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Journal articles on the topic 'Stacked transformer'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Hu, Xiao Ping, Bin Chen, Ye Long Zhong, and Jun Ming Xu. "Design of UHF Thin Film Transformer and Research of its S-Parameter Performance." Advanced Materials Research 482-484 (February 2012): 1542–46. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.1542.

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A novel spiral stacked thin film transformer was designed based on Si IC technology in this paper. And also, the stacked air core and ferrite core thin film transformers with different turns ratio were prepareed. Then, the S-parameter performance of these two kinds of transformers were measured. The measurement results show that the magnetic core thin film transformer has a better electronic transmission performance. It obtains maximum transmission efficiency 93.7% at the frequency range from 10MHz to 20GHz, and the air core transformer obtains maximum 89% at the same frequency range. Many tes

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2

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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3

Gomha, S., and K. Langat. "Performance Investigation of Different Topologies of 1-100 GHz on-chip Transformers using 130 nm SiGe BiCMOS." Engineering, Technology & Applied Science Research 9, no. 6 (2019): 5006–10. https://doi.org/10.5281/zenodo.3566549.

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In this study, modeling and designing different topologies of on-chip transformers are presented using 130nm SiGe BiCMOS technology. Interleaved, stacked, and full symmetrical interleaved transformers are investigated. Octagon and square shapes are used for designing transformers with flipped and non-flipped feed lines. A comparison between performances of various configurations is presented using a full-wave simulator. The octagon stacked transformer with flipped feed lines showed a good performance at around 60GHz. The simulated results demonstrated a coupling factor K of 0.94, minimum inser

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Zhu, Xinxin, Lixiang Li, Jing Liu, Haipeng Peng, and Xinxin Niu. "Captioning Transformer with Stacked Attention Modules." Applied Sciences 8, no. 5 (2018): 739. http://dx.doi.org/10.3390/app8050739.

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5

McRory, J. G., G. G. Rabjohn, and R. H. Johnston. "Transformer coupled stacked FET power amplifiers." IEEE Journal of Solid-State Circuits 34, no. 2 (1999): 157–61. http://dx.doi.org/10.1109/4.743760.

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6

Lin, K. C., E. E. Zook, and J. W. Crockett. "Material usage in stacked transformer cores." Journal of Materials Engineering 12, no. 1 (1990): 51–57. http://dx.doi.org/10.1007/bf02834489.

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7

Gomha, S., and K. Langat. "Performance Investigation of Different Topologies of 1-100 GHz on-chip Transformers using 130 nm SiGe BiCMOS." Engineering, Technology & Applied Science Research 9, no. 6 (2019): 5006–10. http://dx.doi.org/10.48084/etasr.3205.

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In this study, modeling and designing different topologies of on-chip transformers are presented using 130nm SiGe BiCMOS technology. Interleaved, stacked, and full symmetrical interleaved transformers are investigated. Octagon and square shapes are used for designing transformers with flipped and non-flipped feed lines. A comparison between performances of various configurations is presented using a full-wave simulator. The octagon stacked transformer with flipped feed lines showed a good performance at around 60GHz. The simulated results demonstrated a coupling factor K of 0.94, minimum inser

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8

Namoune, Abdelhadi, Azzedine Hamid, and Rachid Taleb. "Stacked Transformer: Influence of the Geometrical and Technological Parameters." International Journal of Engineering Research in Africa 21 (December 2015): 148–64. http://dx.doi.org/10.4028/www.scientific.net/jera.21.148.

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In this work, we study the effect of the geometrical and technological parameters on the performance of stacked transformer in crystal metal oxide silicon (CMOS). It also presents the equivalent electrical model of on-chip stacked transformer based on the “2-p” architecture and contains the equations to evaluate its components values. These equations depend on both technological and geometric characteristics of the transformer. The inductance (primary or secondary) and the quality factor (primary or secondary) of on chip stacked transformer depend on the geometrical and technological parameter

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9

Passaglia, R., and S. Zannella. "Stacked transformer cores using amorphous POWERCORE strip." IEEE Transactions on Magnetics 27, no. 6 (1991): 5265–67. http://dx.doi.org/10.1109/20.278808.

