Search Results (15)

In this paper, we discuss the results of hydroacoustic sounding in the frequency range of units of kHz, conducted during voyage No. 90 of the research vessel “Akademik Mstislav Keldysh”. The employment of a vertical antenna array and electronic phasing to the recorded data made it possible to determine both the reflection coefficient from the bottom at incidence close to normal, and the diffuse scattering coefficients at oblique angles for the surface and the bottom. Based on the processing of experimental data, and with the help of computer modeling, an analysis of the structure of hydroacoustic signals scattered by the bottom and free surface of water was carried out. An approach combining the Green’s function and the scattering function was used to model the reverberation signal. The models of formation of the Doppler spectrum of the scattered acoustic signal were refined, taking into account the influence of sound propagation conditions in the marine environment. The comparison of the results of experimental studies of bottom reverberation in the waters of the Barents and Kara Seas with numerical calculations of the ray structure of the acoustic field demonstrates good agreement. Full article

The International Convention for the Control and Management of Ships’ Ballast Water and Sediments (BWM Convention) in 2024, gives us at least 7 years of data to analyze accounting for technology samples in marine and ships’ ballast water monitoring and forecasting. Marine technology played a crucial role in closing the gap between the adoption of the Convention and its coming into force, a 13-year gap that involved world-wide experimentation and investment in marine technologies which had to be approved for compliance by the International Maritime Organization (IMO) as well as Port States. Compliance Monitoring Devices (CMDs) with rapid results have been generally used in providing marine environmental invasion controls and have been used by Port States to enforce compliance in ballast water management. Qualitative, regulatory and quantitative data have been synthesized in this study from select experiments to test for compliance monitoring success. Going forward, rapid result devices will remain ideal in ship and port time management, particularly with a move towards electronic ballast water record books which also serve as an important forecasting tool for compliance as such data can be pre-delivered to the electronic records of the Port State for reaction prior to ship arrival, that way major issues can be dealt with while the ship is en voyage, for pre-emptive national security measures. Full article

Automatic identification system (AIS) data record a ship’s position, speed over ground (SOG), course over ground (COG), and other behavioral attributes at specific time intervals during a ship’s voyage. At present, there are few studies in the literature on ship trajectory classification, especially the clustering of trajectory segments, to measure the multi-dimensional information of trajectories. Therefore, it is necessary to fully utilize the multi-dimensional information from AIS data when utilizing ship trajectory classification methods. Here, we propose a ship trajectory classification method based on multi-attribute trajectory similarity metrics which utilizes the following steps: (1) Improve the Douglas–Peucker (DP) algorithm by considering the SOG and COG; (2) use a multi-attribute symmetric segmentation path distance (MSSPD) for the similarity metric between trajectories; (3) cluster the segmented sub-trajectories based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm; (4) adaptively determinate the optimal input parameters based on the proposed comprehensive clustering performance metrics. The proposed method was tested on real AIS data from Bohai Sea waters, and the experimental results show that the algorithm can accurately cluster the ship trajectory groups and extract traffic distributions in key waters. Full article

This paper realizes the simultaneous optimization of a vessel’s course and speed for a whole voyage within the estimated time of arrival (ETA), which can ensure the voyage is safe and energy-saving through proper planning of the route and speed. Firstly, a dynamic sea area model with meteorological and oceanographic data sets is established to delineate the navigable and prohibited areas; secondly, some data are extracted from the records of previous voyages, to train two artificial neural network models to predict fuel consumption rate and revolutions per minute (RPM), which are the keys to route optimization. After that, speed configuration is introduced to the optimization process, and a simultaneous optimization model for the ship’s course and speed is proposed. Then, based on a customized version of the A* algorithm, the optimization is solved in simulation. Two simulations of a ship crossing the North Pacific show that the proposed methods can make navigation decisions in advance that ensure the voyage’s safety, and compared with a naive route, the optimized navigation program can reduce fuel consumption while retaining an approximately constant time to destination and adapting to variations in oceanic conditions. Full article

