Relevant bibliographies by topics / Transformer network

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Transformer network'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Transformer network"

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S., S., Thulasi Bikku, P. Muthukumar, K. Sandeep, Jampani Chandra Sekhar, and V. Krishna Pratap. "Enhanced Intrusion Detection Using Stacked FT-Transformer Architecture." Journal of Cybersecurity and Information Management 8, no. 2 (2024): 19–29. http://dx.doi.org/10.54216/jcim.130202.

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The function of network intrusion detection systems (NIDS) in protecting networks from cyberattacks is crucial. Many of the more conventional techniques rely on signature-based approaches, which have a hard time distinguishing between various types of assaults. Using stacked FT-Transformer architecture, this research suggests a new way to identify intrusions in networks. When it comes to dealing with complicated tabular data, FT-Transformers—a variant of the Transformer model—have shown outstanding performance. Because of the inherent tabular nature of network traffic data, FT-Transformers are
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Krupa, Tadeusz. "Elements of Theory of the Correct Operations of Logistics Transforming Networks." Foundations of Management 9, no. 1 (2017): 347–60. http://dx.doi.org/10.1515/fman-2017-0026.

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Abstract In this paper, transformer logistics networks are treated as flow models of discrete manufacturing systems (FMDMS). The purpose is to formalize FMDMS into logical formulas called transformer functions. Transformer logistics networks are able to handle buffers and their production orders in a way that ensures full monitoring of the logic technology stored in the memory of a transforming network (t-network). The structural and functional complexity of the t-network makes it impossible to carry out formal proof of its proper functioning for any new order placement in buffers and transfor
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Alharthi, Musleh, and Ausif Mahmood. "Enhanced Linear and Vision Transformer-Based Architectures for Time Series Forecasting." Big Data and Cognitive Computing 8, no. 5 (2024): 48. http://dx.doi.org/10.3390/bdcc8050048.

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Time series forecasting has been a challenging area in the field of Artificial Intelligence. Various approaches such as linear neural networks, recurrent linear neural networks, Convolutional Neural Networks, and recently transformers have been attempted for the time series forecasting domain. Although transformer-based architectures have been outstanding in the Natural Language Processing domain, especially in autoregressive language modeling, the initial attempts to use transformers in the time series arena have met mixed success. A recent important work indicating simple linear networks out
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Zhou, Mingjie, Jing Xu, Chaojian Xing, Yankai Li, and Shuxin Liu. "Research for transformer operation state prediction method based on BO-CNN-GRU." Journal of Physics: Conference Series 2770, no. 1 (2024): 012013. http://dx.doi.org/10.1088/1742-6596/2770/1/012013.

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Abstract In the realm of electrical engineering, this study introduces a novel approach for forecasting transformer operational conditions by leveraging BO-CNN-GRU (Bayesian Optimized Convolutional Neural Network Gated Loop Unit). The initial step involves an in-depth analysis of the key parameters that significantly impact the operational performance of the transformer. Then, the comprehensive weights of each characteristic parameter of the transformer are obtained by the G1 method, entropy weight method, and CRITIC method, and the comprehensive state data of the transformer is obtained. Fina
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Zhang, Fuping, Pengcheng Zhao, and Jianming Wei. "Channel Transformer Network." IEEE Access 8 (2020): 220762–78. http://dx.doi.org/10.1109/access.2020.3042644.

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Hao, Zixin. "Comparative Analysis of Transformer Integration in U-net Networks for Enhanced Medical Image Segmentation." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 333–40. http://dx.doi.org/10.54097/z4b39y45.

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Transformer is popular in Natural Language Processing (NLP) and is a cornerstone of large models. Transformer has been used by researchers to address the limitations of Convolutional Neural Networks (CNNs) in medical picture segmentation models. Through an extensive literature review and case studies, this paper comparatively analyzes the performance of different models in this field, summarizes different methods of integrating transformers into U-net, and points out existing gaps and challenges. Research has found that the Transformer model can significantly improve the accuracy and efficienc
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Ottele, Andy, and Rahmat Shoureshi. "Neural Network-Based Adaptive Monitoring System for Power Transformer." Journal of Dynamic Systems, Measurement, and Control 123, no. 3 (1999): 512–17. http://dx.doi.org/10.1115/1.1387248.

