Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2020

14.02.2020

Fishing Vessels Behavior Identification for Combating IUU Fishing: Enable Traceability at Sea

verfasst von: Buncha Chuaysi, Supaporn Kiattisin

Erschienen in: Wireless Personal Communications | Ausgabe 4/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Overfishing is a critical catastrophe to the ecosystem and the global food chain. The leading causes are Illegal Unreported and Unregulated Fishing (IUU Fishing) linked to illegal labor. EU and the US have set up the fisheries policy that emphasis on traceability. The traceability principle is to monitor the entire seafood supply chain (Sea to Table). FAO’s technology gap analysis reveals that there is a lack of reliable and affordable automated systems or a lack of links to traceability. The challenge of traceability is tracing back to the catch source with existing data and technology. This study aims at the novel concept of a combination of global and local features of trajectory data for fishing vessel behavior identification and enabling seafood transparency. We present a new technique on a local feature of time series and transform the trajectory pattern to global features for Deep Learning. We apply this technique to AIS and VMS data of Thai fishing vessels (Surrounding Nets, Trawl, Longliner, and Reefer). Fishing vessel behaviors were classified as Fishing, Non-fishing, and Transshipment. Our proposed method gives a robust average accuracy result (97.50%). This concept could solve the IUU Fishing and enable traceability at sea, including monitoring, maritime, and marine resources conservation systems.
Vorheriger Artikel Determining Significant Classification Factors for Senior Learning: A Case Study of Thai Seniors and Social Media Skill Learning
Nächster Artikel A Bayesian Approach for Dynamic Variation of Specific Sectors in Stock Exchange: A Case Study of Stock Exchange Thailand (SET) Indexes