Container terminals are at the center of global logistics, and are highly dependent on the schedule of vessels arriving. Conventional ETA records from ships, utilized for terminal berth planning, lack sufficient accuracy for effective plan implementation. Thus, there is a pressing need for improved ETA prediction methods. In this research, we propose a novel approach that leverages past voyage route patterns to predict the ETA of container vessels arriving at a container terminal at Busan New Port, South Korea. By modeling representative paths based on previous ports of call, the method employs real-time position and speed data from the Automatic Identification System (AIS) to predict vessel arrival times. By inputting AIS data into segmented representative routes, optimal parameters yielding minimal ETA errors for each vessel are determined. The algorithm’s performance evaluation during the modeling period demonstrates its effectiveness, achieving an average Mean Absolute Error (MAE) of approximately 3 h and 14 min. These results surpass the accuracy of existing ETA data, such as ETA in the Terminal Operating System and ETA in the AIS of a vessel, indicating the algorithm’s superiority in ETA estimation. Furthermore, the algorithm consistently outperforms the existing ETA benchmarks during the evaluation period, confirming its enhanced accuracy. Full article

Voyage Data Recorders (VDRs), often referred to as the ‘black boxes’ of the shipping industry, collect and store vital data from key sensors and locations around the ship. This data plays a pivotal role in incident investigation, as was seen in the grounding of the Costa Concordia in 2012, and the sinking of the El Faro in 2015. With such an important role to play, the International Maritime Organization (IMO) has mandated that all SOLAS registered ships carry a VDR, which can demonstrate compliance with internationally agreed standards. Without a VDR compliant with these standards a ship cannot sail. However, the rise in the number, and sophistication, of digital devices are making the sector increasingly vulnerable to cyber-attacks. This paper will demonstrate a number of high-risk VDR cyber security vulnerabilities and review the current international technical standards covering all VDR devices being manufactured and used today, drawing attention to the minimum security requirements. The paper will go on to discuss how these standards fail to promote the necessary levels of cyber security needed to protect VDRs from today’s cyber risks, amidst increased demands for digital connectivity for remote and autonomous operations. The paper will conclude by proposing several amendments (technical and non-technical) to the current standards which, if adopted, will help increase the minimum level of security of VDRs. Industry opinions were gathered on this topic, and their beliefs have been included across this paper. Full article

A vessel sails above the ocean against sea resistance, such as waves, wind, and currents on the ocean surface. Concerning the energy efficiency issue in the marine ecosystem, assigning the right magnitude of shaft power to the propeller system that is needed to move the ship during its operations can be a contributive study. To provide both desired maneuverability and economic factors related to the vessel’s functionality, this research studied the shaft power utilization of a factual vessel operational data of a general cargo ship recorded during 16 months of voyage. A machine learning-based prediction model that is developed using Random Forest Regressor achieved a 0.95 coefficient of determination considering the oceanographic factors and additional maneuver settings from the noon report data as the model’s predictors. To better understand the learning process of the prediction model, this study specifically implemented the SHapley Additive exPlanations (SHAP) method to disclose the contribution of each predictor to the prediction results. The individualized attributions of each important feature affecting the prediction results are presented. Full article

For the investigation of major traffic accidents, larger vessels are obliged to install a voyage data recorder (VDR). However, not every vessel is equipped with a VDR, and the readout is often a manual process that is costly. In addition, not only ship-related information can be relevant for reconstructing traffic accidents, but also information from other entities such as meteorological services or port operators. Moreover, another major challenge is that entities tend to trust only their records, and not those of others as these could be manipulated in favor of the particular recording entity (e.g., to disguise any damage caused). This paper presents an approach to documenting arbitrary data from different entities in a trustworthy, decentralized, and tamper-proof manner to support the conflict resolution process. For this purpose, all involved entities in a traffic situation can contribute to the documentation by persisting their available data. Since maritime stakeholders are equipped with various sensors, a diverse and meaningful data foundation can be aggregated. The data is then signed by a mutually agreed upon timestamping authority (TSA). In this way, everyone can cryptographically verify whether the data has been subsequently changed. This approach was successfully applied in practice by documenting a vessel’s mooring maneuver. Full article

