Ballistic performances of the hourglass lattice sandwich structures under high-velocity fragments

In this paper,the numerical simulation method is used to study the ballistic performances of hourglass lattice sandwich structures with the same mass under the vertical incidence of fragments.Attention is paid to elucidating the influences of rod cross-section dimensions,structure height,structure layer,and rod inclination angle on the deformation mode,ballistic performances,and ability to change the ballistic direction of fragments.The results show that the ballistic performances of hourglass lattice sandwich structures are mainly affected by their structural parameters.In this respect,structural parameters optimization of the hourglass lattice sandwich structures enable one to effectively improve their ballistic limit velocity and,consequently,ballistic performances.

基于YOLOv4-tiny和Hourglass的指针式仪表读数识别

为了降低电力巡检机器人识别变电站指针式仪表的误检率,提高仪表读数识别的精度,设计了一种基于深度学习的指针式仪表检测方法。通过在YOLOv4-tiny网络结构基础上添加残差模块来提高模型的鲁棒性,并对Hourglass网络结构改进,达到精准识别指针式仪表读数的目的。为了验证所提出方法的有效性,以变电站现场仪表图像数据对方法进行测试,并将检测结果与其他方法进行对比。实验结果表明,仪表定位漏检率仅1.25%,指针定位精度在1.125%以内,整体检测时间小于0.5 s。相较于常用的Hough直线检测与ORB结合或基于U-net模型的方法,读数识别的平均误差分别降低了70.8%和58.8%,为变电站指针式仪表的读数识别提供新的思路。

动力响应数值分析中的hourglass现象

结构动力响应数值分析中,通常采用高斯积分一类的数值积分。单点高斯积分的采用可能引起hourglass模态。控制hourglass模态是结构动力响应数值分析中必须解决的问题之一。本文详细分析了hourglass模态产生的机理,并在此基础上讨论了hourglass模态的控制措施、具体应用及对正常响应模态的影响。

基于Stacked Hourglass的智能康复评估系统研究

2020年康复领域异常严峻,一方面,疫情爆发,人群不易聚集,患者不方便去医院或者康复中心做康复及检测身体状况,另一方面,全球人口老龄化问题加剧,老人腿脚不方便,不便于频繁地去康复中心复查,并且全国康复中心资源较少,不仅造成康复师工作压力增大,还会出现医患沟通不充分,延误康复等问题。随着人工智能技术不断发展,机器视觉在人工智能中的表现尤为突出,其中骨架识别不断被认可,应用在了多个领域。因此本次研究设计了基于Stacked Hourglass的智能康复评估系统,期望可以提高康复医师工作效率。本系统主要为智能康复评估,首先构建以量表为中心,建立量表答题结构的数据库,将量表储存在数据库中。其次,将骨架识别移植到移动端软件识别出量表所需数据。然后,将骨架识别数据通过计算,算出填表所需值,智能填表。最后,通过各个量表数据,全部显示出来并提供给康复师,让康复师了解患者状况。通过这些步骤设计量表模型,完成智能康复评估。

Multi-Residual Module Stacked Hourglass Networks for Human Pose Estimation

A multi-residual module stacked hourglass network(MRSH)was proposed to improve the accuracy and robustness of human body pose estimation.The network uses multiple hourglass sub-networks and three new residual modules.In the hourglass sub-network,the large receptive field residual module(LRFRM)and the multi-scale residual module(MSRM)are first used to learn the spatial relationship between features and body parts at various scales.Only the improved residual module(IRM)is used when the resolution is minimized.The final network uses four stacked hourglass sub-networks,with intermediate supervision at the end of each hourglass,repeating high-low(from high resolution to low resolution)and low-high(from low resolution to high resolution)learning.The network was tested on the public datasets of Leeds sports poses(LSP)and MPII human pose.The experimental results show that the proposed network has better performance in human pose estimation.

