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Alsolai, H., Qureshi, S., Iqbal, S. M., Vanichayobon, S., Henesey, L., Lindley, C. & Karrila, S. (2022). A Systematic Review of Literature on Automated Sleep Scoring. IEEE Access, 10, 79419-79443
Open this publication in new window or tab >>A Systematic Review of Literature on Automated Sleep Scoring
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2022 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 79419-79443Article, review/survey (Refereed) Published
Abstract [en]

Sleep is a period of rest that is essential for functional learning ability, mental health, and even the performance of normal activities. Insomnia, sleep apnea, and restless legs are all examples of sleep-related issues that are growing more widespread. When appropriately analyzed, the recording of bio-electric signals, such as the Electroencephalogram, can tell how well we sleep. Improved analyses are possible due to recent improvements in machine learning and feature extraction, and they are commonly referred to as automatic sleep analysis to distinguish them from sleep data analysis by a human sleep expert. This study outlines a Systematic Literature Review and the results it provided to assess the present state-of-the-art in automatic analysis of sleep data. A search string was organized according to the PICO (Population, Intervention, Comparison, and Outcome) strategy in order to determine what machine learning and feature extraction approaches are used to generate an Automatic Sleep Scoring System. The American Academy of Sleep Medicine and Rechtschaffen & Kales are the two main scoring standards used in contemporary research, according to the report. Other types of sensors, such as Electrooculography, are employed in addition to Electroencephalography to automatically score sleep. Furthermore, the existing research on parameter tuning for machine learning models that was examined proved to be incomplete. Based on our findings, different sleep scoring standards, as well as numerous feature extraction and machine learning algorithms with parameter tuning, have a high potential for developing a reliable and robust automatic sleep scoring system for supporting physicians. In the context of the sleep scoring problem, there are evident gaps that need to be investigated in terms of automatic feature engineering techniques and parameter tuning in machine learning algorithms.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
Sleep, Feature extraction, Machine learning, Electroencephalography, StandardsSleep apnea, Deep learning, Artificial neural network, automatic sleep scoring system, big data, feature extraction, inter-rater variability, machine learning, sleep stages
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-23557 (URN)10.1109/access.2022.3194145 (DOI)000836601900001 ()
Note

open access

Available from: 2022-08-19 Created: 2022-08-19 Last updated: 2022-08-19Bibliographically approved
Alsolai, H., Qureshi, S., Iqbal, S. M., Ameer, A., Cheaha, D., Henesey, L. & Karrila, S. (2022). Employing a Long-Short-Term Memory Neural Network to Improve Automatic Sleep Stage Classification of Pharmaco-EEG Profiles. Applied Sciences, 12(10), Article ID 5248.
Open this publication in new window or tab >>Employing a Long-Short-Term Memory Neural Network to Improve Automatic Sleep Stage Classification of Pharmaco-EEG Profiles
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2022 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 10, article id 5248Article in journal (Refereed) Published
Abstract [en]

An increasing problem in today's society is the spiraling number of people suffering from various sleep disorders. The research results presented in this paper support the use of a novel method that employs techniques from the classification of sleep disorders for more accurate scoring. Applying this novel method will assist researchers with better analyzing subject profiles for recommending prescriptions or to alleviate sleep disorders. In biomedical research, the use of animal models is required to experimentally test the safety and efficacy of a drug in the pre-clinical stage. We have developed a novel LSTM Recurrent Neural Network to process Pharmaco-EEG Profiles of rats to automatically score their sleep-wake stages. The results indicate improvements over the current methods; for the case of combined channels, the model accuracy improved by 1% and 3% in binary or multiclass classifications, respectively, to accuracies of 93% and 82%. In the case of using a single channel, binary and multiclass LSTM models for identifying rodent sleep stages using single or multiple electrode positions for binary or multiclass problems have not been evaluated in prior literature. The results reveal that single or combined channels, and binary or multiclass classification tasks, can be applied in the automatic sleep scoring of rodents.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
recurrent neural network (RNN), electroencephalography (EEG), long short-term memory (LSTM), automatic sleep scoring, deep learning
National Category
Computer Sciences Neurosciences
Identifiers
urn:nbn:se:bth-23088 (URN)10.3390/app12105248 (DOI)000801687600001 ()
Note

