Phase field model for electric-thermal coupled discharge breakdown of polyimide nanocomposites under high frequency electrical stress

In contrast to conventional transformers, power electronic transformers, as an integral component of new energy power system, are often subjected to high-frequency and transient electrical stresses, leading to heightened concerns regarding insulation failures. Meanwhile, the underlying mechanism behind discharge breakdown failure and nanofiller enhancement under high-frequency electrical stress remains unclear. An electric-thermal coupled discharge breakdown phase field model was constructed to study the evolution of the breakdown path in polyimide nanocomposite insulation subjected to high-frequency stress. The investigation focused on analyzing the effect of various factors, including frequency, temperature, and nanofiller shape, on the breakdown path of Polyimide(PI) composites. Additionally, it elucidated the enhancement mechanism of nano-modified composite insulation at the mesoscopic scale. The results indicated that with increasing frequency and temperature, the discharge breakdown path demonstrates accelerated development, accompanied by a gradual dominance of Joule heat energy. This enhancement is attributed to the dispersed electric field distribution and the hindering effect of the nanosheets. The research findings offer a theoretical foundation and methodological framework to inform the optimal design and performance management of new insulating materials utilized in high-frequency power equipment.

A critical review of wheel/rail high frequency vibration-induced vibration fatigue of railway bogie in China

Purpose–This review aims to give a critical view of the wheel/rail high frequency vibration-induced vibration fatigue in railway bogie.Design/methodology/approach–Vibration fatigue of railway bogie arising from the wheel/rail high frequency vibration has become the main concern of railway operators.Previous reviews usually focused on the formation mechanism of wheel/rail high frequency vibration.This paper thus gives a critical review of the vibration fatigue of railway bogie owing to the short-pitch irregularities-induced high frequency vibration,including a brief introduction of short-pitch irregularities,associated high frequency vibration in railway bogie,typical vibration fatigue failure cases of railway bogie and methodologies used for the assessment of vibration fatigue and research gaps.Findings–The results showed that the resulting excitation frequencies of short-pitch irregularity vary substantially due to different track types and formation mechanisms.The axle box-mounted components are much more vulnerable to vibration fatigue compared with other components.The wheel polygonal wear and rail corrugation-induced high frequency vibration is the main driving force of fatigue failure,and the fatigue crack usually initiates from the defect of the weld seam.Vibration spectrum for attachments of railway bogie defined in the standard underestimates the vibration level arising from the short-pitch irregularities.The current investigations on vibration fatigue mainly focus on the methods to improve the accuracy of fatigue damage assessment,and a systematical design method for vibration fatigue remains a huge gap to improve the survival probability when the rail vehicle is subjected to vibration fatigue.Originality/value–The research can facilitate the development of a new methodology to improve the fatigue life of railway vehicles when subjected to wheel/rail high frequency vibration.

Ultrafast dynamics of femtosecond laser-induced high spatial frequency periodic structures on silicon surfaces

Femtosecond laser-induced periodic surface structures(LIPSS)have been extensively studied over the past few decades.In particular,the period and groove width of high-spatial-frequency LIPSS(HSFL)is much smaller than the diffraction limit,making it a useful method for efficient nanomanufacturing.However,compared with the low-spatial-frequency LIPSS(LSFL),the structure size of the HSFL is smaller,and it is more easily submerged.Therefore,the formation mechanism of HSFL is complex and has always been a research hotspot in this field.In this study,regular LSFL with a period of 760 nm was fabricated in advance on a silicon surface with two-beam interference using an 800 nm,50 fs femtosecond laser.The ultrafast dynamics of HSFL formation on the silicon surface of prefabricated LSFL under single femtosecond laser pulse irradiation were observed and analyzed for the first time using collinear pump-probe imaging method.In general,the evolution of the surface structure undergoes five sequential stages:the LSFL begins to split,becomes uniform HSFL,degenerates into an irregular LSFL,undergoes secondary splitting into a weakly uniform HSFL,and evolves into an irregular LSFL or is submerged.The results indicate that the local enhancement of the submerged nanocavity,or the nanoplasma,in the prefabricated LSFL ridge led to the splitting of the LSFL,and the thermodynamic effect drove the homogenization of the splitting LSFL,which evolved into HSFL.