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10

Han, Jae-Hyun, Chang-Pyo Yoon, and Chi-Gon Hwang. "Proposal of Stacked SARIMAX-Transformer for Improving Forecasting Accuracy of Time Series Data." Journal of the Korea Institute of Information and Communication Engineering 29, no. 3 (2025): 303–8. https://doi.org/10.6109/jkiice.2025.29.3.303.

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11

Li, Chenming, Runzhou Wang, Zhonghao Chen, Hongmin Gao, and Shufang Xu. "Transformer-inspired stacked-GAN for hyperspectral target detection." International Journal of Remote Sensing 45, no. 15 (2024): 4961–82. http://dx.doi.org/10.1080/01431161.2024.2370500.

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12

Stojanović, Goran, Milan Radovanović, and Vasa Radonić. "A New Fractal-Based Design of Stacked Integrated Transformers." Active and Passive Electronic Components 2008 (2008): 1–8. http://dx.doi.org/10.1155/2008/134805.

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Silicon-based radio-frequency integrated circuits are becoming more and more competitive in wide-band frequency range. An essential component of these ICs is on-chip (integrated) transformer. It is widely used in mobile communications, microwave integrated circuits, low-noise amplifiers, active mixers, and baluns. This paper deals with the design, simulation, and analysis of novel fractal configurations of the primary and secondary coils of the integrated transformers. Integrated stacked transformers, which use fractal curves (Hilbert, Peano, and von Koch) to form the primary and secondary win

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13

Derkaoui, Mokhtaria, and Yamina Benhadda. "Flyback Micro-Converter Design with an Integrated Octagonal Micro-Transformer for DC-DC Conversion." International Journal of Electrical and Electronics Research 11, no. 4 (2023): 886–97. http://dx.doi.org/10.37391/ijeer.110402.

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The work presented in this paper concerns the design of an integrated flyback DC-DC micro-converter operating at high frequencies. The flyback converter consists of only one transformer. The integrated micro-transformer in the flyback micro-converter is composed of two planar stacked coils with spiral octagonal geometry. Basing on Mohan’s method, the geometrical parameters are evaluated. The different parasitic effects created in the stacked layers are grouped perfectly in the equivalent electrical circuit that summarizes all parasitic effects. The integrated micro-transformer is characterized

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14

Murade, Prof G. B. "IOT Based Transformer Monitoring and Controlling." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6940–42. http://dx.doi.org/10.22214/ijraset.2023.53347.

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Abstract: Transformers are critical components of electric powersystems, yet precise fault identificationremains difficult. The study presents a novel transformer defect diagnostic approach based on an Internet of Things (IoT) monitoring sysem and ensemble machine learning (EML). The IoTbased monitoring system is divided into two parts: data measuring subsystemand a data reception subsystem. To begin, the data measuring subsystem measures transformer vibration signals, which are then relayed to the remote server via the data receipt subsystem. Then an EML is proposed that is made up of deep be

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15

Kang, Sungyoon, Minchul Kim, and Junghyun Kim. "U-shaped stacked structure monolithic transformer for efficiency improvement." Microwave and Optical Technology Letters 60, no. 9 (2018): 2325–30. http://dx.doi.org/10.1002/mop.31346.

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Peng, Yuxuan, and Qiang Fu. "Transformer DGA Fault Diagnosis Method Based on Stacked Sparse Auto-Encoders." Journal of Physics: Conference Series 2479, no. 1 (2023): 012044. http://dx.doi.org/10.1088/1742-6596/2479/1/012044.

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Abstract In the field of transformer fault diagnosis, the imbalance of fault samples seriously affects the fault identification performance of the diagnosis model. Focusing on the problems of low accuracy and high leakage rate of diagnosis model caused by unbalanced fault samples of transformer, a transformer fault diagnosis method is proposed. First of all, the Borderline SMOTE algorithm is used to balance the fault data set from a few samples on the boundary, so as to achieve the effect of power transformer fault sample equalization. Secondly, a diagnosis model of the stacked sparse auto-Enc

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17

Huang, Lingbo, Yushi Chen, and Xin He. "Spectral-Spatial Mamba for Hyperspectral Image Classification." Remote Sensing 16, no. 13 (2024): 2449. http://dx.doi.org/10.3390/rs16132449.

