Pub Date : 1900-01-01DOI: 10.3850/978-981-18-2016-8_084-cd
B. Iooss, J. Lonchampt
In uncertainty quantification of numerical simulation model outputs, the classical approaches for quantile estimation requires the availability of the full sample of the studied variable. This approach is sometimes not suitable as large ensembles of simulation runs need to gather a prohibitively large amount of data and computer memory. This problem can be solved thanks to an on-the-fly (iterative) approach based on the Robbins-Monro algorithm. We numerically study this algorithm for estimating a discretized quantile function from samples of limited size (a few hundreds observations). We also define “robust” values of the algorithm parameters in two practical situations: when the final number of the model runs N is a priori fixed and when N is unknown in advance (it can then be minimized during the study in order to save cpu time cost). This method is applied to the estimation of indicators in the field of engineering asset management for offshore wind generation. We show how the proposed algorithm improves the efficiency of the tool to support risk informed decision making in the field of offshore wind generation.
{"title":"Robust Tuning of Robbins-Monro Algorithm for Quantile Estimation -- Application to Wind-Farm Asset Management","authors":"B. Iooss, J. Lonchampt","doi":"10.3850/978-981-18-2016-8_084-cd","DOIUrl":"https://doi.org/10.3850/978-981-18-2016-8_084-cd","url":null,"abstract":"In uncertainty quantification of numerical simulation model outputs, the classical approaches for quantile estimation requires the availability of the full sample of the studied variable. This approach is sometimes not suitable as large ensembles of simulation runs need to gather a prohibitively large amount of data and computer memory. This problem can be solved thanks to an on-the-fly (iterative) approach based on the Robbins-Monro algorithm. We numerically study this algorithm for estimating a discretized quantile function from samples of limited size (a few hundreds observations). We also define “robust” values of the algorithm parameters in two practical situations: when the final number of the model runs N is a priori fixed and when N is unknown in advance (it can then be minimized during the study in order to save cpu time cost). This method is applied to the estimation of indicators in the field of engineering asset management for offshore wind generation. We show how the proposed algorithm improves the efficiency of the tool to support risk informed decision making in the field of offshore wind generation.","PeriodicalId":187633,"journal":{"name":"Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115553964","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 : 1900-01-01DOI: 10.3850/978-981-18-2016-8_303-cd
Sergio Cofre-Martel, Camila Correa-Jullian, E. López Droguett, Katrina M. Groth, Mohammad Modarres
{"title":"Defining Degradation States for Diagnosis Classification Models in Real Systems Based on Monitoring Data","authors":"Sergio Cofre-Martel, Camila Correa-Jullian, E. López Droguett, Katrina M. Groth, Mohammad Modarres","doi":"10.3850/978-981-18-2016-8_303-cd","DOIUrl":"https://doi.org/10.3850/978-981-18-2016-8_303-cd","url":null,"abstract":"","PeriodicalId":187633,"journal":{"name":"Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115709301","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 : 1900-01-01DOI: 10.3850/978-981-18-2016-8_505-cd
E. Menand, N. Jrad, J. Marion, A. Morel, P. Chauvet
With the advent of high-throughput sequencing technologies, the genomic platforms generate a vast amount of high dimensional genomic profiles. One of the fundamental challenges of genomic medicine is the accurate prediction of clinical outcomes from these data. Gene expression profiles are established to be associated with overall survival in cancer patients, and this perspective the univariate Cox regression analysis was widely used as primary approach to develop the outcome predictors from high dimensional transcriptomic data for ovarian cancer patient stratification. Recently, the classical Cox proportional hazards model was adapted to the artificial neural network implementation and was tested with The Cancer Genome Atlas (TCGA) ovarian cancer transcriptomic data but did not result in satisfactory improvement, possibly due to the lack of datasets of sufficient size. Nevertheless, this methodology still outperforms more traditional approaches, like regularized Cox model, moreover, deep survival models could successfully transfer information across diseases to improve prognostic accuracy. We aim to extend the transfer learning framework to “pan - gyn” cancers as these gynecologic and breast cancers share a variety of characteristics being female hormone-driven cancers and could therefore share common mechanisms of progression. Our first results using transfer learning show that deep survival models could benefit from training with multi-cancer datasets in the high-dimensional transcriptomic profiles.
