Detecting obstacles from camera image at open sea

While self-driving cars are a hot topic in these days, fewer people know that the same level of automation is being developed in the maritime industry. To enhance safety on board and to ensure the optimal utilization of crew members, automated assistant solutions are implemented on cargo ships and vessels. This thesis deals with a monocular camera-based system, that is capable of detection obstacles in open sea scenarios, and to estimate surrounding vehicles’ distance and bearing. After a solid research of existing methods and literature, an algorithm has been developed, containing three main parts. First, the real-world measurement data and camera images are being processed. Secondly, object detection is achieved with the YOLO deep learning methods that achieves at a high framerate and can be used for real-time applications. Lastly, distance and bearing values of detected obstacles are estimated based on geometrical calculations and mathematical equations that are validated with ground truth measurement data. Having multiple weeks of recorded measurement data from a RoPax vessel operating from Helsinki, allowed testing and validation already during the development phase. Results have shown that the systems’ detection capability is highly affected by the image resolution, and that distance estimation performance is reliable until 2-3 kilometers, but estimation errors rise at farther distances, due to physical limitations of cameras. In addition, as an interesting evaluation method, a survey has been conducted with industry professionals, to compare human distance estimation capability with the developed system. As a conclusion it can be stated that a significant need and huge potential can be found in automated safety solution in the maritime industry

Cyber Security in the Maritime Industry: A Systematic Survey of Recent Advances and Future Trends

The paper presents a classification of cyber attacks within the context of the state of the art in the maritime industry. A systematic categorization of vessel components has been conducted, complemented by an analysis of key services delivered within ports. The vulnerabilities of the Global Navigation Satellite System (GNSS) have been given particular consideration since it is a critical subcategory of many maritime infrastructures and, consequently, a target for cyber attacks. Recent research confirms that the dramatic proliferation of cyber crimes is fueled by increased levels of integration of new enabling technologies, such as IoT and Big Data. The trend to greater systems integration is, however, compelling, yielding significant business value by facilitating the operation of autonomous vessels, greater exploitation of smart ports, a reduction in the level of manpower and a marked improvement in fuel consumption and efficiency of services. Finally, practical challenges and future research trends have been highlighted

Biorefarmeries: Milking ethanol from algae for the mobility of tomorrow

The idea of this project is to fully exploit microalgae to the best of its potential, possibly proposing a sort of fourth generation fuel based on a continuous milking of macro- and microorganisms (as cows in a milk farm), which produce fuel by photosynthetic reactions. This project proposes a new transportation concept supported by a new socio-economic approach, in which biofuel production is based on biorefarmeries delivering fourth generation fuels which also have decarbonization capabilities, potential negative CO2 emissions plus positive impacts on mobility, the automotive Industry, health and environment and the econom

Discovering ship navigation patterns towards environment impact modeling

Ship positioning and maneuvering information is highly relevant to understand the levels of pollution on coastal cities and sea-life quality, containing latent patterns of vessels behavior, that are of utility on earth sciences and environmental research. Using Automatic Identification System (AIS) data enables air quality models to have finer grain estimations. However, the data as it is, carries uncertainty and errors. Therefore, there is a need for a methodology to filter and clean it and to extract patterns. Ship navigation traces can be understood as time series. Here, we present a methodology for characterizing ships by their navigation traces, using Conditional Restricted Boltzmann Machines (CRBMs) plus classic clustering techniques like k-Means. From the inputs received from ships using the AIS, containing ship positions, speed, and characteristics, we produce a processed cruising trace that a CRBM can encode while preserving the time factor and reducing dimensionality of data. Such codification can be then clustered or pattern-mined, then used not only for ship classification but also to cross such behavior patterns with environmental information. In this paper we detail such methodology and validate it using data from the Spanish Ports Authority records from national and international fishing vessels and passenger and cargo ships. Along the pattern mining methodology we propose how to use Apache Spark for the data cleaning process until it arrives to the Conditional Restricted Boltzmann Machine (CRBM). Finally, we develop a visualization tool for data exploration and pattern evaluation

