Academic literature on the topic 'Transformers network'
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Journal articles on the topic "Transformers network":
1
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.
Abstract:
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.
2
Jianwen, Mo, Mo Lunlin, Yuan Hua, Lin Leping, and Chen Lingping. "CNN with Embedding Transformers for Person Reidentification." Mathematical Problems in Engineering 2023 (July 14, 2023): 1–12. http://dx.doi.org/10.1155/2023/4591991.
Abstract:
For person reidentification (ReID), most slicing methods (such as part-based convolutional baseline (PCB) and AlignedReID) introduce a lot of background devoid of pedestrian parts, resulting in the cross-aliasing of features in the deep network. Besides, the resulting component features are not perfectly aligned with each other, thus affecting model performance. We propose a convolutional neural network (CNN) with embedding transformers (CET) person ReID network architecture based on the respective advantages of CNN and transformer. In CET, first, the residual transformer (RT) structure is first embedded in the backbone network of CNN to obtain a feature extractor, named transformers in CNN. The feature aliasing phenomenon is improved by utilizing transformer’s advantage in grasping the relevance of global information. Second, a feature fuse with learnable vector structure for fusing the output vector is added to the output of the transformer at the end of the network. A two branches loss structure is designed to balance the two different fusion strategies. Finally, the self-attention mechanism in transformer is used for automatic part alignment of human body parts to solve the part alignment problem caused by inaccurate detection frames. The experimental results show that CET network architecture achieves better performance than PCB and some other block-slicing methods.
3
Voitov, O. N., I. I. Golub, L. V. Semenova, E. V. Karpova, and A. L. Buchinsky. "Effects of unbalanced loads in a low-voltage network on flow distribution in a medium-voltage network." iPolytech Journal 28, no. 2 (July 4, 2024): 247–60. http://dx.doi.org/10.21285/1814-3520-2024-2-247-260.
Abstract:
We address the problem of improving the calculation accuracy of power flow in a medium-voltage distribution network based on the measurements of smart meters installed on the secondary side of 6(10)/0.4 kV transformers. In order to account for the effect of unbalanced loads in the low-voltage network on power flow in the medium-voltage network, three-phase three-wire lines were reduced to a single-line option. This enabled the use of symmetric mode calculation programs for the asymmetric mode. The loads in the medium-voltage network were determined by adding power losses in transformer windings and core to the loads measured on the secondary side of transformers. The calculation of winding power losses using the methods of phase coordinates and symmetrical components involves determination of currents in the windings of each phase according to 48 sections of load capacity and voltage module measurements, performed by the smart meter during the day. The correctness of expressions for calculating power losses in transformer windings is confirmed by the equality of total losses in phase coordinates and symmetrical components. The negative sequence power losses in transformer windings were found to be close to zero, while zero sequence losses are significantly lower than the positive sequence losses for almost all transformers with a double star-zero winding connection scheme, regardless of the load factor and rated power. The conducted studies confirmed the possibility and effectiveness of using smart meter measurements for determining loads and calculating power flow in the medium-voltage network. This conclusion was illustrated using an actual distribution network with 26 transformers. Future research should aim to clarify the mathematical models of transformers in the joint calculation of medium- and low-voltage distribution networks.
4
Krupa, Tadeusz. "Elements of Theory of the Correct Operations of Logistics Transforming Networks." Foundations of Management 9, no. 1 (December 20, 2017): 347–60. http://dx.doi.org/10.1515/fman-2017-0026.
Abstract:
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.
5
DAUHIALA, D., V. TIKHANOVICH, and K. BABAMURATOV. "LOSSES IN NETWORK AND SOUND TRANSFORMERS." HERALD OF POLOTSK STATE UNIVERSITY. Series С FUNDAMENTAL SCIENCES, no. 1 (April 18, 2023): 38–43. http://dx.doi.org/10.52928/2070-1624-2023-40-1-38-43.
Abstract:
The causes of losses in transformers are described. The main types of energy losses in the transformer are
revealed. The concepts of Q-factor and efficiency are considered. The estimation of the passage of audio frequency
signals is given, the principles and rules for constructing frequency characteristics are considered. Recommendations
for the design of sound and mains frequency transformers are given. Constructive measures to reduce losses are
considered. The given data can be used for the design of network and sound transformers.
6
Hanus, Oleksii, and Kostiantyn Starkov. "STUDY OF THE NATURE OF OVERVOLTAGES IN THE ELECTRICAL NETWORK ARISING FROM VOLTAGE TRANSFORMERS." Bulletin of the National Technical University "KhPI". Series: Energy: Reliability and Energy Efficiency, no. 1 (2) (July 2, 2021): 28–36. http://dx.doi.org/10.20998/2224-0349.2021.01.05.
Abstract:
A non-linear dynamic mathematical model of voltage transformer has been considered and overvoltages arising on the elements of voltage transformer equivalent circuit during transient processes have been investigated. The influence of voltage transformer secondary circuit capacitance on overvoltage multiplicity in the primary circuits and the duration of transients has been determined. The advantages of approximation of nonlinearity of voltage transformers by hyperbolic sine are used. Mathematical expressions determining the nature of changes in the forced and free components of the transient process in an electrical network with a voltage transformer have been obtained. It is shown that with the increase of the electric network capacitance the duration of the transition process damping increases and the frequency of the forced oscillations and the level of overvoltage decrease. It is proved that even small, in comparison with the primary nominal sinusoidal voltage, aperiodic components of the voltage transient process can lead to significant overvoltages during voltage transformer outages. It has been substantiated that both the secondary resistance and the switching torque influence the overvoltage multiplicity arising in the primary winding of voltage transformers. It is shown that the closed secondary winding worsens the disconnection process of non-linear inductance of voltage transformers. The values to which overvoltages increase in this case are determined. According to the results of calculations it is determined that with open secondary winding of voltage transformers the duration of transient process significantly increases. It has been found that the decrease of frequency of forced oscillations, which occurs in this case, is accompanied by an increase of currents in the primary winding of the voltage transformer, which is dangerous in terms of thermal stability of the winding insulation. It is shown, that closing the secondary winding of voltage transformers leads to significant reduction of transient damping time. It is suggested that this algorithm can be used to provide a rapid breakdown (suppression) of ferroresonant processes. The effectiveness of such a measure of stopping of ferroresonance processes as short-term shunting of secondary winding of voltage transformers has been investigated. The correlation of parameters of electric networks (capacity of busbar sections, nonlinearity of characteristics of voltage transformers, disconnection torque, etc.) at which ferroresonance process may occur and consideration of which may allow, in terms of prevention of ferroresonance processes, to identify substations (electric networks) that require more detailed research has been determined. The results of analytical studies were tested in the electric networks of JSC "Kharkivoblenergo" and used in the electricity distribution system for the selection of specific voltage transformers for certain configurations of electrical networks.
