Gotowa bibliografia na temat „Transformer network”
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Artykuły w czasopismach na temat "Transformer network"
S., S., Thulasi Bikku, P. Muthukumar, K. Sandeep, Jampani Chandra Sekhar i V. Krishna Pratap. "Enhanced Intrusion Detection Using Stacked FT-Transformer Architecture". Journal of Cybersecurity and Information Management 8, nr 2 (2024): 19–29. http://dx.doi.org/10.54216/jcim.130202.
Pełny tekst źródłaKrupa, Tadeusz. "Elements of Theory of the Correct Operations of Logistics Transforming Networks". Foundations of Management 9, nr 1 (20.12.2017): 347–60. http://dx.doi.org/10.1515/fman-2017-0026.
Pełny tekst źródłaZhang, Fuping, Pengcheng Zhao i Jianming Wei. "Channel Transformer Network". IEEE Access 8 (2020): 220762–78. http://dx.doi.org/10.1109/access.2020.3042644.
Pełny tekst źródłaAlharthi, Musleh, i Ausif Mahmood. "Enhanced Linear and Vision Transformer-Based Architectures for Time Series Forecasting". Big Data and Cognitive Computing 8, nr 5 (16.05.2024): 48. http://dx.doi.org/10.3390/bdcc8050048.
Pełny tekst źródłaOttele, Andy, i Rahmat Shoureshi. "Neural Network-Based Adaptive Monitoring System for Power Transformer". Journal of Dynamic Systems, Measurement, and Control 123, nr 3 (11.02.1999): 512–17. http://dx.doi.org/10.1115/1.1387248.
Pełny tekst źródłaMajeed, Issah Babatunde, i Nnamdi I. Nwulu. "Impact of Reverse Power Flow on Distributed Transformers in a Solar-Photovoltaic-Integrated Low-Voltage Network". Energies 15, nr 23 (6.12.2022): 9238. http://dx.doi.org/10.3390/en15239238.
Pełny tekst źródłaAdegboye, B. A. "Power Quality Assessment in a Distribution Network". Advanced Materials Research 62-64 (luty 2009): 53–59. http://dx.doi.org/10.4028/www.scientific.net/amr.62-64.53.
Pełny tekst źródłaKumari, Rekha, Gurpreet Kaur, Aditya Rawat, Harshit Chauhan, Kartik Singh Negi i Rishi Mishra. "ANALYSIS OF TRANSFORMER-DEEP NEURAL NETWORK USING DEEP LEARNING". International Journal of Engineering Applied Sciences and Technology 8, nr 2 (1.06.2023): 313–19. http://dx.doi.org/10.33564/ijeast.2023.v08i02.048.
Pełny tekst źródłaSun, Zhiqing, Yi Xuan, ZikaiCao, Jian Liu, Tiechao Dai, Weihao Liu, Gangjin Ye i in. "Transformer parameter estimation in distribution network based on deformable transformer". Journal of Physics: Conference Series 2758, nr 1 (1.04.2024): 012006. http://dx.doi.org/10.1088/1742-6596/2758/1/012006.
Pełny tekst źródłaAl-Yahya, Maha, Hend Al-Khalifa, Heyam Al-Baity, Duaa AlSaeed i Amr Essam. "Arabic Fake News Detection: Comparative Study of Neural Networks and Transformer-Based Approaches". Complexity 2021 (16.04.2021): 1–10. http://dx.doi.org/10.1155/2021/5516945.
Pełny tekst źródłaRozprawy doktorskie na temat "Transformer network"
Mao, Peilin. "Power transformer fault diagnosis based on wavelet transform and artificial neural network". Thesis, University of Bath, 2000. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.760740.
Pełny tekst źródłaZhang, Yuwen. "An artificial neural network approach to transformer fault diagnosis". Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-08222008-063051/.
Pełny tekst źródłaHardie, Stewart Ramon. "A Prototype Transformer Partial Discharge Detection System". Thesis, University of Canterbury. Electrical and Computer Engineering, 2006. http://hdl.handle.net/10092/1114.
Pełny tekst źródłaSun, Renfei. "Attention Network for Video Based Freezing of Gait Detection". Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28908.
Pełny tekst źródłaTuan, Abdullah Tuan Ab Rashid Bin. "Optimal management of failures, spare parts and transformer reconnections in an electrical distribution network". Thesis, University of Strathclyde, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501838.
Pełny tekst źródłaZheng, Cong. "Loosely Coupled Transformer and Tuning Network Design for High-Efficiency Inductive Power Transfer Systems". Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/52893.
Pełny tekst źródłaPh. D.
Конденко, Віктор Анатолійович. "Електропостачання станційного виробничо-побутового приміщення залізничої станції". Bachelor's thesis, КПІ ім. Ігоря Сікорського, 2021. https://ela.kpi.ua/handle/123456789/43036.
