Smart Ship Research Articles

Introduction to special issue on maritime safety and smart shipping

Introduction to special issue on maritime safety and smart shipping

"Dronization" of the sea - ships of the future

Maritime areas cover 70% of the surface of the Planet, see the transit of 80% of world trade and accommodate submarine cables through which 99% of telecommunications pass. This carpet of the bottom of the oceans contributes to a wave of digitization of land, air and sea spaces, thus challenging human beings. The development of aerial drones and the autono-mous car has largely inspired the industry of the maritime world. The smart shipping rev-olution is underway. Nothing seems able to prevent it as the expected benefits are numer-ous. Reducing the number of sailors on board ships will reduce the number of premises devoted to human presence, thus freeing up space for that of the goods transported, and will give naval architects freedom to design more hydrodynamic shapes adapted to the as-signments. On this aspect, we are witnessing a transition from automation – which has already enabled significant crew reductions – to autonomy, which will amplify the phenom-enon.

D[formula omitted]-Net: Integrated multi-task convolutional neural network for water surface deblurring, dehazing and object detection

Visual perception is one of the key technologies for smart ships to achieve intelligent navigation in various complex water scenes, which adopts the object detection algorithm based on visible image to detect obstacles. Affected by the rolling and high-speed motion of ships and bad weather, motion blur and haze appear in the images of vision sensor, which seriously interfere with the obstacle detection. To ensure the navigation safety of smart ships, we propose the integrated multi-task deblurring, dehazing and object detection convolutional neural network (D3-Net). Firstly, the proposed method is able to complete deblurring, dehazing and object detection within a single network, which is more suitable to be applied in intelligent navigation. Secondly, different from traditional methods that reconstruct enhanced images first and then perform object detection, D3-Net combines image reconstruction and detection feature extraction to realize the integration of image enhancement and detection procedure. Finally, to improve the deblurring and dehazing performance of the network, we propose classification attention-based detection feature loss (CADF-Loss), which uses the image classification features to guide the network perform the detection feature enhancement more specifically. The experimental results in real navigation scenes show that D3-Net not only improves the detection accuracy of blurred and hazy images by 42%, but also has excellent detection efficiency, which is helpful to improve the navigation safety performance of smart ships.

Next-Gen Intelligent Situational Awareness Systems for Maritime Surveillance and Autonomous Navigation [Point of View

Today, the maritime domain is at the cusp of a new era, driven by technological advances in automation, robotics, multisensor perception, and artificial intelligence (AI), together with digitalization and connectivity. Smart ship infrastructure and technology, remotely controlled and autonomous ship operation to improve safety, security, cost efficiency, and sustainability are the future of maritime transportation <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , representing now the engine of 90% of global trade <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> . Ships will soon benefit from recent developments in sensors, telecommunications, and computing technologies to turn the smart shipping revolution into reality <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> and <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> , as it has already happened for autonomous vehicles such as driverless cars, aerial drones, unmanned (or remotely piloted) aircraft, and underwater vehicles.

Advances in Marine Self-Powered Vibration Sensor Based on Triboelectric Nanogenerator

With the rapid development of advanced electronics/materials and manufacturing, marine vibration sensors have made great progress in the field of ship and ocean engineering, which could cater to the development trend of marine Internet of Things (IoT) and smart shipping. However, the use of conventional power supply models requires periodic recharging or replacement of batteries due to limited battery life, which greatly causes too much inconvenience and maintenance consumption, and may also pose a potential risk to the marine environment. By using the coupling effect of contact electrification and electrostatic induction, triboelectric nanogenerators (TENGs) were demonstrated to efficiently convert mechanical vibration movements into electrical signals for sensing the vibration amplitude, direction, frequency, velocity, and acceleration. In this article, according to the two working modes of harmonic vibration and non-harmonic vibration, the latest representative achievements of TENG-based vibration sensors for sensing mechanical vibration signals are comprehensively reviewed. This review not only covers the fundamental working mechanism, rational structural design, and analysis of practical application scenarios, but also investigates the characteristics of harmonic vibration and non-harmonic vibration. Finally, perspectives and challenges regarding TENG-based marine self-powered vibration sensors at present are discussed.