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Dunn, D. C., Jablonicky, C., Crespo, G. O., McCauley, D. J., Kroodsma, D. A., Boerder, K., et al. (2018). Empowering high seas governance with satellite vessel tracking data. Fish and Fisheries, 19(4), 729–739.
2.
Agnew, D. J., Pearce, J., Pramod, G., Peatman, T., Watson, R., Beddington, J. R., et al. (2009). Estimating the worldwide extent of illegal fishing. PLoS ONE, 4(2), e4570.
3.
4.
Davies, R., Cripps, S., Nickson, A., & Porter, G. (2009). Defining and estimating global marine fisheries bycatch. Marine Policy, 33(4), 661–672.
5.
Introini, C., Mme, A., & Cruz Introini, S. (2018). Traceability in the food supply chain: Review of the literature from a technological perspective. Dirección y Organización, 64, 50–55.
6.
7.
Melania, B., & Petter, O. (2016). Seafood traceability systems: Gap analysis of inconsistencies in standards and norms. FAO Fisheries and Aquaculture Circular, C1123, 1.
8.
9.
Nicolae, C. G., Isfan, N., Bahaciu, G. V., Marin, M. P., & Moga, L. M. (2016). Case study in traceability and consumer’s choices on fish and fishery products. Agrolife Scientific Journal, 5(2), 103–107.
10.
Nilsson, J. A., Fulton, E. A., Johnson, C. R., Haward, M., Nilsson, J. A., Fulton, E. A., et al. (2019). How to sustain fisheries: Expert knowledge from 34 nations. Water, 11(2), 213.
11.
12.
13.
Pramod, G., Nakamura, K., Pitcher, T. J., & Delagran, L. (2014). Estimates of illegal and unreported fish in seafood imports to the USA. Marine Policy, 48, 102–113.
14.
Leroy, A., Galletti, F., & Chaboud, C. (2016). The EU restrictive trade measures against IUU fishing. Marine Policy, 64, 82–90.
15.
Miller, N. A., Roan, A., Hochberg, T., Amos, J., & Kroodsma, D. A. (2018). Identifying global patterns of transshipment behavior. Frontiers in Marine Science, 5, 240.
16.
17.
Willette, D. A., & Cheng, S. H. (2017). Delivering on seafood traceability under the new US import monitoring program. Ambio, 47(1), 25–30.
18.
Thompson, M., Sylvia, G., & Morrissey, M. T. (2005). Seafood traceability in the United States: Current trends, system design, and potential applications. Compr Rev Food Sci Food Saf, 4(1), 1–7.
19.
Petersen, A., & Green, D. Seafood traceability: A practical guide for the U.S. industry.
20.
21.
22.
23.
Lewis, S. G., & Boyle, M. (2017). The expanding role of traceability in seafood: Tools and key initiatives. Journal of Food Science, 82, A13–A21.
24.
25.
Shepperson, J. L., Hintzen, N. T., Szostek, C. L., Bell, E., Murray, L. G., & Kaiser, M. J. (2018). A comparison of VMS and AIS data: The effect of data coverage and vessel position recording frequency on estimates of fishing footprints. ICES Journal of Marine Science, 75(3), 988–998.
26.
Yin, P., Ye, M., Lee, W. C., & Li, Z. (2014). Mining GPS data for trajectory recommendation. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), Vol. 8444, LNAI, no. PART 2, pp. 50–61.
27.
Mao, S., Tu, E., Zhang, G., Rachmawati, L., Rajabally, E., & Huang, G.-B. (2017). An automatic identification system (AIS) database for maritime trajectory prediction and data mining. In Proceedings in adaptation, learning and optimization, pp. 241–257.
28.
Watson, J. T., & Haynie, A. C. (2016). Using vessel monitoring system data to identify and characterize trips made by fishing vessels in the United States North Pacific. PLoS ONE, 11(10), e0165173.
29.
Stop Illegal Fishing. (2018). The potential use of ’automatic information systems—AIS as a fisheries monitoring tool. Fish-i Africa, pp. 1–21.
30.
de Souza, E. N., Boerder, K., Matwin, S., & Worm, B. (2016). Improving fishing pattern detection from satellite AIS using data mining and machine learning. PLoS ONE, 11(7), e0158248.
31.
Russo, T., Dandrea, L., Parisi, A., Martinelli, M., Belardinelli, A., Boccoli, F., et al. (2016). Assessing the fishing footprint using data integrated from different tracking devices: Issues and opportunities. Ecological Indicators, 69, 818–827.
32.
Longépé, N., Hajduch, G., Ardianto, R., Joux, R., Nhunfat, B., Marzuki, M. I., et al. (2017). Completing fishing monitoring with spaceborne Vessel Detection System (VDS) and Automatic Identification System (AIS) to assess illegal fishing in Indonesia. Marine Pollution Bulletin, 131, 33–39.
33.
Soleymani, A. (2016). Cross-scale analysis in classification and segmentation of movement. Ph.D. dissertation, University of Zurich.
34.
