基于WOA-BAT的多能源微电网优化调度

为了提高微电网日常运行的经济性和环保性,以及解决鲸鱼算法(WOA)收敛速度较慢、易陷入局部最优的问题,提出了一种鲸鱼-蝙蝠算法(WOA-BAT)。首先,建立微电网优化调度模型,在模型中引入BAT算法对WOA算法进行优化;其次,将鲸鱼觅食行为和蝙蝠搜索策略相结合,将WOA-BAT算法用于仿真微电网并网模式下的多目标优化调度。仿真结果表明:WOA-BAT算法相比于传统的WOA、BAT和粒子群优化算法(PSO),能有效提高微电网日运行总效益及系统运行稳定性。

Descriptive and Comparative Histology of the Urinary System of the Hematophagous Bats Desmodus rotundus and Diphylla ecaudata

Desmodus rotundus and Diphylla ecaudata, both of which are mammals of the order Chiroptera, Desmodontidae family, their diet consisting exclusively of blood. D. rotundus is the main vector and transmitter of the rabies virus, which affects human beings as well as several livestock species so the study of this bat species is of high importance within the fields of animal agriculture and public health. The present study describes and compares the histologic characteristics of the urinary system of two hematophagous bat species. A total of 5 bats from each species were captured in the municipalities of Progreso de Obregón, Hidalgo (D. rotundus), and Huayacocotla, Veracruz (D. ecaudata). Organs belonging to the urinary system were extracted: kidneys, ureters, urinary bladder, and urethra;samples were fixed using 10% formalin and processed by the paraffin embedding technique, obtaining sections of 5 µm thickness, which in turn were stained using hematoxylin-eosin (H-E) and Gomori trichrome (GT) stains. From the obtained histologic preparations, a descriptive and comparative analysis of the structural organography of the urinary system of both species was made, and no noteworthy histological differences between samples were noted. The present research is intended to provide a framework for future studies of these species’ currently understudied microscopic anatomy.

Myotis bat STING attenuates aging-related inflammation in female mice

Bats,notable as the only flying mammals,serve as natural reservoir hosts for various highly pathogenic viruses in humans(e.g.,SARS-CoV and Ebola virus).Furthermore,bats exhibit an unparalleled longevity among mammals relative to their size,particularly the Myotis bats,which can live up to 40 years.However,the mechanisms underlying these distinctive traits remain incompletely understood.In our prior research,we demonstrated that bats exhibit dampened STING-interferon activation,potentially conferring upon them the capacity to mitigate virus-or aging-induced inflammation.To substantiate this hypothesis,we established the first in vivo bat-mouse model for aging studies by integrating Myotis davidii bat STING(MdSTING)into the mouse genome.We monitored the genotypes of these mice and performed a longitudinal comparative transcriptomic analysis on MdSTING and wild-type mice over a 3-year aging process.Blood transcriptomic analysis indicated a reduction in aging-related inflammation in female MdSTING mice,as evidenced by significantly lower levels of pro-inflammatory cytokines and chemokines,immunopathology,and neutrophil recruitment in aged female MdSTING mice compared to aged wild-type mice in vivo.These results indicated that MdSTING knock-in attenuates the aging-related inflammatory response and may also improve the healthspan in mice in a sex-dependent manner.Although the underlying mechanism awaits further study,this research has critical implications for bat longevity research,potentially contributing to our comprehension of healthy aging in humans.

Chromosome-level genome and population genomics of the intermediate horseshoe bat(Rhinolophus affinis)reveal the molecular basis of virus tolerance in Rhinolophus and echolocation call frequency variation

