Pub Date : 2020-06-01DOI: 10.1109/SoSE50414.2020.9130504
György Fekete, A. Molnár
In this paper, we go around two completely different levels of program design of a biomechanical program. First, the broadest level is the data level, where we show that we can use the whole world’s data. This is covered by the System of Systems engineering. The second and most particular level is the algorithm level. Our goal is to achieve the fastest program run we can. For this, we overview the possibilities and show an example of how a parallel paradigm accelerates our program.
{"title":"Special design aspects of a biomechanical program Two completely different levels of program design","authors":"György Fekete, A. Molnár","doi":"10.1109/SoSE50414.2020.9130504","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130504","url":null,"abstract":"In this paper, we go around two completely different levels of program design of a biomechanical program. First, the broadest level is the data level, where we show that we can use the whole world’s data. This is covered by the System of Systems engineering. The second and most particular level is the algorithm level. Our goal is to achieve the fastest program run we can. For this, we overview the possibilities and show an example of how a parallel paradigm accelerates our program.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"14 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":"133366131","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/sose50414.2020.9130471
{"title":"SoSE 2020 Committees","authors":"","doi":"10.1109/sose50414.2020.9130471","DOIUrl":"https://doi.org/10.1109/sose50414.2020.9130471","url":null,"abstract":"","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"23 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":"132818340","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/SoSE50414.2020.9130505
J. Mattioli, Paolo Perico, Pierre-Olivier Robic
In a System Engineering perspective, asset management (AM) is related to a subset of techniques focusing on the in-service phase, aligned with product life-cycle management discipline. Today, within AM solution market, the integration of Artificial Intelligence (AI) technics above traditional entreprise solution is a key trend. This paper is focusing on how symbolic AI and data driven AI could improve some issues of the AM life cycle, in particular in asset acquisition, performance analysis and forecasting, asset monitoring, predictive and prescriptive maintenance, supply chain optimisation including spare parts management…
{"title":"Artificial Intelligence based Asset Management","authors":"J. Mattioli, Paolo Perico, Pierre-Olivier Robic","doi":"10.1109/SoSE50414.2020.9130505","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130505","url":null,"abstract":"In a System Engineering perspective, asset management (AM) is related to a subset of techniques focusing on the in-service phase, aligned with product life-cycle management discipline. Today, within AM solution market, the integration of Artificial Intelligence (AI) technics above traditional entreprise solution is a key trend. This paper is focusing on how symbolic AI and data driven AI could improve some issues of the AM life cycle, in particular in asset acquisition, performance analysis and forecasting, asset monitoring, predictive and prescriptive maintenance, supply chain optimisation including spare parts management…","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"13 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":"129737911","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/sose50414.2020.9130506
{"title":"SoSE 2020 Table of Contents","authors":"","doi":"10.1109/sose50414.2020.9130506","DOIUrl":"https://doi.org/10.1109/sose50414.2020.9130506","url":null,"abstract":"","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"41 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":"129282114","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/SoSE50414.2020.9130426
Lushun Ding, Zhen Jia, Yufan Zhou
This paper proposes an operational tasks analysis method based on complex network theory. The construction method of operational tasks analysis network is developed by combining the characteristics of complex network with the operational domain knowledge; the time analysis method of operational tasks is designed on the basis of operational tasks analysis network; the mathematic models of operational tasks force cost analysis are established, and the solving methods to these models are explored based on complex network theory. This method can assist operational planners to analyze the time and force cost consumed by operational tasks.
{"title":"Research on Operational Tasks Analysis Method Based on Complex Network Theory","authors":"Lushun Ding, Zhen Jia, Yufan Zhou","doi":"10.1109/SoSE50414.2020.9130426","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130426","url":null,"abstract":"This paper proposes an operational tasks analysis method based on complex network theory. The construction method of operational tasks analysis network is developed by combining the characteristics of complex network with the operational domain knowledge; the time analysis method of operational tasks is designed on the basis of operational tasks analysis network; the mathematic models of operational tasks force cost analysis are established, and the solving methods to these models are explored based on complex network theory. This method can assist operational planners to analyze the time and force cost consumed by operational tasks.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"82 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":"115686913","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/SoSE50414.2020.9130498
Tibor Gergely Markovits, G. Rácz
In the last two decades, multiprocessing technology has become a more important feature of complex digital systems, such as railway interlocking. Such systems can be implemented as distributed systems, especially the spur plan based interlocking systems, in case of which the lineside railway equipments are controlled by logically separate hardware units, connected with dedicated trace cables. Due to the growing complexity of the signalling system logic and the new GSM-R technology based ETCS systems often cause conflicting requirements. In fulfilling the requirements, the designer cannot avoid to use different generic or special purpose processing units, like CPU, CPLD, FPGA or ASSP-s. These kind of system architectures called heterogeneous multiprocessing architectures (HMA). There is no proven practice for designing a HMA based heterogeneous multiprocessor system (HMS), and this is often the cause of many intuitive design steps. During developing a HMS, the designer may utilize various high level logic synthesis (HLS) tools. Among other things, dataflow-graphs can be used as a formal method of task description and graph decomposition algorithms can be used for generating proper segments of the task. This paper presents how to use the spectral properties of the data flow graphs for decomposition in the design of HMS. Such systematic design methodologies may also benefit later maintenance and reliability of the designed system.
