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Register a new userAutomating and Improving the Data Acquisition System of Water Meters based on Distributed PLCs Networks
أتمتة و تحسين نظام تحصيل البيانات لعدادات المياه بالاعتماد على شبكات PLCs الموزعة
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أحمد كردي( باحث )
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من باب الحرص على مواكبة أحدث التقنيات التي تم التوصل إليها عالمياً و بهدف ملاحقة الركب العالمي في مجال التكنولوجيا كان لابد لنا من السعي الحثيث للبحث في أحدث السبل التكنولوجية لتحسين واقع عمل المؤسسات ضمن الجمهورية العربية السورية. يهدف هذا البحث إلى بناء شبكة من المتحكمات المنطقية (PLCs) القابلة للبرمجة مع شاشات (HMI) و أنظمة SCADA و دراسة أنسب البروتوكولات لبناء الشبكة مع تحديد الأسباب. بعد د ا رسة و تحديد البروتوكولات الأنسب لبناء الشبكة, يتم استخدام هذه الشبكة الصناعية المؤتمتة في تحسين الواقع الحالي لشركة المياه في مجال تحصيل بيانات العدادات بالدقة و السرعة المطلوبتين. بنيت الشبكة بالاعتماد على مجموعة من المتحكمات المنطقية القابلة للبرمجة بحيث يتم استخدام كل متحكم لتحصيل البيانات من حي من أحياء محافظة حماة تربط هذه المتحكمات الموزعة عبر موجه مركزي لنقل البيانات إلى المركز الرئيس لشركة المياه ,و للتواصل مع النظام تستخدم واجهات التخاطب البشرية HMI و نظام SCADA لإظهار و تخزين البيانات التي تم تحصيلها تربط هذه الواجهة ضمن الشبكة نفسها الخاصة بالمتحكمات المنطقية القابلة للبرمجة لنحصل بذلك على شبكة صناعية موزعة.
In order to keep up with the latest international technologies which have been achieved recently. We have to search continuously for the technological methods in order to improve the present situation of the institutions in the Syrian Arab Republic. This research aims to build a network of Programmable Logic Controllers (PLCs) with Human Machine Interface (HMI) and supervisory control and data acquisition (SCADA) system, as well as, studying the most suitable protocols to build the network beside identifying the causes of choosing the protocol. After studying and identifying it, we had better to use the automated industrial network to improve the present situation of water company concerning with getting meters data accurately as fast as possible. We build the mentioned network by means of a group of PLCs, so that, each one could be used to get the data for an avenue in Hama city. These distributed controllers would be connected by a central router in order to pass all the data to the water company center. We will use a HMI and SCADA system for showing collected data, controlling, and saving the collected data. This HMI and SCADA system will be connected to the same PLCs, and thus, we will get a distributed industrial network.
References used
Tsung-Fu Chien, Hung-Lin Hang, Li-Kang Yang and Pao-Chun Chen,Department of Electrical Engineering, Southern Taiwan University, Taiwan, R.O.C, 2011
Rajeev Bapat, MohitAtale and NitinSapale,Yadavrao Tasgaonkar Institute of Engineering & Technology, 2011
http://www.modbus.org
http://support.automation.siemens.com,2012
Building Adapter for Vehicle On-board Diagnostic,obddiag.net, accessed, 2009
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معالجة الصور
إيماءات الصم و البكم
تعرف على الأشكال
معالجة أنبوبية
مسالك
معالجة تفرعية
دارات قابلة للبرمجة
لغة توصيف مادية
نظام مضمن
نظام ضمن شريحة
تسريع
Deaf and Mute Gestures
Images Processing
Patterns Recognition
Pipeline Processing
Threads
Parallel Processing
(Programmable Circuits (FPGA
(Hardware Description Language (VHDL
(System on Chip (SoC
المزيد..
Most of the time, when dealing with a particular Natural Language Processing task, systems are compared on the basis of global statistics such as recall, precision, F1-score, etc. While such scores provide a general idea of the behavior of these syst
ems, they ignore a key piece of information that can be useful for assessing progress and discerning remaining challenges: the relative difficulty of test instances. To address this shortcoming, we introduce the notion of differential evaluation which effectively defines a pragmatic partition of instances into gradually more difficult bins by leveraging the predictions made by a set of systems. Comparing systems along these difficulty bins enables us to produce a finer-grained analysis of their relative merits, which we illustrate on two use-cases: a comparison of systems participating in a multi-label text classification task (CLEF eHealth 2018 ICD-10 coding), and a comparison of neural models trained for biomedical entity detection (BioCreative V chemical-disease relations dataset).
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