7
Lakehal, Abdelaziz, and Fouad Tachi. "Bayesian Duval Triangle Method for Fault Prediction and Assessment of Oil Immersed Transformers." Measurement and Control 50, no. 4 (May 2017): 103–9. http://dx.doi.org/10.1177/0020294017707461.
Abstract:
Dissolved gas analysis of transformer insulating oil is considered the best indicator of a transformer’s overall condition and is most widely used. In this study, a Bayesian network was developed to predict failures of electrical transformers. The Duval triangle method was used to develop the Bayesian model. The proposed prediction model represents a transformer fault prediction, possible faulty behaviors produced by this transformer (symptoms), along with results of possible dissolved gas analysis. The model essentially captures how possible faults of a transformer can manifest themselves by symptoms (gas proportions). Using our model, it is possible to produce a list of the most likely faults and a list of the most informative gas analysis. Also, the proposed approach helps to eliminate the uncertainty that could exist, regarding the fault nature due to gases trapped in the transformer, or faults that result in more simultaneous gas percentages. The model accurately provides transformer fault diagnosis and prediction ability by calculating the probability of released gases. Furthermore, it predicts failures based on their relationships in the Bayesian network. Finally, we show how the approach works for five distinct electrical transformers of a power plant, by describing the advantages of having available a Bayesian network model based on the Duval triangle method for the fault prediction tasks.
8
Azmi Murad Abd Aziz, Mohd Aizam Talib, Ahmad Farid Abidin, and Syed Abdul Mutalib Al Junid. "Development of Power Transformer Health Index Assessment Using Feedforward Neural Network." Journal of Advanced Research in Applied Sciences and Engineering Technology 30, no. 3 (May 15, 2023): 276–89. http://dx.doi.org/10.37934/araset.30.3.276289.
Abstract:
The role of a power transformer is to convert the electrical power level and send it to the consumer, making it an essential component of a power system. In addition, transformer asset management is essential for monitoring the functioning of transformers in the system to prevent failure and anticipating the health state of transformers, using a technique known as the health index (HI). However, the calculation and computation to determine the transformer HI based on a scoring and ranking technique is complex and required expert validation. Therefore, this paper presents a transformer HI prediction using a feedforward neural network (FFNN) to improve the existing complex scoring and ranking technique. Levenberg–Marquardt (LM), Bayesian Regularized (BR), and Scaled Conjugate Gradient (SCG) are the FFNN training techniques presented in this study to forecast the transformer HI. To validate the techniques, the HI values generated by different FFNN techniques were compared to the scoring and ranking system. Then, the performance of the proposed ANN was evaluated using the correlation coefficient and mean square error (MSE). As a result, the transformer HI was successfully predicted by employing three FFNN techniques, namely the LM, BR, and SCG techniques, which were able to determine whether the transformer's condition is very good, good, fair, or poor. In conclusion, the ANN suggested in this study has also been validated with the ranking and scoring approach, which provides high similarity score in comparison to the transformer health index.
9
Varga, Aleksandr A., and Galaktion V. Shvedov. "Estimating the Error of Calculating the Load Losses in 6—10 kV Distribution Electric Networks." Vestnik MEI, no. 5 (2021): 37–43. http://dx.doi.org/10.24160/1993-6982-2021-5-37-43.
Abstract:
The aim of the study is to estimate the level of load losses for actual (nonuniform) loading of transformer substations in comparison with the load loss level under the conditions of their being uniformly loaded for a typical 6 kV urban distribution electric network. The extent to which such networks are equipped with automated information and measurement systems for fiscal electricity metering is extremely poor. Therefore, not only the load curves of the 6--10/0.4 kV substation transformers, but also their load factors are unknown in the majority of cases. Under these conditions, in calculating the load losses in 6--10 kV distribution electric networks, it is assumed that all substation transformers are uniformly (equally) loaded. The load losses in these electrical networks are calculated with a certain error associated with the assumption according to which the substation transformers are supposed to be uniformly loaded. Under these conditions, estimation of this error becomes of issue. The article describes the modeling, calculation and analysis of technical losses of electricity under the conditions of nonuniformly loaded transformers of a typical urban distribution network consisting of four transformers and four cables interconnecting them. The modeling and calculation of power losses were carried out using the RAP-10-st computer program for several different groups of transformer loading factors. Within each group, different subgroups were produced by rearranging the group loading factors. Each subgroup modeled a nonuniform transformers loading mode in the studied network. For each of these modes, the power losses were calculated and studied with the use of the RAP-10-st computer program. A conclusion has been drawn from the obtained analysis results regarding the error in determining the load losses associated with the assumption about the uniform loading of the substation transformers in the network. The obtained results may prompt electric grid companies to increase the extent of fitting their networks with automated information and measurement systems for fiscal electricity metering to improve the accuracy of determining the load losses.
10
Nadhirah, Nurul Fatin, Hana Abdull Halim, Nurhakimah Mohd Mukhtar, and Samila Mat Zali. "Varying the energisation condition to mitigate sympathetic inrush current." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (December 1, 2023): 5975. http://dx.doi.org/10.11591/ijece.v13i6.pp5975-5985.
Abstract:
Transformers are generally easy to access and can contribute significantly to entire power system. When a transformer is turned on for the first time, it produces a magnetising inrush current which acts as a starting current. Energisation of transformer has a substantial impact on inrush current and transformer that are connected in parallel. Sympathetic inrush current is a phenomenon that appears when a transformer is switched-on in network whereas the other transformers that was earlier energised. Besides, when sympathetic inrush phenomena occur, the peak and period fluctuate significantly. In this paper, the transformers will be energised in three different ways and each condition will be explored in depth. The operation time of the transformer’s energisation whether it is energised simultaneously or at different times are tested and analysed in terms of their characteristics. It is performed using power system computer aided design (PSCAD) software, starting with a develop model of the energisation and then generate the outcomes. The results of the simulation demonstrate that energising the transformer in different ways can give different effect on the sympathetic inrush current, as well as the variables that affect it and methods for reducing it.
Dissertations / Theses on the topic "Transformers network":
1
Hughes, Timothy Howard. "On the synthesis of passive networks without transformers." Thesis, University of Cambridge, 2014. https://www.repository.cam.ac.uk/handle/1810/265924.
