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Dr. Helen Leligou
synelixis solutions S.a.

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Journal article
Published: 21 June 2021 in Journal of Marine Science and Engineering
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The maritime industry is one of the most competitive industries today. However, there is a tendency for the profit margins of shipping companies to reduce due to an increase in operational costs, and it does not seem that this trend will change in the near future. The most important reason for the increase in operating costs relates to the increase in fuel prices. To compensate for the increase in operating costs, shipping companies can either renew their fleet or try to make use of new technologies to optimize the performance of their existing one. The software structure in the maritime industry has changed and is now leaning towards the use of Artificial Intelligence (AI) and, more specifically, Machine Learning (ML) for calculating its operational scenarios as a way to compensate the reduction of profit. While AI is a technology for creating intelligent systems that can simulate human intelligence, ML is a subfield of AI, which enables machines to learn from past data without being explicitly programmed. ML has been used in other industries for increasing both availability and profitability, and it seems that there is also great potential for the maritime industry. In this paper the authors compares the performance of multiple regression algorithms like Artificial Neural Network (ANN), Tree Regressor (TRs), Random Forest Regressor (RFR), K-Nearest Neighbor (kNN), Linear Regression, and AdaBoost, in predicting the output power of the Main Engines (M/E) of an ocean going vessel. These regression algorithms are selected because they are commonly used and are well supported by the main software developers in the area of ML. For this scope, measured values that are collected from the onboard Automated Data Logging & Monitoring (ADLM) system of the vessel for a period of six months have been used. The study shows that ML, with the proper processing of the measured parameters based on fundamental knowledge of naval architecture, can achieve remarkable prediction results. With the use of the proposed method there was a vast reduction in both the computational power needed for calculations, and the maximum absolute error value of prediction.

ACS Style

Kiriakos Alexiou; Efthimios Pariotis; Theodoros Zannis; Helen Leligou. Prediction of a Ship’s Operational Parameters Using Artificial Intelligence Techniques. Journal of Marine Science and Engineering 2021, 9, 681 .

AMA Style

Kiriakos Alexiou, Efthimios Pariotis, Theodoros Zannis, Helen Leligou. Prediction of a Ship’s Operational Parameters Using Artificial Intelligence Techniques. Journal of Marine Science and Engineering. 2021; 9 (6):681.

Chicago/Turabian Style

Kiriakos Alexiou; Efthimios Pariotis; Theodoros Zannis; Helen Leligou. 2021. "Prediction of a Ship’s Operational Parameters Using Artificial Intelligence Techniques." Journal of Marine Science and Engineering 9, no. 6: 681.

Original paper
Published: 09 March 2021 in SN Social Sciences
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Our economies and societies are becoming more and more knowledge based which implies that increasing numbers of people need to be educated and trained on new subjects and processes. Thus, the reduction of the effort needed to design and prepare educational and training programmes that meet the needs of the society and the market is of paramount importance. To achieve this goal, first, we define a learning programme model so that programme designers can easily exchange and re-use programme structures and learning materials. The proposed model additionally enables easier creation of interdisciplinary programmes which is another need of today’s market. Second, we deploy a web-based tool that adopts this model towards facilitating the re-use of structures and materials. Third, to reduce the time required for the training actors to sense the market needs, we propose the establishment of an educational programme marketplace. All three endeavours have been validated in the energy transition sector and (positively) evaluated by experts during an international workshop.

ACS Style

Helen C. Leligou; Ferdinanda Ponci; Rosanna De Rosa; Panagiotis A. Karkazis; Constantinos S. Psomopoulos. Designing an innovative educational toolbox to support the transition to new technologies. SN Social Sciences 2021, 1, 1 -22.

AMA Style

Helen C. Leligou, Ferdinanda Ponci, Rosanna De Rosa, Panagiotis A. Karkazis, Constantinos S. Psomopoulos. Designing an innovative educational toolbox to support the transition to new technologies. SN Social Sciences. 2021; 1 (3):1-22.

Chicago/Turabian Style

Helen C. Leligou; Ferdinanda Ponci; Rosanna De Rosa; Panagiotis A. Karkazis; Constantinos S. Psomopoulos. 2021. "Designing an innovative educational toolbox to support the transition to new technologies." SN Social Sciences 1, no. 3: 1-22.

Journal article
Published: 06 December 2020 in Information
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With the advent of Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies, the networking infrastructures are becoming increasingly agile in their attempts to offer the quality of services needed by the users, maximizing the efficiency of infrastructure utilization. This in essence mandates the statistical multiplexing of demands across the infrastructures of different Network Providers (NPs), which would allow them to cope with the increasing demand, upgrading their infrastructures at a slower pace. However, to enjoy the benefits of statistical multiplexing, a trusted authority to govern it would be required. At the same time, blockchain technology aspires to offer a solid advantage in such untrusted environments, enabling the development of decentralized solutions that ensure the integrity and immutability of the information stored in the digital ledger. To this end, in this paper, we propose a blockchain-based solution that allows NPs to trade their (processing and networking) resources. We implemented the solution in a test-bed deployed on the cloud and we present the gathered performance results, showing that a blockchain-based solution is feasible and appropriate. We also discuss further improvements and challenges.