{"title":"Predicting Clinical Outcomes of Ovarian Cancer Patients: Deep Survival Models and Transfer Learning","authors":"E. Menand, N. Jrad, J. Marion, A. Morel, P. Chauvet","doi":"10.3850/978-981-18-2016-8_505-cd","DOIUrl":"https://doi.org/10.3850/978-981-18-2016-8_505-cd","url":null,"abstract":"With the advent of high-throughput sequencing technologies, the genomic platforms generate a vast amount of high dimensional genomic profiles. One of the fundamental challenges of genomic medicine is the accurate prediction of clinical outcomes from these data. Gene expression profiles are established to be associated with overall survival in cancer patients, and this perspective the univariate Cox regression analysis was widely used as primary approach to develop the outcome predictors from high dimensional transcriptomic data for ovarian cancer patient stratification. Recently, the classical Cox proportional hazards model was adapted to the artificial neural network implementation and was tested with The Cancer Genome Atlas (TCGA) ovarian cancer transcriptomic data but did not result in satisfactory improvement, possibly due to the lack of datasets of sufficient size. Nevertheless, this methodology still outperforms more traditional approaches, like regularized Cox model, moreover, deep survival models could successfully transfer information across diseases to improve prognostic accuracy. We aim to extend the transfer learning framework to “pan - gyn” cancers as these gynecologic and breast cancers share a variety of characteristics being female hormone-driven cancers and could therefore share common mechanisms of progression. Our first results using transfer learning show that deep survival models could benefit from training with multi-cancer datasets in the high-dimensional transcriptomic profiles.","PeriodicalId":187633,"journal":{"name":"Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124298586","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 : 1900-01-01DOI: 10.3850/978-981-18-2016-8_254-cd
Maria Hanini, S. Khebbache, L. Bouillaut, Makhlouf Hadji
{"title":"Grouping Maintenance Strategies Optimization for Complex Systems: A Constrained-Clustering Approach","authors":"Maria Hanini, S. Khebbache, L. Bouillaut, Makhlouf Hadji","doi":"10.3850/978-981-18-2016-8_254-cd","DOIUrl":"https://doi.org/10.3850/978-981-18-2016-8_254-cd","url":null,"abstract":"","PeriodicalId":187633,"journal":{"name":"Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114853508","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 : 1900-01-01DOI: 10.3850/978-981-18-2016-8_087-cd
T. Myklebust, T. Stålhane, G. Jenssen, Inga Sofie Haug
{"title":"Trust Me, We Have a Safety Case for the Public","authors":"T. Myklebust, T. Stålhane, G. Jenssen, Inga Sofie Haug","doi":"10.3850/978-981-18-2016-8_087-cd","DOIUrl":"https://doi.org/10.3850/978-981-18-2016-8_087-cd","url":null,"abstract":"","PeriodicalId":187633,"journal":{"name":"Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115012811","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 : 1900-01-01DOI: 10.3850/978-981-18-2016-8_062-cd
Din Gang, Cui Lijie, Zhang Lin, Cong Jiping
{"title":"Research on Concept Modeling of Mission-Based Aviation Equipment Support System of System","authors":"Din Gang, Cui Lijie, Zhang Lin, Cong Jiping","doi":"10.3850/978-981-18-2016-8_062-cd","DOIUrl":"https://doi.org/10.3850/978-981-18-2016-8_062-cd","url":null,"abstract":"","PeriodicalId":187633,"journal":{"name":"Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114559442","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 : 1900-01-01DOI: 10.3850/978-981-18-2016-8_175-cd
Thomas Termin, D. Lichte, K. Wolf
Protection against car theft, involving organized crime, is a growing threat for car owners as well as fleet management providers. This brings the use of security technologies into automotive industry. The evaluation of security and the justified use of measures to reduce vulnerability of car security systems is perceived as a special challenge for vendors and users of mobile access systems (MAS), as usually only limited resources for design and analysis are available. A lack of adequate reference works and specifications in the form of concrete recommendations for action, guidelines or standards often leads to proprietary security assessments heavily relying on compliance checks. These assessments often lack sufficiency regarding application-specificity and target-orientation in terms of a good cost benefit ratio. This is true for MAS in particular, as they are relatively new products with specific use cases and boundary conditions. The open-available Performance Risk-based Integrated Security Methodology (PRISM) allows a performance-based physical security assessment of critical infrastructures (CRITIS) and initiated a paradigm shift towards performance-based methods within this area. However, PRISM comprises semi-quantitative approaches only and thus does not allow for the consideration of uncertainty impact. Moreover, the approach has not been applied to mobile access systems (MAS) yet. This paper aims at applying the concept of PRISM to the use case of MAS by extending and optimizing it to enable a holistic risk assessment considering uncertainties.