Pełny tekst źródłaDuring the implementation of the diploma project, the load calculations of the railway station shop were calculated. Supply networks up to 1 kV and above 1 kV, power transformers, protection devices and automation are selected. The calculation of short-circuit currents is carried out. As a special issue, the problems of quality and reliability of power supply of the shop were analyzed, the most accessible possible technical solution was selected and electrical installations with the necessary characteristics were selected.
Singh, Arvind. "A multi-layer neural network approach to identification of mechanical damage in power transformer windings". Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/5677.
Pełny tekst źródłaMousavi, Seyed Ali. "Electromagnetic Modelling of Power Transformers with DC Magnetization". Licentiate thesis, KTH, Elektroteknisk teori och konstruktion, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105395.
Pełny tekst źródłaQC 20121121
Dronzeková, Michaela. "Analýza polygonálních modelů pomocí neuronových sítí". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-417253.
Pełny tekst źródłaKsiążki na temat "Transformer network"
IEEE Power Engineering Society. Power Systems Relaying Committee., IEEE Standards Board i American National Standards Institute, red. IEEE guide for the protection of network transformers. New York, N.Y., USA: The Institute of Electrical and Electronics Engineers, 1989.
Znajdź pełny tekst źródłaJarvis, Cheryl. The necklace: Thirteen women and the experiment that transformed their lives. New York: Ballantine Books, 2008.
Znajdź pełny tekst źródłaLi, Charlene. Groundswell: Winning in a world transformed by social technologies. Boston, Mass: Harvard Business Review Press, 2011.
Znajdź pełny tekst źródłaJosh, Bernoff, red. Groundswell: Winning in a world transformed by social technologies. Boston, Mass: Harvard Business Review Press, 2011.
Znajdź pełny tekst źródłaLi, Charlene. Groundswell: Winning in a world transformed by social technologies. Boston, Mass: Harvard Business Press, 2008.
Znajdź pełny tekst źródłaI, Watson C., Paek Eung Gi i National Institute of Standards and Technology (U.S.), red. Effect of resolution and image quality on combined optical and neural network fingerprint matching. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1998.
Znajdź pełny tekst źródłaSpectrum and network measurements. Atlanta, Ga: Noble Pub. Corp., 2001.
Znajdź pełny tekst źródłaWitte, Robert A. Spectrum and network measurements. Englewood Cliffs, N.J: Prentice Hall, 1993.
Znajdź pełny tekst źródłaJarvis, Cheryl. The necklace: Thirteen women and the experiment that transformed their lives. New York: Ballantine Books, 2008.
Znajdź pełny tekst źródłaCheryl, Jarvis, i Women of Jewelia, red. The necklace: Thirteen women and the experiment that transformed their lives. Waterville, Me: Thorndike Press, 2009.
Znajdź pełny tekst źródłaCzęści książek na temat "Transformer network"
Shu, Chang, Xi Chen, Chong Yu i Hua Han. "A Refined Spatial Transformer Network". W Neural Information Processing, 151–61. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04182-3_14.
Pełny tekst źródłaTan, Ruxin, Jiahui Sun, Bo Su i Gongshen Liu. "Transformer-DW: A Transformer Network with Dynamic and Weighted Head". W Neural Information Processing, 504–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36711-4_42.
Pełny tekst źródłaWang, Zehong, Qi Li, Donghua Yu i Xiaolong Han. "Temporal Graph Transformer for Dynamic Network". W Lecture Notes in Computer Science, 694–705. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15931-2_57.
Pełny tekst źródłaSarasua, Ignacio, Sebastian Pölsterl i Christian Wachinger. "TransforMesh: A Transformer Network for Longitudinal Modeling of Anatomical Meshes". W Machine Learning in Medical Imaging, 209–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87589-3_22.
Pełny tekst źródłaChen, Dong, Gang Hua, Fang Wen i Jian Sun. "Supervised Transformer Network for Efficient Face Detection". W Computer Vision – ECCV 2016, 122–38. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46454-1_8.
Pełny tekst źródłaWang, Tuo, Meng Jian, Ge Shi, Xin Fu i Lifang Wu. "Multi-intent Compatible Transformer Network for Recommendation". W Pattern Recognition and Computer Vision, 344–55. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18907-4_27.
Pełny tekst źródłaAzad, Reza, Moein Heidari, Yuli Wu i Dorit Merhof. "Contextual Attention Network: Transformer Meets U-Net". W Machine Learning in Medical Imaging, 377–86. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21014-3_39.
Pełny tekst źródłaLiu, Zhongqiang, Li Zhang, Chunxiao Zhang, Xiangfei Kong i Anan Shen. "Transformer Fault Diagnosis Based on Elman Network". W Advances in Intelligent Systems and Computing, 479–86. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25128-4_60.