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Power transformers are major elements of the electric power transmission and distribution infrastructure. Transformer failure has severe economical impacts from the utility industry and customers. This paper presents analysis, design, development, and experimental evaluation of a robust failure diagnostic technique. Hopfield neural networks are used to identify variations in physical parameters of the system in a systematic way, and adapt the transformer model based on the state of the system. In addition, the Hopfield network is used to design an observer which provides accurate estimates of
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Majeed, Issah Babatunde, and Nnamdi I. Nwulu. "Impact of Reverse Power Flow on Distributed Transformers in a Solar-Photovoltaic-Integrated Low-Voltage Network." Energies 15, no. 23 (2022): 9238. http://dx.doi.org/10.3390/en15239238.

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Modern low-voltage distribution systems necessitate solar photovoltaic (PV) penetration. One of the primary concerns with this grid-connected PV system is overloading due to reverse power flow, which degrades the life of distribution transformers. This study investigates transformer overload issues due to reverse power flow in a low-voltage network with high PV penetration. A simulation model of a real urban electricity company in Ghana is investigated against various PV penetration levels by load flows with ETAP software. The impact of reverse power flow on the radial network transformer load
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Adegboye, B. A. "Power Quality Assessment in a Distribution Network." Advanced Materials Research 62-64 (February 2009): 53–59. http://dx.doi.org/10.4028/www.scientific.net/amr.62-64.53.

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The paper explores power quality disturbances on a specified section of the distribution network of a Textile Industry in Kaduna State of Nigeria. The 33kV PHCN incoming to the industry is stepped down to 11kV by a 7.5MVA, 33/11kV three-phase transformer. This transformer supplies various 11/.415kV transformers present in the distribution network. Another 11kV PHCN incoming is used in event of any failure from the 33/11kV transformer. The paper focuses on Transformer No. 1, a 150kVA, 11/.415kV three-phase transformer operating at 0.9 power factor, located at printing and dying (P/D) building 1
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Kumari, Rekha, Gurpreet Kaur, Aditya Rawat, Harshit Chauhan, Kartik Singh Negi, and Rishi Mishra. "ANALYSIS OF TRANSFORMER-DEEP NEURAL NETWORK USING DEEP LEARNING." International Journal of Engineering Applied Sciences and Technology 8, no. 2 (2023): 313–19. http://dx.doi.org/10.33564/ijeast.2023.v08i02.048.

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Transformers were first used for natural language processing (NLP) tasks, but they quickly spread to other deep learning fields, including computer vision. They assess the interdependence of pairs. Attention is a part that enables to dynamically highlight relevant features of the input data (words in the case of text strings, parts of images in the case of visual Transformers). The cost grows continually with the number of tokens. The most common Trans- former Architecture for image classification uses only the Transformer Encoder to transform the various input tokens. However, the decoder com
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Dissertations / Theses on the topic "Transformer network"

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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.

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Zhang, 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/.

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Hardie, Stewart Ramon. "A Prototype Transformer Partial Discharge Detection System." Thesis, University of Canterbury. Electrical and Computer Engineering, 2006. http://hdl.handle.net/10092/1114.

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Increased pressure on high voltage power distribution components has been created in recent years by a demand to lower costs and extend equipment lifetimes. This has led to a need for condition based maintenance, which requires a continuous knowledge of equipment health. Power transformers are a vital component in a power distribution network. However, there are currently no established techniques to accurately monitor and diagnose faults in real-time while the transformer is on-line. A major factor in the degradation of power transformer insulation is partial discharging. Left unattended,
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Sun, Renfei. "Attention Network for Video Based Freezing of Gait Detection." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28908.

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Freezing of gait (FoG) is a typical symptom of Parkinson's disease (PD), which is a brief, episodic absence or marked reduction despite the patients' intention of walking. It is important to timely identify FoG events for clinical assessments. However, well-trained experts are required to identify FoG events, which is subjective and time-consuming. Therefore, automatic FoG identification methods are highly demanded. In this study, we address this task as a human action detection problem based on vision inputs. Two novel attention based deep learning architectures, namely convolutional 3D a
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Tuan, 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.

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Zheng, 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.

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Transfer signal without wire has been widely accepted after the introduction of cellular technology and WiFi technology, hence the power cable is the last wire that has yet to be eliminated. Inductive power transfer (IPT) has drawn substantial interest in both academia and industry due to its advantages including convenience, nonexistence of cable and connector, no electric shock issue, ability to work under some extreme environment, and so on. After performing thorough literature review of IPT systems, two major drawbacks including low power efficiency and coil displacement sensitivity are id
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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.