{"title":"Utilizing the spectral properties of weighted data flow graphs for designing railway signaling systems","authors":"Tibor Gergely Markovits, G. Rácz","doi":"10.1109/SoSE50414.2020.9130498","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130498","url":null,"abstract":"In the last two decades, multiprocessing technology has become a more important feature of complex digital systems, such as railway interlocking. Such systems can be implemented as distributed systems, especially the spur plan based interlocking systems, in case of which the lineside railway equipments are controlled by logically separate hardware units, connected with dedicated trace cables. Due to the growing complexity of the signalling system logic and the new GSM-R technology based ETCS systems often cause conflicting requirements. In fulfilling the requirements, the designer cannot avoid to use different generic or special purpose processing units, like CPU, CPLD, FPGA or ASSP-s. These kind of system architectures called heterogeneous multiprocessing architectures (HMA). There is no proven practice for designing a HMA based heterogeneous multiprocessor system (HMS), and this is often the cause of many intuitive design steps. During developing a HMS, the designer may utilize various high level logic synthesis (HLS) tools. Among other things, dataflow-graphs can be used as a formal method of task description and graph decomposition algorithms can be used for generating proper segments of the task. This paper presents how to use the spectral properties of the data flow graphs for decomposition in the design of HMS. Such systematic design methodologies may also benefit later maintenance and reliability of the designed system.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"8 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":"114054013","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/SoSE50414.2020.9130532
J. Axelsson
This paper puts forward the hypothesis that all systems-of-systems (SoS) need to deal with geospatial information. It discusses some fundamental aspects of such geodata, including entities, coordinate systems, features, and representation. It then presents how geodata can be used for various purposes in SoS and suggests architectural strategies for handling geodata in this context, including the use of linked data to represent both geodata and other information; triple stores for databases; and cloud servers for executing geodata related constituent system functionality.
{"title":"Needs and Architectural Strategies Related to Geospatial Information in Systems-of-Systems","authors":"J. Axelsson","doi":"10.1109/SoSE50414.2020.9130532","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130532","url":null,"abstract":"This paper puts forward the hypothesis that all systems-of-systems (SoS) need to deal with geospatial information. It discusses some fundamental aspects of such geodata, including entities, coordinate systems, features, and representation. It then presents how geodata can be used for various purposes in SoS and suggests architectural strategies for handling geodata in this context, including the use of linked data to represent both geodata and other information; triple stores for databases; and cloud servers for executing geodata related constituent system functionality.","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":"116605370","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/SoSE50414.2020.9130519
Abdallah Benhamida, A. Várkonyi-Kóczy, M. Kozlovszky
Nowadays, Machine Learning applications are spreading widely in different science and research fields which gave, in fact, the possibility to enhance the results of all kind of both, automated tasks and further possible application areas. Autonomous smart driving cars presents one of the major fields that uses machine learning technics to push further the automated tasks inside the car systems. Many types of research related to this topic enabled real application to fully automate some parts of the car driving process. Road lane detection, pedestrian and car approximation detection, and fastest road finding using real-time traffic statistics present some of the possible application areas that could use the Machine learning technics to improve autonomous driving cars systems. Traffic signs present an important part of the daily driving routine, therefore, traffic signs recognition for mobile-based application is a great solution that provides a new layer for autonomous car driving systems. In this paper, we propose a powerful tool for traffic signs recognition in a mobile-based application. This tool uses TensorFlow together with transfer learning technic that makes it easier to train our dataset on a pre-trained Model using the convolutional network (ConvNet). The used model is a Single Shot MultiBox Detector (SSD) MobileNet V2 based model which uses one single deep network to train the model on multiple objects per image. This network uses 300x300 annotated input images with multiple objects to provide faster training time and faster detection results compared to other types of neural networks. The annotation is made by providing the coordinates of the rectangle that surrounds the given object together with its label which defines the name of the object. The coordinates are usually given by providing the (x,y) coordinates of the top-left and bottom-right points of the surrounding rectangle. This presents a powerful technic for real-time detection on mobile devices with low computational capabilities. The resulting model of the training is then converted to a TensorFlow Lite quantized model using TensorFlow Lite converter which provides compatibility with mobile devices with low computational capacity. The quantized model showed 4 times faster detection compared to the float model on the mobile device.
