2023 IEEE 48th Conference on Local Computer Networks (LCN)
Quantum computing is a fast-developing field that has the ability to tackle complex problems that... more Quantum computing is a fast-developing field that has the ability to tackle complex problems that conventional computers struggle with. Recently, quantum fingerprinting localization has been introduced, which allows for the implementation of large-scale location determination systems across the globe. In this paper, we introduce QRadar, a localization system that uses quantum similarity fingerprints based on the Euclidean similarity metric, which is an early and widely used measure. The computational complexity of QRadar is exponentially superior to classical systems. We explain how to generate the quantum fingerprint, how to encode the received signal strength (RSS) measurements as quantum particles, and we describe the quantum algorithm for computing the Euclidean similarity distance between the online RSS measurements and the fingerprint ones. Additionally, we investigate various sources of errors in quantum machines and how they affect the accuracy of QRadar. We then discuss how to choose an appropriate quantum machine to minimize localization errors. We installed QRadar on a real IBM Quantum Experience machine. The results we obtained from both the installation and simulations conducted on two real test sites confirm that QRadar can accurately determine the estimated location with a significant improvement in processing time when compared to conventional classical methods.
The present study was designed to estimate the substantial equivalence of genetically modified (G... more The present study was designed to estimate the substantial equivalence of genetically modified (GM) potato Spunta lines (SpG2, SpG3 and Sp6A-3) with the cry1Ic1 gene compared to conventional non-transgenic potato Spunta cultivar (control group) through rat feeding experiments and through the determination of nutritional composition such as: protein, ash, fiber, fat, starch, total carbohydrates, amino acids, fatty acids, micro and macroelements and vitamins B1, B2 and C. Short-term feeding study was conducted for nine days using experimental rats to estimate the digestibility parameters: True Digestibility, (T.D.), Biological Value (B.V.) and Net Protein Utilization (N.P.U.) as well as the behavior of rats under investigation. Rats in each group (SpG2, SpG3 and Sp6A-3 and control groups) grew normally without marked differences in appearance, behavior or in mortality rate. No marked differences were observed in the T.D., B.V. and N.P.U. in all groups. All tested chemical parameters s...
In the present paper, we report the photometric and spectroscopic observations obtained by the 1.... more In the present paper, we report the photometric and spectroscopic observations obtained by the 1.88 m telescope at the Kottamia astronomical observatory of the pulsating star BL Cam. Fourier analysis of the light curves reveals that the fundamental mode has two harmonics. The O-C method is used to establish the period changes. So far, the analysis has been very successful in mapping the pulsation amplitude of the star across the instability strip. By using the formalism of Eddington and Plakidis (1929), we found significant results and strong indications of the evolutionary period change. A total of 55 new maximum light timings are reported. New values of (1/P) dP/dt are estimated using the O-C diagram based on all newly obtained times of maximum light combined with those taken from the literature, assuming the periods are decreasing and changing smoothly. To compute the effective temperature and surface gravity of the star, we performed model atmosphere analysis on its spectra. The physical parameters of the star are calculated and compared with the evolutionary models.
Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference, 2020
We consider the propagation of disturbances in the electrical distribution network of CERN's (Eur... more We consider the propagation of disturbances in the electrical distribution network of CERN's (European Organization for Nuclear Research) Large Hadron Collider (LHC), which is a complex system made of a large number of mutually interconnected and interdependent components. The objective of this work is the identification of the components most critical in the determination of the system operating/failed state. Given the complexity of the system, the identification is to be based on the operational data collected from the monitoring systems. The critical components are sought as those whose condition monitoring signals are most correlated to the system operating/failed state. The method of the identification is based on the use of the Relief feature selection technique to rank the monitoring signals according to their importance with respect to the classification of the system operating/failed state. The criticality of the identified signals and the corresponding components has been validated by the system technical responsibles.
