3D Web Reconstruction of a Fibrous Filter Using Sequential Multi-Focus Images

A fibrous filtering material is a kind of fiber assembly whose structure exhibits a three-dimensional(3D)network with dense microscopic open channels.The geometrical/morphological attributes,such as orientations,curvatures and compactness,of fibers in the network is the key to the filtration performance of the material.However,most of the previous studies were based on materials’2D micro-images,which were unable to accurately measure these important 3D features of a filter’s structure.In this paper,we present an imaging method to reconstruct the 3D structure of a fibrous filter from its optical microscopic images.Firstly,a series of images of the fiber assembly were captured at different depth layers as the stage moved vertically.Then a fusion image was established by extracting fiber edges from each layered image.Thirdly,the 3D coordinates of the fiber edges were determined using the sharpness/clarity of each edge pixel in the layered images.Finally,the 3D structure the fiber system was reconstructed through distance transformation based on the locations of fiber edges.

Modeling Additional Twists of Yarn Spun by Lateral Compact Spinning with Pneumatic Groove

Compact spinning with pneumatic grooves is a spinning process to gather fibers by blended actions of airflow and mechanical forces.Modified from the ring spinning system,the lateral compact spinning with pneumatic grooves can improve yarn appearance and properties due to generated additional twists.In this study,we investigated additional twists of the lateral compact spinning with pneumatic grooves via a finite element(FE)method.An elastic thin rod was used to model a fiber to simulate its dynamic deformation in the three-dimensional space,and the space bar unit was used to simplify the fiber model for the dynamic analysis.The stiffness equation of the elastic rod element and the dynamic equation of the rigid body mass element were derived from the differential equation of the elastic thin rod.In the analysis of the nonlinear geometric displacement of the space elastic thin rod unit,the large deformation problem was solved with the stepwise loading successive approximation.The simulation results explained the mechanism of generating additional twists,and the experiment results proved the existence of additional twists.The study demonstrated that the FE model is effective for predicting additional twists of fiber bundles in the agglomeration zone,and for simulating the fiber motion in the compact spinning with pneumatic grooves.

Classifying Abdominal Fat Distribution Patterns byUsing Body Measurement Data

This study aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues(VAT and SAT)measured by magnetic resonance imaging(MRI),to analyze the relationship between the VAT-SAT distribution patterns and the novel body shape descriptors(BSDs),and to develop a classifier to predict the fat distribution clusters using the BSDs.In the study,66 male and 54 female participants were scanned by MRI and a stereovision body imaging(SBI)to measure participants’abdominal VAT and SAT volumes and the BSDs.A fuzzy c-means algorithm was used to form the inherent grouping clusters of abdominal fat distributions.A support-vector-machine(SVM)classifier,with an embedded feature selection scheme,was employed to determine an optimal subset of the BSDs for predicting internal fat distributions.A fivefold cross-validation procedure was used to prevent over-fitting in the classification.The classification results of the BSDs were compared with those of the traditional anthropometric measurements and the Dual Energy X-Ray Absorptiometry(DXA)measurements.Four clusters were identified for abdominal fat distributions:(1)low VAT and SAT,(2)elevated VAT and SAT,(3)higher SAT,and(4)higher VAT.The cross-validation accuracies of the traditional anthropometric,DXA and BSD measurements were 85.0%,87.5% and 90%,respectively.Compared to the traditional anthropometric and DXA measurements,the BSDs appeared to be effective and efficient in predicting abdominal fat distributions.