open access

Available from: 2022-06-10 Created: 2022-06-10 Last updated: 2022-11-15Bibliographically approved
Paulauskas, V., Henesey, L., Paulauskas, D. & Simutis, M. (2022). Optimizing transportation between ports and the hinterland for decreasing impact to the environment. In: Bottani E., Bruzzone A.G., Longo F., Merkuryev Y., Piera M.A. (Ed.), International Conference on Harbour, Maritime and Multimodal Logistics Modelling and Simulation: . Paper presented at 24th International Conference on Harbor, Maritime and Multimodal Logistic Modeling and Simulation, HMS 2022, Rome, 19 September through 21 September 2022 (pp. 1-14). CAL-TEK
Open this publication in new window or tab >>Optimizing transportation between ports and the hinterland for decreasing impact to the environment
2022 (English)In: International Conference on Harbour, Maritime and Multimodal Logistics Modelling and Simulation / [ed] Bottani E., Bruzzone A.G., Longo F., Merkuryev Y., Piera M.A., CAL-TEK , 2022, p. 1-14Conference paper, Published paper (Refereed)
Abstract [en]

Today different transport modes use to deliver cargo between regions, from ports to final destination location or visa-versa. It is quite common to use road transport, which can deliver cargo “from door to door” but road transport causes big environmental impact. Considering alternative possibilities (road, railway and/or inland waterway transport) to decrease environmental impact from transport, it is very important. Based on theoretical and experimental tests, were find optimal solutions, which transport mode make minimum environmental impact and could be the most technically and economically effective solution. Traffic congestion on the roads, in some cases very high railway traffic in some regions, generates requirements by many stakeholders on ways to decrease the environmental impact from transport modes, which studded in Article to find and identify optimal transportation solutions with minimum environmental impact. A theoretical method evaluation conducted on the optimal transportation possibility that minimizes environmental impact. A transport modes environmental comparative index (ECI) is developed and used for evaluations. This paper presents possible alternative transportation conditions based on multi-criteria evaluation system, proposes theoretical basis for the optimal solutions from environmental and economic point of view, and provides for experimental testing during the specific case study, and finally provides recommendations and conclusions. © 2022 The Authors.

Place, publisher, year, edition, pages
CAL-TEK, 2022
Series
International Conference on Harbour, Maritime and Multimodal Logistics Modelling and Simulation, ISSN 27240339
Keywords
emissions, environmental comparative analysis, environmental comparative index, environmental impact, transport modes
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:bth-23750 (URN)10.46354/i3m.2022.hms.008 (DOI)2-s2.0-85139052216 (Scopus ID)9788885741744 (ISBN)
Conference
24th International Conference on Harbor, Maritime and Multimodal Logistic Modeling and Simulation, HMS 2022, Rome, 19 September through 21 September 2022
Note

open access

Available from: 2022-10-14 Created: 2022-10-14 Last updated: 2022-10-14Bibliographically approved
Paulauskas, V., Henesey, L., Plačiene, B., Jonkus, M., Paulauskas, D., Barzdžiukas, R., . . . Simutis, M. (2022). Optimizing Transportation between Sea Ports and Regions by Road Transport and Rail and Inland Waterway Transport Means Including “Last Mile” Solutions. Applied Sciences, 12(20), Article ID 10652.
Open this publication in new window or tab >>Optimizing Transportation between Sea Ports and Regions by Road Transport and Rail and Inland Waterway Transport Means Including “Last Mile” Solutions
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2022 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 20, article id 10652Article in journal (Refereed) Published
Abstract [en]

Optimization transportation cargo and passengers between ports and regions are very important, because industrial regions are located some distance from ports. The demand for energy request for the movement of transport is a necessity in the modern world. Transport and activity called transportation are used daily, everywhere, and a lot of energy is needed to power the various transport modes. Today different transport modes are being used to transport passengers and cargo. It is quite common to use road transport, which can transport passengers and cargo from door to door. Considering alternative possibilities (road, railway and/or inland waterway transport), it is important, based on theoretical and experimentation, to identify optimal solutions. In finding transport modes that are either most technically or economically effective, we could unearth possible solutions which would require minimal energy use. Unfortunately, with increased transportation, this often leads to traffic congestion on the roads, which requires additional energy (fuel). This situation generates requirements from many stakeholders in terms of finding ways to decrease the transportation time and energy (fuel) consumed by transport modes. A theoretical method evaluation is conducted on the optimal transportation possibility that minimizes transportation time and energy (fuel) use by employing graph theory, which is presented in this paper. The scientific contribution is the development of a transport modes comparative index, which is then used for evaluations. This paper presents possible alternative transportation conditions based on a multi-criteria evaluation system, proposes a theoretical basis for the optimal solutions from an eco-economic perspective that considers energy, and provides for experimental testing during a specific case study. The final results from the case study provide recommendations and conclusions. © 2022 by the authors.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
alternative fuels, connection to sea ports, energy consumption, optimal transportation solutions, transport mode comparative index, transport modes
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:bth-23826 (URN)10.3390/app122010652 (DOI)000872193800001 ()2-s2.0-85140450472 (Scopus ID)
Note