A high frequency vibration compensation approach for ultrahigh resolution SAR imaging based on sinusoidal frequency modulation Fourier-Bessel transform

Ultrahigh resolution synthetic aperture radar(SAR)imaging for ship targets is significant in SAR imaging,but it suffers from high frequency vibration of the platform,which will induce defocus into SAR imaging results.In this paper,a novel compensation method based on the sinusoidal frequency modulation Fourier-Bessel transform(SFMFBT)is proposed,it can estimate the vibration errors,and the phase shift ambiguity can be avoided via extracting the time frequency ridge consequently.By constructing the corresponding compensation function and combined with the inverse SAR(ISAR)technique,well-focused imaging results can be obtained.The simulation imaging results of ship targets demonstrate the validity of the proposed approach.

基于边缘引导和动态可变形Transformer的遥感图像变化检测

卷积神经网络(Convolutional Neural Network,CNN)和Transformer的混合架构能够有效建模图像的局部与全局特征,已成为遥感图像变化检测任务的主流网络.然而这类网络仍面临着一些挑战. CNN分支中的卷积和池化运算通常会抑制遥感图像中的高频信息,降低目标边界的精度;此外,Transformer分支对图像像素进行等同长程依赖关系建模,忽略了变化目标的形状及语义关联信息,导致网络对变化目标特征的表达不足.为解决上述问题,提出了基于边缘引导和动态可变形Transformer的遥感图像变化检测网络.在CNN分支中设计了边缘信息引导模块,利用高频信息增强目标区域的边缘信息,从而改善变化目标的轮廓精度.同时设计了一种新颖的动态可变形Transformer,能够自适应地匹配形状不同的变化目标,选择与变化相关的特征建模长程依赖关系,以提高网络的特征表达能力.实验结果表明,提出的方法在三个公开数据集LEVIR-CD、CDD和DSIFN-CD上显著提高了检测精度,在变化目标的边界精度和内部完整性方面都明显优于当前的主流网络.

Analysis of Thermal Behavior of High Frequency Transformers Using Finite Element Method

High frequency transformer is used in many applications among the Switch Mode Power Supply (SMPS), high voltage pulse power and etc can be mentioned. Regarding that the core of these transformers is often the ferrite core;their functions partly depend on this core characteristic. One of the characteristics of the ferrite core is thermal behavior that should be paid attention to because it affects the transformer function and causes heat generation. In this paper, a typical high frequency transformer with ferrite core is designed and simulated in ANSYS software. Temperature rise due to winding current (Joule-heat) is considered as heat generation source for thermal behavior analysis of the transformer. In this simulation, the temperature rise and heat distribution are studied and the effects of parameters such as flux density, winding loss value, using a fan to cool the winding and core and thermal conductivity are investigated.

Development of a Medium Voltage, High Power, High Frequency Four-Port Solid State Transformer

The power and voltage levels of renewable energy resources is growing with the evolution of the power electronics and switching module technologies.For that,the need for the development of a compact and highly efficient solid-state transformer is becoming a critical task in-order to integrate the current AC grid with the new renewable energy systems.The objective of this paper is to present the design,implementation,and testing of a compact multi-port solid-state transformer for microgrid integration applications.The proposed system has a four-port transformer and four converters connected to the ports.The transformer has four windings integrated on a single common core.Thus,it can integrate different renewable energy resources and energy storage systems.Each port has a rated power of 25 kW,and the switching frequency is pushed to 50 k Hz.The ports are chosen to represent a realistic industrial microgrid model consisting of grid,energy storage system,photovoltaic system,and load.The grid port is designed to operate at 4.16 k VAC corresponding to 7.2 kV DC bus voltage,while the other three ports operate at 500 VDC.Moreover,the grid,energy storage and photovoltaic ports are active ports with dual active bridge topologies,while the load port is a passive port with full bridge rectifier one.The proposed design is first validated with simulation results,and then the proposed transformer is implemented and tested.Experimental results show that the designed system is suitable for 4.16 k VAC medium voltage grid integration.

