Literatura científica selecionada sobre o tema "Deployment error estimation"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "Deployment error estimation".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Artigos de revistas sobre o assunto "Deployment error estimation"
Gutierrez, Edgar Andres, Ivan Fernando Mondragon, Julian D. Colorado e Diego Mendez Ch. "Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations". Plants 11, n.º 13 (21 de junho de 2022): 1636. http://dx.doi.org/10.3390/plants11131636.
Texto completo da fonteLin, Feilong, Wenbai Li e Liyong Yuan. "Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor Networks". Sensors 18, n.º 10 (6 de outubro de 2018): 3338. http://dx.doi.org/10.3390/s18103338.
Texto completo da fonteRolling, Craig A., Yuhong Yang e Dagmar Velez. "COMBINING ESTIMATES OF CONDITIONAL TREATMENT EFFECTS". Econometric Theory 35, n.º 6 (6 de novembro de 2018): 1089–110. http://dx.doi.org/10.1017/s0266466618000397.
Texto completo da fonteHsiao, Chiu-Han, Frank Yeong-Sung Lin, Hao-Jyun Yang, Yennun Huang, Yu-Fang Chen, Ching-Wen Tu e Si-Yao Zhang. "Optimization-Based Approaches for Minimizing Deployment Costs for Wireless Sensor Networks with Bounded Estimation Errors". Sensors 21, n.º 21 (27 de outubro de 2021): 7121. http://dx.doi.org/10.3390/s21217121.
Texto completo da fontePark, Dongjoo, Soyoung You, Jeonghyun Rho, Hanseon Cho e Kangdae Lee. "Investigating optimal aggregation interval sizes of loop detector data for freeway travel-time estimation and prediction". Canadian Journal of Civil Engineering 36, n.º 4 (abril de 2009): 580–91. http://dx.doi.org/10.1139/l08-129.
Texto completo da fonteMadsen, Tatiana, Hans-Peter Schwefel, Lars Mikkelsen e Annelore Burggraf. "Comparison of WLAN Probe and Light Sensor-Based Estimators of Bus Occupancy Using Live Deployment Data". Sensors 22, n.º 11 (28 de maio de 2022): 4111. http://dx.doi.org/10.3390/s22114111.
Texto completo da fonteRathore, Kapil Singh, Sricharan Vijayarangan, Preejith SP e Mohanasankar Sivaprakasam. "A Multifunctional Network with Uncertainty Estimation and Attention-Based Knowledge Distillation to Address Practical Challenges in Respiration Rate Estimation". Sensors 23, n.º 3 (1 de fevereiro de 2023): 1599. http://dx.doi.org/10.3390/s23031599.
Texto completo da fonteMcLoughlin, Benjamin, Harry Pointon, John McLoughlin, Andy Shaw e Frederic Bezombes. "Uncertainty Characterisation of Mobile Robot Localisation Techniques using Optical Surveying Grade Instruments". Sensors 18, n.º 7 (13 de julho de 2018): 2274. http://dx.doi.org/10.3390/s18072274.
Texto completo da fonteUkani, Neema Amish, e Saurabh S. Chakole. "Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective". Journal of Physics: Conference Series 2327, n.º 1 (1 de agosto de 2022): 012026. http://dx.doi.org/10.1088/1742-6596/2327/1/012026.
Texto completo da fonteAljohani, Nader, Tierui Zou, Arturo S. Bretas e Newton G. Bretas. "Multi-Area State Estimation: A Distributed Quasi-Static Innovation-Based Model with an Alternative Direction Method of Multipliers". Applied Sciences 11, n.º 10 (13 de maio de 2021): 4419. http://dx.doi.org/10.3390/app11104419.
Texto completo da fonteTeses / dissertações sobre o assunto "Deployment error estimation"
Sarr, Jean Michel Amath. "Étude de l’augmentation de données pour la robustesse des réseaux de neurones profonds". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS072.
