基于高效Transformer的图像去雾研究

随着Transformer技术发展使其在计算机视觉(CV)领域中大放异彩,其构建全局依赖关系能力是卷积神经网络(CNN)所不具备的,而Transformer对图像尺寸变化敏感与处理图像位置信息方面却不如CNN,因此,近几年基于Transformer与CNN的网络结构在CV领域是研究热点。图像去雾技术可用于车载避让系统、遥感系统以及物联网设备被设为公共领域的热点问题,因此,本文提出基于Transformer的网络结构实现图像去雾。结合Transformer与UNet构建网络框架,针对Self-Attention因二次判别导致高计算复杂度的问题,我们使用聚焦线性注意力替换Softmax函数,将二次计算复杂度降为O(n),并且为了解决使用线性注意力引起性能下降的问题,使用恢复模块增加输出特征的多样性,实现性能的恢复。经实验表明,与现有的去雾霾网络相比,文章提出的方法在公开数据集RESIDE上获得了更好的实验结果以及更愉悦的去雾效果。

支路开关自放电对大型多路并联FLTD脉冲源可靠性的影响

快脉冲直线型变压器驱动源(fast linear transformer driver,FLTD)是建设下一代大型Z箍缩装置最有前景的技术路线之一。大型FLTD脉冲源中数以万计气体开关的可靠运行是提高Z箍缩装置可靠性的重要因素。该文基于15 MAZ箍缩科学实验装置的FLTD脉冲源设计,采用Monte-Carlo方法建立考虑支路开关自放电及其载荷共享效应的FLTD脉冲源可靠性计算模型,分析开关故障模式及其触发策略对脉冲源可靠性的影响。结果表明,主支路开关自放电产生的故障电压会引起开关级联自放电,降低装置可靠性。若主支路开关工作系数设定在0.5~0.7范围内,FLTD脉冲源故障率可低于1×10^(-4)。此外,触发器及触发支路开关的高可靠性对于提升脉冲源可靠性至关重要,增加触发器脉冲数量、降低触发器自放电故障率能够有效提升脉冲源可靠性,当触发器脉冲数量提升至4倍后,FLTD脉冲源故障率有望降低至1×10^(-5)以下。研究结果为大型FLTD脉冲源的开关工作系数及触发策略的选取提供参考,具有重要的工程应用价值。

A Multi-Gap Multi-Channel Gas Switch for the Linear Transformer Driver

A multi-gap and multi-channel gas switch with convexo-convex discal planet electrodes was designed and investigated. Eight gaps are formed in series by a trigger electrode, six intermediate electrodes and two high voltage electrodes with a uniform gap length of 5 ram. The self breakdown and triggered breakdown performance of the switch are reported. Both the delay time and jitter decrease with the increase in the trigger voltage, switching coefficient and the decrease in the trigger isolating resistor. The delay time of the switch is about 40 ns, and the jitter is less than 2 ns when charged with 4-85 kV and triggered by a voltage pule of -75 kV. The inductance of the switch is about 30 nH.

Numerical Analysis of the Output-Pulse Shaping Capability of Linear Transformer Drivers

Output-pulse shaping capability of a linear transformer driver （LTD） module under different conditions is studied, by conducting the whole circuit model simulation by using the PSPICE code. Results indicate that a higher impedance profile of the internal transmission line would lead to a wider adjustment range for the output current rise time and a narrower adjustment range for the current peak. The number of cavities in series has a positive effect on the output- pulse shaping capability of LTD. Such an improvement in the output-pulse shaping capability can primarily be ascribed to the increment in the axial electric length of LTD. For a triggering time interval longer than the time taken by a pulse to propagate through the length of one cavity, the output parameters of LTD could be improved significantly. The present insulating capability of gas switches and other elements in the LTD cavities may only tolerate a slightly longer deviation in the triggering time interval. It is feasible for the LTD module to reduce the output current rise time, though it is not useful to improve the peak power effectively.

Influences of Switching Jitter on the Operational Performances of Linear Transformer Drivers-Based Drivers

A whole circuit model of a linear transformer drivers （LTD） module composed of 60 cavities in series was developed in the software PSPICE to study the influence of switching jitter on the operational performances of LTDs. In the model, each brick in each cavity is capable of operating with jitter in its switch. Additionally, the manner of triggering cables entering into cavities was considered. The performances of the LTD module operating with three typical cavity-triggering sequences were simulated and the simulation results indicate that switching jitter affects slightly the peak and starting time of the output current pulse. However, the enhancement in switching jitter would significantly lengthen the rise time of the output current pulse. Without considering other factors, a jitter lower than 10 ns may be necessary for the switches in the LTD module to provide output current parameters with an acceptable deviation.

