Advanced Materials Research Vols. 403-408

Abstract: Since chemical reactors are utilized to produce specific and valuable products, concentration of products should be regulated at a specified level. As a disturbance input, a change in the inlet concentrations can vary the product concentration. So, in order to regulate the product concentration, the inlet concentrations and the product concentration should be measured. However, measurement of concentration encounters some problems such as high cost and time delay. For compensation of these failures, estimation of concentration has been proposed. In this work, the inlet concentration and the product concentration of a continuous stirred-tank reactor (CSTR) are estimated based on the moving horizon state estimation (MHSE), and the product concentration is regulated based on the model predictive control (MPC). Simulation results indicate that the proposed strategy improves the performance of the CSTR compared with the method in which the inlet concentration is not estimated.

Abstract: A novel approach for the implementation of nonlinear model predictive control (NMPC) is proposed based on Individual particle optimizer (IPO1) while functional link neural network (FLNN) is introduced as a nonlinear model of the plant where individual particle optimization (IPO2) is applied for training of the neural network. The IPO algorithm is used as a real-time optimal tuning technique, which is applied to the neural network so that the proposed optimized FLNN can be used in nonlinear model predictive control scheme. Finally, the proposed NMPC applied to the Load frequency control (LFC) problem. Simulation results verify that the proposed IPO based technique possesses efficient performance in the sense of speed up and set point tracking.).

Abstract: This paper puts across the various approaches and methods that have been proposed in the context of Fuzzy Mathematical Morphology. The underlying principles of Dilation & Erosion, the structuring elements used in various techniques, the unique variations put forth by researchers, new applications in spatial relationships, decision making, segmentation of medical images have been discussed.

Abstract: In this paper a state-space average model for boost switching regulators is presented. The presented model includes the most of the regulator’s parameters and uncertainties. This model can be used to design a precise and robust controller that can satisfy stability and performance conditions. In modeling, the load current is assumed to be unknown, and it is assumed that the inductor, capacitor, diode and regulator active switch are non ideal and they have a resistance in conducting condition. Other non ideal effects are also considered. After presenting the complete model, the boost converter Benchmark circuit is simulated in PSpice and its results are compared with our model simulation results in MATLAB.

Abstract: Very recently work has been done to develop efficient disaster forecasting systems utilizing WSN technology. Such networks pose a tremendous design challenge such as the ability to cope with node failure, limited power, distributed prediction, wide variety of sensors and the need for communication over a large area. Our paper introduces a Predictive Environmental Sensor Network (PESN) Architecture which employs a minimal deployment scheme to ensure connectivity among the nodes involved within the network. On this connected network we run our distributed statistical model for forecasting. The statistical process used for this real time prediction uses multiple variable regression method providing the advantages of simplicity and robustness much needed in low power and limited ability sensor nodes. The versatility of the forecasting model is proved on its independence on the number of parameters, as it can incorporate as many variables into the algorithm as required, as long as there is sufficient positive correlation with the instantaneous river water level.

Abstract: Fault tree analysis has been widely used for providing logical functional relationships among subsystems and components of a system and identifying the root causes of the undesired failures in a system. This paper analyzes the reliability of Computer Security System through the method of intuitionistic fuzzy fault tree, which is based on L-R type triangular intuitionistic fuzzy set. In this paper, a new approach of intuitionistic fuzzy fault-tree analysis is proposed to calculate fault interval of system components from integrating expert’s knowledge and experience in terms of providing the possibility of failure of bottom events and to find the most critical system component that affects the reliability of the system, which could be used for managerial decision-making. For numerical verification, the proposed method is applied for the failure analysis problem of Computer Security System to generate the fault-tree, fault-tree nodes, then directly compute the intuitionistic fuzzy fault-tree interval, traditional reliability, and the intuitionistic fuzzy reliability interval.

Abstract: The paper investigates on the use of Differential Evolution (DE) for training the system identification model particularly when the measurement data are available at different sensor nodes. Under such situation the conventional DE algorithms cannot be applied directly. Hence in this paper two distributed learning algorithms known as incremental DE (IDE) and diffusion DE (DDE) have been developed to meet the requirements. The identification of nonlinear plants under different noise conditions has been obtained through simulation study and the results have been compared with distributed PSO algorithms. The performance of the proposed algorithms in terms of convergence rate and minimum mean squared error indicate that the distributed DE algorithms exhibit superior performance compared to its PSO counter parts.

Abstract: The recent progress in the digital multimedia technologies has offered many facilities in the transmission, reproduction and manipulation of data. However, this advance has also brought the problem such as copyright protection for content providers. Digital watermarking is one of the proposed solutions for copyright protection of multimedia. This paper proposes a blind watermarking algorithm based on fractal model in discrete wavelet domain for copyright protection. The idea of the presented scheme is to hide a binary image as a watermark with fractal parameters in wavelet domain of host image. Fractal compression technique is used to encode a gray image and fractal codes are embedded into the wavelet coefficients of the gray image according to well-connected watermark algorithm. The experimental results show that the algorithm is robust against geometric and non geometric attacks.

Abstract: Security has become an important issue for networks. Intrusion detection technology is an effective approach in dealing with the problems of network security. In this paper, we present an intrusion detection model based on hybrid fuzzy logic and neural network. The key idea is to take advantage of different classification abilities of fuzzy clustering and neural network for intrusion detection system. The new model has ability to recognize an attack, to differentiate one attack from another (i.e. classifying attacks), and the most important, to detect new attacks with high detection rate and low false negative. Training and testing data were obtained from the Defense Advanced Research Projects Agency intrusion detection evaluation data set.

Abstract: Nonlinear system Identification based on Volterra filter are widely used for the nonlinearity identification in various application. A standard algorithm for LMS-Volterra filter for system identification simulation, tested with several convergence criteria is presented in this paper. We analyze the steady-state mean square error (MSE) convergence of the LMS algorithm when random functions are used as reference inputs. In this paper, we make a more precise analysis using the deterministic nature of the reference inputs and their time-variant correlation matrix. Simulations performed under MATLAB show remarkable differences between convergence criteria with various value of the step size. Along with that the least mean squared (LMS) adaptive filtering algorithm may experience uncontrolled parameter drift when its input signal is not persistently exciting, leading to serious consequences when implemented with finite word-length. The second order LMS Volterra filter with variable step size for system identification are analyzed and comparing the theoretical value with experimental value. Copyright © 2009 IFSA.