Abstract Chemical process operation optimization aims at obtaining the optimal operating set-poin... more Abstract Chemical process operation optimization aims at obtaining the optimal operating set-points by real-time solution of an optimization problem that embeds a steady-state model of the process. This task is challenged by unavoidable Uncertain Parameters (UPs) variations. MultiParametric Programming (MPP) is an approach for solving this challenge, where the optimal set-points must be updated online, reacting to sudden changes in the UPs. MPP provides algebraic functions describing the optimal solution as a function of the UPs, which allows alleviating large computational cost required for solving the optimization problem each time the UPs values vary. However, MPP applicability requires a well-constructed mathematical model of the process, which is not suited for process operation optimization, where complex, highly nonlinear and/or black-box models are usually used. To tackle this issue, this paper proposes a machine learning-based methodology for multiparametric solution of continuous optimization problems. The methodology relies on the offline development of data-driven models that accurately approximate the multiparametric behavior of the optimal solution over the UPs space. The models are developed using data generated by running the optimization using the original complex process model under different UPs values. The models are, then, used online to, quickly, predict the optimal solutions in response to UPs variation. The methodology is applied to benchmark examples and two case studies of process operation optimization. The results demonstrate the methodology effectiveness in terms of high prediction accuracy (less than 1% of NRMSE, in most cases), robustness to deal with problems of different natures (linear, bilinear, quadratic, nonlinear and/or black boxes) and significant reduction in the complexity of the solution procedure compared to traditional approaches (a minimum of 67% reduction in the optimization time).
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Resumen Introducción y objetivos: El diagnóstico del colesteatoma se basa en los hallazgos clínic... more Resumen Introducción y objetivos: El diagnóstico del colesteatoma se basa en los hallazgos clínicos y en la tomografía computarizada. Actualmente, con las nuevas técnicas de resonancia magnética potenciada en difusión no ecoplanares, sin necesidad de contraste intravenoso, es posible diferenciar entre colesteatoma y tejido de granulación o inflamatorio. Por ello, esta técnica muestra su máxima utilidad en la valoración de recidivas de colesteatoma tras timpanoplastias, sobre todo en técnicas cerradas, ya que puede evitar un alto porcentaje de cirugías de revisión. Otras indicaciones de la técnica son los casos de diagnóstico complejo y el colesteatoma congénito. El objetivo de este estudio es valorar la validez (sensibilidad y especificidad) y la seguridad (valor predictivo positivo y valor predictivo negativo) de la secuencia de difusión PROPELLER, una de las técnicas potenciada en difusión no ecoplanar en el diagnóstico del colesteatoma. Métodos: Estudio prospectivo de 52 pacientes con sospecha de colesteatoma en el que se correlacionan hallazgos clínicos y quirúrgicos con los obtenidos del estudio de resonancia magnética, que incluía una secuencia potenciada en difusión no ecoplanar (PROPELLER) de oídos. Resultados: La sensibilidad de la prueba para el grupo fue del 92,85%, la especificidad del 92,30%, el valor predictivo positivo del 92,85% y el valor predictivo negativo del 92,30%. Conclusiones: La resonancia magnética con imagen potenciada en difusión no ecoplanar utilizando la secuencia PROPELLER, es una técnica eficaz en el control del colesteatoma, permitiendo diagnosticar lesiones mayores de 2 mm.
Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference
This work presents a novel method for inferring causal dependencies among abnormal behaviours of ... more This work presents a novel method for inferring causal dependencies among abnormal behaviours of components in Complex Technical Infrastructures (CTIs) from large-scale databases of alarm messages. The proposed method extracts causal relationships from association rules performing a probabilistic analysis of the alarm occurrence times and applying a modified version of the quicksort algorithm. Its capability and effectiveness is illustrated by application to a real large-scale databases of alarm messages collected in the technical infrastructure of the European Organization for Nuclear Research (CERN).
