A hierarchical fuzzy system for modeling driver’s behavior

The study of human behavior during driving is of primary importance for improving the driver??s security. In this study, we propose a hierarchical driver_vehicle_environment fuzzy system to analyze driver??s behavior under stress conditions on a road. We include climate, road and car conditions in fuzzy modeling. For obtaining fuzzy rules, experts?? opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. The number of fuzzy rules is optimized by Particle Swarm Optimization (PSO) algorithm. Also the frequency of pressing on brake and gas pedals and the number of car??s direction changes are used to determine the driver??s behavior under different conditions. Three different positions are considered for driving and decision making; one position in driving lane and two positions in opposite lane. A fuzzy model called Model 1 is presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. The behaviors of different drivers under two stress conditions are investigated. Also we obtained two other models based on fuzzy rules called Model 2 and Model 3 by using Sugeno fuzzy inference. Model 2 has two linguistic terms and Model 3 has four linguistic terms for estimating the time distances with other cars. The results of three models are compared. The comparative studies have shown that simulation results are in good agreement with the real world situations.     

On-line determination of supersaturation of a kcl-nacl aqueous solution based on density measurement

A method is developed for the calculation of the saturation temperature of a KCI-NaCI aqueous solution, based on the measurement of the density and temperature of a sample solution, and prior knowledge of the NaCl concentration. Experimental density and solubility data for solutions saturated with KCI in the temperature range of 299 to 321 K with concentrations of NaCl greater than 0.200 kg/kg H₂O were used to develop the empirical correlation allowing the calculation of the solution saturation level. The method is applicable in the on-line determination of the level of supersaturation in a KCI crystallizer in which the NaCl concentration is known. Knowledge of the prevailing supersaturation is necessary for the control of crystal purity and crystal size distribution in industrial potash crystallizers.     

Stability of nonlinear hybrid dynamical systems with time delay via sum of squares decomposition

Considering an infinite number of eigenvalues for time delay systems, it is difficult to determine their stability. We have developed a new approach for the stability test of time delay nonlinear hybrid systems. Construction of Lyapunov functions for hybrid systems is generally a difficult task, but once these functions are found, stability’s analysis of the system is straight-forward. In this paper both delay-independent and delay-dependent stability tests are proposed, based on the construction of appropriate Lyapunov-Krasovskii functionals. The methodology is based on the sum of squares decomposition of multivariate polynomials and the algorithmic construction is achieved through the use of semidefinite programming. The reduction techniques provide numerical solution of large-scale instances; otherwise they will be computationally infeasible to solve. The introduced method can be used for hybrid systems with linear or nonlinear vector fields. Finally simulation results show the correctness and validity of the designed method. Recommended by Editorial Board member Young Soo Suh under the direction of Editor Jae Weon Choi. The authors wish to express their thanks to Dr. A. Papachristodoulou and Dr. M. Peet for their helpful comments and suggestions. Mohammad Ali Badamchizadeh was born in Tabriz, Iran, in December 1975. He received the B.S. degree in Electrical Engineering from University of Tabriz in 1998 and the M.Sc. degree in Control Engineering from University of Tabriz in 2001. He received the Ph.D. degree in Control Engineering from University of Tabriz in 2007. He is now an Assistant Professor in the Faculty of Electrical and Computer Engineering at University of Tabriz. His research interests include Hybrid dynamical systems, Stability of systems, Time delay systems, Robot path planning. Sohrab Khanmohammadi received the B.S. degree in Industrial Engineering from Sharif University, Iran in 1977 and the M.Sc. degree in Automatic from University Paul Sabatie, France in 1980 and the Ph.D. degree in Automatic from National University, ENSAE, France in 1983. He is now a Professor of Electrical Engineering at University of Tabriz. His research interests are Fuzzy control, Artificial Intelligence applications in control and simulation on industrial systems and human behavior. Gasem Alizadeh was born in Tabriz, Iran in 1967. He received the B.S. degree in Electrical Engineering from Sharif University, Iran in 1990 and the M.Sc. degree from Khajeh Nasir Toosi University, Iran in 1993 and the Ph.D. degree in Electrical Engineering from Tarbiat Modarres University, Iran in 1998. From 1998, he is a Member of University of Tabriz in Iran. His research interests are robust and optimal control, guidance, navigation and adaptive control. Ali Aghagolzadeh was born in Babol, Iran. He received the B.S. degree in Electrical Engineering in 1985 from University of Tabriz, Tabriz, Iran, and the M.Sc. degree in Electrical Engineering in 1988 from the Illinois Institute of Technology, Chicago, IL. He also attended the School of Electrical Engineering at Purdue University in August 1998 where he was also employed as a part-time research assistant and received the Ph.D. degree in 1991. He is currently an Associate Professor of Electrical Engineering at University of Tabriz, Tabriz, Iran. His research interests include digital signal and image processing, image coding and communication, computer vision, and image analysis.     

