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A Bootstrap Comparison of Robust Regression Estimators
Kalina, Jan; Janáček, Patrik
2022 - English
The ordinary least squares estimator in linear regression is well known to be highly vulnerable to the presence of outliers in the data and available robust statistical estimators represent more preferable alternatives.
Keywords:
linear regression; robust estimation; nonparametric bootstrap; bootstrap hypothesis testing
Fulltext is available at external website.
A Bootstrap Comparison of Robust Regression Estimators
The ordinary least squares estimator in linear regression is well known to be highly vulnerable to the presence of outliers in the data and available robust statistical estimators represent more ...
Jak to v těch datech najít?
Zitová, Barbara; Šorel, Michal
2022 - Czech
Přednáška si klade za cíl seznámit odbornou veřejnost s aktivitami oddělení Zpracování obrazové informace ÚTIA AV ČR v.v.i. v oblasti analýz dat projektu Copernicus. Oddělení se dlouhodobě zabývá vývojem metod digitálního zpracování obrazu a hlubokého učení. Během posledních dvou let vzniklo několik demonstračních studentských prací ve spolupráci s MFF UK a FJFI ČVUT využívajících data z družic Sentinel, jako například rozpoznávání typů plodin z časových řad snímků ze satelitu Sentinel-2, automatická segmentace oblastí podle způsobu využití či typu povrchu pomocí metod strojového učení, přesnější detekce mraků v datech ze Sentinel-2, ve spolupráci s Ústavem pro hydrodynamiku AV ČR postupy pro odhad vlhkosti povrchové vrstvy krajiny z dat družice Sentinel-2 a zvyšování rozlišení tepelných dat Sentinel-3 pomocí metod hlubokého učení. V druhé části budou přiblíženy možnosti aplikace metod vyvinutých pro jiné oblasti (separace zdrojů \ninformace) v DPZ. The lecture aims to introduce the activities of the Image Processing Department of the Institute of Image Information of the CAS in the field of Copernicus data analysis to the professional public. The department has long been involved in the development of digital image processing and deep learning methods. During the last two years, in cooperation with the MFF UK and FJFI CTU, several student demonstration projects using data from Sentinel satellites have been finished, such as crop type recognition from Sentinel-2 time-series images, automatic segmentation of areas by land use or surface type using machine learning methods learning, more accurate cloud detection in Sentinel-2 data, in collaboration with the Institute of Hydrodynamics of the CAS Czech Republic, procedures for estimating landscape surface moisture from Sentinel-2 data and increasing the resolution of Sentinel-3 thermal data using deep learning methods. The second part will present the application of developed methods for other areas in remote sensing.
Keywords:
remote sensing; satellite imaging; machine learning
Fulltext is available at external website.
Jak to v těch datech najít?
Přednáška si klade za cíl seznámit odbornou veřejnost s aktivitami oddělení Zpracování obrazové informace ÚTIA AV ČR v.v.i. v oblasti analýz dat projektu Copernicus. Oddělení se dlouhodobě zabývá ...
Two Composition Operators for Belief Functions Revisited
Jiroušek, Radim; Kratochvíl, Václav; Shenoy, P. P.
2022 - English
In probability theory, compositional models are as powerful as Bayesian networks. However, the relation between belief-function graphical models and the corresponding compositional models is much more complicated due to several reasons. One of them is that there are two composition operators for belief functions. This paper deals with their main properties and presents sufficient conditions under which they yield the same results.
Keywords:
probability theory; Bayesian networks; belief functions
Fulltext is available at external website.
Two Composition Operators for Belief Functions Revisited
In probability theory, compositional models are as powerful as Bayesian networks. However, the relation between belief-function graphical models and the corresponding compositional models is much more ...
Modeling COVID Pandemics: Strengths and Weaknesses of Epidemic Models
Šmíd, Martin
2022 - English
We generally discuss modeling the present COVID pandemics. We argue that useful models have to be simple in the first case, yet their uncertainty has to be handled properly. In order to study circumstances of the upcoming wave of infection,\nwe construct a simple stochastic model and present predictions it gives. We conclude that the autumn wave is most likely unavoidable and suggest concentrating to mitigation.
