Effect of wheelset flexibility on wheel–rail contact behavior and a specific coupling of wheel–rail contact to flexible wheelset

The fexibility of a train＇s wheelset can have a large effect on vehicle–track dynamic responses in the medium to high frequency range.To investigate the effects of wheelset bending and axial deformation of the wheel web,a specifi coupling of wheel–rail contact with a fexible wheelset is presented and integrated into a conventional vehicle–track dynamic system model.Both conventional and the proposed dynamic system models are used to carry out numerical analyses on the effects of wheelset bending and axial deformation of the wheel web on wheel–rail rolling contact behaviors.Excitations with various irregularities and speeds were considered.The irregularities included measured track irregularity and harmonic irregularities with two different wavelengths.The speeds ranged from 200 to400km/h.The results show that the proposed model can characterize the effects of fexible wheelset deformation on the wheel–rail rolling contact behavior very well.

Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

The brain tumour is the mass where some tissues become old or damaged,but they do not die or not leave their space.Mainly brain tumour masses occur due to malignant masses.These tissues must die so that new tissues are allowed to be born and take their place.Tumour segmentation is a complex and time-taking problem due to the tumour’s size,shape,and appearance variation.Manually finding such masses in the brain by analyzing Magnetic Resonance Images(MRI)is a crucial task for experts and radiologists.Radiologists could not work for large volume images simultaneously,and many errors occurred due to overwhelming image analysis.The main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning approaches.This research study proposed an automatic model for tumor segmentation in MRI images.The proposed model has a few significant steps,which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative(NIFTI)volumes into the 3D NumPy array.In the second step,the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated parameters.In the third step,the proposed model uses state-of-the-art Medical Image Computing and Computer-Assisted Intervention(MICCAI)BRATS 2018 dataset withMRI modalities such as T1,T1Gd,T2,and Fluidattenuated inversion recovery(FLAIR).Tumour types in MRI images are classified according to the tumour masses.Labelling of these masses carried by state-of-the-art approaches such that the first is enhancing tumour(label 4),edema(label 2),necrotic and non-enhancing tumour core(label 1),and the remaining region is label 0 such that edema(whole tumour),necrosis and active.The proposed model is evaluated and gets the Dice Coefficient(DSC)value for High-grade glioma(HGG)volumes for their test set-a,test set-b,and test set-c 0.9795, 0.9855 and 0.9793, respectively. DSC value for the Low-gradeglioma (LGG) volumes for the test set is 0.9950, which shows the proposedmodel has achieved significant results in segmenting the tumour in MRI usingdeep learning approaches. The proposed model is fully automatic that canimplement in clinics where human experts consumemaximumtime to identifythe tumorous region of the brain MRI. The proposed model can help in a wayit can proceed rapidly by treating the tumor segmentation in MRI.

Residue Based Open-Loop Modal Analysis Method for Detecting LFMR of PMSG-WFs Penetrated Power Systems

This paper proposes a residue based open-loop modal analysis method to detect low frequency modal resonance(LFMR),including asymmetric low frequency modal attraction(ALFMA)and asymmetric low frequency modal repulsion(ALFMR),of permanent magnetic synchronous generator based wind farms(PMSG-WFs)penetrated power systems.The formation of ALFMA and ALFMR caused by two open-loop low frequency oscillation(LFO)modes moving close and apart is analyzed in detail.Via predicting the trajectories of closed-loop LFO modes based on calculation of residue of open-loop LFO modes,both ALFMA and ALFMR can be detected.The proposed method can select LFO modes which move to the right half complex plane as control parameters vary.Simulation studies are carried out on a three-machine power system and a four-machine 11-bus power system to verify the properties of the proposed method.

Observation of photoacoustic/photothermal effect with a liquid-core optical ring resonator

Photothermal/photoacoustic（PT/PA） spectroscopy provides useful knowledge about optical absorption, as well as the thermal and acoustical properties of a liquid sample. For microfluidic biosensing and bioanalysis where an extremely small volume of liquid sample is encapsulated, simultaneous PT/PA detection remains a challenge. In this work, we present a new optofluidic device based on a liquid-core optical ring resonator（LCORR） for the investigation of PT and PA effects in fluid samples. A focused 532 nm pulsed light optically heats the absorptive fluid in a capillary to locally create a transient temperature rise, as well as acoustic waves. A1550 nm CW laser light is quadrature-locked to detect the resonance spectrum shift of the LCORR and study thermal diffusion and acoustic wave propagation in the capillary. This modality provides an optofluidic investigative platform for biological/biochemical sensing and spectroscopy.