Effects of multiple shapes for steady flow with transformer oil+Fe_(3)O_(4)+TiO_(2) between two stretchable rotating disks

In this study,we examine the effects of various shapes of nanoparticles in a steady flow of hybrid nanofluids between two stretchable rotating disks.The steady flow of hybrid nanofluids with transformer oil as the base fluid and Fe_(3)O_(4)+TiO_(2)as the hybrid nanofluid is considered.Several shapes of Fe_(3)O_(4)+TiO_(2)hybrid nanofluids,including sphere,brick,blade,cylinder,and platelet,are studied.Every shape exists in the same volume of a nanoparticle.The leading equations(partial differential equations(PDEs))are transformed to the nonlinear ordinary differential equations(ODEs)with the help of similarity transformations.The system of equations takes the form of ODEs depending on the boundary conditions,whose solutions are computed numerically by the bvp4c MATLAB solver.The outputs are compared with the previous findings,and an intriguing pattern is discovered,such that the tangential velocity is increased for the rotation parameter,while it is decreased by the stretching values because of the lower disk.For the reaction rate parameter,the concentration boundary layer becomes shorter,and the activation energy component increases the rate at which mass transfers come to the higher disk but have the opposite effect on the bottom disk.The ranges of various parameters taken into account are Pr=6.2,Re=2,M=1.0,φ_(1)=φ_(2)=0.03,K=0.5,S=-0.1,Br=0.3,Sc=2.0,α_(1)=0.2,γ=0.1,E_(n)=2.0,and q=1.0,and the rotation factor K is within the range of 0 to 1.

Weak Fault Feature Extraction of the Rotating Machinery Using Flexible Analytic Wavelet Transform and Nonlinear Quantum Permutation Entropy

Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery,where weak fault characteristic signals hinder accurate fault state representation,we propose a novel feature extraction method that combines the Flexible Analytic Wavelet Transform(FAWT)with Nonlinear Quantum Permutation Entropy.FAWT,leveraging fractional orders and arbitrary scaling and translation factors,exhibits superior translational invariance and adjustable fundamental oscillatory characteristics.This flexibility enables FAWT to provide well-suited wavelet shapes,effectively matching subtle fault components and avoiding performance degradation associated with fixed frequency partitioning and low-oscillation bases in detecting weak faults.In our approach,gearbox vibration signals undergo FAWT to obtain sub-bands.Quantum theory is then introduced into permutation entropy to propose Nonlinear Quantum Permutation Entropy,a feature that more accurately characterizes the operational state of vibration simulation signals.The nonlinear quantum permutation entropy extracted from sub-bands is utilized to characterize the operating state of rotating machinery.A comprehensive analysis of vibration signals from rolling bearings and gearboxes validates the feasibility of the proposed method.Comparative assessments with parameters derived from traditional permutation entropy,sample entropy,wavelet transform(WT),and empirical mode decomposition(EMD)underscore the superior effectiveness of this approach in fault detection and classification for rotating machinery.

Partial Discharge Source Classification and De-Noising in Rotating Machines Using Discrete Wavelet Transform and Directional Coupling Capacitor

This paper introduces a new method to separate PD1 from other disturbing signals present on the high voltage genera-tors and motors. The method is based on combination of a pattern classifier, the Discrete Wavelet Transform (DWT), to de-noise PD and Time-Of-Arrival method to separate PD sources. Furthermore, it will be shown that it can recognize PD sources including rotating machine’s internal and external discharge pulses (e.g. on the bus bar).

