Level Crossing Probabilities for Multipath Acoustic Processes with Bimodal Spectra

A general method of evaluating upcrossing probabilities for a class of random processes consisting of two narrow_band signals is presented. One of the two significant frequencies of the corresponding bimodal spectra is assumed to be dominant. The method approximates the maxima of these processes by the corresponding values of the envelope processes. It is also assumed that the discrete processes of the maxima are Markov. The results have several applications. Two prominent examples are detection problems of multipath partially saturated processes in underwater acoustics and the problem of the structural reliability of marine diesel engine shafting systems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86229/1/Perakis11.pd

A survey of shipping finance research: setting the future research agenda

Financing shipping related investment projects has always been a focal area of debate and research within the international maritime industry since access to funding can determine the competitiveness of a capital-intensive business as well as its success or failure under adverse market conditions. This paper provides, for the first time, a comprehensive and structured survey of all published research in the area of shipping finance and investment. The review spans approximately four decades (1979-2018) of empirical evidence, including 162 studies published in 48 scholarly journals, complemented with select books and book chapters. The study provides a bibliometric analysis and comprehensive synthesis of existing research offering an invaluable source of information for both the academic community and business practice, shaping the future research agenda in shipping finance and investment

Forecasting Tanker Market Using Artificial Neural Networks

Investing in the tanker market, especially in the VLCC sector constitutes a risky undertaking due to the volatility of tanker freight rates. This paper attempts to uncover the benefits of using Artificial Neural Networks (ANNs) in forecasting VLCC spot freight rates. This is achieved by analysing the period from October 1979 to December 2002, in order to detect possible causes of fluctuations, thus determine the independent variables of the analysis, and then use them to construct reliable ANNs. The aim is to reduce error and, most important, allow the model to maintain a stable error variance during high volatility periods. Among the findings are: ANNs can, with the appropriate architecture and training, constitute valuable decision-making tools especially when the tanker market is volatile; the use of variables in differential form enhances the ANN performance in high volatility periods while variables in normal form demonstrated better performance in median periods; ANN demonstrated mean errors comparable to the naïve model for 1-month forecasts but significantly outperformed it in the 3-, 6-, 9- and 12-month cases; finally, the use of informative variables such as the arbitrage between types of crude oil as well as Capesize rates can improve ANN performance. Maritime Economics & Logistics (2004) 6, 93–108. doi:10.1057/palgrave.mel.9100097