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Journal articles on the topic 'Transformer network'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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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.

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The function of network intrusion detection systems (NIDS) in protecting networks from cyberattacks is crucial. Many of the more conventional techniques rely on signature-based approaches, which have a hard time distinguishing between various types of assaults. Using stacked FT-Transformer architecture, this research suggests a new way to identify intrusions in networks. When it comes to dealing with complicated tabular data, FT-Transformers—a variant of the Transformer model—have shown outstanding performance. Because of the inherent tabular nature of network traffic data, FT-Transformers are

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Krupa, Tadeusz. "Elements of Theory of the Correct Operations of Logistics Transforming Networks." Foundations of Management 9, no. 1 (2017): 347–60. http://dx.doi.org/10.1515/fman-2017-0026.

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Abstract In this paper, transformer logistics networks are treated as flow models of discrete manufacturing systems (FMDMS). The purpose is to formalize FMDMS into logical formulas called transformer functions. Transformer logistics networks are able to handle buffers and their production orders in a way that ensures full monitoring of the logic technology stored in the memory of a transforming network (t-network). The structural and functional complexity of the t-network makes it impossible to carry out formal proof of its proper functioning for any new order placement in buffers and transfor

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Alharthi, Musleh, and Ausif Mahmood. "Enhanced Linear and Vision Transformer-Based Architectures for Time Series Forecasting." Big Data and Cognitive Computing 8, no. 5 (2024): 48. http://dx.doi.org/10.3390/bdcc8050048.

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Time series forecasting has been a challenging area in the field of Artificial Intelligence. Various approaches such as linear neural networks, recurrent linear neural networks, Convolutional Neural Networks, and recently transformers have been attempted for the time series forecasting domain. Although transformer-based architectures have been outstanding in the Natural Language Processing domain, especially in autoregressive language modeling, the initial attempts to use transformers in the time series arena have met mixed success. A recent important work indicating simple linear networks out

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Zhou, Mingjie, Jing Xu, Chaojian Xing, Yankai Li, and Shuxin Liu. "Research for transformer operation state prediction method based on BO-CNN-GRU." Journal of Physics: Conference Series 2770, no. 1 (2024): 012013. http://dx.doi.org/10.1088/1742-6596/2770/1/012013.

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Abstract In the realm of electrical engineering, this study introduces a novel approach for forecasting transformer operational conditions by leveraging BO-CNN-GRU (Bayesian Optimized Convolutional Neural Network Gated Loop Unit). The initial step involves an in-depth analysis of the key parameters that significantly impact the operational performance of the transformer. Then, the comprehensive weights of each characteristic parameter of the transformer are obtained by the G1 method, entropy weight method, and CRITIC method, and the comprehensive state data of the transformer is obtained. Fina

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Zhang, Fuping, Pengcheng Zhao, and Jianming Wei. "Channel Transformer Network." IEEE Access 8 (2020): 220762–78. http://dx.doi.org/10.1109/access.2020.3042644.

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

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Transformer is popular in Natural Language Processing (NLP) and is a cornerstone of large models. Transformer has been used by researchers to address the limitations of Convolutional Neural Networks (CNNs) in medical picture segmentation models. Through an extensive literature review and case studies, this paper comparatively analyzes the performance of different models in this field, summarizes different methods of integrating transformers into U-net, and points out existing gaps and challenges. Research has found that the Transformer model can significantly improve the accuracy and efficienc

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Ottele, Andy, and Rahmat Shoureshi. "Neural Network-Based Adaptive Monitoring System for Power Transformer." Journal of Dynamic Systems, Measurement, and Control 123, no. 3 (1999): 512–17. http://dx.doi.org/10.1115/1.1387248.