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Під час виконання дипломного проекту було проведено розрахунок навантажень цеху залізничної станції. Вибрано живлячі мережі напругою до 1 кВ та вище 1 кВ, силові трансформатори, апарати захисту та автоматики. Проведено розрахунок струмів короткого замикання. У якості спец питання було проаналізовано проблеми якості та надійності електропостачання цеху, вибрано найбільш доступне можливе технічне рішення та підібрано електричні установки з необхідними характеристиками.<br>During the implementation of the diploma project, the load calculations of the railway station shop were calculated. Supply
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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.

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Power transformers are among the most critical of assets for electric utilities, in the financial impact that their failure can bring. Asset Managers need to be able to determine the right time for replacement, refurbishment or relocation of these devices, with an increasing degree of confidence, in order to minimize the total cost of operation over the equipments’ life. This has brought a change from scheduled maintenance to condition based monitoring (CBM), where the state of the transformer is continuously monitored to evaluate its working condition. A key method of transformer CBM, which e
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Mousavi, 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.

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DC currents that flow through the ground can be injected to the star windings of power transformers from their grounded neutral points and close their path with transmission lines. The geomagnetically induced currents (GICs) and AC/DC convertors of high voltage direct current (HVDC) systems are the sources of such DC currents. These currents may cause saturation of the core in power transformers that leads to destruction in the transformer performance. This phenomenon results in unwanted influences on power transformers and the power system. Very asymmetric magnetization current, increasing lo
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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.

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This thesis deals with rotation estimation of 3D model of human jaw. It describes and compares methods for direct analysis od 3D models as well as method to analyze model using rasterization. To evaluate perfomance of proposed method, a metric that computes number of cases when prediction was less than 30° from ground truth is used. Proposed method that uses rasterization, takes  three x-ray views of model as an input and processes it with convolutional network. It achieves best preformance, 99% with described metric. Method to directly analyze polygonal model as a sequence uses attention mech
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Books on the topic "Transformer network"

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IEEE Power Engineering Society. Power Systems Relaying Committee., IEEE Standards Board, and American National Standards Institute, eds. IEEE guide for the protection of network transformers. The Institute of Electrical and Electronics Engineers, 1989.

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Jarvis, Cheryl. The necklace: Thirteen women and the experiment that transformed their lives. Ballantine Books, 2008.

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Li, Charlene. Groundswell: Winning in a world transformed by social technologies. Harvard Business Review Press, 2011.

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Li, Charlene. Groundswell: Winning in a world transformed by social technologies. Harvard Business Press, 2008.

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Josh, Bernoff, ed. Groundswell: Winning in a world transformed by social technologies. Harvard Business Review Press, 2011.

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I, Watson C., Paek Eung Gi, and National Institute of Standards and Technology (U.S.), eds. Effect of resolution and image quality on combined optical and neural network fingerprint matching. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1998.

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Witte, Robert A. Spectrum and network measurements. Prentice Hall, 1993.

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Jarvis, Cheryl. The necklace: Thirteen women and the experiment that transformed their lives. Ballantine Books, 2008.

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Cheryl, Jarvis, and Women of Jewelia, eds. The necklace: Thirteen women and the experiment that transformed their lives. Thorndike Press, 2009.

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Marzecki, Jerzy. Metody badania rozwoju i wyznaczania optymalnego rozmieszczenia stacji transformatorowo-rozdzielczych 110 kV/SN w aglomeracji miejskiej. Oficyna Wydawnicza Politechniki Warszawskiej, 1995.

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Book chapters on the topic "Transformer network"

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Shu, Chang, Xi Chen, Chong Yu, and Hua Han. "A Refined Spatial Transformer Network." In Neural Information Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04182-3_14.

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Tan, Ruxin, Jiahui Sun, Bo Su, and Gongshen Liu. "Transformer-DW: A Transformer Network with Dynamic and Weighted Head." In Neural Information Processing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36711-4_42.

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Lee, Seongju, Junseok Lee, Yeonguk Yu, Taeri Kim, and Kyoobin Lee. "MART: MultiscAle Relational Transformer Networks for Multi-agent Trajectory Prediction." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72848-8_6.

