Journal of Modeling and Simulation
Volume:45 Issue: 2, Autumn 2013

Ontology is the main infrastructure of the Semantic Web which provides facilities for integration, searching and sharing of information on the web. Development of ontologies as the basis of semantic web and their heterogeneities have led to the existence of ontology matching. By emerging large-scale ontologies in real domain, the ontology matching systems faced with some problem like memory consumption. Therefore, partitioning the ontology was proposed. In this paper, a new clustering method for the concepts within ontologies is proposed, which is called SeeCC. The proposed method is a seeding-based clustering method which reduces the complexity of comparison by using clusters’ seed. The SeeCC method facilitates the memory consuming problem and increases their accuracy in the large-scale matching problem as well. According to the evaluation of SeeCC's results with Falcon-AO and the proposed system by Algergawy accuracy of the ontology matching is easily observed. Furthermore, compared to OAEI (Ontology Alignment Evaluation Initiative), SeeCC has acceptable result with the top ten systems.

In this paper a mesh-free model of the functionally graded material (FGM) plate is presented. The piezoelectric material as a sensor and actuator has been distributed on the top and bottom of the plate, respectively. The formulation of the problem is based on the classical laminated plate theory (CLPT) and the principle of virtual displacements. Moreover, the Particle Swarm optimization (PSO) algorithm is used for the vibration control of the (FGM) plate. In this study a function of the sliding surface is considered as an objective function and then the control effort is produced by the particle swarm method and sliding mode control strategy. To verify the accuracy and stability of the proposed control system, a traditional sliding mode control system is designed to suppressing the vibration of the FGM plate. Besides, a genetic algorithm sliding mode (GASM) control system is also implemented to suppress the vibration of the FGM plate. The performance of the proposed PSO sliding mode than the GASM and traditional sliding mode control system are demonstrated by some simulations.

Time-delays are important components of many dynamical systems that describe coupling or interconnection between dynamics, propagation or transport phenomena, and heredity and competition in population dynamics. The stabilization with time delay in observation or control represents difficult mathematical challenges in the control of distributed parameter systems. It is well-known that the stability of closed-loop system achieved by some stabilizing output feedback laws may be destroyed by whatever small time delay there exists in observation. In this paper a new method for eigenvalue assignment of discrete-time linear systems with state and input time-delays by static output feedback matrix is presented. The main result is an iterative method that only requires linear equations to be solved at each iteration. In this scheme, first a linear delayed system by defining an augmented vector is changed to standard form, then output feedback matrix K is calculated by inverse eigenvalue problem. We investigate all types of delays in the states, inputs or both for discrete – time linear systems. A simple algorithm and an illustrative example are presented to show the advantages of this new technique.

One of the most important applications of multi-objective optimization is adjusting parameters ofpractical engineering problems in order to produce a more desirable outcome. In this paper, the decoupled sliding mode control technique (DSMC) is employed to stabilize an inverted pendulum which is a classic example of inherently unstable systems. Furthermore, a new Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is implemented for optimizing the DSMC parameters in order to decrease the normalized angle error of the pole and normalized distance error of the cart, simultaneously. The results of simulation are presented which consist of results with and without disturbances. The proposed Pareto front for the DSMC problem demonstrates that the Ingenious-MOPSO operates much better than other multi-objective evolutionary algorithms.

In this paper, a new smooth second order sliding mode control is proposed. This algorithm is a modified form of Super Twisting algorithm. The Super Twisting guarantees the asymptotic stability, but the finite time stability of proposed method is proved with introducing a new particular Lyapunov function. The Proposed algorithm which is able to control nonlinear systems with matched structured uncertainty, is able to guarantee the finite time stability. The main advantage of this second order sliding mode control is reaching to sliding surface with high precision without chattering in control signal. In simulation section, the proposed algorithm is compared with the boundary layer sliding mode control and then is applied to designing a finite time nonlinear guidance law that is robust with respect to target maneuvers. Simulation results show that the control input in this algorithm is smooth and has no chattering and by applying this method, sliding variables will converge to zero in a given desired finite time.

One-third of the people with an age over twenty have some signs of degenerated discs. However, in most of the patients the mere presence of degenerative discs is not a problem leading to pain, neurological compression, or other symptoms. This paper presents an interval type-2 fuzzy hybrid rule-based system to diagnose the abnormal degenerated discs where pain variables are represented by interval type-2 membership functions. For this purpose, Mamdani interval type-2 fuzzy sets are utilized in the inference engine. The main contribution of this paper is to present the interval type-2 fuzzy hybrid rule-based system, which is the combination of forward and backward chaining approach in its inference engine. Combining forward and backward chaining leads to detect the exact location of degenerated disc that shows some spinal instability. The phase of forward chaining diagnoses the severity of the degeneration based on taking history of the patient. The second phase uses backward chaining approach to find the exact location of the degenerated disc by investigating related clinical examinations. Using parametric operations for the fuzzy calculations increases the robustness of the system. The system is tested for 11 patients and the results are compared with the neurosurgeon’s diagnosis. Results indicate that the hybrid of forward and backward chaining approaches provide fast and accurate diagnosis of degenerative disc disease, and determine the necessity of taking MRI. Concluding, the proposed system could be a valuable tool in hand of the physicians in clinics and imaging centers to support diagnosis of the degenerated discs.