Mathematical Model and Machining Method for Spiral Flute Rake Faces of Hourglass Worm Gear Hob

To improve the cutting performance of an hourglass worm gear hob and the accuracy of the resulting worm gear,the rake angles of the teeth on the pitch circle should be reduced.A method of forming the spiral flutes by using a variable transmission ratio was developed.This method ensures that the rake angles on the indexing torus of each tooth are approximately 0°.Based on the gear meshing theory and the hourglass worm forming method,the discretization mathematical model of the rake face of a planar double enveloping worm gear hob was established by using cylindrical generating surface,and the feed parameters for machining the rake face for a variable transmission ratio were obtained.The spiral flute was simulated and the hob was machined on a four axis CNC milling machine.A contourgraph was used to measure the rake angle.The measurement results showed that the proposed method can reduce the absolute value of the rake angle on both sides of the cutting teeth,which can be used for machining the spiral flute rake face of an hourglass worm gear hob.

Temperature Field Simulation and Experimental Study of Anti‑backlash Single‑Roller Enveloping Hourglass Worm Gear

In gear transmission,temperature rise has a non-negligible impact on the accuracy,noise and transmission efficiency.However,there is no relevant research on the temperature rise of the anti-backlash single-roller enveloping hourglass worm(ASEHW)gear.To solve this problem,based on tribology principle and Hertz contact theory,the thermal power calculation method of the ASEHW gear was proposed for the first time and thermal analysis was carried out by Ansys software.The bulk temperature of the ASEHW gear under four different rotating speed(300 r/min,600 r/min,900 r/min,1200 r/min)is calculated.The main factors causing temperature rise of the ASEHW gear are analyzed theoretically.Meanwhile,an experimental study is performed to verify the simulation results and validate the theory methods.The theory presented in this paper provides a solution for the thermal power calculation of ASEHW gear.This research provides a theoretical basis for further optimization of ASEHW gear.

SQoE KQIs Anomaly Detection in Cellular Networks: Fast Online Detection Framework with Hourglass Clustering

The explosive growth of data volume in mobile networks makes fast online diagnose a pressing search problem. In this paper, an object-oriented detection framework with a two-step clustering, named as Hourglass Clustering, is given. Where three object parameters are chosen as Synthetical Quality of Experience（SQo E） Key Quality Indicators（KQIs） to reflect accessibility, integrality, and maintainability of networks. Then, we choose represented Key Performance Indicators（r KPIs） as cause parameters with correlation analysis. For these two kinds of parameters, a hybrid algorithm combining the self-organizing map（SOM） and

Contact analysis and load distribution of double-envelop hourglass worm gearing affected by errors and load

An approach for the contact analysis and load distribution of double-envelop hourglass worm gearing is presented, which is based on a 3-D elastic contact finite element method (FEM) model that accommodates the influence of errors and load. As compared with existing tooth contact analysis model that assumes rigidity for the contacting surfaces, the proposed model provides a more realistic analysis on the contact patterns, the distribution of contact load and transmission errors. It is also capable of exploring the influence of different errors on meshing performances, the contact deformation, the shift of the contact zone and load share among the meshing tooth-pairs under different load.

A New Manufacturing Method for Hourglass Worm Gear Hob Based on a Semi⁃automatic CAD

The top relief surfaces of an hourglass worm gear hob are ground manually in the traditional manufacturing process,which cannot ensure the width of the land surfaces of the hob.Moreover,each geometric feature of the hob has been produced through different manufacturing techniques and machine tools,which results in low efficiency.To solve this problem,we propose a semi-automatic computer aided design(CAD)method for hobs.The point clouds of each feature surface of a hob are calculated by combing mathematical equations of the top relief surfaces built by the proposed method with other existing equations of hob surfaces.According to the point clouds,the method can achieve the automatic modeling for the hob in three-dimensional(3D)software by classifying and extracting the parameter information of the feature-hierarchical knowledge of the hob.Based on the generated 3D model,the entire surfaces of the hob can be manufactured on a four-axis computer numerical control(CNC)milling machine through only twice clamping.Verification of the width of the land surface of the hob manufactured by semi-automatic CAD method on a measuring projector proved the precision of the designed width can be ensured.The edge of the contact area on the worm wheel in a meshing experiment is clear and distinct,which means the worm gear drive is meshed well and the hob manufactured by the proposed method has improved machinability.The method simplifies the processing technique,and improves the design efficiency and production accuracy.