Abstract:
This thesis is concerned with the synthesis of passive networks, motivated by the recent invention of a new mechanical component, the inerter, which establishes a direct analogy between mechanical and electrical networks. We investigate the minimum numbers of inductors, capacitors and resistors required to synthesise a given impedance, with a particular focus on transformerless network synthesis. The conclusions of this thesis are relevant to the design of compact and cost-effective mechanical and electrical networks for a broad range of applications. In Part 1, we unify the Laplace-domain and phasor approach to the analysis of transformerless networks, using the framework of the behavioural approach. We show that the autonomous part of any driving-point trajectory of a transformerless network decays to zero as time passes. We then consider the trajectories of a transformerless network, which describe the permissible currents and voltages in the elements and at the driving-point terminals. We show that the autonomous part of any trajectory of a transformerless network is bounded into the future, but need not decay to zero. We then show that the value of the network's impedance at a particular point in the closed right half plane can be determined by finding a special type of network trajectory. In Part 2, we establish lower bounds on the numbers of inductors and capacitors required to realise a given impedance. These lower bounds are expressed in terms of the extended Cauchy index for the impedance, a property defined in that part. Explicit algebraic conditions are also stated in terms of a Sylvester and a Bezoutian matrix. The lower bounds are generalised to multi-port networks. Also, a connection is established with continued fraction expansions, with implications for network synthesis. In Part 3, we first present four procedures for the realisation of a general impedance with a transformerless network. These include two known procedures, the Bott-Duffin procedure and the Reza-Pantell-Fialkow-Gerst simplification, and two new procedures. We then show that the networks produced by the Bott-Duffin procedure, and one of our new alternatives, contain the least possible number of reactive elements (inductors and capacitors) and resistors, for the realisation of a certain type of impedance (called a biquadratic minimum function), among all series-parallel networks. Moreover, we show that these procedures produce the only series-parallel networks which contain exactly six reactive elements and two resistors and realise a biquadratic minimum function. We further show that the networks produced by the Reza-Pantell-Fialkow-Gerst simplification, and the second of our new alternatives, contain the least possible number of reactive elements and resistors for the realisation of almost all biquadratic minimum functions among the class of transformerless networks. We group the networks obtained by these two procedures into two quartets, and we show that these are the only quartets of transformerless networks which contain exactly five reactive elements and two resistors and realise all of the biquadratic minimum functions. Finally, we investigate the minimum number of reactive elements required to realise certain impedances, of greater complexity than the biquadratic minimum function, with series-parallel networks.
2
Das, Debrup. "Dynamic control of grid power flow using controllable network transformers." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/43739.
Abstract:
The objective of the research is to develop a cost-effective, dynamic grid controller called the controllable network transformer (CNT) that can be implemented by augmenting existing load tap changing (LTC) transformers with an AC-AC converter. The concept is based on using a fractionally rated direct AC-AC converter to control the power through an existing passive LTC. By using a modulation strategy based on virtual quadrature sources (VQS), it is possible to control both the magnitude and the phase angle of the output voltage of the CNT without having any inter-phase connections. The CNT architecture has many advantages over existing power flow controllers, like absence of low frequency storage, fractional converter rating, retro-fitting existing assets and independent per-phase operation making it potentially attractive for utility applications.
The independent control of the magnitude and the phase angle of the output voltage allow independent real and reactive power flow control through the CNT-controlled line. In a meshed network with asymmetric network stresses this functionality can be used to redirect power from critically loaded assets to other relatively under-utilized parallel paths. The power flow controllability of CNT can thus be used to lower the overall cost of generation of power. The solid state switches in the CNT with fast response capability enable incorporation of various additional critical functionalities like grid fault ride through, bypassing internal faults and dynamic damping. This bouquet of features makes the CNT useful under both steady state and transient conditions without compromising the grid reliability.
3
Wu, Wei. "CFD calibrated thermal network modelling for oil-cooled power transformers." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/cfd-calibrated-thermal-network-modelling-for-oilcooled-power-transformers(9199cbcc-c6df-4f26-aa9b-dde055ef44ea).html.
Abstract:
Power transformers are key components of electric system networks; their performance inevitably influences the reliability of electricity transmission and distribution systems. To comprehend the thermal ageing of transformers, hot-spot prediction becomes of significance. As the current method to estimate the hot-spot temperature is based on empirical hot-spot factor and is over-simplified, thermal network modelling has been developed due to its well balance between computation speed and approximation details. The application of Computational Fluid Dynamics (CFD) on transformer thermal analysis could investigate detailed and fundamental phenomena of cooling oil flow, and the principle of this PhD thesis is then to develop more accurate and reliable network modelling tools by utilising CFD.In this PhD thesis the empirical equations employed in network model for Nusselt number (Nu), friction coefficient and junction pressure losses (JPL) are calibrated for a wide range of winding dimensions used by power transformer designs from 22 kV to 500 kV, 20 MVA to 500 MVA, by conducting large sets of CFD simulations. The newly calibrated Nu equation predicts a winding temperature increase as the consequence of on average 15% lower Nu values along horizontal oil ducts. The new friction coefficient equation predicts a slightly more uniform oil flow rate distribution across the ducts, and also calculates a higher pressure drop over the entire winding. The new constant values for the JPL equations shows much better match to experimental results than the currently used 'off-the-shelf' constants and also reveals that more oil will tend to flow through the upper half of a pass if at a high inlet oil flow rate. Based on a test winding model in the laboratory, the CFD calibrated network model's calculation results are compared to both CFD and experimental results. It is concluded that the deviation between the oil pressure drop over the pass calculated by the network model and the CFD and the measured values is acceptably low. It proves that network modelling could deliver quick and reliable calculation results of the oil pressure drop over windings and thereby assist to choose capable oil pumps at the thermal design stage. However the flow distribution predicted by network model deviates from the one by CFD; this is particularly obvious for the cases with high flow rates probably due to the entry eddy circulation phenomena observed in CFD. As no experiment validation has been conducted, further investigation is necessary. The CFD calibrated network model is also applied to conduct a set of sensitivity studies on various thermal design parameters as well as loads. Because the studies are on a directed oil cooling winding case, an oil pump model is incorporated. From the studies recommendations are given for optimising thermal design, e.g. narrowed horizontal ducts will reduce average winding and hot-spot temperatures, and narrowed vertical ducts will however increase the temperatures. Doubled oil block washers are found to be able to significantly reduce the disc temperatures, although there is a slight reduction of the total oil flow rate, due to the increase of winding hydraulic impedance. The impact of different loadings, 50%~150% of rated load, upon the forced oil flow rate is limited, relative change below 5%. The correlations between the average winding and hot-spot temperatures versus the load factors follow parabolic trends.
4
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.
Abstract:
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
5
Cozzi, Riccardo. "Using semantic entities to improve the distillation of transformers." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
Abstract:
In the last decade, the size of deep neural architectures implied in Natural Language Processing (NLP) has increased exponentially, reaching in some cases with hundreds of billions of parameters. Although, training and deploying these huge architectures is an extremely resource-demanding process and the costs are often not affordable in real-world applications. For these reasons, lots of research and industrial efforts are investigating solutions to reduce the size of these models but at the same time maintain high performance.
This work was about studying and experimenting Knowledge Distillation techniques with the goal of training smaller and cheaper models while attempting to produce a good approximation of large pre-trained ones. The conducted experiments consist of a first reproduction of a recent promising work of DistilBERT while trying to further reduce the resources implied in the process. In fact, we discovered it is possible to achieve approximately the same score of the state-of-the-art but involving only a small fraction of data and training resources.