ACS Style

Michael Xevgenis; Dimitrios Kogias; Panagiotis Karkazis; Helen Leligou; Charalampos Patrikakis. Application of Blockchain Technology in Dynamic Resource Management of Next Generation Networks. Information 2020, 11, 570 .

AMA Style

Michael Xevgenis, Dimitrios Kogias, Panagiotis Karkazis, Helen Leligou, Charalampos Patrikakis. Application of Blockchain Technology in Dynamic Resource Management of Next Generation Networks. Information. 2020; 11 (12):570.

Chicago/Turabian Style

Michael Xevgenis; Dimitrios Kogias; Panagiotis Karkazis; Helen Leligou; Charalampos Patrikakis. 2020. "Application of Blockchain Technology in Dynamic Resource Management of Next Generation Networks." Information 11, no. 12: 570.

Journal article
Published: 24 September 2020 in Sensors
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The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.

ACS Style

Panagiotis Trakadas; Pieter Simoens; Panagiotis Gkonis; Lambros Sarakis; Angelos Angelopoulos; Alfonso P. Ramallo-González; Antonio Skarmeta; Christos Trochoutsos; Daniel Calvο; Tomas Pariente; Keshav Chintamani; Izaskun Fernandez; Aitor Arnaiz Irigaray; Josiane Xavier Parreira; Pierluigi Petrali; Nelly Leligou; Panagiotis Karkazis. An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications. Sensors 2020, 20, 5480 .

AMA Style

Panagiotis Trakadas, Pieter Simoens, Panagiotis Gkonis, Lambros Sarakis, Angelos Angelopoulos, Alfonso P. Ramallo-González, Antonio Skarmeta, Christos Trochoutsos, Daniel Calvο, Tomas Pariente, Keshav Chintamani, Izaskun Fernandez, Aitor Arnaiz Irigaray, Josiane Xavier Parreira, Pierluigi Petrali, Nelly Leligou, Panagiotis Karkazis. An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications. Sensors. 2020; 20 (19):5480.

Chicago/Turabian Style

Panagiotis Trakadas; Pieter Simoens; Panagiotis Gkonis; Lambros Sarakis; Angelos Angelopoulos; Alfonso P. Ramallo-González; Antonio Skarmeta; Christos Trochoutsos; Daniel Calvο; Tomas Pariente; Keshav Chintamani; Izaskun Fernandez; Aitor Arnaiz Irigaray; Josiane Xavier Parreira; Pierluigi Petrali; Nelly Leligou; Panagiotis Karkazis. 2020. "An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications." Sensors 20, no. 19: 5480.

Journal article
Published: 04 January 2020 in Journal of Sensor and Actuator Networks
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5G is considered to be the technology that will accommodate the development and management of innovative services with stringent and diverse requirements from end users, calling for new business models from the industry. In this context, the development and efficient management of Network Services (NS) serving specific vertical industries and spanning across multiple administrative domains and heterogeneous infrastructures is challenging. The main challenges regard the efficient provision of NSs considering the Quality of Service (QoS) requirements per vertical industry along with the optimal usage of the allocated resources. Towards addressing these challenges, this paper details an innovative approach that we have developed for managing and orchestrating such NSs, called SONATA, and compare it with OSM and Cloudify, which are two of the most known open-source Management and Orchestration (MANO) frameworks. In addition to examining the supported orchestration mechanisms per MANO framework, an evaluation of main operational and functional KPIs is provided based on experimentation using a real testbed. The final aim is the identification of their strong and weak points, and the assessment of their suitability for serving diverse vertical industry needs, including of course the Internet of Things (IoT) service ecosystem.

ACS Style

Panagiotis Trakadas; Panagiotis Karkazis; Helen C. Leligou; Theodore Zahariadis; Felipe Vicens; Arturo Zurita; Pol Alemany; Thomas Soenen; Carlos Parada; Jose Bonnet; Eleni Fotopoulou; Anastasios Zafeiropoulos; Evgenia Kapassa; Marios Touloupou; Dimosthenis Kyriazis. Comparison of Management and Orchestration Solutions for the 5G Era. Journal of Sensor and Actuator Networks 2020, 9, 4 .

AMA Style

Panagiotis Trakadas, Panagiotis Karkazis, Helen C. Leligou, Theodore Zahariadis, Felipe Vicens, Arturo Zurita, Pol Alemany, Thomas Soenen, Carlos Parada, Jose Bonnet, Eleni Fotopoulou, Anastasios Zafeiropoulos, Evgenia Kapassa, Marios Touloupou, Dimosthenis Kyriazis. Comparison of Management and Orchestration Solutions for the 5G Era. Journal of Sensor and Actuator Networks. 2020; 9 (1):4.

Chicago/Turabian Style

Panagiotis Trakadas; Panagiotis Karkazis; Helen C. Leligou; Theodore Zahariadis; Felipe Vicens; Arturo Zurita; Pol Alemany; Thomas Soenen; Carlos Parada; Jose Bonnet; Eleni Fotopoulou; Anastasios Zafeiropoulos; Evgenia Kapassa; Marios Touloupou; Dimosthenis Kyriazis. 2020. "Comparison of Management and Orchestration Solutions for the 5G Era." Journal of Sensor and Actuator Networks 9, no. 1: 4.