Research

24 pages, 6927 KiB

Open AccessArticle

A Method to Detect Anomalies in Complex Socio-Technical Operations Using Structural Similarity

by Syed Nasir Danial, Doug Smith and Brian Veitch

J. Mar. Sci. Eng. 2021, 9(2), 212; https://doi.org/10.3390/jmse9020212 - 18 Feb 2021

Cited by 3 | Viewed by 1519

Traditional techniques for accident investigation have hindsight biases. Specifically, they isolate the process of the accident event and trace backward from the event to determine the factors leading to the accident. Nonetheless, the importance of the contributing factors towards a successful operation is [...] Read more.

Traditional techniques for accident investigation have hindsight biases. Specifically, they isolate the process of the accident event and trace backward from the event to determine the factors leading to the accident. Nonetheless, the importance of the contributing factors towards a successful operation is not considered in conventional accident modeling. The Safety-II approach promotes an examination of successful operations as well as failures. The rationale is that there is an opportunity to learn from successful operations, in addition to failure, and there is an opportunity to further differentiate failure processes from successful operations. The functional resonance analysis method (FRAM) has the capacity to monitor the functionality and performance of a complex socio-technical system. The method can model many possible ways a system could function, then captures the specifics of the functionality of individual operational events in functional signatures. However, the method does not support quantitative analysis of the functional signatures, which may demonstrate similarities as well as differences among each other. This paper proposes a method to detect anomalies in operations using functional signatures. The present work proposes how FRAM data models can be converted to graphs and how such graphs can be used to estimate anomalies in the data. The proposed approach is applied to human performance data obtained from ice-management tasks performed by a cohort of cadets and experienced seafarers in a ship simulator. The results show that functional differences can be captured by the proposed approach even though the differences were undetected by usual statistical measures. Full article

(This article belongs to the Special Issue Human-Automation Integration in the Maritime Sector)

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16 pages, 7180 KiB

Open AccessArticle

Augmented Reality Lights for Compromised Visibility Navigation

by Doupadi Bandara, Michael Woodward, Christopher Chin and Danchi Jiang

J. Mar. Sci. Eng. 2020, 8(12), 1014; https://doi.org/10.3390/jmse8121014 - 11 Dec 2020

Cited by 5 | Viewed by 2688

Abstract

This paper considers the feasibility of using augmented reality (AR) as a tool for enhancing visualization in maritime operations to avoid collision in different environmental conditions. According to the International Maritime Organization (IMO 2010), 90% of maritime accidents due to collisions at sea [...] Read more.

This paper considers the feasibility of using augmented reality (AR) as a tool for enhancing visualization in maritime operations to avoid collision in different environmental conditions. According to the International Maritime Organization (IMO 2010), 90% of maritime accidents due to collisions at sea are caused in part by human error. This study investigates the new technology (AR) used to superimpose holographic images onto the real world; now reaching a state of readiness for commercial application. This paper demonstrates the competence of AR technology to serve as a maritime navigational aid. The research explores the viability of improving navigational safety in low visibility by projecting holograms of real-world objects in the same geo-location as the real object to make them “visible”. The paper presents the logical deconstruction of the technical problems and identified solutions, together with results of experiments used to validate the concept and technology readiness for real word maritime application. The paper presents a verified demonstrator; a proposed holographic bridge interface with an innovative way of presenting information using AR technology. Furthermore, it identifies that new technologies offer the opportunity for enhanced operator performances, with the expectation being that this should lead to reduce risk to persons, property, and the environment. Full article

(This article belongs to the Special Issue Human-Automation Integration in the Maritime Sector)

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20 pages, 1350 KiB

Open AccessArticle

Arctic Vision: Using Neural Networks for Ice Object Classification, and Controlling How They Fail

by Ole-Magnus Pedersen and Ekaterina Kim

J. Mar. Sci. Eng. 2020, 8(10), 770; https://doi.org/10.3390/jmse8100770 - 30 Sep 2020

Cited by 6 | Viewed by 1956

Abstract

Convolutional neural networks (CNNs) have been shown to be excellent at performing image analysis tasks in recent years. Even so, ice object classification using close-range optical images is an area where their use has barely been touched upon, and how well CNNs perform [...] Read more.