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Power transformers are major elements of the electric power transmission and distribution infrastructure. Transformer failure has severe economical impacts from the utility industry and customers. This paper presents analysis, design, development, and experimental evaluation of a robust failure diagnostic technique. Hopfield neural networks are used to identify variations in physical parameters of the system in a systematic way, and adapt the transformer model based on the state of the system. In addition, the Hopfield network is used to design an observer which provides accurate estimates of

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Majeed, Issah Babatunde, and Nnamdi I. Nwulu. "Impact of Reverse Power Flow on Distributed Transformers in a Solar-Photovoltaic-Integrated Low-Voltage Network." Energies 15, no. 23 (2022): 9238. http://dx.doi.org/10.3390/en15239238.

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Modern low-voltage distribution systems necessitate solar photovoltaic (PV) penetration. One of the primary concerns with this grid-connected PV system is overloading due to reverse power flow, which degrades the life of distribution transformers. This study investigates transformer overload issues due to reverse power flow in a low-voltage network with high PV penetration. A simulation model of a real urban electricity company in Ghana is investigated against various PV penetration levels by load flows with ETAP software. The impact of reverse power flow on the radial network transformer load

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Adegboye, B. A. "Power Quality Assessment in a Distribution Network." Advanced Materials Research 62-64 (February 2009): 53–59. http://dx.doi.org/10.4028/www.scientific.net/amr.62-64.53.

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The paper explores power quality disturbances on a specified section of the distribution network of a Textile Industry in Kaduna State of Nigeria. The 33kV PHCN incoming to the industry is stepped down to 11kV by a 7.5MVA, 33/11kV three-phase transformer. This transformer supplies various 11/.415kV transformers present in the distribution network. Another 11kV PHCN incoming is used in event of any failure from the 33/11kV transformer. The paper focuses on Transformer No. 1, a 150kVA, 11/.415kV three-phase transformer operating at 0.9 power factor, located at printing and dying (P/D) building 1

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Kumari, Rekha, Gurpreet Kaur, Aditya Rawat, Harshit Chauhan, Kartik Singh Negi, and Rishi Mishra. "ANALYSIS OF TRANSFORMER-DEEP NEURAL NETWORK USING DEEP LEARNING." International Journal of Engineering Applied Sciences and Technology 8, no. 2 (2023): 313–19. http://dx.doi.org/10.33564/ijeast.2023.v08i02.048.

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

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Al-Yahya, Maha, Hend Al-Khalifa, Heyam Al-Baity, Duaa AlSaeed, and Amr Essam. "Arabic Fake News Detection: Comparative Study of Neural Networks and Transformer-Based Approaches." Complexity 2021 (April 16, 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

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Liu, Yuqian, Chujie Zhao, Yizhou Jiang, Ying Fang, and Feng Chen. "LDD: High-Precision Training of Deep Spiking Neural Network Transformers Guided by an Artificial Neural Network." Biomimetics 9, no. 7 (2024): 413. http://dx.doi.org/10.3390/biomimetics9070413.

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The rise of large-scale Transformers has led to challenges regarding computational costs and energy consumption. In this context, spiking neural networks (SNNs) offer potential solutions due to their energy efficiency and processing speed. However, the inaccuracy of surrogate gradients and feature space quantization pose challenges for directly training deep SNN Transformers. To tackle these challenges, we propose a method (called LDD) to align ANN and SNN features across different abstraction levels in a Transformer network. LDD incorporates structured feature knowledge from ANNs to guide SNN

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Kirui, Kemei Peter, David K. Murage, and Peter K. Kihato. "Impacts of Placement of Wind Turbine Generators on IEEE 13 Node Radial Test Feeder In-Line Transformer Fuse-Fuse Protection Coordination." European Journal of Engineering Research and Science 5, no. 6 (2020): 665–74. http://dx.doi.org/10.24018/ejers.2020.5.6.1939.

