Seismic Analysis of the Connections of Buried Segmented Pipes

Seismic analysis of buried pipes has been one study focus during the last decades,but the systematic seismic research of pipe connections,especially its relationship with the connected straight pipe,is nearly blank.On the basis,the influence of pipe connections on the joint deformations(JDs)of buried segmented pipes is analyzed in detail by considering different parameters,namely,connection shapes,ground conditions,pipe diameters,branch angles,seismic incident angles,and input ground motions.Moreover,an influence coefficient,which measures the influence of pipe connections on pipe JDs,is calculated.Results show that pipe connections can reduce the JDs of segmented pipes by 40%-50%.Furthermore,the JD is more sensitive to the connection shape,ground condition and pipe diameter than the incident angle and characteristics of seismic waves.An influence coefficient of 0.65 is recommended conservatively for the design of the buried segmented pipes.

Unsupervised Segmentation Method of Multicomponent Images based on Fuzzy Connectivity Analysis in the Multidimensional Histograms

Image segmentation denotes a process for partitioning an image into distinct regions, it plays an important role in interpretation and decision making. A large variety of segmentation methods has been developed;among them, multidimensional histogram methods have been investigated but their implementation stays difficult due to the big size of histograms. We present an original method for segmenting n-D (where n is the number of components in image) images or multidimensional images in an unsupervised way using a fuzzy neighbourhood model. It is based on the hierarchical analysis of full n-D compact histograms integrating a fuzzy connected components labelling algorithm that we have realized in this work. Each peak of the histo- gram constitutes a class kernel, as soon as it encloses a number of pixels greater than or equal to a secondary arbitrary threshold knowing that a first threshold was set to define the degree of binary fuzzy similarity be- tween pixels. The use of a lossless compact n-D histogram allows a drastic reduction of the memory space necessary for coding it. As a consequence, the segmentation can be achieved without reducing the colors population of images in the classification step. It is shown that using n-D compact histograms, instead of 1-D and 2-D ones, leads to better segmentation results. Various images were segmented;the evaluation of the quality of segmentation in supervised and unsupervised of segmentation method proposed compare to the classification method k-means gives better results. It thus highlights the relevance of our approach, which can be used for solving many problems of segmentation.

Disharmonious Ventricular Relationship and Topology for the Given Atrioventricular Connections.Contemporary Diagnostic Approach Using 3D Modeling and Printing

In the last issue,two case reports separately present examples of the extremely rare and complex congenital heart diseases that show concordant atrioventricular connections to the L-looped ventricles in the presence of situs solitus.Both cases highlight that the relationship between the two ventricles within the ventricular mass is not always harmonious with the given atrioventricular connection.Such disharmony between the connections and relationships requires careful assessment of the three basic facets of cardiac building blocks,namely their morphology,the relationship of their component parts,and their connections with the adjacent segments.3D imaging and printing can now facilitate an otherwise difficult diagnosis in such complex situations.Rotation of either the 3D images or the models permit accurate assessment of the ventricular topologic pattern by creating the right ventricular en-face septal view,thus facilitating placement of the observer’s hands.As we now emphasize,an alternative approach,which might prove more attractive to imagers,is to rotate the ventricular mass to provide the ventricular apical view,thus permitting determination of the ventricular relationship without using the hands.

Public Transit Connectivity Measures and Analysis of Weak Segments

Bus and any other public transit connectivity issues facilitate an understanding of the importance of transit planning in enhancing existing or new transit services. Improving transit connectivity is one of the most vital tasks in transit-operations planning. A poor connection can cause some passengers to stop using the transit service. Service-design criteria always contain postulates to improve routing and scheduling coordination （intra- and inter-agency transfer centers/points and synchronized/timed transfers）. Ostensibly the lack of well-defined connectivity measures precludes the weighing and quantifying of the result of any coordination effort. This work provides an initial methodological framework and concepts for （1） quantifying transit connectivity measures and （2） directions and tools for detecting weak segments in inter-route and inter-modal chains （paths） for possible revisions/changes.

