Smart Ship Research Articles

Research on AIS Data Analysis and Its Integration into Discipline-specific Teaching

The processing of ship AIS (Automatic Identification System) data plays a crucial role in the digital transformation of the shipping industry. From data collection to data storage, data analysis, and data visualization, AIS data processing technology provides robust support for the shipping sector. In terms of data collection, the platform utilizes distributed message queues to aggregate AIS data. For data storage, the platform efficiently manages and stores massive amounts of AIS data using data warehouses. When it comes to data analysis, the platform mines and analyzes AIS data through Lambda, extracting valuable information such as ship trajectories and sailing speeds, thereby supporting areas such as ship management, navigation safety, and marine research. In data visualization, the platform intuitively displays ship navigation status through charts and maps, providing strong support for decision-making. As an essential component of big data applications, AIS data analysis technology is playing an increasingly important role in the shipping industry. The big data technology major of Jiangsu Maritime Institute integrates big data applications like AIS data analysis into its curriculum, taking the upgrading needs of the shipping industry as an entry point. It actively plans the cultivation of composite big data skilled talents under the backdrop of smart shipping, comprehensively serving the upgrading of the shipping industry and promoting the transformation from a major shipping country to a shipping powerhouse.

Visual Navigation Systems for Maritime Smart Ships: A Survey

The rapid development of artificial intelligence has greatly ensured maritime safety and made outstanding contributions to the protection of the marine environment. However, improving maritime safety still faces many challenges. In this paper, the development background and industry needs of smart ships are first studied. Then, it analyzes the development of smart ships for navigation from various fields such as the technology industry and regulation. Then, the importance of navigation technology is analyzed, and the current status of key technologies of navigation systems is deeply analyzed. Meanwhile, this paper also focuses on single perception technology and integrated perception technology based on single perception technology. As the development of artificial intelligence means that intelligent shipping is inevitably the trend for future shipping, this paper analyzes the future development trend of smart ships and visual navigation systems, providing a clear perspective on the future direction of visual navigation technology for smart ships.

Bridge Detection in Autonomous Shipping: A YOLOv8 Approach with Autodistill and GroundedSAM

Abstract Inland waterway transportation remains a crucial and cost-effective mode of global transport despite challenges such as skilled personnel shortages, increasing competition, and ecological impacts. Autonomous technology enhances vessel navigation, scheduling, safety, and efficiency, making it a viable solution for smart ship and port operations. Reliable object detection is essential for autonomous ships to navigate safely and avoid collisions with static structures like bridges, piers, bollards, and locks. This paper presents an innovative approach for training a network using the Autodistill pipeline for bridge detection and segmentation. We generate a labeled bridge dataset using GroundedSAM, which integrates Grounding DINO and the Segment Anything Model (SAM) to detect and segment regions based on text input. The system focuses on identifying ‘bridge’ and ‘water’ classes, producing high-quality labeled data. Manual filtering improves label quality, enhancing the training of the YOLOv8 model, known for its superior object detection capabilities. Our approach demonstrates high performance in accurately detecting bridges, confirmed through evaluations with and without manual filtering. To validate our solution’s feasibility in real-world applications, we deployed the model on a NVIDIA Jetson AGX Orin for performance evaluation. Future work will extend this approach to additional static and mobile objects relevant to smart ship and port operations, such as ship locks and various ship types.

Manoeuvring in shallow and confined water with model predictive track controllers

Abstract Autonomous and computer-assisted navigation, for example through portable pilot units and data-driven decision supportive systems, is in full development. Remote-controlled vessels (manned but without a captain steering continuously on board) are used in Belgium (by Seafar in inland shipping) but also in other countries. A model predictive controller has been used to investigate environmental and operational parameters for the passage of the river Seine in Paris by different ship types. These fast-time simulations have been compared with simulations in real time executed by skippers on a ship manoeuvring simulator or measured real life tracks. A good predictability of the real situation requires accurate manoeuvring models. But what if this accuracy cannot be guaranteed? An illustration is made by comparing the tracks if, in the prescience prediction phase for the controller, wind or bank effects are neglected or doubled. The development of model predictive deep reinforcement learning control algorithms for navigation will be one of the challenges of the Data-driven Smart Shipping project that started May 2024.

