Pub Date : 2020-06-01DOI: 10.1109/SoSE50414.2020.9130425
Karol Kiš, Martin Klauco, A. Mészáros
Chemical technologies benefit greatly from optimization-based control strategies. This paper addressed the problem of substituting the optimization based controller with a neural network (NN). The NN-based controller offers several advantages, first it can be derived in an analytical form and second, it can make the closed-loop implementation tunable. It is not possible to incorporate these aspects into optimizationbased controller easily.The contribution is also to address the problem of the quality of the neural net, that approximates the control law. We show, which activation functions and structure yield the best approximation.
{"title":"Neural Network Controllers in Chemical Technologies","authors":"Karol Kiš, Martin Klauco, A. Mészáros","doi":"10.1109/SoSE50414.2020.9130425","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130425","url":null,"abstract":"Chemical technologies benefit greatly from optimization-based control strategies. This paper addressed the problem of substituting the optimization based controller with a neural network (NN). The NN-based controller offers several advantages, first it can be derived in an analytical form and second, it can make the closed-loop implementation tunable. It is not possible to incorporate these aspects into optimizationbased controller easily.The contribution is also to address the problem of the quality of the neural net, that approximates the control law. We show, which activation functions and structure yield the best approximation.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115588498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SoSE50414.2020.9130466
Omar Al-Debagy, P. Martinek
There is a migration trend toward microservices architecture coming from the monolithic applications. This research proposes a decomposition method that extracts microservices’ candidates through analyzing the application programming interface in order to extract the operations and the parameters. Then the operation names are converted into word representations using word embedding models. Next, semantically similar operations are clustered together to provide a microservice’ candidate. Additional step is to evaluate the proposed candidate using cohesion and complexity metrics. The proposed algorithm improved the decomposition approach for big applications but did not affect the decomposition of smaller applications.
{"title":"Extracting Microservices’ Candidates from Monolithic Applications: Interface Analysis and Evaluation Metrics Approach","authors":"Omar Al-Debagy, P. Martinek","doi":"10.1109/SoSE50414.2020.9130466","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130466","url":null,"abstract":"There is a migration trend toward microservices architecture coming from the monolithic applications. This research proposes a decomposition method that extracts microservices’ candidates through analyzing the application programming interface in order to extract the operations and the parameters. Then the operation names are converted into word representations using word embedding models. Next, semantically similar operations are clustered together to provide a microservice’ candidate. Additional step is to evaluate the proposed candidate using cohesion and complexity metrics. The proposed algorithm improved the decomposition approach for big applications but did not affect the decomposition of smaller applications.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129701356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SoSE50414.2020.9130467
M. Hillebrand, Matthias Greinert, R. Dumitrescu, O. Herzog
Sensors, actuators, machine learning, communication and robotics are paving the way for the introduction of autonomous systems. Autonomous Systems in safety-critical applications require resilient operation of the intended functionality throughout the mission. Especially they must be safe and highly available. However, it is not possible to fully anticipate evolving threats, vulnerabilities and faults during the lifetime of those systems. This requires a resilient systems architecture of the autonomous system. Therefore, a thorough testing and evaluation of such systems is mandatory. In this paper, we present a monkey testing framework for evaluating resilience capabilities of autonomous systems. The framework contains a set of agents with specific role concepts and strategy sets. The framework can be applied to virtual, physical and hybrid testbeds. Due to its modularity the framework is extensible, scalable and also adaptable to different autonomous systems (e.g. mobile robot, manipulator). The monkey testing framework is able to work pseudo-randomized and thus reproducible on a connected system. A logging mechanism annotates the data so that the data can be used for machine learning (e.g. anomaly detection algorithm, selfhealing). We applied the framework on a mobile robotic system in virtual scenarios.
