Overview and Analysis of PM Starter/Generator for Aircraft Electrical Power Systems

More Electrical Aircraft(MEA)which replaces the hydraulic and pneumatic power by electrical power leads to reducing emissions and fuel consumption.The MEA concept has led to a growing use of the starter/generator(S/G)system.Permanent magnet(PM)machines have been gaining interests for aircraft S/G system application over the last few years.This is mainly due to the several advantages,including high power density,high efficiency and high speed ability.The shortcoming of the PM machines is the de-­excitation problem in case of a failure,which is a main issue for the aircraft application.However,by using a PM machine with high reactance or multiphase configuration,the fault-­tolerant ability can be improved.In terms of the aircraft S/G system,this paper is going to present a comprehensive analysis of PM machines.Firstly,the state-­of-­the-­art of PM starter/generator(PMS/G)is summarized and the basic structure of PMS/G system is analyzed.Next,key technologies of the PMS/G system are summarized and analyzed.Finally,a flux weakening fault protection strategy that is used to suppress the turn-­to-­turn short circuit(SC)current is studied,simulated and verified.With the breakthrough of key technologies based on the development of high temperature electromagnetic material and high temperature power electronics,the PMS/G will be a potential candidate for aircraft S/G system including the embedded power generation system.

A narrative review of glaucoma screening from fundus images

The objective of the paper is to provide a general view for automatic cup to disc ratio(CDR)assessment in fundus images.As for the cause of blindness,glaucoma ranks as the second in ocular diseases.Vision loss caused by glaucoma cannot be reversed,but the loss may be avoided if screened in the early stage of glaucoma.Thus,early screening of glaucoma is very requisite to preserve vision and maintain quality of life.Optic nerve head(ONH)assessment is a useful and practical technique among current glaucoma screening methods.Vertical CDR as one of the clinical indicators for ONH assessment,has been well-used by clinicians and professionals for the analysis and diagnosis of glaucoma.The key for automatic calculation of vertical CDR in fundus images is the segmentation of optic cup(OC)and optic disc(OD).We take a brief description of methodologies about the OC and disc optic segmentation and comprehensively presented these methods as two aspects:hand-craft feature and deep learning feature.Sliding window regression,super-pixel level,image reconstruction,super-pixel level low-rank representation(LRR),deep learning methodologies for segmentation of OD and OC have been shown.It is hoped that this paper can provide guidance and bring inspiration to other researchers.Every mentioned method has its advantages and limitations.Appropriate method should be selected or explored according to the actual situation.For automatic glaucoma screening,CDR is just the reflection for a small part of the disc,while utilizing comprehensive factors or multimodal images is the promising future direction to furthermore enhance the performance.

Multi-rater Prism:Learning self-calibrated medical image segmentation from multiple raters

In medical image segmentation,it is often necessary to collect opinions from multiple experts to make the final decision.This clinical routine helps to mitigate individual bias.However,when data is annotated by multiple experts,standard deep learning models are often not applicable.In this paper,we propose a novel neural network framework called Multi-rater Prism(MrPrism)to learn medical image segmentation from multiple labels.Inspired by iterative half-quadratic optimization,MrPrism combines the task of assigning multi-rater confidences and calibrated segmentation in a recurrent manner.During this process,MrPrism learns inter-observer variability while taking into account the image's semantic properties and finally converges to a self-calibrated segmentation result reflecting inter-observer agreement.Specifically,we propose Converging Prism(ConP)and Diverging Prism(DivP)to iteratively process the two tasks.ConP learns calibrated segmentation based on multi-rater confidence maps estimated by DivP,and DivP generates multi-rater confidence maps based on segmentation masks estimated by ConP.Experimental results show that the two tasks can mutually improve each other through this recurrent process.The final converged segmentation result of MrPrism outperforms state-of-the-art(SOTA)methods for a wide range of medical image segmentation tasks.The code is available at https://github.-com/WuJunde/MrPrism.

The standardized design and application guidelines: A primary-oriented artificial intelligence screening system of the lesion sign in the macular region based on fundus color photography

With the popularity and development of artificial intelligence(AI),disease screening systems based on AI algorithms are gradually emerging in the medical field.Such systems can be used for primary screening of diseases to relieve the pressure on primary health care.In recent years,AI algorithms have demonstrated good performance in the analysis and identification of lesion signs in the macular region of fundus color photography,and a screening system for fundus lesion signs applicable to primary screening is bound to emerge in the future.Therefore,to standardize the design and clinical application of macular region lesion sign screening systems based on AI algorithms,the Ocular Fundus Diseases Group of Chinese Ophthalmological Society,in collaboration with relevant experts,developed this guideline after investigating issues,discussing production evidence,and holding guideline workshops.It aimed to establish uniform standards for the definition of the macular region and lesion signs,AI adoption scenarios,algorithm model construction,dataset establishment and labeling,architecture and function design,and image data acquisition for the screening system to guide the implementation of the screening work.