Analysis of Bi-LSTM CRF Series Models for Semantic Classification of NAVTEX Navigational Safety Messages

NAVTEX is a key component in the Global Maritime Distress and Safety System (GMDSS) that automatically transmits urgent maritime safety information such as navigational and meteorological warnings and forecasts to vessels. For the safe navigation of smart ships, this information from different systems should be shared harmoniously in the Common Maritime Data Structure (CMDS). To share NAVTEX messages as CMDS, words in NAVTEX messages must be semantically classified and placed within the CMDS structure. While traditional parsing methods are typically used to understand message semantics, NAVTEX requires natural language processing methods with deep learning due to its unstructured messages. This paper applies six types of Bi-LSTM CRF-based deep learning models to NAVTEX navigational safety messages and analyzes the results to find the most suitable model for understanding the semantics of each word in NAVTEX messages. This technique can be applied to accurately convey the meaning of NAVTEX navigational safety messages to equipment that requires navigational safety information on smart ships without human intervention.

Smart Ship Draft Reading by Dual-Flow Deep Learning Architecture and Multispectral Information.

In maritime transportation, a ship's draft survey serves as a primary method for weighing bulk cargo. The accuracy of the ship's draft reading determines the fairness of bulk cargo transactions. Human visual-based draft reading methods face issues such as safety concerns, high labor costs, and subjective interpretation. Therefore, some image processing methods are utilized to achieve automatic draft reading. However, due to the limitations in the spectral characteristics of RGB images, existing image processing methods are susceptible to water surface environmental interference, such as reflections. To solve this issue, we obtained and annotated 524 multispectral images of a ship's draft as the research dataset, marking the first application of integrating NIR information and RGB images for automatic draft reading tasks. Additionally, a dual-branch backbone named BIF is proposed to extract and combine spectral information from RGB and NIR images. The backbone network can be combined with the existing segmentation head and detection head to perform waterline segmentation and draft detection. By replacing the original ResNet-50 backbone of YOLOv8, we reached a mAP of 99.2% in the draft detection task. Similarly, combining UPerNet with our dual-branch backbone, the mIoU of the waterline segmentation task was improved from 98.9% to 99.3%. The inaccuracy of the draft reading is less than ±0.01 m, confirming the efficacy of our method for automatic draft reading tasks.

Sensor-Based Fuzzy Inference of COVID-19 Transmission Risk in Cruise Ships.

Cruise ships are densely populated ecosystems where infectious diseases can spread rapidly. Hence, early detection of infected individuals and risk assessment (RA) of the disease transmissibility are critical. Recent studies have investigated the long-term assessment of transmission risk on cruise ships; however, short-term approaches are limited by data unavailability. To this end, this work proposes a novel short-term knowledge-based method for RA of disease transmission based on fuzzy rules. These rules are constructed using knowledge elicited from domain experts. In contrast to previous approaches, the proposed method considers data captured by several sensors and the ship information system, according to a recently proposed smart ship design. Evaluation with agent-based simulations confirms the effectiveness of the proposed method across various cases.

Analysis of Evolving Technologies and Impact of Smart Shipping on the Future Workforce in the Maritime Industry

Purpose: There is a shift in phase within the maritime industry toward smart shipping, where IoT and AI are used. The real question revolved around these technologies' impact on future employees in ships and shipping. The reason is to understand how this rapidly changing technological world affects these maritime professionals. Study design/methodology/approach: A systematic review was performed according to the system of Preferred Reporting Items for Systematic Reviews and Meta−Analyses (PRISMA). The literature review aimed at analysing a comprehensive spectrum from academic publications to industry reports and white papers in order… The Smart Shipping case studies were studied to provide practical knowledge on the integration of IoT and AI in maritime operations. Findings: The resulting analysis was a diverse and changing technological environment, including blockchain, AI, IoT, autonomous vessels, and innovations that are driven by sustainability. The use of blockchain applications allows for achieving increased transparency, while AI-based developments bring improvements in navigation, logistics and maintenance. The introduction of IoT allows industries to transform via the collection of data in real time. Autonomous vessels offer both challenges and opportunities designed to impact seafaring jobs. Sustainability innovations aim at clean energies and more ecological applications. Smart Shipping case studies demonstrated various positive outcomes on the deployment of IoT and AI applications that are realised in terms of operational efficiency gains up to issues with workforce adaptation. Originality/value: The paper sheds light on the maritime environment as well as its potential impact on determining the future workforce. The strategies to deal with the changes, such as job role change, workforce adaptation strategy and overcoming challenges, should be considered essential. Whereas merits arising from technical development enable the companies to benefit as far as efficiency is concerned, missing opportunities for careers in seafaring imply that active measures to build workforce capacities have become inevitable. The paper ends with recommendations for policy-makers, industrial representatives, and researchers who can help take successful steps in the face of changing fields associated with technology use encompassing maritime operations.