Unmanned Vehicle Systems & Operations on Air, Sea, Land

Unmanned Vehicle Systems & Operations On Air, Sea, Land is our fourth textbook in a series covering the world of Unmanned Aircraft Systems (UAS) and Counter Unmanned Aircraft Systems (CUAS). (Nichols R. K., 2018) (Nichols R. K., et al., 2019) (Nichols R. , et al., 2020)The authors have expanded their purview beyond UAS / CUAS systems. Our title shows our concern for growth and unique cyber security unmanned vehicle technology and operations for unmanned vehicles in all theaters: Air, Sea and Land – especially maritime cybersecurity and China proliferation issues. Topics include: Information Advances, Remote ID, and Extreme Persistence ISR; Unmanned Aerial Vehicles & How They Can Augment Mesonet Weather Tower Data Collection; Tour de Drones for the Discerning Palate; Underwater Autonomous Navigation & other UUV Advances; Autonomous Maritime Asymmetric Systems; UUV Integrated Autonomous Missions & Drone Management; Principles of Naval Architecture Applied to UUV’s; Unmanned Logistics Operating Safely and Efficiently Across Multiple Domains; Chinese Advances in Stealth UAV Penetration Path Planning in Combat Environment; UAS, the Fourth Amendment and Privacy; UV & Disinformation / Misinformation Channels; Chinese UAS Proliferation along New Silk Road Sea / Land Routes; Automaton, AI, Law, Ethics, Crossing the Machine – Human Barrier and Maritime Cybersecurity.Unmanned Vehicle Systems are an integral part of the US national critical infrastructure The authors have endeavored to bring a breadth and quality of information to the reader that is unparalleled in the unclassified sphere. Unmanned Vehicle (UV) Systems & Operations On Air, Sea, Land discusses state-of-the-art technology / issues facing U.S. UV system researchers / designers / manufacturers / testers. We trust our newest look at Unmanned Vehicles in Air, Sea, and Land will enrich our students and readers understanding of the purview of this wonderful technology we call UV.https://newprairiepress.org/ebooks/1035/thumbnail.jp

Annual General Assembly of the International Association of Maritime Universities

978-84-947311-7-

Aufsätze zu Internationalem Handel, Prognose und dem Containerschiffnetzwerk

Seaborne vessels transport approximately 70% of global trade by value according to the United Nations Conference on Trade and Development. The grounding of the 400-meter long container ship “Ever Given” and the ensuing blockage of the Suez Canal for six days in March 2021 clearly demonstrated the importance of a wellfunctioning maritime transport sector for the flow of goods. Container ships play a particularly crucial role in this transportation network as they transport 66% of the maritime cargo by value. At the same time, the Automatic Identification System (AIS), which was developed to avoid collisions at sea using high frequency radio signals, generates extremely recent information on ships’ positions, course and draught. The essays below have in common that they utilize the daily positions of all approximately 6,000 container ships after the year 2015 for economic analysis. The studies rely on the data to develop and test methods for forecasting trade flows, to investigate the impact of oil prices on trade costs and to quantify the disruptive effects of tropical cyclones and piracy on maritime shipping and trade. The first chapter uses the AIS data to derive 880 time series at the port and sea area level to reflect seaborne cargo flows. Using a combination of the least absolute shrinkage and selection operator and the partial least squares, the paper demonstrates that these time series can reliably forecast unilateral and bilateral trade flows. The second chapter dervies a highly dimensional data for shipping that sllows the study of trade costs from container shipping. Oil prices are caculated to increase the time of transportation and increase freight rates. The following chapter investigates the effects of tropical cyclones on internaitonal trade and the container shipping network. In the last chapter, research finds that maritime piracy reduces global trade flows