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The ever increasing global demand on the electrical energy has lead to the integration of Distributed Generators (DGs) onto the distribution power systems networks to supplement on the deficiencies on the electrical energy generation capacities. The high penetration levels of DGs on the electrical distribution networks experienced over the past decade calls for the grid operators to periodically and critically asses the impacts brought by the DGs on the distribution network operations. The assessment on the impacts brought by the DGs on the distribution network operations is done by simulating

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Kirui, Kemei Peter, David K. Murage, and Peter K. Kihato. "Impacts of Placement of Wind Turbine Generators on IEEE 13 Node Radial Test Feeder In-Line Transformer Fuse-Fuse Protection Coordination." European Journal of Engineering and Technology Research 5, no. 6 (2020): 665–74. http://dx.doi.org/10.24018/ejeng.2020.5.6.1939.

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The ever increasing global demand on the electrical energy has lead to the integration of Distributed Generators (DGs) onto the distribution power systems networks to supplement on the deficiencies on the electrical energy generation capacities. The high penetration levels of DGs on the electrical distribution networks experienced over the past decade calls for the grid operators to periodically and critically asses the impacts brought by the DGs on the distribution network operations. The assessment on the impacts brought by the DGs on the distribution network operations is done by simulating

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15

Bogdanov, M. R., G. R. Shakhmametova, and N. N. Oskin. "Possibility of Using the Attention Mechanism in Multimodal Recognition of Cardiovascular Diseases." Programmnaya Ingeneria 15, no. 11 (2024): 578–88. http://dx.doi.org/10.17587/prin.15.578-588.

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The paper is about studying the possibility of using the attention mechanism in diagnosing various cardiovascular diseases. Biomedical data were presented in different modalities (text, images, and time series). A comparison of the efficiency of 5 transformers based on the attention mechanism (Dosovitsky transformer, compact convolutional transformer, transformer with external attention, transformer based on tokenization with patch shift and local self-attention, transformer based on multiple deep attention) was carried out with the Exception convolutional neural network, three fully connecte

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Vasilevskij, V. V., and M. O. Poliakov. "Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems." Electrical Engineering & Electromechanics, no. 1 (February 23, 2021): 10–14. http://dx.doi.org/10.20998/2074-272x.2021.1.02.

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Introduction. One of the parameters that determine the state of the insulation of power transformers is the degree of moisture content of cellulose insulation and transformer oil. Modern systems of continuous monitoring of transformer equipment have the ability to accumulate data that can be used to reproduce the dynamics of moisture content in insulation. The purpose of the work is to reproduce the curve of the of humidity of transformer oil based on the results of measuring the temperature of the upper and lower layers of oil without the need for direct measurement of moisture content by spe

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Moon, Ji-Hwan, Gyuho Choi, Yu-Hwan Kim, and Won-Yeol Kim. "PCTC-Net: A Crack Segmentation Network with Parallel Dual Encoder Network Fusing Pre-Conv-Based Transformers and Convolutional Neural Networks." Sensors 24, no. 5 (2024): 1467. http://dx.doi.org/10.3390/s24051467.

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Cracks are common defects that occur on the surfaces of objects and structures. Crack detection is a critical maintenance task that traditionally requires manual labor. Large-scale manual inspections are expensive. Research has been conducted to replace expensive human labor with cheaper computing resources. Recently, crack segmentation based on convolutional neural networks (CNNs) and transformers has been actively investigated for local and global information. However, the transformer is data-intensive owing to its weak inductive bias. Existing labeled datasets for crack segmentation are rel

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18

Azizian, Davood, and Mehdi Bigdeli. "A new cast-resin transformer thermal model based on recurrent neural networks." Archives of Electrical Engineering 66, no. 1 (2017): 17–28. http://dx.doi.org/10.1515/aee-2017-0002.

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Abstract Thermal modeling in the transient condition is very important for cast-resin dry-type transformers. In the present research, two novel dynamic thermal models have been introduced for the cast-resin dry-type transformer. These models are based on two artificial neural networks: the Elman recurrent networks (ELRN) and the nonlinear autoregressive model process with exogenous input (NARX). Using the experimental data, the introduced neural network thermal models have been trained. By selecting a typical transformer, the trained thermal models are validated using additional experimental r

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Sun, Zhiqing, Yi Xuan, ZikaiCao, et al. "Transformer parameter estimation in distribution network based on deformable transformer." Journal of Physics: Conference Series 2758, no. 1 (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

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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 (2024): 247–60. http://dx.doi.org/10.21285/1814-3520-2024-2-247-260.