基于Transformer大规模MIMO的CSI反馈网络

针对频分双工大规模多输入多输出系统中信道状态信息反馈方法反馈精度低的问题,提出了一种名为CDTransformer的基于Transformer和卷积分解(convolutional decomposition,CD)的CSI反馈网络。CDTransformer将卷积分解融入改进的Transformer结构网络中,在不增加计算复杂度的情况下提升网络性能,并通过对网络的全连接层进行二值化实现轻量级部署。针对用户端功率有限的情况,提出了一种MixedTransformer网络模型。其中编码器采用计算成本较低、结构简单的单层卷积神经网络,而解码器则采用与CDTransformer模型相同的结构。CDTransformer模型融合了Transformer结构和卷积分解,提高了CSI反馈精度并实现轻量级部署。此外,引入了MixedTransformer模型,结合了CDTransformer和卷积神经网络的优点,以在功率有限情况下提供更好的性能。结果显示,相比于CsiTransformer网络模型,CDTransformer网络模型在归一化均方误差和余弦相似度方面分别提高了37.7%和0.2%。

基于CNN-Swin Transformer Network的LPI雷达信号识别

针对在低信噪比(SNR)条件下,低截获概率雷达信号调制方式识别准确率低的问题,提出一种基于Transformer和卷积神经网络(CNN)的雷达信号识别方法。首先,引入Swin Transformer模型并在模型前端设计CNN特征提取层构建了CNN+Swin Transformer网络(CSTN),然后利用时频分析获取雷达信号的时频特征,对图像进行预处理后输入CSTN模型进行训练,由网络的底部到顶部不断提取图像更丰富的语义信息,最后通过Softmax分类器对六类不同调制方式信号进行分类识别。仿真实验表明:在SNR为-18 dB时,该方法对六类典型雷达信号的平均识别率达到了94.26%,证明了所提方法的可行性。

Enhanced Wideband Frequency Estimation via FFT: Leveraging Polynomial Interpolation and Array Indexing

Accurate frequency estimation in a wideband digital receiver using the FFT algorithm encounters challenges, such as spectral leakage resulting from the FFT’s assumption of signal periodicity. High-resolution FFTs pose computational demands, and estimating non-integer multiples of frequency resolution proves exceptionally challenging. This paper introduces two novel methods for enhanced frequency precision: polynomial interpolation and array indexing, comparing their results with super-resolution and scalloping loss. Simulation results demonstrate the effectiveness of the proposed methods in contemporary radar systems, with array indexing providing the best frequency estimation despite utilizing maximum hardware resources. The paper demonstrates a trade-off between accurate frequency estimation and hardware resources when comparing polynomial interpolation and array indexing.

基于Transformer的出铝量决策算法研究与应用

在传统电解铝工业生产中,铝电解槽的生产决策通常根据工艺技术人员多年的经验制定,其中出铝量是一个具有强耦合性的重要决策变量,其决策的好坏对电解槽的生产稳定性和产铝效率具有直接且重要的影响。本文提出了一种可深度挖掘数据频率特征和特征筛选的Transformer架构模型FDisformer,优化了传统的Transformer的encode编码层,进而挖掘数据趋势变化和更深层次的特征信息,同时引入了特征蒸馏模块,确保筛选出与出铝量决策这一任务强相关的特征。FDisformer在出铝量决策方面具有更高的性能指标,该模型的建立可以为后续的铝电解槽出铝量每日决策提供参考依据。

小波分频自注意力Transformer图像去雨网络

针对视觉Transformer对高频信息捕捉能力弱以及目前许多图像去雨方法易丢失细节的问题,提出小波分频自注意力Transformer图像去雨网络(WFDST-Net)。小波分频自注意力Transformer(WFDST)作为WFDST-Net的主要模块,其利用不可分提升小波变换获取特征图的低频分量和高频分量,分别在低频和高频中进行自注意力交互,使模块从低频中学习恢复全局结构的能力,在高频中强化捕捉雨纹等线条细节的能力,增强对不同频域特征的建模能力。WFDST-Net采用U形架构并通过不可分提升小波变换获取多尺度特征,可在捕获不同形状高频雨纹的同时保证信息的完整性。相比其他图像去雨相关的Transformer,WFDST-Net具有更低的参数量。此外,提出VOCRain250数据集用于联合图像去雨和语义分割任务,该数据集比目前广泛使用的BDD150更具优势。实验表明,所提方法增强了视觉Transformer对不同频域信息的捕获能力,并在合成和真实数据集以及VOCRain250中的表现优于目前先进的去雨方法,能有效去除复杂雨纹并保留更多细节特征。

Research on Inter-turn Short-circuit Fault Diagnosis Method Based on High Frequency Voltage Residual for PMSM