Dodge, S., Weibel, R., & Forootan, E. (2009). Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects. Computers, Environment and Urban Systems, 33(6), 419–434.
35.
Teimouri, M., Indahl, U., Sickel, H., & Tveite, H. (2018). Deriving animal movement behaviors using movement parameters extracted from location data. ISPRS International Journal of Geo-Information, 7(2), 78.
36.
Benhamou, S. (2004). How to reliably estimate the tortuosity of an animal’s path: Straightness, sinuosity, or fractal dimension? Journal of Theoretical Biology, 229(2), 209–220.MathSciNetMATH
37.
Postlethwaite, C. M., Brown, P., & Dennis, T. E. (2013). A new multi-scale measure for analysing animal movement data. Journal of Theoretical Biology, 317, 175–185.MathSciNetMATH
38.
Dodge, S., Weibel, R., & Lautenschütz, A.-K. (2008). Towards a taxonomy of movement patterns. Information Visualization, 7, 240–252.
39.
Yoon, H., & Shahabi, C. (2008). Robust time-referenced segmentation of moving object trajectories. In Proceedings—IEEE international conference on data mining, ICDM, pp. 1121–1126.
40.
Wan, Y., Zhou, C., & Pei, T. (2017). Semantic-geographic trajectory pattern mining based on a new similarity measurement. ISPRS International Journal of Geo-Information, 6(7), 212.
41.
Ying, J. J.-C., Lee, W.-C., & Tseng, V. S. (2013). Mining geographic-temporal-semantic patterns in trajectories for location prediction. ACM Transactions on Intelligent Systems and Technology, 5(1), 1–33.
42.
Hatami, N., Gavet, Y., & Debayle, J. (2018). Classification of time-series images using deep convolutional neural networks. In A. Verikas, P. Radeva, D. Nikolaev, J. Zhou (Eds.), Tenth international conference on machine vision (ICMV 2017), vol. 10696, International Society for Optics and Photonics. SPIE, pp. 242–249. https://​doi.​org/​10.​1117/​12.​2309486.​
43.
Karim, F., Majumdar, S., & Darabi, H. (2019). Insights into LSTM fully convolutional networks for time series classification. IEEE Access, 7, 67-718–67-725.
44.
Hua, Y., Zhao, Z., Li, R., Chen, X., Liu, Z., & Zhang, H. (2019). Deep learning with long short-term memory for time series prediction. IEEE Communications Magazine, 57(6), 114–119.
45.
McMaster, R. B. (1986). A statistical analysis of mathematical measures for linear simplification. The American Cartographer, 13(2), 103–116.MathSciNet
46.
47.
Buchin, M., & Purves, R. S. (2013). Computing similarity of coarse and irregular trajectories using space-time prisms. In Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems—SIGSPATIAL’13, ACM Press, New York, pp. 456–459.
48.
49.
Vlachos, M., Kollios, G., & Gunopulos, D. (2002). Discovering similar multidimensional trajectories. In Proceedings 18th international conference on data engineering, pp. 673–684.
50.
51.
Mikolov, T., Karafiát, M., Burget, L., & Khudanpur, S. (2010). Recurrent neural network based language model. Proceedings of INTERSPEECH, pp. 1045–1048.
52.
Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166.
53.
Switonski, A., Josinski, H., & Wojciechowski, K. (2018). Dynamic time warping in classification and selection of motion capture data. Multidimensional Systems and Signal Processing, 2018, 1–32.MATH
54.
55.
Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.MATH
56.
Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43–49.MATH
57.
58.
Abraham, Z., & Tan, P.-N. (2010). An integrated framework for simultaneous classification and regression of time-series data. In Proceedings of the 2010 SIAM international conference on data mining, Society for Industrial and Applied Mathematics, Philadelphia, pp. 653–664.
59.
Díaz, S. P., & Vilar, J. A. (2010). Comparing several parametric and nonparametric approaches to time series clustering: A simulation study. Journal of Classification, 27(3), 333–362.MathSciNetMATH
60.
Montero, P., & Vilar, J. A. (2014). TSclust: An R package for time series clustering. Journal of Statistical Software, 62(1), 1–43.
61.
Vaughan, N., & Gabrys, B. (2016). Comparing and combining time series trajectories using dynamic time warping. Procedia Computer Science, 96, 465–474.
62.
Huang, K., Tan, T., & Zhang, Z. (2006). Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In 2006 18th International conference on pattern recognition, Vol. 3. IEEE Computer Society, Los Alamitos, CA, USA,pp. 1135–1138. https://​doi.​org/​10.​1109/​ICPR.​2006.​392.​
63.
64.
Łuczak, M. (2016). Combinseasean catch documentation scheme for marine capture fisheries. Journal of Intelligent & Fuzzy Systems, 1, 373–380.