Overview and Development of Variable Frequency AC Generators for More Electric Aircraft Generation System

With the development of more electric aircraft(MEA),higher demands for electrical energy are put forward in generation systems.Compared to constant frequency AC(CFAC)generation systems,the constant speed drive(CSD)is eliminated and integrated starter/generator(SG)can be realized in variable frequency AC(VFAC)generation systems.In this paper,an overview of VFAC generators for safety-critical aircraft applications is presented,with a particular focus on the key features and requirements of candidate generators and the starting control strategies.Wound rotor synchronous machines(WRSMs)are typical generators used in VFAC generation systems so far.Meanwhile,hybrid excitation synchronous machines(HESMs)and cage-type induction machines are promising candidates for VFAC generation systems.The generation operation of WRSM is relatively mature,however,the SG technology of WRSM is still full of challenges.As one of the most important issues,the starting excitation methods of WRSM are summarized.An HESM-based VFAC SG system is proposed and developed in this paper.The experimental results show that the starting mode,transition mode and generating mode of the VFAC SG system are realized.The continuous progress of VFAC generation system makes great contributions to the realization of MEA.

Behavior and functional modeling methods of doubly salient electromagnetic generators for aircraft electrical power system applications

The Doubly Salient Electromagnetic Generator(DSEG) is a promising candidate in aircraft generator application due to the simplicity, robustness and reliability. However, the field windings and the armature windings are strongly coupled, which makes the inductance characteristics non-linear and too complex to model. The complex model with low precision also leads to difficulties in modeling and analysis of the entire aircraft Electrical Power System(EPS). A behavior level modeling method based on modified inductance Support Vector Machine(SVM) is proposed. The Finite Element Analysis(FEA) inductance data are modified based on the experiment results to improve the precision. A functional level modeling method based on input–output characteristics SVM is also proposed. The two modeling methods are applied to a 9 kW DSEG prototype. The steady state and transient process precision of the proposed methods are proved by comparing with the experiment results. Meanwhile, the modeling time consumption, the application time consumption and the calculation resource demand are compared. The DSEG behavior and functional modeling methods provide precious results with high efficiency, which accelerates theoretical analysis and expands the application foreground of the DSEG in the aircraft EPS.

Annotation and quality control specifications for fundus color photograph

1.Scope This article sets terms and definitions,basic requirements,annotat-ing requirements,and quality control for fundus color photograph an-notating.This article applies to the fundus color photograph of annotating and quality control of four ocular diseases signs for the purpose of referral or screening,including glaucoma,macular disorders,high myopic macular degeneration and diabetic retinopathy.Note:When referring to this article,please pay attention to the scope of annotating the signs of macular disorders in this document.

Handwashing quality assessment via deep learning:a modelling study for monitoring compliance and standards in hospitals and communities

Background Hand hygiene can be a simple,inexpensive,and effective method for preventing the spread of infectious diseases.However,a reliable and consistent method for monitoring adherence to the guidelines within and outside healthcare settings is challenging.The aim of this study was to provide an approach for monitoring handwashing compliance and quality in hospitals and communities.Methods We proposed a deep learning algorithm comprising three-dimensional convolutional neural networks(3D CNNs)and used 230 standard handwashing videos recorded by healthcare professionals in the hospital or at home for training and internal validation.An assessment scheme with a probability smoothing method was also proposed to optimize the neural network’s output to identify the handwashing steps,measure the exact duration,and grade the standard level of recognized steps.Twenty-two videos by healthcare professionals in another hospital and 28 videos recorded by civilians in the community were used for external validation.Results Using a deep learning algorithm and an assessment scheme,combined with a probability smoothing method,each handwashing step was recognized(ACC ranged from 90.64%to 98.87%in the hospital and from 87.39%to 96.71%in the community).An assessment scheme measured each step’s exact duration,and the intraclass correlation coefficients were 0.98(95%CI:0.97-0.98)and 0.91(95%CI:0.88-0.93)for the total video duration in the hospital and community,respectively.Furthermore,the system assessed the quality of handwashing,similar to the expert panel(kappa=0.79 in the hospital;kappa=0.65 in the community).Conclusions This work developed an algorithm to directly assess handwashing compliance and quality from videos,which is promising for application in healthcare settings and communities to reduce pathogen transmis-sion.