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.

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.

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.

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.

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.

A combined forecasting method for intermittent demand using the automotive aftermarket data

Intermittent demand forecasting is an important challenge in the process of smart supply chain transformation,and accurate demand forecasting can reduce costs and increase efficiency for enterprises.This study proposes an intermittent demand combination forecasting method based on internal and external data,builds intermittent demand feature engineering from the perspective of machine learning,predicts the occurrence of demand by classification model,and predicts non-zero demand quantity by regression model.Based on the strategy selection on the inventory side and the stocking needs on the replenishment side,this study focuses on the optimization of the classification problem,incorporates the internal and external data of the enterprise,and proposes two combination forecasting optimization methods on the basis of the best classification threshold searching and transfer learning,respectively.Based on the real data of auto after-sales business,these methods are evaluated and validated in multiple dimensions.Compared with other intermittent forecasting methods,the models proposed in this study have been improved significantly in terms of classification accuracy and forecasting precision,which validates the potential of combined forecasting framework for intermittent demand and provides an empirical study of the framework in industry practice.The results show that this research can further provide accurate upstream inputs for smart inventory and guarantee intelligent supply chain decision-making in terms of accuracy and efficiency.

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.

党参铺膜施肥移栽联合作业机的设计与试验

针对党参等根茎类中药材移栽种植机械化程度低等问题,分析了国内外各根茎类作物移栽机的工作方法及结构特点,设计了一种党参半自动移栽设备,实现深耕、施肥、移栽(人工辅助放苗)、覆膜为一体的联合作业。以中药材党参为参考进行田间测试,从而掌握移栽机的性能及结构特点,为根茎类中药材移栽机实现国产研发推广提供参考。

组合缓冲约束下的多目标混合流水线节能调度

为解决生产阶段间带有无限缓冲和阻塞两种中间缓冲约束的混合流水线节能调度问题,考虑不相关并行机和多时间约束建立数学模型,结合问题特征提出一种改进多目标模因算法以同时最小化最大完工时间和机器总能耗。采用基于不相关机器分配的矩阵编码方案,利用基于Tent混沌映射的混合初始化策略生成初始元胞数组,全局优化算子应用基于参数的自适应遗传策略改进的非支配排序遗传算法,局部增强搜索算子应用一种融合自适应选择邻域搜索和多目标模拟退火的搜索策略以提高算法搜索能力。通过24种不同规模问题的算例实验,验证了所提算法求解该问题的有效性和优越性。实验结果表明:改进多目标模因算法在平均运行时间241.26 s内得到的平均IGD值为47.89,平均SP值为857.25,均低于其他3种对比算法。改进多目标模因算法所求解集具有较好的收敛性、多样性和分布性。

Design and experiment of bionic stubble breaking-deep loosening combined tillage machine

Under the conditions of straw returning operation,there are three major technical bottlenecks in the Phaeozem region of northeast China,namely low stubble breaking rate,poor tillage depth consistency,and high fuel consumption.In this research,a bionic stubble-deep loosening combined tillage machine(BSD)was designed through bionic prototype analysis,coupled bionic analysis,coupled bionic design,theoretical analysis and application of intelligent control techniques.It consists of a bionic stubble breaking device and a bionic self-excited vibratory deep loosening device.Based on the unique biting pattern of locust mouthparts on maize rootstocks,the bionic stubble breaking device adopted a new multi-segment serrated bionic structure and a symmetrical rotational motion,which could significantly increase the stubble breaking rate(p<0.05)and reduce the resistance to stubble breaking operations(p<0.05).Based on the unique biology of the hare’s paws,toes and nails,the bionic self-excited vibration deep loosening device adopted a new series-parallel composite bionic elastic system and an intelligent tilling depth control system with a fuzzy algorithm,which significantly improved the tilling depth consistency(p<0.05).The operational performance of the BSD was verified at different operating speeds through comparative experiments and reveals the mechanism of its excellent performance through theoretical analysis.The final experiment results showed that,at the same operating speed,the BSD improved the stubble breaking rate by 9.62%and 10.67%,reduced the stubble breaking torque by 28 N·m and 33 N·m,reduced the tillage depth coefficient of variation by 12.73%and 13.48%,and reduced the specific fuel consumption by 36 g/km·h and 40 g/km·h compared to the two most common models.The operating performance of the three kinds of machines will decrease with the increase of operating speed,and the BSD has the least decrease.

