Spectral Wave Modeling in Very Shallow Water at Southern Coast of Caspian Sea

This study evaluates the capability of the Simulating WAves Nearshore (SWAN) wave model (version 41.01) in predicting significant wave height and spectral peak energy content for swell waves in very shallow water of surf zone during depth-induced wave breaking and dissipation. The model results were compared with field measurements at five nearshore stations. The results demonstrated that some breaker index formulations were successful for significant wave height prediction in surf zones. However, an incorrect shape of the energy spectrum and overestimated near spectral peak energy content at shallow water stations were obtained using all of the embedded depth-induced wave breaking formulations in SWAN. The dependent breaker index on relative depth (K_pd) formulation, which was successful in predicting near spectral peak energy content, resulted in an average error of 30%. Finally, this formulation was modified to enhance the model performance in reproducing the spectral peak energy content.     

An automated approach to passive sonar classification using binary image features

This paper proposes a new method for ship recognition and classification using sound produced and radiated underwater. To do so, a three-step procedure is proposed. First, the preprocessing operations are utilized to reduce noise effects and provide signal for feature extraction. Second, a binary image, made from frequency spectrum of signal segmentation, is formed to extract effective features. Third, a neural classifier is designed to classify the signals. Two approaches, the proposed method and the fractal-based method are compared and tested on real data. The comparative results indicated better recognition ability and more robust performance of the proposed method than the fractal-based method. Therefore, the proposed method could improve the recognition accuracy of underwater acoustic targets.     

里海南部海岸在极浅水中的波谱建模研究（英文）

This study evaluates the capability of the Simulating WAves Nearshore(SWAN) wave model(version 41.01) in predicting significant wave height and spectral peak energy content for swell waves in very shallow water of surf zone during depth-induced wave breaking and dissipation. The model results were compared with field measurements at five nearshore stations. The results demonstrated that some breaker index formulations were successful for significant wave height prediction in surf zones.However, an incorrect shape of the energy spectrum and overestimated near spectral peak energy content at shallow water stations were obtained using all of the embedded depth-induced wave breaking formulations in SWAN. The dependent breaker index on relative depth(Kpd) formulation, which was successful in predicting near spectral peak energy content, resulted in an average error of 30%. Finally, this formulation was modified to enhance the model performance in reproducing the spectral peak energy content.     

In situ body mount optimization: theory and experiment

In this paper, an in situ method has been used to find the optimum set of body mounts for a given vehicle. The strength of this method is that it does not require the vehicle to be disassembled. Standard noise, vibration and harshness testing procedures are used to obtain frequency response function (FRF) models of the vehicle's subsystems. This is followed by FRF-based substructuring synthesis approach to obtain the overall vehicle model. The model is incorporated into an optimization algorithm to predict the optimum set of body mounts for a desired objective function. The in situ method is presented and applied to an experimental case study of a pick up truck. The experimental results confirmed the validity and effectiveness of this approach in finding the optimum body mount set, resulting in significant reduction in the vibration level of the vehicle.