Five-phase Synchronous Reluctance Machines Equipped with a Novel Type of Fractional Slot Winding

Multi-phase machines are so attractive for electrical machine designers because of their valuable advantages such as high reliability and fault tolerant ability.Meanwhile,fractional slot concentrated windings(FSCW)are well known because of short end winding length,simple structure,field weakening sufficiency,fault tolerant capability and higher slot fill factor.The five-phase machines equipped with FSCW,are very good candidates for the purpose of designing motors for high reliable applications,like electric cars,major transporting buses,high speed trains and massive trucks.But,in comparison to the general distributed windings,the FSCWs contain high magnetomotive force(MMF)space harmonic contents,which cause unwanted effects on the machine ability,such as localized iron saturation and core losses.This manuscript introduces several new five-phase fractional slot winding layouts,by the means of slot shifting concept in order to design the new types of synchronous reluctance motors(SynRels).In order to examine the proposed winding’s performances,three sample machines are designed as case studies,and analytical study and finite element analysis(FEA)is used for validation.

Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.

Research on system combination of machine translation based on Transformer

Influenced by its training corpus,the performance of different machine translation systems varies greatly.Aiming at achieving higher quality translations,system combination methods combine the translation results of multiple systems through statistical combination or neural network combination.This paper proposes a new multi-system translation combination method based on the Transformer architecture,which uses a multi-encoder to encode source sentences and the translation results of each system in order to realize encoder combination and decoder combination.The experimental verification on the Chinese-English translation task shows that this method has 1.2-2.35 more bilingual evaluation understudy(BLEU)points compared with the best single system results,0.71-3.12 more BLEU points compared with the statistical combination method,and 0.14-0.62 more BLEU points compared with the state-of-the-art neural network combination method.The experimental results demonstrate the effectiveness of the proposed system combination method based on Transformer.

Classification of power quality combined disturbances based on phase space reconstruction and support vector machines

Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, Phase Space Reconstruction (PSR), a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. Support Vector Machines (SVMs) are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term dis-turbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) are also reported to show its advantages.

Design and Performance Analysis of Axial Flux Permanent Magnet Machines with Double-Stator Dislocation Using a Combined Wye-Delta Connection

Conventional fractional slot concentrated winding three-phase axial flux permanent magnet machines have an abundance of armature reaction magnetic field harmonics which deteriorate the torque performance of the machine.This paper presents a double-stator dislocated axial flux permanent magnet machine with combined wye-delta winding.A wye-delta(Y-△)winding connection method is designed to eliminate the 6 th ripple torque generated by air gap magnetic field harmonics.Then,the accurate subdomain method is adopted to acquire the no-load and armature magnetic fields of the machine,respectively,and the magnetic field harmonics and torque performance of the designed machine are analyzed.Finally,a 6 k W,4000 r/min,18-slot/16-pole axial flux permanent magnet machine is designed.The finite element simulation results show that the proposed machine can effectively eliminate the 6 th ripple torque and greatly reduce the torque ripple while the average torque is essentially identical to that of the conventional three-phase machines with wye-winding connection.

Adaptive Fuzzy Control System of Servomechanism for Electro-Discharge Machining Combined with Ultrasonic Vibration

