Intelligent Ship Research Articles

Real-Time Risk Detection Method and Protection Strategy for Intelligent Ship Network Security Based on Cloud Computing

When studying an unfamiliar system, we first look for the symmetry that the system has, so that we can make many predictions about the possible properties of the system. The symmetry in ship network security needs to maintain a stable state and maintain a constant state of ship network security. With the rapid development of network information technology, smart ships have become a new hot spot in the international shipping industry. The smart ships cybersecurity discussion is also at the top of the list in the maritime field. More and more shipping companies feel that their smart ship systems need to be upgraded and the main reason behind this is that the systems are maliciously attacked by cyber hackers. Therefore, it is extremely important to detect and protect the security of intelligent ship network systems in real time. The issue of network security has always accompanied the whole process of the development of the Internet. At the same time, with the development of Internet technology, network hacking attacks against the Internet have never stopped developing, and traditional ship network security risk detection and protection cannot achieve good results. After understanding the operation mode of intelligent ship networks, this paper deeply studied the characteristics of cloud computing technology and proposed a real-time risk detection method and protection strategy for intelligent ship network security based on cloud computing. This paper mainly used multi-sensor nodes to analyze data containing malicious attack information and implemented self-execution protection strategy generation nodes to intercept and protect from the attack, so as to achieve the purpose of maintaining the network security of intelligent ships. Through experiments, the virus intrusion detection and defense rate of the algorithm proposed in this paper was able to reach 85% to 95%, while the virus intrusion detection defense rate of the traditional intelligent ship network security protection algorithm was 55% to 65%. The detection rate of the algorithm proposed in this paper was able to reach 96.95% and the false positive rate was 2.56%. The detection rate of the traditional algorithm was only 70.76%, while the false positive rate reached 4.69%. All of the proposed algorithm’s data were significantly better than that of traditional algorithms, which proved that the performance of cloud computing-based real-time risk detection and protection algorithms for intelligent ship network security was significantly better than that of traditional algorithms.

Ship Autonomous Collision-Avoidance Strategies—A Comprehensive Review

Autonomous decision-making for ships to avoid collision is core to the autonomous navigation of intelligent ships. In recent years, related research has shown explosive growth. However, owing to the complex constraints of navigation environments, the Convention of the International Regulations for Preventing Collisions at Sea, 1972 (COLREGs), and the underactuated characteristics of ships, it is extremely challenging to design a decision-making algorithm for autonomous collision avoidance (CA) that is practically useful. Based on the investigation of many studies, current decision-making algorithms can be attributed to three strategies: alteration of course alone, alteration of speed alone, and alteration of both course and speed. This study discusses the implementation methods of each strategy in detail and compares the specific ways, applicable scenes, and limiting conditions of these methods to achieve alteration of course and/or speed to avoid collision, especially their advantages and disadvantages. Additionally, this study quantitatively analyzes the coupling mechanisms of alterations of course and speed for autonomous CA decision-making under different encounter situations, supplementing and optimizing the decision-making theory for ship autonomous CA. Finally, several feasible algorithms and improvement schemes for autonomous CA decision-making, combined with course and speed alterations, are discussed.

Regulation aware dynamic path planning for intelligent ships with uncertain velocity obstacles

Risk assessment and collision avoidance are extremely important for the safe navigation of surface ships. In this paper, we propose a regulation aware dynamic path planning method based on uncertain velocity obstacles for intelligent ships. Specifically, we first use the fuzzy logic theory to evaluate the real-time collision risk of the own ship. Based on the closest point of approach criterion, considering the influences of the relative bearing, the distance, and ship speed ratio, the collision risk is used to activate the path planning process. Moreover, we consider the various ship encountering scenarios including crossing, overtaking and head-on. To deal with the uncertainties in the target ship information, the relative cone angle of the traditional velocity obstacles are systematically expanded using a robust tube idea. The reachable velocity set of the own ship is accordingly divided into three subsets that meet the rules specified by COLREGs in crossing, overtaking and head-on scenarios. Real automatic identification system (AIS) data in an area of the Yangtze River in Wuhan, China, is used to demonstrate the effectiveness of the proposed approach.