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Recently, transformer has gradually attracted interest for its excellence in modeling the long-range dependencies of spatial-spectral features in HSI. However, transformer has the problem of the quadratic computational complexity due to the self-attention mechanism, which is heavier than other models and thus has limited adoption in HSI processing. Fortunately, the recently emerging state space model-based Mamba shows great computational efficiency while achieving the modeling power of transformers. Therefore, in this paper, we first proposed spectral-spatial Mamba (SS-Mamba) for HSI classific

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18

Key, Sopheap, Chang-Sung Ko, Kwang-Jae Song, and Soon-Ryul Nam. "Fast Detection of Current Transformer Saturation Using Stacked Denoising Autoencoders." Energies 16, no. 3 (2023): 1528. http://dx.doi.org/10.3390/en16031528.

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Malfunctions in relay protection devices are predominantly caused by current transformer (CT) saturation which produces distortion in current measurements and disturbances in power system protection. The development of deep learning in power system protection is on the rise recently because of its robustness. This study presents a CT saturation detection where the secondary current becomes distorted. The proposed scheme offers a wide range of saturation detection and consists of a moving-window technique and stacked denoising autoencoders. Moreover, Bayesian optimization was used to minimize t

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19

Liu, Chenlu, Di Jiang, Weiyang Lin, and Luis Gomes. "Robot Grasping Based on Stacked Object Classification Network and Grasping Order Planning." Electronics 11, no. 5 (2022): 706. http://dx.doi.org/10.3390/electronics11050706.

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In this paper, the robot grasping for stacked objects is studied based on object detection and grasping order planning. Firstly, a novel stacked object classification network (SOCN) is proposed to realize stacked object recognition. The network takes into account the visible volume of the objects to further adjust its inverse density parameters, which makes the training process faster and smoother. At the same time, SOCN adopts the transformer architecture and has a self-attention mechanism for feature learning. Subsequently, a grasping order planning method is investigated, which depends on t

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20

M, Geetha, Ramkumar R, Sarankumar C, Velmurugan K, and Boopathi N. "Online Network Protection Firmware for Malware Identification Utilizing Transformer Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 110–13. http://dx.doi.org/10.22214/ijraset.2024.58280.

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Abstract: Malware ID expects a critical part in network security with the expansion in malware improvement. What more, kinds of progress in cutting edge assaults. Noxious programming applications, or malware, are the principal wellspring of different security issues. For different reasons, including the taking of state of the art developments and insightful properties, regulative exhibitions of retaliation, and the modification of sensitive information, to give some examples, these pernicious applications plan to perform unapproved exercises on the host machines to assist their makers. More va

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Yunas, Jumril, Azrul Azlan Hamzah, and Burhanuddin Yeop Majlis. "Fabrication and characterization of surface micromachined stacked transformer on glass substrate." Microelectronic Engineering 86, no. 10 (2009): 2020–25. http://dx.doi.org/10.1016/j.mee.2008.12.091.

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22

Wu, Danruo, Bicheng Zhou, and Mikhail Zimin. "Prediction of landslide displacement based on the CA-stacked transformer model." Alexandria Engineering Journal 124 (June 2025): 389–403. https://doi.org/10.1016/j.aej.2025.03.140.

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23

Yuan, Wei, Lei Qiao, and Liu Tang. "Forest Wildfire Detection from Images Captured by Drones Using Window Transformer without Shift." Forests 15, no. 8 (2024): 1337. http://dx.doi.org/10.3390/f15081337.

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Cameras, especially those carried by drones, are the main tools used to detect wildfires in forests because cameras have much longer detection ranges than smoke sensors. Currently, deep learning is main method used for fire detection in images, and Transformer is the best algorithm. Swin Transformer restricts the computation to a fixed-size window, which reduces the amount of computation to a certain extent, but to allow pixel communication between windows, it adopts a shift window approach. Therefore, Swin Transformer requires multiple shifts to extend the receptive field to the entire image.