Ship Trajectory Planning and Optimization via Ensemble Hybrid A* and Multi-Target Point Artificial Potential Field Model

Ship path planning is the core problem of autonomous driving of smart ships and the basis for avoiding obstacles and other ships reasonably. To achieve this goal, this study improved the traditional A* algorithm to propose a new method for ship collision avoidance path planning by combining the multi-target point artificial potential field algorithm (MPAPF). The global planning path was smoothed and segmented into multi-target sequence points with the help of an improved A* algorithm and fewer turning nodes. The improved APF algorithm was used to plan the path of multi-target points locally, and the ship motion constraints were considered to generate a path that was more in line with the ship kinematics. In addition, this method also considered the collision avoidance situation when ships meet, carried out collision avoidance operations according to the International Regulations for Preventing Collisions at Sea (COLREGs), and introduced the collision risk index (CRI) to evaluate the collision risk and obtain a safe and reliable path. Through the simulation of a static environment and ship encounter, the experimental results show that the proposed method not only has good performance in a static environment but can also generate a safe path to avoid collision in more complex encounter scenarios.

Real-time digital twin of autonomous ships based on virtual-physical mapping model

The advancement of intelligent technology has propelled the development of smart unmanned vessels into a new phase. To address the urgent demands of current smart ship development, this paper develops a comprehensive ship digital twin system based on a virtual-real mapping algorithm, focusing on the fundamental elements of digital twin model construction. Using the smart unmanned experimental ship Dolphin 1 as a prototype, a digital twin virtual model is proposed. This system leverages real-time internal and external data from the entire vessel to track its navigational status, performance indicators, sailing trends, and surrounding flow field information, offering coordinated “human-machine” navigation assistance. Based on historical data collected from the vessel's long-term navigation, a real-time precise prediction of the vessel's navigational state and hydrodynamic performance is conducted using physics-informed neural network algorithm. This establishes a self-learning iterative virtual-physical mapping model that enables autonomous updates and evolution. As the real navigation data of the vessel continuously update, the virtual model can more accurately simulate the vessel's state in real time. The proposed digital twin model has been tested through sea trials under real sea conditions, demonstrating its high accuracy, robustness, and potential for enhancing navigational safety and efficiency. This system marks a significant step forward in the integration of digital twin technology with maritime navigation, providing a valuable tool for the future development of smart shipping.

Developed Status and Forecast of Intelligent Ship Application

The concept of smart ships is not limited to ship autonomous driving, it covers a broader scope, including the full life cycle of ship design, construction, operation and maintenance. By integrating advanced sensors, navigation systems and data analysis platforms, smart ships are able to monitor ship status and the Marine environment in real time, optimize route planning and improve fuel efficiency, thereby reducing energy consumption and greenhouse gas emissions. In addition, smart ships can revolutionize the shipping industry by improving maritime safety and reducing human error through remote control and autonomous decision-making. In recent years, smart ship technology has gradually moved from the theoretical research stage to practical application, and has attracted wide attention from the global shipping industry. A number of international cooperation and research projects have been launched to explore the practical application potential and promotion paths of smart ships. However, the development of smart ships also faces many challenges, including technical difficulties, legal and regulatory restrictions, and public acceptance issues. This paper will deeply discuss the current development status of smart ships, analyze the main challenges it faces, and forecast the future development trend, in order to provide reference and inspiration for the innovative development of the shipping industry.

A Comparative Analysis of Computational Intelligence Methods for Autonomous Navigation of Smart Ships

This paper presents the author’s approaches based on computational intelligence methods for application in the Autonomous Navigation System (ANS) of a smart ship. The considered task is collision avoidance, which is one of the vital functions of the ANS. The proposed methods, applying the Ant Colony Optimization and the Firefly Algorithm, were compared with other artificial intelligence approaches introduced in the recent literature, e.g., evolutionary algorithms and machine learning. The advantages and disadvantages of different algorithms are formulated. Results of simulation experiments carried out with the use of the developed algorithms are presented and discussed. Future trends and challenges of presented smart technologies are also stated.

