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1. |
Record Nr. |
UNISA996211264503316 |
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Titolo |
Medical Computer Vision: Algorithms for Big Data [[electronic resource] ] : International Workshop, MCV 2014, Held in Conjunction with MICCAI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers / / edited by Bjoern Menze, Georg Langs, Albert Montillo, Michael Kelm, Henning Müller, Shaoting Zhang, Weidong (Tom) Cai, Dimitris Metaxas |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014 |
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ISBN |
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Edizione |
[1st ed. 2014.] |
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Descrizione fisica |
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1 online resource (XI, 211 p. 78 illus.) |
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Collana |
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Image Processing, Computer Vision, Pattern Recognition, and Graphics ; ; 8848 |
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Disciplina |
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Soggetti |
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Optical data processing |
Pattern recognition |
User interfaces (Computer systems) |
Computer graphics |
Computer simulation |
Image Processing and Computer Vision |
Pattern Recognition |
User Interfaces and Human Computer Interaction |
Computer Graphics |
Simulation and Modeling |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di contenuto |
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Automatic segmentation and registration -- Localization of anatomical features -- Detection of anomalies. |
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Sommario/riassunto |
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This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision: Algorithms for Big Data, MCV 2014, held in Cambridge, MA, USA, in September 2019, in conjunction with the 17th International Conference on Medical Image Computing and Computer-Assisted |
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Intervention, MICCAI 2014. The one-day workshop aimed at exploring the use of modern computer vision technology and "big data" algorithms in tasks such as automatic segmentation and registration, localization of anatomical features and detection of anomalies emphasizing questions of harvesting, organizing and learning from large-scale medical imaging data sets and general-purpose automatic understanding of medical images. The 18 full and 1 short papers presented in this volume were carefully reviewed and selected from 30 submission. |
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2. |
Record Nr. |
UNINA9910253982303321 |
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Autore |
Yao Ye |
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Titolo |
Modeling and Control in Air-conditioning Systems / / by Ye Yao, Yuebin Yu |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2017 |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (XXI, 479 p. 272 illus., 46 illus. in color.) |
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Collana |
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Energy and Environment Research in China, , 2197-0238 |
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Disciplina |
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Soggetti |
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Energy consumption |
Building construction |
Mathematical models |
Sustainable development |
Energy Efficiency |
Building Physics, HVAC |
Mathematical Modeling and Industrial Mathematics |
Sustainable Development |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Introduction -- State-space modelling for air-conditioning system -- Modelling based on graph theory and structure-matrix theory -- Control design based on state-space model -- Air-conditioning load forecasting model -- Optimal operation and energy analysis modelling |
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for air-conditioning system -- Thermal comfort of human body indoors -- Multizone network modelling of building ventilation and contaminant transport -- Computational fluid dynamics of building environment -- Coupled multizone and CFD modelling of building environment -- New trends of advanced modelling of building environment. |
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Sommario/riassunto |
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This book investigates the latest modeling and control technologies in the context of air-conditioning systems. Firstly, it introduces the state-space method for developing dynamic models of all components in a central air-conditioning system. The models are primarily nonlinear and based on the fundamental principle of energy and mass conservation, and are transformed into state-space form through linearization. The book goes on to describe and discuss the state-space models with the help of graph theory and the structure-matrix theory. Subsequently, virtual sensor calibration and virtual sensing methods (which are very useful for real system control) are illustrated together with a case study. Model-based predictive control and state-space feedback control are applied to air-conditioning systems to yield better local control, while the air-side synergic control scheme and a global optimization strategy based on the decomposition-coordination method are developed so as to achieve energy conservation in the central air-conditioning system. Lastly, control strategies for VAV systems including total air volume control and trim & response static pressure control are investigated in practice. . |
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