Sensors and Materials

We will celebrate the 30th anniversary of Sensors and Materials in 2018.
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
To submit your manuscript, please use our Online Manuscript Submission System.

To subscribe to Sensors and Materials, please contact us.

Sensors and Materials
is covered by Science Citation Index Expanded (Thomson Reuters), Scopus (Elsevier), and other databases.

Guidelines
English    Japanese

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: 81-3-3821-2930
 Fax: 81-3-3827-8547

Twitter

ISSN 0914-4935

Cover of latest issue




Proofreading Services
MYU Group
provides English proofreading, translation, and recording services for your submissions and presentations. The quality of the services is highly regarded by many researchers in Japan and other Asian countries.


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
http://dx.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

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

[PDF]


Forthcoming Regular Issues


Forthcoming Special Issues

Special Issue on Advanced Sensing Technology for Smart Manufacturing
Guest editor, Chien-Hung Liu (National Chung Hsing University)


Special Issue on International Conference on BioSensors, BioElectronics, BioMedical Devices, BioMEMS/NEMS and Applications 2016 (Bio4Apps 2016)
Guest editor, Dzung Dao (Griffith University)
Call for paper


Special Issue on Retinal Prosthesis
Guest editor, Jun Ohta (Nara Institute of Science and Technology), Hiroyuki Tashiro (Kyusyu University), and Yasuo Terasawa (Nidek Co., Ltd.)
Call for paper


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 ICASI2017
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 Open Collaboration for MEMS
Guest editor, Masayoshi Esashi (Tohoku University)


Special Issue on Materials, Devices, Circuits, Analytical Methods for Various Sensors (Selected Papers from ICSEVEN 2017)
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 Dedicated to Professor Toshitsugu Ueda for His Achievements in Sensing Technologies
Guest editor, Satoshi Ikezawa (Waseda University) and Jinxing Liang (Southeast University)


Special Issue on the International Multi-Conference on Engineering and Technology Innovation 2017 (IMETI2017)
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)
Call for paper


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


Special Issue on Micro Energy Harvesting and Storing Technologies
Guest editor, Bin Yang (Shanghai Jiao Tong University)
Call for paper


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


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 Remote Sensing and Sensing Devices
Guest editor, Haruichi Kanaya (Kyushu 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)
Conference website


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 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



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