Keywords:
COVID; Epidemic Models; stochastic model
Fulltext is available at external website.
Modeling COVID Pandemics: Strengths and Weaknesses of Epidemic Models
We generally discuss modeling the present COVID pandemics. We argue that useful models have to be simple in the first case, yet their uncertainty has to be handled properly. In order to study ...
Analysis of Impact of Covariates Entering Stochastic Optimization Problem
Volf, Petr
2022 - English
In the contribution we study consequences of imperfect information to precision of stochastic optimization solution. In particular, it is assumed that the characteristics of optimization problem are influenced by a set of covariates. This dependence is described via a regression model. Hence, the uncertainty is then caused by statistical estimation of regression parameters. The contribution will analyze several regression model cases, together with their application. Precision of results will be explored, both theoretically as well as with the aid of simulations.
Keywords:
stochastic optimization; regression model; statistical estimation
Fulltext is available at external website.
Analysis of Impact of Covariates Entering Stochastic Optimization Problem
In the contribution we study consequences of imperfect information to precision of stochastic optimization solution. In particular, it is assumed that the characteristics of optimization problem are ...
Hybrid evaluation of the industrial global impact on Mexican aquifers under uncertain criteria evaluations
Flores Casamayor, H.; Carpitella, Silvia; Izquierdo, J.; Mora-Rodríguez, J.; Delgado-Galván, X.
2022 - English
The present paper proposes an integrated methodological approach to address the problem of managing five aquifers of Guanajuato state, Mexico, according to such relevant criteria as environmental, social, economic and hydrological aspects. The goal of this research consists in formalizing a structured framework to first evaluate the various degrees of importance of criteria and to secondly get a classification of aquifers by minimizing uncertainty of evaluations. To such an aim, the Analytic Hierarchy Process (AHP) is used for calculating the vector of criteria weights, while the Fuzzy Logic (FL) theory supports in deriving quantitative evaluations of aquifers under each selected criterion. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is then proposed to formalize the final ranking of aquifers, something that will be helpful to understand which alternative matches all the differently weighted criteria in the most suitable way at a practical level. In such a way, getting a comprehensive and strategic overview about the problem of interest will be possible.
Keywords:
Analytic Hierarchy Process (AHP); Fuzzy Logic (FL) theory; Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)
Fulltext is available at external website.
Hybrid evaluation of the industrial global impact on Mexican aquifers under uncertain criteria evaluations
The present paper proposes an integrated methodological approach to address the problem of managing five aquifers of Guanajuato state, Mexico, according to such relevant criteria as environmental, ...
On a class of biped underactuated robot models with upper body: Sensitivity analysis of the walking performance
Papáček, Štěpán; Polach, P.; Prokýšek, R.; Anderle, Milan
2022 - English
Biped underactuated robots with an upper body (being a torso) form a subclass of legged robots. This study deals with the walking performance of such class of legged robot models and has been motivated by the need to implement of the previously developed sensor and control algorithms for the real-time movement of the laboratory walking robot, designed and built at the Department of Control Theory of the Institute of Information Theory and Automation (UTIA) of the Czech Academy of Sciences, see Fig. 1 (left). A detailed description of this underactuated walking-like mechanical system (called further UTIA Walking Robot – UWR) is provided in [2] and [5]. The simplest underactuated walking robot hypothetically able to walk is the so-called Compass gait biped walker, alternatively called the Acrobot, see Fig. 1 (right). For a review of underactuated mechanical systems, i.e. systems with fewer actuators than degrees of freedom, which encounter many applications in different fields (e.g., in robotics, in aeronautical and spatial systems, in marine and underwater systems, and in-flexible and mobile systems), see [3]. As follows, we examine the walking performance of parametrized models for different walking regimes and different values of model parameters. More specifically, the sensitivity analysis (i.e., parameter study) of the walking performance with respect to certain design variables (model parameters) is carried out using the software package alaska/MultibodyDynamics. The main attention is attracted to the role of the upper body mass m3 and position lc3, see Fig. 1 (right). Last but not least, having surveyed the mechanics of planar biped robots, our subsequent goal is the analysis of a 3D biped model where lateral balance is either controlled, suppressed or compensated.
