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Artykuły w czasopismach na temat "Physics-based invertible models"
Bellotti, Renato, Romana Boiger i Andreas Adelmann. "Fast, Efficient and Flexible Particle Accelerator Optimisation Using Densely Connected and Invertible Neural Networks". Information 12, nr 9 (28.08.2021): 351. http://dx.doi.org/10.3390/info12090351.
Pełny tekst źródłaVisone, Ciro, i Mårten Sjöström. "Exact invertible hysteresis models based on play operators". Physica B: Condensed Matter 343, nr 1-4 (styczeń 2004): 148–52. http://dx.doi.org/10.1016/j.physb.2003.08.087.
Pełny tekst źródłaYang, Pan, Minqing Zhang, Riming Wu, Yunxuan Su i Kaiyang Guo. "Hiding Image within Image Based on Deep Learning". Journal of Physics: Conference Series 2337, nr 1 (1.09.2022): 012009. http://dx.doi.org/10.1088/1742-6596/2337/1/012009.
Pełny tekst źródłaTavakkoli, Vahid, Jean Chamberlain Chedjou i Kyandoghere Kyamakya. "A Novel Recurrent Neural Network-Based Ultra-Fast, Robust, and Scalable Solver for Inverting a “Time-Varying Matrix”". Sensors 19, nr 18 (16.09.2019): 4002. http://dx.doi.org/10.3390/s19184002.
Pełny tekst źródłaAl Hayek, Marianne, Catherine Baskiotis, Josselin Aval, Marwa Elbouz i Bachar El Hassan. "Invertible Physics-Based Hyperspectral Signature Models: A review". IEEE Geoscience and Remote Sensing Magazine, 2023, 2–20. http://dx.doi.org/10.1109/mgrs.2023.3315520.
Pełny tekst źródłaXing, Xudong, Zhaobo Chen, Dong Yu, Zhongqiang Feng i Yuechen Liu. "An invertible hysteresis model for magnetorheological damper with improved adaption capability in frequency and amplitude". Smart Materials and Structures, 27.03.2024. http://dx.doi.org/10.1088/1361-665x/ad38a5.
Pełny tekst źródłaRozprawy doktorskie na temat "Physics-based invertible models"
Al, Hayek Marianne. "Modélisation optique de signatures spectrales et polarimétriques d'objets pour augmenter les performances d'un système de reconnaissance". Electronic Thesis or Diss., Brest, 2023. http://www.theses.fr/2023BRES0101.
Pełny tekst źródłaConventional imaging, limited to object shapes and colors, faces limitations in object recognition. To enhance imaging system performance, hyperspectral and polarimetric imaging provides a wealth of information, includingchallenging-to-obtain physical parameters. This facilitates improved object detection, quantitative characterization, and classification. However, the processing of complex data from these modalities remains a challenge. The aim of this work is to propose a generic methodology for the analysis of optical signals, with a primary focus on hyperspectral imaging (HSI). An original classification of invertible physics-based hyperspectral models is presented, along with descriptions of recent diverse models for various applications: MPBOM for algae and bacteria biofilm, MARMIT for soil, PROSPECT for plant leaves, Farrell for turbid biological tissues, Schmitt for human skin, and Hapke for objects in the solar system. A convergence between the PROSPECT and Farrell models for intermediate objects (green apple and leek) paves the way for the development of a new generic and comprehensive modeling approach.Particularly in the field of biology, in collaboration with the ANSES laboratory, we conducted early detection ollowed by quantification of biofilms forming in fish farming basins using hyperspectral and polarimetric imaging. This is crucial as the current visual detection method is not efficient in preventing biofilm accumulation and implementingcleaning and disinfection procedures. Hence, an initial version of a dedicated physical modeling approach called "DNA-HSI" has been established
Streszczenia konferencji na temat "Physics-based invertible models"
ZENG, JICE, MICHAEL D. TODD i HU ZHEN. "DEGRADATION MODEL UPDATING FOR FAILURE PROGNOSTICS USING A SEQUENTIAL LIKELIHOOD- FREE BAYESIAN INFERENCE METHOD AND VIDEO MONITORING DATA". W Structural Health Monitoring 2023. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/shm2023/36804.
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