|Table of Contents|

Intelligent assessment for collaborative simulation training in ship engine room(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2016年06期
Page:
82-90
Research Field:
载运工具运用工程
Publishing date:
2016-12-20

Info

Title:
Intelligent assessment for collaborative simulation training in ship engine room
Author(s):
DUAN Zun-lei;  REN Guang;  ZHANG Jun-dong;  CAO Hui
School of Marine Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China
Keywords:
ship engineering;  engine room simulator;  intelligent assessment;  collaborative training;  genetic algorithm;  neural network;  deep learning
PACS:
U676.4
DOI:
-
Abstract:
The assessment for simulation training in ship engine room was studied based on the theory of man-machine-environment system engineering. The system model called role-mission-resource for collaborative training was constructed, and the intelligent assessment methods based on expert system and machine learning were proposed to improve the three dimensional engine room collaborative training system. The scene knowledge base, mission knowledge base and evaluation index system of simulation training in ship engine room were established. The assessment rules were extracted according to the expert experience and the evaluation criteria. The data were evaluated by using multiple fuzzy comprehensive evaluation and the weights were optimized by genetic algorithm. The structures of machine learning network were constructed appropriately according to the practical assessment problem. The computing steps were simplified by using the advantage of self-learning of BP neural network and deep learning algorithms. The normalized evaluation index was used as the input of evaluation model, and the evaluation results were used as the target data. The features of mass sample data were transformed by using sparse autocoder. Sample features were learned deeply and used for classification evaluation, thus the better evaluation model was achieved after repeated training. A comparative analysis of the intelligent assessment methods based on expert system and machine learning was conducted. Analysis result indicates that after the optimization of genetic algorithm, the assessment result error is smaller apparently. The average absolute error is 0.761 point, the average relative error is 0.983%, the mean square error is 0.938 point, the maximum error is 2.263 point, and the minimum error is 0.248 point. For the evaluation of simpler missions, the maximum absolute error of assessment result based on machine learning is 3.521 point, and the minimum absolute error is 0.304 point. The assessment errors of all indexes in the better assessment network based on deep learning are less than 1 point. 5 tabs, 9 figs, 26 refs.

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Last Update: 2016-12-20