MIA-UNet:Multi-Scale Iterative Aggregation U-Network for Retinal Vessel Segmentation

As an important part of the new generation of information technology,the Internet of Things(IoT)has been widely concerned and regarded as an enabling technology of the next generation of health care system.The fundus photography equipment is connected to the cloud platform through the IoT,so as to realize the realtime uploading of fundus images and the rapid issuance of diagnostic suggestions by artificial intelligence.At the same time,important security and privacy issues have emerged.The data uploaded to the cloud platform involves more personal attributes,health status and medical application data of patients.Once leaked,abused or improperly disclosed,personal information security will be violated.Therefore,it is important to address the security and privacy issues of massive medical and healthcare equipment connecting to the infrastructure of IoT healthcare and health systems.To meet this challenge,we propose MIA-UNet,a multi-scale iterative aggregation U-network,which aims to achieve accurate and efficient retinal vessel segmentation for ophthalmic auxiliary diagnosis while ensuring that the network has low computational complexity to adapt to mobile terminals.In this way,users do not need to upload the data to the cloud platform,and can analyze and process the fundus images on their own mobile terminals,thus eliminating the leakage of personal information.Specifically,the interconnection between encoder and decoder,as well as the internal connection between decoder subnetworks in classic U-Net are redefined and redesigned.Furthermore,we propose a hybrid loss function to smooth the gradient and deal with the imbalance between foreground and background.Compared with the UNet,the segmentation performance of the proposed network is significantly improved on the premise that the number of parameters is only increased by 2%.When applied to three publicly available datasets:DRIVE,STARE and CHASE DB1,the proposed network achieves the accuracy/F1-score of 96.33%/84.34%,97.12%/83.17%and 97.06%/84.10%,respectively.The experimental results show that the MIA-UNet is superior to the state-of-the-art methods.

Cyclic test and numerical study of seismic performance of precast segmental concrete double-columns

The comparative research on the seismic performance of grouted sleeve connected pier(GS)and prestressed precast segmental concrete pier(PC)is mostly carried out by numerical simulation.In this study,the GS pier and the PC pier of the new railway project from Hetian to Ruoqiang are taken into consideration.Two kinds of 1/5-scale assembled double-column specimens are made,and the quasi-static tests are carried out.The overall seismic performance of the two spliced piers is studied,and compared in terms of failure mechanism,bearing capacity,ductility,stiffness and energy dissipation capacity.The results show that the failure modes of both GS pier and PC pier are characterized by bending.However,the specific failure location and form are different.The GS pier presents a complete hysteretic curve,large equivalent stiffness and strong energy dissipation capacity.The hysteretic area of the PC pier is small.However,it has good self-reset ability and quasi-static residual displacement.Finite element models are set up using DispBeamColumn fiber elements and ZeroLength elements.The models that are calibrated with the test data can effectively simulate the damage development under monotonic loading.The load−displacement curves are in good agreement with the backbone curves of the test results.

PCB CT Image Element Segmentation Model Optimizing the Semantic Perception of Connectivity Relationship

Computed Tomography(CT)is a commonly used technology in Printed Circuit Boards(PCB)non-destructive testing,and element segmentation of CT images is a key subsequent step.With the development of deep learning,researchers began to exploit the“pre-training and fine-tuning”training process for multi-element segmentation,reducing the time spent on manual annotation.However,the existing element segmentation model only focuses on the overall accuracy at the pixel level,ignoring whether the element connectivity relationship can be correctly identified.To this end,this paper proposes a PCB CT image element segmentation model optimizing the semantic perception of connectivity relationship(OSPC-seg).The overall training process adopts a“pre-training and fine-tuning”training process.A loss function that optimizes the semantic perception of circuit connectivity relationship(OSPC Loss)is designed from the aspect of alleviating the class imbalance problem and improving the correct connectivity rate.Also,the correct connectivity rate index(CCR)is proposed to evaluate the model’s connectivity relationship recognition capabilities.Experiments show that mIoU and CCR of OSPC-seg on our datasets are 90.1%and 97.0%,improved by 1.5%and 1.6%respectively compared with the baseline model.From visualization results,it can be seen that the segmentation performance of connection positions is significantly improved,which also demonstrates the effectiveness of OSPC-seg.