Convolutional neural networks (CNNs) have been shown to be excellent at performing image analysis tasks in recent years. Even so, ice object classification using close-range optical images is an area where their use has barely been touched upon, and how well CNNs perform this classification task is still an open question, especially in the challenging visual conditions often found in the High Arctic. The present study explores the use of CNNs for such ice object classification, including analysis of how visual distortion of optical images impacts their performance and comparisons to human experts and novices. To account for the model’s tendency to predict the presence of very few classes for any given image, the use of a loss-weighting scheme pushing a model towards predicting a higher number of classes is proposed. The results of this study show that on clean images, given the class definitions and labeling scheme used, the networks perform better than some humans. At least for some classes of ice objects, the results indicate that the network learned meaningful features. However, the results also indicate that humans are much better at adapting to new visual conditions than neural networks. Full article

(This article belongs to the Special Issue Human-Automation Integration in the Maritime Sector)

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27 pages, 44596 KiB

Open AccessArticle

An Inland Shore Control Centre for Monitoring or Controlling Unmanned Inland Cargo Vessels

by Gerben Peeters, Gökay Yayla, Tim Catoor, Senne Van Baelen, Muhammad Raheel Afzal, Christos Christofakis, Stijn Storms, René Boonen and Peter Slaets

J. Mar. Sci. Eng. 2020, 8(10), 758; https://doi.org/10.3390/jmse8100758 - 28 Sep 2020

Cited by 12 | Viewed by 3970

Abstract

Augmenting the automation level of the inland waterway cargo transport sector, coupled with mechatronic innovation in this sector, could increase its competitiveness. This increase might potentially induce a sustainable paradigm shift in the road-dominated inland cargo transport sector. A key enabler of this [...] Read more.

Augmenting the automation level of the inland waterway cargo transport sector, coupled with mechatronic innovation in this sector, could increase its competitiveness. This increase might potentially induce a sustainable paradigm shift in the road-dominated inland cargo transport sector. A key enabler of this envisaged shift may be an inland shore control centre (I-SCC) capable of remotely monitoring and controlling inland vessels. Accordingly, this study investigated the concept and design requirements to achieve an inland I-SCC that provides interaction services when supervising an unmanned surface vessel (USV). This I-SCC can help its operator to develop situational awareness and sensemaking. The conducted experiments offered insights into the performance of both the I-SCC system and its operator, and unlock research on the impact on ship sense and harmony when remotely controlling a USV. The Hull-To-Hull project extends the current I-SCC by providing enhanced motion control. This enhancement enables further performance insights and might improve the future monitoring of USVs. The successful I-SCC construction, the preliminary experiments, and the design-extension demonstrate that the I-SCC can serve as an experimental platform for both mechatronic innovation and human-automation integration research in the inland waterway sector, whilst additionally providing fruitful knowledge for adjacent research domains. Full article

(This article belongs to the Special Issue Human-Automation Integration in the Maritime Sector)

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17 pages, 3605 KiB

Open AccessFeature PaperArticle

Capturing Expert Knowledge to Inform Decision Support Technology for Marine Operations

by Jennifer Smith, Fatemeh Yazdanpanah, Rebecca Thistle, Mashrura Musharraf and Brian Veitch

J. Mar. Sci. Eng. 2020, 8(9), 689; https://doi.org/10.3390/jmse8090689 - 07 Sep 2020

Cited by 13 | Viewed by 2191

Abstract

The digital transformation of the offshore and maritime industries will present new safety challenges due to the rapid change in technology and underlying gaps in domain knowledge, substantially affecting maritime operations. To help anticipate and address issues that may arise in the move [...] Read more.

The digital transformation of the offshore and maritime industries will present new safety challenges due to the rapid change in technology and underlying gaps in domain knowledge, substantially affecting maritime operations. To help anticipate and address issues that may arise in the move to autonomous maritime operations, this research applies a human-centered approach to developing decision support technology, specifically in the context of ice management operations. New technologies, such as training simulators and onboard decision support systems, present opportunities to close the gaps in competence and proficiency. Training simulators, for example, are useful platforms as human behaviour laboratories to capture expert knowledge and test training interventions. The information gathered from simulators can be integrated into a decision support system to provide seafarers with onboard guidance in real time. The purpose of this research is two-fold: (1) to capture knowledge held by expert seafarers, and (2) transform this expert knowledge into a database for the development of a decision support technology. This paper demonstrates the use of semi-structured interviews and bridge simulator exercises as a means to capture seafarer experience and best operating practices for offshore ice management. A case-based reasoning (CBR) model is used to translate the results of the knowledge capture exercises into an early-stage ice management decision support system. This paper will describe the methods used and insights gained from translating the interview data and expert performance from the bridge simulator into a case base that can be referenced by the CBR model. Full article

(This article belongs to the Special Issue Human-Automation Integration in the Maritime Sector)

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19 pages, 1742 KiB

Open AccessFeature PaperArticle

“Are You Planning to Follow Your Route?” The Effect of Route Exchange on Decision Making, Trust, and Safety

by Katie Aylward, Reto Weber, Yemao Man, Monica Lundh and Scott N. MacKinnon

J. Mar. Sci. Eng. 2020, 8(4), 280; https://doi.org/10.3390/jmse8040280 - 13 Apr 2020

Cited by 8 | Viewed by 2694

Abstract

The Sea Traffic Management (STM) Validation project is a European based initiative which focuses on connecting and updating the maritime world in real time, with efficient information exchange. The purpose of this paper is to evaluate two functions developed during the project: a [...] Read more.

