The Experts below are selected from a list of 15612 Experts worldwide ranked by ideXlab platform
Włodzimierz Balicki - One of the best experts on this subject based on the ideXlab platform.
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ICAO AVIATION OCCURRENCE CATEGORIES SIGNIFICANTLY AFFECTING AVIATION SAFETY IN POLAND FROM 2008 TO 2015
Silesian University of Technology, 2017Co-Authors: Paweł GŁowacki, Włodzimierz BalickiAbstract:Poland, as a member of the EU, is represented within the ICAO, by the European Aviation Safety Agency. However, this does not relieve our country from the responsibility of developing a state safety programme (SSP). The need to set up such a programme, which has to be specific to every country involved in aviation operation, was introduced by the ICAO’s Annex 19. One of the important points in Annex 19 is: “5.2.1 Each State shall establish and maintain a safety database to facilitate the effective analysis of information on actual or potential safety deficiencies obtained, including that from its incident reporting systems, and to determine any actions required for the enhancement of safety”. The Polish Civil Aviation Authority, along with other databases, manages the European Coordination Centre for Aviation Incident Reporting Systems (ECCAIRS). The authors (who are specialists dealing with exploitation processes in aviation) have conducted a laborious processing of the data contained in the ECCAIRS database, analysing them based on various criteria: aviation occurrence categories (as defined by the ICAO), phases of flight for different airports in Poland etc. Aircraft with an maximum take-off mass (MTOM) 5,700 kg (commercial aviation) were considered separately. It was found that the most events are those that relate to power plant (SCF-PP) airframes and related system (SCF-NP) failures, followed by collisions with birds (BIRD), events related to airports (ADRM) and events related to the required separation of aircraft (MAC). For lighter aircraft, the dominant categories are ARC, CTOL, GTOW and LOC-I events. The article presents a proposed method for predicting the number of events, determining the alert levels for the next years and assuming a Normal Distribution (Gaussian). It is one of the first attempts to use actual data contained in the database of events on airports in Poland. The results of this analysis may support the decisions of supervisory authorities in the areas where security threats are most important
Ernst A. Ruh - One of the best experts on this subject based on the ideXlab platform.
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Multivariate Normal Distributions Parametrized as a Riemannian Symmetric Space
Journal of Multivariate Analysis, 2000Co-Authors: Miroslav Lovric, Maung Min-oo, Ernst A. RuhAbstract:The construction of a distance function between probability Distributions is of importance in mathematical statistics and its applications. The distance function based on the Fisher information metric has been studied by a number of statisticians, especially in the case of the multivariate Normal Distribution (Gaussian) on Rn. It turns out that, except in the case n=1, where the Fisher metric describes the hyperbolic plane, it is difficult to obtain an exact formula for the distance function (although this can be achieved for special families with fixed mean or fixed covariance). We propose to study a slightly different metric on the space of multivariate Normal Distributions on Rn. Our metric is based on the fundamental idea of parametrizing this space as the Riemannian symmetric space SL(n+1)/SO(n+1). Symmetric spaces are well understood in Riemannian geometry, allowing us to compute distance functions and other relevant geometric data.
Paweł GŁowacki - One of the best experts on this subject based on the ideXlab platform.
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ICAO AVIATION OCCURRENCE CATEGORIES SIGNIFICANTLY AFFECTING AVIATION SAFETY IN POLAND FROM 2008 TO 2015
Silesian University of Technology, 2017Co-Authors: Paweł GŁowacki, Włodzimierz BalickiAbstract:Poland, as a member of the EU, is represented within the ICAO, by the European Aviation Safety Agency. However, this does not relieve our country from the responsibility of developing a state safety programme (SSP). The need to set up such a programme, which has to be specific to every country involved in aviation operation, was introduced by the ICAO’s Annex 19. One of the important points in Annex 19 is: “5.2.1 Each State shall establish and maintain a safety database to facilitate the effective analysis of information on actual or potential safety deficiencies obtained, including that from its incident reporting systems, and to determine any actions required for the enhancement of safety”. The Polish Civil Aviation Authority, along with other databases, manages the European Coordination Centre for Aviation Incident Reporting Systems (ECCAIRS). The authors (who are specialists dealing with exploitation processes in aviation) have conducted a laborious processing of the data contained in the ECCAIRS database, analysing them based on various criteria: aviation occurrence categories (as defined by the ICAO), phases of flight for different airports in Poland etc. Aircraft with an maximum take-off mass (MTOM) 5,700 kg (commercial aviation) were considered separately. It was found that the most events are those that relate to power plant (SCF-PP) airframes and related system (SCF-NP) failures, followed by collisions with birds (BIRD), events related to airports (ADRM) and events related to the required separation of aircraft (MAC). For lighter aircraft, the dominant categories are ARC, CTOL, GTOW and LOC-I events. The article presents a proposed method for predicting the number of events, determining the alert levels for the next years and assuming a Normal Distribution (Gaussian). It is one of the first attempts to use actual data contained in the database of events on airports in Poland. The results of this analysis may support the decisions of supervisory authorities in the areas where security threats are most important
Miroslav Lovric - One of the best experts on this subject based on the ideXlab platform.
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Multivariate Normal Distributions Parametrized as a Riemannian Symmetric Space
Journal of Multivariate Analysis, 2000Co-Authors: Miroslav Lovric, Maung Min-oo, Ernst A. RuhAbstract:The construction of a distance function between probability Distributions is of importance in mathematical statistics and its applications. The distance function based on the Fisher information metric has been studied by a number of statisticians, especially in the case of the multivariate Normal Distribution (Gaussian) on Rn. It turns out that, except in the case n=1, where the Fisher metric describes the hyperbolic plane, it is difficult to obtain an exact formula for the distance function (although this can be achieved for special families with fixed mean or fixed covariance). We propose to study a slightly different metric on the space of multivariate Normal Distributions on Rn. Our metric is based on the fundamental idea of parametrizing this space as the Riemannian symmetric space SL(n+1)/SO(n+1). Symmetric spaces are well understood in Riemannian geometry, allowing us to compute distance functions and other relevant geometric data.
Maung Min-oo - One of the best experts on this subject based on the ideXlab platform.
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Multivariate Normal Distributions Parametrized as a Riemannian Symmetric Space
Journal of Multivariate Analysis, 2000Co-Authors: Miroslav Lovric, Maung Min-oo, Ernst A. RuhAbstract:The construction of a distance function between probability Distributions is of importance in mathematical statistics and its applications. The distance function based on the Fisher information metric has been studied by a number of statisticians, especially in the case of the multivariate Normal Distribution (Gaussian) on Rn. It turns out that, except in the case n=1, where the Fisher metric describes the hyperbolic plane, it is difficult to obtain an exact formula for the distance function (although this can be achieved for special families with fixed mean or fixed covariance). We propose to study a slightly different metric on the space of multivariate Normal Distributions on Rn. Our metric is based on the fundamental idea of parametrizing this space as the Riemannian symmetric space SL(n+1)/SO(n+1). Symmetric spaces are well understood in Riemannian geometry, allowing us to compute distance functions and other relevant geometric data.