open access

Available from: 2022-11-04 Created: 2022-11-04 Last updated: 2022-12-13Bibliographically approved
Mhathesh, T. S., Andrew, J., Martin Sagayam, K. & Henesey, L. (2021). A 3d convolutional neural network for bacterial image classification. In: Peter J.D.,Fernandes S.L.,Alavi A.H.,Alavi A.H. (Ed.), Advances in Intelligent Systems and Computing: . Paper presented at 3rd International Conference on Big-Data and Cloud Computing, ICBDCC 2019, Coimbatore, India, 6 December 2019 through 7 December 2019 (pp. 419-431). Springer, 1167
Open this publication in new window or tab >>A 3d convolutional neural network for bacterial image classification
2021 (English)In: Advances in Intelligent Systems and Computing / [ed] Peter J.D.,Fernandes S.L.,Alavi A.H.,Alavi A.H., Springer , 2021, Vol. 1167, p. 419-431Conference paper, Published paper (Refereed)
Abstract [en]

Identification and analysis of biological microscopy images need high focus and years of experience to master the art. The rise of deep neural networks enables analyst to achieve the desired results with reduced time and cost. Light sheet fluorescence microscopies are one of the types of 3D microcopy images. Processing microscopy images is tedious process as it consists of low-level features. It is necessary to use proper image processing techniques to extract the low-level features of the biological microscopy images. Deep neural networks (DNN) are efficient in extracting the features of images and able to classify with high accuracy. Convolutional neural networks (CNN) are one of the types of neural networks that can provide promising results with less error rates. The ability of CNN to extract the low-level features of images makes it popular for image classification. In this paper, a CNN-based 3D bacterial image classification is proposed. 3D images contain more in-depth features than 2D images. The proposed CNN model is trained on 3D light sheet fluorescence microscopy images of larval zebrafish. The proposed CNN model classifies the bacterial and non-bacterial images effectively. Intense experimental analyses are carried out to find the optimal complexity and to get better classification accuracy. The proposed model provides better results than human comprehension and other traditional machine learning approaches like random forest, support vector classifier, etc. The details of network architecture, regularization, and hyperparameter optimization techniques are also presented. © Springer Nature Singapore Pte Ltd 2021.

Place, publisher, year, edition, pages
Springer, 2021
Keywords
3D light sheet, Bacterial image classification, Convolutional neural network, Deep learning, Feature extraction, Image classification, Bacteria, Big data, Convolution, Convolutional neural networks, Decision trees, Deep neural networks, Fluorescence, Fluorescence microscopy, Network architecture, Random forests, Biological microscopy, Classification accuracy, Experimental analysis, Fluorescence microscopy images, Hyper-parameter optimizations, Image processing technique, Machine learning approaches, Support vector classifiers
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:bth-20306 (URN)10.1007/978-981-15-5285-4_42 (DOI)2-s2.0-85089315384 (Scopus ID)9789811552847 (ISBN)
Conference
3rd International Conference on Big-Data and Cloud Computing, ICBDCC 2019, Coimbatore, India, 6 December 2019 through 7 December 2019
Available from: 2020-08-21 Created: 2020-08-21 Last updated: 2020-08-21Bibliographically approved
Meyer, C., Gerlitz, L. & Henesey, L. (2021). Cross-Border Capacity-Building for Port Ecosystems in Small and Medium-Sized Baltic Ports. Baltic Journal of European studies, 11(1), 113-132
Open this publication in new window or tab >>Cross-Border Capacity-Building for Port Ecosystems in Small and Medium-Sized Baltic Ports
2021 (English)In: Baltic Journal of European studies, ISSN 2228-0596, E-ISSN 2228-0588, Vol. 11, no 1, p. 113-132Article in journal (Refereed) Published
Abstract [en]