联合利华首次收购高科技美妆品牌Hourglass

联合利华宣布与美妆品牌Hourglass达成收购协议,具体交易细节暂未透露。如果并购顺利完成,这将是联合利华首次进军高端化妆品领域,但集团发言人称交易最快将于2017年下半年完成,目前正等待相关部门审批。Hourglass由美容行业资深人士Carisa Janes创立于2004年,其特色是精美包装、高科技含量的美妆产品、创新性配方等。

四边形Mindlin板元的Hourglass控制

本文以四边形 Mindlin 板元作 Hourglass 控制的稳定性方法,可有效地消除不适的运动模式和不产生自锁。本文将控制 Hourglass 模式的稳定矩阵方法应用到三维非线性动力响应问题中,算例表明动力响应解的 Hourglass 模式振荡得到了明显改善.但这个解还不令人十分满意,尚未彻底控制 Hourglass 模式振荡,还有待进一步研究.

Hourglass Control in Dynamic Explicit Simulation of Cold Ring Rolling Process

Hourglass is remarkably aroused in ring rolling simulation with reduced integration element because of violent ring vibration and small contact area of ring with forming rollers. Hourglass energy can be reduced by improving mesh density. Combined approach of artificial damping and artificial stiffness can control the hourglass. A ring with rectangular section is simulated with the weight factor of artificial stiffness resistance set to 0.2. The artificial damping hourglass control coefficient is set to 0.1, and the artificial stiffness hourglass control coefficient is set to 0.001. The ratio of hourglass energy to internal energy is reduced to 2.5 percent. Compared with axial spread width test, the effectiveness of hourglass control in ring rolling simulation is verified.

HourglassNet:一种用于遥感目标检测的改进FCOS算法

针对遥感图像中背景复杂、目标分布密集、目标尺度形态多样等问题,该文在单阶段全卷积(FCOS)目标检测模型的基础上,基于沙漏特征金字塔并且与多尺度上下文场景结合,提出了沙漏网(HourglassNet)。针对卷积神经网络(CNN)中不同深度语义信息和空间信息不均衡的问题,提出了一种沙漏特征金字塔,通过将多尺度特征缩放至中间尺度进行融合和优化以获得全局特征。基于注意力机制将全局特征向不同尺度特征传递,在抑制无关特征的同时增强了有效特征,实现了对多尺度特征的补偿。为了将高层特征的语义信息更加充分地融入不同尺寸的特征图内,设计了多尺度上下文融合模块。利用适当的感受野提取高层特征的上下文信息,提升了特征的鲁棒性和辨识性。分别在DOTA v1.5和NWPU VHR-10公开遥感图像数据集上进行了性能对比与消融实验。结果表明,该文算法的均值平均精度(mAP)相比于FCOS在DOTA v1.5和NWPU VHR-10数据集上分别提升了4.3%和3.4%,且检测性能优于YOLOv3等其它对比方法。

LOAD SHARING AND CONTACT OF HOURGLASS WORM GEARING

Based on solid modeling, the geometry and the FE model of the hourglass worm gearing are established. By the hybrid method of FEM and penalty function, the load sharing among tooth paris and the stress of contact teeth are calculated, the contact zones and the distribution of the contact pressure are investigated. The variation of the contact states with the load is investigated by comparision of its contact patterns under different loads.

基于DCA-CenterNet的棉布瑕疵检测研究

针对棉布生产过程中常存在各类瑕疵,且瑕疵的尺度差异较大、形态各异、对比度低、部分为小目标瑕疵等特点,提出了一种基于CenterNet网络的改进目标检测模型DCA-CenterNet,并首次应用于棉布瑕疵检测.在骨干网络Hourglass的残差模块引入CA注意力机制,可以捕捉到不同位置之间的空间关系和上下文信息,提高了网络对于棉布瑕疵的特征表达能力;设计了基于定位质量的关键点筛选模块,可以有效地捕获关键位置信息,提高算法模型检测精度;采用多组改进的带有基于定位质量的关键点筛选模块的检测器,以更好地适应棉布瑕疵的种类多样性和尺度差异,有效解决棉布瑕疵极端长宽比问题.实验结果表明:提出的模型相较于改进前的模型,mAP提高了4.14%,比YOLOv5、FasterRCNN算法分别高出了4.20%和9.11%,验证了所提模型的有效性.

