Neural Network Configurations Analysis for Multilevel Speech Pattern Recognition System with Mixture of Experts

This chapter proposes to analyze two configurations of neural networks to compose the expert set in the development of a multilevel speech signal pattern recognition system of 30 commands in the Brazilian Portuguese language. Then, multilayer perceptron (MLP) and learning vector quantization (LVQ) networks have their performances verified during the training, validation and test stages in the speech signal recognition, whose patterns are given by two-dimensional time matrices, result from mel-cepstral coefficients coding by the discrete cosine transform (DCT). In order to avoid the pattern separability problem, the patterns are modified by a nonlinear transformation to a high-dimensional space through a suitable set of Gaussian radial base functions (GRBF). The performance of MLP and LVQ experts is improved and configurations are trained with few examples of each modified pattern. Several combinations were performed for the neural network topologies and algorithms previously established to determine the network structures with the best hit and generalization results

FABRICAÇÃO DE PAINÉIS DE PARTÍCULAS DE MADEIRA TAUARI (Couratari oblongifolia) UTILIZANDO RESINA POLIURETANA DE MAMONA

Neste trabalho foi estudada a fabricação de painéis de partículas (particleboard) de madeira tauari (Couratari oblongifolia) como forma de melhorar o aproveitamento da madeira oriunda do manejo extrativo da região amazônica, visando diminuir o impacto ambiental provocado pela retirada destas árvores. Os resíduos foram aglomerados com resina poliuretana de mamona, tipo bi componente. Os compósitos foram conformados com densidade nominal de 1000 kgm-3 por prensagem uniaxial a uma pressão de 5 MPa, a 90, 110 e 130°C. Para a caracterização dos painéis foram realizados os ensaios de Densidade aparente (DAP), Teor de Umidade (U), Absorção de água (AA), Inchamento na espessura (IE), Flexão Estática (MOR e MOE), Tração perpendicular (TPP) e Arrancamento de parafuso (AP), segundo as recomendações da norma NBR 14810-3 da ABNT (2006b). Os resultados mostraram que os painéis fabricados com o resíduo da madeira tauari independentemente da temperatura de conformação apresentam densidade média entre 930 e 941 kgm-3, com valores de tração perpendicular (ligação interna), inchamento na espessura superiores aos estabelecidos pelas normas NBR 14810-2 (ABNT, 2006a) e ANSI A208.1 (1999) sendo classificados de acordo como material de alta densidade recomendado para uso industrial e comercial

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