The second proposed experiment consists of an attempt of performing the same distillation task with an architecture based on LUKE, a powerful entity-aware transformer that has recently shown how injecting semantic entities can positively influence the training of these models. Unfortunately, this second experiment, as we will see, did not give us the result we hoped, meaning that the task needs additional research effort.
6
Moses, Paul S. "Operation and performance of three-phase asymmetric multi-leg power transformers subjected to nonlinear and dynamic electromagnetic disturbances." Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/1529.
Abstract:
Three-phase power transformers continue to be an important fixture in modern power systems since their initial development in the 1880s. While transformer design has fundamentally remained the same, the operating environment has significantly changed. This is apparent through new flexible network operations (e.g., integration of renewable energy sources), growing network complexities (e.g., deployment of micro-grids, smart grids, etc.) and increasing use of nonlinear power electronic equipment (e.g., power converters and motor drives). Thus the issue of power quality in power systems has become an important consideration to utilities and industries as the performance of electrical machines and devices could be adversely affected. This doctoral thesis focuses on the performance of three-phase power transformers under various nonlinear and dynamic electromagnetic disturbances in distorted power networks.The first part of this work is devoted to the development and improvement of nonlinear electromagnetic models of three-phase multi-leg transformer cores for the study of steady-state and transient electromagnetic disturbances. This is mainly achieved by developing new detailed magnetic models for ferromagnetic nonlinearities (e.g., hysteresis) as well as considering core asymmetry and magnetic couplings of core-leg fluxes in three-phase multi-leg iron-core structures. These combined effects have not been considered in conventional electromagnetic transient studies of transformers and are shown in this work for the first time to have a significant impact on predicted steady-state and transient electromagnetic behaviour.In subsequent parts of this thesis, the developed models are applied to the examination of selected nonlinear electromagnetic phenomena such as transformer operation in harmonically distorted power systems (e.g., terminal voltage distortions and nonlinear loads), dc bias caused by geomagnetically induced currents, ferroresonance, and no-load magnetisation and inrush current effects. Furthermore, based on the new modelling approaches, improved methods are presented for estimating transformer aging with wider applicability to three-phase transformers considering load and source imbalances with harmonic distortions.With the advent of newly emerging smart grids, the last part of this thesis is devoted to exploring future transformer operation in new smart grid operating conditions such as plug-in electric vehicle charging. Transformer loading patterns with random uncoordinated PEV charging compared to coordinated charging activity in smart grids is investigated. The investigation highlights the notion of harnessing future smart grid technologies to better manage transformer health and performance.
7
Mousavi, Seyedali. "Electromagnetic Modelling of Power Transformers for Study and Mitigation of Effects of GICs." Doctoral thesis, KTH, Elektroteknisk teori och konstruktion, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-159747.
Abstract:
Geomagnetic disturbances that result from solar activities can affect technological systems such as power networks. They may cause DC currents in power networks and saturation of the core in power transformers that leads to destruction in the transformer performance. This phenomena result 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 malfunction of protective relays menaces the power network stability. 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, a new algorithm for digital measurement of the magnetic 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. Two and three dimensional finite element models of various core types of power transformers are created to study the effect of DC magnetization on transformers. In order to enhance the numerical tools for analysis of low frequency transients related to power transformers and the network, a novel topological based time step transformer model has been outlined. The model can employ a detailed magnetic circuit and consider nonlinearity, hysteresis and eddy current effects of power transformers. Furthermore, the proposed model can be used in the design process of transformers and even extend other application such as analysis of electrical machines. The numerical and experimental studies in this project lead to understanding the mechanism that a geomantic disturbance affects power transformers and networks. The revealed results conclude with proposals for mitigation strategies against these phenomena.QC 20150210
8
Kaminskas, Almundas. "Elektros tinklų rekonstrukcija Skomantų kaime." Bachelor's thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130823_095922-30435.
Abstract:
Elektros inžinerijos bakalauro baigiamojo darbo tema yra aktuali, nes elektros tinklai sumontuoti prieš 30–40 metų jau yra pasenę ir nebeatitinka šiuolaikinių poreikių. Vis daugiau elektros vartotojų keičia dujines virykles į elektrines, įsigija naujų elektros prietaisų, elektros suvartojimas auga, todėl būtina atnaujinti elektros tinklus, siekiant užtikrinti patikimą kokybiškos elektros energijos tiekimą. Šiuo projektu Skomantų kaime numatoma esamos oro linijos L-300 iš transformatorinės KT-V-305 rekonstravimas, ją pakeičiant apskaičiuoto skerspjūvio 0,4 kV elektros kabelių linijomis su reikiamu skaičiumi 0,4 kV skirstomųjų spintų (SS), įvadinių apskaitų skirstomųjų spintų (ĮASS) ir įvadinių apskaitų spintų (ĮAS). Numatoma trumpinti esamą oro liniją L-100 iš transformatorinės KT-V-305, pastatant dvi stulpines transformatorines su 25 kVA ir 40 kVA galios transformatoriais. Planuojamas oro linijos L-100 iš transformatorinės KT-V-305 ruožų nuo atramos Nr. 100/9 iki atramos Nr. 100/25 ir nuo atramos Nr. 101/18 iki atramos Nr. 101/25 demontavimas.His Bachelors’ degree electrical engineering thesis is relevant, because the electricity network built 30-40 years ago in Skomantai village is already old and no longer meets todays’ needs. More and more electricity consumers change gas stoves into electrical stoves, purchase new electrical devices. The usage of electricity is increasing. That’s why the renewal of electrical network is needed in order to maintain reliable supply of high quality electric power. The plan of this project in Skomantai village is the reconstruction of existing transmission line L-300 from the transformer KT-V-305 reconstructing it by changing with calculated cross-section 0,4 kV electricity cables’ lines with needed number 0,4 kV 0,4kV distribution boards (SS), electrical distribution and metering equipment boards (ĮASS) and lead-in metering boards (ĮAS). Shortening of the existing transmission lines L-100 from transformer KT-V-305 and building two pole transformers with 25 kVA and 40 kVA power transformers. Dismantling of transmission line L-100 from transformer KT-V-305 parts from pole No. 100/9 up to pole No. 100/25, and from pole No. 101/18 up to No. 101/25.
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Lecesne, Erwan. "Planification et assistance par fusion d'images multimodales pour l'optimisation de gestes de réparation tissulaire en insuffisance cardiaque." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS001.