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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 winding

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21

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.

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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 p

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Cremasco, Andrea, Wei Wu, Andreas Blaszczyk, and Bogdan Cranganu-Cretu. "Network modelling of dry-type transformer cooling systems." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 37, no. 3 (2018): 1039–53. http://dx.doi.org/10.1108/compel-12-2016-0534.

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Purpose The application of dry-type transformers is growing in the market because the technology is non-flammable, safer and environmentally friendly. However, the unit dimensions are normally larger and material costs become higher, as no oil is present for dielectric insulation or cooling. At designing stage, a transformer thermal model used for predicting temperature rise is fundamental and the modelling of cooling system is particularly important. This paper aims to describe a thermal model used to compute dry transformers with different cooling system configurations. Design/methodology/ap

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Yarymbash, D.S., M.I. Kotsur, S.T. Yarymbash, and I.M. Kylymnyk. "Electromagnetic Processes Simulation of Power Transformers in Operation and in No-load Modes." Problemele Energeticii Regionale 1(45) (January 15, 2020): 1–13. https://doi.org/10.5281/zenodo.3713396.

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In the designing preproduction of power transformers for calculating design data, a circuit models are widely used, which doesn’t fully reflect the structural features of the transformer and spatial energy conversion. This leads to a significant increase of the error of design calculations and calculations of the magnetization parameters in load operation of transformers. This is especially the case in networks with alternative generation of electrical energy. Therefore, the aim of the work is to estimate the influence of design features and non-linear characteristics of electrical steel

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Zhao, Guanghui, Zelin Wang, Yixiong Huang, Huirong Zhang, and Xiaojing Ma. "Transformer-Based Maneuvering Target Tracking." Sensors 22, no. 21 (2022): 8482. http://dx.doi.org/10.3390/s22218482.

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When tracking maneuvering targets, recurrent neural networks (RNNs), especially long short-term memory (LSTM) networks, are widely applied to sequentially capture the motion states of targets from observations. However, LSTMs can only extract features of trajectories stepwise; thus, their modeling of maneuvering motion lacks globality. Meanwhile, trajectory datasets are often generated within a large, but fixed distance range. Therefore, the uncertainty of the initial position of targets increases the complexity of network training, and the fixed distance range reduces the generalization of th

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Abdullah, A. M., R. Ali, S. B. Yaacob, K. Ananda-Rao, and N. A. Uloom. "Transformer Health Index by Prediction Artificial Neural Networks Diagnostic Techniques." Journal of Physics: Conference Series 2312, no. 1 (2022): 012002. http://dx.doi.org/10.1088/1742-6596/2312/1/012002.

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Abstract This paper presents the artificial neural network diagnostic techniques for predicting the health index in transformer. Collection data is measured and tested from insulation resistance in between phase-ground, phase to phase and also the winding resistance transformer. The data was collected from 10 units of transformers from Company Transformer Manufacturing and Servicing (CTMS) in Malaysia. The data was used to calculate condition transformer index or health index transformer. Condition transformer index can identify whether transformer in good condition or not good condition. The

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Jiao, Jinyue, Zhiqiang Gong, and Ping Zhong. "Dual-Branch Fourier-Mixing Transformer Network for Hyperspectral Target Detection." Remote Sensing 15, no. 19 (2023): 4675. http://dx.doi.org/10.3390/rs15194675.

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In recent years, transformers have shown great potential in hyperspectral image processing and have also been gradually applied in hyperspectral target detection (HTD). Nonetheless, applying a typical transformer to HTD remains challenging. The heavy computation burden of the multi-head self-attention (MSA) in transformers limits its efficient HTD, while the limited ability to extract local spectral features can reduce the discrimination of the learned spectral features. To further explore the potential of transformers for HTD, for balance of representation ability and computational efficiency

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Nafisi, Hamed, Mehrdad Abedi, and Gevorg B. Gharehpetian. "Locating Pd in Transformers through Detailed Model and Neural Networks." Journal of Electrical Engineering 65, no. 2 (2014): 75–82. http://dx.doi.org/10.2478/jee-2014-0011.