We develop a degradation indicator for nuclear power plants steam generators, based on the use of... more We develop a degradation indicator for nuclear power plants steam generators, based on the use of signal measurements collected by sensors during plant operational transients between two successive maintenance interventions. Given the unavailability of information about the real degradation state during operation, an unsupervised approach is adopted. It consists in the extraction of several features from raw signals and in the selection of those features which best describe the degradation state evolution within a multi-objective optimization framework. The two considered objectives are the monotonicity and trendability of the features.
Abstract The complexity of optimization problems of optimization problems increases along with th... more Abstract The complexity of optimization problems of optimization problems increases along with the dimensions, non-linearity, and/or the required accuracy of the model constraints and objective functions. Additionally, for mixed-integer multiparametric problems, the discreet and uncertain nature of the variables and parameters to be considered, affect the complexity further more. Recently, machine learning or data-driven techniques have been proposed as alternatives for the solution of complex multiparametric programming problems. However, those methods presents as a main limitation its very high prediction error in variables that show discrete behavior and on the limits of the critical/local regions. This work extends this investigation line via proposing a novel machine learning method for solving these kind of problems based on an iterative process that use Ordinary Kriging as supervised learning tool to classify and model data. Furthermore, Ordinary Kriging can be also used, as an unsupervised tool to cluster data. The proposed methodology is applied to a benchmark case-study and the numerical results exhibits a significant improvements, up to 65% based on the normalized root-mean-square error, compared with reported information that used other modeling techniques.
This paper investigates the extension of a MultiParametric approach based on surrogate models (Me... more This paper investigates the extension of a MultiParametric approach based on surrogate models (Meta-MultiParametric approach, M-MP) in order to handle general Mixed- Integer (MI) optimization problems involving Uncertain Parameters (UPs). The method harnesses metamodeling and clustering techniques in order to approximate black box relations between the optimal values of the continuous variables and the UPs, while Classification Techniques (CT) are employed to identify the optimal values of the integer variables also as a function of the UPs. The results of applying the method to a benchmark case-study show a high prediction accuracy of the optimal solutions, saving computational effort and overpassing the complex mathematical procedures required by the standard MultiParametric Programming methods.
Abstract This paper investigates the data-driven MultiVariate Dynamic Modelling (MVDM) and MultiS... more Abstract This paper investigates the data-driven MultiVariate Dynamic Modelling (MVDM) and MultiStep-Ahead Prediction (MSAP) of nonlinear systems based on the Ordinary Kriging (OK) metamodel. The OK is used to build a set of Nonlinear Autoregressive models with Exogenous inputs (NAREX), able to approximate the system future outputs as a function of previous inputs and outputs considering a specific delay. Then, these OK-based dynamic models are used in a recursive interactive interpolations scheme to predict the process outputs over several time steps. The capabilities of the OK-based dynamic models are compared to other leading techniques, via their application to benchmark cases. The application results reveal the OK promising and competitive capabilities for MVDM of nonlinear systems, in terms of prediction accuracy and prediction time horizon.
Abstract The digitalization of nuclear power plants, with the rapid growth of information technol... more Abstract The digitalization of nuclear power plants, with the rapid growth of information technology, opens the door to the development of new methods of condition-based maintenance. In this work, a semi-supervised method for characterizing the level of degradation of nuclear power plant components using measurements collected during plant operational transients is proposed. It is based on the fusion of selected features extracted from the monitored signals. Feature selection is formulated as a multi-objective optimization problem. The objectives are the maximization of the feature monotonicity and trendability, and the maximization of a novel measure of correlation between the feature values and the results of non-destructive tests performed to assess the component degradation. The features of the Pareto optimal set are normalized and the component degradation level is defined as the median of the obtained values. The developed method is applied to real data collected from steam generators of pressurized water reactors. It is shown able to identify degradation level with errors comparable to those obtained by ad-hoc non-destructive tests.
In this article we propose a new single-source shortest-path algorithm that achieves the same O(n... more In this article we propose a new single-source shortest-path algorithm that achieves the same O(n ⋅ m) time bound as the Bellman-Ford-Moore algorithm but outperforms it and other state-of-the-art algorithms in many cases in practice. Our claims are supported by experimental evidence.