基于Transformer-LSTM网络的轴承寿命预测

轴承是旋转机械设备中的重要部件,由于工况、材质、加工方式等原因,轴承寿命时长相差许多。传统的并行或串行神经网络预测方式,对数据集有一定要求。因此,需要一种能够适用于不同数据长短的轴承剩余使用寿命预测网络。为此提出了一种能够预测不同寿命时长的Transformer-LSTM串并行神经网络预测模型。通过将Transformer解码层进行重构,并与长短期记忆时序神经网络(long short-term memory,LSTM)网络结构融合,实现轴承寿命数据的串并行预测处理。试验结果表明Transformer-LSTM神经网络能够精准预测长、中、短不同寿命时长的轴承失效时间,具有较强的模型泛化能力,提升轴承寿命预测精度与模型的泛化能力。

Optical Diagnostics of Multi-Gap Gas Switches for Linear Transformer Drivers

The trigger characteristics of a multi-gap gas switch with double insulating layers,a square-groove electrode supporter and a UV pre-ionizing structure are investigated aided by a high sensitivity fiber-bundle array detector, a UV fiber detector, and a framing camera, in addition to standard electrical diagnostics. The fiber-bundle-array detector is used to track the turn-on sequence of each electrode gap at a timing precision of 0.6 ns. Each fiber bundle, including five fibers with different azimuth angles, aims at the whole emitting area of each electrode gap and is fed to a photomultiplier tube. The UV fiber detector with a spectrum response of 260-320 nm,including a fused-quartz fiber of 200 μm in diameter and a solar-blinded photomultiplier tube, is adopted to study the effect of UV pre-ionizing on trigger characteristics. The framing camera,with a capacity of 4 frames per shot and an exposure time of 5 ns, is employed to capture the evolution of channel arcs. Based on the turn-on light signal of each electrode gap, the breakdown delay is divided into statistical delay and formative delay. A decrease in both of them, a smaller switch jitter and more channel arcs are observed with lower gas pressure. An increase in trigger voltage can reduce the statistical delay and its jitter, while higher trigger voltage has a relatively small influence on the formative delay and the number of channel arcs. With the UV pre-ionizing structure at 0.24 MPa gas pressure and 60 kV trigger voltage, the statistical delay and its jitter can be reduced by 1.8 ns and 0.67 ns, while the formative delay and its jitter can only be reduced by 0.5 ns and 0.25 ns.

A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation metrics.In this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised information.For this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the network.Specifically,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd images.In addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density estimation.The experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

基于局部Transformer的泰语分词和词性标注联合模型

泰语分词和词性标注任务二者之间存在高关联性,已有研究表明将分词和词性标注任务进行联合学习可以有效提升模型性能,为此,提出了一种针对泰语拼写和构词特点的分词和词性标注联合模型。针对泰语中字符构成音节,音节组成词语的特点,采用局部Transformer网络从音节序列中学习分词特征;考虑到词根和词缀等音节与词性的关联,将用于分词的音节特征融入词语序列特征,缓解未知词的词性标注特征缺失问题。在此基础上,模型采用线性分类层预测分词标签,采用线性条件随机场建模词性序列的依赖关系。在泰语数据集LST20上的试验结果表明,模型分词F1、词性标注微平均F1和宏平均F1分别达到96.33%、97.06%和85.98%,相较基线模型分别提升了0.33%、0.44%和0.12%。

固态LTD的脉宽叠加技术仿真

针对脉冲电磁场生物医学应用中所需求的高电压大电流宽范围脉冲问题,文中采用脉冲叠加的方案设计了一种长脉宽固态直线型变压器驱动源(Linear Transformer Driver, LTD)。该LTD脉冲发生器为了突破脉冲变压器伏秒积限制下的脉宽范围,采用并联叠加的方式增加输出脉宽。设计了脉冲变压器的复位电路,提高了LTD输出脉冲占空比,能够实现最少支路LTD的多次脉冲叠加。通过Pspice搭建仿真电路模型进行仿真实验,结果表明在单匝脉冲变压器伏秒积为146 V·ms的情况下,实现单级电压为300 V,脉宽为1.6μs的脉冲输出,为其原伏秒积的3倍,且所提方案能够通过增加叠加脉冲个数来实现更宽范围的脉冲延长。