Texto completo da fonteIn this thesis, we considered the problem of the robustness of neural networks. That is, we have considered the case where the learning set and the deployment set are not independently and identically distributed from the same source. This hypothesis is called : the i.i.d hypothesis. Our main research axis has been data augmentation. Indeed, an extensive literature review and preliminary experiments showed us the regularization potential of data augmentation. Thus, as a first step, we sought to use data augmentation to make neural networks more robust to various synthetic and natural dataset shifts. A dataset shift being simply a violation of the i.i.d assumption. However, the results of this approach have been mixed. Indeed, we observed that in some cases the augmented data could lead to performance jumps on the deployment set. But this phenomenon did not occur every time. In some cases, the augmented data could even reduce performance on the deployment set. In our conclusion, we offer a granular explanation for this phenomenon. Better use of data augmentation toward neural network robustness is to generate stress tests to observe a model behavior when various shift occurs. Then, to use that information to estimate the error on the deployment set of interest even without labels, we call this deployment error estimation. Furthermore, we show that the use of independent data augmentation can improve deployment error estimation. We believe that this use of data augmentation will allow us to better quantify the reliability of neural networks when deployed on new unknown datasets
Capítulos de livros sobre o assunto "Deployment error estimation"
Ahmed, Qasim Zeeshan, e Lie-Liang Yang. "Comparative Study of Adaptive Multiuser Detections in Hybrid Direct-Sequence Time-Hopping Ultrawide Bandwidth Systems". In Advances in Wireless Technologies and Telecommunication, 459–78. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5170-8.ch018.
Texto completo da fonteGuo, Cheng, R. Venkatesha Prasad, Jing Wang, Vijay Sathyanarayana Rao e Ignas Niemegeers. "Localizing Persons Using Body Area Sensor Network". In Developments in Wireless Network Prototyping, Design, and Deployment, 273–89. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1797-1.ch013.
Texto completo da fonteLin, Kai, Min Chen, Joel J. P. C. Rodrigues e Hongwei Ge. "System Design and Data Fusion in Body Sensor Networks". In Advances in Healthcare Information Systems and Administration, 1–25. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0888-7.ch001.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Deployment error estimation"
Guihen, Damien, e Peter King. "A model Of AUV survey feature resolution and error estimation for deployment optimization". In 2016 IEEE/OES Autonomous Underwater Vehicles (AUV). IEEE, 2016. http://dx.doi.org/10.1109/auv.2016.7778672.
Texto completo da fonteMahboubi, Hamid, Mojtaba Vaezi e Fabrice Labeau. "Distributed deployment algorithms in a network of nonidentical mobile sensors subject to location estimation error". In 2014 IEEE Sensors. IEEE, 2014. http://dx.doi.org/10.1109/icsens.2014.6985374.
Texto completo da fonteLuciani, Sara, Stefano Feraco, Angelo Bonfitto, Andrea Tonoli, Nicola Amati e Maurizio Quaggiotto. "A Machine Learning Method for State of Charge Estimation in Lead-Acid Batteries for Heavy-Duty Vehicles". In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-68469.
Texto completo da fonteBanerjee, Amit, Issam Abu-Mahfouz, Jianyan Tian e A. H. M. Esfakur Rahman. "A Robust Hybrid Machine Learning-Based Modeling Technique for Wind Power Production Estimates". In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-94173.
Texto completo da fonteBooncharoen, Pichita, Thananya Rinsiri, Pakawat Paiboon, Supaporn Karnbanjob, Sonchawan Ackagosol, Prateep Chaiwan e Ouraiwan Sapsomboon. "Pore Pressure Estimation by Using Machine Learning Model". In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21490-ms.
Texto completo da fonteKalanovic, V. D., K. Padmanabhan e C. H. Jenkins. "A Discrete Cell Model for Shape Control of Precision Membrane Antennae and Reflectors". In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0548.
Texto completo da fonteAL-Qutami, Tareq Aziz, e Fatin Awina Awis. "Personnel Real Time Tracking in Hazardous Areas Using Wearable Technologies and Machine Learning". In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21426-ms.
Texto completo da fonteRamos Gurjao, Kildare George, Eduardo Gildin, Richard Gibson e Mark Everett. "Estimation of Far-Field Fiber Optics Distributed Acoustic Sensing DAS Response Using Spatio-Temporal Machine Learning Schemes and Improvement of Hydraulic Fracture Geometric Characterization". In SPE Hydraulic Fracturing Technology Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/209119-ms.
Texto completo da fonteJenkins, C. H., V. D. Kalanovic, S. M. Faisal, K. Padmanabhan e M. Tampi. "Adaptive Shape Control of Precision Membrane Antennae and Reflectors". In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-0951.
Texto completo da fonteWani, Ankit, Jyotsana Singh, Deepa Kumari, Avinash Ithape e Govind Rapanwad. "“FEV’s ‘CogniSafe’: An Innovative Deep Learning-Based AI Driver Monitoring System for the Future of Mobility”". In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2012.
Texto completo da fonte