Horseshoe bats(genus Rhinolophus,family Rhinolophidae)represent an important group within chiropteran phylogeny due to their distinctive traits,including constant high-frequency echolocation,rapid karyotype evolution,and unique immune system.Advances in evolutionary biology,supported by high-quality reference genomes and comprehensive whole-genome data,have significantly enhanced our understanding of species origins,speciation mechanisms,adaptive evolutionary processes,and phenotypic diversity.However,genomic research and understanding of the evolutionary patterns of Rhinolophus are severely constrained by limited data,with only a single published genome of R.ferrumequinum currently available.In this study,we constructed a high-quality chromosome-level reference genome for the intermediate horseshoe bat(R.affinis).Comparative genomic analyses revealed potential genetic characteristics associated with virus tolerance in Rhinolophidae.Notably,we observed expansions in several immune-related gene families and identified various genes functionally associated with the SARS-CoV-2 signaling pathway,DNA repair,and apoptosis,which displayed signs of rapid evolution.In addition,we observed an expansion of the major histocompatibility complex class II(MHC-II)region and a higher copy number of the HLA-DQB2 gene in horseshoe bats compared to other chiropteran species.Based on whole-genome resequencing and population genomic analyses,we identified multiple candidate loci(e.g.,GLI3)associated with variations in echolocation call frequency across R.affinis subspecies.This research not only expands our understanding of the genetic characteristics of the Rhinolophus genus but also establishes a valuable foundation for future research.

What Is the Impact of Ploughing on Eucalyptus Plantations in Bambou Mingali (Plateaux Batéké, Republic of Congo)?

Soil ploughing is an important stage in the preparation of planting, causing disturbance to the physical, chemical and biological properties of the soil. Soil ploughing can affect the availability of nutrients and water resources, and its effect can be short, medium or long-term. Soil ploughing accelerates surface heating and air circulation and encourages mineralisation by transforming organic matter into mineral salts, making nutrients soluble and accessible to plants. The aim of this study is to determine how soil ploughing affects the distribution of nutrients in the soil profile. The study focuses on nitrogen, carbon, phosphorus, calcium and magnesium, which are major elements of soil fertility on the Batéké plateaux in Congo. The results indicate that ploughing significantly modifies the distribution at depth des elements nutritifs: there is more accumulation at the surface than at depth (ei: nitrogen 1.34 t/ha ± 0.035 at 10 cm compared with 1.034 t/ha ± 0.098 at 50 cm) with a higher concentration of carbon (13.89 t/ha ± 0.87) followed by nitrogen (1.34 t/ha ± 0.035).

Sedimentary Dynamics of the Sands of the Cover Formation in the Batéké Plateaus (Republic of Congo): Paleoenvironmental Implications

The depositional environment of the sands of the cover formation is discussed. This study aims to determine the paleoenvironments of deposition of the sands of the cover formation in the Batéké Plateaus by studying sedimentary dynamics based on the description of lithological facies in the field and granulometric analyses in the laboratory. In the field, six (6) lithostratigraphic logs were surveyed and 42 sand samples were taken for laboratory analysis. In the laboratory, the samples underwent granulometric, sieving and sedimentometry analyses, after washing with running water using a 63 µm sieve. These analyses made it possible to determine the granulometric classes of the samples. The sieving results allowed to determine the granulometric parameters (mean, standard deviation, mode, median, skewness, flattening or kurtosis) using the method of moments with the software “Gradistat V.8”, granulometric parameters with which the granulometric facies, the mode of transport and the deposition environment were determined using the diagrams. Morphoscopy made it possible to determine the form and aspect of the surface of the quartz grains constituting these sands. Granulometric analyses show that these silty-clay or clayey-silty sands are fine sands and rarely medium sands, moderately to well sorted and rarely well sorted. The dominant granulometric facies is hyperbolic (sigmoid), with parabolic facies being rare. The primary mode of transport of these sands is saltation, which dominates rolling. The dispersion of points in the diagrams shows that these sands originate from two depositional environments: aeolian and fluvial. Morphoscopic analysis reveals the presence of clean rounded matt grains (RM), dirty rounded matt grains (RS), shiny blunt grains (EL) and shiny rounded grains (RL). The rounded matt grains exhibit several impact marks. The presence of dirty rounded grains with a ferruginous cement on their surface indicates that these sands have been reworked. These sands have undergone two types of transport, first by wind (aeolian environment) and then by water (fluvial environment).