A soft robotics nonlinear hybrid position/force control for tendon driven catheters

Minimally invasive steerable catheters, commonly implemented in cardiac ablation, are currently operated by interventionalists exposing them to X-ray radiation and requiring the dexterity for accurate steering. To conduct robot-assisted cardiac ablation, highly accurate stable control platform for precise force/position control on the moving tissue is required. This paper introduces hybrid force/position control strategy to apply a constant force to the cardiac tissue while tracking the desired trajectory. The position controller is based on a nonlinear model predictive tracking control satisfying the input constraints. Cosserat rod theory is incorporated for the distal shaft modeling of tendon-driven catheters, and the model is reformulated for controller design and stability proof. Lyapunov-based stability analysis is conducted. To apply the controller, the force-displacement mapping of the cardiac tissue is obtained through ex vivo experimental tests. The performance of the controller is evaluated, and the catheter is capable of regulating the force with the RMSE of 4.9 mN and tracking the position with the RMSE of 0.89 mm. The promising results verify the potential of the application of the introduced approach in real applications including in vitro and clinical cardiac ablation.     

Adaptive synchronization between two non-identical BAM neural networks with unknown parameters and time-varying delays

International Journal of Control, Automation and Systems - In this paper, the synchronization of two non-identical bidirectional associative memory (BAM) neural networks with unknown parameters and...     

A primary unit commitment approach with a modification process

This paper introduces an approach to one of the most important problems in electrical power system called the Unit Commitment (UC). The proposed method PUC-MP which stands for the primary unit commitment-modification process, addresses this problem firstly by using a simple and new priority for operating the generating units in each hour, and then, using a modification process which enhances the solution quality with lower cost. The PUC-MP takes advantage of both deterministic and stochastic algorithms in its structure to solve the discrete-variable part of the UC problem for choosing a suitable combination of units in each hour, and also, continuous-variable part of it which is dispatching the operating units’ output power to the power network load economically. The latter part which is called economic dispatch (ED) has been solved using an intelligent algorithm which in turn has been customized by two new ideas to increase its efficiency. Simulation results show that this new approach even without using its modification process can be considered as an effective approach which surpasses some other popular and recently reported methods in producing near-optimal and robust solutions.     

Synthesis and characterization of poly(ethylene tetrasulfide)/graphene oxide nanocomposites by in situ polymerization method

Poly(ethylene tetrasulfide) (PSP) is synthesized via interfacial polycondensation of 1,2 dichloroethane and sodium tetrasulfide, in the presence of graphene oxide (GO). This process resulted in homogeneously dispersed PSP/GO nanocomposites. Nanocomposites of 0.3 and 0.5?wt% of GO are synthesized and their morphology, chemical characteristics behavior are studied employing field emission scanning electron microscopy, Fourier transform infrared spectroscopy and X-ray diffraction techniques. Thermal characterization of composites is performed by differential scanning calorimetry and thermogravimetry analysis. Results indicate that the addition of only small amounts (0.5?wt%) of well-dispersed GO can increase the melting point more than 16°C resulting in better thermal properties for the composite. The solubility of nanocomposite is also studied and results show that the solubility depends on solvent concentration in addition to reinforcement (GO) deals.     

Determination of phenolic profile and antioxidant activity of pistachio hull using high-performance liquid chromatography–diode array detector–electro-spray ionization–mass spectrometry as affected by ultrasound and microwave

The phytochemicals content and radical scavenging activity of pistachio (Pistacia vera L.) hull extract obtained by different solvents (water, ethanol, and butanol) were measured and compared. Water was selected as superior solvent. Ultrasound-assisted aqueous extraction of the hull by power ultrasound (35 kHz) was more efficient in ascending the phytochemicals content than the sonochemical ultrasonication (130 kHz). High-performance liquid chromatography-mass spectrometry showed increased amounts of vanillic acid, p-coumaric acid, naringenin, and catechin in ultrasound-assisted extracts. Post-extraction sonication declined significantly the phenolics amount and antioxidant property of the aqueous extract. Microwave-assisted extraction increased the phenolics and flavonoids content at extract in a power-dependent trend.     

Asphaltene precipitation and deposition in oil reservoirs – Technical aspects,experimental and hybrid neural network predictive tools

Precipitation of asphaltene is considered as an undesired process during oil production via natural depletion and gas injection as it blocks the pore space and reduces the oil flow rate. In addition, it lessens the efficiency of the gas injection into oil reservoirs. This paper presents static and dynamic experiments conducted to investigate the effects of temperature, pressure, pressure drop, dilution ratio, and mixture compositions on asphaltene precipitation and deposition. Important technical aspects of asphaltene precipitation such as equation of state, analysis tools, and predictive methods are also discussed. Different methodologies to analyze asphaltene precipitation are reviewed, as well. Artificial neural networks (ANNs) joined with imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) are employed to approximate asphaltene precipitation and deposition with and without CO₂ injection. The connectionist model is built based on experimental data covering wide ranges of process and thermodynamic conditions. A good match was obtained between the real data and the model predictions. Temperature and pressure drop have the highest influence on asphaltene deposition during dynamic tests. ICA-ANN attains more reliable outputs compared with PSO-ANN, the conventional ANN, and scaling models. In addition, high pressure microscopy (HPM) technique leads to more accurate results compared with quantitative methods when studying asphaltene precipitation.