FOREX Press I. J. of Electrical & Electronics Research
Support Open Access

Research Article |

Evaluation of IIOT based Pd-MaaS using CNN with Ensemble Subspace Discriminate – for Indian Ship Building in Maritime Industry

Author(s): PNV Srinivasa Rao 1* and PVY Jayasree2

Publisher : FOREX Publication

Published : 15 March 2023

e-ISSN : 2347-470X

Page(s) : 103-111




PNV Srinivasa Rao*, Department of EECE, GITAM Institute of Technology, GITAM University, Visakhapatnam, India; Email: pnvsrinu@yahoo.com

PVY Jayasree, Department of EECE, GITAM Institute of Technology, GITAM University, Visakhapatnam, India

    [1] Herterich, M. M., Uebernickel, F., & Brenner, W. (2015). The impact of cyber-physical systems on industrial services in manufacturing. Procedia Cirp, 30, 323-328. [Cross Ref]
    [2] Mascaraque-Ramírez, C., & Para-González, L. (2022). Can the six dimensions of Marketing Promotion enhance performance in the international shipbuilding industry? Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 236(1), 245-256. [Cross Ref]
    [3] Mourougane, A. (2020). Present Scenario of Ship Building Industry in Indian. Center for Development Economic, 6(06), 29-38.
    [4] Thangam, K. M., & Sureshkumar, D. (2015). Competitiveness of Indian ship building industry. International Journal of Innovative Research & Development, 4(7), 18-25.
    [5] Jaison, M. (2021). TOWARDS GREATER SHIPBUILDING SUPPLY CHAIN SURPLUS IN INDIA–A REVIEW. Industrial Engineering Journal (ISSN-0970-2555), 14(1), 5-14.
    [6] Sender, J., Illgen, B., Klink, S., & Flügge, W. (2021). Integration of learning effects in the design of shipbuilding networks. Procedia CIRP, 100, 103-108. [Cross Ref]
    [7] Shahbakhsh, M., Emad, G. R., & Cahoon, S. (2022). Industrial revolutions and transition of the maritime industry: The case of Seafarer’s role in autonomous shipping. The Asian Journal of Shipping and Logistics, 38(1), 10-18. [Cross Ref]
    [8] Ichimura, Y., Dalaklis, D., Kitada, M., & Christodoulou, A. (2022). Shipping in the era of digitalization: Mapping the future strategic plans of major maritime commercial actors. Digital Business, 2(1), 100022. [Cross Ref]
    [9] Theodoropoulos, P., Spandonidis, C. C., & Fassois, S. (2022). Use of Convolutional Neural Networks for vessel performance optimization and safety enhancement. Ocean Engineering, 248, 110771. [Cross Ref]
    [10] Taşdemir, A., & Nohut, S. (2021). An overview of wire arc additive manufacturing (WAAM) in shipbuilding industry. Ships and Offshore Structures, 16(7), 797-814. [Cross Ref]
    [11] Kiel, D., Arnold, C., & Voigt, K. I. (2017). The influence of the Industrial Internet of Things on business models of established manufacturing companies–A business level perspective. Technovation, 68, 4-19. [Cross Ref]
    [12] Kiran, S., & Sriramoju, S. B. (2018). A Study on the Applications of IOT. Indian Journal of Public Health Research & Development, 9(11).
    [13] Bi, Z., Da Xu, L., & Wang, C. (2014). Internet of things for enterprise systems of modern manufacturing. IEEE Transactions on industrial informatics, 10(2), 1537-1546. [Cross Ref]
    [14] Schneider, S. (2017). The industrial internet of things (iiot) applications and taxonomy. Internet of Things and Data Analytics Handbook, 41-81. [Cross Ref]
    [15] Kim, G. S., & Lee, Y. H. (2021). Transformation towards a smart maintenance factory: The case of a vessel maintenance depot. Machines, 9(11), 267. [Cross Ref]
    [16] Wang, K., Yan, X., Yuan, Y., & Li, F. (2016). Real-time optimization of ship energy efficiency based on the prediction technology of working condition. Transportation Research Part D: Transport and Environment, 46, 81-93. [Cross Ref]