65.
Yuan, J., Zheng, Y., & Xie, X. (2012). Discovering regions of different functions in a city using human mobility and pois. In Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’12, ACM, New York, NY, USA, pp. 186–194. https://​doi.​org/​10.​1145/​2339530.​2339561
66.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27–34.
67.
Fayyad, U., Piatetsky-shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–54.
68.
Gullo, F. (2015). From patterns in data to knowledge discovery: What data mining can do. Physics Procedia, 62, 18–22.
69.
70.
Bertrand, S., Burgos, J., Gerlotto, F., & Atiquipa, J. (2005). Levy trajectories of Peruvian purse-seiners as an indicator of the spatial distribution of anchovy. ICES Journal of Marine Science, 62(3), 477–482.
71.
Lee, J., South, A. B., & Jennings, S. (2010). Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data. ICES Journal of Marine Science, 67(6), 1260–1271.
72.
Gong, L., Hitomi Sato, B., Toshiyuki Yamamoto, B., Tomio Miwa, B., & Takayuki Morikawa, B. (2015). Identification of activity stop locations in GPS trajectories by density-based clustering method combined with support vector machines. Journal of Modern Transportation, 23(3), 202–213.
73.
Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Technical report, University of Munich.
74.
Fabio Mazzarella, G. O., Vespe, M., Damalas, D. (2014). Discovering vessel activities at sea using AIS data: Mapping of fishing footprints. In 17th International conference on information fusion (FUSION).
75.
Dodge, S., Laube, P., & Weibel, R. (2012). Movement similarity assessment using symbolic representation of trajectories. International Journal of Geographical Information Science, 26(9), 1563–1588.
76.
Shamoun-Baranes, J., Bouten, W., Camphuysen, C. J., & Baaij, E. (2011). Riding the tide: Intriguing observations of gulls resting at sea during breeding. IBIS, 153(2), 411–415.
77.
Cheung, A., Zhang, S., Stricker, C., & Srinivasan, M. V. (2007). Animal navigation: The difficulty of moving in a straight line. Biological Cybernetics, 97(1), 47–61.MathSciNetMATH
78.
Laube, P., & Purves, R. S. (2011). How fast is a cow? Cross-scale analysis of movement data. Transactions in GIS, 15(3), 401–418.
79.
Marzuki, M. I., Gaspar, P., Garello, R., Kerbaol, V., & Fablet, R. (2017). Fishing gear identification from vessel-monitoring-system-based fishing vessel trajectories. IEEE Journal of Oceanic Engineering, 43(3), 3–7.
80.
Li, X. (2014). Using complexity measures of movement for automatically detecting movement types of unknown GPS trajectories. American Journal of Geographic Information System, 3(2), 63–74.
81.
Kitamura, T., & Imafuku, M. (2015). Behavioural mimicry in flight path of Batesian intraspecific polymorphic butterfly Papilio polytes. Proceedings of Biological Sciences, 282(1809), 20150483.
82.
Shamble, P. S., Hoy, R. R., Cohen, I., & Beatus, T. (2017). Walking like an ant: A quantitative and experimental approach to understanding locomotor mimicry in the jumping spider Myrmarachne formicaria. Proceedings of the Royal Society B: Biological Sciences, 284(1858), 20170308.
83.
McLean, D. J., & Skowron Volponi, M. A. (2018). trajr: An R package for characterisation of animal trajectories. Ethology, 124(6), 440–448.
84.
Zhang, Z., Tavenard, R., Bailly, A., Tang, X., Tang, P., & Corpetti, T. (2017). Dynamic time warping under limited warping path length. Information Sciences, 393, 91–107.
Metadaten
Titel
Fishing Vessels Behavior Identification for Combating IUU Fishing: Enable Traceability at Sea
verfasst von
Buncha Chuaysi
Supaporn Kiattisin
Publikationsdatum
14.02.2020
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2020
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-020-07200-w

Weitere Artikel der Ausgabe 4/2020

Wireless Personal Communications 4/2020 Zur Ausgabe

OriginalPaper

Increasing Web Accessibility Through a Personalized Web Browser for Older Adults

OriginalPaper

Performance Analysis for Asynchronous Requests in Wireless Networks with Multiple Packet Reception Capability

OriginalPaper

A Method of Motion-Estimation-Based H.264 Video Coding Using Optimal Search-Range

OriginalPaper

Portfolio Risk and Return with a New Simple Moving Average of Price Change Ratio

OriginalPaper

P2P Control Model for Synchronous Battery-less Solar-Powered Serial Water Pumps

OriginalPaper

A Monolithic Patch Antenna on a Semi-insulated Alumina () Substrate for Active Integrated Antenna