{"title":"Traffic Signs Recognition in a mobile-based application using TensorFlow and Transfer Learning technics","authors":"Abdallah Benhamida, A. Várkonyi-Kóczy, M. Kozlovszky","doi":"10.1109/SoSE50414.2020.9130519","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130519","url":null,"abstract":"Nowadays, Machine Learning applications are spreading widely in different science and research fields which gave, in fact, the possibility to enhance the results of all kind of both, automated tasks and further possible application areas. Autonomous smart driving cars presents one of the major fields that uses machine learning technics to push further the automated tasks inside the car systems. Many types of research related to this topic enabled real application to fully automate some parts of the car driving process. Road lane detection, pedestrian and car approximation detection, and fastest road finding using real-time traffic statistics present some of the possible application areas that could use the Machine learning technics to improve autonomous driving cars systems. Traffic signs present an important part of the daily driving routine, therefore, traffic signs recognition for mobile-based application is a great solution that provides a new layer for autonomous car driving systems. In this paper, we propose a powerful tool for traffic signs recognition in a mobile-based application. This tool uses TensorFlow together with transfer learning technic that makes it easier to train our dataset on a pre-trained Model using the convolutional network (ConvNet). The used model is a Single Shot MultiBox Detector (SSD) MobileNet V2 based model which uses one single deep network to train the model on multiple objects per image. This network uses 300x300 annotated input images with multiple objects to provide faster training time and faster detection results compared to other types of neural networks. The annotation is made by providing the coordinates of the rectangle that surrounds the given object together with its label which defines the name of the object. The coordinates are usually given by providing the (x,y) coordinates of the top-left and bottom-right points of the surrounding rectangle. This presents a powerful technic for real-time detection on mobile devices with low computational capabilities. The resulting model of the training is then converted to a TensorFlow Lite quantized model using TensorFlow Lite converter which provides compatibility with mobile devices with low computational capacity. The quantized model showed 4 times faster detection compared to the float model on the mobile device.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"84 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":"123206926","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/SoSE50414.2020.9130550
T. Fülöp, Ágnes Győrfi, Szabolcs Csaholczi, L. Kovács, L. Szilágyi
Ensemble learning methods are frequently employed in medical decision support. In image segmentation problems the ensemble based decisions require a postprocessing, because the ensemble cannot adequately handle the strong correlation of neighbor voxels. This paper proposes a brain tumor segmentation procedure based on an ensemble cascade. The first ensemble consisting of binary decision trees is trained to separate focal lesions from normal tissues based on four observed and 100 computed features. Starting from the intermediary labels provided by the first ensemble, six local features are computed for each voxel that serve as input for the second ensemble. The second ensemble is a classical random forest that enforces the correlation between neighbor pixels, regularizes the shape of the lesions. The segmentation accuracy is characterized by 85.5% overall Dice Score, 0.5% above previous solutions.
{"title":"Brain Tumor Segmentation from Multi-Spectral MRI Data Using Cascaded Ensemble Learning*","authors":"T. Fülöp, Ágnes Győrfi, Szabolcs Csaholczi, L. Kovács, L. Szilágyi","doi":"10.1109/SoSE50414.2020.9130550","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130550","url":null,"abstract":"Ensemble learning methods are frequently employed in medical decision support. In image segmentation problems the ensemble based decisions require a postprocessing, because the ensemble cannot adequately handle the strong correlation of neighbor voxels. This paper proposes a brain tumor segmentation procedure based on an ensemble cascade. The first ensemble consisting of binary decision trees is trained to separate focal lesions from normal tissues based on four observed and 100 computed features. Starting from the intermediary labels provided by the first ensemble, six local features are computed for each voxel that serve as input for the second ensemble. The second ensemble is a classical random forest that enforces the correlation between neighbor pixels, regularizes the shape of the lesions. The segmentation accuracy is characterized by 85.5% overall Dice Score, 0.5% above previous solutions.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"22 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":"125094469","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/SoSE50414.2020.9130518
J. Axelsson
Online education is changing how teaching is done at universities and provides new opportunities to reach out to practitioners. In this paper, the development of an online course in systems-of-systems engineering is presented, as well as results from the first instance of the course. The paper describes how the course was designed; how it was produced; and experiences from giving it. Challenges with online education in the systems engineering subjects are also highlighted.
{"title":"Systems-of-Systems Engineering Online Education: An Experience Report","authors":"J. Axelsson","doi":"10.1109/SoSE50414.2020.9130518","DOIUrl":"https://doi.org/10.1109/SoSE50414.2020.9130518","url":null,"abstract":"Online education is changing how teaching is done at universities and provides new opportunities to reach out to practitioners. In this paper, the development of an online course in systems-of-systems engineering is presented, as well as results from the first instance of the course. The paper describes how the course was designed; how it was produced; and experiences from giving it. Challenges with online education in the systems engineering subjects are also highlighted.","PeriodicalId":121664,"journal":{"name":"2020 IEEE 15th International Conference of System of Systems Engineering (SoSE)","volume":"27 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":"127876009","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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