In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet function. This approach has been performed very successfully, with better results
obtained with the FFNN with modified wavelet activation function (FFMW) when compared with classic
FFNN with Sigmoid activation function (FFS) .One can notice from the simulation that the FFMW can be
capable of identifying the 4-Links of SCARA robot more efficiently than the classic FFS.
The research has deal with the relationship between organizational justice and empowerment and their impact on the achievement of organizational commitment in the office of Labour and Vocational Training. To study the research problem which is represented a sense that employees with low levels of organizational justice and empowerment and the reflection on the organizational commitment of the employees, so that Has been collecting data and information relating to research by designing a questionnaire, were distributed to a sample of (50) people in the office mentioned, and the results of the study to confirm the research hypotheses. and the key results of the research was the presence of correlation relationships and the effect o
... Show MoreThe investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o
... Show MoreImage Fusion Using A Convolutional Neural Network
In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi
... Show MoreA new 4-thiazolidinone, substitutedbenzylidene-thiazolidinone and tetrazole were synthesized from thiosemicarbazone and hydrazone. The thiosemicarbazone was prepared by the reaction of thiosemicarbazide with aldehyde derivative from L-ascorbic acid in absolute ethanol using glacial acetic acid as a catalyst. 1, 3-thiazolidin-4-ones were synthesized from the condensation of thiosemicarbazones with chloroacetic acid in presence of anhydrous sodium acetate. A 1, 3- thiazolidine-4-one was reaction with several 4-substitutedaldehydes to produce new derivatives with a double bond at the position-5 of the 4-thiazolidinone ring. While the tetrazole compounds were synthesized by 1, 3-cycloaddition reaction of sodium azide and hydrazone compounds in
... Show MoreTo enhance interfacial bonding between carbon fibers and epoxy matrix, the carbon fibers have been modified with multiwall carbon nanotubes (MWCNTs) using the dip- coating technique. FT-IR spectrum of the MWCNTs shows a peak at 1640 cm−1 corresponding to the stretching mode of the C=C double bond which forms the framework of the carbon nanotube sidewall. The broad peak at 3430 cm−1 is due to O–H stretching vibration of hydroxyl groups and the peak at 1712 cm−1 corresponds to the carboxylic (C=O) group attached to the carbon fiber. The peaks at 2927 cm−1 and 2862 cm−1 ar
The durability of asphalt pavement is associated with the properties and performance of the binder. This work-study intended to understand the impact of blending Styrene-Butadiene-Styrene (SBS) to conventional asphalt concrete mixtures and calculating the Optimum Asphalt Content (OAC) for conventional mixture also; compare the performance between SBS modified with the conventional mixture. Two different kinds of asphalt penetration grades, A.C. (40-50) and A.C. (60-70), were improved with 2.5 and 3.5% SBS polymer, respectively. Marshall properties were determined in this work. Optimum Asphalt Content (OAC) was 4.93 and 5.1% by weight of mixture for A.C. (40-50) and (60-70), respectively. Marshall properties results show an increasem
... Show MoreA particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation results show the effectiveness of the proposed adaptive PID neural control algorithm in terms of minimum tracking error and smoothness control signal obtained for non-linear dynamical CSTR system.
The increase globally fossil fuel consumption as it represents the main source of energy around the world, and the sources of heavy oil more than light, different techniques were used to reduce the viscosity and increase mobility of heavy crude oil. this study focusing on the experimental tests and modeling with Back Feed Forward Artificial Neural Network (BFF-ANN) of the dilution technique to reduce a heavy oil viscosity that was collected from the south- Iraq oil fields using organic solvents, organic diluents with different weight percentage (5, 10 and 20 wt.% ) of (n-heptane, toluene, and a mixture of different ratio
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