Cumincad : CUMINCAD References : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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	Schlarp, JS, Csencsics, EC and Schitter, Georg (2019) Scanning laser triangulation sensor geometry maintaining imaging condition , Elsevierjournal, 52(15), pp. 301-306

	Che, E, Jung, J and Olsen, MJ (2019) Object recognition, segmentation, and classification of mobile laser scanning point clouds: A state of the art review , Sensors, 19(4), p. 810

	Jo, Y. & Hong, S. . (2019) Three-dimensional digital documentation of cultural heritage site based on the convergence of terrestrial laser scanning and unmanned aerial vehicle photogrammetry , International Journal of Geo-Information, 8(2)

	Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N. & Lee, S.-I. (2019) Explainable AI for Trees: From Local Explanations to Global Understanding , ArXiv:1905.04610 [Cs, Stat]. Retrieved January 10, 2022, from http://arxiv.org/abs/1905.04610Menges, A., & Reichert, S. (2012). Material Capacity: Embedded Responsiveness. Architectural Design, 82(2), 52–59. https://doi.org/10.1002/ad.1379Olsson, A., & Oscarsson, J. (2017). Strength grading on the basis of high resolution laser scanning and dynamic excitation: A full scale investigation of performance. European Journal of Wood and Wood Products, 75(1), 17–31. https://doi.org/10.1007/s00107-016-1102-6Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12(85), 2825–2830

	Wang, Q, Kim, Mk, Cheng, JCP and Sohn, H (2016) Automated quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning , Automation in Construction, 68, pp. 170-182

	A. Harichandran, B. Raphael, and A. Mukherjee (2019) Determination of automated construction operations from sensor data using machine learning , Proceedings of the 4th International Conference on Civil and Building Engineering Informatics

	Abbasabadi, N. and Ashayeri, M. (2019) Urban energy use modelling methods and tools: A review and an outlook , Building and Environment, 161, 106270. https://doi.org/10.1016/j.buildenv.2019.106270Alhamwi, A., Medjroubi, W., Vogt, T. and Agert, C. (2017). GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas. Applied Energy, 191, 1-9. https://doi.org/10.1016/j.apenergy.2017.01.048Chen, Y. and Hong, T. (2018). Impacts of building geometry modelling methods on the simulation results of urban building energy models. Applied Energy, 215, 717-735. https://doi.org/10.1016/j.apenergy.2018.02.073Chen, Y., Hong, T., Luo, X. and Hooper, B. (2019). Development of city buildings dataset for urban building energy modelling. Energy and Buildings, 183, 252-265. https://doi.org/10.1016/j.enbuild.2018.11.008Davila, C.C., Reinhart, C.F., and Bemis, J.L. (2016). Modelling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets. Energy, 117, 237-250. https://doi.org/10.1016/j.energy.2016.10.057Dogan, T. and Reinhart, C. (2017). Shoeboxer: An algorithm for abstracted rapid multi-zone urban building energy model generation and simulation. Energy and Buildings, 140, 140-153. https://doi.org/10.1016/j.enbuild.2017.01.030EC. (2021). 2030 Climate Target Plan. European Commission. Retrieved June 1, 2021, from https://ec.europa.eu/clima/eu-action/european-green-deal/2030-climate-target-plan_en

	Abhari, M and Abhari, K (2019) Design with Perfect Sense: The Adoption of Smart Sensor Technologies in Architectural Practice , HICSS 2019

	Alom, Md Zahangir, Chris Yakopcic, Mahmudul Hasan, Tarek Taha, and Vijayan Asari (2019) Recurrent Residual U-Net for Medical Image Segmentation , Journal of Medical Imaging 6 (1): 014006. https://doi.org/10.1117/1.JMI.6.1.014006

	Andreas Wulff-Abramsson, Adam Lopez, and Luis Antonio Mercado Cerda (2019) Paint with Brainwaves—A Step Towards a Low Brain Effort Active BCI Painting Prototype , Mobile Brain-Body Imaging and the Neuroscience of Art, Innovation and Creativity, 183–188, Springer Series on Bio- and Neurosystems, vol 10

	Arac, A., Zhao, P., Dobkin, B. H., Carmichael, S. T., & Golshani, P. (2019) DeepBehavior: A deep learning toolbox for automated analysis of animal and human behavior imaging data , Frontiers in systems neuroscience, 13, 20

	Arieno, AA, Chan, AC and Destounis, SVD (2019) A review of the role of augmented intelligence in breast imaging: From automated breast density assessment to risk stratification , American Journal of Roentgenology, 212, pp. 259-270

	Bard, J, Cupkova, D, Washburn, N and Zeglin, G (2019) Thermally Informed Robotic Topologies:Pro fi le-3D-Printing for the RoboticConstruction of Concrete Panels, ThermallyTuned Through High ResolutionSurface Geometry , Willmann, J, Block, P, Hutter, M, Byrne, K and Schork, T (eds), Robotic Fabrication in Architecture, Art and Design 2018, Springer Nature Switzerland AG, Basel, pp. 113-125

	Bassier M and Vergauwen M (2018) Clustering of wall geometry from unstructured point clouds using conditional random fields , Remote Sens 2019; 11: 1586.

	Bassier M, Van Genechten B and Vergauwen M (2019) Classification of sensor independent point cloud data of building objects using random forests , J Build Eng 2019; 21: 468–477

	Belley, Denis, Isabelle Duchesne, Steve Vallerand, Julie Barrette, and Michel Beaudoin (2019) Computed Tomography (CT) Scanning of Internal Log Attributes Prior to Sawing Increases Lumber Value in White Spruce (Picea Glauca) and Jack Pine (Pinus Banksiana) , Samaniego, Spencer MIXED REALITY MIXED REALITY REALIGNMENTS REALIGNMENTS 23111 Canadian Journal of Forest Research 49 (12): 1516–24

	Bonduel, M, Wagner, A, Pauwels, P, Vergauwen, M and Klein, R (2019) Including widespread geometry schemas into Linked Data-based BIM applied to built heritage , Proc. Inst. Civ. Eng. - Smart Infrastruct. Constr., pp. 34-51

	Bonduel, M., Wagner, A., Pauwels, P., Vergauwen, M., Klein, R., (2019) Including widespread geometry formats in semantic graphs using RDF literals , Proceedings of the 2019 European Conference on Computing in Construction, Chania, Greece, pp. 341-350

	Capone M and Lanzara E (2019) Scan-to-bim vs 3d ideal model hbim: parametric tools to study domes geometry , International Archives of the Photogrammetry. Remote Sensing & Spatial Information Sciences, 2019.

	Capone, M. & Lanazara. E. (2019) Scan-to-BIM vs 3D Ideal model HBIM: parametric tools to study domes geometry , The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Volume XLII-2/W9, 2019)

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