Numerical study of progressive damage analysis on filament wound composite tubes embedded with metal joints

Song Lin,Linan Xu,Shuangwen Li,Xuran Liu,Weili Jiang,Xiaolong Jia 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.11

This paper investigates the progressive damage of filament wound composite tubes embedded within metal joints. Finite element analysis (FEA) modellings were provided through CADWIND filament wound simulation software, and progressive damage analysis of composite tubes was carried out using the Puck criterion under tensile loading. Besides, the ultimate tensile loading of composite tube was carried out. It was found that the inside metal joint extruded the outer composite layers, which resulted in further failure damage of the composite tubes. Furthermore, thickening the composite layer outside metal joint was beneficial to improve the tensile load of the composite tube. Moreover, the numerical results of the FEA provided a reasonably good agreement with the experimental results, which was less than 10 % on the ultimate tensile loading value. Finally, the FEA with the Puck criterion was suitable to predict the progressive damage of filament wound composite tube embedded with metal joints.

Spatial Patterns, Longitudinal Development, and Hemispheric Asymmetries of Cortical Thickness in Infants from Birth to 2 Years of Age

Li, Gang,Lin, Weili,Gilmore, John H.,Shen, Dinggang Society for Neuroscience 2015 The Journal of neuroscience Vol.35 No.24

<P>Cortical thickness (CT) is related to normal development and neurodevelopmental disorders. It remains largely unclear how the characteristic patterns of CT evolve in the first 2 years. In this paper, we systematically characterized for the first time the detailed vertex-wise patterns of spatial distribution, longitudinal development, and hemispheric asymmetries of CT at 0, 1, and 2 years of age, via surface-based analysis of 219 longitudinal magnetic resonance images from 73 infants. Despite the dynamic increase of CT in the first year and the little change of CT in the second year, we found that the overall spatial distribution of thin and thick cortices was largely present at birth, and evolved only modestly during the first 2 years. Specifically, the precentral gyrus, postcentral gyrus, occipital cortex, and superior parietal region had thin cortices, whereas the prefrontal, lateral temporal, insula, and inferior parietal regions had thick cortices. We revealed that in the first year thin cortices exhibited low growth rates of CT, whereas thick cortices exhibited high growth rates. We also found that gyri were thicker than sulci, and that the anterior bank of the central sulcus was thicker than the posterior bank. Moreover, we showed rightward hemispheric asymmetries of CT in the lateral temporal and posterior insula regions at birth, which shrank gradually in the first 2 years, and also leftward asymmetries in the medial prefrontal, paracentral, and anterior cingulate cortices, which expanded substantially during this period. This study provides the first comprehensive picture of early patterns and evolution of CT during infancy.</P>

Scalable joint segmentation and registration framework for infant brain images

Dong, Pei,Wang, Li,Lin, Weili,Shen, Dinggang,Wu, Guorong Elsevier 2017 Neurocomputing Vol.229 No.-

<P><B>Abstract</B></P> <P>The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We developed an efficient approach to deal with the tissue segmentation and registration for the infant brain MR images. </LI> <LI> Our proposed framework is scalable to various registration tasks in early brain development studies. </LI> <LI> Promising results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old. </LI> <LI> The proposed technique can be very useful for many ongoing early brain development studies. </LI> </UL> </P>

Segmentation of perivascular spaces in 7T MR image using auto-context model with orientation-normalized features

Park, Sang Hyun,Zong, Xiaopeng,Gao, Yaozong,Lin, Weili,Shen, Dinggang Elsevier 2016 NeuroImage Vol.134 No.-

