Bibliographies thématiques / Transformer network

Littérature scientifique sur le sujet « Transformer network »

Auteur : Grafiati
Publié le 25 mai 2024

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Articles de revues sur le sujet "Transformer network"

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S., S., Thulasi Bikku, P. Muthukumar, K. Sandeep, Jampani Chandra Sekhar et 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 an attractive option for intrusion detection jobs. In this area, our study looks at how FT-Transformers outperform more conventional machine learning (ML) methods. Our working hypothesis is that, in comparison to single-layered ML models, FT-Transformers will achieve better detection accuracy due to their intrinsic capacity to grasp long-range correlations in network traffic data. We also test the FT-Transformer model on several network traffic datasets that include various protocols and attack kinds to see how well it performs and how generalizable it is. The purpose of this research is to shed light on how well and how versatile FT-Transformers perform for detecting intrusions in networks. We aim to prove that FT-Transformers can secure networks from ever-changing cyber threats by comparing their performance to that of classic ML models and by testing their generalizability.
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Krupa, Tadeusz. « Elements of Theory of the Correct Operations of Logistics Transforming Networks ». Foundations of Management 9, no 1 (20 décembre 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 transformers. This is because with the growing capacity in buffers, the number of available states of tnetworks also increases, and as such, the number of transformers and buffers unable to effectively generate new production orders that protect the t-network is also increasing. The problem therefore becomes to maintain t-network equilibrium technology that guarantees the continuity of the logical operations and processes of resource transformation.
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Zhang, Fuping, Pengcheng Zhao et Jianming Wei. « Channel Transformer Network ». IEEE Access 8 (2020) : 220762–78. http://dx.doi.org/10.1109/access.2020.3042644.

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Alharthi, Musleh, et Ausif Mahmood. « Enhanced Linear and Vision Transformer-Based Architectures for Time Series Forecasting ». Big Data and Cognitive Computing 8, no 5 (16 mai 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 outperform transformer-based designs. We investigate this paradox in detail comparing the linear neural network- and transformer-based designs, providing insights into why a certain approach may be better for a particular type of problem. We also improve upon the recently proposed simple linear neural network-based architecture by using dual pipelines with batch normalization and reversible instance normalization. Our enhanced architecture outperforms all existing architectures for time series forecasting on a majority of the popular benchmarks.
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Ottele, Andy, et Rahmat Shoureshi. « Neural Network-Based Adaptive Monitoring System for Power Transformer ». Journal of Dynamic Systems, Measurement, and Control 123, no 3 (11 février 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 the internal states of the transformer that can not be accessed or measured during operation. Analytical and experimental results of this adaptive observer for power transformer diagnostics are presented.
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Majeed, Issah Babatunde, et Nnamdi I. Nwulu. « Impact of Reverse Power Flow on Distributed Transformers in a Solar-Photovoltaic-Integrated Low-Voltage Network ». Energies 15, no 23 (6 décembre 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 loadings is examined for high PV penetrations. Using the least squares method, simulation results are modelled in Excel software. Transformer backflow limitations are determined by correlating operating loads with PV penetration. At high PV penetration, the models predict reverse power flow into the transformer. Interpolations from the correlation models show transformer backflow operating limits of 78.04 kVA and 24.77% at the threshold of reverse power flow. These limits correspond to a maximum PV penetration limit of 88.30%. In low-voltage networks with high PV penetration; therefore, planners should consider transformer overload limits caused by reverse power flow, which degrades transformer life. This helps select control schemes near substation transformers to limit reverse power flow.
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Adegboye, B. A. « Power Quality Assessment in a Distribution Network ». Advanced Materials Research 62-64 (février 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. Majority of the loads on it are inductive. Measurements were taken at the secondary terminal of this transformer by the use of the Harmonitor 3000 power analyzer, which generates the voltage and current waveforms, power factor, voltage and current total harmonic distortion and the apparent power of the red, yellow and blue phases of the transformer. Analyses of these data reveal the disturbances due to harmonics in the phases and neutral of the transformer. The effect of the harmonic current is seen as poor power factor of the transformer. Considering the observations and analyses of the power quality of the transformer 1 (P/D), the paper proposes some recommendations for improving the power quality of the distribution network under study.
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Kumari, Rekha, Gurpreet Kaur, Aditya Rawat, Harshit Chauhan, Kartik Singh Negi et Rishi Mishra. « ANALYSIS OF TRANSFORMER-DEEP NEURAL NETWORK USING DEEP LEARNING ». International Journal of Engineering Applied Sciences and Technology 8, no 2 (1 juin 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 component of the traditional Transformer Architecture is also used in a variety of other applications. In this section, we first introduce the Attention Mechanism (Section 1), followed by the Basic Transformer Block, which includes the Vision Transformer (Section 2).
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Sun, Zhiqing, Yi Xuan, ZikaiCao, Jian Liu, Tiechao Dai, Weihao Liu, Gangjin Ye et al. « Transformer parameter estimation in distribution network based on deformable transformer ». Journal of Physics : Conference Series 2758, no 1 (1 avril 2024) : 012006. http://dx.doi.org/10.1088/1742-6596/2758/1/012006.

