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Journal articles on the topic 'Automation of aquaculture systems'

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1

Gustilo, Reggie C., and Elmer P. Dadios. "Behavioural Response Analysis Using Vision Engineering (BRAVENet)." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 2 (2017): 211–20. http://dx.doi.org/10.20965/jaciii.2017.p0211.

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A new engineering methodology is proposed to improve the automation process in monitoring the water quality in a small scale aquaculture system. Behavioural Response Analysis using Vision Engineering Network or BRAVENet is proposed, as a support system to a traditional sensor-based system, to monitor critical water quality parameters such as temperature, pH, salinity and dissolved oxygen. BRAVENet is based on the reactions or behavioural responses of tiger prawns to different water conditions. The performance of both the sensor-based system and BRAVENet are analysed and discussed. It is shown
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2

Scattini, Noel, and Stanislaw Paul MAJ. "Aquaponic Integration and Automation – A Critical Evaluation." Modern Applied Science 11, no. 9 (2017): 165. http://dx.doi.org/10.5539/mas.v11n9p165.

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Aquaponics is technology developed from the aquaculture industry that integrates intensive farming of fish and utilizes plants (integrates hydroponics) in a continuous closed loop to clean the water for the fish. The plants clean the water of nitrate (waste form is initially ammonia) which has been converted into a form that is not toxic to fish by bacteria and is accessible to plants. Hydroponics technology is a technique used to grow plants and vegetables that does not incorporate soil, but nutrients that are dissolved in water and plants are either floated or treated with a nutrient film de
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3

Martinez-de Dios, J. R., C. Serna, and A. Ollero. "Computer vision and robotics techniques in fish farms." Robotica 21, no. 3 (2003): 233–43. http://dx.doi.org/10.1017/s0263574702004733.

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This paper presents new low-cost systems for the automation of some fish farm operations. Particularly, computer vision is applied to non-contact fish weight estimation. Stereo vision systems with synchronised convergent cameras are employed to perform fish 3-D segmentation in tanks and sea cages. Several pre-processing algorithms are applied to compensate for illumination local variations. The approach applied for fish 3-D segmentation consists in detecting in both images certain fish features. Once these points have been detected and validated in both images, the fish are 3-D segmented by ap
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4

Shafait, Faisal, Euan S. Harvey, Mark R. Shortis, et al. "Towards automating underwater measurement of fish length: a comparison of semi-automatic and manual stereo–video measurements." ICES Journal of Marine Science 74, no. 6 (2017): 1690–701. http://dx.doi.org/10.1093/icesjms/fsx007.

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Abstract Underwater stereo–video systems are widely used for counting and measuring fish in aquaculture, fisheries, and conservation management. Length measurements are generated from stereo–video recordings by a software operator using a mouse to locate the head and tail of a fish in synchronized pairs of images. This data can be used to compare spatial and temporal changes in the mean length and biomass or frequency distributions of populations of fishes. Since the early 1990s stereo–video has also been used for measuring the lengths of fish in aquaculture for quota and farm management. Howe
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5

Volvenko, Igor V. "Regional Data Center (RDC) of FSUE «TINRO-centre»: its role in prediction of resource state for national fisheries and principal directions of activities." Izvestiya TINRO 176, no. 1 (2014): 3–15. http://dx.doi.org/10.26428/1606-9919-2014-176-3-15.

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Regional Data Center (RDC) is 40 years old in 2014. Its main goals are accumulation of research data on Far-Eastern marine biological resources and their environments, as well as supervision for the data collection, validation and processing. Principal activities of RDC are: 1) development and implementation of computerize workplaces for scientific and technical personnel aboard research vessels and ashore; 2) logging of primary cruise materials on paper and their digitizing; 3) development and management of large databases (DB) for the information gathered in research cruises and fishery stat
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6

Huu, Phat Nguyen, and Hoan Nguyen Duc. "Propose an Automatic Ammonia Concentration of Water Measuring System Combining Image Processing for Aquaculture." Journal Européen des Systèmes Automatisés 54, no. 3 (2021): 453–60. http://dx.doi.org/10.18280/jesa.540308.

