Developing a Ship Collision Risk Index estimation model based on Dempster-Shafer theory

2021 ◽  
Vol 113 ◽  
pp. 102735
Author(s):  
Misganaw Abebe ◽  
Yoojeong Noh ◽  
Chanhee Seo ◽  
Donghyun Kim ◽  
Inwon Lee
Keyword(s):  
Risk Index ◽  
Collision Risk ◽  
Estimation Model ◽  
Model Based ◽  
Dempster Shafer Theory ◽  
Ship Collision ◽  
Shafer Theory ◽  
Index Estimation

scholarly journals An Estimation of Ship Collision Risk Based on Relevance Vector Machine

2021 ◽  
Vol 9 (5) ◽  
pp. 538
Author(s):  
Jinwan Park ◽  
Jung-Sik Jeong
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Risk Estimation ◽  
Good Method ◽  
Relevance Vector Machine ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Collision Risk ◽  
Estimation Model ◽  
Ship Collision

According to the statistics of maritime collision accidents over the last five years (2016–2020), 95% of the total maritime collision accidents are caused by human factors. Machine learning algorithms are an emerging approach in judging the risk of collision among vessels and supporting reliable decision-making prior to any behaviors for collision avoidance. As the result, it can be a good method to reduce errors caused by navigators’ carelessness. This article aims to propose an enhanced machine learning method to estimate ship collision risk and to support more reliable decision-making for ship collision risk. In order to estimate the ship collision risk, the conventional support vector machine (SVM) was applied. Regardless of the advantage of the SVM to resolve the uncertainty problem by using the collected ships’ parameters, it has inherent weak points. In this study, the relevance vector machine (RVM), which can present reliable probabilistic results based on Bayesian theory, was applied to estimate the collision risk. The proposed method was compared with the results of applying the SVM. It showed that the estimation model using RVM is more accurate and efficient than the model using SVM. We expect to support the reasonable decision-making of the navigator through more accurate risk estimation, thus allowing early evasive actions.

A Trust Transitivity Model Based-on Dempster-Shafer Theory

2010 ◽  
Vol 5 (9) ◽  
Author(s):  
Xiang Qiu ◽  
Li Zhang ◽  
Shouxin Wang ◽  
Guanqun Qian
Keyword(s):  
Model Based ◽  
Dempster Shafer Theory ◽  
Shafer Theory

scholarly journals Multi-Ship Collision Avoidance Decision-Making Based on Collision Risk Index

2020 ◽  
Vol 8 (9) ◽  
pp. 640
Author(s):  
Yingjun Hu ◽  
Anmin Zhang ◽  
Wuliu Tian ◽  
Jinfen Zhang ◽  
Zebei Hou
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Risk Analysis ◽  
Real Time ◽  
Collision Avoidance ◽  
Evaluation Method ◽  
Risk Index ◽  
Collision Risk ◽  
Ship Collision ◽  
Risk Thresholds

Most maritime accidents are caused by human errors or failures. Providing early warning and decision support to the officer on watch (OOW) is one of the primary issues to reduce such errors and failures. In this paper, a quantitative real-time multi-ship collision risk analysis and collision avoidance decision-making model is proposed. Firstly, a multi-ship real-time collision risk analysis system was established under the overall requirements of the International Code for Collision Avoidance at Sea (COLREGs) and good seamanship, based on five collision risk influencing factors. Then, the fuzzy logic method is used to calculate the collision risk and analyze these elements in real time. Finally, decisions on changing course or changing speed are made to avoid collision. The results of collision avoidance decisions made at different collision risk thresholds are compared in a series of simulations. The results reflect that the multi-ship collision avoidance decision problem can be well-resolved using the proposed multi-ship collision risk evaluation method. In particular, the model can also make correct decisions when the collision risk thresholds of ships in the same scenario are different. The model can provide a good collision risk warning and decision support for the OOW in real-time mode.

Studies on Ships Collision Avoidance with SAPSO Arithmetic

2014 ◽  
Vol 971-973 ◽  
pp. 1338-1342 ◽  
Author(s):  
Bo Tian ◽  
Wei Jie Gao ◽  
Qian Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Collision Avoidance ◽  
Risk Index ◽  
Science Research ◽  
Collision Risk ◽  
Swarm Optimization ◽  
Index Model ◽  
Collision Prevention ◽  
Ship Collision

Vessel collision prevention issue has always been the focus of the nautical science research. This paper considers a variety of factors that affect the safety of the ship collision avoidance to optimize the research on multi-boat collision avoidance magnitude, by using the improved collision risk index model and simulated annealing particle swarm optimization. The result of the simulation indicates that SAPSO can deal with the problems of angle of avoiding collision, results are accurate and feasible.

scholarly journals Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data

2021 ◽  
Vol 11 (19) ◽  
pp. 9230
Author(s):  
Wei Guo ◽  
Yifeng Yang ◽  
Hengqian Zhao ◽  
Rui Song ◽  
Ping Dong ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Spectral Index ◽  
North China ◽  
Disease Index ◽  
Accurate Estimation ◽  
Estimation Model ◽  
Model Based ◽  
Index Estimation ◽  
Band Combinations ◽  
Take All

Wheat take-all, caused by two variants of the fungus Gaeumannomyces gramnis (Sacc.) Arx & D. Olivier, was common in spring wheat areas in northwest and north China and occurred in winter wheat areas in north China. The yield of common disease areas was reduced by more than 20% and the yield of severe cases was reduced by more than 50%. Large-scale rapid and accurate estimation of the incidence of wheat take-all plays an important role in guiding field control and agricultural yield estimation. In this study, a portable ground spectrometer was used to collect the spectral reflectance in the 350–1050 nm band range of wheat canopy after take-all infection in the wheat grain filling stage and combined with the ground disease survey data.Then a winter wheat take-all disease index estimation model was proposed based on the spectral band division interval and selected band combination. According to the normalized difference spectral index (NDSI) and the determinative coefficient of the disease index formed by any two band combinations, the spectral index band combinations corresponding to the spectral index with high correlation in each region were screened by dividing spectral intervals. Partial least-squares regression was used to establish a binary and ternary disease index calibration model. The results showed that the model based on spectral indices of ternary variables had the highest coefficient of determination. Finally, the optimal regression model of wheat take-all disease condition index composed of NDSI(R590,R598), NDSI(R534,R742) and NDSI(R810,R834) was established: Y = 134.577 − 70.301 NDSI(R590,R598) − 223.533 NDSI(R534,R742) + 51.584 NDSI(R810,R834) (R2 = 0.743, RMSEP = 0.094, df = 15), which was the most suitable model for winter wheat take-all estimation. The construction of this model can provide new method and technical support for future evaluation and monitoring of wheat take-all disease on the field.

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