General information about the DOI system can be found here and here. A DOI name is a digital object identifier for any object of intellectual property. A DOI name provides a means of persistently identifying a piece of intellectual property on a digital network and associating it with related current data in a structured extensible way. DOI was accepted as an ISO standard in 2010.
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If you have ever tried to follow an URL in an article older than 5-10 years, more often than not you will find that the URL is no longer active. The DOI system is an attempt to overcome this deficiency by providing stable and permanent references for intellectual property on the web.

The MIC journal has implemented the DOI system for every single article published in MIC since the foundation year in 1980. The DOI prefix for MIC is 10.4173 and an individual article has been assigned a DOI on the following format: 10.4173/ For example, the first article published in MIC by Oddvar Hallingstad has the following DOI: 10.4173/mic.1980.1.1 and the following permanent URL This permanent URL links back to the website. If the MIC website is moved in the future, the DOI information will be updated to point to the new address.

Another advantage of the DOI system, is the possibility to register all the references in an article in a structured manner. All the references made in MIC articles starting from 1980 have been submitted into the DOI system. The effect is an increased visibility of MIC articles, which again will lead to a wider audience. MIC also participates in the 'cited-by' system, which can be seen for this article. 'cited-by' shows which other papers have included the actual paper in the reference lists.

The MIC class files for pdfLaTeX found in the Author Information have commands for embedding DOI information in the PDF files. Prospective authors for future MIC articles will receive the DOI identification when the article is accepted. Authors are encouraged to embed the tag into the PDF file themselves using pdflatex prior to publication. Authors are also encouraged to embed DOI tags in their reference lists.

Click on the links below to see the external DOI forward links to MIC:
1980    1981    1982    1983    1984    1985    1986    1987    1988    1989    
1990    1991    1992    1993    1994    1995    1996    1997    1998    1999    
2000    2001    2002    2003    2004    2005    2006    2007    2008    2009    
2010    2011    2012    2013    2014    2015    2016    2017    2018    2019    
2020    2021    2022    2023    

DOI Forward Links to MIC for Year: 2021

 Total number of MIC articles in 2021  14
 Total number of DOI citations  16
 Average citations per article   1.14 