Journal of the Egyptian National Cancer Institute, 2018
Osteosarcoma (OS) is a primary bone malignancy, characterized by spindle cells producing osteoid.... more Osteosarcoma (OS) is a primary bone malignancy, characterized by spindle cells producing osteoid. The objective of this study is to describe the magnetic resonance imaging (MRI) features of different OS subtypes, record their attenuation diffusion coefficient (ADC) values and to point to the relation of their pathologic base and their corresponding ADC value. We performed a retrospective observational lesion-based analysis for 31 pathologically proven osteosarcoma subtypes: osteoblastic (n = 9), fibroblastic (n = 8), chondroblastic (n = 6), para-osteal (n = 3), periosteal (n = 1), telangiectatic (n = 2), small cell (n = 1) and extra-skeletal (n = 1). On conventional images we recorded: bone of origin, epicenter, intra-articular extension, and invasion of articulating bones, skip lesions, distant metastases, pathological fractures, ossified matrix, hemorrhage and necrosis. We measured the mean ADC value for each lesion. Among the included OS lesions, 51.6% originated at the femur, 29...
Different reasons can hinder the application of multiparametric programming formulations to solve... more Different reasons can hinder the application of multiparametric programming formulations to solve optimization problems under uncertainty, as the high nonlinearity of the optimization model, and/or its complicated structure. This work presents a complementary method that can assist in such situations. The proposed tool uses kriging metamodels to provide global multiparametric metamodels that approximate the optimal solutions as functions of the problem uncertain parameters. The method has been tested with two benchmark problems of different characteristics, and applied to a case study. The results show the high accuracy of the methodology to predict the multiparametric behavior of the optimal solution, high robustness to deal with different problem types using small number of data, and significant reduction in the solution procedure complexity in comparison with classical multiparametric programming approaches.
2023 IEEE 48th Conference on Local Computer Networks (LCN)
Quantum computing is a fast-developing field that has the ability to tackle complex problems that... more Quantum computing is a fast-developing field that has the ability to tackle complex problems that conventional computers struggle with. Recently, quantum fingerprinting localization has been introduced, which allows for the implementation of large-scale location determination systems across the globe. In this paper, we introduce QRadar, a localization system that uses quantum similarity fingerprints based on the Euclidean similarity metric, which is an early and widely used measure. The computational complexity of QRadar is exponentially superior to classical systems. We explain how to generate the quantum fingerprint, how to encode the received signal strength (RSS) measurements as quantum particles, and we describe the quantum algorithm for computing the Euclidean similarity distance between the online RSS measurements and the fingerprint ones. Additionally, we investigate various sources of errors in quantum machines and how they affect the accuracy of QRadar. We then discuss how to choose an appropriate quantum machine to minimize localization errors. We installed QRadar on a real IBM Quantum Experience machine. The results we obtained from both the installation and simulations conducted on two real test sites confirm that QRadar can accurately determine the estimated location with a significant improvement in processing time when compared to conventional classical methods.
The present study was designed to estimate the substantial equivalence of genetically modified (G... more The present study was designed to estimate the substantial equivalence of genetically modified (GM) potato Spunta lines (SpG2, SpG3 and Sp6A-3) with the cry1Ic1 gene compared to conventional non-transgenic potato Spunta cultivar (control group) through rat feeding experiments and through the determination of nutritional composition such as: protein, ash, fiber, fat, starch, total carbohydrates, amino acids, fatty acids, micro and macroelements and vitamins B1, B2 and C. Short-term feeding study was conducted for nine days using experimental rats to estimate the digestibility parameters: True Digestibility, (T.D.), Biological Value (B.V.) and Net Protein Utilization (N.P.U.) as well as the behavior of rats under investigation. Rats in each group (SpG2, SpG3 and Sp6A-3 and control groups) grew normally without marked differences in appearance, behavior or in mortality rate. No marked differences were observed in the T.D., B.V. and N.P.U. in all groups. All tested chemical parameters s...
