Autonomous ship navigation technology is a promising approach to reduce human errors in maritime accidents. Artificial Potential Field method has been successfully employed for path planning of autonomous mobile robot and it can also be effective for ship navigation at sea. The problem of local minima, however, should be properly tackled. Harmonic functions, by virtue of their extrema appearing only on the domain boundary, are novel approach for autonomous ship navigation. In this paper, harmonic functions are used in defining the potential field. A fluid dynamic analogy is introduced and proper boundary conditions are chosen to solve the navigation planning problem for ships. With harmonic potential field, ship navigation route can be generated automatically in constrained water areas and the tracks of the ships can follow the navigational regulations for the specific region as well. Computer simulations are conducted for two typical water areas such as Traffic Separation Scheme and narrow channel. The simulations illustrate satisfied results for path planning of ship navigation.

Elevation information and its derivation, such as grade, are very important in analyses of traffic operation, safety performance, and energy consumption on highways. Google Earth (GE) is considered a reliable source of elevation information of ground surface and highway elevation. Data were extracted from GE. However, the authors found that raw GE elevation data on highways contains various anomalies and noises. The primary objective of this study was to evaluate the use of the ensemble empirical mode decomposition (EEMD) method for anomaly detection and cleaning of highway elevation data extracted from GE. Three interstate highways’ segments were studied, and typical anomalies that existed in raw GE elevation data were identified. The EEMD method was then applied to decompose elevation data into different compositions with different details of original data, which were determined into those containing true information or white noise. The modeling results showed that the EEMD method was effective in excluding noises and obtaining accurate elevation data. Findings of this study can help transport authorities to create an accurate elevation data set for all highways or other road classes.

The electrodeposition technique was used to fabricate graphene and Cr particle-reinforced Ni–Cr–graphene coatings. The Rietveld refinement was utilized to analyze the microstructure of Ni deposits in the coatings. The properties including micro-hardness and corrosion behaviors of the coatings were also tested. Results showed that the addition of graphene particles contributed to the dendrite like structure on the surface of the Ni–Cr–graphene coating. The crystallite size and [200] texture of the Ni deposits in the Ni–Cr–graphene coatings were significantly decreased by the graphene particles. The crystallite size of 149.8 nm in the Ni-25–Cr-0–graphene coating was reduced to 35 nm in the Ni-25–Cr-8–graphene coating due to the addition of 8 g/L graphene to the electrolyte. The microstructure evolution of the Ni–Cr–graphene coatings brought about an enhancement in micro-hardness and corrosion resistance of the coatings. The micro-hardness of the coatings was improved from 260.1 HV_0.2 of the pure Ni coating to 285.9 HV_0.2 of the Ni-25–Cr-0–graphene coating and continually to 461.8 HV_0.2 of the Ni-25–Cr-8–graphene coating. In corrosion solution (3.5 wt.% NaCl), the corrosion current (6.22 μA/cm²) of the Ni-25–Cr-0–graphene coating could be decreased by about an order of magnitude through the addition of graphene particles, which was 0.33 μA/cm² for the Ni-25–Cr-8–graphene coating.

Maritime accidents frequently occurred because of human errors. It is important to generate safe route automatically in collision situations. Genetic algorithm is a new method for this purpose. However, the International Regulations for Preventing Collision at Sea have not been taken into account in previous researches. In this paper, Bayesian model is employed to calculate the post probability of each trajectory according to the rules and general practice of seaman. The fitness function is formed with path cost and information content. Experiments illustrate that evasion routes generated by Bayesian genetic algorithm are more rational than that of pure GA, considering the rules for preventing collision and general practice of seaman.

Spectrum analysis based saliency detection approaches have been studied and applied in computer vision. They are either generalized filtering or spectrum whitening of amplitude spectrum of image. However, as resultant salient regions may be inconsistent, a better result can be achieved by combining several scales. This paper proposes a novel saliency detection method for small maritime target using singular value decomposition (SVD) of amplitude spectrum. Color and intensity features of image are extracted and Fourier transform is employed on them. SVD is conducted on amplitude spectrums of each feature and their saliency maps are defined as the inverse Fourier transform of non-dominant components, whose amplitude spectrum is measured by difference between original amplitude spectrums and their main components. Taking into account the characteristics of maritime visual scene, the dominant parts of amplitude spectrum are selected according to threshold which is the ratio of singular values. Color saliency map and intensity saliency map are linearly combined to achieve the saliency map. The proposed method is conducted on the original image size, thus maintaining information of small maritime target. Experiments are made to show the validity and effectiveness. Comparison between proposed method and related approaches demonstrates that the new method is more suitable for saliency detection of small maritime target. Effect of threshold is also analyzed, and 50% is best for saliency detection of small maritime targets. The new saliency detection approach can be used for small maritime target detection and tracking.

Autonomous collision avoidance research for ships has been one of important research topics in the field of transportation. Artificial potential field (APF) method is widely used for path planning of mobile robots, due to its elegant mathematical analysis and simplicity. This method is especially suitable for 2-D low-speed systems. This paper proposes an improved APF method to realize the autonomous collision avoidance for ships. A novel APF model for ship collision avoidance is defined, and the velocity and maneuvering behavior of ship are introduced to the corresponding virtual force functions. The problem of local minima is addressed and solutions are presented.