An innovative approach of model order reduction using ant colony optimization
In this paper, a generic solution in the area of model order reduction is presented. The proposed method is based on artificial intelligence, which depends on a probabilistic optimization algorithm called Ant Colony. This approach aims to obtain a reduced-order model out of a relatively complex model represented as a transfer function. This reduced order model is obtained in a reasonable run time. It has acceptable error in time domain represented by the unit step time response, and the least error in frequency domain represented by Bode plot compared to other conventional methods in model order reduction regardless the targeted transfer function.
Model order reduction using artificial neural networks
In this paper, we present a new technique for model order reduction (MOR) that is based on an artificial neural network (ANN) prediction. The ANN-based MOR can be applied for different scale systems with substructure preservation. In the proposed technique, the ANN is implemented for predicting the unknown elements of the reduced order model. Prediction of the ANN architecture is based on minimizing the cost function obtained by the difference between the actual and desired system behavior. The ANN prediction process is pursued while maintaining the full order substructure in the reduced model. The proposed ANN-based model order reduction method is compared to recently published work on MOR techniques. Simulation results verify the validity of the new MOR technique.
Detection of Fractures Based on Directional Filtering in Image Logging
According to the feature of the direction of fractures in image logging, a method of fracture detection in image logging based on directional filtering is presented in this paper. The fracture image is filtered by the corresponding filter based on the local direction of fractures, which enhances the fracture traces in the direction of fractures in image logging. The final fracture traces are extracted by the combination of the results of both directional filtering and the threshold segmentation. Experimental results show this method effectively detect the fractures in image logging.