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Abstract In a power transformer as one of the major component in electric power networks, partial discharge (PD) is a major source of insulation failure. Therefore the accurate and high speed techniques for locating of PD sources are required regarding to repair and maintenance. In this paper an attempt has been made to introduce the novel methods based on two different artificial neural networks (ANN) for identifying PD location in the power transformers. In present report Fuzzy ARTmap and Bayesian neural networks are employed for PD locating while using detailed model (DM) for a power transf

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Braña, L., A. Costa, and R. Lopes. "Development of a power transformer model for high-frequency transient phenomena." Renewable Energy and Power Quality Journal 19 (September 2021): 217–21. http://dx.doi.org/10.24084/repqj19.260.

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In recent years, the proliferation of distributed renewable energy sources and the application of new rules for the exploitation of electrical networks imposed by the markets have dictated increasingly demanding operating conditions for electric power transformers, creating new challenges in their exploration and conservation. Transformers that, in addition to the transmission lines, are certainly the most important and critical element of any electrical energy system. Adequate models are necessary to accurately describe transformer behavior and internal response when submitted to different ex

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Lynch, A. C. "Blumlein's transformer-bridge network." Engineering Science and Education Journal 2, no. 3 (1993): 117. http://dx.doi.org/10.1049/esej:19930037.

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Lin, Yubin, Jiyu Li, Xiaofei Ruan, Xiaoyu Huang, and Jinbo Zhang. "Energy consumption analysis of power grid distribution transformers based on an improved genetic algorithm." PeerJ Computer Science 9 (October 26, 2023): e1632. http://dx.doi.org/10.7717/peerj-cs.1632.

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With the promotion of energy transformation, the utilization ratio of electrical power is progressively rising. Since electrical power is challenging to store, real-time production and consumption become imperative, imposing significant demands on the dependability and operational efficiency of electrical power apparatus. Suppose the load distribution among multiple transformers within a transformer network exhibits inequality. In such instances, it will amplify the total energy consumption during the voltage conversion process, and local, long-term high-load transformer networks become more s

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V.V., Vasilevskij, and Poliakov M.O. "Reproducing of the humidity curve of power transformers oil using adaptive neuro-fuzzy systems." Electrical Engineering & Electromechanics, no. 1 (February 25, 2021): 10–14. https://doi.org/10.20998/2074-272X.2021.1.02.

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<strong><em>Introduction</em></strong><em>. One of the parameters that determine the state of the insulation of power transformers is the degree of moisture content of cellulose insulation and transformer oil. Modern systems of continuous monitoring of transformer equipment have the ability to accumulate data that can be used to reproduce the dynamics of moisture content in insulation. The <strong>purpose</strong> of the work is to reproduce the curve of the of humidity of transformer oil based on the results of measuring the temperature of the upper and lower layers of oil without t

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Bernadić, Alen, and Zahira Anane. "NEUTRAL POINT CONNECTIONS IN MV POWER NETWORKS WITH GROUNDING ZIGZAG TRANSFORMERS – ANALYSIS AND SIMULATIONS." Journal of Energy - Energija 68, no. 1 (2019): 42–48. http://dx.doi.org/10.37798/20196812.

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Treatment of transformer neutral point in middle-voltage (MV) networks become an important issue with increasing proportion of MV cables in power networks. As consequence, overall capacitance of MV network is increased and moreover earth fault currents magnitudes. In MV networks with feeding transformer winding in delta connection (isolated networks), that earth fault current increase requires forming of artificial ground point – a neutral connection point on a three-phase ungrounded power system. Grounding transformer use, in zigzag or delty-wye connection, is common, well-known solution for

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Xu, Honghua, Yong Li, Lei Zhu, and Ziqiang Xu. "Condition assessment of transformers in wind farm based on modified one-dim residual neural network." Journal of Physics: Conference Series 2378, no. 1 (2022): 012078. http://dx.doi.org/10.1088/1742-6596/2378/1/012078.