Intelligent ship inspection analytics: Ship deficiency data mining for port state control

To provide safe, secure, and environment-friendly maritime transport, it is an essential point to define and take appropriate countermeasures against the substandard ships in maritime transport. Port State Control (PSC) is one of the critical inspection regimes recognized worldwide to define substandard ships. However, one of the biggest challenge faced in ship inspection is the necessity of checking many items through limited time and qualified human resources. Therefore, in this study, it is aimed to develop a decision support system to increase the effectiveness of ship inspections. In accordance with this, we proposed an intelligent ship inspection analytics (I-SIA) model based on the Knowledge Discovery in Database (KDD) process by utilizing fuzzy c-means clustering and apriori algorithms. The I-SIA model provides the determination of ship deficiency patterns based on specific ship attributes, and subsequently predicts main/sub inspection items to be focused on through former inspection records of the ship to be inspected. In the case study performed for a selected ship, I-SIA predicts critical 5 main and 23 sub-inspection items that need to be checked with high priority, among 17 main inspection items and out of more than 500 sub-inspection items. Herewith, I-SIA provides a ship-specific and dynamic ship inspection based on deficiencies recorded in previous inspections and detected during inspection. Thus, it becomes possible to enhance the effectiveness of the inspection via defined deficiency patterns.

A Novel Intelligent Ship Detection Method Based on Attention Mechanism Feature Enhancement

The intelligent perception ability of the close-range navigation environment is the basis of autonomous decision-making and control of unmanned ships. In order to realize real-time perception of the close-range environment of unmanned ships, an enhanced attention mechanism YOLOv4 (EA-YOLOv4) algorithm is proposed. First of all, on the basis of YOLOv4, the convolutional block attention module (CBAM) is used to search for features in channel and space dimensions, respectively, to improve the model’s feature perception of ship targets. Then, the improved-efficient intersection over union (EIoU) loss function is used to replace the complete intersection over union (CIoU) loss function of the YOLOv4 algorithm to improve the algorithm’s perception of ships of different sizes. Finally, in the post-processing of algorithm prediction, soft non-maximum suppression (Soft-NMS) is used to replace the non-maximum suppression (NMS) of YOLOv4 to reduce the missed detection of overlapping ships without affecting the efficiency. The proposed method is verified on the large data set SeaShips, and the average accuracy rate of mAP0.5–0.95 reaches 72.5%, which is 10.7% higher than the original network YOLOv4, and the FPS is 38 frames/s, which effectively improves the ship detection accuracy while ensuring real-time performance.

Traffic-conflict and fuzzy-logic-based collision risk assessment for constrained crossing scenarios of a ship

The crossing of a ship is a common type of traffic event, that becomes especially critical in constrained crossing scenarios, leading to numerous ship collision accidents. To derive a solution to mitigate ship collision risks, this study derives the basic mode of constrained crossing scenarios of a ship, then traces the collision risks from the global evolution of the crossing event with the help of the traffic conflict technique, proposes a fuzzy logic model to pre-estimate the adaptabilities of opportunity and interspace of the crossing gap and assesses ship collision risks at the conflict point in the instances of obtaining and missing the proper crossing opportunity. The results indicate that the risks resulting from the proposed model are in line with those perceived by operators of the crossing ship in typical crossing scenarios and operating conditions. The proposed model can be applied in various crossing scenarios and operating conditions after enriching the fuzzification criteria and fuzzy rules, that can provide effective support for the intelligent ship navigation. Selecting a suitable crossing gap with a favourable opportunity and adequate interspace, and obtaining substantial assistance of the crossed ships are feasible approaches to mitigating ship handling risks.