{"title":"Advanced Monkey Testing for connected autonomous systems","authors":"M. Hillebrand, Matthias Greinert, R. Dumitrescu, O. Herzog","doi":"10.1109/SoSE50414.2020.9130467","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130467","url":null,"abstract":"Sensors, actuators, machine learning, communication and robotics are paving the way for the introduction of autonomous systems. Autonomous Systems in safety-critical applications require resilient operation of the intended functionality throughout the mission. Especially they must be safe and highly available. However, it is not possible to fully anticipate evolving threats, vulnerabilities and faults during the lifetime of those systems. This requires a resilient systems architecture of the autonomous system. Therefore, a thorough testing and evaluation of such systems is mandatory. In this paper, we present a monkey testing framework for evaluating resilience capabilities of autonomous systems. The framework contains a set of agents with specific role concepts and strategy sets. The framework can be applied to virtual, physical and hybrid testbeds. Due to its modularity the framework is extensible, scalable and also adaptable to different autonomous systems (e.g. mobile robot, manipulator). The monkey testing framework is able to work pseudo-randomized and thus reproducible on a connected system. A logging mechanism annotates the data so that the data can be used for machine learning (e.g. anomaly detection algorithm, selfhealing). We applied the framework on a mobile robotic system in virtual scenarios.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127482909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SoSE50414.2020.9130503
Martin Kortwinkel
The article introduces an approach and framework that combines both variability in product line development and reuse of product line assets in customer projects using proven architecture frameworks with some enhancements in one approach.
{"title":"MBSE Product Line Engineering – Variability Overview lost?","authors":"Martin Kortwinkel","doi":"10.1109/SoSE50414.2020.9130503","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130503","url":null,"abstract":"The article introduces an approach and framework that combines both variability in product line development and reuse of product line assets in customer projects using proven architecture frameworks with some enhancements in one approach.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129099737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SoSE50414.2020.9130538
Muhammad Monjurul Karim, C. Dagli
Infrastructure inspection using unmanned aerial drones has a great potential to support complex inspection tasks especially where inspection task can be dangerous, dull or dirty. The increased number of systems in this type of inspection process makes it a very complex systems-of-systems (SoS) which is hard to assess. As a result, it becomes very difficult to satisfy all stakeholder needs and requirements. Therefore, an assessment system is required that can efficiently assess the meta-architecture of drone based inspection system. This paper presents a method to generate and evaluate systems of systems (SoS) architecture model for aerial inspection with drones. Where, a meta-architecture containing system component and a system to system interface is presented. To map the desired SoS attributes from stakeholders, different characteristics of the architecture capabilities are evaluated using some linguistic terms called key performance attributes (KPA). KPAs are combined in a Fuzzy Inference System (FIS) to evaluate an overall fitness value that is optimized using a Genetic Algorithm (GA) for the SoS within the meta-architecture. The integrated evaluation method presented in this paper utilizes the SoS explorer to evaluate the SoS meta-architecture using synthetic parameter values.
{"title":"SoS Meta-Architecture Selection for Infrastructure Inspection System Using Aerial Drones","authors":"Muhammad Monjurul Karim, C. Dagli","doi":"10.1109/SoSE50414.2020.9130538","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130538","url":null,"abstract":"Infrastructure inspection using unmanned aerial drones has a great potential to support complex inspection tasks especially where inspection task can be dangerous, dull or dirty. The increased number of systems in this type of inspection process makes it a very complex systems-of-systems (SoS) which is hard to assess. As a result, it becomes very difficult to satisfy all stakeholder needs and requirements. Therefore, an assessment system is required that can efficiently assess the meta-architecture of drone based inspection system. This paper presents a method to generate and evaluate systems of systems (SoS) architecture model for aerial inspection with drones. Where, a meta-architecture containing system component and a system to system interface is presented. To map the desired SoS attributes from stakeholders, different characteristics of the architecture capabilities are evaluated using some linguistic terms called key performance attributes (KPA). KPAs are combined in a Fuzzy Inference System (FIS) to evaluate an overall fitness value that is optimized using a Genetic Algorithm (GA) for the SoS within the meta-architecture. The integrated evaluation method presented in this paper utilizes the SoS explorer to evaluate the SoS meta-architecture using synthetic parameter values.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121449653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SoSE50414.2020.9130488
Samuel Vanfossan, C. Dagli, Benjamin J. Kwasa
Sponsored by expedient technologic innovation, consumers frequently expect manufacturer offerings to exhibit extensive product variety and regular product advancement. These expectations have rendered many traditional production practices less applicable. Chiefly impacted is the notion of mass produced, low-variety artifacts via massive assembly lines. These operations have difficulty meeting the high-customization, short life-cycle requirements imposed by contemporary demand. Many industries and organizations have begun the transformation from these rigid assembly mechanisms to a more versatile, cellular production strategy known as seru production. To facilitate this transition, methods are needed to aid manufacturers in appropriately selecting and arranging seru system components, a critical step in seru system design. Herein, a generalized model is proposed utilizing a system-of-systems architecting approach to determine the component assembly best suiting the needs of the manufacturing entity. Candidate architectures are generated and evaluated using a multi-objective genetic algorithm from which a preferred alternative is selected through a fuzzy inference system. Directing this genetic algorithm, domain-independent objectives are presented, maintaining applications to most seru production design scenarios. The proposed method is then applied to a camera production example, culminating in the identification of a well-performing architecture. The presented method should find increased use as organizations further adopt this flexible production methodology.
