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Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling
Mohammad Afshar,Mojtaba Asoodeh,Amin Gholami 한국화학공학회 2014 Korean Journal of Chemical Engineering Vol.31 No.3
Bubble point pressure is a critical pressure-volume-temperature (PVT) property of reservoir fluid, whichplays an important role in almost all tasks involved in reservoir and production engineering. We developed two sophisticatedmodels to estimate bubble point pressure from gas specific gravity, oil gravity, solution gas oil ratio, andreservoir temperature. Neural network and adaptive neuro-fuzzy inference system are powerful tools for extractingthe underlying dependency of a set of input/output data. However, the mentioned tools are in danger of sticking in localminima. The present study went further by optimizing fuzzy logic and neural network models using the genetic algorithmin charge of eliminating the risk of being exposed to local minima. This strategy is capable of significantly improvingthe accuracy of both neural network and fuzzy logic models. The proposed methodology was successfully applied toa dataset of 153 PVT data points. Results showed that the genetic algorithm can serve the neural network and neurofuzzymodels from local minima trapping, which might occur through back-propagation algorithm.
Intelligent Mechatronic Model Reference Theory for Robot End-effector Control
Mohammad sadegh Dahideh,Mohammad Najafi,AliReza Zarei,Yaser Barmayeh,Mehran Afshar 보안공학연구지원센터(IJUNESST) 2015 International Journal of u- and e- Service, Scienc Vol.8 No.1
The control problem for manipulators is to determine the joint inputs required to case the end-effector execute the commanded motion. The nonminimum phase characteristic of a rigid manipulator makes the design of stable controller that ensures stringent tracking requirements a highly nontrivial and challenging problem. A useful controller in the computed torque family is the gravity controller. To compensate the dynamic parameters, fuzzy logic methodology is used and applied parallel to this method. When the arm is at rest, the only nonzero terms in the dynamic is the gravity. Proposed method can cancels the effects of the terms of gravity. In this case inorder to decrease the error and satteling time, higher gain controller is design and applied to nonlinear system.
Air Pollution Reduction Based on Intelligent Nonlinear Control Methodology
Yaser Barmayeh,Mehran Afshar,Mohammad Sadegh Dahideh,Mohammad Najafi,Ali Reza Zarei 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.1
This paper expands a Multi Input Multi Output (MIMO) fuzzy baseline control (FBC) which controller coefficient is off-line tuned by gradient descent algorithm. The main goal is to adjust the optimal value for fuel ratio (FR) in motor engine. The fuzzy inference system in proposed methodology is works based on Mamdani-Lyapunov fuzzy inference system (FIS). To reduce dependence on the gain updating factor coefficients of the fuzzy methodology, PID baseline method is introduced. This new method provides an optimal setting for other factors which created by PID baseline method. The gradient descent methodology is off-line tune all coefficients of baseline fuzzy based on mathematical optimization methodology. Simulation results signify good performance of fuel ratio in presence of different torque load and external disturbance.
Mohammad J. Emami Skardi,Abbas Afshar,Samuel Sandoval Solis 대한토목학회 2013 KSCE JOURNAL OF CIVIL ENGINEERING Vol.17 No.6
A new cooperative watershed management methodology is designed for developing an equitable and efficient Best Management Practice cost allocation among landowners in a watershed. The approach intends to control the total sediment yield in the watershed,considering landowners’ conflicting interests. Wet detention ponds, are considered as the only available options to the landowners. The quality of the storm water is evaluated by the Total Suspended Solid loading from the watersheds. The proposed methodology combines a watershed simulation model, named Soil Water Accounting Tool (SWAT), with an Ant Colony Optimization (ACO)module and the cooperative game theory approach. Integration of SWAT and ACO modules provide the best set of designs for any constraints on target sediment removal set forth by non-cooperative and cooperative behaviors of the stakeholders to participate in the coalition to minimize the total cost of management practice. Nash Bargaining Theory is used to investigate how the maximum saving on cost of the participating players in a coalition can be fairly allocated. The proposed method is illustrated by a hypothetical example. The results demonstrate the applicability of the methodology. For the hypothetical case example, the proposed methodology with grand coalition leads to approximately 48 percent cost saving.
Developing an optimal valve closing rule curve for real-time pressure control in pipes
Mohammad Reza Bazargan-Lari,Reza Kerachian,Hossein Afshar,Seyyed Nasser Bashi-Azghadi 대한기계학회 2013 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.27 No.1
Sudden valve closure in pipeline systems can cause high pressures that may lead to serious damages. Using an optimal valve closing rule can play an important role in managing extreme pressures in sudden valve closure. In this paper, an optimal closing rule curve is developed using a multi-objective optimization model and Bayesian networks (BNs) for controlling water pressure in valve closure instead of traditional step functions or single linear functions. The method of characteristics is used to simulate transient flow caused by valve closure. Non-dominated sorting genetic algorithms-II is also used to develop a Pareto front among three objectives related to maximum and minimum water pressures, and the amount of water passes through the valve during the valve-closing process. Simulation and optimization processes are usually time-consuming, thus results of the optimization model are used for training the BN. The trained BN is capable of determining optimal real-time closing rules without running costly simulation and optimization models. To demonstrate its efficiency, the proposed methodology is applied to a reservoir-pipe-valve system and the optimal closing rule curve is calculated for the valve. The results of the linear and BN-based valve closure rules show that the latter can significantly reduce the range of variations in water hammer pressures.