Keywords:
Walking robot; ALASCA; Simulation
Fulltext is available at external website.
On a class of biped underactuated robot models with upper body: Sensitivity analysis of the walking performance
Biped underactuated robots with an upper body (being a torso) form a subclass of legged robots. This study deals with the walking performance of such class of legged robot models and has been ...
Probabilistic representation of spatial fuzzy sets
Soukup, Lubomír
2022 - English
Membership function of a given fuzzy set is expressed by probability that a point belongs in the fuzzy set. Such a membership function is derived from probability distribution of points on the boundary of the fuzzy set. Polygonal boundary is considered. Spatial operations (conjunction, disjunction, complement) are defined accordingly. Several application areas are mentioned, namely classification of land cover, cadastral mapping, material quality analysis, interferometric monitoring of bridges.
Keywords:
fuzzy set theory; probability theory; uncertainty; geographic information system
Fulltext is available at external website.
Probabilistic representation of spatial fuzzy sets
Membership function of a given fuzzy set is expressed by probability that a point belongs in the fuzzy set. Such a membership function is derived from probability distribution of points on the ...
Characterizing Uncertainty In Decision-Making Models For Maintenance In Industry 4.0
Ahmed, U.; Carpitella, Silvia; Certa, A.
2022 - English
Decision-making involves our daily life at any level, something that entails uncertainty and potential occurrence of risks of varied nature. When dealing with industrial engineering systems, effective decisions are fundamental in terms of maintenance planning and implementation. Specifically, several forms of uncertainty may affect decision-making procedures, for which adopting suitable techniques seems to be a good strategy to attain the main maintenance goals by taking into account system criticality along with decision-maker(s) opinions. A wide variety of factors contributes to uncertainty, being some of them greatly important while other ones less significant. However, all of these factors in synergy can impact the functioning of systems in a positive, neutral, or negative way. In this case, the question is whether obtaining a complete picture of such uncertainty can improve decision-making capabilities and mitigate both through-life costs and unforeseen problems. The fundamental issues include dealing with ambiguity in the maintenance decision-making process by employing numerous evaluation criteria and dealing with real-world scenarios in the maintenance environment. In this study, the Multi-Criteria Decision-Making (MCDM) approach is analysed, with particular reference to the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS), technique capable to effectively rank alternatives while dealing with uncertainty for maintenance decision-making. A final case study is developed to demonstrate the applicability of the method to the field of maintenance in industry 4.0. The proposed study may be useful in supporting intelligent and efficient decisions resulting in favorable maintenance outcomes.
Keywords:
Decision-Making; Uncertainty; Industry 4.0
Fulltext is available at external website.
Characterizing Uncertainty In Decision-Making Models For Maintenance In Industry 4.0
Decision-making involves our daily life at any level, something that entails uncertainty and potential occurrence of risks of varied nature. When dealing with industrial engineering systems, effective ...
Computing the Decomposable Entropy of Graphical Belief Function Models
Jiroušek, Radim; Kratochvíl, Václav; Shenoy, P. P.
2022 - English
In 2018, Jiroušek and Shenoy proposed a definition of entropy for Dempster-Shafer (D-S) belief functions called decomposable entropy. Here, we provide an algorithm for computing the decomposable entropy of directed graphical D-S belief function models. For undirected graphical belief function models, assuming that each belief function in the model is non-informative to the others, no algorithm is necessary. We compute the entropy of each belief function and add them together to get the decomposable entropy of the model. Finally, the decomposable entropy generalizes Shannon’s entropy not only for the probability of a single random variable but also for multinomial distributions expressed as directed acyclic graphical models called Bayesian networks.
Keywords:
Decomposable Entropy; DempsterShafer belief functions; Bayesian networks
Fulltext is available at external website.
Computing the Decomposable Entropy of Graphical Belief Function Models
In 2018, Jiroušek and Shenoy proposed a definition of entropy for Dempster-Shafer (D-S) belief functions called decomposable entropy. Here, we provide an algorithm for computing the decomposable ...
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