面向樱桃番茄采摘识别的轻量化Transformer架构优化研究

为进一步提升穗收型樱桃番茄识别准确率和识别速度,实现设施环境番茄自动采摘,提出了一种基于改进Transformer的轻量化樱桃番茄穗态识别模型。首先,构建了包含不同光照环境和采摘姿态的樱桃番茄数据集,并对樱桃番茄果穗姿态进行了划分。然后,提出了一种基于改进RE-DETR的轻量化穗收樱桃番茄识别模型,通过引入一个轻量级的骨干网络EfficientViT替换RT-DETR原有的骨干网络,显著减少了模型参数和计算量;同时设计了一个自适应细节融合模块,旨在高效处理并融合不同尺度特征图,并进一步降低计算复杂度。最后,引入加权函数滑动机制和指数移动平均思想来优化损失函数,来处理样本分类中的不确定性。实验结果表明,该轻量化模型在保持高识别准确率(90%)的同时,实现了快速检测(41.2 f/s)和低计算量(8.7×10^(9) FLOPs)。与原始网络模型、Faster R-CNN和Swin Transformer相比,平均识别准确率提高1.24%~15.38%,每秒处理帧数(FPS)提高25.61%~255.17%,同时浮点运算量实现了69.37%~92.37%的大幅降低。该模型在综合性能上有着较强的鲁棒性,兼顾了精度与速度,可为番茄采摘机器人完成视觉任务提供技术支撑。

基于RT-DETR-Faster的苹果采摘机器人实时目标检测算法

为了解决苹果采摘中目标小,实时性要求高等问题,提出了一种基于RT-DETR的采摘机器人目标检测方法,名为RT-DETR-Faster。首先,采用FasterNet部分卷积替换主干网络的传统卷积,有效提升了模型的运算速度;其次,使用改进的级联注意力编码器替换原始的编码器,使网络更专注于目标区域;最后,引入Faster_Rep融合特征模块,保留更多有效特征并减少计算量。该文在实际的果园图像上进行了实验,结果表明,该文提出的算法与原始的RT-DETR算法相比,FPS提升了34%,帧数达到了47.9,同时准确率更高,适用于苹果采摘机器人的实时目标检测任务。

Exact Solution of Second Grade Fluid in a Rotating Frame through Porous Media Using Hodograph Transformation Method

In this paper exact solution for a homogenous incompressible, second grade fluid in a rotating frame through porous media has been provided using hodograph-Legendre transformation method. Results are summarised in the form of theorems. Two examples have been taken and streamline patterns are shown for the solutions.

Fault Feature Extraction of Rotating Machinery Based on Wavelet Transformation and Multi-resolution Analysis

This paper expounded in detail the principle of energy spectrum analysis based on discrete wavelet transformation and multiresolution analysis. In the aspect of feature extraction method study, with investigating the feature of impact factor in vibration signals and considering the non-placidity and non-linear of vibration diagnosis signals, the authors import wavelet analysis and fractal theory as the tools of faulty signal feature description. Experimental results proved the validity of this method. To some extent, this method provides a good approach of resolving the wholesome problem of fault feature symptom description.

Analysis of Transforming dq Impedances of Different Converters to A Common Reference Frame in Complex Converter Networks

DQ impedance-based method has been widely used to study the stability of three-phase converter systems.As the dq impedance model of each converter depends on its local dq reference frame,the dq impedance modeling of complex converter networks gets complicated.Because the reference frames of different converters might not fully align,depending on the structure.Thus,in order to find an accurate impedance model of a complex network for stability analysis,converting the impedances of different converters into a common reference frame is required.This paper presents a comprehensive investigation on the transformation of dq impedances to a common reference frame in complex converter networks.Four different methods are introduced and analyzed in a systematic way.Moreover,a rigorous comparison among these approaches is carried out,where the method with the simplest transformation procedure is finally suggested for the modeling of complex converter networks.The performed analysis is verified by injecting two independent small-signal perturbations into the d and the q axis,and doing a point-by-point impedance measurement.

Magneto-Photo-Thermoelastic Excitation Rotating Semiconductor Medium Based on Moisture Diffusivity

In this research,we focus on the free-surface deformation of a one-dimensional elastic semiconductor medium as a function of magnetic field and moisture diffusivity.The problem aims to analyze the interconnection between plasma and moisture diffusivity processes,as well as thermo-elastic waves.The study examines the photothermoelasticity transport process while considering the impact of moisture diffusivity.By employing Laplace’s transformation technique,we derive the governing equations of the photo-thermo-elastic medium.These equations include the equations for carrier density,elastic waves,moisture transport,heat conduction,and constitutive relationships.Mechanical stresses,thermal conditions,and plasma boundary conditions are used to calculate the fundamental physical parameters in the Laplace domain.By employing numerical techniques,the Laplace transform is inverted to get complete time-domain solutions for the primary physical domains under study.Referencemoisture,thermoelastic,and thermoelectric characteristics are employed in conjunction with a graphical analysis that takes into consideration the effects of applied forces on displacement,moisture concentration,carrier density,stress due to forces,and temperature distribution.