{"title":"A system-of-systems meta-architecting approach for seru production system design","authors":"Samuel Vanfossan, C. Dagli, Benjamin J. Kwasa","doi":"10.1109/SoSE50414.2020.9130488","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130488","url":null,"abstract":"Sponsored by expedient technologic innovation, consumers frequently expect manufacturer offerings to exhibit extensive product variety and regular product advancement. These expectations have rendered many traditional production practices less applicable. Chiefly impacted is the notion of mass produced, low-variety artifacts via massive assembly lines. These operations have difficulty meeting the high-customization, short life-cycle requirements imposed by contemporary demand. Many industries and organizations have begun the transformation from these rigid assembly mechanisms to a more versatile, cellular production strategy known as seru production. To facilitate this transition, methods are needed to aid manufacturers in appropriately selecting and arranging seru system components, a critical step in seru system design. Herein, a generalized model is proposed utilizing a system-of-systems architecting approach to determine the component assembly best suiting the needs of the manufacturing entity. Candidate architectures are generated and evaluated using a multi-objective genetic algorithm from which a preferred alternative is selected through a fuzzy inference system. Directing this genetic algorithm, domain-independent objectives are presented, maintaining applications to most seru production design scenarios. The proposed method is then applied to a camera production example, culminating in the identification of a well-performing architecture. The presented method should find increased use as organizations further adopt this flexible production methodology.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130098020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SoSE50414.2020.9130501
Jiang Jiang, Yaqian You, Jianbin Sun, Xuan-nan Li
Public services are one of the basic functions of governments. How to improve the quality of public services under the limited budget has attracted significant attention. Most studies have tried to analysis the satisfaction of public services from statistical description or subjective decomposition. In this study, the evidential network model is employed to describe the relationship between the public services to analysis the satisfaction of them systematically. The advantage of the Evidential Network model is that the relationship between the public services can be represented using directed acyclic graph, and the subjective uncertainty in the relationship can be reflected by the D-S evidence theory. This study uses the Evidential Network structure learning approach to mine the relationship between public services from the questionnaire data, and analyzes the satisfaction of public services on this basis. A case study of the China General Social Survey (2015) is presented to demonstrate the methodology proposed in this study.
{"title":"Satisfaction analysis of public services using Evidential Network model","authors":"Jiang Jiang, Yaqian You, Jianbin Sun, Xuan-nan Li","doi":"10.1109/SoSE50414.2020.9130501","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130501","url":null,"abstract":"Public services are one of the basic functions of governments. How to improve the quality of public services under the limited budget has attracted significant attention. Most studies have tried to analysis the satisfaction of public services from statistical description or subjective decomposition. In this study, the evidential network model is employed to describe the relationship between the public services to analysis the satisfaction of them systematically. The advantage of the Evidential Network model is that the relationship between the public services can be represented using directed acyclic graph, and the subjective uncertainty in the relationship can be reflected by the D-S evidence theory. This study uses the Evidential Network structure learning approach to mine the relationship between public services from the questionnaire data, and analyzes the satisfaction of public services on this basis. A case study of the China General Social Survey (2015) is presented to demonstrate the methodology proposed in this study.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130958641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SoSE50414.2020.9130514
C. Binder, Michael Fischinger, C. Neureiter, G. Lastro, Katharina Polanec, Jounes-Alexander Gross
The advancement of the electricity system in the upcoming years faces major challenges due to the rising number of decentralized and self-managed participants that individually react to the current load. These participants, regardless whether being an electric Vehicle or a Photovoltaic System, can have diverse behaviors, whose interplaying one mutual system is still widely unexplored. As conventional engineering methods reach their limits when trying to predict the collective behaviors of such Systems of Systems (SoS), the utilization of advanced tools such as the SGAM Toolbox and Mosaik framework is necessary. Each of the tools provide a distinct functionality in their unique field of application, however, their interconnection has yet to be introduced. Therefore, this work has been dedicated to explore the possibility of extending an SGAM based model so that the generation of components and their behavior for usage in the Co-Simulation environment of Mosaik is supported. The developed artifacts are thereby evaluated with a suitable realworld case study making use of different kinds of Electric Vehicle (EV) behaviors. Based on the results of this approach, entire Co-Simulation scenarios can be set up according to previously modeled Smart Grid architectures, which enables the analysis of different system behaviors in a considerably simplified way.
