Auswahl der wissenschaftlichen Literatur zum Thema „Deployment error estimation“
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Zeitschriftenartikel zum Thema "Deployment error estimation"
Gutierrez, Edgar Andres, Ivan Fernando Mondragon, Julian D. Colorado und Diego Mendez Ch. „Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations“. Plants 11, Nr. 13 (21.06.2022): 1636. http://dx.doi.org/10.3390/plants11131636.
Der volle Inhalt der QuelleLin, Feilong, Wenbai Li und Liyong Yuan. „Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor Networks“. Sensors 18, Nr. 10 (06.10.2018): 3338. http://dx.doi.org/10.3390/s18103338.
Der volle Inhalt der QuelleRolling, Craig A., Yuhong Yang und Dagmar Velez. „COMBINING ESTIMATES OF CONDITIONAL TREATMENT EFFECTS“. Econometric Theory 35, Nr. 6 (06.11.2018): 1089–110. http://dx.doi.org/10.1017/s0266466618000397.
Der volle Inhalt der QuelleHsiao, Chiu-Han, Frank Yeong-Sung Lin, Hao-Jyun Yang, Yennun Huang, Yu-Fang Chen, Ching-Wen Tu und Si-Yao Zhang. „Optimization-Based Approaches for Minimizing Deployment Costs for Wireless Sensor Networks with Bounded Estimation Errors“. Sensors 21, Nr. 21 (27.10.2021): 7121. http://dx.doi.org/10.3390/s21217121.
Der volle Inhalt der QuellePark, Dongjoo, Soyoung You, Jeonghyun Rho, Hanseon Cho und Kangdae Lee. „Investigating optimal aggregation interval sizes of loop detector data for freeway travel-time estimation and prediction“. Canadian Journal of Civil Engineering 36, Nr. 4 (April 2009): 580–91. http://dx.doi.org/10.1139/l08-129.
Der volle Inhalt der QuelleMadsen, Tatiana, Hans-Peter Schwefel, Lars Mikkelsen und Annelore Burggraf. „Comparison of WLAN Probe and Light Sensor-Based Estimators of Bus Occupancy Using Live Deployment Data“. Sensors 22, Nr. 11 (28.05.2022): 4111. http://dx.doi.org/10.3390/s22114111.
Der volle Inhalt der QuelleRathore, Kapil Singh, Sricharan Vijayarangan, Preejith SP und Mohanasankar Sivaprakasam. „A Multifunctional Network with Uncertainty Estimation and Attention-Based Knowledge Distillation to Address Practical Challenges in Respiration Rate Estimation“. Sensors 23, Nr. 3 (01.02.2023): 1599. http://dx.doi.org/10.3390/s23031599.
Der volle Inhalt der QuelleMcLoughlin, Benjamin, Harry Pointon, John McLoughlin, Andy Shaw und Frederic Bezombes. „Uncertainty Characterisation of Mobile Robot Localisation Techniques using Optical Surveying Grade Instruments“. Sensors 18, Nr. 7 (13.07.2018): 2274. http://dx.doi.org/10.3390/s18072274.
Der volle Inhalt der QuelleUkani, Neema Amish, und Saurabh S. Chakole. „Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective“. Journal of Physics: Conference Series 2327, Nr. 1 (01.08.2022): 012026. http://dx.doi.org/10.1088/1742-6596/2327/1/012026.
Der volle Inhalt der QuelleAljohani, Nader, Tierui Zou, Arturo S. Bretas und Newton G. Bretas. „Multi-Area State Estimation: A Distributed Quasi-Static Innovation-Based Model with an Alternative Direction Method of Multipliers“. Applied Sciences 11, Nr. 10 (13.05.2021): 4419. http://dx.doi.org/10.3390/app11104419.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleIn 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
Buchteile zum Thema "Deployment error estimation"
Ahmed, Qasim Zeeshan, und 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.
Der volle Inhalt der QuelleGuo, Cheng, R. Venkatesha Prasad, Jing Wang, Vijay Sathyanarayana Rao und 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.
Der volle Inhalt der QuelleLin, Kai, Min Chen, Joel J. P. C. Rodrigues und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Deployment error estimation"
Guihen, Damien, und 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.
Der volle Inhalt der QuelleMahboubi, Hamid, Mojtaba Vaezi und 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.
Der volle Inhalt der QuelleLuciani, Sara, Stefano Feraco, Angelo Bonfitto, Andrea Tonoli, Nicola Amati und 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.
Der volle Inhalt der QuelleBanerjee, Amit, Issam Abu-Mahfouz, Jianyan Tian und 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.
Der volle Inhalt der QuelleBooncharoen, Pichita, Thananya Rinsiri, Pakawat Paiboon, Supaporn Karnbanjob, Sonchawan Ackagosol, Prateep Chaiwan und 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.
Der volle Inhalt der QuelleKalanovic, V. D., K. Padmanabhan und 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.
Der volle Inhalt der QuelleAL-Qutami, Tareq Aziz, und 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.
Der volle Inhalt der QuelleRamos Gurjao, Kildare George, Eduardo Gildin, Richard Gibson und 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.
Der volle Inhalt der QuelleJenkins, C. H., V. D. Kalanovic, S. M. Faisal, K. Padmanabhan und 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.
Der volle Inhalt der QuelleWani, Ankit, Jyotsana Singh, Deepa Kumari, Avinash Ithape und 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.
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