For electro-discharge machining, only in the optimum state could the highest material removal rate be realized. In practical machining process, the timely elevation of the tool electrode is needed to eliminate chipping, which ordinarily occupies quite a lot of time. Therefore, besides the control of the machining parameters, the control of the optimum discharge gap and the conversion of different machining states is also needed. In this paper, the adaptive fuzzy control system of servomechanism for EDM combined with ultrasonic vibration is studied, the servomechanism of which is composed of the stepping motor comprising variable steps and the inductive synchronizer. The fuzzy control technology is used to realize the control of the frequency and the step of the servomechanism. The adaptive fuzzy controller has three inputs and two outputs, which can well meet the actual control requirements. The constitution of the fuzzy control regulation for the step frequency is the key to the design of the whole fuzzy control system of the servomechanism. The step frequency is mainly determined by the position error and the change rate of the position error. When the value of the position error is high or medium, the controlled parameters are selected to eliminate the error; when the position error is lower, the controlled parameters are selected to avoid the over-orientation and thus keep the stability of the system. According to these, a fuzzy control table is established in advanced, which is used to express the relations between the fuzzy input parameters and the fuzzy output parameters. The input parameters and the output parameters are all expressed by the level-values in fuzzy field. Therefore, the output parameters used for control can be obtained for the fuzzy control table according to the detected actual input parameters, by which the EDM combined with ultrasonic vibration is improved and the machining efficiency is increased. In addition, a stimulation program is designed by means of Microsoft Visual Basic

The application of machine learning under supervision in identification of shale lamina combination types——A case study of Chang 7_(3)sub-member organic-rich shales in the Triassic Yanchang Formation,Ordos Basin,NW China

Organic rich laminated shale is one type of favorable reservoirs for exploration and development of continental shale oil in China.However,with limited geological data,it is difficult to predict the spatial distribution of laminated shale with great vertical heterogeneity.To solve this problem,taking Chang 73 sub-member in Yanchang Formation of Ordos Basin as an example,an idea of predicting lamina combinations by combining'conventional log data-mineral composition prediction-lamina combination type identification'has been worked out based on machine learning under supervision on the premise of adequate knowledge of characteristics of lamina mineral components.First,the main mineral components of the work area were figured out by analyzing core data,and the log data sensitive to changes of the mineral components was extracted;then machine learning was used to construct the mapping relationship between the two;based on the variations in mineral composition,the lamina combination types in typical wells of the research area were identified to verify the method.The results show the approach of'conventional log data-mineral composition prediction-lamina combination type identification'works well in identifying the types of shale lamina combinations.The approach was applied to Chang 73 sub-member in Yanchang Formation of Ordos Basin to find out planar distribution characteristics of the laminae.

Reduction of Cogging Torque and Electromagnetic Vibration Based on Different Combination of Pole Arc Coefficient for Interior Permanent Magnet Synchronous Machine

Cogging torque and electromagnetic vibration are two important factors for evaluating permanent magnet synchronous machine(PMSM)and are key issues that must be considered and resolved in the design and manufacture of high-performance PMSM for electric vehicles.A fast and accurate magnetic field calculation model for interior permanent magnet synchronous machine(IPMSM)is proposed in this article.Based on the traditional magnetic potential permeance method,the stator cogging effect and complex boundary conditions of the IPMSM can be fully considered in this model,so as to realize the rapid calculation of equivalent magnetomotive force(MMF),air gap permeance,and other key electromagnetic properties.In this article,a 6-pole 36-slot IPMSM is taken as an example to establish its equivalent solution model,thereby the cogging torque is accurately calculated.And the validity of this model is verified by a variety of different magnetic pole structures,pole slot combinations machines,and prototype experiments.In addition,the improvement measure of the machine with different combination of pole arc coefficient is also studied based on this model.Cogging torque and electromagnetic vibration can be effectively weakened.Combined with the finite element model and multi-physics coupling model,the electromagnetic characteristics and vibration performance of this machine are comprehensively compared and analyzed.The analysis results have well verified its effectiveness.It can be extended to other structures or types of PMSM and has very important practical value and research significance.

