Literatura científica selecionada sobre o tema "Sawn oak timber pieces"
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Artigos de revistas sobre o assunto "Sawn oak timber pieces"
Riesco Muñoz, Guillermo, e Andrés Remacha Gete. "Prediction of Strength and Elasticity in Oak Beams on the Basis of External Wood Characteristics". Advanced Materials Research 778 (setembro de 2013): 152–58. http://dx.doi.org/10.4028/www.scientific.net/amr.778.152.
Texto completo da fonteМаксименков e Alexey Maksimenkov. "Influence of ways of teeth set and feed rate of band saws on roughness of sawn timber". Forestry Engineering Journal 4, n.º 1 (21 de abril de 2014): 178–82. http://dx.doi.org/10.12737/3366.
Texto completo da fonteKRZOSEK, SŁAWOMIR, IZABELA BURAWSKA-KUPNIEWSKA, PIOTR MAŃKOWSKI e MAREK GRZEŚKIEWICZ. "Comparison results of visual and machine strength grading of Scots pine sawn timber from the Silesian Forestry Region in Poland". Annals of WULS, Forestry and Wood Technology 107 (30 de setembro de 2019): 24–30. http://dx.doi.org/10.5604/01.3001.0013.7633.
Texto completo da fonteBukara, Bogdan, e Goran Milic. "Razvoj diskoloracija i profili vlaznosti u hrastovoj rezanoj gradji tokom konvencionalnog susenja". Bulletin of the Faculty of Forestry, n.º 128 (2023): 21–30. http://dx.doi.org/10.2298/gsf2328021b.
Texto completo da fonteNocetti, Michela, Giovanni Aminti, C. Brand Wessels e Michele Brunetti. "Applying Machine Strength Grading System to Round Timber Used in Hydraulic Engineering Works". Forests 12, n.º 3 (28 de fevereiro de 2021): 281. http://dx.doi.org/10.3390/f12030281.
Texto completo da fonteSejdiu, Muharrem, Pandeli Marku e Rrahim Sejdiu. "The Use of Sawn Timber for Barrel Production from Oak Wood (Quercus Petraea L.)". European Journal of Engineering and Formal Sciences 2, n.º 2 (12 de junho de 2018): 101. http://dx.doi.org/10.26417/ejef.v2i2.p101-107.
Texto completo da fonteKrzosek, Sławomir, Izabela Burawska-Kupniewska e Piotr Mańkowski. "The Influence of Scots Pine Log Type (Pinus sylvestris L.) on the Mechanical Properties of Lumber". Forests 11, n.º 12 (26 de novembro de 2020): 1257. http://dx.doi.org/10.3390/f11121257.
Texto completo da fonteWieruszewski, Marek, Adrian Trociński, Jakub Kawalerczyk, Adam Derkowski e Radosław Mirski. "The Strength of Pine (Pinus sylvestris L.) Sawn Timber in Correlation with Selected Wood Defects". Materials 15, n.º 11 (2 de junho de 2022): 3974. http://dx.doi.org/10.3390/ma15113974.
Texto completo da fonteGrześkiewicz, Marek, Sławomir Krzosek, Izabela Burawska, Piotr Borysiuk e Piotr Mańkowski. "Influence of Thermo-Mechanical Densification (TMD) on the Properties of Structural Sawn Timber (Pinus sylvestris L.)". Forests 14, n.º 2 (26 de janeiro de 2023): 231. http://dx.doi.org/10.3390/f14020231.
Texto completo da fonteZhou, Hai Bin, e Hai Qing Ren. "Factors Influencing Structural Performance of Finger-Jointed Chinese Fir Lumber". Applied Mechanics and Materials 174-177 (maio de 2012): 635–40. http://dx.doi.org/10.4028/www.scientific.net/amm.174-177.635.
Texto completo da fonteTeses / dissertações sobre o assunto "Sawn oak timber pieces"
Dahbi, Radouan. "Conception d’une chaîne de traitements pour la segmentation texture d’images multimodales de pièces de bois en chêne. Application à la détection des singularités et la discrimination du grain du bois". Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0143.
Texto completo da fonteThe work presented in this CIFRE thesis, associating CRAN and CRITT Bois for the ANR-OPTIFIN project, contributes to the development of an image processing chain for the texture segmentation of multimodal images of sawn oak timber pieces. The idea is to combine multimodal acquisition techniques in visible and near-infrared (NIR) range with texture analysis methods using covariance matrices and texture segmentation methods in the Riemannian manifold, for the detection of singularities and discrimination of wood grain. In the first chapter, we present a state of the art on automated inspection of wood pieces; with a special focus on hardwood species (e.g. oak) for which inspection is still an open problem. The second chapter deals with the implementation of the multimodal imagery platform (PIM) and the calibration of color, grayscale, direct and scatter images in the visible range and abundance maps, obtained from NIR hyperspectral images. We propose an original methodology for the scatter images by optimizing the acquisition parameters on sawn oak timber pieces. The third chapter concerns the study of the registration of monomodal and multimodal images and the application of a method for the suppression of their background. In the fourth chapter, we propose a texture analysis methodology based on the fusion of multimodal images and/or their textural images (LBP, nriLBP, GLCM and Gradient) by covariance matrices. We exploit the covariance matrices by K-means clustering andk-ppv supervised classification methods, extended to the Riemannian case, for segmentation. In the last chapter, we present results ensuring a relevant and fast segmentation of the covariance matrices. They are obtained after having determined the best parameters for the K-means setting. The clustering results show that the use of multimodal images alone leads to an optimal segmentation of compact singularities. They also show the importance of integrating textural images in the modality sets to obtain a better segmentation of regional type singularities. For wood grain, an efficient segmentation is obtained by using only textural images. Finally, we propose to apply k-ppv in the Riemannian manifold on the selected modalities to obtain a more accurate segmentation
Capítulos de livros sobre o assunto "Sawn oak timber pieces"
Rebeggiani, Sabina, Vijeth Reddy, Linus Olofsson e Magnus Fredriksson. "Towards In-Line Measurements of Sawn Wood Surfaces". In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde240150.
Texto completo da fonteOrr, David W. "The Ecology of Giving and Consuming". In The Nature of Design. Oxford University Press, 2002. http://dx.doi.org/10.1093/oso/9780195148558.003.0027.
Texto completo da fonte