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Abstract With the large number of distributed power sources and the dynamic change of load, the abnormal parameters of distribution transformers become more and more complicated. So it is particularly important to estimate their parameters accurately. For a low voltage distribution network with a limited number of measuring equipment, a Transformer parameter estimation method based on a Deformable Transformer is proposed in this paper. Firstly, a Transformer parameter estimation model based on a Deformable Transformer network is established by using historical measurement data. Then, a quality evaluation method of parameter estimation is proposed to test the accuracy of parameter estimation. Finally, the effectiveness of the proposed method is verified by practical data.
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Al-Yahya, Maha, Hend Al-Khalifa, Heyam Al-Baity, Duaa AlSaeed et Amr Essam. « Arabic Fake News Detection : Comparative Study of Neural Networks and Transformer-Based Approaches ». Complexity 2021 (16 avril 2021) : 1–10. http://dx.doi.org/10.1155/2021/5516945.

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Fake news detection (FND) involves predicting the likelihood that a particular news article (news report, editorial, expose, etc.) is intentionally deceptive. Arabic FND started to receive more attention in the last decade, and many detection approaches demonstrated some ability to detect fake news on multiple datasets. However, most existing approaches do not consider recent advances in natural language processing, i.e., the use of neural networks and transformers. This paper presents a comprehensive comparative study of neural network and transformer-based language models used for Arabic FND. We examine the use of neural networks and transformer-based language models for Arabic FND and show their performance compared to each other. We also conduct an extensive analysis of the possible reasons for the difference in performance results obtained by different approaches. The results demonstrate that transformer-based models outperform the neural network-based solutions, which led to an increase in the F1 score from 0.83 (best neural network-based model, GRU) to 0.95 (best transformer-based model, QARiB), and it boosted the accuracy by 16% compared to the best in neural network-based solutions. Finally, we highlight the main gaps in Arabic FND research and suggest future research directions.
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Thèses sur le sujet "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, partial discharges (PDs) will eventually cause complete insulation failure. PDs generate a variety of signals, including electrical pulses that travel through the windings of the transformer to the terminals. A difficulty with detecting these pulses in an on-line environment is that they can be masked by external electrical interference. This thesis develops a method for identifying PD pulses and determining the number of PD sources while the transformer is on-line and subject to external interference. The partial discharge detection system (PDDS) acquires electrical signals with current and voltage transducers that are placed on the transformer bushings, making it unnecessary to disconnect or open the transformer. These signals are filtered to prevent aliasing and to attenuate the power frequency, and then digitised and analysed in Matlab, a numerical processing software package. Arbitrary narrowband interference is removed with an automated Fourier domain threshold filter. Internal PD pulses are separated from stochastic wideband pulse interference using directional coupling, which is a technique that simultaneously analyses the current and voltage signals from a bushing. To improve performance of this stage, the continuous wavelet transform is used to discriminate time and frequency information. This provides the additional advantage of preserving the waveshapes of the PD pulses for later analysis. PD pulses originating within the transformer have their waveshapes distorted when travelling though the windings. The differentiation of waveshape distortion of pulses from multiple physical sources is used as an input to a neural network to group pulses from the same source. This allows phase resolved PD analysis to be presented for each PD source, for instance, as phase/magnitude/count plots. The neural network requires no prior knowledge of the transformer or pulse waveshapes. The thesis begins with a review of current techniques and trends for power transformer monitoring and diagnosis. The description of transducers and filters is followed by an explanation of each of the signal processing steps. Two transformers were used to conduct testing of the PDDS. The first transformer was opened and modified so that internal PDs could be simulated by injecting artificial pulses. Two test scenarios were created and the performance of the PDDS was recorded. The PDDS identified and extracted a high rate of simulated PDs and correctly allocated the pulses into PD source groups. A second identically constructed transformer was energised and analysed for any natural PDs while external interference was present. It was found to have a significant natural PD source.
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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 attention network (C3DAN) and higher order polynomial transformer (HP-Transformer), are proposed to investigate fine-grained FoG patterns. The C3DAN addresses the FoG detection task by exploring the appearance features in detail to learn an informative region for more effective detection. The network consists of two main parts: Spatial Attention Network (SAN) and 3-dimensional convolutional network (C3D). SAN aims to generate an attention regions from coarse to fine, while C3D extracts discriminative features. Our proposed approach is able to localize attention region without manual annotation and to extract discriminative features in an end-to-end way. The HP-Transformer incorporates pose and appearance feature sequences to formulate fine-grained FoG patterns. In particular, higher order self-attentions are proposed based on higher order polynomials. To this end, linear, bilinear and trilinear transformers are formulated in pursuit of discriminative fine-grained representations. These representations are treated as multiple streams and further fused by a self-attention based fusion strategy for FoG detection. Comprehensive experiments on a large in-house dataset collected during clinical assessments demonstrate the effectiveness of the proposed methods. The two methods both achieved promising results and in particular, the HP-Transformer achieved an AUC of 0.92 in the FoG detection task.
