The latest methods based on deep learning have achieved amazing results regarding the complex work of inpainting large missing areas in an image. But this type of method generally attempts to generate one single “optimal” result, ignoring many other plausible results. Considering the uncertainty of the inpainting task, one sole result can hardly be regarded as a desired regeneration of the missing area. In view of this weakness, which is related to the design of the previous algorithms, we propose a novel deep generative model equipped with a brand new style extractor which can extract the style feature (latent vector) from the ground truth. Once obtained, the extracted style feature and the ground truth are both input into the generator. We also craft a consistency loss that guides the generated image to approximate the ground truth. After iterations, our generator is able to learn the mapping of styles corresponding to multiple sets of vectors. The proposed model can generate a large number of results consistent with the context semantics of the image. Moreover, we evaluated the effectiveness of our model on three datasets, i.e., CelebA, PlantVillage, and MauFlex. Compared to state-of-the-art inpainting methods, this model is able to offer desirable inpainting results with both better quality and higher diversity. The code and model will be made available on https://github.com/vivitsai/PiiGAN .

PiiGAN: Generative Adversarial Networks for Pluralistic Image Inpainting

A Big Data Analytics Method for the Evaluation of Ship - Ship Collision Risk reflecting Hydrometeorological Conditions

A collision avoidance decision-making system for autonomous ship based on modified velocity obstacle method

A predictive analytics method for maritime traffic flow complexity estimation in inland waterways

Global path planning for autonomous ship: A hybrid approach of Fast Marching Square and velocity obstacles methods

Fuzzy logic based dynamic decision-making system for intelligent navigation strategy within inland traffic separation schemes

The collision avoidance of intelligent vessels is an important issue to promote traffic fluency and safety in the waterway This paper proposes an approach to simulate maritime traffic flow based on historical data and Agent-based models In the first one, the combination of collision potential fields of different obstacles is described using the Artificial Potential Field method, ie piers, and buoys The different static models are established according to the sharps and measures of the obstacles Furthermore, the agent model is designed for simulating vessels' behaviors where simulated vessels always choose their routes based on the consideration of the trade-off between the collision potential distribution and operation convenience The simulation result indicated that the collision avoidance action of vessels of waterway is described exactly using the Artificial Potential Field method indeed Subsequently, by creating a large amount of Agent-based vessels with this method, arbitrary maritime traffic flow can be simulated in the software platform Particularly, not only the simulated traffic flow might conform to the statistical characteristics of the real one, but also any individual simulated vessel should behave like a real sailing vessel Eventually, the efficiency of the proposed approach is validated in the case in The Yangtze River in Wuhan section

An artificial potential field-based simulation approach for maritime traffic flow

A novel system-theoretic approach for human-system collaboration safety: Case studies on two degrees of autonomy for autonomous ships

A Probabilistic Decision-Making System for Joining Traffic Lanes Within an Inland Traffic Separation Scheme

The crossing of a channel is a typical scenario for the ferries in the Yangtze River, which increases the navigation risk for both the ferry and the ships navigating in the channel. From the perspective of crossing behavior, this paper proposes a predictive method based on Hidden Markov Model (HMM) to analyze the dynamic process of crossing. The observation factors are established by analyzing expertise of captains of passenger ships and ferries. The state transfer matrix between safe crossing and unsafe crossing, together with the emission matrix between hidden states and observation, is proposed by using statistical data. The proposed method is applied to the real crossing scenario of a WUDU2HAO ferry in the Changzhou port of Yangtze River as a test. The result state that the proposed method for rationality prediction of crossing behaviors of ferries is reasonable.

Ting-ting Cheng

Papers

Fuzzy logic based dynamic decision-making system for intelligent navigation strategy within inland traffic separation schemes

An artificial potential field-based simulation approach for maritime traffic flow

A novel system-theoretic approach for human-system collaboration safety: Case studies on two degrees of autonomy for autonomous ships

A Probabilistic Decision-Making System for Joining Traffic Lanes Within an Inland Traffic Separation Scheme

Prediction of Rationality for Ferry Crossing Behavior Based on Multi Hidden Markov Model