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The underwater environmental monitoring system applying IoT is now developing for 4.0 industry. It helps not only to simplify work but also to increase efficiency and reduce costs and execution time as well as ensure health to avoid contact with toxic solutions. In this paper, we first model the process of measuring NH3 concentration manually to automate. Secondly, the proposed model is combined with the process of processing output image automatically and displaying the results on the server. Thirdly, the system is able to measure the concentration by VNC viewer connecting with Raspberry pi4 v
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7

Wang, Guangxu, Akhter Muhammad, Chang Liu, Ling Du, and Daoliang Li. "Automatic Recognition of Fish Behavior with a Fusion of RGB and Optical Flow Data Based on Deep Learning." Animals 11, no. 10 (2021): 2774. http://dx.doi.org/10.3390/ani11102774.

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The rapid and precise recognition of fish behavior is critical in perceiving health and welfare by allowing farmers to make informed management decisions on recirculating aquaculture systems while reducing labor. The conventional recognition methods are to obtain movement information by implanting sensors on the skin or in the body of the fish, which can affect the normal behavior and welfare of the fish. We present a novel nondestructive method with spatiotemporal and motion information based on deep learning for real-time recognition of fish schools’ behavior. In this work, a dual-stream 3D
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8

Zhang, Song, Xinting Yang, Yizhong Wang, et al. "Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model." Animals 10, no. 2 (2020): 364. http://dx.doi.org/10.3390/ani10020364.

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In intensive aquaculture, the number of fish in a shoal can provide valuable input for the development of intelligent production management systems. However, the traditional artificial sampling method is not only time consuming and laborious, but also may put pressure on the fish. To solve the above problems, this paper proposes an automatic fish counting method based on a hybrid neural network model to realize the real-time, accurate, objective, and lossless counting of fish population in far offshore salmon mariculture. A multi-column convolution neural network (MCNN) is used as the front en
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9

Radford, C., and M. Slater. "Soundscapes in aquaculture systems." Aquaculture Environment Interactions 11 (February 21, 2019): 53–62. http://dx.doi.org/10.3354/aei00293.

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10

Piedrahita, Raul H. "Detritus‐based aquaculture systems." Food Reviews International 6, no. 3 (1990): 317–31. http://dx.doi.org/10.1080/87559129009540875.

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11

Wrobel, A., and M. Placzek. "Visualization systems for industrial automation systems." IOP Conference Series: Materials Science and Engineering 400 (September 18, 2018): 062032. http://dx.doi.org/10.1088/1757-899x/400/6/062032.

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12

Козир, Олег Васильович, and Юліан Михайлович Туз. "Measurement systems analysis automation." MECHANICS OF GYROSCOPIC SYSTEMS, no. 31 (May 20, 2016): 87–94. http://dx.doi.org/10.20535/0203-377131201683588.

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13

Diedrich, Christian, and Peter Neumann. "Standardisation in Automation Systems." IFAC Proceedings Volumes 31, no. 15 (1998): 53–58. http://dx.doi.org/10.1016/s1474-6670(17)40528-3.

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14

Helander, Martin G. "Emerging Office Automation Systems." Human Factors: The Journal of the Human Factors and Ergonomics Society 27, no. 1 (1985): 3–20. http://dx.doi.org/10.1177/001872088502700102.

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15

Talukder, Md Sadiqul Hasan. "Automation Technologies and Systems." IOSR Journal of Mechanical and Civil Engineering 13, no. 04 (2016): 39–45. http://dx.doi.org/10.9790/1684-1304063945.

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16

Benel, R. A., R. D. Dancey, J. D. Dehn, J. C. Gutmann, and D. M. Smith. "Advanced Automation Systems design." Proceedings of the IEEE 77, no. 11 (1989): 1653–60. http://dx.doi.org/10.1109/5.47728.

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17

Kumar, G. V. Sunil, and M. Saravanan. "A Complete Automation Unit to Increase the Efficiency of Aquaculture Farming." Journal of Computational and Theoretical Nanoscience 15, no. 6 (2018): 2169–73. http://dx.doi.org/10.1166/jctn.2018.7430.

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18

Wu, Q. H., D. P. Buse, J. Q. Feng, P. Sun, and J. Fitch. "e-Automation, an architecture for distributed industrial automation systems." International Journal of Automation and Computing 1, no. 1 (2004): 17–25. http://dx.doi.org/10.1007/s11633-004-0017-6.

