• Advanced Data Analytics towards Energy Efficient and Emission Reduction Retrofit Technology Integration in Shipping 

      Perera, Lokukaluge Prasad; Ventikos, N P; Rolfsen, Sven; Öster, Anders (Chapter; Bokkapittel, 2021)
      An overview of integrating two energy efficient and emission reduction technologies to improve ship energy efficiency under advanced data analytics is presented in this study. The proposed technologies consist of developing engine and propulsion innovations that will be experimented under laboratory conditions and large-model-scale sea trials, respectively. These experiments will collect large ...
    • An AIS-based deep learning framework for regional ship behavior prediction 

      Murray, Brian; Perera, Lokukaluge Prasad (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-05-27)
      This study presents a deep learning framework to support regional ship behavior prediction using historical AIS data. The framework is meant to aid in proactive collision avoidance, in order to enhance the safety of maritime transportation systems. In this study, it is suggested to decompose the historical ship behavior in a given geographical region into clusters. Each cluster will contain trajectories ...
    • An AIS-Based Multiple Trajectory Prediction Approach for Collision Avoidance in Future Vessels 

      Murray, Brian; Perera, Lokukaluge Prasad (Peer reviewed; Bok; Chapter, 2019-11-11)
      This study presents a method to predict the future trajectory of a target vessel using historical AIS data. The purpose of such a prediction is to aid in collision avoidance in future vessels. The method presented in this study extracts all trajectories present in an initial cluster centered about a vessel position. Features for each trajectory are then generated using Principle Component Analysis ...
    • The Compliance Challenges in Emissions Control Regulations to Reduce Air Pollution from Shipping 

      Bui, Khanh Quang; Perera, Lokukaluge Prasad (Peer reviewed; Chapter; Bokkapittel, 2019-10-14)
      One of the thorny problems perplexing the maritime industry is to comply with existing and soon-to-be-enacted regulations towards emissions reduction from shipping. This problem has been discussed in this study under three viewpoints: i) the marine engine efficiency, ii) the decision-making process for selecting the technologies for reducing air emissions from shipping and iii) the challenges in the ...
    • Decentralized System Intelligence in Data Driven Networks for Shipping Industrial Applications: Digital Models to Blockchain Technologies 

      Perera, Lokukaluge Prasad; Czachorowski, Karen Vanessa (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-10-14)
      Data driven networks applicable for shipping industrial applications to create decentralized system intelligence are considered in this study. Such system intelligence can facilitate to improve the respective operational efficiency in local (i.e. vessel operations) and global (i.e. logistics operations) scales in shipping as the main advantage. The main features of these data driven networks are ...
    • Deep Learning towards Autonomous Ship Navigation and Possible COLREGs Failures 

      Perera, Lokukaluge Prasad (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-12-17)
      A structured technology framework to address navigation considerations, including collision avoidance, of autonomous ships is the focus of this study. That consists of adequate maritime technologies to achieve the required level of navigation integrity in ocean autonomy. Since decision-making facilities in future autonomous vessels can play an important role under ocean autonomy, these technologies ...
    • A dual linear autoencoder approach for vessel trajectory prediction using historical AIS data 

      Murray, Brian; Perera, Lokukaluge Prasad (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-05-21)
      Advances in artificial intelligence are driving the development of intelligent transportation systems, with the purpose of enhancing the safety and efficiency of such systems. One of the most important aspects of maritime safety is effective collision avoidance. In this study, a novel dual linear autoencoder approach is suggested to predict the future trajectory of a selected vessel. Such predictions ...
    • Failure intensity of offshore power plants under varying maintenance policies 

      Perera, Lokukaluge Prasad; Machado, Mario M.; Valland, Anders; Manguinho, Diego A.P. (Journal article; Peer reviewed; Tidsskriftartikkel, 2019-01-06)
      System reliability of an offshore power plant with several gas turbine engines is analyzed in this study to understand the failure intensity of a selected gas turbine engines under varying maintenance activities. A set of event data of a selected gas turbine engine is considered to identify system failure intensity levels, where unknown maintenance actions were implemented (i.e. various repairs ...
    • Possible COLREGs Failures under Digital Helmsman of Autonomous Ships 

      Perera, Lokukaluge Prasad; Batalden, Bjørn-Morten (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-10-19)
      Autonomous navigation will play an important role in the future of the shipping industry. Hence, this study illustrates several concepts that should support the navigation side in relation to the collision avoidance of autonomous ships. The concept of system intelligence, i.e., cloned by human navigators, as the digital helmsman to navigate future vessels is discussed in the first part of this study. ...
    • Proactive Collision Avoidance for Autonomous Ships: Leveraging Machine Learning to Emulate Situation Awareness 

      Murray, Brian; Perera, Lokukaluge Prasad (Journal article; Tidsskriftartikkel; Peer reviewed, 2021)
      Autonomous ship technology is developing at a rapid pace, with the aim of facilitating safe ship operations. Collision avoidance is one of the most critical tasks that autonomous ships must handle. To support the level of safety associated with collision avoidance, this study suggests to provide autonomous ships with the ability to conduct proactive collision avoidance maneuvers. Proactive collision ...
    • Ship behavior prediction via trajectory extraction-based clustering for maritime situation awareness 

      Murray, Brian; Perera, Lokukaluge Prasad (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-03-20)
      This study presents a method in which historical AIS data are used to predict the future trajectory of a selected vessel. This is facilitated via a system intelligence-based approach that can be subsequently utilized to provide enhanced situation awareness to navigators and future autonomous ships, aiding proactive collision avoidance. By evaluating the historical ship behavior in a given geographical ...
    • Ship Performance and Navigation Data Compression and Communication under Autoencoder System Architecture 

      Perera, Lokukaluge Prasad; Mo, Brage (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-04-21)
      Modern vessels are designed to collect, store and communicate large quantities of ship performance and navigation information through complex onboard data handling processes. That data should be transferred to shore based data centers for further analysis and storage. However, the associated transfer cost in large-scale data sets is a major challenge for the shipping industry, today. The same cost ...
    • Ship Performance and Navigation Information under High Dimensional Digital Models 

      Perera, Lokukaluge Prasad; Mo, Brage (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-03-04)
      Future vessels will be facilitated by modern internet of things to collect various ship performance and navigation information. Such information is collected as large-scale data sets, the so-called Big Data, that should be utilized towards digitalization of the shipping industry. However, various data handling challenges are encountered by the shipping industry during the phase of digitalization, ...
    • Ship speed power performance under relative wind profiles in relation to sensor fault detection 

      Perera, Lokukaluge Prasad; Mo, Brage (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-11-19)
      Statistical Data analysis and visualization approaches to identify ship speed power performance under relative wind (i.e. apparent wind) profiles are considered in this study. Ship performance and navigation data of a selected vessel are analyzed, where various data anomalies, i.e. sensor related erroneous data conditions, are identified. Those erroneous data conditions are investigated and several ...