One of the key challenges related to the threat posed by the COVID-19 pandemic is preservation of employment and protecting staff who are working in port operations and struggling to keep ports operating for ship calls. These activities performed by port labour are deemed to be crucial for the EU and European ports, since 75% of the EU external trade and 30% of intra-EU transport goods are moved by waterborne transport. As a response to the global lockdown and the vulnerability of global supply chains, the majority of international organisations and maritime ports networks have shortlisted measures necessary to keep the severe effects of the lockdown to a minimum. One of the key measures identified is how to limit physical interaction. As an effect, millions of people and organisations across the globe have had to use and/or increase their deployment of digital technologies, such as digital documentation, tracing information systems and digital group-working platforms. Hence, blockchain and data-enabling systems have become to be recognised as a core element maintaining the uninterrupted flow of goods and services at ports. In pursuing uninterrupted trade and keeping ports open and running, this research paper addresses how the current situation afflicts the small and medium-sized ports located on the Baltic Sea which are argued to be critical actors of the port-centric logistics' ecosystem. Given the topicality of this research and addressing the research gap, the authors suggest a conceptual capacity-building framework for port employees. This suggested framework is based on empirical insights: primary and secondary data collected from the project Connect2SmallPorts, part-financed by the Interreg South Baltic Programme 2014-2020 from the European Regional Development Fund (ERDF). The conceptual framework aims towards a practical training programme dedicated to fill in the missing skills or expand the limited competence of human resources and ports' capacity when adapting or advancing digitalisation in the ports' ecosystems. In particular, specific areas of capacity building are addressed and individual solutions suggested to foster a digital transformation of ports. The conceptual training framework is designed as a training tool indicating opportunities to help ports upgrade their competences with the blockchain technology, and to advance their transportation, environmental and economic performance with improved digitalisation. For this purpose, the conducted research employed mixed methods and applied concepts and approaches based on the field of management. For example, the construct of absorptive capacity, organisational learning, transformation, resource-based view and the concept of dynamic capabilities are included in the ecosystem discourse and are linked with open innovation and service design. The research presented in this article provides both theoretical and practical contributions, in which the affected stakeholders can test and utilise the developed tool as well as transfer it to other regions. © 2021 Christopher Meyer et al., published by Sciendo 2021.

Place, publisher, year, edition, pages
De Gruyter Open, 2021
Keywords
blockchain, capacity building, digital transformation, digitalisation, small ports, the Baltic Sea, training
National Category
Transport Systems and Logistics Business Administration
Identifiers
urn:nbn:se:bth-22347 (URN)10.2478/bjes-2021-0008 (DOI)000884551800008 ()2-s2.0-85107558522 (Scopus ID)
Note

open access

Available from: 2021-11-11 Created: 2021-11-11 Last updated: 2023-01-02Bibliographically approved
Henesey, L., Silonosov, A., Meyer, C. & Gerlitz, L. (2021). Smart Container Stacking in the Yard. In: International Conference on Harbour, Maritime and Multimodal Logistics Modelling and Simulation: . Paper presented at 23rd International Conference on Harbor, Maritime and Multimodal Logistic Modeling and Simulation, HMS 2021, Virtual, Online, 15 September through 17 September 2021 (pp. 37-44). CAL-TEK
Open this publication in new window or tab >>Smart Container Stacking in the Yard
2021 (English)In: International Conference on Harbour, Maritime and Multimodal Logistics Modelling and Simulation, CAL-TEK , 2021, p. 37-44Conference paper, Published paper (Refereed)
Abstract [en]

The workloads at seaport container terminals are increasing; thus, to enhance performance, the focus on improving container stacking is argued to be an integral factor that should be studied. The main problem is the number of unproductive moves of handling containers. A well-planned stacking area is argued to be a key requirement in order to increase the performance of the terminal operations and assist in maximum utilization of existing resources. In this work, we investigated and then propose the best possible solution by evaluating GAs in order to minimize the unproductive moves often witnessed in terminal operations. A discrete-event simulation CSS model has been developed to study the inbound container stacking that considers in the model the following: the working of the yard crane, Automated Guided Vehicles, delivery trucks and obtain the simulation-based results of GA. We propose a mathematical model to minimize the container handling costs during stacking and retrieval operations in the container terminal yard. © 2021 The Authors.