Computationally guided design and synthesis of dual-drug loaded polymeric nanoparticles for combination therapy

Single-drug therapies or monotherapies are often inadequate,particularly in the case of life-threatening diseases like cancer.Consequently,combination therapies emerge as an attractive strategy.Cancer nanomedicines have many benefits in addressing the challenges faced by small molecule therapeutic drugs,such as low water solubility and bioavailability,high toxicity,etc.However,it remains a significant challenge in encapsulating two drugs in a nanoparticle.To address this issue,computational methodologies are employed to guide the rational design and synthesis of dual-drug-loaded polymer nanoparticles while achieving precise control over drug loading.Based on the sequential nanoprecipitation technology,five factors are identified that affect the formulation of drug candidates into dual-drug loaded nanoparticles,and then screened 176 formulations under different experimental conditions.Based on these experimental data,machine learning methods are applied to pin down the key factors.The implementation of this methodology holds the potential to signif-icantly mitigate the complexities associated with the synthesis of dual-drug loaded nanoparticles,and the co-assembly of these compounds into nanoparticulate systems demonstrates a promising avenue for combination therapy.This approach provides a new strategy for enabling the streamlined,high-throughput screening and synthesis of new nanoscale drug-loaded entities.

Sentence-Level Paraphrasing for Machine Translation System Combination

In this paper, we propose to enhance machine translation system combination (MTSC) with a sentence-level paraphrasing model trained by a neural network. This work extends the number of candidates in MTSC by paraphrasing the whole original MT translation sentences. First we train a neural paraphrasing model of Encoder-Decoder, and leverage the model to paraphrase the MT system outputs to generate synonymous candidates in the semantic space. Then we merge all of them into a single improved translation by a state-of-the-art system combination approach (MEMT) adding some new paraphrasing features. Our experimental results show a significant improvement of 0.28 BLEU points on the WMT2011 test data and 0.41 BLEU points without considering the out-of-vocabulary (OOV) words for the sentence-level paraphrasing model.

Maturity Classification of Rapeseed Using Hyperspectral Image Combined with Machine Learning

Oilseed rape is an important oilseed crop planted worldwide.Maturity classification plays a crucial role in enhancing yield and expediting breeding research.Conventional methods of maturity classification are laborious and destructive in nature.In this study,a nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms.Initially,hyperspectral images were captured for 3 distinct ripeness stages of rapeseed,and raw spectral data were extracted from the hyperspectral images.The raw spectral data underwent preprocessing using 5 pretreatment methods,namely,Savitzky-Golay,first derivative,second derivative(D2nd),standard normal variate,and detrend,as well as various combinations of these methods.Subsequently,the feature wavelengths were extracted from the processed spectra using competitive adaptive reweighted sampling,successive projection algorithm(SPA),iterative spatial shrinkage of interval variables(IVISSA),and their combination algorithms,respectively.The classification models were constructed using the following algorithms:extreme learning machine,k-nearest neighbor,random forest,partial least-squares discriminant analysis,and support vector machine(SVM)algorithms,applied separately to the full wavelength and the feature wavelengths.A comparative analysis was conducted to evaluate the performance of diverse preprocessing methods,feature wavelength selection algorithms,and classification models,and the results showed that the model based on preprocessing-feature wavelength selection-machine learning could effectively predict the maturity of rapeseed.The D2nd-IVISSA-SPA-SVM model exhibited the highest modeling performance,attaining an accuracy rate of 97.86%.The findings suggest that rapeseed maturity can be rapidly and nondestructively ascertained through hyperspectral imaging.

Estimation of Potato Biomass and Yield Based on Machine Learning from Hyperspectral Remote Sensing Data

The estimation of potato biomass and yield can optimize the planting pattern and tap the production potential.Based on partial least square(PLSR),multiple linear regression(MLR),support vector machine(SVM),random forest(RF),BP neural network and other machine learning algorithms,the biomass estimation model of potato in different growth stages is constructed by using single variables such as original spectrum,first-order differential spectrum,combined spectrum index and vegetation index(VI)and their coupled combination variables.The accuracy of the models is compared and analyzed,and the best modeling method of biomass in different growth stages is selected.Based on the optimized modeling method,the biomass of each growth stage is estimated,and the yield estimation model of different growth stages is constructed based on the estimation results and the linear regression analysis method,and the accuracy of the model is verified.The results showed that in tuber formation stage,starch accumulation stage and maturity stage,the biomass estimation accuracy based on combination variable was the highest,the best modeling method was MLR and SVM,in tuber growth stage,the best modeling method was MLR,the effect of yield estimation is good.It provides a reference for the algorithm selection of crop biomass and yield models based on machine learning.

