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.

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.

Multi-layer quasi-zero-stiffness meta-structure for high-efficiency vibration isolation at low frequency

An easily stackable multi-layer quasi-zero-stiffness(ML-QZS)meta-structure is proposed to achieve highly efficient vibration isolation performance at low frequency.First,the distributed shape optimization method is used to design the unit cel,i.e.,the single-layer QZS(SL-QZS)meta-structure.Second,the stiffness feature of the unit cell is investigated and verified through static experiments.Third,the unit cells are stacked one by one along the direction of vibration isolation,and thus the ML-QZS meta-structure is constructed.Fourth,the dynamic modeling of the ML-QZS vibration isolation metastructure is conducted,and the dynamic responses are obtained from the equations of motion,and verified by finite element(FE)simulations.Finally,a prototype of the ML-QZS vibration isolation meta-structure is fabricated by additive manufacturing,and the vibration isolation performance is evaluated experimentally.The results show that the vibration isolation performance substantially enhances when the number of unit cells increases.More importantly,the ML-QZS meta-structure can be easily extended in the direction of vibration isolation when the unit cells are properly stacked.Hence,the ML-FQZS vibration isolation meta-structure should be a fascinating solution for highly efficient vibration isolation performance at low frequency.

Switching Frequency Improvement of a High Speed on/off Valve Based on Pre-excitation Control Algorithm

The high-speed on/off valve(HSV)serves as the fundamental component responsible for generating discrete fluids within digital hydraulic systems.As the switching frequency of the HSV increases,the properties of the generated discrete fluid approach those of continuous fluids.Therefore,a higher frequency response characteristic of HSV is the key to ensure the control accuracy of digital hydraulic systems.However,the current research mainly focuses on its dynamic performance,but neglect its FRC.This paper presents a theoretical analysis demonstrating that the FRC of the HSV can be enhanced by minimizing its switching time.The maximum switching frequency(MSF)is mainly determined by opening dynamic performance when HSV operates with low switching duty ratio(SDR),whereas the closing dynamic performance limits the MSF when HSV operates with high SDR.Building upon these findings,the pre-excitation control algorithm(PECA)is proposed to reduce the switching time of the HSV,and consequently enhance its FRC.Experimental results demonstrate that PECA shortens the opening delay time of HSV by 1.12 ms,the closing delay time by 2.54 ms,and the closing moving time by 0.47 ms in comparison to the existing advanced control algorithms.As a result,a larger MSF of 417 Hz and a wider controllable SDR range from 20%to 70%were achieved at a switching frequency of 250 Hz.Thus,the proposed PFCA in this paper has been verified as an effective and promising approach for enhancing the control performance of digital hydraulic systems.

Mid-infrared Optical Frequency Comb-based Fourier Transform Spectrometer for Broadband Molecular Spectroscopy

Optical frequency combbased Fourier transform spectroscopy has the features of broad spectral bandwidth,high sensitivity,andmultiplexed trace gas detection,which has valuable application potential in the fields of precision spectroscopy and trace gas detection.Here,we report the development of a mid-infrared Fourier transform spectrometer based on an optical frequency comb combined with a Herriott-type multipass cell.Using this instrument,the broadband absorption spectra of several important molecules,including methane,acetylene,water molecules and nitrous oxide,are measured by near real-time data acquisition in the 2800-3500 cm^(-1)spectral region.The achieved minimum detectable absorption of the instrument is 4.4×10^(-8)cm^(-1)·Hz^(-1/2)per spectral element.Broadband spectra of H_(2)0 are fited using the Voigt profile multispectral fitting technique and the consistency of the concentration inversion is 1%.Our system also enables precise spectroscopic measurements,and it allows the determination of the spectral line positions and upper state constants of N_(2)O in the(0002)-(1000)band,with results in good agreement with those reported by Toth[Appl.Opt.30,5289(1991)].

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.

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.

High Frequency Transformers for DC/DC Converter Used in Solar PV System

This paper presents and investigates planar and coaxial high frequency power transformers used for DC/DC converters in a three phase photo voltaic （PV） power systems. The winding structure including a Faraday shield between the primary and secondary windings is designed to minimize eddy current losses, skin and proximity effects, and to reduce the leakage inductance, and the inter winding coupling capacitance. Finite Element Method is employed to analyze the magnetic flux and eddy current distributions. The two different kinds of prototype high frequency transformers are designed and tested. The simulation and experiment results are demonstrated and compared with non-shielded transformers. The shielded transformers have achieved the expected results with a relatively small coupling capacitance, compared with the conventional high frequency transformer. This shield decreases the inter-winding coupling capacitance Cps. The topology of this shield has to be such that it acts as a Faraday screen while avoiding eddy current generation.