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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 identified as the main obstacles that have to be solved in order for these systems to reach full functionality and compete with existing wired solutions. To address the limitations and design challenges in the IPT systems, a detailed electric circuit modeling of individual part of the IPT DC-DC stage is performed. Several resonant DC-AC inverters and output AC-DC rectifiers are compared based on their performance and feasibility in inductive charging applications. Different equivalent circuit models for the loosely coupled transformer (LCT) are derived which allows for better understanding on how power is distributed among the circuit components. Five compensation networks to improve the power transfer efficiency are evaluated and their suitable application occasions are identified. With comprehensive circuit model analysis, the influence of the resonant compensation tank parameters has been investigated carefully for efficient power transfer. A novel tuning network parameters design methodology is proposed based on multiple given requirement such as battery charging profile, geometry constraints and operating frequency range, with the aim of avoiding bifurcation phenomenon during the whole charging process and achieving decent efficiency. A 4-kW hardware prototype based on the proposed design approach is built and tested under different gap and load conditions. Peak IPT system DC-DC efficiencies of 98% and 96.6% are achieved with 4-cm and 8-cm air gap conditions, which is comparable to the conventional plug-in type or wired charging systems for EVs. A long-hour test with real EV batteries is conducted to verify the wireless signal transmission and CC/CV mode seamless transition during the whole charging profile without bifurcation. To reduce the IPT system sensitivity to the gap variation or misalignment, a novel LCT design approach without additional complexity for the system is proposed. With the aid of FEA simulation software, the influence of coil relative position and geometry parameters on the flux distribution and coupling coefficient of the transmitter and receiver is studied from an electromagnetic perspective. An asymmetrical LCT based on the proposed design method is built to compare with a traditional symmetrical LCT. With fixed 10-mm gap and 0 to 40-mm misalignment variation, the coupling coefficient for the symmetrical LCT drops from 0.354 to 0.107, and the corresponding efficiency decrease is 16.6%. The operating frequency variation is nearly 100 kHz to maintain same input/output condition. When employing the proposed asymmetrical LCT, the coupling coefficient changes between 0.312 and 0.273, and the maximum efficiency deviation is kept within 0.67% over the entire 40-mm misalignment range. Moreover, the required frequency range to achieve same operation condition is less than 10 kHz. Lastly, some design considerations to further improve the IPT system efficiency are proposed on the basis of the designed asymmetrical LCT geometry. For given circuit specifications and LCT coupling conditions, determination of the optimal primary winding turns number could help achieve minimal winding loss and core loss. For lower output power, the optimal primary winding turns number tends to be larger compared to that for higher output power IPT system. Two asymmetrical LCT with similar dimension but different number of turns are built and tested with a 100-W hardware prototype for laptop inductive charging. The proposed efficiency improvement methodology is validated by the winding loss and core loss from experimental results.
Ph. D.
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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.
During 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.
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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 effectively detects mechanical damage to the structure of the transformer windings, is Frequency Response Analysis (FRA). FRA relies on comparison of electrical admittance signatures to determine if the winding has become deformed. One of the major problems it still faces is the interpretation of differences in the signatures. To date, experts are needed to analyse graphs, drawing from experience in order to produce educated guesses as to what the differences in admittance functions denote. However, in the recent past, there has been some headway in programming computer based solutions for the problem of interpretation. The use of Artificial Neural Networks (ANNs) has perhaps been the most promising in this respect. ANNs perform in the same way that human experts do, drawing upon experience to map a change in shape of a signature to a physical change in the winding system. However, one of the major drawbacks of these methods is the large training data-sets required for the neural network to learn. The work reported in this thesis seeks to address this problem by generating training datasets from analytical models of the transformer. Due to the large number of simulations that need to be performed a customized solution method was developed to speed up computations. A combination of back propagation and radial basis function networks were then used to classify the type, location and severity of winding movement. The results showed that the neural network approach was not only accurate but tolerant to high noise levels.
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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 losses and creation of hot spots in the core, in the windings, and the metallic structural parts are adverse effects that occur in transformers. Also, increasing demand of reactive power and misoperation of protective relays menaces the power network. Damages in large power transformers and blackouts in networks have occurred due to this phenomenon Hence, studies regarding this subject have taken the attention of researchers during the last decades. However, a gap of a comprehensive analysis still remains. Thus, the main aim of this project is to reach to a deep understanding of the phenomena and to come up with a solution for a decrease of the undesired effects of GIC. Achieving this goal requires an improvement of the electromagnetic models of transformers which include a hysteresis model, numerical techniques, and transient analysis. In this project until now, a new algorithm for digital measurement of the core materials is developed and implemented. It enhances the abilities of accurate measurements and an improved hysteresis model has been worked out. Also, a novel differential scalar hysteresis model is suggested that easily can be implemented in numerical methods. Three dimensional finite element models of various core types of power transformers are created to study the effect on them due to DC magnetization. In order to enhance the numerical tools for analysis of low frequency transients related to power transformers and the network, a distributed reluctance network method has been outlined. In this thesis a method for solving such a network problem with coupling to an electrical circuit and taking hysteresis into account is suggested.