Abstract:
Les travaux de cette thèse s’inscrivent dans le contexte clinique visant à optimiser le geste lors des interventions endoventriculaires cardiaques. Cette recherche se concentre principalement sur le guidage en vue du diagnostic et du traitement des affections endoventriculaires à l’aide de cathéters. L’intervention considérée est la biopsie endoventriculaire utilisée pour diagnostiquer les patients atteints de sarcoïdose cardiaque. En effet, le cathéter doit être guidé avec précision vers la zone de fibrose. Cependant, l’absence d’informations visuelles précises sur la localisation de la fibrose pendant l’intervention accroît le risque de faux négatifs pour les échantillons prélevés. De plus, il existe un risque de complications telles que la perforation myocardique, également appelée tamponnade cardiaque. Les objectifs de cette thèse sont articulés en deux parties distinctes :La première partie, préopératoire, consiste à élaborer un modèle 3D du cœur, englobant le ventricule gauche, le ventricule droit et le myocarde. Ce modèle est construit à partir de segmentations d’images d’IRM, notamment des séquences ciné pour les structures principales et LGE pour localiser les zones de fibrose. Les méthodes de segmentation développées reposent sur l’apprentissage profond, et la méthode de segmentation de la fibrose fait l’objet d’une publication en cours. La seconde partie, peropératoire, vise à assis- ter la procédure en fournissant des informa- tions précises sur l’anatomie et la localisation de la zone fibrosée. Cela permet d’optimiser le positionnement du cathéter en périphérie de cette zone fibrosée, contribuant ainsi à améliorer la précision et l’efficacité de l’intervention. Enfin, l’ensemble de la chaîne de traitement a été expérimenté avec succès sur trois patients, procurant ainsi un retour d’expérience du clinicien. Ces avancées visent à réduire les risques liés à la biopsie endoventriculaire et à accroître la précision du diagnostic de la sarcoïdose cardiaque, ouvrant ainsi la voie à des progrès significatifs dans la prise en charge de cette pathologieThe research in this thesis is situated in the clinical context aimed at optimizing procedures during cardiac endoventricular interventions. This study primarily focuses on guidance for the diagnosis and treatment of endoventricular conditions using catheters. The specific intervention under consideration is the endoventricular biopsy used for diagnosing patients with cardiac sarcoidosis. Indeed, the catheter must be precisely guided to the fibrotic zone. However, the lack of precise visual information on the location of fibrosis during the intervention increases the risk of false negatives for the collected samples. Additionally, there is a risk of complications such as myocardial perforation, also known as cardiac tamponade. The objectives of this thesis are articulated in two distinct parts: The first part, preoperative, involves developing a 3D model of the heart, encompassing the left ventricle, right ventricle, and myocardium. This model is constructed from segmentations of MRI images, including cine sequences for the main structures and late gadolinium-enhanced (LGE) images to locate fibrotic zones. The segmentation methods developed rely on deep learning, and the fibrosis segmentation method is the subject of an ongoing publication. The second part, intraoperative, aims to assist the procedure by providing precise information about the anatomy and location of the fibrotic zone. This optimizes the positioning of the catheter on the periphery of this fibrotic zone, thereby contributing to improving the precision and efficiency of the intervention. Finally, the entire processing pipeline has been successfully tested on three patients, providing valuable feedback for clinicians. These advancements aim to reduce the risks associated with endoventricular biopsy and enhance the precision of cardiac sarcoidosis diagnosis, paving the way for significant progress in the management of this pathology
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Smailes, Michael Edward. "Hybrid HVDC transformer for multi-terminal networks." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31173.
Abstract:
There is a trend for offshore wind farms to move further from the point of common coupling to access higher and more consistent wind speeds to reduce the levelised cost of energy. To accommodate these rising transmission distances, High Voltage Direct Current (HVDC) transmission has become increasingly popular. However, existing HVDC wind farm topologies and converter systems are ill suited to the demands of offshore operation. The HVDC and AC substations have been shown to contribute to more than 20% of the capital cost of the wind farm and provide a single point of failure. Therefore, many wind farms have experienced significant delays in construction and commissioning, or been brought off line until faults could be repaired. What is more, around 75% of the cost of the HVDC and AC substations can be attributed to structural and installation costs. Learning from earlier experiences, industry is now beginning to investigate the potential of a modular approach. In place of a single large converter, several converters are connected in series, reducing substation individual size and complexity. While such options somewhat reduce the capital costs, further reductions are possible through elimination of the offshore substations altogether. This thesis concerns the design and evaluation the Hybrid HVDC Transformer, a high power, high voltage, DC transformer. This forms part of the platform-less (i.e. without substations) offshore DC power collection and distribution concept first introduced by the Offshore Renewable Energy Catapult. By operating in the medium frequency range the proposed Hybrid HVDC Transformer can be located within each turbine’s nacelle or tower and remove the need for expensive offshore AC and DC substations. While solid state, non-isolating DC-DC transformers have been proposed in the literature, they are incapable of achieving the step up ratios required for the Hybrid HVDC transformer [1]– [3]. A magnetic transformer is therefore required, although medium frequency and non-sinusoidal operation does complicate the design somewhat. For example, inter-winding capacitances are more significant and core losses are increased due to the added harmonics injected by the primary and secondary converters [1], [2]. To mitigate the impact of these complications, an investigation into the optimal design was conducted, including all power converter topologies, core shapes and winding configurations. The modular multilevel converter in this case proved to be the most efficient and practical topology however, the number of voltage levels that could be generated on the primary converter was limited by the DC bus voltage. To avoid the use of pulse width modulation and hence large switching losses, a novel MMC control algorithm is proposed to reduce the magnitude of the converter generated harmonics while maintaining a high efficiency. The development and analysis of this High Definition Modular Multilevel Control algorithm forms the bulk of this thesis’ contribution. While the High Definition Modular Multilevel Control algorithm was developed initially for the Hybrid HVDC Transformer, analysis shows it has several other potential applications particularly in medium and low voltage ranges.
Books on the topic "Transformers 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. New York, N.Y., USA: The Institute of Electrical and Electronics Engineers, 1989.
2
Wasylkiwskyj, Wasyl. Signals and Transforms in Linear Systems Analysis. New York, NY: Springer New York, 2013.
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Benkler, Yochai. The wealth of networks: How social production transforms markets and freedom. New Haven: Yale University Press, 2006.
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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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Li, Charlene. Groundswell: Winning in a world transformed by social technologies. Boston, Mass: Harvard Business Review Press, 2011.
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Witte, Robert A. 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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Li, Charlene. Groundswell: Winning in a world transformed by social technologies. Boston, Mass: Harvard Business Press, 2008.
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Marzecki, Jerzy. Metody badania rozwoju i wyznaczania optymalnego rozmieszczenia stacji transformatorowo-rozdzielczych 110 kV/SN w aglomeracji miejskiej. Warszawa: Oficyna Wydawnicza Politechniki Warszawskiej, 1995.
Book chapters on the topic "Transformers network":
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Michail, Othon, and Paul G. Spirakis. "Connectivity Preserving Network Transformers." In Emergent Computation, 337–59. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46376-6_15.
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Karpov, Ilia, and Nick Kartashev. "SocialBERT – Transformers for Online Social Network Language Modelling." In Lecture Notes in Computer Science, 56–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16500-9_6.
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Zhang, Yaning, Tianyi Wang, Minglei Shu, and Yinglong Wang. "A Robust Lightweight Deepfake Detection Network Using Transformers." In Lecture Notes in Computer Science, 275–88. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20862-1_20.