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19

Wik, Torsten E. I., Björn T. Lindén, and Per I. Wramner. "Integrated dynamic aquaculture and wastewater treatment modelling for recirculating aquaculture systems." Aquaculture 287, no. 3-4 (2009): 361–70. http://dx.doi.org/10.1016/j.aquaculture.2008.10.056.

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20

Ebeling, James M. "Engineering Aspects of Recirculating Aquaculture Systems." Marine Technology Society Journal 34, no. 1 (2000): 68–78. http://dx.doi.org/10.4031/mtsj.34.1.8.

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Intensive recirculating aquaculture systems utilizing water recirculation and pure oxygen injection are examined in terms of the individual unit processes that are required to handle the wastes generated by fish at stocking densities as high as 120‐150 kg/m3. These unit processes include solid waste removal, nitrification of ammonia and nitrite, aeration or oxygenation, carbon dioxide removal, and control and monitoring systems. Overall system integration is reviewed and an example of a research/commercial intensive recirculating system is presented.
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21

Silvert, W. "Decision support systems for aquaculture licensing." Journal of Applied Ichthyology 10, no. 4 (1994): 307–11. http://dx.doi.org/10.1111/j.1439-0426.1994.tb00170.x.

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22

Izquierdo, M., and M. Carrillo. "Optimization of aquaculture systems in Spain." Energy Conversion and Management 38, no. 9 (1997): 879–88. http://dx.doi.org/10.1016/s0196-8904(96)00094-5.

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23

Poole, Bruce M. "FILTRATION TECHNIQUE FOR RECIRCULATING AQUACULTURE SYSTEMS." Journal of the World Mariculture Society 14, no. 1-4 (2009): 485–94. http://dx.doi.org/10.1111/j.1749-7345.1983.tb00100.x.

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24

Leung, PingSun. "Applications of systems modeling in aquaculture." Aquacultural Engineering 5, no. 2-4 (1986): 171–82. http://dx.doi.org/10.1016/0144-8609(86)90015-4.

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25

van Rijn, Jaap. "Waste treatment in recirculating aquaculture systems." Aquacultural Engineering 53 (March 2013): 49–56. http://dx.doi.org/10.1016/j.aquaeng.2012.11.010.

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26

Luo, Guozhi, Jinxiang Xu, and Haoyan Meng. "Nitrate accumulation in biofloc aquaculture systems." Aquaculture 520 (April 2020): 734675. http://dx.doi.org/10.1016/j.aquaculture.2019.734675.

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27

Hargreaves, John A. "Photosynthetic suspended-growth systems in aquaculture." Aquacultural Engineering 34, no. 3 (2006): 344–63. http://dx.doi.org/10.1016/j.aquaeng.2005.08.009.

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28

Fuller, R. J. "Solar heating systems for recirculation aquaculture." Aquacultural Engineering 36, no. 3 (2007): 250–60. http://dx.doi.org/10.1016/j.aquaeng.2006.12.005.

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29

莫, 伟. "Environmentally Friendly New Recirculating Aquaculture Systems." Open Journal of Fisheries Research 08, no. 02 (2021): 76–83. http://dx.doi.org/10.12677/ojfr.2021.82009.

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30

Thilsted, Shakuntala Haraksingh. "Diversifying aquaculture systems to nourish nations." Nature Food 2, no. 7 (2021): 450–51. http://dx.doi.org/10.1038/s43016-021-00326-5.

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31

Palaniappan, Satish, Naveen Hariharan, Naren T Kesh, Vidhyalakshimi S, and Angel Deborah S. "Home Automation Systems - A Study." International Journal of Computer Applications 116, no. 11 (2015): 11–18. http://dx.doi.org/10.5120/20379-2601.

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32

Ciucias, Michał, Waldemar Nowakowski, and Daniel Pietruszczak. "Safety of industrial automation systems." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 24, no. 6 (2019): 50–55. http://dx.doi.org/10.24136/atest.2019.124.

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In order to minimize the risks associated with the automation of industrial processes, it is necessary to unify standards of safety assessment. The aim of this article is the comparative analysis of safe-ty assessment methods of industrial automation systems. Authors presented two techniques of ensuring safety based on risk analysis, i.e. Performance Level (PL) and Safety Integrity Level (SIL) in relation to the applicable standards and regulations.
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33

Levkin, D. A. "Automation of multilayered systems researches." Energy and automation, no. 1(47) (February 26, 2020): 51–58. http://dx.doi.org/10.31548/energiya2020.01.051.