QC 20121121

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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 mechanism to do so and was inspired by transformer architecture. A special pooling function was proposed for this network that decreases memory requirements of the network. This method achieves 88%, but does not use rasterization and can process polygonal model directly. It is not as good as rasterization method with x-ray display, byt it is better than rasterization method with model not rendered as x-ray.  The last method uses graph representation of mesh. Graph network had problems with overfitting, that is why it did not get good results and I think this method is not very suitable for analyzing plygonal model.
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Livres sur le sujet "Transformer network"

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IEEE Power Engineering Society. Power Systems Relaying Committee., IEEE Standards Board et American National Standards Institute, dir. IEEE guide for the protection of network transformers. New York, N.Y., USA : 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. New York : Ballantine Books, 2008.

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

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

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

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I, Watson C., Paek Eung Gi et National Institute of Standards and Technology (U.S.), dir. 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.

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Spectrum and network measurements. Atlanta, Ga : Noble Pub. Corp., 2001.

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Witte, Robert A. Spectrum and network measurements. Englewood Cliffs, N.J : Prentice Hall, 1993.

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

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

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Chapitres de livres sur le sujet "Transformer network"

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Shu, Chang, Xi Chen, Chong Yu et Hua Han. « A Refined Spatial Transformer Network ». Dans Neural Information Processing, 151–61. Cham : 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 et Gongshen Liu. « Transformer-DW : A Transformer Network with Dynamic and Weighted Head ». Dans Neural Information Processing, 504–15. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36711-4_42.

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Wang, Zehong, Qi Li, Donghua Yu et Xiaolong Han. « Temporal Graph Transformer for Dynamic Network ». Dans Lecture Notes in Computer Science, 694–705. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15931-2_57.