<P><B>Abstract</B></P> <P>Quantitative study of perivascular spaces (PVSs) in brain magnetic resonance (MR) images is important for understanding the brain lymphatic system and its relationship with neurological diseases. One of the major challenges is the accurate extraction of PVSs that have very thin tubular structures with various directions in three-dimensional (3D) MR images. In this paper, we propose a learning-based PVS segmentation method to address this challenge. Specifically, we first determine a region of interest (ROI) by using the anatomical brain structure and the vesselness information derived from eigenvalues of image derivatives. Then, in the ROI, we extract a number of randomized Haar features which are normalized <I>with respect to</I> the principal directions of the underlying image derivatives. The classifier is trained by the random forest model that can effectively learn both discriminative features and classifier parameters to maximize the information gain. Finally, a sequential learning strategy is used to further enforce various contextual patterns around the thin tubular structures into the classifier. For evaluation, we apply our proposed method to the 7T brain MR images scanned from 17 healthy subjects aged from 25 to 37. The performance is measured by voxel-wise segmentation accuracy, cluster-wise classification accuracy, and similarity of geometric properties, such as volume, length, and diameter distributions between the predicted and the true PVSs. Moreover, the accuracies are also evaluated on the simulation images with motion artifacts and lacunes to demonstrate the potential of our method in segmenting PVSs from elderly and patient populations. The experimental results show that our proposed method outperforms all existing PVS segmentation methods.</P>

Determination of meaty peptide in enzymatic hydrolyzate of beef protein by HPLC-MS

Yanping Wang,Songrong Zeng,Xiaojia Bai,Weili Lin,Ming Yang,Haipeng Xing 한국화학공학회 2008 Korean Journal of Chemical Engineering Vol.25 No.5

The purpose of this study is to detect beefy meaty peptide (BMP) in beef hydrolyzate. The synthesized BMP is used as a standard sample in the study. High performance liquid chromatography (HPLC)/ion trap electrospray ionization mass spectrometry (ESI-MS) with UV detection was applied in qualitative analysis of the peptides. Six beef protein enzymatic hydrolyzate samples were separated on a Surveyor HPLC system through a SUPELCO Discovery® C18 analytical column (5 μm, 15 cm×2.1 mm i.d.). The column was eluted at a flow rate of 0.2 mL/min in a linear gradient elution mode of acetronitrile-water solution with 0.1% trifluoroacetic acid. The concentration of acetronitrile was increased from 5% to 50% in 40 minutes. A Finnigan LCQ Advantage MAX instrument was used as detector to analyze with ESI-MS and ESI-MS/MS in positive mode. Among the six samples of beef protein enzymatic hydrolysate, the BMP is detected and confirmed in sample No.4 with a higher intensity of characteristic peak and is further investigated by ESI-MS/MS. As a result, BMP exists in sample No.4. The study proves that HPLC-ESI-MS/MS is a simple, rapid, sensitive method to analyze target peptides from complex polypeptides

Welding Performance of Low-Yield-Strength Steel Shear Panel Dampers Under Large Plastic Deformation

Chaofeng Zhang,Shixi Chen,Junhua Zhao,Xuchuan Lin,Lingxin Zhang,Jiajia Zhu,Weili Wang 한국강구조학회 2021 International Journal of Steel Structures Vol.21 No.6

Characterized by large plastic deformation and energy-dissipation capacity, low-yield-strength steel shear panel dampers (LYSPDs) are widely used as energy-dissipating members in seismic engineering. However, the eff ect of welding on the properties of LYSPDs remains unclear. Hence, in this study, we investigated the matching performance between low-yieldstrength steel (LYS) and two diff erent welding materials. Subsequently, the quality of welding between LYS and dissimilar metals was investigated. Furthermore, the fracture characteristics of the welding seam of LYS under large plastic deformation were explored. Finally, the welding performance of LYSPDs under large plastic deformation was verifi ed under cyclic loading. The results of this study are signifi cant in terms of the eff ect of welding on the properties of LYSPDs.

Mapping Longitudinal Development of Local Cortical Gyrification in Infants from Birth to 2 Years of Age

Li, Gang,Wang, Li,Shi, Feng,Lyall, Amanda E.,Lin, Weili,Gilmore, John H.,Shen, Dinggang Society for Neuroscience 2014 The Journal of neuroscience Vol.34 No.12