Exploring the impact of industry 4.0 on marine mechanical engineering in the Philippines: Opportunities and challenges in the 21st century

Industry 4.0 is revolutionizing the way we do business and operate our industries today. The marine mechanical engineering industry in the Philippines is not exempt from this digital transformation. The advancement of technology has paved the way for the development of smart ships and the implementation of automated systems in the maritime industry. This review paper explores the impact of Industry 4.0 on marine mechanical engineering in the Philippines, the opportunities it presents, and the challenges it brings. The paper starts with a brief introduction to Industry 4.0 and its various components that are relevant to the marine mechanical engineering industry. It then delves into the current state of the industry in the Philippines, including its strengths and weaknesses. The review paper discusses the opportunities that Industry 4.0 presents in terms of improving efficiency, reducing costs, and increasing safety in the maritime industry. It also highlights the challenges that come with implementing new technologies, such as cybersecurity threats and the need for upskilling the workforce. The paper concludes by emphasizing the need for the marine mechanical engineering industry in the Philippines to embrace Industry 4.0 to stay competitive and relevant in the 21st century. The authors suggest that stakeholders must work together to develop a clear roadmap for the adoption of new technologies and address any barriers to implementation. The review paper provides valuable insights for policymakers, industry players, and researchers in the field of marine mechanical engineering in the Philippines. Keywords: Industry 4.0, Marine Mechanical Engineering, Philippines, Opportunities, Challenges

The Development of a Machine Learning-Based Carbon Emission Prediction Method for a Multi-Fuel-Propelled Smart Ship by Using Onboard Measurement Data

Zero-carbon shipping is the prime goal of the seaborne trade industry at this moment. The utilization of ammonia and liquid hydrogen propulsion in a carbon-free propulsion system is a promising option to achieve net-zero emission in the maritime supply chain. Meanwhile, optimal ship voyage planning is a candidate to reduce carbon emissions immediately without new buildings and retrofits of the alternative fuel-based propulsion system. Due to the voyage options, the precise prediction of fuel consumption and carbon emission via voyage operation profile optimization is a prerequisite for carbon emission reduction. This paper proposes a novel fuel consumption and carbon emission quantity prediction method which is based on the onboard measurement data of a smart ship. The prediction performance of the proposed method was investigated and compared to machine learning and LSTM-model-based fuel consumption and gas emission prediction methods. The results had an accuracy of 81.5% in diesel mode and 91.2% in gas mode. The SHAP (Shapley additive explanations) model, an XAI (Explainable Artificial Intelligence), and a CO2 consumption model were employed to identify the major factors used in the predictions. The accuracy of the fuel consumption calculated using flow meter data, as opposed to power load data, improved by approximately 21.0%. The operational and flow meter data collected by smart ships significantly contribute to predicting the fuel consumption and carbon emissions of vessels.

Research and Development of Inland Green and Smart Ship Technologies in China

Nowadays, the development of green and smart ships has become a trend in the global shipping industry. Some countries such as Japan and Korea, as well as several European countries, have already made some progress and advantages. In recent years, China has issued a series of policies to encourage and support the development of green and smart ships. Beyond that, the demonstration of green and smart ships has been promoted along with the trend of larger-sized and standardized inland vessels. The Chinese status and plans of green technologies are summarized including air layer drag reduction, efficient propulsion, DC networking, and clean energy. Some smart technologies have also been illustrated, for instance, intelligent driving, intelligent engine rooms, energy efficiency, hulls, cargo control, and information integration. Smart ship technologies need to be fully validated in order to improve industrialization applications. In the shipping industry, the development and application of smart ship technology need international and inter-regional cooperation, so as to achieve much higher energy savings, ensure environmental protection, achieve collaborative intelligence, and ensure safety and reliability. In turn, this will aid economic advances worldwide.

Factor Analysis of Machine Selection in Aquatec Smart Ship Design for Fish Farming Using The SPSS Method

Pada pembuatan sebuah bangunan apung seperti kapal memerluka mesin untuk dapat bergerak. Penelitian ini memiliki tujuan untuk menganalisis faktor yang dapat mempengaruhi pemilihan mesin yang akan berdapak pada performa kapal. Metode yang dilakukan melalui pengumpulan data yang disebarkan kepada 30 responden. Analisis yang dilakukan mencakup validitas dan reabilitas. Hasil dari analisis yang dilakukan berupa nilai alpha lebih dari 0,6 dan nilai cronch’s bach 0,705 sehingga faktor yang digunakan realiabel. Dimana faktor yang memiliki nilai tertinggi berupa perhitungan tahanan yaitu 0,609. Selanjutnya dilakukan perhitungan nilai tahanan untuk menentukan daya mesin. Nilai selisih dari tahanan melalui bantuan aplikasi bantuan aplikasi maxsuf dan perhitungan manual adalah 0,91. Sehingga hasil dari perhitungan kebutuhan daya desain aquatec SMART ship yaitu 15 Hp.