BAT评分联合CTA点征对幕上自发性脑出血患者早期血肿扩大的预测价值研究

目的研究BAT评分联合CTA点征对幕上自发性脑出血患者早期血肿扩大的预测价值。方法回顾性分析2021年9月—2022年4月连续于首都医科大学附属北京天坛医院急诊就诊且发病至一站式CT检查时间在6 h内的幕上自发性脑出血患者的资料,根据发病后24~48 h复查头颅CT与基线CT的血肿体积对比,分为血肿扩大组和非血肿扩大组。比较两组患者的基本临床资料、CTA点征及非增强CT(non-contrast computed tomography sign,NCCT)特点(低密度征、混合征等)影像资料、BAT评分(blend sign,any hypodensity,timing of NCCT score,BAT)。采用单因素分析和多因素logistic回归分析血肿扩大的危险因素,并绘制ROC曲线分析BAT评分、CTA点征及两者结合对自发性脑出血患者早期血肿扩大的预测价值。结果共纳入97例患者,血肿扩大组28例,非血肿扩大组69例。血肿扩大组患者CTA点征,NCCT低密度征、混合征的发生率均高于非血肿扩大组,BAT评分中位数和BAT评分≥3分的患者比例均高于非血肿扩大组,发病至基线影像时间短于非血肿扩大组,基线出血体积和基线随机血糖水平高于非血肿扩大组,上述差异均有统计学意义。多因素logistic回归分析结果显示,CTA点征阳性(OR 31.828,95%CI 5.350~189.337,P<0.01)、BAT评分≥3分(OR 71.976,95%CI 5.391~960.899,P<0.01)、基线出血体积(OR 1.029,95%CI 1.003~1.055,P=0.03)和基线随机血糖(OR 1.355,95%CI 1.070~1.714,P=0.01)可独立预测血肿扩大。ROC曲线分析显示,基线出血体积预测血肿扩大的AUC为0.762,BAT评分≥3分预测血肿扩大的AUC为0.716,CTA点征阳性预测血肿扩大的AUC为0.756,BAT评分≥3分联合CTA点征阳性预测血肿扩大的AUC为0.833。De Long检验提示两者联合预测效力优于单独预测效力(P<0.05)。结论幕上自发性脑出血患者发病6 h内,BAT评分≥3分及CTA点征阳性都可有效预测血肿扩大,两者联合预测的效力更佳。

高血压脑出血患者短期预后的影响因素及BAT评分的预测效果

目的 分析高血压脑出血患者短期预后的影响因素及非增强CT 5分预测法(BAT)评分的预测效果。方法 2016年1月—2020年12月在中国人民解放军南部战区海军第二医院治疗的618例高血压脑出血患者,将其中术后28 d内死亡的62例患者设为死亡组,其余556例存活的患者设为存活组。分析高血压脑出血患者术后28 d内死亡的影响因素,并采用受试者操作特征(ROC)曲线分析BAT评分对高血压脑出血患者术后28 d内死亡的预测价值。结果 术前24 h格拉斯哥昏迷量表(GCS)评分<6分、BAT评分≥4分、首次CT距发病时间短、合并术后24 h并发症为高血压脑出血患者术后28 d内死亡的影响因素(P<0.05)。ROC曲线分析显示,BAT评分预测高血压脑出血患者术后28 d内死亡的ROC曲线下面积(AUC)为0.892(P<0.01),95%CI为0.789~0.932,最佳截断值为4分,敏感度为88.29%,特异度为81.43%。结论 术前24 h GCS评分、BAT评分、首次CT距发病时间、术后24 h并发症为高血压脑出血患者术后28 d内死亡的影响因素,且BAT评分对患者预后有较佳的预测价值。