An Adaptive Parameter-Free Optimal Number of Market Segments Estimation Algorithm Based on a New Internal Validity Index

An appropriate optimal number of market segments(ONS)estimation is essential for an enterprise to achieve successful market segmentation,but at present,there is a serious lack of attention to this issue in market segmentation.In our study,an independent adaptive ONS estimation method BWCON-NSDK-means++is proposed by integrating a newinternal validity index(IVI)Between-Within-Connectivity(BWCON)and a newstable clustering algorithmNatural-SDK-means++(NSDK-means++)in a novel way.First,to complete the evaluation dimensions of the existing IVIs,we designed a connectivity formula based on the neighbor relationship and proposed the BWCON by integrating the connectivity with other two commonly considered measures of compactness and separation.Then,considering the stability,number of parameters and clustering performance,we proposed the NSDK-means++to participate in the integrationwhere the natural neighbor was used to optimize the initial cluster centers(ICCs)determination strategy in the SDK-means++.At last,to ensure the objectivity of the estimatedONS,we designed a BWCON-based ONS estimation framework that does not require the user to set any parameters in advance and integrated the NSDK-means++into this framework forming a practical ONS estimation tool BWCON-NSDK-means++.The final experimental results showthat the proposed BWCONand NSDK-means++are significantlymore suitable than their respective existing models to participate in the integration for determining theONS,and the proposed BWCON-NSDK-means++is demonstrably superior to the BWCON-KMA,BWCONMBK,BWCON-KM++,BWCON-RKM++,BWCON-SDKM++,BWCON-Single linkage,BWCON-Complete linkage,BWCON-Average linkage and BWCON-Ward linkage in terms of the ONS estimation.Moreover,as an independentmarket segmentation tool,the BWCON-NSDK-means++also outperforms the existing models with respect to the inter-market differentiation and sub-market size.

3D Kronecker Convolutional Feature Pyramid for Brain Tumor Semantic Segmentation in MR Imaging

Brain tumor significantly impacts the quality of life and changes everything for a patient and their loved ones.Diagnosing a brain tumor usually begins with magnetic resonance imaging(MRI).The manual brain tumor diagnosis from the MRO images always requires an expert radiologist.However,this process is time-consuming and costly.Therefore,a computerized technique is required for brain tumor detection in MRI images.Using the MRI,a novel mechanism of the three-dimensional(3D)Kronecker convolution feature pyramid(KCFP)is used to segment brain tumors,resolving the pixel loss and weak processing of multi-scale lesions.A single dilation rate was replaced with the 3D Kronecker convolution,while local feature learning was performed using the 3D Feature Selection(3DFSC).A 3D KCFP was added at the end of 3DFSC to resolve weak processing of multi-scale lesions,yielding efficient segmentation of brain tumors of different sizes.A 3D connected component analysis with a global threshold was used as a post-processing technique.The standard Multimodal Brain Tumor Segmentation 2020 dataset was used for model validation.Our 3D KCFP model performed exceptionally well compared to other benchmark schemes with a dice similarity coefficient of 0.90,0.80,and 0.84 for the whole tumor,enhancing tumor,and tumor core,respectively.Overall,the proposed model was efficient in brain tumor segmentation,which may facilitate medical practitioners for an appropriate diagnosis for future treatment planning.