马铃薯整地施肥播种联合作业机的研制与试验

依据现阶段马铃薯播种机械发展现状,结合本地农民对播种机的现实需求,设计了一款马铃薯整地施肥播种联合作业机。对机具进行总体结构设计和主要参数确定,在对关键部件进行详细设计计算的基础上,制作了马铃薯联合作业机的试验样机,并进行大量田间试验。试验结果表明,设计的马铃薯联合作业机各项指标满足国家标准要求。

基于CEEMDAN-GRU组合模型的碳排放交易价格预测研究

准确的碳价格预测有助于监管部门观测碳交易市场运行状况及投资者进行科学决策,对实现碳达峰和碳中和具有重要作用。但碳价序列具有非线性、非平稳性和高噪声的特性,很难对其进行准确预测。将完全自适应噪声集合经验模态分解(CEEMDAN)方法与门控循环单元(GRU)相结合,构建一个碳排放交易价格预测模型。该模型基于分解、集成思想,利用CEEMDAN将原始碳价序列分解,获得不同频率的本征模函数(IMF)和残差序列,使用GRU神经网络分别为各子序列建立预测模型,最后集成预测结果得到碳价预测值。以湖北省碳交易市场的日度成交价为例进行实证分析,结果表明:相较于其他5种基准模型,CEEMDAN-GRU模型具有更小的预测误差和更高的拟合优度,在碳价格预测上具有一定的优势。

基于可解释机器学习的黄河源区径流分期组合预报

黄河源区是黄河流域重要的产流区和我国重要的清洁能源基地,提高黄河源区径流预报准确率可为流域水资源科学调配和水风光清洁能源高效利用提供重要支撑。以黄河源区唐乃亥和玛曲水文站为研究对象,基于不同月份径流组分的差异,考虑积雪覆盖率及融雪水当量变化,构建了中长期径流分期组合机器学习预报模型及其可解释性分析框架。研究结果表明:1)年内的径流预报时段可划分为融雪影响期(3—6月)和非融雪主导(以降雨和地下水补给为主)期(7月—次年2月);2)与传统不分期模型相比,唐乃亥站和玛曲站分期组合预报模型的纳什效率系数分别达0.897、0.835,确定系数(R2)分别达0.897、0.839,均方根误差分别降低了10%、17%,提高了径流预报准确率,通过分位数映射校正,唐乃亥站和玛曲站预报模型的R2分别进一步提升至0.926和0.850;3)基于SHAP机器学习可解释性分析框架,辨识了预报因子对径流预报结果的贡献程度,由高到低依次为降水、前一个月流量、蒸发、气温、相对湿度、融雪水当量等,发现了不同预报因子之间交互作用散点分布具有拖尾式或阶跃式的特征。

暴雨洪涝灾害转移安置人数的组合预测模型研究

为了更加科学精准地预测暴雨洪涝灾害下需要转移安置的人数,收集2011—2018年全国范围内严重暴雨洪涝灾害案例,通过Pearson相关性分析检验转移安置人数与表征暴雨洪涝灾害严重程度影响因素之间的关系;分别使用基于主成分分析的回归模型和支持向量机(SVM)预测暴雨洪涝灾害下需要转移安置人数,并以2种方法的结果为基础,提出1种组合预测方法对暴雨洪涝灾害转移人数进行修正。研究结果表明:组合预测法的MSE、MAE均小于回归预测和SVM模型预测。使用组合预测方法对洪涝灾害转移安置人数进行预测,可以充分结合单一预测模型的优势,提高组合预测模型的预测精度和泛化能力。研究结果可为确定暴雨洪涝灾害的避难需求并制定避难疏散计划提供参考。

