航海模拟器中人员落水救助操纵智能化评估

doi:10.16182/j.issn1004731x.joss.21-FZ0754

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (11): 2647-2655.doi: 10.16182/j.issn1004731x.joss.21-FZ0754

航海模拟器中人员落水救助操纵智能化评估

王海超, 尹勇^*

大连海事大学航海动态仿真和控制交通部重点实验室,辽宁大连 116026

收稿日期:2021-06-03 修回日期:2021-08-14 出版日期:2021-11-18 发布日期:2021-11-17
通讯作者: 尹勇(1969-),男,博士,教授,博士生导师,研究方向为航海仿真、航海智能等。E-mail：bushyin@163.com
作者简介:王海超(1993-),男,博士生,研究方向为船舶救助操纵智能评估方法等。E-mail：wanghaichao@dlmu.edu.cn
基金资助:
工信部智能船舶综合测试与验证研究(2018-437); 中央高校基本科研业务费专项资金(3132019312); 辽宁省自然科学基金指导计划(2019-ZD-0152)

Intelligent Evaluation of Rescuing Persons From Water in Navigation Simulator

Wang Haichao, Yin Yong^*

Key Laboratory of Marine Simulation &Control for Ministry of Transportation Dalian Maritime University, Dalian 116026, China

Received:2021-06-03 Revised:2021-08-14 Online:2021-11-18 Published:2021-11-17

摘要/Abstract

摘要： 针对航海模拟器中人员落水救助训练评估主观评估存在的随意性大、标准不统一的问题,对Williamson旋回救助落水人员操纵过程进行分析。利用专家调查法获取评价指标体系;利用航海模拟器获取人员落水救助操纵的样本数据,结合专家调查法,得到各样本的主观得分;运用BP神经网络对样本进行训练和测试,得到人员落水救助智能评估模型,实现智能评估。实验分析表明：该模型的精度较高,成绩评定与船员的操纵水平相符,证明该评估模型的合理性。

关键词: 航海模拟器, Williamson旋回, 人员落水救助, 智能评估, BP神经网络

Abstract: Aiming at the arbitrariness and inconsistent standards in the subjective assessment of the personnel overboard rescue training evaluation in the navigation simulator, the maneuvering process of Williamson turn rescue overboard personnel is analyzed. The evaluation index system is obtained by using the expert investigation method. The sample data of the personnel overboard rescue operation is obtained by the navigation simulator. Combining the expert investigation method, the subjective score of each sample is obtained. By using the BP neural network to train and test the samples, the intelligent evaluation model of personnel overboard rescue is obtained, and the intelligent evaluation is realized. Experimental analysis shows that the accuracy of the model is high, and the performance evaluation is consistent with the maneuvering level of the crew, which proves the rationality of the evaluation model.

Key words: navigation simulator, Williamson turn, personnel overboard rescue, intelligent evaluation, BP neural network

中图分类号:

王海超, 尹勇. 航海模拟器中人员落水救助操纵智能化评估[J]. 系统仿真学报, 2021, 33(11): 2647-2655.

Wang Haichao, Yin Yong. Intelligent Evaluation of Rescuing Persons From Water in Navigation Simulator[J]. Journal of System Simulation, 2021, 33(11): 2647-2655.