In the present paper, we report the photometric and spectroscopic observations obtained by the 1.... more In the present paper, we report the photometric and spectroscopic observations obtained by the 1.88 m telescope at the Kottamia astronomical observatory of the pulsating star BL Cam. Fourier analysis of the light curves reveals that the fundamental mode has two harmonics. The O-C method is used to establish the period changes. So far, the analysis has been very successful in mapping the pulsation amplitude of the star across the instability strip. By using the formalism of Eddington and Plakidis (1929), we found significant results and strong indications of the evolutionary period change. A total of 55 new maximum light timings are reported. New values of (1/P) dP/dt are estimated using the O-C diagram based on all newly obtained times of maximum light combined with those taken from the literature, assuming the periods are decreasing and changing smoothly. To compute the effective temperature and surface gravity of the star, we performed model atmosphere analysis on its spectra. The physical parameters of the star are calculated and compared with the evolutionary models.
Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference, 2020
We consider the propagation of disturbances in the electrical distribution network of CERN's (Eur... more We consider the propagation of disturbances in the electrical distribution network of CERN's (European Organization for Nuclear Research) Large Hadron Collider (LHC), which is a complex system made of a large number of mutually interconnected and interdependent components. The objective of this work is the identification of the components most critical in the determination of the system operating/failed state. Given the complexity of the system, the identification is to be based on the operational data collected from the monitoring systems. The critical components are sought as those whose condition monitoring signals are most correlated to the system operating/failed state. The method of the identification is based on the use of the Relief feature selection technique to rank the monitoring signals according to their importance with respect to the classification of the system operating/failed state. The criticality of the identified signals and the corresponding components has been validated by the system technical responsibles.
Abstract Chemical process operation optimization aims at obtaining the optimal operating set-poin... more Abstract Chemical process operation optimization aims at obtaining the optimal operating set-points by real-time solution of an optimization problem that embeds a steady-state model of the process. This task is challenged by unavoidable Uncertain Parameters (UPs) variations. MultiParametric Programming (MPP) is an approach for solving this challenge, where the optimal set-points must be updated online, reacting to sudden changes in the UPs. MPP provides algebraic functions describing the optimal solution as a function of the UPs, which allows alleviating large computational cost required for solving the optimization problem each time the UPs values vary. However, MPP applicability requires a well-constructed mathematical model of the process, which is not suited for process operation optimization, where complex, highly nonlinear and/or black-box models are usually used. To tackle this issue, this paper proposes a machine learning-based methodology for multiparametric solution of continuous optimization problems. The methodology relies on the offline development of data-driven models that accurately approximate the multiparametric behavior of the optimal solution over the UPs space. The models are developed using data generated by running the optimization using the original complex process model under different UPs values. The models are, then, used online to, quickly, predict the optimal solutions in response to UPs variation. The methodology is applied to benchmark examples and two case studies of process operation optimization. The results demonstrate the methodology effectiveness in terms of high prediction accuracy (less than 1% of NRMSE, in most cases), robustness to deal with problems of different natures (linear, bilinear, quadratic, nonlinear and/or black boxes) and significant reduction in the complexity of the solution procedure compared to traditional approaches (a minimum of 67% reduction in the optimization time).