Place, publisher, year, edition, pages
CAL-TEK, 2021
Series
International Conference on Harbour, Maritime and Multimodal Logistics Modelling and Simulation, ISSN 27240339
Keywords
Automated guided vehicle (AGV), Container stacking, Container Terminal, Genetic Algorithm (GA)
National Category
Control Engineering Computational Mathematics
Identifiers
urn:nbn:se:bth-23753 (URN)10.46354/i3m.2021.hms.005 (DOI)2-s2.0-85139046868 (Scopus ID)9788885741591 (ISBN)
Conference
23rd International Conference on Harbor, Maritime and Multimodal Logistic Modeling and Simulation, HMS 2021, Virtual, Online, 15 September through 17 September 2021
Funder
European Regional Development Fund (ERDF), Connect2SmallPorts
Note

open access

Available from: 2022-10-14 Created: 2022-10-14 Last updated: 2022-10-14Bibliographically approved
Paulauskas, V., Philipp, R., Henesey, L., Paulauskas, D., Sutnikas, A., Meyer, C., . . . Silonosov, A. (2021). Smart Ports’ Influence on Coastal Sustainability. In: Transport Means - Proceedings of the International Conference: . Paper presented at 25th International Scientific Conference Transport Means 2021, Kaunas, Lithuania, Virtual,6 October 2021 through 8 October 2021 (pp. 396-401). Kauno Technologijos Universitetas
Open this publication in new window or tab >>Smart Ports’ Influence on Coastal Sustainability
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2021 (English)In: Transport Means - Proceedings of the International Conference, Kauno Technologijos Universitetas , 2021, p. 396-401Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays, ports are actively seeking ways to improve their safety and operational activity. An essential driver in this context is digitalisation. Since seaports are also key actors for the sustainable development of coastal regions, it is important that they transform into smart port ecosystems. Hence, the automation and digitisation of ports’ operations are important not only for the ports themselves, but also for the regions and countries hosting regional port ecosystems. Studies on the digitalisation level of ports bear the potential to detect optimal ways for increasing safety, security and visibility in terms of the digital transformation, as well as attracting passengers and freight flows, which in turn positively affects not only the ports, but particularly also the sustainable development of coastal regions. Therefore, the paper presents the results of a conducted assessment of small and medium-sized ports’ digitisation level as well as introduces ways and recommendations how to improve the level of digitisation on the path towards becoming a smarter port ecosystem. The research builds upon key insights from the Connect2SmallPorts project, part-financed by INTERREG South Baltic Programme 2014–2020. Thereby, the research utilises collected primary data concerning ports located in the Baltic, North and Mediterranean Sea Regions. Thus, the study bases on well-grounded theoretical and practical findings in the maritime science field in the nexus of digital transformation. © 2021 Kaunas University of Technology. All rights reserved.

Place, publisher, year, edition, pages
Kauno Technologijos Universitetas, 2021
Series
Transport Means - Proceedings of the International Conference, ISSN 1822296X ; 2021
Keywords
Digitalisation level, Port digitalisation, Port ecosystem, Smart port, Ecosystems, Planning, Ports and harbors, Sustainable development, Coastal regions, Coastal sustainability, Digital transformation, Digitalization level, Digitisation, Operational activity, Port digitalization, Safety activities, Coastal zones
National Category
Transport Systems and Logistics Information Systems, Social aspects Business Administration
Identifiers
urn:nbn:se:bth-22600 (URN)2-s2.0-85123187987 (Scopus ID)
Conference
25th International Scientific Conference Transport Means 2021, Kaunas, Lithuania, Virtual,6 October 2021 through 8 October 2021
Available from: 2022-02-08 Created: 2022-02-08 Last updated: 2022-02-08Bibliographically approved
Martin Sagayam, K., Timothy, A. J., Ho, C. C., Henesey, L. & Bestak, R. (2020). Augmented reality-based solar system for e-magazine with 3-D audio effect. International Journal of Simulation and Process Modelling, 15(6), 524-534
Open this publication in new window or tab >>Augmented reality-based solar system for e-magazine with 3-D audio effect
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2020 (English)In: International Journal of Simulation and Process Modelling, ISSN 1740-2123, E-ISSN 1740-2131, Vol. 15, no 6, p. 524-534Article in journal (Refereed) Published
Abstract [en]