Davoud Afshar,Farzaneh Rafiee,Mozhgan Kheirandish,Solmaz Ohadian Moghadam,Mohammad Azarsa 대한백신학회 2020 Clinical and Experimental Vaccine Research Vol.9 No.2
Purpose: N-acetylmuramoyl-l-alanine amidase known as lytA, is an immunogenic protein that plays an important role in the pathogenesis of Streptococcus pneumoniae. It is highly conserved among S. pneumoniae strains and is absent among other Streptococcus species. In the present study, the level of antibodies against the lytA recombinant protein was evaluated in healthy individuals’ sera. Materials and Methods: DNA was extracted from S. pneumoniae ATCC 49619 to amplify lytA gene by polymerase chain reaction assay. The lytA amplicon and pET28a vector were separately double digested using Nde-1 and Xho1 restriction enzymes and then ligated together with ligase enzyme. The recombinant plasmid was expressed in Escherichia coli BL21 strain and the lytA recombinant protein purified using nickel-nitrilotriacetic acid affinity chromatography. Western blot was carried to detect lytA recombinant protein. Sixty healthy individual’s sera (at three age groups: group 1, <2; group 2, 2–40; and group 3, 60–90 years old) were collected and the titers of anti-lytA antibodies were determined. Results: The lytA gene was highly expressed in E. coli BL21 host. The recombinant lytA protein was purified and confirmed by western blotting. Tukey test analysis showed that there were no significant differences among the age groups considering the anti-lytA titer of 10. However, at the anti-lytA titer of 60, significant differences were observed between group 1 vs. group 2 (p<0.001); group 1 vs. group 3 (p=0.003), and group 2 vs. group 3 (p=0.024). Conclusion: The lytA protein seems to be a highly immunogenic antigen and a potential target for developing vaccines against pneumococcal infections.
Soheil Ganjefar,Mohammad Afshar,Mohammad Hadi Sarajch,Zhufeng Shao 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.5
In this study, a new controller method based on wavelet neural adaptive proportional plus conventional integral-derivative (WNAP+ID) controller through adaptive learning rates (ALRs) for the Internet-based bilateral teleoperation system is developed. The PID controller design suffers from dealing with a plant with an intricate dynamic model. To make an adaptive essence for PID controller, this study uses a trained offline self-recurrent wavelet neural network as a processing unit (SRWNN-PU) in parallel with conventional PID controller. The SRWNN-PU parameters are updated online using an SRWNN-identifier (SRWNNI) in order to reduce the controller error in realtime function. Using feedback linearization method and a PID controller, the presented control method reduced the tracking error in the subsystems of the teleoperation system, i.e., master and slave which are stabilized, respectively. Additionally, time-varying delay in teleoperation systems is considered as noise making the master signals be modulated because wavelt neural networks have a high susceptibility to remove the noise, thus the WNAP+ID controller is able to eliminate the noise effect. In this paper, we concentrated on the efficiency and stability of the teleoperation system with time-varying parameters through simulation outcomes. Moreover, the results of the WNNs are compared with those of multi-layer perceptron neural networks (MLPNNs).
Internal Combustion Engine Control Based on CFM Strategy
Ali Reza Zarei,Mohammad Sadegh Dahideh,Mohammad Najafi,Mehran Afshar,Yaser Barmayeh 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.2
Internal combustion (IC) engines are optimized to meet exhaust emission requirements with the best fuel economy. The mean-value engine model can be used to validate control strategies for different types of controllers that are model-based. The equations that are used to calculate the outputs of the model are approximated over an engine cycle. A significant advantage of the mean-value engine model is its low computational throughput which makes it possible for real-time simulation. In order to reduce engine emissions and improving engine fuel economy, closed loop combustion control, which requires cycleto-cycle combustion measurement such as cylinder pressure, is a necessity. The addition of a cylinder pressure signal to a mean value engine model will allow for developing closed loop combustion control strategies (or other strategies that involve cylinder pressure) to be validated. This is because the cylinder pressure model can produce a cylinder pressure signal for a complete engine operational map and a mean value engine model can produce real world engine parameters and conditions. The performance of the baseline computed fuel controller is compared with that of a baseline proportional, integral, and derivative (PID) controller.