Classifying ships by their acoustic signals with a cross-bispectrum algorithm and a radial basis function neural network

An algorithm for estimating the cross-bispectrum of an acoustic vector signal was formulated. Composed features of sound pressure and acoustic vector signals are extracted by the proposed algorithm and other estimating algorithms for secondary and higher order spectra. Its effectiveness was tested with lake and sea trial data. These features can be used to construct an input vector set for a radial basis function neural network. The classification of vessels can then be made based on the extracted features. It was shown that the composed features of acoustic vector signals are more easily divided into categories than those of pressure signals. When using the composed features of acoustic vector signals, the recognition rate of underwater acoustic targets improves.

Ship detection and classification from optical remote sensing images: A survey

Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing filed. This article collects the methods of ship detection and classification for practically testing in optical remote sensing images, and provides their corresponding feature extraction strategies and statistical data. Basic feature extraction strategies and algorithms are analyzed associated with their performance and application in ship detection and classification.Furthermore, publicly available datasets that can be applied as the benchmarks to verify the effectiveness and the objectiveness of ship detection and classification methods are summarized in this paper. Based on the analysis, the remaining problems and future development trends are provided for ship detection and classification methods based on optical remote sensing images.

Prediction Method of Ice Resistance and Propulsion Power for Polar Ships

Polar ships need to meet stringent safety and environmental requirements.Usually those ships are classified by diferent ice classes based on ice operation capability.However,the polar ships are also trapped by severe ice condition due to low propulsion power.Therefore,it is a realistic question to design the appropriate minimum propulsion power for ice operation.This paper focuses on the ice resistance and its related propulsion power for the ships with polar code(PC)classes.In consideration of seven typical polar ice conditions related to the PC rule of International Association of Classification Societies(IACS),a prediction method of ice resistance is developed by Lindqvist's model.The results are compared with those of Lindqvist's model and Riska's model by using two real ship lines.The comparison among propulsion requirements of representative classification societies is made,and a formula of minimum propulsion power is presented on the basis of ice resistance by revised Finnish-Swedish Ice Class Rules(FSICR)method.The results are verified by the actual values from seven ice class ships.A relatively good agreement is achieved.As a conclusion,the presented prediction method of ice resistance and minimum propulsion power is recommended for evaluation of ice resistance and its related propulsion power during the process of developing polar ships.