Hierarchical level fault detection and diagnosis of ship engine systems

As smart ships are developed, research into engine management through remote monitoring and support from data collected at onshore control centers is being conducted. However, ship engine data collected from various sensors have high dimensions, large measurement errors, few labeled data, and insufficient amounts and quality of data, making it difficult to determine the condition of engines with complex failure modes under various operating environments.This study proposes a hierarchical level fault detection and diagnosis (HL-FDD) method that combines domain knowledge of ship engines and advanced data analysis techniques. The developed method extracts key features of reduced dimensions from the original variables (sensors) through an optimal hierarchical clustering and dimension-reduction model, allowing a hierarchy divided into the top (the entire system combining all features), middle (subsystems and feature sets), and bottom (components and sensors) levels. The reduced key features are used to generate robust regression models and dynamic thresholds (prediction intervals) according to the engine load, and dynamic thresholds determine whether the engine’s condition is normal or abnormal. The dynamic thresholds are able to automatically label abnormal conditions of the engine. Once anomalies are detected at the top level, the proposed method can sequentially search for data on features belonging to the middle and bottom hierarchies for detailed fault diagnosis to determine which engine subsystem or component(s) caused the engine fault(s). Actual data collected by ship operators verify the proposed method’s efficiency, reliability, and accuracy.

Pixel-Wise Ship Identification From Maritime Images via a Semantic Segmentation Model

Accurately identifying ships from maritime surveillance videos attracts increasing attention in the smart ship community, considering that the videos provide informative yet easily understandable spatial–temporal traffic information for varied maritime traffic participants. Previous studies (e.g., ship detection and ship tracking) are conducted by learning distinct features from training samples labeled in terms of bounding boxes, and thus, background pixels may be wrongly trained as ship features. To bridge the gap, we propose a novel approach for achieving a pixel-wise ship segmentation and identification task through a novel design of U-Net deep learning architecture (denoted as EU-Net). The encoder of the EU-Net extracts distinct ship features from input maritime images, and its decoder outputs ship segmentation results in the pixel-wise manner. The proposed EU-Net model consists of encoder and decoder parts via the help of a convolution layer, a depth separable convolution layer, a softmax layer, and so on. More specifically, the EU-Net model identifies each pixel into ship or non-ship as the final output. Experimental results suggest that our proposed model can accurately identify ship (in terms of pixels), considering that the ship segmentation accuracies were larger than 99%. The proposed ship segmentation framework can be further adaptively deployed in the ship sensing system for maritime traffic situation awareness and intelligent visual navigation in a smart ship era.

A bibliometric review on maritime inspection analysis: Current and future insights

This study conducts a comprehensive bibliometric review of 123 papers, authored by 359 scholars, on maritime inspection analysis published in 77 scientific journals. The bibliographical data are analyzed in R software using the Bibliometrix tool. The most influential publications, journals, authors, and institutions are identified using citation analysis metrics. Three underlying research clusters are identified using the bibliographic coupling methodology: (i) analytic approach-based, (ii) research methodology-based, and (iii) research field-based clusters. Indeed, research collaboration networks are demonstrated at the institution, author, and country levels to address current tendencies in maritime inspection analysis. Besides the compliance solution studies, the future insights of inspection regimes along with green and smart shipping are extensively discussed. The study has great potential to encourage the forthcoming initiatives and research on inspection analytics and digitalization in maritime, offshore, logistics, and relevant fields.

Towards system-theoretic risk assessment for future ships: A framework for selecting Risk Control Options

While the concept of smart shipping is expected to shape the future of the maritime industry, its safety is still a major concern. New risks might emerge when shifting from human controllers onboard, to autonomous software controllers and remote human controllers. The uncertainties associated with the emerging risks require an efficient decision-making methodology to ensure ship safety. This paper proposes a framework for selecting Risk Control Options (RCOs) of ships with higher degrees of autonomy in the context of marine risk assessment and Formal Safety Assessment (FSA). The framework uses the System Theoretic Process Analysis (STPA) for the hazard analysis and the identification of RCOs, while Bayesian Network is employed in the framework for estimating the system risk. Integrating STPA and BN offers the possibility to cover most of the steps of both risk assessment and FSA and permits the prioritization of the identified RCOs. The proposed method is applied to a concept of an autonomous seawater cooling system (SWC) as an illustrative case study. The results indicate that the RCOs including sensors health monitoring and software testing should be prioritized to reduce the risk. This is unveiled by the STPA analysis which shows the risk contribution of the associated causal scenarios.

