Improved Structure Detection For Polynomial NARX Models Using a Multiobjective Error Reduction Ratio

This paper addresses the problem of structure detection for polynomial NARX models. It develops MERR, a multiobjective extension of a methodology well-known as the error reduction ratio (ERR). It is shown that it is possible to choose terms which take into account dynamics of prediction error and other types of affine information, such as fixed points or static curve. Two examples are included to illustrate the proposed methodology. A numerical example shows that the technique is able to reconstruct the structure of a system, known a priori. The identification of a pilot DC–DC buck converter shows that the proposed approach is capable to find models valid over a wide range of operation points. In this latter example, MERR is compared with ERR in two forms: (i) affine information is applied only in the structure selection for MERR and (ii) affine information is applied for structure selection for MERR and for parameter estimation for both MERR and ERR. In both comparisons, MERR presented nondominated solutions of Pareto set

Automation of the process for accessing lip forces

The decrease of lip strength results in the absence of lip contact, which may result in musculature neuromuscular imbalance and affect several functions, such as harmonic dental growth, swallowing, speech and breathing. The measurement of lip strength is an important task in clinical speech pathology practice. This paper describes the development of a measurement system to be used in the processes of force assessment of lips. The user can follow the measurement by means of an interface, which allows registration of information, such as patient personal data, measurement and a brief report. The developed system may be used on personal computer at Windows platfor

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Improved Structure Detection For Polynomial NARX Models Using a Multiobjective Error Reduction Ratio

This paper addresses the problem of structure detection for polynomial NARX models. It develops MERR, a multiobjective extension of a methodology well-known as the error reduction ratio (ERR). It is shown that it is possible to choose terms which take into account dynamics of prediction error and other types of affine information, such as fixed points or static curve. Two examples are included to illustrate the proposed methodology. A numerical example shows that the technique is able to reconstruct the structure of a system, known a priori. The identification of a pilot DC–DC buck converter shows that the proposed approach is capable to find models valid over a wide range of operation points. In this latter example, MERR is compared with ERR in two forms: (i) affine information is applied only in the structure selection for MERR and (ii) affine information is applied for structure selection for MERR and for parameter estimation for both MERR and ERR. In both comparisons, MERR presented nondominated solutions of Pareto set

Improved Structure Detection For Polynomial NARX Models Using a Multiobjective Error Reduction Ratio

This paper addresses the problem of structure detection for polynomial NARX models. It develops MERR, a multiobjective extension of a methodology well-known as the error reduction ratio (ERR). It is shown that it is possible to choose terms which take into account dynamics of prediction error and other types of affine information, such as fixed points or static curve. Two examples are included to illustrate the proposed methodology. A numerical example shows that the technique is able to reconstruct the structure of a system, known a priori. The identification of a pilot DC–DC buck converter shows that the proposed approach is capable to find models valid over a wide range of operation points. In this latter example, MERR is compared with ERR in two forms: (i) affine information is applied only in the structure selection for MERR and (ii) affine information is applied for structure selection for MERR and for parameter estimation for both MERR and ERR. In both comparisons, MERR presented nondominated solutions of Pareto set

Automation of the process for accessing lip forces

The decrease of lip strength results in the absence of lip contact, which may result in musculature neuromuscular imbalance and affect several functions, such as harmonic dental growth, swallowing, speech and breathing. The measurement of lip strength is an important task in clinical speech pathology practice. This paper describes the development of a measurement system to be used in the processes of force assessment of lips. The user can follow the measurement by means of an interface, which allows registration of information, such as patient personal data, measurement and a brief report. The developed system may be used on personal computer at Windows platfor