The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

The pupil recognition method is helpful in many real-time systems,including ophthalmology testing devices,wheelchair assistance,and so on.The pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size,occlusion of eyelids,and eyelashes.Deep Convolutional Neural Networks(DCNN)are being used in pupil recognition systems and have shown promising results in terms of accuracy.To improve accuracy and cope with larger datasets,this research work proposes BOC(BAT Optimized CNN)-IrisNet,which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil region.The proposed method is based on very deep architecture and many tricks from recently developed popular CNNs.Experiment results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight,easy to implement,and of cutting-edge accuracy.Finally,the region-based black box method for determining pupil center coordinates was introduced.The proposed architecture was tested using various IRIS databases,including the CASIA(Chinese academy of the scientific research institute of automation)Iris V4 dataset,which has 99.5%sensitivity and 99.75%accuracy,and the IIT(Indian Institute of Technology)Delhi dataset,which has 99.35%specificity and MMU(Multimedia University)99.45%accuracy,which is higher than the existing architectures.

基于HS-Bat算法的电网负荷卸载协调分配系统设计

多电网负荷联合分配后,系统中将产生较多的剩余负荷。为了实现剩余负荷的优化分配,设计了基于和声搜索-蝙蝠(HSBat)算法的电网负荷卸载协调分配系统。硬件设计调整了电网负荷卸载异步数据的接收装置,将时钟点位与异步数据调整为同步状态,以提升电网负荷卸载分配精度。软件设计基于HS-Bat计算电网剩余负荷最大值,建立电网负荷分配模型。设定单电网联合分配与多电网联合分配的目标函数,划分整体与局部功率的约束条件,设计电网负荷卸载协调分配算法。以剩余负荷峰谷差和剩余艾伦方差为指标进行性能测试。测试结果表明:设计系统应用后,剩余负荷峰谷差和剩余艾伦方差低于1.4,电网负荷分配效果更好,提高了电网负荷协调分配后的系统稳定性。

On the use of cross polarization in solid-state NMR:1 H spinlock versus adiabatic demagnetization in the rotating frame

Cross polarization(CP)is a widely used solid-state nuclear magnetic resonance(NMR)technique for enhancing the polarization of dilute S spins from much larger polarization of abundant I spins such as 1 H.To achieve such a polarization transfer,the I spin should either be spin-locked or be converted to the dipolar ordered state through adiabatic demagnetization in the rotating frame.In this work,we analyze the spin dynamics of the Hartmann-Hahn CP(HHCP)utilizing the 1 H spin-locking,and the dipolar-order CP(DOCP)having the 1 H adiabatic demagnetization.We further propose an adiabatic demagnetization CP(ADCP)where a constant radio-frequency pulse is applied on the S spin while 1 H is adiabatically demagnetized.Our analyses indicate that ADCP utilizes the adiabatic passage to effectively achieve the polarization transfer from the 1 H to S spins.In addition,the dipolar ordered state generated during the 1 H demagnetization process could also be converted into the observable S polarization through DOCP,further enhancing the polarized signals.It is shown by both static and magic-angle-spinning(MAS)NMR experiments that ADCP has dramatically broadened the CP matching condition over the other CP schemes.Various samples have been used to demonstrate the polarization transfer efficiency of this newly proposed ADCP scheme.

Bat 26 Microsatellite Instability in Oral Cavity Cancers in Senegal

Oral cavity cancers are part of head and neck cancers. They have become frequent in the world in general and Senegal in particular. This study evaluates microsatellite instability tumors in oral cavity cancers in Senegal. Forty cancerous tissues, 20 healthy tissues, and 12 blood tissues were included in this study. These tissues were collected from each patient during the biopsy after obtaining consent. DNA extraction, Polymerase Chain Reaction (PCR) and sequencing were carried out to obtain sequences. Mutation surveyor, Bioedit and Dnasp software were used to perform our analyses. High instability was found in 57.5% of patients with cancer. Moreover, 90% of the patients had the same motif on healthy and cancerous tissue. Furthermore, 26.12%, 20.72%, and 11.71% polymorphic sites were found in cancerous, healthy and blood tissue respectively. Thus, a similarity between cancerous and healthy tissues seems to exist. This implies that instability of the Bat 26 microsatellite could occur early in the occurrence of oral cavity cancers.