基于改进实时检测Transformer的塔机上俯视场景小目标检测模型

针对塔机吊钩相互碰撞导致物体跌落以及塔机倒塌致使人员伤亡等一系列施工现场人员安全保障的问题,提出一种基于改进实时检测Transformer(Real-Time DEtection TRansformer,RT-DETR)的塔机上俯视场景小目标检测模型。首先,在原始模型中加入应用模型的重参数化思想设计的多路训练和单路推理结构以提升检测速度;其次,重新设计FasterNet Block中的卷积模块替换原始BackBone之中的BasicBlock以提升检测模型性能;再次,利用新的损失函数Inner-SIoU(Inner-Structured Intersection over Union)进一步提升模型精度与收敛速度;最后,进行消融实验与对比实验验证模型性能。结果表明,在检测塔机顶部俯视小目标图像时,所提模型的精度达到94.7%,高于原始RT-DETR模型6.1个百分点;所提模型的每秒检测帧数(FPS)达到59.7,检测速度相较于原模型提升了21%。在公共数据集COCO 2017上所提模型的平均精度(AP)比YOLOv5、YOLOv7和YOLOv8分别高2.4、1.5和1.3个百分点。可见所提模型满足塔机上俯视场景下的小目标检测精度和速度的要求。

基于旋转去噪Transformer的遥感图像目标端到端检测

提出一种基于旋转去噪Transformer的遥感图像旋转目标端到端检测方法。首先,针对遥感图像滑窗切片时存在目标切断影响网络学习的问题,提出基于密集栅格的遥感图像数据增强方法,提升网络对不完整目标的预测能力。其次,提出旋转去噪Transformer架构的端到端检测网络,采用PVTv2提取多尺度特征,通过Transformer编码器对特征进行聚合与增强,利用Transformer解码器对目标解码,通过分类头和回归头直接实现旋转目标预测,即端到端检测。最后,提出旋转对比去噪学习方法,将去噪学习用于旋转目标检测,加速网络收敛。在2023全国大数据与计算智能挑战赛数据集上,所提方法的m AP达到了98.38%。

火针联合董氏奇穴针刺治疗肩袖损伤的疗效观察及对关节功能及血清PGE_(2)和TGF-β1的影响

目的观察火针联合董氏奇穴针刺治疗肩袖损伤的临床疗效及对关节功能及血清前列腺素E_(2)(prostaglandin E_(2),PGE_(2))和转化生长因子-β1(transforming growth factor-β1,TGF-β1)的影响。方法将100例肩袖损伤患者随机分为对照组(50例)和观察组(50例)。对照组予以电针联合董氏奇穴针刺治疗,观察组予以火针联合董氏奇穴针刺治疗。检测视觉模拟量表(visual analog scale,VAS)和美国加利福尼亚大学洛杉矶分校(University of California,Los Angeles,UCLA)肩关节评分、肩关节活动度(range of motion,ROM)及血清PGE_(2)和TGF-β1水平。结果观察组总有效率高于对照组(P<0.05)。治疗后及4周随访时,两组VAS评分及血清PGE_(2)和TGF-β1水平均低于治疗前(P<0.05),且观察组均低于对照组(P<0.05);两组UCLA肩关节评分和肩关节ROM均高于治疗前(P<0.05),且观察组均高于对照组(P<0.05)。4周随访时,两组VAS评分、UCLA肩关节评分、肩关节ROM及血清PGE_(2)和TGF-β1水平与治疗后比较差异无统计系意义(P>0.05)。治疗后血清PGE_(2)、TGF-β1水平与VAS评分呈正相关,与UCLA肩关节评分呈负相关。结论火针和电针联合董氏奇穴针刺治疗肩袖损伤均可缓解疼痛、改善肩关节功能,但火针优于电针,其机制可能与降低血清PGE_(2)和TGF-β1水平有关。