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24

Chee, Sze Keat, Hirofumi Suzuki, Mutsumi Okada, Takeshi Yano, Toshiro Higuchi, and Wei Min Lin. "Precision Polishing of Micro Mold by Using Piezoelectric Actuator Incorporated with Mechanical Amplitude Magnified Mechanism." Advanced Materials Research 325 (August 2011): 470–75. http://dx.doi.org/10.4028/www.scientific.net/amr.325.470.

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Demands of precision molds with complicated microstructures for digital devices such as DVD pick-up system, medical devices such as μ-TAS and solar optics etc. are increasing [1 - 5]. The structured molds must be polished after grinding or cutting in order to improve the surface roughness. In this paper, a two dimensional low frequency vibration (2DLFV) polishing actuator using PZT is proposed and developed. The 2DLFV consists of 4 mechanical amplitude magnified actuators (MechaTrans), a multilayer stacked piezoelectric actuator (PZT) incorporated with mechanical transformer, and a center piec

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25

Löffler, F., H. Pfützner, T. Booth, C. Bengtsson, and K. Gramm. "Influence of overlap length in stacked transformer cores consisting of several packages." Journal of Magnetism and Magnetic Materials 133, no. 1-3 (1994): 561–63. http://dx.doi.org/10.1016/0304-8853(94)90622-x.

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26

Loffler, F., H. Pfutzner, T. Booth, C. Bengtsson, and K. Gramm. "Influence of air gaps in stacked transformer cores consisting of several packages." IEEE Transactions on Magnetics 30, no. 2 (1994): 913–15. http://dx.doi.org/10.1109/20.312443.

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27

Mechler, G. F., and R. S. Girgis. "Calculation of spatial loss distribution in stacked power and distribution transformer cores." IEEE Transactions on Power Delivery 13, no. 2 (1998): 532–37. http://dx.doi.org/10.1109/61.660925.

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28

Kim, Jina, Hyeongwon Kang, and Pilsung Kang. "Time-series anomaly detection with stacked Transformer representations and 1D convolutional network." Engineering Applications of Artificial Intelligence 120 (April 2023): 105964. http://dx.doi.org/10.1016/j.engappai.2023.105964.

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29

Zhang, Lei, Zhongyang Xu, Chen Lu, Tianjiao Qiao, Hongzhi Su, and Yazhou Luo. "Transformer fault diagnosis based on adversarial generative networks and deep stacked autoencoder." Heliyon 10, no. 9 (2024): e30670. http://dx.doi.org/10.1016/j.heliyon.2024.e30670.

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30

Zentner, Radovan, Juraj Bartolic, and Ervin Zentner. "Broadband matching of stacked patch antennas using a single line-transformer technique." Microwave and Optical Technology Letters 39, no. 3 (2003): 178–83. http://dx.doi.org/10.1002/mop.11162.

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31

Guangliang, Pan, Li Jie, and Li Minglei. "Multi-channel multi-step spectrum prediction using transformer and stacked Bi-LSTM." China Communications 22, no. 5 (2025): 1–13. https://doi.org/10.23919/jcc.ja.2022-0667.

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32

Krishna, Ram, Agbotiname Lucky Imoize, Rajveer Singh Yaduvanshi, Harendra Singh, Arun Kumar Rana, and Subhendu Kumar Pani. "Analysis of Multi-Stacked Dielectric Resonator Antenna with Its Equivalent R-L-C Circuit Modeling for Wireless Communication Systems." Mathematical and Computational Applications 28, no. 1 (2022): 4. http://dx.doi.org/10.3390/mca28010004.

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The dielectric resonator antenna (DRA) can be modeled as a series and parallel combination of electrical networks consisting of a resistor (R), inductor (L), and capacitor (C) to address peculiar challenges in antennas suitable for application in emerging wireless communication systems for higher frequency range. In this paper, a multi-stacked DRA has been proposed. The performance and characteristic features of the DRA have been analyzed by deriving the mathematical formulations for dynamic impedance, input impedance, admittance, bandwidth, and quality factor for fundamental and high-order re

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Qaadan, Sahar, Aiman Alshare, Abdullah Ahmed, and Haneen Altartouri. "Stacked Ensembles Powering Smart Farming for Imbalanced Sugarcane Disease Detection." Applied Sciences 15, no. 5 (2025): 2788. https://doi.org/10.3390/app15052788.