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Shi, Jiangpeng, Xiaochun Cheng, and Jianfeng Wang. "Pair-Wise Convolution Network with Transformers for Sequential Recommendation." In Communications in Computer and Information Science, 433–45. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9031-3_38.
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Samet, Haidar, Farshid Nasrfard Jahromi, Arash Dehghani, and Afsaneh Narimani. "Improving Current and Voltage Transformers Accuracy Using Artificial Neural Network." In IFIP Advances in Information and Communication Technology, 435–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33409-2_45.
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Smirnov, Alexander, Nikolay Teslya, Nikolay Shilov, Diethard Frank, Elena Minina, and Martin Kovacs. "Quantitative Comparison of Translation by Transformers-Based Neural Network Models." In Enterprise Information Systems, 155–74. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39386-0_8.
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Ye, Wei, Jiasai Sun, Min Xu, Xuemeng Yang, Hongliang Li, and Yong Liu. "Detecting Aging Substation Transformers by Audio Signal with Deep Neural Network." In Transactions on Edutainment XVI, 70–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61510-2_7.
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Idjaton, Koubouratou, Xavier Desquesnes, Sylvie Treuillet, and Xavier Brunetaud. "Transformers with YOLO Network for Damage Detection in Limestone Wall Images." In Lecture Notes in Computer Science, 302–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13324-4_26.
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Yu, Long, Yuliang Lu, Xuehu Yan, and Xianhui Wang. "Generative Text Steganography via Multiple Social Network Channels Based on Transformers." In Natural Language Processing and Chinese Computing, 606–17. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17120-8_47.
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Ding, Ning, Yankai Lin, Zhiyuan Liu, and Maosong Sun. "Sentence and Document Representation Learning." In Representation Learning for Natural Language Processing, 81–125. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1600-9_4.
Abstract:
AbstractSentence and document are high-level linguistic units of natural languages. Representation learning of sentences and documents remains a core and challenging task because many important applications of natural language processing (NLP) lie in understanding sentences and documents. This chapter first introduces symbolic methods to sentence and document representation learning. Then we extensively introduce neural network-based methods for the far-reaching language modeling task, including feed-forward neural networks, convolutional neural networks, recurrent neural networks, and Transformers. Regarding the characteristics of a document consisting of multiple sentences, we particularly introduce memory-based and hierarchical approaches to document representation learning. Finally, we present representative applications of sentence and document representation, including text classification, sequence labeling, reading comprehension, question answering, information retrieval, and sequence-to-sequence generation.
Conference papers on the topic "Transformers network":
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Divan, Deepak, and Jyoti Sastry. "Controllable Network Transformers." In 2008 IEEE Power Electronics Specialists Conference - PESC 2008. IEEE, 2008. http://dx.doi.org/10.1109/pesc.2008.4592291.
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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.
Abstract:
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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Blaszczyk, A., N. Boulet, M. Buffoni, B. Galletti, C. Pethrick, and W. Wu. "Coupled CFD-Network Simulations for Cooling of Transformers." In 2019 6th International Advanced Research Workshop on Transformers (ARWtr). IEEE, 2019. http://dx.doi.org/10.23919/arwtr.2019.8930192.
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Tanksale, Vinayak. "Transformers for Network Traffic Prediction." In 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE). IEEE, 2023. http://dx.doi.org/10.1109/csce60160.2023.00042.
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Wen, Qingsong, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun. "Transformers in Time Series: A Survey." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/759.
Abstract:
Transformers have achieved superior performances in many tasks in natural language processing and computer vision, which also triggered great interest in the time series community. Among multiple advantages of Transformers, the ability to capture long-range dependencies and interactions is especially attractive for time series modeling, leading to exciting progress in various time series applications. In this paper, we systematically review Transformer schemes for time series modeling by highlighting their strengths as well as limitations. In particular, we examine the development of time series Transformers in two perspectives. From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis. From the perspective of applications, we categorize time series Transformers based on common tasks including forecasting, anomaly detection, and classification. Empirically, we perform robust analysis, model size analysis, and seasonal-trend decomposition analysis to study how Transformers perform in time series. Finally, we discuss and suggest future directions to provide useful research guidance.
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Das, Debrup, Deepak Divan, and Ronald G. Harley. "Implementation of loadflow for networks with controllable network transformers." In 2013 North American Power Symposium (NAPS). IEEE, 2013. http://dx.doi.org/10.1109/naps.2013.6666923.
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Das, D., and D. Divan. "Power flow control in networks using controllable network transformers." In 2009 IEEE Energy Conversion Congress and Exposition. ECCE 2009. IEEE, 2009. http://dx.doi.org/10.1109/ecce.2009.5316320.
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Lin, Yang, Tianyu Zhang, Peiqin Sun, Zheng Li, and Shuchang Zhou. "FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/164.
Abstract:
Network quantization significantly reduces model inference complexity and has been widely used in real-world deployments. However, most existing quantization methods have been developed mainly on Convolutional Neural Networks (CNNs), and suffer severe degradation when applied to fully quantized vision transformers. In this work, we demonstrate that many of these difficulties arise because of serious inter-channel variation in LayerNorm inputs, and present, Power-of-Two Factor (PTF), a systematic method to reduce the performance degradation and inference complexity of fully quantized vision transformers. In addition, observing an extreme non-uniform distribution in attention maps, we propose Log-Int-Softmax (LIS) to sustain that and simplify inference by using 4-bit quantization and the BitShift operator. Comprehensive experiments on various transformer-based architectures and benchmarks show that our Fully Quantized Vision Transformer (FQ-ViT) outperforms previous works while even using lower bit-width on attention maps. For instance, we reach 84.89% top-1 accuracy with ViT-L on ImageNet and 50.8 mAP with Cascade Mask R-CNN (Swin-S) on COCO. To our knowledge, we are the first to achieve lossless accuracy degradation (~1%) on fully quantized vision transformers. The code is available at https://github.com/megvii-research/FQ-ViT.
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Bermann, Jiri, and Martin Prochazka. "Distance protection of big network transformers." In 2016 17th International Scientific Conference on Electric Power Engineering (EPE). IEEE, 2016. http://dx.doi.org/10.1109/epe.2016.7521720.
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Wang, Yan, Bin Lin, Xiaoyan Cao, and Shaolei Wang. "The Design and Finite Element Analyses of the Super-Long Ultrasonic Amplitude Transformer." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-62484.
Abstract:
Super-long amplitude transformer is an indispensable part of ultrasonic vibration system, often used in high power ultrasound applications. This paper put forward a new method to design and analyze super-long amplitude transformer. Firstly, by using the mechanical four-terminal network method to solve one dimensional longitudinal vibration equation, the frequency and vibration ratio of the ultrasonic transformer are obtained. Thereafter, two types amplitude transformers design scheme can be got by theoretical calculation, i.e. exponential and conical amplitude transformer. Finally, using FEM software ANSYS to analyze the modal, stress distribution and harmonic response of the amplitude transformer. According to the finite element simulation results, each of amplitude transformers has advantages and disadvantages. Compared with theoretical results, it is satisfied with demands of engineering. A new way is carried out for design and optimality of super-long amplitude transformer.
