Sensors and Materials
ISSN 0914-4935

Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

Guidelines
English    日本語

Template
English

General Notes on Format
English

Publisher
 MYU K.K.
 Sensors and Materials
 1-23-3-303 Sendagi,
 Bunkyo-ku, Tokyo 113-0022, Japan
 Tel: 81-3-3827-8549
 Fax: 81-3-3827-8547


MYU Research

(proofreading and recording)


MYU K.K.
(translation service)


The Art of Writing Scientific Papers

(How to write scientific papers)
(Japanese Only)

Sensors and Materials,
Volume 29, Number 9(2) (2017)

Copyright(C) MYU K.K. All Rights Reserved.
pp. 1291-1303
S&M1423
https://doi.org/10.18494/SAM.2017.1588
Published: September 27, 2017

A Prediction Method for Deck Motion of Aircraft Carrier Based on Particle Swarm Optimization and Kernel Extreme Learning Machine [PDF]

Xixiang Liu, Qiming Wang, Yongjiang Huang, Qing Song, and Liye Zhao

(Received March 1, 2017; Accepted August 23, 2017)

Keywords: prediction of deck motion, extreme learning machine, support vector machine, particle swarm optimization

The prediction of deck motion is an effective and potential means of improving the landing/ take-off safety of carrier-based aircraft using current and historical deck-motion measurements when deck motion in six degrees of freedom cannot be effectively controlled or restrained. The prediction models of deck motion should have excellent nonlinear fitting ability to cope with the deck-motion characteristics of randomness and nonlinearity caused by waves and wind; and should not use heavy computation to fulfill the requirement of real-time prediction for deck motion. It is generally believed that classical feed-forward neural networks, such as the back-propagation (BP) network, have excellent nonlinear fitting ability but suffer from slow training processes and reduced local optimum, thus failing to satisfy the requirements of real-time and high accuracy for deck-motion prediction. In addition, the extreme learning machine (ELM) is easy to train but it is difficult for ELM to determine the number of hidden layer nodes; an incorrect number of hidden layer nodes will introduce poor stability and generalization ability. To fulfill the requirements of deck motion prediction, a prediction method based on ELM, support vector machine, and particle swarm optimization [particle swam optimization kernel extreme learning machine (PSO-KELM)] is designed. In this method, the fundamental structure of the ELM is used and the kernel function from the support vector machine (SVM) is introduced to replace the hidden function in ELM. Further aiming at the acquisition of optimal parameters, including the penalty coefficient and kernel parameters for the kernel function, autoadaptive particle swarm optimization is adopted. Simulation results indicate that a prediction method based on PSO-KELM has the advantages of a simple structure, fast training speed, and powerful generalization ability, and thus can satisfy the requirements of real-time and high-accuracy deck-motion prediction. Compared with the prediction data from BP and the ELM, high-precision prediction data can be obtained with PSOKELM. PSO-KELM has a significantly reduced training time compared with BP.

Corresponding author: Xixiang Liu


Cite this article
Xixiang Liu, Qiming Wang, Yongjiang Huang, Qing Song, and Liye Zhao, A Prediction Method for Deck Motion of Aircraft Carrier Based on Particle Swarm Optimization and Kernel Extreme Learning Machine, Sens. Mater., Vol. 29, No. 9, 2017, p. 1291-1303.


Forthcoming Regular Issues


Forthcoming Special Issues

Special Issue on Micro Energy Harvesting and Storing Technologies
Guest editor, Bin Yang (Shanghai Jiao Tong University); Submission deadline, extended to March 20, 2018


Special Issue on Internet of Things (IoT) and Applications for Improving Quality of Life
Guest editor, Hidetaka Nambo (Kanazawa University)


4th Special Issue on the Workshop of Next-Generation Front-Edge Optical Science Research
Guest editor, Hidehito Nanto (Kanazawa Institute of Technology)


Special Issue on the International Multi-Conference on Engineering and Technology Innovation 2017 (IMETI2017) (1)
Guest editor, Wen-Hsiang Hsieh (National Formosa University)
Conference website


Special Issue on the International Multi-Conference on Engineering and Technology Innovation 2017 (IMETI2017) (2)
Guest editor, Wen-Hsiang Hsieh (National Formosa University)
Conference website


Special Issue on Sensors and Materials for Cyber-Physical Applications
Guest editor, Pitikhate Sooraksa (King Mongkut’s Institute of Technology Ladkrabang)


Special Issue on Innovative and Intelligent Sensing Analysis and Experiment for Functional Materials
Guest editor, Cheng-Chi Wang (National Chin-Yi University of Technology)
Conference website


Special Issue on Biosensing Materials and Engineering for Electrobiology
Guest editor, Toshiya Sakata (The University of Tokyo)


Special Issue on Remote Sensing and Sensing Devices
Guest editor, Haruichi Kanaya (Kyushu University)


Special Issue on Advances in Devices and Materials for Stress-Strain Sensing
Guest editor, Toshiyuki Toriyama and Taeko Ando (Ritsumeikan University)
Call for paper


Special Issue on International Conference on BioSensors, BioElectronics, BioMedical Devices, BioMEMS/NEMS and Applications 2017 (Bio4Apps 2017)
Guest editor, Toshihiro Itoh (The University of Tokyo); Submission deadline, May 15, 2018
Conference website
Call for paper


Special Issue on Nanostructure and Its Application in Sensors
Guest editor, Tie Li (Shanghai Institute of Microsystem and Information Technology)
Call for paper


Special Issue on Micro/Nano Sensing Platforms Exploring Biomedical Innovation
Guest editor, Ryoji Kurita (National Institute of Advanced Industrial Science and Technology)
Call for paper


Special Issue on Mechanical and Thermal Reliability of Micro/Nanomaterials
Guest editor, Takahiro Namazu (Aichi Institute of Technology) and Shugo Miyake (Kobe City College of Technology)
Call for paper


Special Issue on Universal Power Supply Technologies for Trillion Sensors Era
Guest editor, Keiji Takeuchi (NTT Data Institute of Management Consulting, Inc.)
Call for paper


Special Issue on the Workshop on Sensors and Applications for Fishery and Agricultural Industries
Guest editor, Masaaki Wada (Future University Hakodate) and Katsumori Hatanaka (Tokyo University of Agriculture); Submission deadline, October 12, 2018
Call for paper


Special Issue on Selected Papers from ICASI2018
Guest editor, Teen-Hang Meen (National Formosa University), Shoou-Jinn Chang National Cheng Kung University), and Stephen D. Prior (University of Southampton)
Conference website
Call for paper


Special Issue on the International Multi-Conference on Engineering and Technology Innovation 2018 (IMETI2018)
Guest editor, Wen-Hsiang Hsieh (National Formosa University)
Conference website


Special Issue on Materials, Devices, Circuits, Analytical Methods for Various Sensors (Selected Papers from ICSEVEN 2018)
Guest editor, Chien-Jung Huang (National University of Kaohsiung), Chi-Chih Liao (II-V Epiworks, Inc.), and Ja-Hao Chen (Feng Chia University)
Conference website
Call for paper


Special Issue on Carbon Material-based Chemical and Biochemical Sensors
Guest editor, Yuko Ueno (NTT Basic Research Laboratories) and Osamu Niwa (Saitama Institute of Technology)
Call for paper



Copyright(C) MYU K.K. All Rights Reserved.