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Resumen Introducción y objetivos: El diagnóstico del colesteatoma se basa en los hallazgos clínic... more Resumen Introducción y objetivos: El diagnóstico del colesteatoma se basa en los hallazgos clínicos y en la tomografía computarizada. Actualmente, con las nuevas técnicas de resonancia magnética potenciada en difusión no ecoplanares, sin necesidad de contraste intravenoso, es posible diferenciar entre colesteatoma y tejido de granulación o inflamatorio. Por ello, esta técnica muestra su máxima utilidad en la valoración de recidivas de colesteatoma tras timpanoplastias, sobre todo en técnicas cerradas, ya que puede evitar un alto porcentaje de cirugías de revisión. Otras indicaciones de la técnica son los casos de diagnóstico complejo y el colesteatoma congénito. El objetivo de este estudio es valorar la validez (sensibilidad y especificidad) y la seguridad (valor predictivo positivo y valor predictivo negativo) de la secuencia de difusión PROPELLER, una de las técnicas potenciada en difusión no ecoplanar en el diagnóstico del colesteatoma. Métodos: Estudio prospectivo de 52 pacientes con sospecha de colesteatoma en el que se correlacionan hallazgos clínicos y quirúrgicos con los obtenidos del estudio de resonancia magnética, que incluía una secuencia potenciada en difusión no ecoplanar (PROPELLER) de oídos. Resultados: La sensibilidad de la prueba para el grupo fue del 92,85%, la especificidad del 92,30%, el valor predictivo positivo del 92,85% y el valor predictivo negativo del 92,30%. Conclusiones: La resonancia magnética con imagen potenciada en difusión no ecoplanar utilizando la secuencia PROPELLER, es una técnica eficaz en el control del colesteatoma, permitiendo diagnosticar lesiones mayores de 2 mm.
Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference
This work presents a novel method for inferring causal dependencies among abnormal behaviours of ... more This work presents a novel method for inferring causal dependencies among abnormal behaviours of components in Complex Technical Infrastructures (CTIs) from large-scale databases of alarm messages. The proposed method extracts causal relationships from association rules performing a probabilistic analysis of the alarm occurrence times and applying a modified version of the quicksort algorithm. Its capability and effectiveness is illustrated by application to a real large-scale databases of alarm messages collected in the technical infrastructure of the European Organization for Nuclear Research (CERN).
We develop a degradation indicator for nuclear power plants steam generators, based on the use of... more We develop a degradation indicator for nuclear power plants steam generators, based on the use of signal measurements collected by sensors during plant operational transients between two successive maintenance interventions. Given the unavailability of information about the real degradation state during operation, an unsupervised approach is adopted. It consists in the extraction of several features from raw signals and in the selection of those features which best describe the degradation state evolution within a multi-objective optimization framework. The two considered objectives are the monotonicity and trendability of the features.
Abstract The complexity of optimization problems of optimization problems increases along with th... more Abstract The complexity of optimization problems of optimization problems increases along with the dimensions, non-linearity, and/or the required accuracy of the model constraints and objective functions. Additionally, for mixed-integer multiparametric problems, the discreet and uncertain nature of the variables and parameters to be considered, affect the complexity further more. Recently, machine learning or data-driven techniques have been proposed as alternatives for the solution of complex multiparametric programming problems. However, those methods presents as a main limitation its very high prediction error in variables that show discrete behavior and on the limits of the critical/local regions. This work extends this investigation line via proposing a novel machine learning method for solving these kind of problems based on an iterative process that use Ordinary Kriging as supervised learning tool to classify and model data. Furthermore, Ordinary Kriging can be also used, as an unsupervised tool to cluster data. The proposed methodology is applied to a benchmark case-study and the numerical results exhibits a significant improvements, up to 65% based on the normalized root-mean-square error, compared with reported information that used other modeling techniques.
This paper investigates the extension of a MultiParametric approach based on surrogate models (Me... more This paper investigates the extension of a MultiParametric approach based on surrogate models (Meta-MultiParametric approach, M-MP) in order to handle general Mixed- Integer (MI) optimization problems involving Uncertain Parameters (UPs). The method harnesses metamodeling and clustering techniques in order to approximate black box relations between the optimal values of the continuous variables and the UPs, while Classification Techniques (CT) are employed to identify the optimal values of the integer variables also as a function of the UPs. The results of applying the method to a benchmark case-study show a high prediction accuracy of the optimal solutions, saving computational effort and overpassing the complex mathematical procedures required by the standard MultiParametric Programming methods.