用于电感负载的全固态双极性LTD型脉冲电流发生器

在基于脉冲功率技术的肿瘤治疗方法中,利用双极性脉冲磁场处理的治疗方法具有明显的优势。针对此应用,该文结合模块化全桥型多电平换流器(FB-MMC)拓扑及直线变压器驱动(LTD)拓扑的优势,提出了一种用于电感负载的全固态双极性LTD型脉冲电流发生器。首先,对该文提出的拓扑结构及原理进行介绍,详细分析了发生器在电感负载下的工作过程;然后,对发生器的硬件电路进行了设计与选型;最后,研制了一台4级全固态双极性LTD型脉冲电流发生器样机并对其进行性能测试。测试结果表明,该发生器可在电感负载下输出±800A的脉冲电流,电流的上升时间为600 ns;发生器的最高工作频率为10 kHz,并具备灵活的波形调制功能,能输出三角波、梯形波及阶梯波等多种波形。

Experimental Investigation of the Deformation of Helical Gears with Double－Circular－Arc Tooth Profile Using Linear Variable Differential Transformer

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Effect of New Suggested Ferroresonance Limiter on the Stability Domain of Chaotic Ferroresonance in the Power Transformer with Linear Core Model

This Paper studies the effect of new suggested ferroresonance limiter on controlling ferroresonance oscillations in the power transformer. It is expected that this limiter generally can control the ferroresonance. For studying these phenomena, at first ferroresonance is introduced and a general modeling approach is given. A simple case of ferroresonance in a three phase transformer is used to illustrate these phenomena. Then, effect of new suggested ferroresonance limiter on the onset of chaotic ferroresonance and control of these oscillations in a power transformer including linear core losses is studied. Simulation is done on a three phase power transformer while one of its phases is opened, and effect of varying input voltage on occurring ferroresonance overvoltage is studied. Results show that connecting the ferroresonance limiter to the transformer exhibits a great controlling effect on the ferroresonance overvoltage. Phase plane diagram, FFT analysis along with bifurcation diagrams are also presented. Significant effect on occurring chaotic ferroresonance, the range of parameter values that may lead to overvoltage and magnitude of ferroresonance overvoltage is obtained, showed and tabulated.

Transformer Characteristics of Linear Motor-Transformer Apparatus

The characteristics of linear transformer are studied analytically. The transformer is composed in one of modes of linear motor-transformer apparatus proposed for future wireless light rail vehicle (LRV). The secondary (onboard) power factor can be adjusted at any value by an onboard converter. The equivalent circuit is used to study the transferred power control. The parameters are determined by three-dimensional finite element method (FEM) analysis for one pole-pair model. Under the rated primary (input) and secondary voltage and current, which are specified for linear motor operation, the characteristics of the secondary power factor are cleared. It is also shown that the input capacitor can improve the primary power factor and decrease the input power capacity, but does not change the efficiency. This linear transformer has the efficiency of 91% and the input power factor of 0.87 when the apparatus without input capacitor is controlled at the secondary power factor of 0.4.

西北核技术研究所强脉冲辐射模拟装置近年发展综述

西北核技术研究所是我国强脉冲辐射环境建设的主体单位,成立至今自主成功研制了一系列大型辐射模拟装置,形成了具有我国自主特色的辐射环境模拟体系和技术体系。近10年来,随着应用需求的持续拓展,西北核技术研究所在辐射模拟装置及其技术方面取得了新突破。本文重点介绍了西北核技术研究所近年新建成的大型强脉冲辐射模拟装置,包括大面积脉冲γ射线试验装置“云光一号”、4 MV脉冲X射线闪光照相装置“剑光二号”及强脉冲X射线试验装置等,分析了感应电压叠加、快脉冲直线变压器、多型射线负载等方面取得的最新技术进展,提出了未来强脉冲辐射模拟装置建设及其关键技术发展的初步设想。

基于RSMA的无线光通信中预编码与功率分配研究

【目的】针对无线光通信(OWC)系统中应用速率分拆接入(RSMA)技术的资源分配问题,提出一种基于线性逼近的联合预编码矩阵和速率分配算法,以提高系统的可靠性和性能。【方法】采用变量转换和线性逼近的方法将非凸分阶函数转化为凸函数。通过联合预编码矩阵和速率分配算法,有效地分配系统资源,提高系统性能。为了求解最优解,采用连续迭代算法进行优化。【结果】仿真结果表明,文章所提出的联合优化方案显著降低了系统中断概率,并具有快速的收敛速率。这意味着所提方案能够在基于RSMA的OWC(RSMA⁃OWC)系统中提供更可靠和高效的通信性能。【结论】文章所提方案为OWC系统中的RSMA技术提供了有效的资源分配方法,提高了系统的可靠性和性能。进一步的研究应聚焦于RSMA⁃OWC系统的中断概率问题以及资源分配、预编码设计和应用扩展等方面的挑战。该研究为推动RSMA技术在OWC领域的应用和发展提供了有价值的参考。