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Sugarcane is a vital crop, accounting for approximately 75% of the global sugar production. Ensuring its health through the early detection and classification of diseases is essential in maximizing crop yields and productivity. While recent deep learning advancements, such as Vision Transformers, have shown promise in sugarcane disease classification, these methods often rely on resource-intensive models, limiting their practical applicability. This study introduces a novel stacking-based ensemble framework that combines embeddings from multiple state-of-the-art deep learning methods. It offer

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Kefalas, Themistoklis D., George Loizos, and Antonios G. Kladas. "Normal Flux Distribution at Step-Lap Joints of Si-Fe Wound Cores." Materials Science Forum 670 (December 2010): 284–90. http://dx.doi.org/10.4028/www.scientific.net/msf.670.284.

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Even though, the flux distribution at joints of stacked type transformer cores has been investigated thoroughly many issues remain unclear in the case of wound transformer cores. The paper addresses this lack of information by longitudinal and normal flux measurements at step-lap joints of Si-Fe wound cores. Flux measurements are verified by an original finite element analysis where the necessary excitation is performed by means of a pseudo-source. The advantage of the proposed technique is the accurate estimation of the flux distribution at step-lap joints, with a two dimensional model of sim

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Gabralla, Lubna Abdelkareim, Ali Mohamed Hussien, Abdulaziz AlMohimeed, et al. "Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence." Diagnostics 13, no. 18 (2023): 2939. http://dx.doi.org/10.3390/diagnostics13182939.

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Colon cancer is the third most common cancer type worldwide in 2020, almost two million cases were diagnosed. As a result, providing new, highly accurate techniques in detecting colon cancer leads to early and successful treatment of this disease. This paper aims to propose a heterogenic stacking deep learning model to predict colon cancer. Stacking deep learning is integrated with pretrained convolutional neural network (CNN) models with a metalearner to enhance colon cancer prediction performance. The proposed model is compared with VGG16, InceptionV3, Resnet50, and DenseNet121 using differe

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Kim, Insung, Hyeonkyu Joo, Soonjong Jeong, Minsoo Kim, Jaesung Song, and Vo Viet Thang. "Output Power of a Ring-type Stacked Piezoelectric Transformer with Pure Ag Electrodes." Journal of the Korean Physical Society 58, no. 3(1) (2011): 627–31. http://dx.doi.org/10.3938/jkps.58.627.

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37

Ardebili, M., and A. J. Moses. "Investigation of surface scratched silicon steels in three-phase stacked model transformer cores." Journal of Magnetism and Magnetic Materials 112, no. 1-3 (1992): 409–12. http://dx.doi.org/10.1016/0304-8853(92)91215-f.

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38

Dharmik, Bhushan Y., and Nitin Kumar Lautre. "Influence of heat on the performance of stack welded thin sheets of CRNGO electrical steel." Metallurgical Research & Technology 119, no. 3 (2022): 310. http://dx.doi.org/10.1051/metal/2022022.

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An attempt is made to investigate mechanical-microstructural-magnetic properties on the welded Cold Rolled Non-Oriented Electrical steel (CRNGO) sheets. Single 0.5 mm thin sheets susceptible to Tungsten Inert gas (TIG) welding current are stacked and edge welded using a range of welding current from 30 A to 110 A. The influence of weld with varying current is analyzed through various testing for joint performance, micro-hardness, Residual stress, weld seam characterization, Grain size variation and Magnetic property evaluation of post-welded samples. The results showed the variations of micro-

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Han, Song, Xiaoping Liu, and Gang Wang. "Visual Sorting Method Based on Multi-Modal Information Fusion." Applied Sciences 12, no. 6 (2022): 2946. http://dx.doi.org/10.3390/app12062946.