The environmental characteristics of the Taiwan Strait (TS) have been linked to variations in the abundance and distribution of greater amberjack (Seriola dumerili) populations. Greater amberjack is a commercially and ecologically valuable species in ecosystems, and its spatial distribution patterns are pivotal to fisheries management and conservation. However, the relationship between the catch rates of S. dumerili and the environmental changes and their impact on fish communities remains undetermined in the TS. The goal of this study was to determine the spatiotemporal distribution pattern of S. dumerili with environmental characteristics in the TS from south to north (20°N–29°N and 115°E–127°E), applying generalized additive models (GAMs) and spatiotemporal fisheries data from logbooks and voyage data recorders from Taiwanese fishing vessels (2014–2017) as well as satellite-derived remote sensing environmental data. We used the generalized linear model (GLM) and GAM to analyze the effect of environmental factors and catch rates. The predictive performance of the two statistical models was quantitatively assessed by using the root mean square difference. Results reveal that the GAM outperforms the GLM model in terms of the functional relationship of the GAM for generating a reliable predictive tool. The model selection process was based on the significance of model terms, increase in deviance explained, decrease in residual factor, and reduction in Akaike’s information criterion. We then developed a species distribution model based on the best GAMs. The deviance explained indicated that sea surface temperature, linked to high catch rates, was the key factor influencing S. dumerili distributions, whereas mixed layer depth was the least relevant factor. The model predicted a relatively high S. dumerili catch rate in the northwestern region of the TS in summer, with the area extending to the East China Sea. The target species is strongly influenced by biophysical environmental conditions, and potential fishing areas are located throughout the waters of the TS. The findings of this study showed how S. dumerili populations respond to environmental variables and predict species distributions. Data on the habitat preferences and distribution patterns of S. dumerili are essential for understanding the environmental conditions of the TS, which can inform future priorities for conservation planning and management. Full article

Each of the giant planets, Jupiter, Saturn, Uranus, and Neptune, has been observed by at least one robotic spacecraft mission. However, these missions are infrequent; Uranus and Neptune have only had a single flyby by Voyager 2. The Hubble Space Telescope, particularly the Wide Field Camera 3 (WFC3) and Advanced Camera for Surveys (ACS) instruments, and large ground-based telescopes with adaptive optics systems have enabled high-spatial-resolution imaging at a higher cadence, and over a longer time, than can be achieved with targeted missions to these worlds. These facilities offer a powerful combination of high spatial resolution, often <0.05”, and broad wavelength coverage, from the ultraviolet through the near infrared, resulting in compelling studies of the clouds, winds, and atmospheric vertical structure. This coverage allows comparisons of atmospheric properties between the planets, as well as in different regions across each planet. Temporal variations in winds, cloud structure, and color over timescales of days to years have been measured for all four planets. With several decades of data already obtained, we can now begin to investigate seasonal influences on dynamics and aerosol properties, despite orbital periods ranging from 12 to 165 years. Future facilities will enable even greater spatial resolution and, combined with our existing long record of data, will continue to advance our understanding of atmospheric evolution on the giant planets. Full article

Mixed fisheries refer to fishing activities that catch more than one species simultaneously, and a species may be fished using different gear. A trawl fishery shares these features to exploit multiple species simultaneously, with diverse fishing gear and strategies. The situation becomes more complex when interactions among fleet dynamics, fishing activities, and fishery resources are involved and influence each other. Information regarding the operational patterns may be hidden in a set of long-term big data. This study aims to investigate the fishery structure and fleet dynamics of trawl fisheries in Taiwan for spatial planning and management, based on a long-term dataset from a management system that collects information by using voyage data recorders (VDR) and dockside observers. We applied a two-step data mining process with a clustering algorithm to classify the main groups of fishery resources and then identified 18 catch métiers based on catch composition. The target species, operation pattern, and fishing season were determined for each métier, and associated with the relevant fishery resources and the fishing gear used. Additionally, fishing effects on target species were estimated using information on fishing grounds and trajectories from VDR. The métier-based approach was successfully applied to define the six major fishery resources targeted by trawlers. We examined the key features of fishing activity associated with catch composition and spatial-temporal fishing metrics, which could be used to provide suggestions for the spatial planning and management of the mixed trawl fishery in the offshore waters of Taiwan. Full article