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Abstract The working environment of transformers in the wind farm is more complex than others, which brings the difference in condition assessment. Moreover, many condition assessment methods based on characteristics or machine learning have difficulty in recognition in cases of multiple transformers, conditions and measuring points. To assess conditions, this paper establishes a condition classification model of the transformer with a modified one-dim residual neural network and uses vibration signal, current and voltage as inputs. The built network mode has faster convergence speed and class

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Fiennes, J., and C. R. de Souza. "The Complex Transformer as a Network-Model Element." International Journal of Electrical Engineering & Education 40, no. 1 (2003): 27–35. http://dx.doi.org/10.7227/ijeee.40.1.3.

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Zheng, Jianhan, Shengqing Gui, and Haomin Zhang. "Transformer Vibration Analysis Based on Double Branch Convolutional Neural Network." Journal of Physics: Conference Series 2503, no. 1 (2023): 012092. http://dx.doi.org/10.1088/1742-6596/2503/1/012092.

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Abstract The power transformer is one of the important pieces of equipment in the power grid system, and its normal operation is related to the safety and reliability of the whole power system. There are many factors influencing transformer vibration in operation, and its characteristics are complex, so it is difficult to be directly used for transformer state analysis. This paper proposes a method for vibration signal analysis based on a continuous wavelet time-frequency graph. The segmented samples of transformer vibration signals are selected by the time-domain sample segmentation method, a

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Lin, Jun, Lei Su, Yingjie Yan, Gehao Sheng, Da Xie, and Xiuchen Jiang. "Prediction Method for Power Transformer Running State Based on LSTM_DBN Network." Energies 11, no. 7 (2018): 1880. http://dx.doi.org/10.3390/en11071880.

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It is of great significance to accurately get the running state of power transformers and timely detect the existence of potential transformer faults. This paper presents a prediction method of transformer running state based on LSTM_DBN network. Firstly, based on the trend of gas concentration in transformer oil, a long short-term memory (LSTM) model is established to predict the future characteristic gas concentration. Then, the accuracy and influencing factors of the LSTM model are analyzed with examples. The deep belief network (DBN) model is used to establish the transformer operation usi

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Jaiswal, Sushma, Harikumar Pallthadka, Rajesh P. Chinchewadi, and Tarun Jaiswal. "Optimized Image Captioning: Hybrid Transformers Vision Transformers and Convolutional Neural Networks: Enhanced with Beam Search." International Journal of Intelligent Systems and Applications 16, no. 2 (2024): 53–61. http://dx.doi.org/10.5815/ijisa.2024.02.05.

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Deep learning has improved image captioning. Transformer, a neural network architecture built for natural language processing, excels at image captioning and other computer vision applications. This paper reviews Transformer-based image captioning methods in detail. Convolutional neural networks (CNNs) extracted image features and RNNs or LSTM networks generated captions in traditional image captioning. This method often has information bottlenecks and trouble capturing long-range dependencies. Transformer architecture revolutionized natural language processing with its attention strategy and

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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 (2023): 276–89. http://dx.doi.org/10.37934/araset.30.3.276289.

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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 predi

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Lara, Hector, and Esteban Inga. "Efficient Strategies for Scalable Electrical Distribution Network Planning Considering Geopositioning." Electronics 11, no. 19 (2022): 3096. http://dx.doi.org/10.3390/electronics11193096.