2021, Vol. 42, No. 4:
1.Anna Tupitsina, Jan-Henri Montonen, Jani Alho, Paula Immonen, Mika Lauren, Pia Lindh and Tuomo Lindh, “Simulation tool for dimensioning power train of hybrid working machine”, pp. 143-158
DOI forward links to this article:
[1] Mika Lauren, Giota Goswami, Anna Tupitsina, Suraj Jaiswal, Tuomo Lindh and Jussi Sopanen (2021), doi:10.3390/machines10010026
[2] Shruti Singh, Ilya Petrov, Juha Pyrhonen and Peter Sergeant (2022), doi:10.1109/ICEM51905.2022.9910846
2.Simon Christensen, Xuerong Li and Shaoping Bai, “Modeling and Analysis of Physical Human-Robot Interaction of an Upper Body Exoskeleton in Assistive Applications”, pp. 159-172
DOI forward links to this article:
[1] David Scherb, Sandro Wartzack and Jorg Miehling (2023), doi:10.3389/fbioe.2022.1044275
3.Sihan Gao, Lars Christian Gansel, Guoyuan Li and Houxiang Zhang, “An Integrated Approach to Modelling Fish Cage Response in the Flow”, pp. 173-184
4.Mishiga Vallabhan, Jose Matias and Christian Holden, “Feedforward, Cascade and Model Predictive Control Algorithms for De-Oiling Hydrocyclones: Simulation Study”, pp. 185-195
DOI forward links to this article:
[1] Mishiga Vallabhan K G, Christian Holden and Sigurd Skogestad (2022), doi:10.2118/209576-PA
[2] Jaroslav Hlava and Shereen Abouelazayem (2022), doi:10.3390/s22082847
5.Martinius Knudsen, Sverre Hendseth, Gunnar Tufte and Axel Sandvig, “Model-Free All-Source-All-Destination Learning as a Model for Biological Reactive Control”, pp. 197-204
2021, Vol. 42, No. 3:
1.Hans K.R. Holen, Alexander M. Sjøberg and Olav Egeland, “Estimation of Ship-Deck Motion using LIDAR,Gyroscopes and Cameras”, pp. 99-112
2.Konrad J. Jensen, Morten K. Ebbesen and Michael R. Hansen, “Development of 3D Anti-Swing Control for Hydraulic Knuckle Boom Crane”, pp. 113-129
DOI forward links to this article:
[1] Konrad Johan Jensen, Morten Kjeld Ebbesen and Michael Rygaard Hansen (2022), doi:10.3390/robotics11020034
3.Jani Alho, Tuomo Lindh, Pasi Peltoniemi, Jan-Henri Montonen, Andrey Lana, Antti Pinomaa and Olli Pyrhönen, “Optimal control of powertrain and energy balance to recover an equipment fault on a marine vessel”, pp. 131-141
2021, Vol. 42, No. 2:
1.Joacim Dybedal and Geir Hovland, “CNN-based People Detection in Voxel Space using Intensity Measurements and Point Cluster Flattening”, pp. 37-46
2.Savin Viswanathan, Christian Holden, Olav Egeland and Marilena Greco, “An Open-Source Python-Based Boundary-Element Method Code for the Three-Dimensional, Zero-Froude, Infinite-Depth, Water-Wave Diffraction-Radiation Problem”, pp. 47-81
3.Eirik B. Njaastad, Geir Ole Tysse and Olav Egeland, “Residual vibration control for robotic 3D scanning with application to inspection of marine propellers”, pp. 83-98
2021, Vol. 42, No. 1:
1.Alberto Maximiliano Crescitelli, Lars Christian Gansel and Houxiang Zhang, “NorFisk: fish image dataset from Norwegian fish farms for species recognition using deep neural networks”, pp. 1-16
DOI forward links to this article:
[1] Jennifer L. Bell, Randy Mandel, Andrew S. Brainard, Jon Altschuld and Richard J. Wenning (2022), doi:10.1002/ieam.4622
[2] K Banno, H Kaland, AM Crescitelli, SA Tuene, GH Aas and LC Gansel (2022), doi:10.3354/aei00432
[3] Ricardo J. M. Veiga, Inigo E. Ochoa, Adela Belackova, Luis Bentes, Joao P. Silva, Jorge Semiao and Joao M. F. Rodrigues (2022), doi:10.3390/app12125910
[4] Alexa Sugpatan Abangan, Dorothee Kopp and Robin Faillettaz (2023), doi:10.3389/fmars.2023.1010761
2.Robert Skulstad, Guoyuan Li, Thor I. Fossen, Tongtong Wang and Houxiang Zhang, “A Co-operative Hybrid Model For Ship Motion Prediction”, pp. 17-26
DOI forward links to this article:
[1] Motoyasu Kanazawa, Robert Skulstad, Guoyuan Li, Lars Ivar Hatledal and Houxiang Zhang (2021), doi:10.1109/JSEN.2021.3119069
[2] Motoyasu Kanazawa, Robert Skulstad, Tongtong Wang, Guoyuan Li, Lars Ivar Hatledal and Houxiang Zhang (2022), doi:10.1109/JSEN.2022.3171036
[3] Motoyasu Kanazawa, Lars Ivar Hatledal, Guoyuan Li and Houxiang Zhang (2022), doi:10.1007/978-3-031-12429-7_13
[4] Motoyasu Kanazawa, Tongtong Wang, Robert Skulstad, Guoyuan Li and Houxiang Zhang (2022), doi:10.1016/j.oceaneng.2022.112998
[5] Gianluca Antonelli, Stefano Chiaverini and Paolo Di Lillo (2022), doi:10.1007/s11071-022-08192-x
3.Fredrik Bengtsson and Torsten Wik, “Finding feedforward configurations using gramian based interaction measures”, pp. 27-35
DOI forward links to this article:
[1] M Manogna, B Amarendra Reddy and K Padma (2023), doi:10.1088/2631-8695/acc0dc