A Novel BeiDou Satellite Transmission Framework With Missing Package Imputation Applied to Smart Ships

Obtaining voyage data through a vessel monitoring system (VMS) and transmitting it to a remote monitoring center through satellites is an important link to realize the information perception of smart ships. However, improving data quality to ensure the integrity of information is still an open question. Taking into account the complex and harsh marine environment, a BeiDou satellite transmission link will inevitably suffer the challenge of packet loss. Therefore, this paper proposes a novel BeiDou satellite transmission framework with missing package imputation to improve the information perception ability of smart ships. In particular, we propose a two-stage missing packet imputation strategy (T-MPI). First, a bidirectional recurrent neural network (Bi-RNN) based on temporal view imputation is designed to learn the time correlation of the data. Second, considering the attributive view imputation of multivariate data, an autoencoder with a linear aggregation module (LA-AE) is proposed to capture sudden changes in the ship’s complex sailing state. An experimental platform was established to verify the developed framework, and real freighter transportation data were used to prove the effectiveness of our proposed imputation strategy under complex sailing conditions.

Prediction of tidal-driven currents using convolutional neural network

Efficient forecasting models of coastal currents are required for the predictive control of smart vessels with autonomous or remote operating capabilities. Accurate physics-based numerical models demand computational resources that often are unavailable onboard ships; moreover, internet bandwidth at remote locations might not be sufficient to exchange the large computational files with onshore servers. In this case, light and efficient Data-Driven Models (DDM) may be developed to be used as a surrogate for a physics-based model.Currents and surface elevation are hydrodynamic variables driven by astronomic tide potential, allowing for harmonic analysis and synthesis of otherwise irregular solutions of Navier-Stokes equations. Once numerical solutions are obtained and analysed in the Fourier domain, the identified harmonics (tide constituents) can be used as a dataset to train the DDM. One approach to building a DDM is by utilizing a Convolutional Neural Network (CNN).CNN is mainly applied in image recognition processes, where it learns the spatial features of image matrices of pixels arranged in columns and rows and uses the learned features to predict an output. This approach can be applied to 2D or 3D fields of predominantly tidal-driven coastal currents. This paper extends the CNN framework to approximate and predict coastal hydrodynamic state variables in space and time. The depth-averaged Navier-Stokes equations were used to create the dataset necessary to train the adopted CNN autoencoder architecture.Two methods of CNN model development are explored. The first method assesses CNN’s capabilities for time forward prediction, using 2D time series of hydrodynamic variables. The second method investigates CNN’s parameter (using Manning number) prediction ability using 2D spatial plots formed by the top 20 most energetic tidal constituents that were obtained from the numerical solution. The hyperparameters of the CNN model for both methods were carefully optimized and tuned using the loss function, Root Mean Square Error (RMSE). The coefficient of determination, the relative error of the time series, and its amplitude and phase were used to gauge the performance of the CNN model for both methods at the final testing stage.Once accurate solutions are achieved, the trained DDM is almost instantaneous and uses negligible computational resources as compared to traditional fluid solvers. Therefore, trained DDM can be deployed onboard ships where global forecast information (e.g., from Global Forecast System) can be used to provide an accurate prediction of the current forcing. To generate the instantaneous current predictions for navigation and smart ship control, the global data can be pulled or pushed to the ship before the mission.