The Sea Traffic Management (STM) Validation project is a European based initiative which focuses on connecting and updating the maritime world in real time, with efficient information exchange. The purpose of this paper is to evaluate two functions developed during the project: a ship to ship route exchange (S2SREX) function and rendezvous (RDV) information layer, collectively referred to as S2SREX/RDV. S2SREX displays the route segment consisting of the next seven waypoints of the monitored route of a collaborating ship and the RDV layer that predicts a meeting point. S2SREX/RDV provides supplementary information to data acquired by existing navigation systems and is intended to improve situational awareness and safety through a more comprehensive understanding of the surrounding traffic. Chalmers University of Technology and Solent University completed an experiment using twenty-four experienced navigators in bridge simulators. Six traffic scenarios were developed by subject matter experts and tested with and without S2SREX/RDV functionalities. Qualitative data were collected using post-test questionnaires and group debriefs to evaluate the participants’ perceptions of S2SREX/RDV in the various traffic scenarios, and quantitative data were collected to assess the ship distances and behavior in relation to the International Regulations for Preventing Collisions at Sea (COLREGs). The results revealed that participants generally trusted the S2SREX/RDV information, and most used S2SREX/RDV for decision support. The quantitative assessment revealed that the COLREGs were breached more often when S2SREX/RDV was used. Experimental findings are discussed in relation to safety, trust, reliance, situational awareness, and human-automation interaction constructs. Full article

(This article belongs to the Special Issue Human-Automation Integration in the Maritime Sector)

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Review

Jump to: Research, Other

20 pages, 981 KiB

Open AccessReview

Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains

by Raheleh Kari and Martin Steinert

J. Mar. Sci. Eng. 2021, 9(4), 385; https://doi.org/10.3390/jmse9040385 - 05 Apr 2021

Cited by 21 | Viewed by 3897

Abstract

The idea of remote controlling ships for operational and commercial uses has developed beyond concepts. Controlling and monitoring vessels from a distant location requires updating the concept and requirements of shore control centers (SCCs), where human operators control the fleet via cameras, GPS, [...] Read more.

The idea of remote controlling ships for operational and commercial uses has developed beyond concepts. Controlling and monitoring vessels from a distant location requires updating the concept and requirements of shore control centers (SCCs), where human operators control the fleet via cameras, GPS, and many other types of sensors. While remote ship operation promises to reduce operational and maintenance costs, while increasing loading capacity and safety, it also brings significant uncertainty related to both the human-machine and human-human interactions which will affect operations. Achieving safe, reliable, and efficient remote ship operations requires consideration of both technological, cultural, social and human factor aspects of the system. Indeed, operators will act as captain and crew remotely, from the SCC, introducing new types of hardware and software interactions. This paper provides an overview of human factor issues that may affect human-machine and human-human interactions in the course of remote ship operations. In doing so, the literature related to remote operations in the domains of shipping, aerial vehicles, cranes, train transportation, automobiles, and mining is reviewed. Findings revealed that human factor issues are likely to fall into 13 distinct groups based on the type of human interactions that take place in SCCs. Full article

(This article belongs to the Special Issue Human-Automation Integration in the Maritime Sector)

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Other

Jump to: Research, Review

13 pages, 1927 KiB

Open AccessConcept Paper

Assisting Maritime Search and Rescue (SAR) Personnel with AI-Based Speech Recognition and Smart Direction Finding

by Aylin Gözalan, Ole John, Thomas Lübcke, Andreas Maier, Maximilian Reimann, Jan-Gerrit Richter and Ivan Zverev

J. Mar. Sci. Eng. 2020, 8(10), 818; https://doi.org/10.3390/jmse8100818 - 20 Oct 2020

Cited by 6 | Viewed by 3622

Abstract

Communication for processing relevant information plays a paramount role in developing a comprehensive understanding of Search and Rescue (SAR) situations and conducting operations in a successful and reliable manner. Nevertheless, communication systems have not changed considerably in the context of simplifying very high [...] Read more.

Communication for processing relevant information plays a paramount role in developing a comprehensive understanding of Search and Rescue (SAR) situations and conducting operations in a successful and reliable manner. Nevertheless, communication systems have not changed considerably in the context of simplifying very high frequency (VHF) maritime communication and enhancing the value of SAR practices. The Automated Transcription of Maritime VHF Radio Communication for SAR Mission Coordination (ARTUS) project approaches this problem with the development of an assistance system which employs AI-based speech recognition and smart direction finding. First, ideas and specified needs of end users for designing the user interface are presented in this paper. Further, preliminary accomplishments of domain specific language training for maritime speech recognition, and the direction-finding algorithms for localizing senders are sketched out. While the preliminary results build a solid ground, additional field experiments will be conducted in order to enhance the accuracy and reliability of speech recognition and direction finding. The identified end user requirements across different personnel groups show commonalities, but call for a differentiated approach in order to meet the challenges and peculiar needs of the various working contexts. Full article

(This article belongs to the Special Issue Human-Automation Integration in the Maritime Sector)

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