The rising tide of the autonomous ships market

The rising tide of the autonomous ships market Anastasiya Azarko, Junior Innovation Consultant and Marco Molica Colella, Managing Consultant at CiaoTech – PNO Group, tell us about the autonomous ships market and some of the initiatives that are broadening capabilities within the maritime industry. In 2020, analysts valued the autonomous ship market close to 90 billion with a steady growth (ca. 7% annually) for the next decade. Almost 18% of this market was accounted for in Europe, with the chance to double by 2030. This upward trajectory is primarily attributed to the ongoing emergence of autonomous ships, which include a whole set of definitions, like smart ships. These ships are equipped with advanced software and hardware systems that allow operating, or being operated, without human direct intervention on board, adopting different levels of autonomy in different phases of the journey.

Empowering 6G maritime communications with distributed intelligence and over-the-air model sharing

Introduction: Shipping and maritime transportation have gradually gained a key role in worldwide economical strategies and modern business models. The realization of Smart Shipping (SMS) powered by advanced 6G communication networks, as well as innovative Machine Learning (ML) solutions, has recently become the focal point in the maritime sector. However, conventional centralized learning schemes are unsuitable in the maritime domain, due to considerable data communication overhead, stringent energy constraints, increased transmission failures in the harsh propagation environment, as well as data privacy concerns.Methods: To overcome these challenges, we propose the joint adoption of Federated Learning (FL) principles and the utilization of the Over-the-Air computation (AirComp) wireless transmission framework. Thus, this paper initially describes the mathematical considerations of a 6G maritime communication system, focusing on the heterogeneity of the relevant nodes and the channel models, including an Unmanned Aerial Vehicle (UAV)-aided relaying model that is usually required in maritime communications. The communication network, enhanced with the AirComp technique for efficiency purposes, forms the technical basis for the collaborative learning across multiple Internet of Maritime Things (IoMT) nodes in FL tasks. The workflow of the FL/AirComp scheme is illustrated and proposed as a communication-efficient and privacy-aware SMS framework, considering spectrum and energy efficiency aspects under a sum transmitting power constraint.Results: Then, the performance of the proposed methodology is assessed in an important ML task, related to intelligent maritime transportation systems, namely, the prediction of the Cargo Ship Propulsion Power using real data originating from six cargo ships and utilizing long-short-term-memory (LSTM) neural networks. Upon extensive experimentation, FL showed higher prediction accuracy relative to the typical Ensemble Learning technique by a factor of 3.04. The AirComp system performance was evaluated under varying noise conditions and number of IoMT nodes, using simulation data for the channel state information by regulating the power of the transmitting IoMT entities and the scaling factor at the shore base station.Discussion: The results clearly indicate the efficiency of the proposed FL/AirComp scheme in achieving low computation error, collaborative learning, spectrum efficiency and privacy protection in wireless maritime communications, while providing adequate accuracy levels with respect to the optimization objective.

Zero-Trust Security Model Applied to Smart Shipping: Towards a Feasible Architecture

Securing information systems and assets within smart shipping environments is of utmost importance. In practice, however, securing smart shipping is a difficult and tedious task because smart shipping environments are highly dynamic, distributed, and loosely coupled, which cause having large threat/attack vectors without having all security measures under own control. The Zero Trust Security Model (ZTSM) has been suggested by security experts and many national cybersecurity centers as a promising approach for addressing the shortcomings of the traditional perimeter-based security architecture. However, a scalable ZTSM architecture which is applicable to large networks, like those of smart shipping, is missing. In this contribution we aim at investigating how the ZTSM can be made suitable for securing smart shipping. We study smart shipping security requirements and describe three off-the-shelf security services that can contribute to the realization of the ZTSM in such environments. Investigating these example security services inspired us to propose a ZTSM architecture for smart shipping environments, which relies on metadata exchange for trust establishment at various levels among federations of organizations, human or business controlled context and content transfer, and monitoring and controlling data and service usage. The proposed architecture can embody the ZTSM deployment within large networks of cooperating organizations (like those within smart environments). Although this architecture is proposed for and based on the security requirements of smart shipping, we conjecture that it can be applicable to other forms of smart environments as well.

FedShip: Federated Over-the-Air Learning for Communication-Efficient and Privacy-Aware Smart Shipping in 6G Communications

FedShip: Federated Over-the-Air Learning for Communication-Efficient and Privacy-Aware Smart Shipping in 6G Communications