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Sarasua, Ignacio, Sebastian Pölsterl et Christian Wachinger. « TransforMesh : A Transformer Network for Longitudinal Modeling of Anatomical Meshes ». Dans Machine Learning in Medical Imaging, 209–18. Cham : 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 et Jian Sun. « Supervised Transformer Network for Efficient Face Detection ». Dans Computer Vision – ECCV 2016, 122–38. Cham : 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 et Lifang Wu. « Multi-intent Compatible Transformer Network for Recommendation ». Dans Pattern Recognition and Computer Vision, 344–55. Cham : 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 et Dorit Merhof. « Contextual Attention Network : Transformer Meets U-Net ». Dans Machine Learning in Medical Imaging, 377–86. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21014-3_39.

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Liu, Zhongqiang, Li Zhang, Chunxiao Zhang, Xiangfei Kong et Anan Shen. « Transformer Fault Diagnosis Based on Elman Network ». Dans 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.

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Chiam, Dar Hung, et King Hann Lim. « Power Quality Disturbance Classification Using Transformer Network ». Dans 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.

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Zhang, Yungeng, Yuru Pei et Hongbin Zha. « Learning Dual Transformer Network for Diffeomorphic Registration ». Dans 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.

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Actes de conférences sur le sujet "Transformer network"

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Ottele, Andy, Rahmat Shoureshi, Duane Torgerson et John Work. « Neural Network-Based Adaptive Monitoring System for Power Transformer ». Dans 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 estimates of the internal states of the transformer that can not be accessed or measured during operation. The estimated physical parameters are then passed to a neural network to be classified into regions representing various parameter patterns. Analytical and experimental results of this adaptive observer for power transformer diagnostics are presented.
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Neimark, Daniel, Omri Bar, Maya Zohar et Dotan Asselmann. « Video Transformer Network ». Dans 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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Girdhar, Rohit, Joao Joao Carreira, Carl Doersch et Andrew Zisserman. « Video Action Transformer Network ». Dans 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 et Euntai Kim. « Video Multitask Transformer Network ». Dans 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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Mishra, Naman, Avinash Yadav, Arzoo Yadav, Pritam Yadav et Pinki Yadav. « Transformer : Health Monitoring System Based on IoT ». Dans International Research Conference on IOT, Cloud and Data Science. Switzerland : Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-d7mojr.

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Distribution Transformers performs a critical role and is one of the maximum critical electric gadget in electricity device network. For the reliability of the electricity device network, protection and tracking of transformer will become our first precedence. But sometimes due to overloading, leakages in transformer oil, overheating and overcurrent faults occurs in distribution transformer. To overcome such problems proper tracking of distribution transformer will become essential. This paper gives the one of the approach used for tracking of distribution transformer that's primarily based totally on IoT. This proposed device is designed and manufactured to screen and locate the faults of distribution transformer and straight away dispatched the message concerning fault to authenticated or related person. This approach may be very green and correct for tracking of fitness of distribution transformer.
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Liu, Ruolan, Xiao Chen et Xue Wen. « Voice Conversion with Transformer Network ». Dans ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054523.

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Gao, Jiarui, Yanwei Fu, Yu-Gang Jiang et Xiangyang Xue. « Frame-Transformer Emotion Classification Network ». Dans ICMR '17 : International Conference on Multimedia Retrieval. New York, NY, USA : ACM, 2017. http://dx.doi.org/10.1145/3078971.3079030.

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Shahid, Mohammad, et Kai-lung Hua. « Fire Detection using Transformer Network ». Dans ICMR '21 : International Conference on Multimedia Retrieval. New York, NY, USA : ACM, 2021. http://dx.doi.org/10.1145/3460426.3463665.

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Escher, Rafael Molossi, Rodrigo Andrade de Bem et Paulo Lilles Jorge Drews. « Fast Spatial-Temporal Transformer Network ». Dans 2021 34th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2021. http://dx.doi.org/10.1109/sibgrapi54419.2021.00018.

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Nechikkat, Mubashira I., Bhagyasree V. Pattilikattil, Soumya Varma et Ajay James. « Video captioning using transformer network ». Dans THE 2ND UNIVERSITAS LAMPUNG INTERNATIONAL CONFERENCE ON SCIENCE, TECHNOLOGY, AND ENVIRONMENT (ULICoSTE) 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0107029.