A Novel Apprehension of the Primary Lung Meridian, Sinew Channel, Divergent Channel, Luo-Connecting Channel Acting as a Single Unit System to Serve Respiration Function Based on Modern Neurophysiology and Kinesiology

The TCM philosophy of a meridian and associated channels pertains to the specific function of one or more organs. We define the Lung Primary Meridian (LUM) together with the Lung Sinew (LUSC), Divergent (LUDC), Luo-connecting (LULCC) Channels as a system of routes plus some parts of the body (such as muscles) to fulfil respiration, as a main function under different situations. There is very limited information about the Lung associated channels in classical literature of TCM. With a clear focus on the function of respiration, we have carried out a detailed analysis of the biomedical consequence of stimulating the LUM, analysed the roles played by LUSC, LUDC, and LULCC. The updated LUM and LUDC include acupoints of other meridians, serving the same purpose of performing satisfactory respiration starting from checking the quality of the inflow through the nose. The LUSC includes the respiratory muscles (plus the associated connective tissues) extending to various parts of the body. The muscles of the limb (as part of the LUSC) embrace the nerves that provide routes for somatosensory reflexes and play the role of locomotion, providing voluntary respiration via the pectoralis muscles. The muscles of LUSC are bounded by stiff connective tissue layers, forming compartments, and are part of the pulley system for various body locomotions. Within a compartment, the interstitial fluid, blood, lymph flows must be potent to protect the associated nerves related to LUM;the healthy state of the LUSC also provides freedom of various types of locomotion. The LULCC exists because the vagus nerve has a part of it passing through the spinal cords all the way down to the sacrum domain, with exiting nerve innervating two-third of the large intestine. The crucial steps of our deductions are supported by experimental evidence based on modern neurophysiology and kinesiology. We discover that all the four channels stated above work as a unit system to allow respiration to be possible under various postures/conditions. The complexity of structures and processes is eased off by providing 29 figures and 13 tables for the relevant muscles and nerves. In addition to respiration, the Lung system in TCM context includes interaction of this system with the sweat gland and neuroendocrine system;such aspects will be left to another study.

基于暂态导数极性分段优化的VSG二次调频控制策略

二次调频能使电力系统在一次调频过程中始终满足频率稳定的要求。采用虚拟同步发电机VSG控制策略的并网逆变器在离网运行时仅具备一次调频特性,若VSG的输出频率超出安全工作区间时将会对微电网的稳定运行产生影响,因此,需在有功控制环加入固定积分系数来实现频率无差控制的二次调频,但传统二次调频存在恢复时间与频率振荡的固有矛盾。提出一种根据暂态导数极性分段优化的改进VSG二次调频策略,该策略基于频率恢复特性,将频率恢复过程划分为2个阶段,并分别配置不同的积分系数,使得整个频率恢复时间缩短,且避免了频率的振荡,并给出了所提方法关键参数的设定依据。最后通过仿真与基于HIL的半实物平台验证了所提策略的可靠性。

基于最大最小蚁群系统的车载自组网路由策略

城市车载自组织网络中具有拓扑变化频繁,车辆分布不均匀等特性,因此如何选择下一跳车辆和确定最优传输路径是在复杂城市环境下设计高效路由协议的两个具有挑战性的问题.针对目前车载自组网中基于地理位置的算法具有下一跳车辆选取不合理,数据的传输路径缺少整体规划等问题,提出了一种基于最大最小蚁群系统的车载自组网路由策略.首先,采用基于分段连通度的最大最小蚁群探索机制进行路径探索.其次,采用基于接收节点驱动的转发机制优化数据包在车辆之间的多跳转发方式.仿真结果表明,与经典的基于地理位置的GPSR协议和基于GPSR协议改进的MM-GPSR算法相比较,本算法在数据包投递率和平均端到端时延方面均优于对比算法.

融合残差连接的图像语义分割方法

由于传统SegNet模型在采样过程中产生了大量信息损失,导致图像语义分割精度较低,为此提出了一种融合残差连接的新型编-解码器网络结构:文中引入了多残差连接策略,更为全面地保留了多尺度图像中包含的大量细节信息,降低还原降采样所带来的信息损失;为进一步加速网络训练的收敛效率,改善样本的不平衡问题,设计了一种带平衡因子的交叉熵损失函数,对正负样本不平衡现象予以针对性的优化,使得模型的训练更加高效;实验表明该方法较好地解决了语义分割中信息损失以及分割不准确的问题,与SegNet相比,本网络在Cityscapes数据集上进行精细标注的mIoU值提高了约13%。

