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Register a new userمراقبة وادارة الشبكات باستخدام نظام zabbix
899
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5.0
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Added by
Tishreen University مشروع تخرج
Publication date
2020
fields
Informatics Engineering
and research's language is
العربية
Created by
Yazzan Sulaiman
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مراقبة و ادارة الشبكات الحاسوبية باستخدام نظام zabbix حيث تتم الادارة و المراقبة من خلال triggers تطبق على كامل اجهزة الشبكة
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أتت فكرة المشروع من الأهمية المتزايدة للنظم المفتوحة المصدر في أيامنا هذه لاسيما الإمكانات الواسعة التي تتيحها هذه النظم في مجال إدارة الشبكات, حيث يهدف مشروعنا إلى إظهار مزايا نظام Ubuntu وذلك من خلال عرض وإعداد مجموعة من الخدمات التي يقدها في مجال
إدارة الشبكات, وبالتالي إظهار الفائدة العلمية والعملية منها, حيث نرى الجانب العلمي من خلال شرح ماتقوم به كل خدمة وماهي البروتوكولات والآليات التي تبنى عليها الخدمة, والتي أيضاً تظهر بشكل واضح من خلال الجانب العملي لكل خدمة لمافيه من عرض شامل للفائدة التي يمكن الحصول عليها.
Traffic jam is a serious problem in our life, it causes waste of time and energy, conventional traffic light control system works with a fixed time and fixed cycle. This paper proposes an intelligent traffic light control system with a changeable gre
en time and cycle depending on traffic density. Traffic parameters (cars’ numbers, density, flow) are collected by Loop Detectors located at each traffic signal, this data will be transmitted to the PLC controller then, PLC processes this data to produce controlling commands, it is also connected to a SCADA system which supervises the process and provides an automatic and manual control.
The proposal system applies Green Wave method to connect between two junctions based on a real car velocity and sets priority for emergency car, when Loop Detector detects this car the program will be interrupted to open the traffic light needed.
This intelligent system is experimented with conventional control system s’ data the obtained result is promising, it can reduce the green time, cycle time, and delayed time of each car at traffic light to its’ minimum value.
This paper introduces a system to recognize labels of time plans, where labels are
extracted from time plan. This labels are images, so spatial segmentation is used to extract
images of labels only. Size of images of labels are made same using medi
an's algorithm for
two purposes. The first one is to create database training for used neural networks. The
second is to recognizing's processing. Two methods of recognizing are dependent on using
neural networks technic: classification using perceptron network and recognizing using
back propagation network. Perceptron network is built to take image as input and to give
classification index as output for label. Then label is recognize dependent on stored table
of ASCII for label. Back propagation network is designed to recognize images for all
letters of English alphabet that are used in time plan. Results of research appear efficiency
of designed system to recognize labels of time plan from their images for both methods
after system had been applied on three time plans.
In recent years, the problem of classifying objects in images has increased by using deep learning as a result of the industrial sector requirements. Despite of many algorithms used in this field, such as Deep Learning Neural Network DNN and Convolut
ional Neural Network CNN, the proposed systems to address this problem Lack of comprehensive solution to the difficulties of long training time and floating memory during the training process, low rating classification. Convolutional Neural Networks (CNNs), which are the most used algorithms for this task, were a mathematical pattern for analyzing images data. A new deep-traversal network pattern was proposed to solve the above problems. The aim of the research is to demonstrate the performance of the recognition system using CNNs networks on the available memory and training time by adapting appropriate variables for the bypass network. The database used in this research is CIFAR10, which consists of 60000 colorful images belonging to ten categories, as every 6,000 images are for a class of these items. Where there are 50,000 training images and 10,000 test tubes. When tested on a sample of selected images from the CIFAR10 database, the model achieved a rating classification of 98.87%.
This study has reached to that ANN (5-9-1) (five neurons in input
layer_nine neurons in hidden layer _ one neuron in output layer) is the
optimum artificial network that hybrid system has reached to it with
mean squared error equals (1*10^-4) (0.7
m3/sec), where this software
has summed up millions of experiments in one step and in limited time, it
has also given a zero value of a number of network connections, such as
some connections related of relative humidity input because of the lake
of impact this parameter on the runoff when other parameters are
avaliable.
This study recommend to use this technique in forecasting of
evaporation and other climatic elements.
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