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AbstractMulti-agent trajectory prediction is crucial to autonomous driving and understanding the surrounding environment. Learning-based approaches for multi-agent trajectory prediction, such as primarily relying on graph neural networks, graph transformers, and hypergraph neural networks, have demonstrated outstanding performance on real-world datasets in recent years. However, the hypergraph transformer-based method for trajectory prediction is yet to be explored. Therefore, we present a MultiscAle Relational Transformer (MART) network for multi-agent trajectory prediction. MART is a hypergr
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Wang, Zehong, Qi Li, Donghua Yu, and Xiaolong Han. "Temporal Graph Transformer for Dynamic Network." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15931-2_57.

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Sarasua, Ignacio, Sebastian Pölsterl, and Christian Wachinger. "TransforMesh: A Transformer Network for Longitudinal Modeling of Anatomical Meshes." In Machine Learning in Medical Imaging. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87589-3_22.

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Chen, Dong, Gang Hua, Fang Wen, and Jian Sun. "Supervised Transformer Network for Efficient Face Detection." In Computer Vision – ECCV 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46454-1_8.

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Wang, Tuo, Meng Jian, Ge Shi, Xin Fu, and Lifang Wu. "Multi-intent Compatible Transformer Network for Recommendation." In Pattern Recognition and Computer Vision. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18907-4_27.

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Azad, Reza, Moein Heidari, Yuli Wu, and Dorit Merhof. "Contextual Attention Network: Transformer Meets U-Net." In Machine Learning in Medical Imaging. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21014-3_39.

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Zhu, Xu, Zhiwei Qi, Kun Yue, Yunshan Su, and Liang Duan. "Directed Brain Network Transformer for Psychiatric Diagnosis." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-78195-7_14.

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Ma, Denghao, Li Chong, Yueguo Chen, and Liang Shen. "Category-Highlighting Transformer Network for Question Retrieval." In Database Systems for Advanced Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30675-4_33.

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Conference papers on the topic "Transformer network"

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Dai, JiaXuan, LiangLi Ma, and JiWei Qin. "COM-transformers: research on automatic knowledge graph construction based on transformer." In Third International Conference on Algorithms, Network and Communication Technology (ICANCT 2024), edited by Fabrizio Marozzo. SPIE, 2025. https://doi.org/10.1117/12.3060407.

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Zou, Jie, Yingying Li, Zijian Hu, and Yong Wang. "Multi-scale Transformer-Based Crowd Counting Network." In 2024 17th International Conference on Advanced Computer Theory and Engineering (ICACTE). IEEE, 2024. https://doi.org/10.1109/icacte62428.2024.10871514.

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Shi, Liang, Dunwei Han, Guifang Luo, et al. "DenoiseNet: towards advanced denoising with transformer network." In Seventeenth International Conference on Digital Image Processing (ICDIP 2025), edited by Xudong Jiang, Jindong Tian, Ting-Chung Poon, and Zhaohui Wang. SPIE, 2025. https://doi.org/10.1117/12.3075566.

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Sangeetha, M., G. Sunil, Ammar Hameed Shnain, Shruthi B. S, and A. U. Shabeer Ahamed. "Landmark Scene Classification Using Vision Transformer." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691072.

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Ottele, Andy, Rahmat Shoureshi, Duane Torgerson, and John Work. "Neural Network-Based Adaptive Monitoring System for Power Transformer." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0069.

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Abstract Power transformers are major elements of the electric power transmission and distribution infrastructure. Transformer failure has severe economical impacts from the utility industry and customers. This paper presents analysis, design, development and experimental results of a robust failure diagnostic technique. Hopfield neural networks are used to identify variations in physical parameters of the system in a systematic way, and adapt the transformer model based on the state of the system. In addition, the Hopfield network is used to design an observer which provides accurate estimate
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Neimark, Daniel, Omri Bar, Maya Zohar, and Dotan Asselmann. "Video Transformer Network." In 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE, 2021. http://dx.doi.org/10.1109/iccvw54120.2021.00355.

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Sauseng, Alexander, Alice Reinbacher-Kostinger, Peter Hamberger, Manfred Kaltenbacher, and Klaus Roppert. "Network-Based Transformer Models – A Transient Analysis." In XXVIII. Symposium Electromagnetic Phenomena in Nonlinear Circuits (EPNC 2024). University of Maribor Press, 2025. https://doi.org/10.18690/um.feri.4.2025.23.

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This contribution is about network-based transformer models. Two models (single- and three-phase) are derived, whereas Hopkinson’s analogy is used to depict the magnetic domain. Therein, an energy-based hysteresis model is incorporated to represent the magnetic core. The solution strategy is a second-order variable step size backward differentiation formula (BDF2) in time domain, yielding the transient response of the two transformers. Finally, the simulation results are compared to measurements.
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Xia, Jianqiang, Dianxi Shi, Ke Song, et al. "Unified Single-Stage Transformer Network for Efficient RGB-T Tracking." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/163.