A Function-Oriented Electronic and Electrical Architecture of Remote Control Ship on Inland River: Design, Verification, and Evaluation

The remote control is an important function of intelligent ships. In this article, first, the functions and communication requirements of remote control ships are sorted out based on the “Rules for Intelligent Ships (2020)” issued by the China Classification Society (CCS). Second, a domain controller-based electronic and electrical architecture (EEA) for inland remote control ships is proposed. The functional domain of the ship is divided, combined with the physical layout of the ship, into the perception domain, communication domain, motion control domain, marine bridge domain, deck domain, cargo management domain, and cabin domain. Third, the real-time network communication system and the central gateway controller are designed, and the efficient information interaction between domain controllers is realized through a service-oriented communication architecture. The simulation experiment shows that the designed domain controller architecture and communication network can meet the information interaction requirements of the ship. Compared with the traditional distributed architecture, the ship EEA based on the domain controller has apparent advantages in system cost, system redundancy, functional load distribution, architecture scalability, communication load distribution, and information security and can give a design reference for inland remote control ships.

Dynamic Data-Driven Ship Motion Simulation toward Visual-aided Navigation on Water

Visual aids for navigation systems are a key function of intelligent onboard navigation units for ships. These systems visualize the planning decisions of an intelligent navigation unit for computer-aided visual navigation to reduce the cognitive workload of the driving control personnel of an intelligent ship. In this study, we propose a dynamic data driven method to simulate the motion of ships on the water. We constructed a validation dataset for ship motion simulation toward visual-aided navigation considering the combined effects of wind, waves and currents. According to the validation results on this dataset, the proposed simulation method exhibited good accuracy and real-time performance. Under the given control and environmental input series, the mean errors of simulated heading and simulated trajectory were 5.7°/h and 282.6 m/h, respectively; the simulation time was 475 ms for each hour of physical duration. Overall, the proposed simulation method satisfies the need for visual navigation aids and could improve the situational awareness of the driving control personnel of the intelligent ship.

An INS-aided MASS autonomous navigation algorithm considering virtual motion constraints and the leeway and drift angle

Autonomous navigation is one of the critical enabling technologies in developing MASS or intelligent ships. This study proposes an autonomous navigation algorithm with INS-aided shipborne GNSS. The proposed algorithm constructs the ship's virtual motion attitude by considering the leeway and drift angle, and takes into account the one-dimensional speed constraints in the virtual ship body reference frame. This approach addresses the issue that the conventional INS and shipborne GNSS integrated navigation scheme can utilize the three-dimensional position information only, without considering the measurement update from the one-dimensional speed obtained by shipborne GNSS. The algorithm is verified by actual ship departure and cruising experiments in natural marine environments. The experimental results show that the proposed algorithm has a higher accuracy than the conventional algorithm when the GNSS speed and position information are available. In the scenario when the GNSS position information is temporarily lost, the proposed algorithm can continue to derive relatively reliable navigation parameters for the safe navigation of ships with only one-dimensional speed measurement.

Design and implementation of a testing platform for ship control: A case study on the optimal switching controller for ship motion

Fully autonomous navigation is the highest level of ship autonomy, which has high requirements for the adaptability of the ship control system. Taking navigation with large turning errors as an example, this paper combines the intelligent optimization controller with the traditional controller and adopts the controller switching strategy to guide the ship’s path following. In order to illustrate the feasibility of optimizing the switching mode of the motion controller to reduce the large turning error during ship navigation, this paper designs a test platform for the intelligent navigation of the model ship, and tests the effect of the optimal switching strategy of the controller. Firstly, the structure of the intelligent ship system is analyzed, and the framework of the intelligent navigation system is emphasized. Secondly, based on the analysis of the characteristics of controllers, the optimal switching strategy of the motion controller is proposed. Then, the construction process of the testing platform is described. Finally, the optimal switching strategy of the motion controller is verified on this test platform for the case of a large turning error of the ship. The results show that the implementation of the switching strategy can effectively reduce the control error compared with the mode without using the strategy. The experiment extends the ship motion control mode strategically and improves the adaptability of ship motion control. Based on the testing platform, this experiment can simulate the process of the control system autonomously selecting an appropriate controller according to the ship’s motion state and navigation scenarios, which is helpful to enhance the adaptability of the ship in the face of special navigation scenarios.