A Study on the Development Priority of Smart Shipping Items—Focusing on the Expert Survey

As smart technology in the shipping industry accelerates, demand for technology development, which aims to increase competitiveness by utilizing the latest smart technologies, is increasing. The shipping industry in each country is pushing for developing its smart technologies at a rapid pace, and it is urgent to prioritize the demand for smart technologies in the shipping industry for sustainable industry growth. The purpose of the present study is to verify the importance and urgency of smart technologies in the shipping industry, and the present study surveys 74 domestic and foreign shipping industry officials, covering shipping companies, brokers, governments, etc. The importance and urgency of each smart technology item in the shipping industry are questioned in a survey. It is necessary to focus investment on specific smart shipping items under a limited budget, and, since the priorities presented in this study were derived through comprehensive collection of opinions from 74 shipping experts, the priorities presented in this study are expected to be effective reference values when implementing sustainable shipping investment policies. According to an interview analysis on importance and urgency, both “autonomous ships” and “blockchain” items show high importance and urgency. Since both importance and urgency are high, strategic priorities are the highest, and it is expected to contribute effectively to enhancing the smartness of the shipping industry when it focuses its investment capabilities in those fields.

A Path Planning Method for Ship Collision Avoidance Considering Spatial–Temporal Interaction Effects

Efficient and reliable path planning is crucial for smart ships when avoiding collisions with static and dynamic obstacles in complex marine environments. This research proposes a novel path planning method based on the fast marching method to specifically assist with safe navigation for autonomous ships. At the very beginning, a unified representation is specially produced to describe the path planning space based on the parametric fast marching speed function. In addition, the spatial–temporal interaction effects of dynamic obstacles are considered and integrated into the construction of planning space. Subsequently, a path optimization strategy is put forward based on the trajectory prediction of dynamic objects. Particularly, the effectiveness of the method has been validated and evaluated through a number of simulations, which proves that such a method is practical in narrow and crowded waterways.

IoT Enabled Shipping Containers with Location Tracking and Environment Monitoring

Abstract: The Internet of Things (IoT) is a network of physical devices and objects that connect to provide services to users. Up to 50 billion IoT devices are expected to be deployed by 2020, according to estimates. new products and services For example, integrating IoT into traditional transportation systems provides increased visibility and traceability, allowing for remote monitoring of transported products. Containers carrying donated organs should be tightly sealed, kept below a particular temperature, and positioned in a physically safe location in traditional shipping and freight systems to reduce the risk of harm from jerking and unintentional dropping. This paper describes a smart shipping container system that incorporates the Internet of Things (IoT), cloud computing, the Message Queuing Telemetry Transport (MQTT) protocol, and Docker images for shipping container surveillance that is both effective and remote. Keywords: IoT, Shipping Containers, Arduino, DHT11 Sensor, LDR Sensor, MQ2 sensor, LCD, Cloud, GPS

Design of the Comprehensive Monitoring System of Ro-Ro Ship Power Plant Based on LabVIEW

In this paper, using power machinery monitoring technology, testing technology, signal analysis and processing technology, combined with the characteristics of the dual-engine and double-propeller arrangement, carrying, complex waters and variable operating conditions of the power unit of the ro-ro ship, a set of virtual instruments is designed for the power of the ro-ro ship. Device integrated monitoring system. The system has a complete set of comprehensive monitoring system, which can monitor the power output working status of the power unit of the ro-ro ship. Under different working conditions, the system can collect and analyse signals through sensors, and automatically obtain information such as combustion work in the cylinder, gearbox gear transmission and meshing, shaft trajectory dynamic vibration and shaft power, and is a monitoring platform for marine power plants. The development and intelligent cabin provide technical support. The system realizes the automatic monitoring of ships, effectively improves the safety and reliability of ship operations, and reduces and avoids the occurrence of major accidents. At the same time, it has also completed the integration and digitization of ship power plant monitoring, providing support for decision-making in the use, operation and control, maintenance, and management of equipment and systems, effectively improving the safety and reliability of ship operations, and further promoting the development of smart ships and smart engine rooms.

