يهدف البحث الحالي إلى دراسة تأثير المناخ التنظيمي لشعب الرياضة المدرسية في اقسام الانشطة الرياضية على مدرسي التربية الرياضية لأهمية المناخ التنظيمي لما يمثله من الخصائص والسلوكيات التي يتأثر بها العاملون التي قد تتباين من مكان لآخر والبيئة الجغرافية وثقافتها النوعية وذلك باستخدام المنهج الوصفي ولتتحقق من خلال الدراسة الميدانية, يتمثل مجتمع البحث الحالي من مدرسي التربية الرياضية في مدارس محافظة بغداد وال

The facilities layout are among the most important of the most influential factors in the efficiency of the production system, and represents the systematic layout of the various work centers, tools, persons, and other auxiliary services within the factory. Careful selection of the technique that would be used in Re-layout, represents an important step in reaching to the optimum layout that keeps on reducing handling costs and reduce unnecessary movement of materials, as well as the regularity and functioning of the flow of materials through the facilities. The research aims to propose a new layout of (Al-M'ammon Factory / The General Company for Vegetable Oils Industry), and then re-layout according to the new propose layout by using of te

In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that

Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re

 

    The combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet networks are feed-forward neural networks using wavelets as activation function. Wavelets networks have been used in classification and identification problems with some success.

  In this work we proposed a fuzzy wavenet network (FWN), which learns by common back-propagation algorithm to classify medical images. The library of medical image has been analyzed, first. Second, Two experimental tables’ rules provide an excellent opportunity to test the ability of fuzzy wavenet network due to the high level of information variability often experienced with this type of images.

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This paper presents on the design of L-Band Multiwavelength laser for Hybrid Time Division Multiplexing/ Wavelength Division Multiplexing (TDM/WDM) Passive Optical Network (PON) application. In this design, an L-band Mulltiwavelength Laser is designed as the downstream signals for TDM/WDM PON. The downstream signals ranging from 1569.865 nm to 1581.973 nm with 100GHz spacing. The multiwavelength laser is designed using OptiSystem software and it is integrated into a TDM/WDM PON that is also designed using OptiSystem simulation software. By adapting multiwavelength fiber laser into a TDM/WDM network, a simple and low-cost downstream signal is proposed. From the simulation design, it is found that the proposed design is suitable to be used

Palm vein recognition is a one of the most efficient biometric technologies, each individual can be identified through its veins unique characteristics, palm vein acquisition techniques is either contact based or contactless based, as the individual's hand contact or not the peg of the palm imaging device, the needs a contactless palm vein system in modern applications rise tow problems, the pose variations (rotation, scaling and translation transformations) since the imaging device cannot aligned correctly with the surface of the palm, and a delay of matching process especially for large systems, trying to solve these problems. This paper proposed a pose invariant identification system for contactless palm vein which include three main

Ziegler and Nichols proposed the well-known Ziegler-Nichols method to tune the coefficients of PID controller. This tuning method is simple and gives fixed values for the coefficients which make PID controller have weak adaptabilities for the model parameters variation and changing in operating conditions. In order to achieve adaptive controller, the Neural Network (NN) self-tuning PID control is proposed in this paper which combines conventional PID controller and Neural Network learning capabilities. The proportional, integral and derivative (K_P, K_I, K_D) gains are self tuned on-line by the NN output which is obtained due to the error value on the desired output of the system under control. The conventio

Sewer systems are used to convey sewage and/or storm water to sewage treatment plants for disposal by a network of buried sewer pipes, gutters, manholes and pits. Unfortunately, the sewer pipe deteriorates with time leading to the collapsing of the pipe with traffic disruption or clogging of the pipe causing flooding and environmental pollution. Thus, the management and maintenance of the buried pipes are important tasks that require information about the changes of the current and future sewer pipes conditions. In this research, the study was carried on in Baghdad, Iraq and two deteriorations model's multinomial logistic regression and neural network deterioration model NNDM are used to predict sewers future conditions. The results of the

Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.