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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	(2016) The use of close-range photogrammetry in zooarchaeology: Creating accurate 3D models of wolf crania to study dog domestication , Journal of Archaeological Science: Reports, 9, 87–93. https://doi.org/10.1016/j.jasrep.2016.06.028

	(2016) Algorithmic approach toward Transit-Oriented Development neighborhoods: (Para)metric tools for evaluating and proposing rapid transit-based districts , International Journal of Architectural Computing, 14(2), 131-146

	(2016) Improving collaborative work and project management in a nuclear power plant design team: A Human-Centered Design approach , Annals of Nuclear Energy. Elsevier, ANE4864

	(2016) Cloud-based BIM Data Transmission: Current Status and Challenges , Proceedings of the 33rd International Symposium on Automation and Robotics Construction (ISARC). Auburn, Alabama

	3M (2016) Respiratory Protection for Health Care Workers: Fitting a 3MTM 1863 Unvalved FFP3 Respirator , 3M HSO guidelines

	3XN Architects, et al. (2016) Bladerunner, 1st Edition , KLS PurePrint, Denmark

	?sterg?rd, T., Jensen, R. L. and Maagaard, S. E. (2016) Building simulations supporting decision making in early design-A review , Renewable and Sustainable Energy Reviews, 61, pp. 187-201

	A. Brock, T. Lim, J. M. Ritchie, and N. Weston (2016) Generative and Discriminative Voxel Modeling with Convolutional Neural Networks , ArXiv Preprint

	A. Körner, A. Mader, S. Saffarian, and J. Knippers (2016) Bio-Inspired Kinetic Curved-Line Folding for Architectural Applications , ACADIA16: Posthuman Frontiers; Proceedings of the 36th Annual Conference ofthe Association for Computer Aided Design in Architecture. 270–279

	A. Lamb, V. Dumoulin, and A. Courville (2016) Discriminative Regularization for Generative Models , ArXiv

	A. McRobie (2016) Maxwell and Rankine reciprocal diagrams via Minkowski sums for two-dimensional and three-dimensional trusses under load , International Journal of Space Structures 31 (2-4): 203–216

	Aaditya, G and Mani, M (2016) BIPV:a real-time guilding performance study for a roof-integrated facility , International Journal of Sustainable Energy, 37(3), pp. 249-267

	Abadi, M, Agarwal, A, Barham, P, Brevdo, E, Chen, Z, Citro, C, Corrado, GS, Davis, A, Dean, J, Devin, M and Ghemawat, S (2016) Tensorflow: Large-scale machine learning on heterogeneous distributed systems , OSDI, pp. 265-238

	Abadi, M, Agarwal, A, Barham, P, Brevdo, E, Chen, Z, Citro, C, Corrado, GS, Davis, A, Dean, J, Devin, M and Ghemawat, S (2016) Tensorflow: Large-scale machine learning on heterogeneous distributed systems , OSDI, 16, pp. 265-283

	Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., & others. (2016) TensorFlow: A system for Large-Scale machine learning , 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), 265-283

	Abadi, Martín, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al. (2016) Tensorflow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , arXiv preprint arXiv:1603.04467

	Abadi, Martín, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado et al. (2016) TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , arXiv preprint arXiv:1603.04467

	Abadi, Martín, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado et al. (2016) TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , arXiv preprint arXiv:1603.04467

	Abarca-Gómez, L. et al. (2017) Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults , The Lancet, 390(10113), pp. 2627 - 2642 https://doi.org/10.1016/S0140-6736(17)32129-3

	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

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