融合编码器多尺度特征的RGB-D图像语义分割

针对语义分割任务中,室内场景中目标物体尺寸变化较大的问题,在ACFNet的基础上,提出融合编码器多尺度特征的RGB-D语义分割网络。首先,为有效利用网络提取的多尺度特征,提出结合池化操作的多尺度特征融合模块(PMFM),选择编码器不同阶段RGB和深度特征的融合特征作为该模块的输入;其次,设计改进的跳跃连接模块(ISCM),使用下一层级包含更多语义信息的特征图辅助修正当前层级的特征图,再经跳跃连接以拼接的方式传输到解码器对应阶段。将提出的网络模型应用到NYUD V2和SUN RGB-D数据集上,平均交并比分别达到了52.6%和48.8%。通过这两项改进,实验结果表明,上述方法达到了较高的分割准确率,优于对比的语义分割方法。

新型预制节段拼装桥墩抗震性能及塑性铰区变形分析

为探究采用两种不同连接方式(Fiber Reinforced Concrete(FRC)+锥套锁紧纵筋和Ultra-High Performance Concrete(UHPC)+纵筋错位搭接)预制节段拼装桥墩塑性铰性能,设计并制作了4组大比例缩尺(1∶2.5)桥墩。通过拟静力试验,采用数字图像相关(Digital Image Correlation,DIC)技术得到应变场和位移场,利用应变场和位移场捕捉并计算4个桥墩试件塑性铰区的裂缝发展趋势和宽度。结果表明:在大比例尺试件上,采用DIC技术测量位移具有较高的可靠性。4个桥墩试件加载至中高水平荷载时塑性铰区位移呈现非线性变化,具有明显的塑性位移发展趋势;两种连接方式接缝处裂缝宽度约为其他部位的2~8倍,表明接缝处为预制节段拼装桥墩变形的主要集中区域。

压弯扭作用下新型承插装配式桥墩的抗震性能

为明确并提升承插式拼装桥墩抵抗压弯扭等复合荷载的能力,提出了一种结合灌浆套筒和承插口组合连接的新型承插装配式墩,通过复合荷载作用下的拟静力试验对比了现浇(reinforced concrete,RC)、灌浆套筒(grouting and sleeve,GS)、承插口(socket with ultra-high performance concrete,SU)和结合套筒连接钢筋的新型承插(grouting sleeve and socket with ultra-high performance concrete,GSU)连接拼装桥墩的损伤机理和滞回性能,结合有限元模型重点讨论了承插口深度对滞回性能的影响。结果表明:4个构件的破坏模式都是以受弯破坏为主的弯扭破坏,其中SU构件出现了轻微拔起的现象,而对应的GSU构件并未出现该现象,与RC构件接近;各构件的剪力-墩顶位移骨架发展趋势比较一致,由于GSU构件纵向钢筋连续,具有更好的整体性能,其抗弯承载力与RC构件接近,且明显大于SU和GS构件,4个构件弯曲滞回耗能较为接近;承插口深度为1.0倍截面宽度的GSU构件抗扭承载力略高于RC构件,且明显大于其余装配式墩,GSU构件的扭转刚度、延性系数和耗能能力均大于其他3个墩;当承插口深度采用0.5倍构件截面宽度时,新型承插GSU构件的抗弯和抗扭承载力均略高于整体现浇构件,具有良好的抵抗压弯扭荷载的能力,可以实现浅承插口连接。研究结果可为压弯扭复合作用下装配式墩的应用提供试验依据。

偏心作用下预制拼装桥墩-盖梁体系抗震性能研究

目的 研究不同盖梁节段和盖梁-桥墩连接方式对预制拼装双柱墩偏心作用下的抗震性能的影响。方法 采用有限元软件ABAQUS建立7组预制拼装双柱墩,对其进行偏心作用下拟静力模拟研究,改变桥墩的盖梁节段连接和盖梁-桥墩连接方式,对比分析拼装桥墩和现浇桥墩的滞回曲线、骨架曲线、破坏形态、耗能性能和延性,研究不同盖梁节段连接和盖梁-桥墩连接方式的桥墩抗震性能。结果 盖梁节段键齿连接桥墩,采用灌浆套筒和预应力连接的承载能力与现浇桥墩相比均有小幅度提高,提高幅度在10%以内,抗震性能参数几乎一致。结论 盖梁斜键齿连接,盖梁-桥墩灌浆套筒和盖梁-桥墩预应力连接的预制拼装桥墩与现浇桥墩性能基本一致,同样具有良好抗震性能和整体性,满足抗震要求。