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。

基于DFT的频率敏感双分支Transformer多变量长时间序列预测方法

在进行多变量长时间序列预测时,仅利用时域分析通常无法充分捕捉长时间序列依赖,而这会导致信息利用率不足、预测精度不够高。因此,结合频域时域分析,提出一种基于离散傅里叶变换(DFT)的频率敏感双分支多变量长时间序列预测(FSDformer)方法。首先,通过DFT实现时间和频率的相互转换,从而将复杂的时间序列数据分解为结构简单的低频趋势项、中频季节项和高频余项3个分量;其次,采用双分支结构,针对中高频分量预测,应用Encoder-Decoder结构,设计了周期性增强注意力机制;针对低频趋势分量预测,采用多层感知机(MLP)结构;最后将中高频分量与低频分量预测结果相加,得到多变量长时间序列的最终预测结果。在2个数据集上把FSDformer与其他5个经典算法进行了对比分析,在Electricity数据集上,当历史序列长度为96,预测序列长度为336时,相较于Autoformer等对比算法,FSDformer的平均绝对误差(MAE)下降了11.5%~29.1%,均方误差(MSE)下降了20.9%~43.7%,达到了最优预测精度。实验结果表明,FSDformer能有效捕捉长时间序列的相关依赖,在提升预测精度和计算效率的同时,增强了模型预测的稳定性。

Exploration of instantaneous frequency for local control assessment in real-time hybrid simulation

Local control parameters such as instantaneous delay and instantaneous amplitude play an essential role in evaluating the performance and maintaining the stability of real-time hybrid simulation(RTHS).However,existing methods have limitations in obtaining this local assessment in either the time domain or frequency domain.In this study,the instantaneous frequency is introduced to determine local control parameters for actuator tracking assessment in a real-time hybrid simulation.Instantaneous properties,including amplitude,delay,frequency and phase,are then calculated based on analytic signals translated from actuator tracking signals through the Hilbert transform.Potential issues are discussed and solutions are proposed for calculation of local control parameters.Numerical simulations are first conducted for sinusoidal and chirp signals with time varying amplitude error and delay to demonstrate the potential of the proposed method.Laboratory tests also are conducted for a predefined random signal as well as the RTHS of a single degree of freedom structure with a self-centering viscous damper to experimentally verify the effectiveness of the proposed use of the instantaneous frequency.Results from the ensuing analysis clearly demonstrate that the instantaneous frequency provides great potential for local control assessment,and the proposed method enables local tracking parameters with good accuracy.

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。

Research on blind source separation of operation sounds of metro power transformer through an Adaptive Threshold REPET algorithm

Purpose–The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system.Through voiceprint technology,the sounds emitted by the transformer can be monitored in real-time,thereby achieving real-time monitoring of the transformer’s operational status.However,the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer,severely impacting the accuracy and reliability of voiceprint identification.Therefore,effective preprocessing steps are required to identify and separate the sound signals of transformer operation,which is a prerequisite for subsequent analysis.Design/methodology/approach–This paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique(REPET)algorithm to separate and denoise the transformer operation sound signals.By analyzing the Short-Time Fourier Transform(STFT)amplitude spectrum,the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold,effectively distinguishing and extracting stable background signals from transient foreground events.The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period,then constructs a repeating segment model.Through comparison with the amplitude spectrum of the original signal,repeating patterns are extracted and a soft time-frequency mask is generated.Findings–After adaptive thresholding processing,the target signal is separated.Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects.Originality/value–A REPET method with adaptive threshold is proposed,which adopts the dynamic threshold adjustment mechanism,adaptively calculates the threshold for blind source separation and improves the adaptability and robustness of the algorithm to the statistical characteristics of the signal.It also lays the foundation for transformer fault detection based on acoustic fingerprinting.

一种融合CNN与Transformer的高鲁棒性目标跟踪算法

针对因目标物体形变、尺度变化、快速运动和遮挡等导致目标跟踪算法性能下降的问题,基于孪生网络架构提出了一种融合CNN与Transformer的高鲁棒性目标跟踪算法。在特征提取阶段,使用标准卷积提取浅层局部特征信息,在深层网络中设计了一种类卷积Transformer模块建模全局信息,并采用滑窗方式计算Transformer中的像素值,大大降低了计算量。在特征聚合阶段,采用多头交叉注意力模块构建特征增强与聚合网络,滤除干扰信息,突出与模板相关的信息以提高特征的判别性。与目前的主流算法相比,所提算法在OTB2015数据集上的形变、尺度变化、快速运动和遮挡4种不同挑战下的评估指标均为最优。在GOT-10K数据集上的平均重叠度为70.8%,相比TransT和SiamR-CNN算法分别提高3.7%和5.9%。在LaSOT、UAV123数据集上成功率分别为67.7%、71.9%,相比TransT和SiamR-CNN算法分别提高2.8%、2.8%和2.9%、7%。在VOT2018和VOT2019数据集上的鲁棒性评估结果,所提算法跟踪失败次数最少,鲁棒性指标得分分别为0.112和0.266,相比Ocean算法分别提高0.5%和5%,进一步验证了所提算法具有更高鲁棒性。

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。