Abstract This paper investigates the data-driven MultiVariate Dynamic Modelling (MVDM) and MultiS... more Abstract This paper investigates the data-driven MultiVariate Dynamic Modelling (MVDM) and MultiStep-Ahead Prediction (MSAP) of nonlinear systems based on the Ordinary Kriging (OK) metamodel. The OK is used to build a set of Nonlinear Autoregressive models with Exogenous inputs (NAREX), able to approximate the system future outputs as a function of previous inputs and outputs considering a specific delay. Then, these OK-based dynamic models are used in a recursive interactive interpolations scheme to predict the process outputs over several time steps. The capabilities of the OK-based dynamic models are compared to other leading techniques, via their application to benchmark cases. The application results reveal the OK promising and competitive capabilities for MVDM of nonlinear systems, in terms of prediction accuracy and prediction time horizon.
Abstract The digitalization of nuclear power plants, with the rapid growth of information technol... more Abstract The digitalization of nuclear power plants, with the rapid growth of information technology, opens the door to the development of new methods of condition-based maintenance. In this work, a semi-supervised method for characterizing the level of degradation of nuclear power plant components using measurements collected during plant operational transients is proposed. It is based on the fusion of selected features extracted from the monitored signals. Feature selection is formulated as a multi-objective optimization problem. The objectives are the maximization of the feature monotonicity and trendability, and the maximization of a novel measure of correlation between the feature values and the results of non-destructive tests performed to assess the component degradation. The features of the Pareto optimal set are normalized and the component degradation level is defined as the median of the obtained values. The developed method is applied to real data collected from steam generators of pressurized water reactors. It is shown able to identify degradation level with errors comparable to those obtained by ad-hoc non-destructive tests.
In this article we propose a new single-source shortest-path algorithm that achieves the same O(n... more In this article we propose a new single-source shortest-path algorithm that achieves the same O(n ⋅ m) time bound as the Bellman-Ford-Moore algorithm but outperforms it and other state-of-the-art algorithms in many cases in practice. Our claims are supported by experimental evidence.
Journal of the Egyptian National Cancer Institute, 2018
Osteosarcoma (OS) is a primary bone malignancy, characterized by spindle cells producing osteoid.... more Osteosarcoma (OS) is a primary bone malignancy, characterized by spindle cells producing osteoid. The objective of this study is to describe the magnetic resonance imaging (MRI) features of different OS subtypes, record their attenuation diffusion coefficient (ADC) values and to point to the relation of their pathologic base and their corresponding ADC value. We performed a retrospective observational lesion-based analysis for 31 pathologically proven osteosarcoma subtypes: osteoblastic (n = 9), fibroblastic (n = 8), chondroblastic (n = 6), para-osteal (n = 3), periosteal (n = 1), telangiectatic (n = 2), small cell (n = 1) and extra-skeletal (n = 1). On conventional images we recorded: bone of origin, epicenter, intra-articular extension, and invasion of articulating bones, skip lesions, distant metastases, pathological fractures, ossified matrix, hemorrhage and necrosis. We measured the mean ADC value for each lesion. Among the included OS lesions, 51.6% originated at the femur, 29...
Different reasons can hinder the application of multiparametric programming formulations to solve... more Different reasons can hinder the application of multiparametric programming formulations to solve optimization problems under uncertainty, as the high nonlinearity of the optimization model, and/or its complicated structure. This work presents a complementary method that can assist in such situations. The proposed tool uses kriging metamodels to provide global multiparametric metamodels that approximate the optimal solutions as functions of the problem uncertain parameters. The method has been tested with two benchmark problems of different characteristics, and applied to a case study. The results show the high accuracy of the methodology to predict the multiparametric behavior of the optimal solution, high robustness to deal with different problem types using small number of data, and significant reduction in the solution procedure complexity in comparison with classical multiparametric programming approaches.
Uploads
Papers by Ahmed Shokry