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34

Böhme, B., and U. Starke. "Expert Systems in Process Automation." IFAC Proceedings Volumes 22, no. 10 (1989): 305–10. http://dx.doi.org/10.1016/s1474-6670(17)53190-0.

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35

Dowlin, Kenneth E. "Library Automation and Telecommunications Systems." Public Library Quarterly 8, no. 3-4 (1988): 3–6. http://dx.doi.org/10.1300/j118v08n03_02.

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36

Collins, Hugh. "Standards for office automation systems." Facilities 3, no. 9 (1985): 10–11. http://dx.doi.org/10.1108/eb006342.

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37

Oliver, Hilary, Matthew Shin, David Matthews, et al. "Workflow Automation for Cycling Systems." Computing in Science & Engineering 21, no. 4 (2019): 7–21. http://dx.doi.org/10.1109/mcse.2019.2906593.

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38

Leeves, Juliet. "Automation of ILL Management Systems." Interlending & Document Supply 21, no. 3 (1993): 12–17. http://dx.doi.org/10.1108/02641619310154656.

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39

Clarke, T. "Editorial: Systems Engineering for Automation." IEE Proceedings - Control Theory and Applications 143, no. 2 (1996): 113. http://dx.doi.org/10.1049/ip-cta:19960416.

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40

Little, James N. "The Zymate Laboratory Automation Systems." Journal of Liquid Chromatography 9, no. 14 (1986): 3197–201. http://dx.doi.org/10.1080/01483918608074177.

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41

Heinemann, Luba. "Elegance in Library Automation Systems." Library Hi Tech 3, no. 3 (1985): 43–46. http://dx.doi.org/10.1108/eb047607.

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42

Takahagi, Takayuki, and Akira Ishitani. "Automation systems for chemical analysis." Kobunshi 34, no. 12 (1985): 986–89. http://dx.doi.org/10.1295/kobunshi.34.986.

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43

Humphreys, S. "Substation automation systems in review." IEEE Computer Applications in Power 11, no. 2 (1998): 24–30. http://dx.doi.org/10.1109/67.659623.

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44

KANDA, Yuichi. "Manufacturing Systems and Factory Automation." Journal of the Society of Mechanical Engineers 107, no. 1027 (2004): 454–55. http://dx.doi.org/10.1299/jsmemag.107.1027_454.

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45

Hill, Darren. "Cutler‐Hammer open automation systems." Assembly Automation 19, no. 4 (1999): 313–17. http://dx.doi.org/10.1108/01445159910295212.

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46

Granzer, Wolfgang, Fritz Praus, and Wolfgang Kastner. "Security in Building Automation Systems." IEEE Transactions on Industrial Electronics 57, no. 11 (2010): 3622–30. http://dx.doi.org/10.1109/tie.2009.2036033.

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47

Sudo, T. "Design automation systems in Japan." IEEE Design & Test of Computers 5, no. 6 (1988): 14–21. http://dx.doi.org/10.1109/54.9268.

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48

Mikhailov, M. N., M. I. Rozhdestvenskii, and S. G. Ukharov. "Automation of nuclear power systems." Atomic Energy 103, no. 1 (2007): 553–59. http://dx.doi.org/10.1007/s10512-007-0088-x.

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49

Guckes, Michael. "Requirements of Modern Automation Systems." ATZproduction worldwide 6, no. 1 (2019): 20–23. http://dx.doi.org/10.1007/s38312-019-0009-8.

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50

Mishchuk, Yevhen, and Dmytro Mishchuk. "IoT-based industrial automation systems." Gіrnichі, budіvelnі, dorozhnі ta melіorativnі mashini, no. 96 (December 31, 2020): 42–50. http://dx.doi.org/10.32347/gbdmm2020.96.0501.

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"Internet of Things" approaches in comparison with classical industrial automation allow to create system architectures which appear more economical, flexible, productive and effective that is reached at the expense of communication and interaction with industrial devices of automation (industrial controllers), sensors. , actuators, drives, machine vision systems, video, robotic systems.
 The basis of the "Internet of Things" (IoT) is the technology of interaction of machines (M2M), when machines use mobile networks to exchange information with each other or transmit it to data processing
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