Augmented reality (AR) is the newest technology that can be applied to computer vision, audio, video and other sensor-based input projects into 3D vision. It is the backbone for all specialisation of science, medical and engineering concepts. Currently, the reading and learning method through AR-based approaches are quite highly intensive than the existing methods such as papers, books and magazines. This strategy is more expensive but it is more interactive to the user in understanding the root concepts in an effective manner. This paper explores the experiment on solar system revolution pattern along with 3D audio effect in spatial dimension. This novel idea inculcates more vibrancy in the current generation of students to understand the concepts with the clear illustrations and demonstrations. © 2020 Inderscience Enterprises Ltd.

Place, publisher, year, edition, pages
Inderscience Publishers, 2020
Keywords
3D audio effect, 3D modelling, 3D vision, Augmented reality, Computer vision, Solar system, 3-D vision, 3D audio, Audio effects, Current generation, Engineering concepts, Learning methods, Spatial dimension, Specialisation, Learning systems
National Category
Computer Sciences Human Computer Interaction
Identifiers
urn:nbn:se:bth-20997 (URN)10.1504/IJSPM.2020.112460 (DOI)2-s2.0-85099575468 (Scopus ID)
Available from: 2021-01-29 Created: 2021-01-29 Last updated: 2021-01-29Bibliographically approved
Henesey, L., Lizneva, Y., Philipp, R., Meyer, C. & Gerlitz, L. (2020). Improved load planning of RoRo vessels by adopting blockchain and internet-of-things. In: Bottani E.,Bruzzone A.G.,Longo F.,Merkuryev Y.,Piera M.A. (Ed.), 22nd International Conference on Harbor, Maritime and Multimodal Logistics Modelling and Simulation, HMS 2020: . Paper presented at 22nd International Conference on Harbor, Maritime and Multimodal Logistics Modelling and Simulation, HMS 2020, Vitual, Online, 16 September 2020 through 18 September 2020 (pp. 58-65). Dime University of Genoa
Open this publication in new window or tab >>Improved load planning of RoRo vessels by adopting blockchain and internet-of-things
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2020 (English)In: 22nd International Conference on Harbor, Maritime and Multimodal Logistics Modelling and Simulation, HMS 2020 / [ed] Bottani E.,Bruzzone A.G.,Longo F.,Merkuryev Y.,Piera M.A., Dime University of Genoa , 2020, p. 58-65Conference paper, Published paper (Refereed)
Abstract [en]

Ports are vital to the global economy, as up to 90% of goods are transferred through seaports. With increasing vessel sizes, cargo volumes and higher demand for supply-chain optimization, seaports are required to be more efficient and competitive. In the present study, a proposed solution incorporating IoT and Blockchain is considered into automating many of the activities in the load planning process, which is then evaluated via simulation. Real data is collected concerning different types of cargo for RoPax vessels with the intended goal of reducing planning time in a seaport. The results contribute as one piece of the mosaic on the avenue towards becoming a “Smart Port”, which deploys various digitalization technologies in order to become a fully automated port. The suggested approach to be integrated, builds upon IoT sensors in combination with the lightweight version of a Blockchain to improve balance indicators on a trim of a vessel. A developed simulation tool was used for evaluating a number of scenarios, with each scenario run set to 2500 times. The simulation results indicate an improvement of 50-160% from the current load planning operations for RoPax vessels. © 2020 The Authors.

Place, publisher, year, edition, pages
Dime University of Genoa, 2020
Keywords
Ballast, Blockchain, Internet-of-Things, Port Automation, Simulation, Competition, Ports and harbors, Supply chains, Cargo volume, Current loads, Fully automated, Global economies, Load planning, Planning time, Ro-Ro vessel, Supply chain optimization, Internet of things
National Category
Computer Sciences Media and Communication Technology
Identifiers
urn:nbn:se:bth-20882 (URN)10.46354/i3m.2020.hms.009 (DOI)2-s2.0-85097714657 (Scopus ID)9788885741478 (ISBN)
Conference
22nd International Conference on Harbor, Maritime and Multimodal Logistics Modelling and Simulation, HMS 2020, Vitual, Online, 16 September 2020 through 18 September 2020
Funder
European Regional Development Fund (ERDF)
Available from: 2021-01-04 Created: 2021-01-04 Last updated: 2021-01-04Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0518-6532

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