{"title":"Towards a Tool-Based Approach for Dynamically Generating Co-Simulation Scenarios based on complex Smart Grid System Architectures","authors":"C. Binder, Michael Fischinger, C. Neureiter, G. Lastro, Katharina Polanec, Jounes-Alexander Gross","doi":"10.1109/SoSE50414.2020.9130514","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130514","url":null,"abstract":"The advancement of the electricity system in the upcoming years faces major challenges due to the rising number of decentralized and self-managed participants that individually react to the current load. These participants, regardless whether being an electric Vehicle or a Photovoltaic System, can have diverse behaviors, whose interplaying one mutual system is still widely unexplored. As conventional engineering methods reach their limits when trying to predict the collective behaviors of such Systems of Systems (SoS), the utilization of advanced tools such as the SGAM Toolbox and Mosaik framework is necessary. Each of the tools provide a distinct functionality in their unique field of application, however, their interconnection has yet to be introduced. Therefore, this work has been dedicated to explore the possibility of extending an SGAM based model so that the generation of components and their behavior for usage in the Co-Simulation environment of Mosaik is supported. The developed artifacts are thereby evaluated with a suitable realworld case study making use of different kinds of Electric Vehicle (EV) behaviors. Based on the results of this approach, entire Co-Simulation scenarios can be set up according to previously modeled Smart Grid architectures, which enables the analysis of different system behaviors in a considerably simplified way.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127052815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SoSE50414.2020.9130423
Gábor Kertész, I. Felde
Representation learning of images using deep neural networks have shown great results in classificational tasks. In case of instance recognition, or object re-identification other approaches are used. Siamese architectured convolutional networks were the first approach to learn from semantic distances, and give the similarity of two inputs. Triplet networks apply the triplet loss based on the furthest positive and the closest negative pair. In this paper we present a method to apply multi-directional image projections as an initial transformation to compress image data, whereafter the discriminative ability remains. After performing the training on vehicle images, the model is evaluated by measuring the one-shot classification accuracy.
{"title":"One-Shot Re-identification using Image Projections in Deep Triplet Convolutional Network","authors":"Gábor Kertész, I. Felde","doi":"10.1109/SoSE50414.2020.9130423","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130423","url":null,"abstract":"Representation learning of images using deep neural networks have shown great results in classificational tasks. In case of instance recognition, or object re-identification other approaches are used. Siamese architectured convolutional networks were the first approach to learn from semantic distances, and give the similarity of two inputs. Triplet networks apply the triplet loss based on the furthest positive and the closest negative pair. In this paper we present a method to apply multi-directional image projections as an initial transformation to compress image data, whereafter the discriminative ability remains. After performing the training on vehicle images, the model is evaluated by measuring the one-shot classification accuracy.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133274675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SoSE50414.2020.9130472
G. Muller
The sustainability transition is a systems of systems challenge to the power ‘n’. The amount of systems and organizations that is involved is inconceivable for most humans. A roadmap is a means to structure much information and to help humans and organizations to understand their role in this transition. This paper describes a roadmap for a local municipality in the Netherlands. It explains the roadmap contents and the current experiences in using and evolving the roadmap. The general response to the roadmap is quite positive. However, we need to evolve the roadmap to turn it into a living entity that is helping the sustainability transition.
{"title":"A Roadmap for Sustainability for a Community in The Netherlands","authors":"G. Muller","doi":"10.1109/SoSE50414.2020.9130472","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130472","url":null,"abstract":"The sustainability transition is a systems of systems challenge to the power ‘n’. The amount of systems and organizations that is involved is inconceivable for most humans. A roadmap is a means to structure much information and to help humans and organizations to understand their role in this transition. This paper describes a roadmap for a local municipality in the Netherlands. It explains the roadmap contents and the current experiences in using and evolving the roadmap. The general response to the roadmap is quite positive. However, we need to evolve the roadmap to turn it into a living entity that is helping the sustainability transition.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133441268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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