Ship feature recognition methods for deep learning in complex marine environments

With the advancement of edge computing, the computing power that was originally located in the center is deployed closer to the terminal, which directly accelerates the iteration speed of the "sensing-communication-decision-feedback" chain in the complex marine environments, including ship avoidance. The increase in sensor equipment, such as cameras, have also accelerated the speed of ship identification technology based on feature detection in the maritime field. Based on the SSD framework, this article proposes a deep learning model called DP-SSD. By adjusting the size of the detection frame, different feature parameters can be detected. Through actual data learning and testing, it is compatible with Faster RCNN, SSD and other classic algorithms. It was found that the proposed method provided high-quality results in terms of the calculation time, the processed frame rate, and the recognition accuracy. As an important part of future smart ships, this method has theoretical value and an influence on engineering.

Measuring the impact of an exogenous factor: An exponential smoothing model of the response of shipping to COVID-19

Following the outbreak of the COVID-19 pandemic, various lockdown strategies restrained global economic growth bringing a significant decline in maritime transportation. However, the previous studies have not adequately recognized the specific impacts of COVID-19 on maritime transportation. In this study, a series of analyses of the Baltic Dry Index (BDI), the China Coastal Bulk Freight Index (CCBFI) and of container throughputs with and without the impact of COVID-19 were carried out to assess changing trends in dry bulk and container transportation. The results show that global dry bulk transportation was largely affected by lockdown policies in the second month during COVID-19, and BDI presented a year-on-year decrease of approximately 35.5% from 2019 to 2020. The CCBFI showed an upward trend in the second month during COVID-19, one month ahead of the BDI. The container throughputs at Shanghai Port, the Ports of Hong Kong, the Ports of Singapore and the Ports of Los Angeles from 2019 to 2020 presented the largest year-on-year drops of approximately 19.6%, 7.1%, 10.6% and 30.9%, respectively. In addition, the authors developed exponential smoothing models of BDI, CCBFI, and container transportation, and calculated the percentage prediction error between the observed and predicted values to examine the impact of exogenous effects on the shipping industry due to the outbreak of COVID-19. The results are consistent with the conclusions obtained from the comparison of BDI, CCBFI, and container transportation during the same period in 2020 and 2019. Finally, on the basis of the findings, smart shipping and special support policies are proposed to reduce the negative impacts of COVID-19.

A Game Theory-Based Fusion Algorithm for Autonomous Navigation of Smart Ships

A Game Theory-Based Fusion Algorithm for Autonomous Navigation of Smart Ships

해양무인이동체산업 활성화를 위한 교육훈련체계 수립에 대한 연구

Marine unmanned vehicles, which are the culmination of the 4th industrial revolution technology, including marine drones, maritime autonomous surface ships(MASS), unmanned ships, and smart ships, are being applied and utilized in various industrial fields on land and at sea as an essential factor for innovative growth. In this regard, major developed countries are specializing in the marine sector based on the general unmanned vehicle industry, selecting it as a new growth driving industry in the future, and promoting policies for technology development, market preoccupation, and cultivation of professionals. Marine unmanned vehicles such as MASS, USVs, UUVs, wave gliders, sea gliders, and ROVs are required to have a future-oriented education and training system for professionals, which is the core of industrial development. Therefore, this study aims to establish and recommend the vision and strategy of education and training by comparing the macroenvironmental analysis(PEST) and the environment of internal education users/stakeholders after defining concepts related to various marine unmanned vehicles and analyzing technology trends of related companies.

Design and research of port automatic magnetic mooring device based on electromagnetic technology

With the rapid development of modern science and technology, ship design and construction are becoming more and more large-scale, professional and intelligent, and the degree of integration, intelligence and modularization of port and terminal equipment is also getting higher and higher. Smart ships and wisdom The construction of ports has become an important trend in the development of the shipping industry. Although the rope materials of modern ships have been greatly improved, the main mooring method of ships still relies on the traditional operation method of manual mooring [1], which has low operation efficiency, high degree of danger and high risk factor. This article mainly studies the use of electromagnetic technology to realize the automatic berthing and unberthing of ships. The electromagnetic mooring device is controlled by the remote control system to realize the automatic berthing and unberthing operations of the ship, and the height and angle are automatically adjusted according to the tide and loading conditions to realize berthing monitoring.