线性正则正余弦加权卷积及其应用

针对积分方程的求解问题,本文提出了利用卷积运算及其卷积定理来讨论两类卷积类积分方程组的解。首先,在线性正则正弦变换与线性正则余弦变换的基础上,定义了线性正则正余弦卷积运算及其加权卷积运算;其次,推导了相应的卷积定理,研究了该卷积与傅里叶正余弦变换卷积运算的关系;最后,讨论了两类卷积类积分方程组的解,给出了该方程解的一般形式。

新常态下海洋经济新旧动能转换测评与影响因素分析

海洋经济新旧动能转换是坚持新发展理念、深化海洋供给侧结构性改革、推动海洋经济转型升级的重要标志,对实现海洋经济高质量发展有重要意义。基于海洋经济新旧动能转换内涵与机理,从需求侧、供给侧、结构转换动能三个维度构建综合评价指标体系;运用主客观赋权法、TOPSIS模型和核密度估计,对中国2006—2020年海洋经济新旧动能转换进行测算与评价;引入GWR模型探讨中国沿海11个省份各影响因素的空间异质性。结果表明,中国海洋经济新旧动能转换综合指数总体呈现波动上升趋势。其中,结构转换动能提升最为显著,需求侧动能总体呈波动上升态势,供给侧动能转换指数整体发展水平不高,对海洋经济新旧动能转换贡献力度较小;2006—2020年,中国海洋经济新旧动能转换指数差异呈先缩小后扩大继而再缩小的变化过程;陆域经济发展水平、海洋产业结构水平、海洋科技创新水平对海洋经济动能转换的提升起正向作用,各因素影响效应具有空间异质性。

基于SwinT-YOLOX模型的自动扶梯行人安全检测算法

自动扶梯被广泛应用在公共场合,乘客摔倒事故如果不能被及时发现并处理,会造成严重的人身伤害,因此实现自动扶梯智能化监控管理势在必行。受自动扶梯运行环境复杂、行人多以及局部遮挡情况的影响,传统的人体姿态特征摔倒检测模型效果不佳且检测速度减慢。融合Swin Transformer和YOLOX目标检测算法的优秀策略,提出一种基于SwinT-YOLOX网络模型的自动扶梯行人摔倒检测算法。采用Swin Transformer模型作为骨干网络,颈部网络使用添加注意力机制的YOLOX模型,进一步提升特征图的多样性和表达能力。此外,利用漏斗修正线性单元视觉激活函数构建CBF模块,改进颈部网络和Head网络结构,从而获得更优的特征检测性能。实验结果表明,针对自建扶梯行人摔倒数据库和网络采集实际扶梯行人摔倒事故,与AlphaPose、OpenPose、YOLOv5等算法相比,该算法检测性能明显提高,行人摔倒平均检测精度可以达到95.92%,检测帧率为24.08帧/s,能够快速、精准地检测到乘客摔倒事故发生,监控管理平台立刻采取安全急停措施以保证乘客安全。

数字赋能“有效论”:数字化转型对重污染企业绿色创新的双向效应

当前,以大数据、云计算等为代表的数字技术快速发展,为企业绿色创新提供了新动能。然而,数字赋能的“有效性”问题一直在学术界存在争议。基于2010—2020年中国沪深A股重污染企业数据,实证检验了数字化转型对企业绿色创新的影响及作用机制。研究发现,数字化转型与绿色创新之间存在显著的倒“U”型关系,且样本企业广泛分布于倒“U”型曲线最高点的左侧;管控投入在数字化转型与企业绿色创新的关系中发挥部分中介作用。同时,政府补助能够显著强化数字化转型与企业绿色创新之间的倒“U”型关系。异质性分析表明,上述倒“U”型关系主要存在于国有企业和媒体报道较多的企业中。这说明,“技术驱动”有助于重污染企业绿色创新“增量提质”,但过度数字化会诱使企业陷入“技术鸿沟”,不利于数字化与绿色创新的协同推进。因此,重污染企业应找准数字技术赋能生态环境保护的契合点与着力点,政府可以利用补贴政策进一步激励企业数字赋能绿色创新。