Design method and experiment of machinery for combined application of seed,fertilizer and herbicide

This study aimed to resolve the problems of full wheat straw returning to the field,which might readily cause stalk obstruction,poor sowing quality,and serious weeds at the seedling stage,affecting the growth of maize.Based on the idea of“simultaneous seeding and spraying,closed weeding”,this paper presented a design method for designing a corn seed-fertilizer-herbicide simultaneous operation machine,which focuses on the design of vertical active straw-removing anti-blocking device mechanism,design of nozzle key parameters,nozzle selection,seeding monomer analysis and spatial layout design of seed-fertilizer-herbicide mechanism.In addition,the interrelated formulas were deduced and machine design and field experiment were conducted.The experiment results showed that the average variation coefficient of spray uniformity of machines was 17.70%.The post-experiment weed amount was 8.9%,which was lower than that before sowing,8.5%lower than that before artificially closed weeding,and 14.3%lower than that in unenclosed weeding area.Moreover,the weeds were less in the working area of the machine,and the growth of corn was better.Compared with manual closed weeding,the average plant height uniformity and average stem diameter uniformity increased by 4.4%and 5.1%,respectively.Compared with unclosed weeding,the average plant height uniformity and average stem diameter uniformity increased by 18.3%and 10.8%,respectively.Overall,the rationality of the design method proposed in this paper was validated,and these can lay a foundation for the research and development of the same type of machine.

Protein-Protein Interaction Extraction Based on Convex Combination Kernel Function

Owing to the effect of classified models was different in Protein-Protein Interaction(PPI) extraction, which was made by different single kernel functions, and only using single kernel function hardly trained the optimal classified model to extract PPI, this paper presents a strategy to find the optimal kernel function from a kernel function set. The strategy is that in the kernel function set which consists of different single kernel functions, endlessly finding the last two kernel functions on the performance in PPI extraction, using their optimal kernel function to replace them, until there is only one kernel function and it’s the final optimal kernel function. Finally, extracting PPI using the classified model made by this kernel function. This paper conducted the PPI extraction experiment on AIMed corpus, the experimental result shows that the optimal convex combination kernel function this paper presents can effectively improve the extraction performance than single kernel function, and it gets the best precision which reaches 65.0 among the similar PPI extraction systems.

Research on Combination Winding of Revolving Bodies

A methematical model based upon the theory of differential geometry isestablished for the combination winding of revolving bodies and programs are developedfor the filament winding operations as well.Trial winding on a filament winding machineproved this model is right and useful.

A New Multi-Method Combination Forecasting Model for ESDD Predicting

Equal Salt Deposit Density (ESDD) is a main factor to classify contamination severity and draw pollution distribution map. The precise ESDD forecasting plays an important role in the safety, economy and reliability of power system. To cope with the problems existing in the ESDD predicting by multivariate linear regression (MLR), back propagation (BP) neural network and least squares support vector machines (LSSVM), a nonlinear combination forecasting model based on wavelet neural network (WNN) for ESDD is proposed. The model is a WNN with three layers, whose input layer has three neurons and output layer has one neuron, namely, regarding the ESDD forecasting results of MLR, BP and LSSVM as the inputs of the model and the observed value as the output. In the interest of better reflection of the influence of each single forecasting model on ESDD and increase of the accuracy of ESDD prediction, Morlet wavelet is used to con-struct WNN, error backpropagation algorithm is adopted to train the network and genetic algorithm is used to determine the initials of the parameters. Simulation results show that the accuracy of the proposed combina-tion ESDD forecasting model is higher than that of any single model and that of traditional linear combina-tion forecasting (LCF) model. The model provides a new feasible way to increase the accuracy of pollution distribution map of power network.

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。