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Visual sorting of stacked parcels is a key issue in intelligent logistics sorting systems. In order to improve the sorting success rate of express parcels and effectively obtain the sorting order of express parcels, a visual sorting method based on multi-modal information fusion (VS-MF) is proposed in this paper. Firstly, an object detection network based on multi-modal information fusion (OD-MF) is proposed. The global gradient feature is extracted from depth information as a self-attention module. More spatial features are learned by the network, and the detection accuracy is improved signif

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Wang, Maofa, Bingcheng Yan, Yibo Zhang, et al. "Optimizing Precipitation Forecasting and Agricultural Water Resource Allocation Using the Gaussian-Stacked-LSTM Model." Atmosphere 15, no. 11 (2024): 1308. http://dx.doi.org/10.3390/atmos15111308.

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Our study investigates the use of machine learning models for daily precipitation prediction using data from 56 meteorological stations in Jilin Province, China. We evaluate Stacked Long Short-Term Memory (LSTM), Transformer, and Support Vector Regression (SVR) models, with Stacked-LSTM showing the best performance in terms of accuracy and stability, as measured by the Root Mean Square Error (RMSE). To improve robustness, Gaussian noise was introduced, particularly enhancing predictions for zero-precipitation days. Key predictors identified through variable attribution analysis include tempera

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Feng, Sheng, Xiaoqian Zhu, Shuqing Ma, and Qiang Lan. "GIT: A Transformer-Based Deep Learning Model for Geoacoustic Inversion." Journal of Marine Science and Engineering 11, no. 6 (2023): 1108. http://dx.doi.org/10.3390/jmse11061108.

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Geoacoustic inversion is a challenging task in marine research due to the complex environment and acoustic propagation mechanisms. With the rapid development of deep learning, various designs of neural networks have been proposed to solve this issue with satisfactory results. As a data-driven method, deep learning networks aim to approximate the inverse function of acoustic propagation by extracting knowledge from multiple replicas, outperforming conventional inversion methods. However, existing deep learning networks, mainly incorporating stacked convolution and fully connected neural network

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Tsui, Lok-kun, Yongkun Sui, Jamin R. Pillars, Thomas Michael Hartmann, Joshua Dye, and Judi Lavin. "Multi-Material Additive Manufacturing of Coreless Transformers By Aerosol Jet Printing and Electrochemical Deposition." ECS Meeting Abstracts MA2022-01, no. 57 (2022): 2363. http://dx.doi.org/10.1149/ma2022-01572363mtgabs.

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Coreless transformers are an attractive technology for power electronics due to their light weight and the absence of a brittle magnetic core material.[1] A coreless transformer consists of a primary spiral inductor with few turns and a secondary spiral inductor with many turns stacked together. Aerosol jet printing (AJP) is an additive manufacturing technology which can be used to print metals and polymers with linewidths as narrow as 10 µm. In this work we have used a combined AJP, electroless deposition, and electrodeposition approach to fabricate multi-layer coreless flyback transformers.

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Li, Hui, Xin Zhao, Lin Yu, Yixin Zhao, and Jie Zhang. "DEEDP: Document-Level Event Extraction Model Incorporating Dependency Paths." Applied Sciences 13, no. 5 (2023): 2846. http://dx.doi.org/10.3390/app13052846.

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Document-level event extraction (DEE) aims at extracting event records from given documents. Existing DEE methods handle troublesome challenges by using multiple encoders and casting the task into a multi-step paradigm. However, most of the previous approaches ignore a missing feature by using mean pooling or max pooling operations in different encoding stages and have not explicitly modeled the interdependency features between input tokens, and thus the long-distance problem cannot be solved effectively. In this study, we propose Document-level Event Extraction Model Incorporating Dependency

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Huang, Zhiwen, Jiajie Shao, Panyu Zhou, Baolin Liu, Jianmin Zhu, and Dianjun Fang. "Continuous blood pressure monitoring based on transformer encoders and stacked attention gated recurrent units." Biomedical Signal Processing and Control 99 (January 2025): 106860. http://dx.doi.org/10.1016/j.bspc.2024.106860.

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45

Bautista, John Lorenzo, Yun Kyung Lee, and Hyun Soon Shin. "Speech Emotion Recognition Based on Parallel CNN-Attention Networks with Multi-Fold Data Augmentation." Electronics 11, no. 23 (2022): 3935. http://dx.doi.org/10.3390/electronics11233935.

