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alkej-184
Design of an Adaptive PID Neural Controller for Continuous Stirred Tank Reactor based on Particle Swarm Optimization
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 A 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.

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Publication Date
Fri Oct 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
Application of Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) Method
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The paper aims is to solve the problem of choosing the appropriate project from several service projects for the Iraqi Martyrs Foundation or arrange them according to the preference within the targeted criteria. this is done by using Multi-Criteria Decision Method (MCDM), which is the method of Multi-Objective Optimization by Ratios Analysis (MOORA) to measure the composite score of performance that each alternative gets and the maximum benefit accruing to the beneficiary and according to the criteria and weights that are calculated by the Analytic Hierarchy Process (AHP). The most important findings of the research and relying on expert opinion are to choose the second project as the best alternative and make an arrangement acco

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Publication Date
Wed Jul 01 2015
Journal Name
Journal Of Engineering
Spiking Neural Network in Precision Agriculture
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In this paper, precision agriculture system is introduced based on Wireless Sensor Network (WSN). Soil moisture considered one of environment factors that effect on crop. The period of irrigation must be monitored. Neural network capable of learning the behavior of the agricultural soil in absence of mathematical model. This paper introduced modified type of neural network that is known as Spiking Neural Network (SNN). In this work, the precision agriculture system  is modeled, contains two SNNs which have been identified off-line based on logged data, one of these SNNs represents the monitor that located at sink where the period of irrigation is calculated and the other represents the soil. In addition, to reduce p

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Publication Date
Tue May 23 2023
Journal Name
Journal Of Sensors
On-Board Digital Twin Based on Impedance and Model Predictive Control for Aerial Robot Grasping
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Aerial manipulation of objects has a number of advantages as it is not limited by the morphology of the terrain. One of the main problems of the aerial payload process is the lack of real-time prediction of the interaction between the gripper of the aerial robot and the payload. This paper introduces a digital twin (DT) approach based on impedance control of the aerial payload transmission process. The impedance control technique is implemented to develop the target impedance based on emerging the mass of the payload and the model of the gripper fingers. Tracking the position of the interactional point between the fingers of gripper and payload, inside the impedance control, is achieved using model predictive control (MPD) approach.

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Publication Date
Thu Oct 01 2020
Journal Name
Defence Technology
A novel facial emotion recognition scheme based on graph mining
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Recent years have seen an explosion in graph data from a variety of scientific, social and technological fields. From these fields, emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human, driver safety during driving, pain monitoring during surgery etc. A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region, where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion. T

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Publication Date
Wed Dec 01 2010
Journal Name
Iraqi Journal Of Physics
Particle-Hole State Density Calculations with Non-Equidistant Spacing Model: I. Basic Derivation
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The basic analytical formula for particle-hole state densities is derived based on the non-Equidistant Spacing Model (non-ESM) for the single-particle level density (s.p.l.d.) dependence on particle excitation energy u. Two methods are illustrated in this work, the first depends on Taylor series expansion of the s.p.l.d. about u, while the second uses direct analytical derivation of the state density formula. This treatment is applied for a system composing from one kind of fermions and for uncorrected physical system. The important corrections due to Pauli blocking was added to the present formula. Analytical comparisons with the standard formulae for ESM are made and it is shown that the solution reduces to earlier formulae providing m

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Publication Date
Mon Jan 16 2023
Journal Name
Iraqi National Journal Of Nursing Specialties
Effectiveness of an Intervention Program on Nurses’ Performance toward Neonatal Endotracheal Suctioning Procedure
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Objective(s): To evaluate nurses’ Practice toward neonatal endotracheal suctioning procedure, and to determine the effectiveness of the interventional program on nurses’ practices, as well as to find out the relationship between nurses’ practice and their demographic characteristics.

Methodology: A Pre-experimental, one group design, was carried out to achieve the objectives of the current study using the evaluation approach and the implementation of the education program for the period from January 17 to June 31, 2022. A non- probability, purposive sample of (24) nurses were selected from the Neonatal Intensive Care Unit at Pediatric Teaching Hospital/ Medical City Department. A checklist w

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Publication Date
Tue Mar 31 2015
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Formation Evaluation for Nasiriyah Oil Field Based on The Non-Conventional Techniques
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The unconventional techniques called “the quick look techniques”, have been developed to present well log data calculations, so that they may be scanned easily to identify the zones that warrant a more detailed analysis, these techniques have been generated by service companies at the well site which are among the useful, they provide the elements of information needed for making decisions quickly when time is of essence. The techniques used in this paper are:

  • Apparent resistivity Rwa
  • Rxo /Rt

   The above two methods had been used to evaluate Nasiriyah oil field formations (well-NS-3) to discover the hydrocarbon bearing formations. A compu

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Publication Date
Thu Nov 17 2022
Journal Name
Journal Of Information And Optimization Sciences
Hybrid deep learning model for Arabic text classification based on mutual information
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Publication Date
Wed Apr 15 2020
Journal Name
Al-mustansiriyah Journal Of Science
Adaptation Proposed Methods for Handling Imbalanced Datasets based on Over-Sampling Technique
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Classification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields. These algorithms speak of the problem of imbalanced data, where there are situations that belong to more classes than others. Imbalanced data result in poor performance and bias to a class without other classes. In this paper, we proposed three techniques based on the Over-Sampling (O.S.) technique for processing imbalanced dataset and redistributing it and converting it into balanced dataset. These techniques are (Improved Synthetic Minority Over-Sampling Technique (Improved SMOTE),  Border

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Publication Date
Mon Aug 01 2022
Journal Name
Bulletin Of Electrical Engineering And Informatics
Solid waste recycling and management cost optimization algorithm
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Solid waste is a major issue in today's world. Which can be a contributing factor to pollution and the spread of vector-borne diseases. Because of its complicated nonlinear processes, this problem is difficult to model and optimize using traditional methods. In this study, a mathematical model was developed to optimize the cost of solid waste recycling and management. In the optimization phase, the salp swarm algorithm (SSA) is utilized to determine the level of discarded solid waste and reclaimed solid waste. An optimization technique SSA is a new method of finding the ideal solution for a mathematical relationship based on leaders and followers. It takes a lot of random solutions, as well as their outward or inward fluctuations, t

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Scopus (6)
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