورة للعضو البشري و تشخيص فيما لو كان ذلك العضو مصاباً أم لا. كما ان لهذا البرنامج القدرة على تحديد الجزء المصاب من ذلك العضو بالاعتماد على المقارنة بين صورة العضو السليم المخزونة مسبقاً مع صورته الجديدة.الخطوة الأولى يتم من خلالها اجراء تقريب الى الصورة (Image Approximation) باستخدام شبكة المويجة(Wavenet) والذي من خلاله نحصل على صورة تقريبية مع تقليل القيم الاصلية المكونة للصورة (Data Reduction) والحصول على قيم ضمنية تتعلق بتلك الصورة.الخطوة الثانية يتم من خلالها تقسيم الصورة الناتجة من الخطوة الاولى الى ستة عشرجزءاً متساوياً والتعامل مع كل جزء كصورة مستقلة. الخطوة الثالثة والاخيرة يمر خلالها كل جزء من أجزاء الصورة بمرحلة قياس أقصر مسافة بين صورتين واستخدام النتائج في عملية التشخيص النهائية.


Article
Intelligent Modeling of Metal Oxide Gas Sensor

Authors: Omar F. Lutfy --- Alaa A. Abdul-Hamead
Journal: Engineering and Technology Journal مجلة الهندسة والتكنولوجيا ISSN: 16816900 24120758 Year: 2018 Volume: 36 Issue: 7 Part (A) Engineering Pages: 777-783
Publisher: University of Technology الجامعة التكنولوجية

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Abstract

Due to the complexity of the gas detection process, traditional modeling techniques cannot provide accurate modeling performance to reproduce the behavior of this difficult process. In this paper, an intelligent modeling technique is utilized to develop an accurate model to represent the complex and nonlinear gas detection process. In particular, in this study nickel Oxide NiO gas sensor, which was specifically fabricated by a simple chemical spray pyrolysis technique. In the process, the nickel chloride hexahydrate salt was used at a concentration of (0.05 M) and a temperature of 350 ºC. Because of this process, the thickness of NiO was 0.1μm. Inspection was done using three different testing techniques; X-ray diffraction, scanning electron microscopy, and the sensitivity test of NiO for Methane gas CH4 in the range of (0-500) ppmv. Inspection results show that the film was crystalline, has a cubic system, and without cracks or open pores. On the other hand, the sensitivity results were disparate and low in value within the considered range. From the real-time experiment described above, training samples were gathered to develop the desired process model. The considered modeling technique was based on exploiting the wavelet network (wavenet) to represent the nonlinear function of the nonlinear autoregressive with exogenous input (NARX) structure. In model development process, the experimental data were utilized as the training samples for the wavenet-based NARX model. As the modeling accuracy, the proposed wavenet-based NARX model attained a value of 1.895 × 10-12 for the root mean square of error (RMSE) criterion.

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