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In this paper, an automatic speech emotion recognition (SER) task of classifying eight different emotions was experimented using parallel based networks trained using the Ryeson Audio-Visual Dataset of Speech and Song (RAVDESS) dataset. A combination of a CNN-based network and attention-based networks, running in parallel, was used to model both spatial features and temporal feature representations. Multiple Augmentation techniques using Additive White Gaussian Noise (AWGN), SpecAugment, Room Impulse Response (RIR), and Tanh Distortion techniques were used to augment the training data to furth

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Chu, Xutao, Shengjie Zhao, and Hongwei Dai. "AIFormer: Adaptive Interaction Transformer for 3D Point Cloud Understanding." Remote Sensing 16, no. 21 (2024): 4103. http://dx.doi.org/10.3390/rs16214103.

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Recently, significant advancements have been made in 3D point cloud analysis by leveraging transformer architecture in 3D space. However, it remains challenging to effectively implement local and global learning within irregular and sparse structures of 3D point clouds. This paper presents the Adaptive Interaction Transformer (AIFormer), a novel hierarchical transformer architecture designed to enhance 3D point cloud analysis by fusing local and global features through the adaptive interaction of features. Specifically, AIFormer mainly consists of several stacked AIFormer Blocks. Each AIFormer

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Liu, Yi, and Lanjian Wu. "Intrusion Detection Model Based on Improved Transformer." Applied Sciences 13, no. 10 (2023): 6251. http://dx.doi.org/10.3390/app13106251.

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This paper proposes an enhanced Transformer-based intrusion detection model to tackle the challenges of lengthy training time, inaccurate detection of overlapping classes, and poor performance in multi-class classification of current intrusion detection models. Specifically, the proposed model includes the following: (i) A data processing strategy that initially reduces the data dimension using a stacked auto-encoder to speed up training. In addition, a novel under-sampling method based on the KNN principle is introduced, along with the Borderline-SMOTE over-sampling method, for hybrid data sa

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Park, Karam, Jae Woong Soh, and Nam Ik Cho. "Efficient Attention-Sharing Information Distillation Transformer for Lightweight Single Image Super-Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 6416–24. https://doi.org/10.1609/aaai.v39i6.32687.

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Transformer-based Super-Resolution (SR) methods have demonstrated superior performance compared to convolutional neural network (CNN)-based SR approaches due to their capability to capture long-range dependencies. However, their high computational complexity necessitates the development of lightweight approaches for practical use. To address this challenge, we propose the Attention-Sharing Information Distillation (ASID) network, a lightweight SR network that integrates attention-sharing and an information distillation structure specifically designed for Transformer-based SR methods. We modify

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Yuan, Zhi Jian, Hao Deng, and Wen Jun Liu. "Fault Diagnosis of Power Transformers Based on Membrane Computing Optimizing Neural Network." Applied Mechanics and Materials 214 (November 2012): 740–44. http://dx.doi.org/10.4028/www.scientific.net/amm.214.740.

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Presently, dissolved gas content analysis and fault diagnosis are the important segments of power transformer. As to the problem of the back propagation algorithm of neural network commonly used lies in the optimization procedure getting easily stacked into the minimal value locally and strict requirement on the initial value, a fault diagnostic method is presented, based on the membrane computing optimizing back propagation neural network. Throughout the process, compromise is satisfactorily reached among the network complexity, the convergence and the generalization ability. The results of d

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Zhang, Haotian, Yundong Sun, Yansong Wang, et al. "GTPLM-GO: Enhancing Protein Function Prediction Through Dual-Branch Graph Transformer and Protein Language Model Fusing Sequence and Local–Global PPI Information." International Journal of Molecular Sciences 26, no. 9 (2025): 4088. https://doi.org/10.3390/ijms26094088.

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Currently, protein–protein interaction (PPI) networks have become an essential data source for protein function prediction. However, methods utilizing graph neural networks (GNNs) face significant challenges in modeling PPI networks. A primary issue is over-smoothing, which occurs when multiple GNN layers are stacked to capture global information. This architectural limitation inherently impairs the integration of local and global information within PPI networks, thereby limiting the accuracy of protein function prediction. To effectively utilize information within PPI networks, we propose GTP

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