Technique for Multi-Pass Turning Optimization Based on Gaussian Quantum-Behaved Bat Algorithm

The multi-pass turning operation is one of the most commonly used machining methods in manufacturing field.The main objective of this operation is to minimize the unit production cost.This paper proposes a Gaussian quantum-behaved bat algorithm(GQBA)to solve the problem of multi-pass turning operation.The proposed algorithm mainly includes the following two improvements.The first improvement is to incorporate the current optimal positions of quantum bats and the global best position into the stochastic attractor to facilitate population diversification.The second improvement is to use a Gaussian distribution instead of the uniform distribution to update the positions of the quantum-behaved bats,thus performing a more accurate search and avoiding premature convergence.The performance of the presented GQBA is demonstrated through numerical benchmark functions and amulti-pass turning operation problem.Thirteen classical benchmark functions are utilized in the comparison experiments,and the experimental results for accuracy and convergence speed demonstrate that,in most cases,the GQBA can provide a better search capability than other algorithms.Furthermore,GQBA is applied to an optimization problem formulti-pass turning,which is designed tominimize the production cost while considering many practical machining constraints in the machining process.The experimental results indicate that the GQBA outperforms other comparison algorithms in terms of cost reduction,which proves the effectiveness of the GQBA.

Neighborhood Search Based Improved Bat Algorithm for Web Service Composition

Web services are provided as reusable software components in the services-oriented architecture.More complicated composite services can be combined from these components to satisfy the user requirements represented as a workflow with specified Quality of Service(QoS)limitations.The workflow consists of tasks where many services can be considered for each task.Searching for optimal services combination and optimizing the overall QoS limitations is a Non-deterministic Polynomial(NP)-hard problem.This work focuses on the Web Service Composition(WSC)problem and proposes a new service composition algorithm based on the micro-bats behavior while hunting the prey.The proposed algorithm determines the optimal combination of the web services to satisfy the complex user needs.It also addresses the Bat Algorithm(BA)shortcomings,such as the tradeoff among exploration and exploitation searching mechanisms,local optima,and convergence rate.The proposed enhancement includes a developed cooperative and adaptive population initialization mechanism.An elitist mechanism is utilized to address the BA convergence rate.The tradeoff between exploration and exploitation is handled through a neighborhood search mechanism.Several benchmark datasets are selected to evaluate the proposed bat algorithm’s performance.The simulation results are estimated using the average fitness value,the standard deviation of the fitness value,and an average of the execution time and compared with four bat-inspired algorithms.It is observed from the simulation results that introduced enhancement obtains significant results.

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare.Biomedical Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in nature.Due to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients.In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals.The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)approach.To accomplish this,the proposed IBADL-BECGC model initially pre-processes the input signals.Besides,IBADL-BECGC model applies NasNet model to derive the features from test ECG signals.In addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet approach.Finally,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification method.The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset.The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

Hybridizing Artificial Bee Colony with Bat Algorithm for Web Service Composition

In the Internet of Things(IoT),the users have complex needs,and the Web Service Composition(WSC)was introduced to address these needs.The WSC’s main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services(QoS)constraints.The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints.In this paper,we introduce an extension of our previous works on the Artificial Bee Colony(ABC)and Bat Algorithm(BA).A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search.The bat agent is used to improve the solution of exhausted bees after a threshold(limits),and also an Elitist Strategy(ES)is added to BA to increase the convergence rate.The performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-ofthe-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria(average fitness values,best fitness values,and execution time)that were measured for 30 different runs.These datasets are created from real-world datasets and artificially to form different scale sizes of WSC datasets.The results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to competitors.TheWilcoxon signed-rank significant test is usedwhere the proposed algorithm results significantly differ fromother algorithms on 100%of datasets.