基于热力图预测的免“锚框”人物目标检测算法

传统的目标检测算法通常预设许多候选的目标边界框(“锚框”)穷举待检测目标的潜在位置,在对边界框进行置信度计算后筛除冗余边界框确定人物位置,此类检测方法需要复杂的后处理程序,检测效率低。针对待检测的人物目标,提出基于热力图预测的免“锚框”目标检测算法,将人物目标的检测转化为对人物热力图的最大值,即目标中心点的检测。通过中心点进行目标尺寸的回归,最终确定目标位置,免除对“锚框”的依赖和计算,从而有效降低计算成本,大幅提高目标检测的速度。实验结果表明:与传统基于“锚框”的检测算法Faster R-CNN和SSD相比,所提算法目标检测速度大幅提升,达到45帧/s,同时检测精度相较前两者在不同数据集上均有所改善。在现实场景测试中,即使视频中存在人物交叉遮挡情况,该算法也能实时跟踪和精准检测人物位置,达到实时检测的目的。

基于深度学习的男装款式图制版要素识别

探讨在传统服装制版领域中如何应用深度学习技术,以替代传统制版师在款式图中进行尺寸评价关键点和部件类别信息提取的人工思维过程,从而提高服装结构设计的效率和质量。首先,对经典服装款式图的构成要素进行了分析,以及探讨了制版师在款式图分析中提取关键信息的思维模式。其次,介绍了基于Hourglass和YOLOv5款式图识别模型的设计和实现过程。对包含15个服装尺寸测量关键点的数据集和包含15种常见男装部件的数据集进行了预处理、标注和数据增强等,共涵盖2000张男装款式图。对这两个数据集进行训练,迭代了多个模型,并对模型的性能指标进行了测试、评估和分析。深度学习技术成功实现了对款式图中关键信息的自动提取,有效替代了传统制版师的人工判读过程,从而提高了设计效率和质量。未来的研究方向包括扩大数据集规模、丰富标签类别、探索更复杂的模型结构,并考虑其他领域的应用,如女装、童装款式图识别等。

基于改进轻量级沙漏模型的2D单人姿态估计研究与应用

提出一种基于改进轻量级沙漏模型的2D单人人体姿态估计方法。使用逆残差卷积来构建改进的轻量级沙漏模型,从而降低参数数量与计算量,使用多尺度特征融合以提高轻量级模型在遮挡情况下的关键点检测能力。引入知识蒸馏方法,使得改进的模型在略微降低检测准确度时,能大幅降低训练和部署所需要的计算资源。MPII数据集和实际应用中的检测结果表明,改进的轻量级沙漏模型能有效检测人体骨骼关键点,实时性好、鲁棒性强,能在一定程度上克服遮挡问题。

结合沙漏注意力与渐进式混合Transformer的图像分类方法

Transformer在图像分类任务中具有广泛应用,但在小数据集分类任务中,Transformer受到数据量较少、模型参数量过大等因素的影响,导致分类精度低、收敛速度缓慢。本文提出了一种融合沙漏注意力的渐进式混合Transformer模型。首先,通过下-上采样的沙漏自注意力建模全局特征关系,利用上采样补充下采样操作丢失的信息,同时采用可学习温度参数和负对角掩码锐化注意力的分数分布,避免因层数过多产生过度平滑的现象;其次,设计渐进式下采样模块获得细粒度多尺度特征图,有效捕获低维特征信息;最后,使用混合架构,在顶层阶段使用设计的沙漏注意力,底层阶段使用池化层替代注意力模块,并引入带有深度卷积的层归一化,增加网络局部性。所提方法在T-ImageNet、CIFAR10、CIFAR100、SVHN数据集上进行实验,分类精度可以达到97.42%,计算量和参数量分别为3.41G和25M。实验结果表明,与对比算法相比,该方法的分类精度有明显提升,计算量和参数量有明显降低,提高了Transformer模型在小数据集上的性能表现。