Automatic cruise system for water quality monitoring
Abstract
Keywords: water quality monitoring, GPS, automatic cruise, Android mobile client
DOI: 10.25165/j.ijabe.20181104.2658
Citation: Zhu C Y, Liu X Q, Chen H L, Tian X. Automatic cruise system for water quality monitoring. Int J Agric & Biol Eng, 2018; 11(4): 244-250.
Keywords
Full Text:
PDFReferences
Rathi S, Gupta R A simple sensor placement approach for regular monitoring and contamination detection in water distribution networks. Ksce Journal of Civil Engineering 2016; 20: 597–608. doi: 10.1007/ s12205-015-0024-x
Huang X, Yi J, Chen S, Zhu X. A wireless sensor network-based approach with decision support for monitoring lake water quality. Sensors, 2015; 15: 29273-29296. doi: 10.3390/s151129273
Keum J, Kaluarachchi J J. Development of a decision-making methodology to design a water quality monitoring network. Environmental Monitoring and Assessment 2015, 187. doi: 10.1007/s10661-015-4687-z
Skadsen J, Janke R, Grayman W, Samuels W, Tenbroek M, Steglitz B, et al. Distribution system on-line monitoring for detecting contamination and water quality changes. Journal / American Water Works Association, 2008; 100: 81–94.
Eliades D G, Polycarpou M M. In multi-objective optimization of water quality sensor placement in drinking water distribution networks, 2007 9th European Control Conference, ECC 2007, July 2, 2007 - July 5, 2007, Kos, Greece, 2015; Institute of Electrical and Electronics Engineers Inc.: Kos, Greece, pp.1626–1633.
Simbeye D S, Yang S F. Water quality monitoring and control for aquaculture based on wireless sensor networks. Journal of Networks, 2014; 9: 840–849. doi: 10.4304/jnw.9.4.840-849
Gustilo R C, Dadios E P. Machine vision support system for monitoringwater quality in a small scale tiger prawn aquaculture. Journal of Advanced Computational Intelligence and Intelligent Informatics, 2016; 20: 111–116.
Bergquist D C, Heuberger D, Sturmer L N, Baker S M. Continuous water quality monitoring for the hard clam industry in florida, USA. Environmental Monitoring and Assessment, 2009; 148: 409–419. doi: 10.1007/s10661-008-0171-3
Luo H, Li G, Peng W, Song J, Bai Q. Real-time remote monitoring system for aquaculture water quality. Int J Agric & Biol Eng, 2015; 8(6): 136–143. doi: 10.3965/j.ijabe.20150806.1486
Dong J, Wang G, Yan H, Xu J, Zhang X. A survey of smart water quality monitoring system. Environmental Science and Pollution Research International, 2015; 22: 4893–4906. doi: 10.1007/s11356-014-4026-x
Chung W-Y, Yoo J-H. Remote water quality monitoring in wide area. Sensors and Actuators B: Chemical, 2015; 217: 51–57. doi: 10.1016/j.snb.2015.01.072
Sicard C, Glen C, Aubie B, Wallace D, Jahanshahi-Anbuhi S, Pennings K, et al. Tools for water quality monitoring and mapping using paper-based sensors and cell phones. Water Research, 2015; 70: 360–369. doi: 10.1016/j.watres.2014.12.005
Sun J, Xu X S, Liu Y T, Zhang T, Li Y. Fog random drift signal denoising based on the improved ar model and modified sage-husa adaptive kalman filter. Sensors, 2016; 16: 1073. doi: 10.3390/s16071073
Gao, X.D.; You, D.Y.; Katayama, S. Seam tracking monitoring based on adaptive kalman filter embedded elman neural network during high-power fiber laser welding. IEEE Transactions on Industrial Electronics, 2012; 59: 4315–4325. doi: 10.1109/tie.2012.2193854
Xia B Z, Wang H Q, Wang M W, Sun W, Xu Z H, Lai Y Z. A new method for state of charge estimation of lithium-ion battery based on strong tracking cubature kalman filter. Energies, 2015; 8: 13458–13472. doi: 10.3390/en81212378
Huang W, Xie H S, Shen C, Li J P. A robust strong tracking cubature kalman filter for spacecraft attitude estimation with quaternion constraint. Acta Astronautica, 2016; 121: 153–163. doi: 10.1016/j.actaastro. 2016.01.009
Hu G G, Gao S S, Zhong Y M, Gao B B, Subic A. Modified strong tracking unscented kalman filter for nonlinear state estimation with process model uncertainty. International Journal of Adaptive Control and Signal Processing, 2015; 29: 1561–1577. doi: 10.1002/acs.2572
Motwani A, Liu W W, Sharma S, Sutton R, Bucknall R. An interval kalman filter-based fuzzy multi-sensor fusion approach for fault-tolerant heading estimation of an autonomous surface vehicle. Proceedings of the Institution of Mechanical Engineers Part M-Journal of Engineering for the Maritime Environment, 2016; 230: 491–507. doi: 10.1177/ 1475090215596180
Xu H T, Soares C G. Vector field path following for surface marine vessel and parameter identification based on ls-svm. Ocean Engineering, 2016; 113: 151–161. doi: 10.1016/j.oceaneng.2015.12.037
Xiao H, Wang C, Han Y, Pan W, Zhou F, Yang Y, Li J. In Design of ship straight-line tracking controller based on auto disturbance rejection control technique. 8th World Congress on Intelligent Control and Automation, WCICA 2010, Jinan, China. July 7-9, 2010; pp 2559–2564.
Jin X. Fault tolerant finite-time leader follower formation control for autonomous surface vessels with los range and angle constraints. Automatica, 2016; 68: 228–236. doi: 10.1016/j.automatica.2016.01.064
Zuo L, Yan W S, Cui R X, Gao J. A coverage algorithm for multiple autonomous surface vehicles in flowing environments. International Journal of Control Automation and Systems, 2016; 14: 540–548. doi: 10.1007/s12555-014-0454-0
Shojaei K. Observer-based neural adaptive formation control of autonomous surface vessels with limited torque. Robotics and Autonomous Systems, 2016;78: 83–96. doi:10.1016/j.robot.2016.01.005
Han Y, Xiao H, Wang C, Zhou F. In design and simulation of ship course controller based on auto disturbance rejection control technique, 2009 IEEE International Conference on Automation and Logistics, ICAL 2009, Shenyang, China, August 5-7, 2009; pp 686–691.
Chen W, Zhou F, Li Y, Song R. In the ship nonlinear course system control based on auto disturbance rejection controller. 7th World Congress on Intelligent Control and Automation, WCICA'08, Chongqing, China, June 25-27, 2008; pp 6454–6458.
Stradolini F, Riario S, Boero C, Baj-Rossi C, Taurino I, Surrel G, et al. Wireless monitoring of endogenous and exogenous biomolecules on an android interface. IEEE Sensors Journal, 2016; 16: 3163–3170. doi: 10.1109/jsen.2016.2524631
Kim K-W. An efficient implementation of key frame extraction and sharing in android for wireless video sensor network. KSII Transactions on Internet and Information Systems, 2015; 9: 3357–3376. doi: 10.3837/tiis.2015.09.005
Copyright (c) 2018