BAT批处理程序在图片信息自动处理的辅助应用

文章介绍BAT批处理程序在图片信息自动处理的辅助应用。通过BAT批处理程序引入图片信息提取技术和自动化处理流程,可以实现图片信息自动分类、自动识别和自动存储等功能,从而减少人工干预和提高工作效率;同时,文章主要研究图片信息自动处理在典型领域的应用场景,设置的场景为无人机输电线路巡检图片信息提取,在提高生产力和降低成本的同时,也为企业带来更快捷的服务和更好的用户体验。最后,指出其在办公自动化中的重要价值和应用前景。

Hybrid Deep Learning-Improved BAT Optimization Algorithm for Soil Classification Using Hyperspectral Features

Now a days,Remote Sensing(RS)techniques are used for earth observation and for detection of soil types with high accuracy and better reliability.This technique provides perspective view of spatial resolution and aids in instantaneous measurement of soil’s minerals and its characteristics.There are a few challenges that is present in soil classification using image enhancement such as,locating and plotting soil boundaries,slopes,hazardous areas,drainage condition,land use,vegetation etc.There are some traditional approaches which involves few drawbacks such as,manual involvement which results in inaccuracy due to human interference,time consuming,inconsistent prediction etc.To overcome these draw backs and to improve the predictive analysis of soil characteristics,we propose a Hybrid Deep Learning improved BAT optimization algorithm(HDIB)for soil classification using remote sensing hyperspectral features.In HDIB,we propose a spontaneous BAT optimization algorithm for feature extraction of both spectral-spatial features by choosing pure pixels from the Hyper Spectral(HS)image.Spectral-spatial vector as training illustrations is attained by merging spatial and spectral vector by means of priority stacking methodology.Then,a recurring Deep Learning(DL)Neural Network(NN)is used for classifying the HS images,considering the datasets of Pavia University,Salinas and Tamil Nadu Hill Scene,which in turn improves the reliability of classification.Finally,the performance of the proposed HDIB based soil classifier is compared and analyzed with existing methodologies like Single Layer Perceptron(SLP),Convolutional Neural Networks(CNN)and Deep Metric Learning(DML)and it shows an improved classification accuracy of 99.87%,98.34%and 99.9%for Tamil Nadu Hills dataset,Pavia University and Salinas scene datasets respectively.

Tasks Scheduling in Cloud Environment Using PSO-BATS with MLRHE

Cloud computing plays a significant role in Information Technology(IT)industry to deliver scalable resources as a service.One of the most important factor to increase the performance of the cloud server is maximizing the resource utilization in task scheduling.The main advantage of this scheduling is to max-imize the performance and minimize the time loss.Various researchers examined numerous scheduling methods to achieve Quality of Service(QoS)and to reduce execution time.However,it had disadvantages in terms of low throughput and high response time.Hence,this study aimed to schedule the task efficiently and to eliminate the faults in scheduling the tasks to the Virtual Machines(VMs).For this purpose,the research proposed novel Particle Swarm Optimization-Bandwidth Aware divisible Task(PSO-BATS)scheduling with Multi-Layered Regression Host Employment(MLRHE)to sort out the issues of task scheduling and ease the scheduling operation by load balancing.The proposed efficient sche-duling provides benefits to both cloud users and servers.The performance evalua-tion is undertaken with respect to cost,Performance Improvement Rate(PIR)and makespan which revealed the efficiency of the proposed method.Additionally,comparative analysis is undertaken which confirmed the performance of the intro-duced system than conventional system for scheduling tasks with highflexibility.

基于径向气隙磁密和定子电流的永磁同步电机均匀退磁故障诊断研究

该文提出一种基于径向气隙磁密和定子电流的永磁同步电机均匀退磁故障诊断方法。首先,建立电机的d轴等效磁路模型,分析均匀退磁故障后径向气隙磁密的变化规律;然后,获取电机的径向气隙磁密和定子电流信号,对各极下径向气隙磁密幅值进行归一化处理,并绘制雷达图,根据雷达图和比较相同定子电流幅值下健康电机和故障电机的各极下径向气隙磁密幅值的平均值,来诊断均匀退磁故障;利用空载各极下径向气隙磁密幅值的平均值计算故障程度。最后,仿真和实验结果均验证所提均匀退磁故障诊断方法的有效性。