    [17] Ariany, Z., Pitana, T., & Vanany, I. (2022). Review of the Risk Assessment Methods for Shipbuilding in Indonesia. In IOP Conference Series: Earth and Environmental Science (Vol. 972, No. 1, p. 012056). IOP Publishing. [Cross Ref]
    [18] Kiangala, K. S., & Wang, Z. (2020). An effective predictive maintenance framework for conveyor motors using dual time-series imaging and convolutional neural network in an industry 4.0 environment. Ieee Access, 8, 121033-121049. [Cross Ref]
    [19] Hafeez, T., Xu, L., & Mcardle, G. (2021). Edge intelligence for data handling and predictive maintenance in IIOT. IEEE Access, 9, 49355-49371. [Cross Ref]
    [20] Wang, Y., Li, Y., Song, Y., & Rong, X. (2020). The influence of the activation function in a convolution neural network model of facial expression recognition. Applied Sciences, 10(5), 1897. [Cross Ref]
    [21] Wu, J. (2017). Introduction to convolutional neural networks. National Key Lab for Novel Software Technology. Nanjing University. China, 5(23), 495.
    [22] Shoaran, M., Haghi, B. A., Taghavi, M., Farivar, M., & Emami-Neyestanak, A. (2018). Energy-efficient classification for resource-constrained biomedical applications. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 8(4), 693-707. [Cross Ref]
    [23] Tao, W., Lai, Z. H., Leu, M. C., & Yin, Z. (2018). Worker activity recognition in smart manufacturing using IMU and sEMG signals with convolutional neural networks. Procedia Manufacturing, 26, 1159-1166. [Cross Ref]
    [24] Yuan, Y., Ma, G., Cheng, C., Zhou, B., Zhao, H., Zhang, H. T., & Ding, H. (2018). Artificial intelligent diagnosis and monitoring in manufacturing. arXiv preprint arXiv:1901.02057.
    [25] Ren, L., Sun, Y., Cui, J., & Zhang, L. (2018). Bearing remaining useful life prediction based on deep autoencoder and deep neural networks. Journal of Manufacturing Systems, 48, 71-77. [Cross Ref]
    [26] Li, L., Ota, K., & Dong, M. (2018). Deep learning for smart industry: Efficient manufacture inspection system with fog computing. IEEE Transactions on Industrial Informatics, 14(10), 4665-4673. [Cross Ref]
    [27] Jiang, L., Ge, Z., & Song, Z. (2017). Semi-supervised fault classification based on dynamic Sparse Stacked auto-encoders model. Chemometrics and Intelligent Laboratory Systems, 168, 72-83. [Cross Ref]
    [28] Zhang, W., Guo, W., Liu, X., Liu, Y., Zhou, J., Li, B., ... & Yang, S. (2018). LSTM-based analysis of industrial IoT equipment. IEEE Access, 6, 23551-23560. [Cross Ref]
    [29] Krishnaveni, P., & Sutha, J. (2020). Novel deep learning framework for broadcasting abnormal events obtained from surveillance applications. Journal of Ambient Intelligence and Humanized Computing, 1-15. [Cross Ref]
    [30] Bharathi, S., & Venkatesan, P. (2022). Enhanced Classification of Faults of Photovoltaic Module Through Generative Adversarial Network. IJEER, 10(3), 579-584. [Cross Ref]
    [31] Femy, P. H., & Jayakumar, J. (2021). A Review on the Feasibility of Deployment of Renewable Energy Sources for Electric Vehicles under Smart Grid Environment. IJEER, 9(3), 57-65. [Cross Ref]
    [32] Kulkarni, S., & Thosar, A. (2022). Performance Analysis of Fault Tolerant Operation of PMSM using Direct Torque Control and Fuzzy Logic Control. IJEER, 10(2), 297-307. [Cross Ref]
    [33] Ramanna, D., & Ganesan, V. (2022). Low-Power VLSI Implementation of Novel Hybrid Adaptive Variable-Rate and Recursive Systematic Convolutional Encoder for Resource Constrained Wireless Communication Systems. IJEER, 10(3), 523-528.Fröhlich, B. and Plate, J. 2013. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. [Cross Ref]

PNV Srinivasa Rao and PVY Jayasree (2023), Evaluation of IIOT based Pd-MaaS using CNN with Ensemble Subspace Discriminate – for Indian Ship Building in Maritime Industry. IJEER 11(1), 103-111. DOI: 10.37391/IJEER.110114.