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This article presents a heuristic model to find the optimal route or layout of a subway electrical distribution network, obtaining full coverage of users in different scenarios and respecting technical criteria such as maximum distance to avoid voltage drop and capacity. In this way, the location of the transformer substations is achieved through an analysis of candidate sites. The medium voltage network will connect each transformer to a minimum spanning tree (MST), reducing the cost of materials associated with constructing the electrical grid. This work considers the latitude and longitude

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Qi, Qiang, and Xiao Wang. "TGBFormer: Transformer-GraphFormer Blender Network for Video Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 6559–67. https://doi.org/10.1609/aaai.v39i6.32703.

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Video object detection has made significant progress in recent years thanks to convolutional neural networks (CNNs) and vision transformers (ViTs). Typically, CNNs excel at capturing local features but struggle to model global representations. Conversely, ViTs are adept at capturing long-range global features but face challenges in representing local feature details. Off-the-shelf video object detection methods solely rely on CNNs or ViTs to conduct feature aggregation, which hampers their capability to simultaneously leverage global and local information, thereby resulting in limited detectio

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Hartono, Muharni, Adipura, Martiningsih, Otong, and Muhammad Irvan. "ANALYSIS OF POWER TRANSFORMATOR CONDITIONS USING DGA METHOD USING ARTIFICIAL NEURAL NETWORK IN KRAKATAU ELECTRICAL POWER COMPANY." International Journal of Engineering Technologies and Management Research 7, no. 6 (2020): 77–88. http://dx.doi.org/10.29121/ijetmr.v7.i6.2020.572.

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Test method that can be done for transformer oil with DGA method. In identifying early transformer conditions, one of them is using IEC 60599 Standards. The artificial neural network training process used 341 data in the presence of nine conditions based on the IEC standard. The best network architecture configuration is a configuration with 3 neurons in the input layer, 10 neurons in the first hidden layer, 20 neurons in the second hidden layer, 20 neurons in the third hidden layer and 4 neurons in the output layer with the transfer logic. The results of the training give a regression value o

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Jurisic, Bruno, Marijan Perković, Ivan Novko, Luka Kovačić, Igor Žiger, and Tomislav Župan. "Proposal of Testing Procedure for Resonance and Ferroresonance Inception Possibility in Instrument Transformers." Journal of Energy - Energija 73, no. 2 (2024): 21–24. http://dx.doi.org/10.37798/2024732520.

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This paper deals with the possibility of ferroresonance occurence in the interaction between circuit breakers and inductive instrument transformers. Existing standards lack guidance on testing for ferroresonant behaviour. The paper proposes a standardized testing procedure and presents measurements on a full-scale system. EMTP simulations complement the measurements for a broader network topology analysis, i.e. circuit breaker capacitance combinations. EMTP simulations are validated for a 170 kV voltage transformer and a combined instrument transformer, showing accuracy within 10%. The paper a

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Zhou, Li, Tongqin Shi, Songquan Huang, et al. "Convolutional neural network for real-time main transformer detection." Journal of Physics: Conference Series 2229, no. 1 (2022): 012021. http://dx.doi.org/10.1088/1742-6596/2229/1/012021.

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Abstract For substation constructions, the main transformer is the dominant electrical equipment, and its arrival and operation affect the progress of project directly. In the context of smart grid construction, in order to improve the efficiency of real-time main transformer detection, this paper proposes an identification and detection method based on the SSD algorithm. The SSD algorithm is able to extract the target device (such as main transformer) accurately and the Lenet algorithm module can analyse the features contained in the image. To improve the accuracy of the detection method, the

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Xu, Xiangkai, Zhejun Feng, Changqing Cao, et al. "An Improved Swin Transformer-Based Model for Remote Sensing Object Detection and Instance Segmentation." Remote Sensing 13, no. 23 (2021): 4779. http://dx.doi.org/10.3390/rs13234779.