基于改进DeeplabV3+的水面多类型漂浮物分割方法研究

【目的】为解决传统图像处理方法鲁棒性差、常用深度学习检测方法无法准确识别大片漂浮物的边界等问题,【方法】提出一种基于改进DeeplabV3+的水面多类型漂浮物识别的语义分割方法,提高水面漂浮的识别能力。对所收集实际水面漂浮物进行分类,采用自制数据集进行对比试验。算法选择xception网络作为主干网络以获得初步漂浮物特征,在加强特征提取网络部分引入注意力机制以强调有效特征信息,在后处理阶段加入全连接条件随机场模型,将单个像素点的局部信息与全局语义信息融合。【结果】对比图像分割性能指标,改进后的算法mPA(Mean Pixel Accuracy)提升了5.73%,mIOU(Mean Intersection Over Union)提升了4.37%。【结论】相比于其他算法模型,改进后的DeeplabV3+算法对漂浮物特征的获取能力更强,同时能获得丰富的细节信息以更精准地识别多类型水面漂浮物的边界与较难分类的漂浮物,在对多个水库场景测试后满足实际水域环境中漂浮物检测的需求。

基于DA-Attention U-Net编码-解码结构的浮选矿浆相气泡图像分割

浮选矿浆相气泡图像是采集自浮选槽内部矿浆溶液中的图像数据,与浮选泡沫相图像数据相比视觉特征显著不同。针对使用特殊设备从浮选槽矿浆溶液中原位采集的气泡图像数据,提出了一种基于DA-Attention U-Net编码-解码结构的气泡分割模型。模型以U-Net为基础,引入CBAM模块并依据Residual残差连接思想改进模块结构,使模型同时具有通道注意力和空间注意力的优点,给予包含气泡的前景区域更大权重,减少因下采样次数多导致的信息丢失;引入ASPP模块并基于Dense密集连接思想进行改进,从多尺度提取气泡特征及整合前后特征层信息;并在完成气泡分割的基础上使用热力图与显著图对分割结果进行分析。研究结果表明,与原始U-Net相比,所提模型对气泡图像分割效果更优,训练损失、Dice系数降低了0.416、0.2,分别达到了0.015、0.12,MIoU精度值、F1_Score值提升了0.331、0.229,分别达到了0.952、0.985,并通过消融试验验证了各模块有效性。该模型对气泡图像的精确分割,可为后续提取气泡特征奠定基础,对于未来将矿浆相气泡特征信息用于浮选过程智能控制,具有重要意义。

基于多模态融合和自适应剪枝Transformer的脑肿瘤图像分割算法

脑肿瘤是目前世界上最致命的肿瘤之一,所以脑肿瘤图像的自动分割在临床诊疗中变得日益重要.近年来,基于CNN和Transformer的脑肿瘤分割方法在医学图像分割领域取得了令人欣喜的成就.然而,大多数方法没有充分利用脑肿瘤多模态间的互补性和差异性,并且模型中的Transformer在捕获远程依赖性的同时,忽略了其较大的计算复杂性、冗余依赖性等问题.针对此问题,提出一种基于多模态融合和自适应剪枝Transformer的脑肿瘤图像分割方法(MF-MAPT Swin UNETR),其中多模态融合模块可以充分学习性质相近的模态间信息和不同模态不同尺度的特征变化,为后续分割提供了充分的准备;基于多模态的自适应剪枝Transformer可以降低计算复杂度,对提升性能有一定的帮助,将MF-MAPT Swin UNETR模型在两个公共数据集上进行了实验验证,结果表明,该模型较最先进的方法整体具有突出的分割性能.