参考文献

[1] Harro G.Simulator Training and the Internet[C]// CAORF July 3-7 2000, New York: United States Merchant Marine Academy, 2000: 1-8.
[2] Perera L P, Ferrari V, Santos F P, et al.Experimental Evaluations on Ship Autonomous Navigation and Collision Avoidance by Intelligent Guidance[J]. IEEE Journal of Oceanic Engineering (S0364-9059), 2015, 40(2): 374-387.
[3] 陶俊. 大型船舶进出港训练评估方法的研究与实现[D]. 大连: 大连海事大学, 2009.
Tao Jun.Research and Realization of Assessment Method of Large Ship Entering and Leaving Port Training[D]. Dalian: Dalian Maritime University, 2009.
[4] 蒋效彬, 任鸿翔, 王德龙, 等. 航海模拟器的锚泊操纵自动评估系统[J]. 上海海事大学学报, 2016, 37(1): 82-86.
Jiang Xiaobin, Ren Hongxiang, Wang Delong, et al.An Automatic Evaluation System of Anchoring Operation in Navigation Simulator[J]. Journal of Shanghai Maritime University, 2016, 37(1): 82-86.
[5] 薛满福, 房希旺, 任鸿翔. 船舶靠泊操纵评估模型[J]. 中国航海, 2013, 36(3): 46-49.
Xue Manfu, Fang Xiwang, Ren Hongxiang.An Assessment Model of Ship Berthing[J]. Navigation of China, 2013, 36(3): 46-49.
[6] 万子谦. 船舶航行风险的识别、过滤与评估研究[D]. 哈尔滨: 哈尔滨工程大学, 2017.
Wan Ziqian.Research on Ship Navigation Risk Identification, Filtering and Evaluation[D]. Harbin: Harbin Engineering University, 2017.
[7] 邓小富, 朱银培, 董海亮. 基于模糊层次分析的船舶通航风险评估系统设计[J]. 舰船科学技术, 2017, 39(24): 46-48.
Deng Xiaofu, Zhu Yinpei, Dong Hailiang.Design of Ship Navigation Risk Assessment System Based on Fuzzy Analytic Hierarchy Process[J]. Ship Science and Technology, 2017, 39(24): 46-48.
[8] Abaei M M, Arzaghi E, Abbassi R, et al.Dynamic Reliability Assessment of Ship Grounding Using Bayesian Inference[J]. Ocean Engineering (S0029-8018), 2018, 159: 47-55.
[9] Ozturk U, Birbil S I, Cicek K.Evaluating Navigational Risk of Port Approach Manoeuvrings with Expert Assessments and Machine Learning[J]. Ocean Engineering (S0029-8018), 2019, 192(15/16): 106558.
[10] Huang J C, Nieh C Y, Kuo H C.Risk Assessment of Ships Maneuvering in an Approaching Channel Based on AIS Data[J]. Ocean Engineering (S0029-8018), 2019, 173: 399-414.
[11] 陈锦标, 唐瑶, 肖英杰. 基于GA-BP模型的船舶靠泊操纵智能化评价[J]. 中国航海, 2016, 39(1): 64-68.
Chen Jinbiao, Tang Yao, Xiao Yingjie.Intelligent Evaluation of Ship Berthing Operation Based on GA-BP Model[J]. Navigation of China, 2016, 39(1): 64-68.
[12] 王德龙. 船舶操纵避碰智能评估模型[D]. 大连: 大连海事大学, 2018.
Wang Delong.The Intelligent Assessment Model for Ship Maneuvering and Collision-Avoidance[D]. Dalian: Dalian Maritime University, 2018.
[13] Jia Y, Chen F, Wu P, et al.A Study of Online Function Approximation System Based on BP Neural Network[C]//2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). Guilin: IEEE, 2019: 147-150.
[14] Tang J, He L.Genetic Optimization of BP Neural Network in the Application of Suspicious Financial Transactions Pattern Recognition[C]//International Conference on Management of E-commerce & E-government. Beijing: IEEE, 2012: 280-284.
[15] Wang S, Jiang J, Lu X.Study on the Classification of Pulse Signal Based on the BP Neural Network[J]. Journal of Biosciences and Medicines (S2327-509X), 2020, 8(5): 104-112.
[16] 胡志武, 程葆明, 陈延才. 基于BP神经网络的船员适任性评价模型[J]. 上海海事大学学报, 2010, 31(4): 23-27.
Hu Zhiwu, Cheng Baoming, Chen Yancai.Model of Crew Competency Evaluation Based on BP Neural Network[J]. Journal of Shanghai Maritime University, 2010, 31(4): 23-27.
[17] 洪碧光. 船舶操纵[M]. 大连: 大连海事大学出版社, 2008: 207-209.
Hong Biguang.Ship Manoeuvring[M]. Dalian: Dalian Maritime University Press, 2008: 207-209.
[18] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016: 25-26.
Zhou Zhihua.Machine Learning[M]. Beijing: Tsinghua University Press, 2016: 25-26.
[19] 飞思科技产品研发中心. 神经网络理论与MATLAB7实现[M]. 北京: 电子工业出版社, 2005.
Feisi Technology R&D Center. The Neural Network Theory and Achievement by MATLAB 7[M]. Beijing: Publishing House of Electronics Industry, 2005.

航海模拟器中人员落水救助操纵智能化评估

Intelligent Evaluation of Rescuing Persons From Water in Navigation Simulator

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	孙晓安, 栾小丽, 刘飞. 基于智能优化灰色模型的电子固废预测[J]. 系统仿真学报, 2022, 34(3): 536-542.
[2]	陈立家, 王凯, 李世刚, 田延飞. 基于航空影像的航海模拟器视景快速建模方法[J]. 系统仿真学报, 2021, 33(7): 1565-1573.
[3]	于军琪, 井文强, 赵安军, 任延欢, 周梦, 黄馨乐, 杨雪. 基于改进PSO-BP算法的冷负荷预测模型[J]. 系统仿真学报, 2021, 33(1): 54-61.
[4]	李妮, 李玉红, 龚光红, 黄晓冬. 基于深度学习的体系作战效能智能评估及优化[J]. 系统仿真学报, 2020, 32(8): 1425-1435.
[5]	徐刚, 张磊, 田磊. 航空弹药技术保障模拟训练智能评估[J]. 系统仿真学报, 2020, 32(6): 1103-1116.
[6]	唐少虎, 周进, 尚春琳, 郑国荣. 降雨情景下城市道路交通信号控制优化模型与方法[J]. 系统仿真学报, 2020, 32(2): 149-156.
[7]	贾玉鹏, 尹勇, 朱忠显. 基于泊位坐标的无人船自主靠泊仿真研究[J]. 系统仿真学报, 2020, 32(10): 1910-1917.
[8]	景乾峰, 神和龙, 高正丽, 尹勇. 航海模拟器中海洋环境时空动态变化的仿真[J]. 系统仿真学报, 2020, 32(10): 1972-1980.
[9]	杨文峰, 王艳, 纪志成. 基于RF-GA-BP神经网络的N-乙酰氨基葡萄糖含量预测[J]. 系统仿真学报, 2020, 32(10): 2034-2040.
[10]	孟小凡, 宋华. 基于神经网络的卫星姿控系统故障预测[J]. 系统仿真学报, 2019, 31(11): 2499-2508.
[11]	贾立山, 刘喆, 孙毅. 基于RMBP神经网络的飞机电气故障智能诊断[J]. 系统仿真学报, 2018, 30(9): 3493-3501.
[12]	孔红山, 郁滨. 一种基于BP神经网络的VIRE改进算法研究[J]. 系统仿真学报, 2018, 30(9): 3586-3595.
[13]	沈艳霞, 苗贝贝. 三电平逆变器的开关管开路故障诊断策略[J]. 系统仿真学报, 2018, 30(8): 3058-3065.
[14]	景乾峰, 神和龙, 尹勇, 刘秀文. 航海模拟器中的岸线实时碰撞检测[J]. 系统仿真学报, 2018, 30(7): 2682-2688.
[15]	陶文华, 袁正波. 焦炭质量的DE-BP神经网络预测模型研究[J]. 系统仿真学报, 2018, 30(5): 1650-1656.