Interactive ship cabin layout optimization

Traditionally, the ship cabin layout is designed by experts with the help of CAD, GAD, etc. The layout is usually drawn and designed repeatedly, which is not only labor-intensive but also time-consuming. Although a layout is well-thought-out, potential design issues may still exist in practice. In recent years, some intelligent ship layout design algorithms are proposed. However, the capability of involving humans in the loop has been seriously overlooked in current studies, which are mostly end-to-end approaches. To resolve these challenges, we propose a novel interactive ship cabin layout optimization approach based on integer programming (IP), which is capable to generate diverse layouts and interact with designers for further refinement. Gurobi, an integer programming solver, is adopted to find out the optimal value under the layout constraints in our approach. Moreover, crowd simulation is used to evaluate the layout quality from the perspective of coziness and mobility. The results indicate the proposed method can effectively design and optimize the ship cabin layout in an interactive manner.

A thrust allocation strategy for intelligent ships based on model prediction control

In order to solve the problem of the traditional sequential quadratic programming thrust allocation method, such as limited feasible region and low propulsion efficiency, a thrust allocation strategy based on model predictive control is proposed in this paper. The proposed thrust allocation strategy is realized by rolling optimization of model predictive control, and moreover, considering the possibility of thruster failure, a fault-tolerant thrust allocation strategy is proposed. The simulation results show that the proposed strategy can not only make the ship run in the expected condition but also reduce the average power consumption by 16.3% compared with the traditional sequential quadratic programming method and has good fault tolerance in the face of thruster failure.

Research on coupling mechanism of intelligent ship navigation risk factors based on N-K model

Research on coupling mechanism of intelligent ship navigation risk factors based on N-K model

Decarbonizing in Maritime Transportation: Challenges and Opportunities

As global warming caused by greenhouse gases grows (GHGs) into a global environmental threat, carbon dioxide emissions are drawing increasing attention in these years. Among all emission sources, transportation is a major contributor to climate change because of its high dependence on fossil fuels. The International Maritime Organization (IMO) has therefore been promoting the reduction of fuel usage and carbon emissions for container ships by such measures as improving shipping route selection, shipping speed optimization, and constructing clean energy propulsion systems. In this paper, a review of the impact of carbon dioxide emissions on climate change is presented; the current situations of carbon dioxide emissions, decarbonizing methods, IMO regulations, and possible future directions of decarbonizing in the maritime transportation industry are also discussed. Based on the result, it is found that in the case that non intelligent ships still occupy the vast majority of operating ships, the use of new energy as the main propulsion fuel has the defects of high renewal cost and long effective period. It is more likely to achieve energy conservation and emission reduction in the shipping industry in a short period of time by using intelligent means and artificial intelligence to assist ship operation.

Research on the reform of marine education in the era of intelligent ship development

Intelligent ships integrate new technologies such as information technology and artificial intelligence, and have significant characteristics such as safety and reliability, energy conservation and environmental protection, and economic efficiency. It is the key direction of the future development of commercial ships. Higher education in navigation under the background of ship intelligence should make full use of the advantages of traditional navigation majors, actively master the core technology of intelligent ship development, and actively carry out the cross-integration of computer science, control theory, communication science, artificial intelligent and other information sciences. High-quality compound "new engineering" navigation talents who master information technology and navigation science are cultivated under the background of intelligent ships. Under the background of intelligent ships, reconstructing the training of professional marine technology professionals in higher vocational colleges is conducive to the transformation and upgrading of traditional navigation majors in higher vocational colleges, and provides cutting-edge exploration for the development of high-quality compound "new engineering" navigation majors.

COLREGs Experience-Based Real-Time Route Planning of Intelligent Ship

The route planning of intelligent ship has been studied a lot, especially in the dynamic planning of collision avoidance, but it is still faced with some difficulties, such as complex model structure, poor compatibility of collision avoidance rules, difficult to practice and so on. Therefore, this paper adopts the integration of field model theory and COLREGs for real-time ship route planning, taking into account the model efficiency and application value. It uses FM algorithm as the basic route planning algorithm, introducing the virtual electric field model to construct the ship navigation situation, and gives the guidance strategy of collision avoidance behavior under different encounter situations considering the COLREGs and daily navigation experience, which integrates it into the field model to form a real-time collision avoidance adjustment route planning algorithm. The proposed algorithm has been compared and verified by simulation in multiple scenes, it is worth mentioning that the algorithm can potentially be developed to advance collision-free route planning in unconstructed environment.