ML组合的CYGNSS海面风速反演质量控制模型

卷积神经网络(CNN)可用于气旋全球导航卫星系统(CYGNSS)的海面风速反演。虽然在模型训练前设置了质量控制指标来检测和削弱CYGNSS的异常观测数据,但CYGNSS观测数据中仍存在异常值导致模型反演精度降低,甚至出现错误反演结果。因此,提出一种基于机器学习(ML)组合的海面风速反演模型。在基于CNN回归模型的CYGNSS反演海面风速基础上,ML分类模型生成CNN回归结果的质量标志位,该标志位可以检测并删除CNN回归结果的异常值,进一步提高风速反演结果的数据质量,ML分类模型能够更好地考虑各种数据误差之间的相互作用,而不是单独使用每个条件的阈值,以达到更优的海面风速反演精度的效果。实验对比了Logistic回归(LR)、决策树(DT)、朴素贝叶斯模型、K最邻近(KNN)算法、神经网络(NN)模型、支持向量机(SVM)算法等6个分类模型,其中,基于KNN算法的分类模型对风速反演质量控制的效果最优。所提风速反演组合模型显著提高了反演结果的精度,在0~20 m/s区间内,异常样本过滤率为81.27%,在所有被过滤的数据中,过滤正确率为86.03%;风速反演误差的均方根误差从无ML分类模型的1.7 m/s降低到有ML分类模型的1.44 m/s,其中,训练样本为0~10 m/s的反演结果精度提升效果较为明显,证明了所提风速反演组合模型对风速质量控制的有效性。

高速钢桶焊缝机电机座支撑脚复合模设计

针对高速钢桶焊缝机电机座支撑脚使用2 mm厚的SECC板料进行中小批量冲压成形的要求,设计了3副模具用于制件的4道工序成形。冲孔落料和修边压印采用倒装式复合模结构、切分模和弯曲模采用正装结构,且切分模和弯曲模集成在1副模具内。弯曲模中设计了凸模驱动的双滑块内收弯曲成形机构,该机构在制件弯曲壁的两侧都使用滑块进行夹紧驱动弯曲,能有效防止弯曲回弹,保证了制件的弯曲成形尺寸。模具结构简单实用,成形工艺设计合理,实现了制件的自动化生产。

民航客运量预测方法研究综述

为了提升民航运行效率、准确预测客运量,促进其可持续发展,采用分类学方法将民航客运量预测方法划分为传统统计学、机器学习、组合模型3大类。详述各类方法的改进原理、效果和应用,通过数据处理、权重调整、参数优化和结构改进提高准确性,并总结组合模型相对单一模型的优势。实证研究结果表明,组合预测模型相较于单一模型具有更高的准确性,并指出结合人工智能和大数据技术的发展趋势,构建优秀的组合预测模型将是提高准确性的潜在研究方向。

数据驱动的污水处理高密池混凝加药预测研究

高密池是污水处理工艺流程中关键且复杂的一个环节,而混凝加药过程在高密池中扮演重要的角色,针对混凝加药非线性、大迟滞性、不确定因素较多的特性,为实现加药量的预测控制,达到降低成本的目的。提出主成分分析(Principal Component Analysis,PCA)和极限学习机(Extreme Learning Machine,ELM)以及长短记忆神经网络(Long Short-Term Memory,LSTM)残差组合预测方法,PCA降维和LSTM残差优化能够有效提高ELM的预测精度,同时对模型参数进行优化可以得到最优方法。利用污水处理数据进行验证,所提预测方法的平均绝对误差为0.14%,均方误差根为0.63%。试验结果表明,该方法在预测精度上明显优于随机森林等机器学习预测方法,为混凝加药量的预测和控制提供了可靠的依据,并具有实际应用价值。