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Rapports d'organisations sur le sujet "Transformer network"

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Shi, Jimeng, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang et Giri Narasimhan. Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates. Purdue University, octobre 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), avril 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, janvier 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 networks are likely to be future drivers of economic growth, and the ‘5G competition’ of countries aims to create the necessary technological foundations. Western sanctions against Chinese technology companies will not hold back the construction of 5G and, through it, the future development of the Chinese economy. In addition to establishing a network, countries need companies that really transform their activities by building on new technologies. In the absence of these, 5G rollout can easily remain an unfulfilled promise for economic growth.
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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), mai 1990. http://dx.doi.org/10.2172/6874873.

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Соловйов, В. М., et 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 characteristic way in time periods that characterize the different phases of the behavior and state of the stock market. This fact encouraged to use monitoring and prediction of critical and crisis states in socio-economic systems.
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Enria, Luisa. Citizen Ethnography in Outbreak Response : Guidance for Establishing Networks of Researchers. SSHAP, mai 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 integrate citizen-led ethnographic approaches into their research, and for practitioners working on community engagement or other outbreak responses, who seek to use social science insights in their operations. It was written for SSHAP by Luisa Enria (London School of Hygiene & Tropical Medicine). It is the responsibility of SSHAP.
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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. Fort Belvoir, VA : Defense Technical Information Center, novembre 2008. http://dx.doi.org/10.21236/ada509798.

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Nechaev, V., Володимир Миколайович Соловйов et 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, Mercedes-Benz and some others companies have not been engaged in their traditional business for a long time. Their partner networks performs this function instead of them. The companies themselves perform the function of system integrators. The Tapscott’s idea finds its confirmation within the framework of a new powerful direction of the development of the modern interdisciplinary science – the theory of the complex networks (CN) [2]. CN-s are multifractal objects, the loss of multifractality being the indicator of the system transition from more complex state into more simple state. We tested the multifractal properties of the data using the wavelet transform modulus maxima approach in order to analyze scaling properties of our company. Comparative analysis of the singularity spectrumf(®), namely, the difference between maximum and minimum values of ® (∆ = ®max ¡ ®min) shows that IBM company is considerably more fractal in comparison with Apple Computer. Really, for it the value of ∆ is equal to 0.3, while for the vertically integrated company Apple it only makes 0.06 – 5 times less. The comparison of other companies shows that this dependence is of general character. Taking into consideration the fact that network organization of business has become dominant in the last 5-10 years, we carried out research for the selected companies in the earliest possible period of time which was determined by the availability of data in the Internet, or by historically later beginning of stock trade of computer companies. A singularity spectrum of the first group of companies turned out to be considerably narrower, or shifted toward the smaller values of ® in the pre-network period. The latter means that dynamic series were antipersistant. That is, these companies‘ management was rigidly controlled while the impact of market mechanisms was minimized. In the second group of companies if even the situation did changed it did not change for the better. In addition, we discuss applications to the construction of portfolios of stock that have a stable ratio of risk to return.
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Bedoya-Maya, Felipe, Lynn Scholl, Orlando Sabogal-Cardona et Daniel Oviedo. Who uses Transport Network Companies ? : Characterization of Demand and its Relationship with Public Transit in Medellín. Inter-American Development Bank, septembre 2021. http://dx.doi.org/10.18235/0003621.

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Transport Network Companies (TNCs) have become a popular alternative for mobility due to their ability to provide on-demand flexible mobility services. By offering smartphone-based, ride-hailing services capable of satisfying specific travel needs, these modes have transformed urban mobility worldwide. However, to-date, few studies have examined the impacts in the Latin American context. This analysis is a critical first step in developing policies to promote efficient and sustainable transport systems in the Latin-American region. This research examines the factors affecting the adoption of on-demand ride services in Medellín, Colombia. It also explores whether these are substituting or competing with public transit. First, it provides a descriptive analysis in which we relate the usage of platform-based services with neighborhood characteristics, socioeconomic information of individuals and families, and trip-level details. Next, factors contributing to the election of platform-based services modeled using discrete choice models. The results show that wealthy and highly educated families with low vehicle availability are more likely to use TNCs compared to other groups in Medellín. Evidence also points at gender effects, with being female significantly increasing the probability of using a TNC service. Finally, we observe both transit complementary and substitution patterns of use, depending on the context and by whom the service is requested.
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