Reports on the topic "Transformers network":
1
Shi, Jimeng, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, and Giri Narasimhan. Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates. Purdue University, October 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), April 2020. http://dx.doi.org/10.2172/1615666.
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Bedoya-Maya, Felipe, Lynn Scholl, Orlando Sabogal-Cardona, and Daniel Oviedo. Who uses Transport Network Companies?: Characterization of Demand and its Relationship with Public Transit in Medellín. Inter-American Development Bank, September 2021. http://dx.doi.org/10.18235/0003621.
Abstract:
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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Соловйов, Володимир Миколайович, Наталя Володимирівна Моісеєнко, and Олена Юріївна Тарасова. Complexity theory and dynamic characteristics of cognitive processes. Springer, January 2020. http://dx.doi.org/10.31812/123456789/4143.
Abstract:
The features of modeling of the cognitive component of social and humanitarian systems have been considered. An example of using entropy multiscale, multifractal, recurrence and network complexity measures has shown that these and other synergetic models and methods allow us to correctly describe the quantitative differences of cognitive systems. The cognitive process is proposed to be regarded as a separate implementation of an individual cognitive trajectory, which can be represented as a time series and to investigate its static and dynamic features by the methods of complexity theory. Prognostic possibilities of the complex systems theory will allow to correct the corresponding pedagogical technologies. It has been proposed to track and quantitatively describe the cognitive trajectory using specially transformed computer games which can be used to test the processual characteristics of thinking.
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Huang, Lei, Meng Song, Hui Shen, Huixiao Hong, Ping Gong, Deng Hong-Wen, and Zhang Chaoyang. Deep learning methods for omics data imputation. Engineer Research and Development Center (U.S.), February 2024. http://dx.doi.org/10.21079/11681/48221.
Abstract:
One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or nonmonotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advanced imputation methods, including deep learning-based methods, have been proposed to address these challenges. Due to its capability of modeling complex patterns and relationships in large and high-dimensional datasets, many researchers have adopted deep learning models to impute missing omics data. This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. In addition, this review also discusses the opportunities that deep learning brings and the challenges that it might face in this field.
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Jander, Georg, Gad Galili, and Yair Shachar-Hill. Genetic, Genomic and Biochemical Analysis of Arabidopsis Threonine Aldolase and Associated Molecular and Metabolic Networks. United States Department of Agriculture, January 2010. http://dx.doi.org/10.32747/2010.7696546.bard.
Abstract:
Since the amino acids threonine and isoleucine can be limiting in mammalian diet and there is interest in increasing their abundance in certain crop plants. To meet this need, a BARD proposal was written with two main research objectives: (i) investigate new avenues for manipulating threonine and isoleucine content in plants and (ii) study the role of threonine aldolase in plant metabolism. Research conducted to meet these goals included analysis of the sub-cellular localization of threonine aldolase in the plant, analysis of metabolic flux in developing embryos, over- and under-expression of Arabidopsis threonine aldolases, and transcriptional and metabolic analysis of perturbations resulting from altered threonine aldolase expression. Additionally, the broader metabolic effects of increasing lysine biosynthesis were investigated. An interesting observation that came up in the course of the project is that threonine aldolase activity affects methionine gamma-lyase in Arabidopsis. Further research showed that threonine deaminase and methionine gamma-lyase both contribute to isoleucine biosynthesis in plants. Therefore, isoleucine content can be altered by manipulating the expression of either or both of these enzymes. Additionally, both enzymes contribute to the up to 100-fold increase in isoleucine that is observed in drought-stressed Arabidopsis. Toward the end of the project it was discovered that through different projects, both groups had been able to independently up-regulate phenylalanine accumulation by different mechanisms. The Galili lab transformed Arabidopsis with a feedbackinsensitive bacterial enzyme and the Jander lab found a feedback insensitive mutation in Arabidopsis arogenate dehydratase. Exchange of the respective plant lines has allowed a comparative analysis of the different methods for increasing phenylalanine content and the creation of double mutants. The research that was conducted as part of this BARD project has led to new insights into plant amino acid metabolism. Additionally, new approaches that were found to increase the accumulation of threonine, isoleucine, and phenylalanine in plants have potential practical applications. Increased threonine and isoleucine levels can increase the nutritional value of crop plants. Elevated isoleucine accumulation may increase the osmotic stress tolerance of plants. Up-regulation of phenylalanine biosynthesis can be used to increase the production of downstream higher-value plant metabolites of biofuel feed stocks.
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Seginer, Ido, James Jones, Per-Olof Gutman, and Eduardo Vallejos. Optimal Environmental Control for Indeterminate Greenhouse Crops. United States Department of Agriculture, August 1997. http://dx.doi.org/10.32747/1997.7613034.bard.
Abstract:
Increased world competition, as well as increased concern for the environment, drive all manufacturing systems, including greenhouses, towards high-precision operation. Optimal control is an important tool to achieve this goal, since it finds the best compromise between conflicting demands, such as higher profits and environmental concerns. The report, which is a collection of papers, each with its own abstract, outlines an approach for optimal, model-based control of the greenhouse environment. A reliable crop model is essential for this approach and a significant portion of the effort went in this direction, resulting in a radically new version of the tomato model TOMGRO, which can be used as a prototype model for other greenhouse crops. Truly optimal control of a very complex system requires prohibitively large computer resources. Two routes to model simplification have, therefore, been tried: Model reduction (to fewer state variables) and simplified decision making. Crop model reduction from nearly 70 state variables to about 5, was accomplished by either selecting a subset of the original variables or by forming combinations of them. Model dynamics were then fitted either with mechanistic relationships or with neural networks. To simplify the decision making process, the number of costate variables (control policy parametrs) was recuced to one or two. The dry-matter state variable was transformed in such a way that its costate became essentially constant throughout the season. A quasi-steady-state control algorithm was implemented in an experimental greenhouse. A constant value for the dry-matter costate was able to control simultaneously ventilation and CO2 enrichment by continuously producing weather-dependent optimal setpoints and then maintaining them closely.
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Horejs, Barbara, and Ulrike Schuh, eds. PREHISTORY & WEST ASIAN/NORTHEAST AFRICAN ARCHAEOLOGY 2021–2023. Verlag der Österreichischen Akademie der Wissenschaften, December 2023. http://dx.doi.org/10.1553/oeai.pwana2021-2023.