The optimal fractional Gabor transform based on the adaptive window function and its application

We designed the window function of the optimal Gabor transform based on the time-frequency rotation property of the fractional Fourier transform. Thus, we obtained the adaptive optimal Gabor transform in the fractional domain and improved the time-frequency concentration of the Gabor transform. The algorithm first searches for the optimal rotation factor, then performs the p-th FrFT of the signal and, finally, performs time and frequency analysis of the FrFT result. Finally, the algorithm rotates the plane in the fractional domain back to the normal time-frequency plane. This promotes the application of FrFT in the field of high-resolution reservoir prediction. Additionally, we proposed an adaptive search method for the optimal rotation factor using the Parseval principle in the fractional domain, which simplifies the algorithm. We carried out spectrum decomposition of the seismic signal, which showed that the instantaneous frequency slices obtained by the proposed algorithm are superior to the ones obtained by the traditional Gabor transform. The adaptive time frequency analysis is of great significance to seismic signal processing.

应用SYBR GreenI荧光定量RT-PCR测定糖尿病大鼠肾皮质TGF-β1表达水平

SYBR GreenI荧光定量RTPCR法检测正常、糖尿病和牛磺酸治疗组大鼠TGFβ1mRNA/βactinmRNA比值为(7.0±0.8)×10-3,(64.4±8.0)×10-3和(16.7±2.0)×10-3;终点法RTPCR检测比值分别为0.28±0.12,0.58±0.16和0.43±0.10。与终点法RTPCR相比,TGFβ1mRNASYBRGreenI荧光定量RTPCR实时检测法,方便快捷,敏感特异。

rt-PA与尿激酶溶栓后对MMP-9及脑出血性转化的对比研究

目的:比较重组组织型纤溶酶原激活剂(Recombinant human tissue-type plasiminogen,rt-PA)与尿激酶溶栓后对基质金属蛋白酶-9(Matrix metalloproteinase-9,MMP-9)表达和脑出血性转化的影响。方法:采用大鼠自体血栓塞大脑中动脉模型,缺血6h后静脉应用rt-PA及尿激酶,24h后处死,脑组织中MMP-9表达采用免疫组织化学法,脑出血量的测定采用分光光度计法。结果:脑缺血后MMP-9表达增加,rt-PA与尿激酶溶栓后MMP-9表达均进一步增加。脑缺血对照组、rt-PA溶栓组及UK溶栓组平均光密度值分别为0.158±0.010、0.225±0.0341、0.228±0.022,差异有统计学意义(P<0.01)。溶栓后两组脑出血发生率及脑出血量均较缺血对照组增加,但两组之间无明显差别。结论:rt-PA与尿激酶溶栓后均可上调MMP-9表达并与出血性转化密切相关。

基于Hilbert变换和预设旋转频率的无锁相环电网同步基准正弦信号检测方法

新能源发电并网过程中,电网同步基准正弦信号检测是并网关键之一,特别是在电网不平衡、畸变、频率波动和频率偏移等复杂工况下,对电网同步基准正弦准确检测造成较大干扰。为此提出一种基于Hilbert变换和预设旋转频率的无锁相环电网同步基准正弦信号检测方法。该方法通过预设同步旋转频率进行特定次频率分量正、负转换,然后运用Hilbert变换的幅相特性和一定的运算消除正序谐波分量、基波负序分量和负序谐波分量。所提方法能在复杂的工况下准确提取电网同步基准正弦信号,理论和实验验证了该方法的有效性和可行性。