Inter-turn fault is a serious stator winding short-circuit fault of permanent magnet synchronous machine(PMSM). Once it occurs, it produces a huge short-circuit current that poses a great risk to the safe operation of PMSM. Thus, an inter-turn short-circuit fault(ITSCF) diagnosis method based on high frequency(HF) voltage residual is proposed in this paper with proper HF signal injection. First, the analytical models of PMSM after the ITSCF are deduced. Based on the model, the voltage residual at low frequency(LF) and HF can be obtained. It is revealed that the HF voltage residual has a stronger ITSCF detection capability compared to the LF voltage residual. To obtain optimal fault signature, a 3-phase symmetrical HF voltage is injected into the machine drive system, and the HF voltage residuals are extracted. The fault indicator is defined as the standard deviation of the 3-phase HF voltage residuals. The effectiveness of the proposed ITSCF diagnosis method is verified by experiments on a triple 3-phase PMSM. It is worth noting that no extra hardware equipment is required to implement the proposed method.

基于多尺度transformer的伪造人脸检测方法

考虑到目前大多数伪造人脸分类方法存在分类精度低、泛化能力差的问题,提出一种结合多尺度transformer和卷积块注意力模块的伪造人脸分类方法。多尺度transformer用来学习卷积层所提取特征图的高级语义特征,卷积块注意力模块用来增强此高级语义特征,使其更具有区分性,使用增强后的特征来进行分类。实验结果表明,该方法可以显著提高伪造人脸分类的准确性和泛化能力。

Application of short-time Fourier transform to high-rise frame structural-health monitoring based on change of inherent frequency over time

The high-rise frame structure has become more and more widespread, like its damage from the complication of the environment. The traditional method of damage detection, which is only suitable for the stationary signal, does not apply to a high-rise frame structure because its damage signal is non-stationary. Thus, this paper presents an application of the short-time Fourier transform(STFT) to damage detection of high-rise frame structures. Compared with the fast Fourier transform, STFT is found to be able to express the frequency spectrum property of the time interval using the signal within this interval. Application of STFT to analyzing a Matlab model and the shaking table test with a twelve-story frame-structure model reveals that there is a positive correlation between the slope of the frequency versus time and the damage level. If the slope is equal to or greater than zero, the structure is not damaged. If the slope is smaller than zero, the structure is damaged, and the less the slope is, the more serious the damage is. The damage results from calculation based on the Matlab model are consistent with those from the shaking table test, demonstrating that STFT can be a reliable tool for the damage detection of high-rise frame structures.

A Contribution to the Analysis of Frequency Response and Impedance Measurements to Evaluation and Diagnosis of Power Transformers

This paper has an objective to show a developed quantitative criterion,based in two mathematical variables that explicit the deviation degree of a normal situation,applying simultaneously data from terminal impedances and frequency response.Based in more than 100-measured equipment,of different applications(step-up transformer,transmission transformer,etc.,),for a period of 10 years,the work presents some examples of practical application of this methodology in Brazilian Electrical System.

基于改进Frequency-Bessel变换法的主动源Love波频散分析

近年来,Love波多道分析技术在浅地表结构探测中受到越来越广泛的应用.与Rayleigh波相比,Love波相速度不受纵波速度影响,其反演参数较少,可使反演过程更加稳定、求解的横波速度模型更加可靠.高分辨率和多模式面波频散分析是面波多道分析技术中至关重要的一环.本文利用改进Frequency-Bessel变换法对主动源Love波进行频散分析.通过改进的0阶Frequency-Bessel变换,将时间-空间域多道Love波记录变换到频率-波数域,获得其频散能量谱.通过公式推导和数值模拟,验证了该方法的有效性.实例测试表明,该方法具有较高的分辨率和多模式分辨能力,为多分量面波勘探提供了一种有效的频散分析手段.

基于Swin Transformer的服装图像分类方法研究

在服装图像分类任务中,背景会使服装的轮廓和位置难以确定。为降低背景对服装图像分类的影响,提高分类准确度,提出了一种基于Swin Transformer的服装图像分类方法。这种方法使用新颖的Swin Transformer网络作为基础网络,利用傅里叶变换获取图像的高低频信息,筛选出合适的高低频图像,有效提取高频特征,并引入弱监督选择机制,确定服装在图像中的具体位置。为了确定这一分类方法的有效性,使用部分DeepFashion服装数据集的数据进行验证。实验结果表明,文中提出的引入高频信息和弱监督机制的Swin Transformer方法在服装图像分类上效果显著,可达84.0%的Top1准确率,相比原Swin Transformer基础模型能提升4.1%的准确率。