<P>Human cortical folding is believed to correlate with cognitive functions. This likely correlation may have something to do with why abnormalities of cortical folding have been found in many neurodevelopmental disorders. However, little is known about how cortical gyrification, the cortical folding process, develops in the first 2 years of life, a period of dynamic and regionally heterogeneous cortex growth. In this article, we show how we developed a novel infant-specific method for mapping longitudinal development of local cortical gyrification in infants. By using this method, via 219 longitudinal 3T magnetic resonance imaging scans from 73 healthy infants, we systemically and quantitatively characterized for the first time the longitudinal cortical global gyrification index (GI) and local GI (LGI) development in the first 2 years of life. We found that the cortical GI had age-related and marked development, with 16.1% increase in the first year and 6.6% increase in the second year. We also found marked and regionally heterogeneous cortical LGI development in the first 2 years of life, with the high-growth regions located in the association cortex, whereas the low-growth regions located in sensorimotor, auditory, and visual cortices. Meanwhile, we also showed that LGI growth in most cortical regions was positively correlated with the brain volume growth, which is particularly significant in the prefrontal cortex in the first year. In addition, we observed gender differences in both cortical GIs and LGIs in the first 2 years, with the males having larger GIs than females at 2 years of age. This study provides valuable information on normal cortical folding development in infancy and early childhood.</P>

Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI

Xiang, Lei,Qiao, Yu,Nie, Dong,An, Le,Lin, Weili,Wang, Qian,Shen, Dinggang Elsevier 2017 Neurocomputing Vol.267 No.-

<P><B>Abstract</B></P> <P>Positron emission tomography (PET) is an essential technique in many clinical applications such as tumor detection and brain disorder diagnosis. In order to obtain high-quality PET images, a standard-dose radioactive tracer is needed, which inevitably causes the risk of radiation exposure damage. For reducing the patient's exposure to radiation and maintaining the high quality of PET images, in this paper, we propose a deep learning architecture to estimate the high-quality standard-dose PET (SPET) image from the combination of the low-quality low-dose PET (LPET) image and the accompanying T1-weighted acquisition from magnetic resonance imaging (MRI). Specifically, we adapt the convolutional neural network (CNN) to account for the two channel inputs of LPET and T1, and directly learn the end-to-end mapping between the inputs and the SPET output. Then, we integrate multiple CNN modules following the auto-context strategy, such that the tentatively estimated SPET of an early CNN can be iteratively refined by subsequent CNNs. Validations on real human brain PET/MRI data show that our proposed method can provide competitive estimation quality of the PET images, compared to the state-of-the-art methods. Meanwhile, our method is highly efficient to test on a new subject, e.g., spending ∼2 s for estimating an entire SPET image in contrast to ∼16 min by the state-of-the-art method. The results above demonstrate the potential of our method in real clinical applications.</P>

Structured Learning for 3-D Perivascular Space Segmentation Using Vascular Features

Zhang, Jun,Gao, Yaozong,Park, Sang Hyun,Zong, Xiaopeng,Lin, Weili,Shen, Dinggang IEEE 2017 IEEE Transactions on Biomedical Engineering Vol.64 No.12

<P>Objective: The goal of this paper is to automatically segment perivascular spaces (PVSs) in brain from high-resolution 7T magnetic resonance (MR) images. Methods: We propose a structured-learning-based segmentation framework to extract the PVSs from high-resolution 7T MR images. Specifically, we integrate three types of vascular filter responses into a structured random forest for classifying voxels into two categories, i.e., PVS and background. In addition, we propose a novel entropy-based sampling strategy to extract informative samples in the background for training an explicit classification model. Since the vascular filters can extract various vascular features, even thin and low-contrast structures can be effectively extracted from noisy backgrounds. Moreover, continuous and smooth segmentation results can be obtained by utilizing patch-based structured labels. Results: The performance of our proposed method is evaluated on 19 subjects with 7T MR images, with the Dice similarity coefficient reaching 66%. Conclusion: The joint use of entropy-based sampling strategy, vascular features, and structured learning can improve the segmentation accuracy. Significance: Instead of manual annotation, our method provides an automatic way for PVS segmentation. Moreover, our method can be potentially used for other vascular structure segmentation because of its data-driven property.</P>

Hierarchical and symmetric infant image registration by robust longitudinal-example-guided correspondence detection : Hierarchical and symmetric infant image registration

Wu, Yao,Wu, Guorong,Wang, Li,Munsell, Brent C.,Wang, Qian,Lin, Weili,Feng, Qianjin,Chen, Wufan,Shen, Dinggang Wiley (John WileySons) 2015 Medical physics Vol.42 No.7

<P>To investigate anatomical differences across individual subjects, or longitudinal changes in early brain development, it is important to perform accurate image registration. However, due to fast brain development and dynamic tissue appearance changes, it is very difficult to align infant brain images acquired from birth to 1-yr-old.</P>