{"title":"Physical Security Risk Analysis for Mobile Access Systems Including Uncertainty Impact","authors":"Thomas Termin, D. Lichte, K. Wolf","doi":"10.3850/978-981-18-2016-8_175-cd","DOIUrl":"https://doi.org/10.3850/978-981-18-2016-8_175-cd","url":null,"abstract":"Protection against car theft, involving organized crime, is a growing threat for car owners as well as fleet management providers. This brings the use of security technologies into automotive industry. The evaluation of security and the justified use of measures to reduce vulnerability of car security systems is perceived as a special challenge for vendors and users of mobile access systems (MAS), as usually only limited resources for design and analysis are available. A lack of adequate reference works and specifications in the form of concrete recommendations for action, guidelines or standards often leads to proprietary security assessments heavily relying on compliance checks. These assessments often lack sufficiency regarding application-specificity and target-orientation in terms of a good cost benefit ratio. This is true for MAS in particular, as they are relatively new products with specific use cases and boundary conditions. The open-available Performance Risk-based Integrated Security Methodology (PRISM) allows a performance-based physical security assessment of critical infrastructures (CRITIS) and initiated a paradigm shift towards performance-based methods within this area. However, PRISM comprises semi-quantitative approaches only and thus does not allow for the consideration of uncertainty impact. Moreover, the approach has not been applied to mobile access systems (MAS) yet. This paper aims at applying the concept of PRISM to the use case of MAS by extending and optimizing it to enable a holistic risk assessment considering uncertainties.","PeriodicalId":187633,"journal":{"name":"Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117273606","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 : 1900-01-01DOI: 10.3850/978-981-18-2016-8_234-cd
Moritz Schneider, D. Lichte, Dustin Witte, Stephan Gimbel, E. Brucherseifer
Threats posed by civilian drones are becoming an increasing security risk for critical infrastructures as well as events or companies. In order to protect an asset against a drone intrusion a security system is necessary, which in general is described by its capabilities of protection, detection, and intervention. The variety of different threat scenarios posed by drones raises the need for detailed analysis of scenario specific requirements on detection systems. However, there is a lack of comprehensive scenario analyses in the literature that include relevant parameters for detection. Thus, in this paper a scenario analysis is conducted to identify consistent threat scenarios including factors critical for drone detection. The study is based on morphological analysis and applies methods of influence analysis and Cross-Impact Balance analysis. Using these methods, factors that influence the detectability of drones are specified and key factors identified. Potential states of these key factors are determined based on literature reviews or expert interviews. For the assessment of internal consistency of a scenario, a Cross-Impact-Balance analysis is conducted. Exemplarily, the paper shows how a remaining consistent scenario can be applied to derive requirements for a drone detection system or to validate existing systems regarding suitability for feasible threat scenarios.
{"title":"Scenario Analysis of Threats Posed to Critical Infrastructures by Civilian Drones","authors":"Moritz Schneider, D. Lichte, Dustin Witte, Stephan Gimbel, E. Brucherseifer","doi":"10.3850/978-981-18-2016-8_234-cd","DOIUrl":"https://doi.org/10.3850/978-981-18-2016-8_234-cd","url":null,"abstract":"Threats posed by civilian drones are becoming an increasing security risk for critical infrastructures as well as events or companies. In order to protect an asset against a drone intrusion a security system is necessary, which in general is described by its capabilities of protection, detection, and intervention. The variety of different threat scenarios posed by drones raises the need for detailed analysis of scenario specific requirements on detection systems. However, there is a lack of comprehensive scenario analyses in the literature that include relevant parameters for detection. Thus, in this paper a scenario analysis is conducted to identify consistent threat scenarios including factors critical for drone detection. The study is based on morphological analysis and applies methods of influence analysis and Cross-Impact Balance analysis. Using these methods, factors that influence the detectability of drones are specified and key factors identified. Potential states of these key factors are determined based on literature reviews or expert interviews. For the assessment of internal consistency of a scenario, a Cross-Impact-Balance analysis is conducted. Exemplarily, the paper shows how a remaining consistent scenario can be applied to derive requirements for a drone detection system or to validate existing systems regarding suitability for feasible threat scenarios.","PeriodicalId":187633,"journal":{"name":"Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116002084","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 : 1900-01-01DOI: 10.3850/978-981-18-2016-8_644-cd
Gabriel A. Costa Lima, Luís Augusto Nagasaki Costa, A. M. Teodoro-Filho, Eduardo Otto-Filho
{"title":"Queuing Theory and Regression Approach for Maintenance Personnel Estimation: A Case Study of a Brazilian Power Distribution Company","authors":"Gabriel A. Costa Lima, Luís Augusto Nagasaki Costa, A. M. Teodoro-Filho, Eduardo Otto-Filho","doi":"10.3850/978-981-18-2016-8_644-cd","DOIUrl":"https://doi.org/10.3850/978-981-18-2016-8_644-cd","url":null,"abstract":"","PeriodicalId":187633,"journal":{"name":"Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116435584","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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