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Most existing RGB-T tracking networks extract modality features in a separate manner, which lacks interaction and mutual guidance between modalities. This limits the network's ability to adapt to the diverse dual-modality appearances of targets and the dynamic relationships between the modalities. Additionally, the three-stage fusion tracking paradigm followed by these networks significantly restricts the tracking speed. To overcome these problems, we propose a unified single-stage Transformer RGB-T tracking network, namely USTrack, which unifies the above three stages into a single ViT (Visio
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Girdhar, Rohit, Joao Joao Carreira, Carl Doersch, and Andrew Zisserman. "Video Action Transformer Network." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00033.

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Seong, Hongje, Junhyuk Hyun, and Euntai Kim. "Video Multitask Transformer Network." In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019. http://dx.doi.org/10.1109/iccvw.2019.00194.

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Reports on the topic "Transformer network"

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Shi, Jimeng, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, and Giri Narasimhan. Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317672.

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Raghavan, 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), 2020. http://dx.doi.org/10.2172/1615666.

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Du, Daqiao. Neural network character recognition with a 2-D Fourier transform preprocessor. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6084.

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Goreczky, 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.

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In terms of the number of 5G subscriptions, Northeast Asia, including China, is currently ahead of the rest of the world. The global 5G infrastructure market is expected to continue to grow rapidly over the next five years, but the elite club of global companies capable of building it is not expected to expand in the short term. Despite restrictions on Huawei’s participation in some tenders and possible Chinese retaliation against European companies, neither the Chinese company nor Ericsson is expected to be sidelined from the global market for 5G network infrastructure devices. The new networ
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Fitch, J. The radon transform for data reduction, line detection, and artificial neural network preprocessing. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6874873.

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Соловйов, В. М., та В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.

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From the standpoint of interdisciplinary self-organization theories and synergetics analyzes current approaches to modeling socio-economic systems. It is shown that the complex network paradigm is the foundation on which to build predictive models of complex systems. We consider two algorithms to transform time series or a set of time series to the network: recurrent and graph visibility. For the received network designed dynamic spectral, topological and multiplex measures of complexity. For example, the daily values the stock indices show that most of the complexity measures behaving in a ch
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Cassidy, Anne, Leonor Rodriguez, and Carmel Devaney. The Child and Family Support Networks Research Study. UNESCO Child and Family Research Centre, National University of Ireland Galway, 2018. http://dx.doi.org/10.52516/eaoa2611.

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The research and evaluation team at the UNESCO Child and Family Research Centre (UCFRC), NUI Galway provides research, evaluation and technical support to Tusla’s Development and Mainstreaming Programme for Prevention, Partnership and Family Support (PPFS). This is a new programme of action being undertaken by Tulsa – Child and Family Agency as part of its National Service Delivery Framework. The programme seeks to transform child and family services in Ireland by embedding prevention and early intervention into the culture and operations of Tusla. The research and evaluation carried out by th
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Enria, Luisa. Citizen Ethnography in Outbreak Response: Guidance for Establishing Networks of Researchers. SSHAP, 2022. http://dx.doi.org/10.19088/sshap.2022.001.

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This guidance outlines the steps for designing and implementing ethnographic research which is led by citizens. It explains what citizen ethnography is and then sets out what should be considered throughout the process of working with networks of citizen researchers, from recruitment, training and supporting them to collect and analyse ethnographic data, and how to transform the insights they gain to support preparedness and responses for disease outbreaks. It also provides suggestions for further resources to support the process. The guidance is for social scientists who would like to integra
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DiDonato, Armido. Target Location and ID From a Passive Multistatic Sensor Network Using Time Differences of Arrival (TDOAs) and the Hough Transform. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada509798.

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Nechaev, V., Володимир Миколайович Соловйов, and A. Nagibas. Complex economic systems structural organization modelling. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1118.

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One of the well-known results of the theory of management is the fact, that multi-stage hierarchical organization of management is unstable. Hence, the ideas expressed in a number of works by Don Tapscott on advantages of network organization of businesses over vertically integrated ones is clear. While studying the basic tendencies of business organization in the conditions of globalization, computerization and internetization of the society and the results of the financial activities of the well-known companies, the authors arrive at the conclusion, that such companies, as IBM, Boeing, Merced
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