Pełny tekst źródłaChiam, Dar Hung, i King Hann Lim. "Power Quality Disturbance Classification Using Transformer Network". W Communications in Computer and Information Science, 272–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15784-4_21.
Pełny tekst źródłaZhang, Yungeng, Yuru Pei i Hongbin Zha. "Learning Dual Transformer Network for Diffeomorphic Registration". W Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 129–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87202-1_13.
Pełny tekst źródłaStreszczenia konferencji na temat "Transformer network"
Ottele, Andy, Rahmat Shoureshi, Duane Torgerson i John Work. "Neural Network-Based Adaptive Monitoring System for Power Transformer". W ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0069.
Pełny tekst źródłaNeimark, Daniel, Omri Bar, Maya Zohar i Dotan Asselmann. "Video Transformer Network". W 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2021. http://dx.doi.org/10.1109/iccvw54120.2021.00355.
Pełny tekst źródłaGirdhar, Rohit, Joao Joao Carreira, Carl Doersch i Andrew Zisserman. "Video Action Transformer Network". W 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00033.
Pełny tekst źródłaSeong, Hongje, Junhyuk Hyun i Euntai Kim. "Video Multitask Transformer Network". W 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019. http://dx.doi.org/10.1109/iccvw.2019.00194.
Pełny tekst źródłaMishra, Naman, Avinash Yadav, Arzoo Yadav, Pritam Yadav i Pinki Yadav. "Transformer: Health Monitoring System Based on IoT". W International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-d7mojr.
Pełny tekst źródłaLiu, Ruolan, Xiao Chen i Xue Wen. "Voice Conversion with Transformer Network". W ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054523.
Pełny tekst źródłaGao, Jiarui, Yanwei Fu, Yu-Gang Jiang i Xiangyang Xue. "Frame-Transformer Emotion Classification Network". W ICMR '17: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3078971.3079030.
Pełny tekst źródłaShahid, Mohammad, i Kai-lung Hua. "Fire Detection using Transformer Network". W ICMR '21: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460426.3463665.
Pełny tekst źródłaEscher, Rafael Molossi, Rodrigo Andrade de Bem i Paulo Lilles Jorge Drews. "Fast Spatial-Temporal Transformer Network". W 2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2021. http://dx.doi.org/10.1109/sibgrapi54419.2021.00018.
Pełny tekst źródłaNechikkat, Mubashira I., Bhagyasree V. Pattilikattil, Soumya Varma i Ajay James. "Video captioning using transformer network". W THE 2ND UNIVERSITAS LAMPUNG INTERNATIONAL CONFERENCE ON SCIENCE, TECHNOLOGY, AND ENVIRONMENT (ULICoSTE) 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0107029.
Pełny tekst źródłaRaporty organizacyjne na temat "Transformer network"
Shi, Jimeng, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang i Giri Narasimhan. Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates. Purdue University, październik 2023. http://dx.doi.org/10.5703/1288284317672.
Pełny tekst źródłaRaghavan, Ajay. TRANSENSOR: Transformer Real-time Assessment INtelligent System with Embedded Network of Sensors and Optical Readout. Final Report. Office of Scientific and Technical Information (OSTI), kwiecień 2020. http://dx.doi.org/10.2172/1615666.
Pełny tekst źródłaDu, Daqiao. Neural network character recognition with a 2-D Fourier transform preprocessor. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.6084.
Pełny tekst źródłaGoreczky, Péter. 5G Network Rollout: a Contest of Countries or Companies? Külügyi és Külgazdasági Intézet, 2021. http://dx.doi.org/10.47683/kkielemzesek.e-2021.14.
Pełny tekst źródłaFitch, J. The radon transform for data reduction, line detection, and artificial neural network preprocessing. Office of Scientific and Technical Information (OSTI), maj 1990. http://dx.doi.org/10.2172/6874873.
Pełny tekst źródłaСоловйов, В. М., i В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.
Pełny tekst źródłaEnria, Luisa. Citizen Ethnography in Outbreak Response: Guidance for Establishing Networks of Researchers. SSHAP, maj 2022. http://dx.doi.org/10.19088/sshap.2022.001.
Pełny tekst źródłaDiDonato, Armido. Target Location and ID From a Passive Multistatic Sensor Network Using Time Differences of Arrival (TDOAs) and the Hough Transform. Fort Belvoir, VA: Defense Technical Information Center, listopad 2008. http://dx.doi.org/10.21236/ada509798.
Pełny tekst źródłaNechaev, V., Володимир Миколайович Соловйов i A. Nagibas. Complex economic systems structural organization modelling. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1118.
Pełny tekst źródłaBedoya-Maya, Felipe, Lynn Scholl, Orlando Sabogal-Cardona i Daniel Oviedo. Who uses Transport Network Companies?: Characterization of Demand and its Relationship with Public Transit in Medellín. Inter-American Development Bank, wrzesień 2021. http://dx.doi.org/10.18235/0003621.
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