Shanghai Maritime University

Guichen Zhang

Yanji Liu

Yifan Li

Qili Wu

Chunchang Zhang

Lizhe Yang

Siyu Zhang

Zhijian Huang

Hongren Wang

Provided is a method for adjusting lubrication optimization of a marine main engine cylinder based on computer vision, and belongs to the technical field of marine power systems. The method includes: establishing a marine main engine lubrication database for cylinder lubrication analysis and quality evaluation; collecting cylinder lubrication data of each current cylinder, and preprocessing cylinder lubrication image set data; performing image segmentation and cluster analysis on the preprocessed cylinder lubrication image set, and comparing the preprocessed cylinder lubrication image set with the marine main engine lubrication database to obtain feature recognition results of carbon deposition, dirts, scratches and a wear degree; and according to the feature recognition results, evaluating a cylinder lubrication quality to obtain evaluation grades, and adjusting cylinder lubrication optimization according to the evaluation grades.

Method for adjusting lubrication optimization of marine main engine cylinder based on computer vision

An intelligent course planning method and controller for unmanned surface vehicle are provided, an overall optimization from starting point to end point of course deflection angle is achieved based on an optimization principle of approximate dynamic programming; an optimization of minimization of the course deflection angle and a control signal is achieved based on a quadratic form cost function and a performance index by designing a virtual radial basis function neural network and a least square method, a target course expression is obtained, and a stability and a degree of convergence are ensured through a positive-definite constraint of the Hessian matrix of the performance index. Compared with the related art, an overshoot of course deflection and the control signal is reduced, an optimization of flight and steering energy consumption is achieved, and completely data-driven for intelligent course planning for unmanned surface vehicle and a high-accuracy feedback adjustment are achieved.

Intelligent course planning method and controller for unmanned surface vehicle

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Shanghai, China

Guichen Zhang