Information regarding the oceanic environment is crucial for determining species distributions and their habitat preferences. However, in studies on crustaceans, especially swimming crabs, such information remains poorly utilized, and its effects on crab communities in the Taiwan Strait (TS) has not been well documented. The purpose of this study was to understand the relationship between the catch rates of three swimming crab species and environmental factors in the TS. We fitted generalized additive models (GAMs) to logbooks and voyage data recorder data from Taiwanese crab vessels (2011–2015), developed a species distribution model, and predicted catch rates for these three swimming crab species based on the GAM output. The chlorophyll-a (Chl-a) concentration was related to the high catch rates of Chrybdis feriatus and Portunus sanguinolentus, whereas bottom temperature (BT) was related to high catch rates of Portunus pelagicus. The variance percentages for each crab species indicated that high catch rates of C. feriatus and P. sanguinolentus occurred in a Chl-a concentration > 0.5 mg/m³, whereas P. pelagicus catch rates exhibited negative correlations with BTs > 25 °C. The model predicted high catch rates of C. feriatus in the north of the TS during autumn and winter, whereas P. pelagicus was observed to the south during summer and autumn. P. sanguinolentus was predicted to be widely distributed around the TS and distributed further to the northern area during autumn and winter. These findings revealed that each species responds to spatiotemporal environmental variations. Understanding the distributions and habitats of these three crabs is vital in fisheries resource management and conservation planning. Full article

One of the world’s longest sea transport routes of live sheep for slaughter is from Australia to the Middle East. Heat stress is a major cause of mortality in shipments of sheep, particularly in sheep leaving Australia in the Southern Hemisphere winter to arrive in the Middle Eastern summer. Temperature and mortality data were utilized and recorded from fourteen voyages from Australia to the Middle East in May to December, 2016–2018, with the aim of determining when the welfare of the sheep began to be affected by elevated temperatures. Increases in heat stress were recorded at temperatures normally experienced in 50% of voyages between July and September offloading sheep at two of the four Persian Gulf ports, Doha and Dubai; however, small increases in recorded heat stress were not sufficient to increase mortality. Temperatures increased most rapidly when sheep were offloaded initially at Doha first, followed by other Gulf ports, and this resulted in higher mortality than when sheep were offloaded at other ports first. These results confirm benefits of restricting voyages leaving Australia in the Southern Hemisphere winter and suggest that shipments offloading at multiple ports should not offload at the hottest port, Doha, first. Full article

The shipping industry constantly strives to achieve efficient use of energy during sea voyages. Previous research that can take advantages of both ethnographic studies and big data analytics to understand factors contributing to fuel consumption and seek solutions to support decision making is rather scarce. This paper first employed ethnographic research regarding the use of a commercially available fuel-monitoring system. This was to contextualize the real challenges on ships and informed the need of taking a big data approach to achieve energy efficiency (EE). Then this study constructed two machine-learning models based on the recorded voyage data of five different ferries over a one-year period. The evaluation showed that the models generalize well on different training data sets and model outputs indicated a potential for better performance than the existing commercial EE system. How this predictive-analytical approach could potentially impact the design of decision support navigational systems and management practices was also discussed. It is hoped that this interdisciplinary research could provide some enlightenment for a richer methodological framework in future maritime energy research. Full article

The aim of this study was to monitor acute response patterns of autonomic and central nervous system activity during an encounter with Antarctica by synchronously recording heart rate variability (HRV) and electroencephalography (EEG). On three different time-points during the two-week sea journey, the EEG and HRV were recorded from nine male scientists who participated in “The First Turkish Antarctic Research Expedition”. The recordings were performed in a relaxed state with the eyes open, eyes closed, and during a space quantity perception test. For the EEG recordings, the wireless 14 channel EPOC-Emotiv device was used, and for the HRV recordings, a Polar heart rate monitor S810i was used. The HRV data were analyzed by time/frequency domain parameters and ordinal pattern statistics. For the EEG data, spectral band power in the conventional frequency bands, as well as permutation entropy values were calculated. Regarding HRV, neither conventional nor permutation entropy calculations produced significant differences for the different journey time-points, but only permutation entropy was able to differentiate between the testing conditions. During the cognitive test, permutation entropy values increased significantly, whereas the conventional HRV parameters did not show any significant differences. In the EEG analysis, the ordinal pattern statistics revealed significant transitions in the course of the sea voyage as permutation entropy values decreased, whereas spectral band power analysis could not detect any significant difference. Permutation entropy analysis was further able to differentiate between the three testing conditions as well between the brain regions. In the conventional spectral band power analysis, alpha band power could separate the three testing conditions and brain regions, and beta band power could only do so for the brain regions. This superiority of permutation entropy in discerning subtle differences in the autonomic and central nervous system’s responses to an overwhelming subjective experience renders it suitable as an analysis tool for biomonitoring in extreme environments. Full article