Three-Dimensional Nonlinear Integral Operator with the Modelling of Majorant Function

 تقدم هذه الورقة البحثية طريقة  لايجاد الحل التقريبي لمؤثر فولتيرا التكاملي  الثلاثي الأبعاد غير الخطي في  R3. حيث يتم استخدام مفهوم (Majorant function) وباستخدام طريقة نيوتن المعدلة  لتحويل مؤثر فولتيرا التكاملي  الثلاثي الأبعاد غير الخطي  إلى متتالية  لمؤثر فولتيرا التكاملي  الثلاثي الأبعاد الخطي ومن يتم استخدام طريقة (Gaussian-Legendre)  التربيعية لايجاد الحل التقريبي لمؤثر فولتيرا التكاملي  الثلاثي الأبعاد الخطي من خلال التعامل مع نظام جبري خطي.تم مناقشة وجود ووحدانية الحل للطريقة المستخدمة مع اعطاء أمثلة توضيحية لإظهار دقة وكفاءة الطريقة.In this paper, the process for finding an approximate solution of nonlinear three-dimensional (3D) Volterra type integral operator equation (N3D-VIOE) in R3 is introduced. The modelling of the majorant function (MF) with the modified Newton method (MNM) is employed to convert N3D-VIOE to the linear 3D Volterra type integral operator equation (L3D-VIOE). The method of trapezoidal rule (TR) and collocation points are utilized to determine the approximate solution of L3D-VIOE by dealing with the linear form of the algebraic system. The existence of the approximate solution and its uniqueness are proved, and illustrative examples are provided to show the accuracy and efficiency of the model. Mathematical Subject Classification (2010):  45P05, 45G10, 47H9

Monitoring and Wireless Controlling of Power Generation by LabView

In this paper we will try to improve and enhance the nation's energy infrastructure, by achieve controlling and monitoring of power generations remotely all over the world to evaluate performance of plants and investigate the voltage ,amperage, temperature and etc for each plant and also to decrease the workers number, cost saving in management and maintenance, predict failure before accrue and etc since the wireless networks  made an advantages of  low cost nature over traditional communication technologies used in today’s electric power systems. This paper include design and implementation of internet based remote monitoring and controlling of power generation where the platform based on the design of an interface circuit with Arduino board, implemented by LabView software ALCS (Arduino-LabView Control System).The complete system can be controlled remotely where the remote control implemented by using a VSAT (Very Small Aperture Terminal) system. In this paper will focus on monitoring of coolant temperature and calculate the Mean Absolute Percentage Error (MAPE) of temperature between the Programming Logic Controller (PLC) reading that already install on power generation unit and the ALCS reading. Keywords: Remote Monitoring and Contro

Mathematical Modelling of Magnetic Abrasive Machining Hybrid Operation: A Review

الانهاء بالحث الممغنط هو طريقة انهاء سطحي غير تقليدي لإنتاج اجزاء ذات نوعية عالية والتي يسيطر عليها بالطاقة المغناطيسية. طورت عملية الحك الممغنط بعض الخواص الميكانيكية للسطح. بعض طرق الانهاء السطحي التقليدية مثل تنعيم السطح الداخلي، التجليخ والتنعيم الخارجي بدلت الان بهذه الطريقة. في هذا البحث (مراجعة) سنتناول بالتفصيل اساس عملية الانهاء بالحث الممغنط، متغيرات العملية وتأثيرها على المخرجات (الاستجابة)، نمذجة العملية وتطورها للسطوح المستقيمة. اضافة الى ذلك هناك نوع جديد من الانهاء بالحث الممغنط المندمج مع التشغيل الكهروكيمياوي لإنتاج التشغيل بالحك الممغنط الكهروكيمياوي. اداء النموذج الرياضي والامثلية المتعددة لتنبأ المخرجات مثل معدل الازالة المعدنية، الانهاء السطحي والمنطقة المتأثرة بالحرارة.... الخ وجدت للمقارنة بدلالة دقة وسرع التنبؤ.   Magnetic Abrasive Finishing (MAF) or super finishing is a modern unconventional finishing technique to produce high quality of parts, which is controlled by a magnetic energy. Magnetic abrasive operation develops some of the mechanical properties such as the surface quality. Nowadays, many of the traditional finishing technique such as honing, polishing and grinding are now being replaced by this process. In this review, principles of the MAF process, processing factors and their influence on the responses, the process modeling and development of the MAF method for flat surfaces will be examined in details research work in the literature. Additionally, there is a new type of MAF connected with Electrochemical Machining (ECM) to produce Electrochemical Magnetic Abrasive Machining (EMAM). The performance of models and multi-optimizing for predicting the responses such as metal removal rate (MRR), surface finish (SF), heat affected zone (HAZ) etc. are found to comparable in terms of the prediction accuracy and speed. &nbsp

Statistical Properties and Applications of Empirical Mode Decomposition

Signal analysis is key to extracting information buried in noise. The decomposition of signal is a data analysis tool for determining the underlying physical components of a processed data set. However, conventional signal decomposition approaches such as wavelet analysis, Wagner-Ville, and various short-time Fourier spectrograms are inadequate to process real world signals. Moreover, most of the given techniques require \emph{a prior} knowledge of the processed signal, to select the proper decomposition basis, which makes them improper for a wide range of practical applications. Empirical Mode Decomposition (EMD) is a non-parametric and adaptive basis driver that is capable of breaking-down non-linear, non-stationary signals into an intrinsic and finite components called Intrinsic Mode Functions (IMF). In addition, EMD approximates a dyadic filter that isolates high frequency components, e.g. noise, in higher index IMFs. Despite of being widely used in different applications, EMD is an ad hoc solution. The adaptive performance of EMD comes at the expense of formulating a theoretical base. Therefore, numerical analysis is usually adopted in literature to interpret the behavior. This dissertation involves investigating statistical properties of EMD and utilizing the outcome to enhance the performance of signal de-noising and spectrum sensing systems. The novel contributions can be broadly summarized in three categories: a statistical analysis of the probability distributions of the IMFs and a suggestion of Generalized Gaussian distribution (GGD) as a best fit distribution; a de-noising scheme based on a null-hypothesis of IMFs utilizing the unique filter behavior of EMD; and a novel noise estimation approach that is used to shift semi-blind spectrum sensing techniques into fully-blind ones based on the first IMF. These contributions are justified statistically and analytically and include comparison with other state of art techniques