An Automatic-Identification-System-Based Vessel Security System

As today’s transportation systems have gradually developed into intelligent transportation systems, the fundamental elements of the system consist of vessels, harbors, and ship-shore information-communication technology applications. Safety is the priority for intelligent transportation, securing the transportation environment for vessels. Because there are more dangerous situations when sailing in the sea, our research deploys artificial intelligence of thing with the 5G network to ensure ship safety. To achieve the goal of intelligent ships for vessels to share information between groups; moreover, the geofencing technology can protect vessels from sailing into risky zones. The security mechanism of our system can detect malicious attacks from Dynamic Domain Name System and radio jamming attacks. Additionally, the network security mechanism proposed in this article can safeguard data reliability and safety, enabling vessels to detect collisions in the front and improve safety through the 5G network and the sensor. The performance analysis has proven that the network security approach of this article surpasses other studies; regarding the geofencing part, this article has also conducted a practical experiment to introduce it into the automatic-identification-system (AIS) and 5G system. The experimental results prove that the suggested approach can ensure the AIS network security in vessels; moreover, the system can precisely judge whether there are obstacles in front of the ship, making sure the vessel safety.

Research on the path of artificial intelligence to empower intelligent port upgrading and transformation

As the key nodes of the global comprehensive transportation network, ports play a pivotal role in global trade and shipping. With the progress of artificial intelligence technology and the promotion of intelligent port development concept, intelligent port will be the inevitable trend and choice of all countries in the world. This paper analyzes the application status and problems of artificial intelligence in promoting the construction of smart ports, discusses the typical application of artificial intelligence in the intelligent port upgrading, and details the practical application of artificial intelligence in the construction of intelligent port, including container electronic tags, unmanned driving technology, intelligent ship stowage, and intelligent port scheduling. Finally, the corresponding optimization countermeasures of intelligent port are put forward.

Identification modeling of ship nonlinear motion based on nonlinear innovation

In order to further improve the accuracy of parameter identification of ship nonlinear motion mathematical model, grey wolf optimizer-support vector regression (GWO-SVR) is embedded into the nonlinear innovation processed by hyperbolic tangent function. A novel identification scheme called nonlinear innovation GWO-SVR(NGWO-SVR) is proposed for identification of ship nonlinear motion mathematical model. Based on the full-scale trial data of vessel YUKUN, the NGWO-SVR is used to identify the parameters of the nonlinear motion mathematical model. Using full-scale trial data different from the identified samples, the generalization of the parameters obtained from SVR, GWO-SVR and NGWO-SVR are verified. From the evaluation indicator of the prediction results, the parameters identified by the proposed algorithm are more generalized under the condition of effectiveness of certain accuracy. In addition, the indirect sensitivity analysis method is used to analyze the parameters of the nonlinear motion mathematical model. The research results can provide a priori characteristics for intelligent ship motion control and path planning and a reference scheme for the optimization of ship motion mathematical model in marine simulator.

Ship collision avoidance based on brain storm optimization near offshore wind farm

An intelligent ship collision avoidance mechanism based on improved swarm intelligence is presented. To expedite optimal decision-making system to prevent vessel collision, an adaptive brain storm optimization (ABSO) with variational inference-based expectation maximum for Mean-shift is proposed. The threshold for random update of individuals in brain storm optimization algorithm was adaptively renewed. The fitness was based on turning amplitude, and distance from original route. The proposed algorithm was tested under different scenarios of ship encounter, including crossing, head-on, and overtaking situations. The rationale is validated via typical cases. One of the minor objectives of the optimization was to prevent ships from steering to the side of the wind farm, which ensures the safety of navigation near offshore wind farm. This algorithm was used in collision avoidance near the offshore wind farm. Results show that calculations based on the proposed optimization method contribute to reasonable and effective decision-making.