Abstract:
The long-established research of Prehistory and West Asian/Northeast African archaeology (the former Institute for Oriental and European Archaeology, OREA) at the Austrian Academy of Sciences was transformed into a department of the »new« Austrian Archaeological Institute (OeAI) at the Austrian Academy of Sciences in 2021. This merging of several institutes into the new OeAI offers a wide range of new opportunities for basic and interdisciplinary research, which support the traditional research focus as well as the development of new projects in world archaeology. The research areas of the Department of Prehistory and West Asian/Northeast African Archaeology include Quaternary archaeology, Prehistory, Near Eastern archaeology and Egyptology. The groups cover an essential cultural area of prehistoric and early historical developments in Europe, Northeast Africa and West Asia. Prehistory is embedded in the world archaeology concept without geographical borders, including projects beyond this core zone, as well as a scientific and interdisciplinary approach. The focus lies in the time horizon from the Pleistocene about 2.6 million years ago to the transformation of societies into historical epochs in the 1st millennium BC. The chronological expertise of the groups covers the periods Palaeolithic, Neolithic, Chalcolithic, Bronze Age and Early Iron Age. The archaeology of West Asia and Northeast Africa is linked to the Mediterranean and Europe, which enables large-scale and chronologically broad basic research on human history. The department consists of the following seven groups: »Quaternary Archaeology«, »Prehistoric Phenomena«, »Prehistoric Identities«, »Archaeology in Egypt and Sudan«, »Archaeology of the Levant«, »Mediterranean Economies« and »Urnfield Culture Networks«. The groups conduct fieldwork and material analyses in Austria, Hungary, Poland, the Czech Republic, Slovakia, Italy, Croatia, Bosnia and Herzegovina, Serbia, North Macedonia, Bulgaria, Romania, Moldova, Greece, Cyprus, Türkiye, Iran, the United Arab Emirates, Lebanon, Israel, Egypt, Sudan and South Africa.
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Rudyk, Myroslava. BLOGGING PLATFORMS AS ARENAS FOR THE MEDIA ACTIVITIES OF CITIZEN JOURNALISTS. Ivan Franko National University of Lviv, March 2024. http://dx.doi.org/10.30970/vjo.2024.54-55.12164.
Abstract:
The article is dedicated to the investigation of blogging platforms as information and communication arenas during wartime. It analyzes the media activities of citizen journalists as a crucial component of the overall information landscape in Ukraine, outlining the peculiarities of their work, the specifics of information dissemination, and the prospects for the development of citizen journalism as a whole. The advantages and disadvantages of citizen journalism are highlighted. Journalism is one of the most dynamic spheres of our existence, and the information space in Ukraine has undergone significant changes since the full-scale war. The development of technological tools always transforms journalism, elevating it to new levels of possibilities. The use of modern blogging platforms, widely accessible not only to professional journalists but also to activists, has contributed to powerful changes in the information sector. Citizen journalism during the Russo-Ukrainian war has taken on entirely different dimensions than before, prompting new reflections on the role of citizen journalists. This expands the scope of the researched issue, from ethical considerations and adherence to journalistic standards to understanding the safety of information activities for both journalists and humanity as a whole. Not every blogger can be called a citizen journalist. The crucial characteristic of citizen journalism, as opposed to mere blogging, is self-awareness, active social action, dedication to an idea, drawing attention to a problem, lobbying for public interests, and serving the function of public control. In the article were examined the activities of citizen journalists, who meet professional standards and exemplify citizen journalism. These include Ihor Lachenkov, Serhiy Sternenko, and Kostyantyn Liberov. Their blogging platforms and social networks were analysed, their content characterised, and posting frequency assessed. The activities of citizen journalists become most in-demand when media representatives cannot capture an event promptly at a particular moment. Citizen journalists find it easier to disseminate information through blogging platforms and social media, especially considering the tremendous trust these platforms enjoy. Survey results illustrate the colossal trust in these platforms. When asked, «What mass media tools did citizens use to get news in 2022?» the responses were as follows: 1. Social networks – 74%, 2. News websites – 42%, 3. Television – 36%, Radio – 11%, Print media – 3%. We observe a trend in the Ukrainian information space where some bloggers position themselves as citizen journalists, even though they previously did not have such a pronounced civic position. This trend became particularly evident at the beginning of Russia’s full-scale invasion of Ukraine when influencers started using their blogs to inform their audience about the events in Ukraine, mobilizing them for active volunteer actions and resisting enemy challenges. Keywords: citizen journalism, information dissemination, blogging platforms, war.
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Rohwerder, Brigitte. Apoyo equitativo para la obtención de medios de sustento y alimentos. Institute of Development Studies, August 2023. http://dx.doi.org/10.19088/core.2023.009.
Abstract:
La pandemia de Covid-19 y las políticas públicas creadas para contener su propagación tuvieron repercusiones serias en la seguridad alimentaria y los medios de sustento de las personas. La pandemia expuso las deficiencias y desigualdades de los sistemas de protección social, la producción y la distribución de alimentos, la seguridad laboral, las políticas económicas, y afectó más que nada a aquellos que ya se encontraban en una situación de marginación y vulnerabilidad (Bolton and Georgalakis 2022; Thompson et al. 2021; Rohwerder 2020). Los aislamientos obligatorios, el cierre de fronteras, el cierre o la reducción de negocios y oficinas públicas causaron recesiones económicas y pérdidas de medios de sustento, y arrastraron a millones de personas al borde de la pobreza y la indigencia (Thompson et al. 2021). La pandemia tuvo repercusiones significativas en la producción y el acceso de los hogares a alimentos nutritivos de calidad debido a la pérdida de ingresos, el aumento en los precios de los alimentos y las restricciones de movimiento de personas, insumos y productos (ibid.). Las mujeres y aquellos que trabajan en las economías informales fueron los más afectados en zonas urbanas y rurales a causa de desigualdades profundamente arraigadas (ibid.; Bolton and Georgalakis 2022; Juárez Padilla, Machorro and Pira 2021). Estas repercusiones continúan cuatro años después. Así, el Informe Mundial sobre las Crisis Alimentarias 2023 identifica que los efectos socioeconómicos prolongados de la pandemia son uno de los factores que aumenta la inseguridad alimentaria aguda (FSIN and Global Network Against Food Crises 2023). Los efectos socioeconómicos de la pandemia han tenido consecuencias negativas en la resiliencia de las personas y ha dado lugar a períodos de recuperación más extensos y a una incapacidad para afrontar futuras crisis (ibid.). De acuerdo con la investigación respaldada por el Covid-19 Responses for Equity Programme (CORE), que cuenta con el apoyo del Centro Internacional de Investigaciones para el Desarrollo (IDRC), esta guía destaca las lecciones clave y acciones prioritarias que se deben realizar para abordar los desafíos en torno a la seguridad alimentaria y los medios de subsistencia a fin de transformar los sistemas alimentarios en sistemas resilientes y equitativos. Esto incluye apoyar las necesidades inmediatas; construir flexibilidad, resiliencia e inclusión en los sistemas alimentarios y de protección social, y desarrollar políticas económicas que sean equitativas y que tengan una perspectiva de género (Bolton and Georgalakis 2022).