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Remote sensing image object detection and instance segmentation are widely valued research fields. A convolutional neural network (CNN) has shown defects in the object detection of remote sensing images. In recent years, the number of studies on transformer-based models increased, and these studies achieved good results. However, transformers still suffer from poor small object detection and unsatisfactory edge detail segmentation. In order to solve these problems, we improved the Swin transformer based on the advantages of transformers and CNNs, and designed a local perception Swin transforme

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Astashev, Mikhail G., Artem S. Vanin, Vladimir M. Korolev, Dmitriy I. Panfilov, Pavel A. Rashitov, and Vladimir N. Tulskii. "Assessment of the Technical and Economic Effect from Using Automatic Voltage Control Devices on 10/0.4 kV Transformers in Power Distribution Networks." Vestnik MEI, no. 5 (2021): 27–36. http://dx.doi.org/10.24160/1993-6982-2021-5-27-36.

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The article addresses the problem of ensuring permissible voltage levels in distribution electrical networks of various types: distribution networks of large cities, regional distribution electrical networks, and distribution electrical networks containing renewable energy sources. The most typical factors causing the voltage to go beyond the permissible limits specified by the relevant regulatory documents are pointed out. The negative factors conducive to the voltage at the consumer end deviating from the permissible limits, including a long length of network lines, high network load, low co

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Isijola, Ayomitope, Michael Asefon, Ufuoma Ogude, Adetoro Mayowa Sola, Temiloluwa Adebowale, and Isabella Akunekwu. "Network Anomaly Detection System using Transformer Neural Networks and Clustering Techniques." International Journal of Artificial Intelligence 12, no. 1 (2025): 37–52. https://doi.org/10.36079/lamintang.ijai-01201.837.

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This study proposes a hybrid approach for network anomaly detection by integrating a Transformer-based model with clustering techniques. The methodology begins with the application of K-means clustering as a preprocessing step to group similar network traffic data, thereby reducing data complexity and highlighting significant patterns. The clustered data is then fed into a Transformer model, which utilizes multi-head self-attention mechanisms to capture intricate temporal dependencies and contextual relationships within sequential data. This dual-stage approach enhances the model’s ability to

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Albalawi, Fahad. "Prediction of the Insulating Paper State of Power Transformers Using Artificial Neural Network." International Journal of Energetica 9, no. 1 (2024): 10. https://doi.org/10.47238/ijeca.v9i1.242.

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Power transformers are considered the heart of power systems. The malfunction or undesirable outage of the power transformer will cause a tremendous revenue loss for the utilities. Therefore, a regular or preventive test must be accomplished on the transformer to check its state. Some standards, such as the American Transformer Diagnosis Guide and the American Society for Testing and Materials, have instructions for testing the transformers. The current works addressed which tests can be accomplished to predict the insulating paper state, which is the indicator of transformer aging. Furthermor

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Goran Jerbić. "APPLICATION OF PHASE SHIFTING TRANSFORMERS IN THE CROATIAN POWER SUPPLY SYSTEM." Journal of Energy - Energija 56, no. 2 (2022): 216–31. http://dx.doi.org/10.37798/2007562353.

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The phase shifting transformers with on load tap changer are increasingly found in modern power supply systems, especially under the present conditions of the full opening of the electricity market. The construction of the Žerjavinec TS 400/220/110 kV for the first time introduces into the Croatian transmission system a 400/220/(10,5) kV 400 MVA phase shifting network transformer with on load tap changer. The present article highlights some specific aspects of phase shifting transformers in the light of their application in the Croatian system. For a more efficient use of the advantages of pha

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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.

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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 fir

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Alibašić, Emir, Predrag Marić, and Srete N. Nikolovski. "Transient Phenomena during the Three-Phase 300MVA Transformer Energization on the Transmission Network." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (2016): 2499. http://dx.doi.org/10.11591/ijece.v6i6.11406.

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<p>Connecting the transformer to the network may incur inrush current, which is significantly higher than the rated current of the transformer. The main cause of this phenomenon lies in the nonlinearity of the magnetic circuit. The value of the inrush current depends of the time moment of the energization and the residual magnetism in the transformer core. While connecting, the operating point of the magnetization characteristic can be found deep in the saturation region resulting in occurrence of large transformer currents that can trigger the transformer protection. Tripping of protect

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