基于RO-SBM的Hilbert-Huang变换端点效应抑制方法

针对Hilbert-Huang变换方法中由于信号经验模态分解(empirical mode decomposition,简称EMD)过程中所存在的端点效应问题,分析了现有数据延拓方式的利弊,并在基于斜率(slope based method,简称SBM)方法以及改进方法(improved slope based method,简称ISBM)的基础上提出了一种全新的基于斜率再优化(reoptimization slope based method,简称RO-SBM)方法用于信号序列的极值点延拓,然后对延拓后的数据进行EMD分解,得到相应的本征模函数(intrinsic mode function,简称IMF)分量。数值仿真结果表明,采用基于RO-SBM方法进行数据延拓,相比镜像延拓以及ISBM方法,可以更有效地抑制EMD中的端点效应问题,提升HHT方法的信号分析性能。通过基于RO-SBM方法进行数据延拓的HHT方法准确分离出了某转子系统的局部碰摩径向振动信号中所包含的故障特征分量,并将此方法成功应用于旋转机械故障诊断领域。

急性缺血性脑卒中rt-PA静脉溶栓后出血性转化的特殊类型

目的溶栓后出血性转化(hemorrhagic transformation,HT)是重组组织型纤维蛋白溶酶原激活剂(rt-PA)治疗急性缺血性脑卒中的一个重要安全指标。HT有不同的亚型,而不同亚型的预后也不尽相同。我们对急性缺血性脑卒中患者rt-PA静脉溶栓后出现的特殊型HT进行分析。方法对发病3zh内的98例缺血性卒中患者用rt-PA(剂量0．6 mg/kg,最大剂量5 0 mg)进行静脉溶栓治疗,溶栓前后行头颅CT、MRI或数字减影血管造影(DSA)检查判断是否有HT,并判定这种HT与责任病灶的关系。结果溶栓后经CT或MRI检查发现4种特殊的远端HT类型,1例发生蛛网膜下腔出血(SAH),1例梗死部位的对侧出现明显占位效应的脑实质出血,1例出现梗死灶对侧的侧脑室出血,1例出现梗死部位对侧的腔隙性出血。这4例患者所引起的4类HT在临床上均为无症状,预后好。结论对急性缺血性脑卒中的溶栓治疗要坚持动态观和平衡观,对症状性出血性转化的诊断要慎重,充分考虑HT的分型和程度,从而正确判断HT对预后的影响。

应用SYBR荧光实时定量RT-PCR法检测TGF-β1诱导瘢痕疙瘩成纤维细胞中PAI-1mRNA表达

目的应用SYBR荧光实时定量RT—PCR法检测转化生长因子-β1（transforming growth factor betal，TGF-β1）诱导瘢痕疙瘩成纤维细胞（keloid fibroblasts，KFS）中纤溶酶原激活物抑制剂-1（plasminogen activator inhibitor1，PAI-1）mRNA的表达。方法体外分离培养KFs，应用不同浓度（1．25～20pmol·L-1）TGF-β1刺激KFs3h或TGF-β1（10pmol·L-1）刺激KFs不同时间（0．5～6h），采用SYBRGreenI荧光实时定量RT—PCR法检测，以B—actin基因作为内参，计算各组PAI-1mRNA的相对表达量。结果与空白对照组相比，TGF-β1能明显诱导KFs中PAI-1mRNA的表达，10、20pmol·L-1浓度组表达比值分别增至4．19及4．44倍（P〈0．01）；TGF-β1（10pmol·L。）的1、2h时问组表达比值分别增至2．29及2．41倍（P〈0．05），3、6h时间组分别增至4．19及5．83倍（P〈0．01）。提示，TGF-β1诱导KFs中PAI=1mRNA表达的最适浓度为10pmol·L-1、最适时间为3h。结论利用SYBR荧光实时定量RT—PCR法检测TGF-β1诱导KFs中PAI-1mRNA的表达，为进一步研究瘢痕疙瘩中TGF-β1信号通路的靶基因调控机制，以及开发治疗瘢痕疙瘩的新药提供了基础。

RotateTransform函数在船舶仪表控件编写中的运用及数学分析

应用最新的GDI+和Visual C#.net编程技术中的旋转函数Rotate Transform()法和计算坐标的方法来编写了船舶仪表控件中的电流表,介绍了两种不同编写方法的思考路径,阐述了这两种编写转动仪表控件方法的优点和缺点。