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.

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

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.

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.

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.

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.

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.

密度和灌溉定额对76 cm等行距棉田土壤硝态氮分布和氮素利用的影响

【目的】研究76 cm等行距机采棉种植模式下,土壤硝态氮分布和氮素利用对密度和灌溉定额调控的响应机制,为优化76 cm等行距机采棉栽培模式提供理论依据。【方法】在大田试验基础上,设置3个种植密度(低密度M_(1),13.5×10^(4)株/hm^(2);中密度M 2,18×10^(4)株/hm^(2);高密度M_(3),22.5×10^(4)株/hm^(2))和灌溉定额[重度亏缺W_(1)(50%ET_(C)),3150 m^(3)/hm^(2);轻度亏缺W_(2)(75%ET_(C)),4050 m^(3)/hm^(2);充分灌溉W 3(100%ET_(C)),4980 m^(3)/hm^(2)]。研究其对76 cm等行距机采棉田土壤无机氮含量、土壤硝态氮分布及氮素利用的影响。【结果】在相同灌溉定额下,密度对土壤铵态氮含量、硝态氮分布的均匀性均有显著影响(P<0.05),随密度的升高,土壤铵态氮含量降低,上层土壤硝态氮占比呈先升高后降低,而硝态氮分布的均匀性则完全相反。在相同密度处理下,随灌溉定额的增加,土壤硝态氮含量、分布的均匀性、上层土壤硝态氮占比、表聚性显著降低。密度和灌溉定额及两因素交互对棉田氮生产效率、氮素偏生产力均有显著性影响(P<0.05),随密度的升高,氮生产效率呈先降低后升高趋势,氮素偏生产力则逐渐降低;随灌溉定额的增加,氮生产效率逐渐降低,氮素偏生产力则相反。以低度与重度亏缺灌溉处理氮素生产效率最高,而氮素偏生产力则以低密度、中密度与充分灌溉处理组合最高,但与高密度与重度亏缺灌溉处理组合没有显著差异。【结论】氮素偏生产力以低密度、中密度与充分灌溉处理组合最高,分别为13.56、13.54 kg/kg,与高密度与重度亏缺灌溉处理组合没有显著差异。

基于CM-AHP的石羊河流域水质评价

为准确描述石羊河流域水环境质量状态,明晰流域水质的主要污染因子,根据甘肃省石羊河流域典型断面水质数据的分析结果,选取溶解氧、化学需氧量、氨氮、总磷、氟化物5项污染因子作为水质评价指标,建立了基于CM-AHP、熵权法的组合赋权综合评价模型,对石羊河流域主要污染因子进行研究。结果表明,总磷、氨氮污染组合权重占比最大,分别为0.438、0.296,是石羊河流域主要污染因子;石羊河流域从上游至中下游,水质逐步变差。

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。

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。

玛湖凹陷风城组岩石力学参数自适应权重组合预测

准噶尔盆地玛湖凹陷风城组岩性复杂,为准确预测其岩石力学参数,提出了一种自适应权重组合预测方法。首先分析、对比传统方法和不同机器学习算法(BP神经网络、XGBoost、支持向量机(SVM)、随机森林(RF)、卷积神经网络(CNN)、决策树(CART)、长短时记忆神经(LSTM)网络等)的预测效果,传统方法难以准确预测岩石力学参数,而不同机器学习算法的预测效果不同,其中抗压强度、抗张强度和脆性指数预测的最优机器学习算法模型为SVM,弹性模量为BP,泊松比为RF,内聚力为XGBoost,内摩擦角和断裂韧性为LSTM网络;单一机器学习算法难以实现对多个岩石力学参数的同步准确预测。在此基础上,通过对不同岩石力学参数选取不同预测基模型,再根据基模型预测效果赋予权重并进行组合,以开展自适应权重组合预测。结果表明,该方法能够有效提升机器学习算法的预测精度和泛化性能,可实现复杂岩性地层多个岩石力学参数的同步准确预测。

弱电解质溶液EDM/ECM复合加工机理研究

弱电解质溶液中利用电沉积补偿电极损耗的电火花/电化学复合加工技术可以大幅降低电极损耗,对提高微细电火花加工效率具有重要意义。由于该工艺方法是EDM/ECM复合加工领域一个新的研究方向,研究成果很少。为加深对利用电沉积补偿电极损耗的电火花/电化学复合加工技术的认识,基于实验结果,对弱电解质溶液中的电火花/电化学复合加工的材料去除机理、放电通道形成机理及电极损耗机理进行了初步探索,得到了以下结果:电火花放电蚀除和电化学溶解共同将工件材料去除;大量气泡存在于电极间隙使复合加工放电通道的形成异于电火花放电加工;电沉积作用和电火花放电蚀除共同对工具电极损耗产生影响。

大规模激光相干合成主动相位控制技术研究进展

大规模激光相干合成是突破单口径激光特性极限、获得超高峰值/平均功率、超大脉冲能量、超高空/谱亮度等极端特性激光的有效技术路径之一,而大规模激光相干合成的关键是主动相位控制。主动相位控制技术可以对各路光束相位进行主动控制,补偿相位噪声引起的相干特性退化、合成效率下降,获得高品质的合成激光。自相干合成技术提出以来,研究人员开发了多种主动相位控制方法用于相干合成相位校正,其中适用于大规模激光相干合成的主动相位控制方法也得到了飞速发展。系统梳理了大规模激光相干合成的主动相位控制方法,深入分析了不同方法的原理、特点、适用场景和扩展能力,介绍了不同方法取得的相干合成研究最新进展及标志性成果,报道了19通道闭环上升时间仅6μs的相干合成锁相控制突破性结果,最后总结和展望了主动相位控制方法的发展趋势。

基于联合收获机平台的覆秸式油菜联合播种机研制

针对长江中下游稻油轮作区水稻收获后留茬高、传统旋耕装置秸秆还田能力不足导致油菜成苗率不高等问题,该研究设计了一种基于联合收获机平台的覆秸式油菜联合播种机。为提高机具通过性,结合曲柄滑块机构运动原理,根据运动学分析对悬挂装置结构进行改进,增大后悬挂装置离地间隙;为实现秸秆覆盖还田,基于联合收获机脱粒装置结构特点增设秸秆粉碎侧抛装置,对秸秆粉碎和抛送过程开展动力学和运动学分析,确定了秸秆粉碎和铺放质量的影响因素。以机具前进速度、发动机动力输出转速为试验因素,以碎土率、厢面平整度、秸秆粉碎合格率和秸秆铺放均匀性为试验指标,开展单因素试验与二因素三水平中心复合试验,得到各因素对试验指标的影响规律及响应面模型。试验结果表明,前进速度和发动机动力输出转速对种床质量及秸秆粉碎铺放效果均影响显著(P<0.05),在前进速度2.89 km/h、发动机动力输出转速2210 r/min时碎土率为85.65%,厢面平整度为20.06 mm,秸秆粉碎合格率为90.31%,秸秆铺放均匀性为89.33%,机具作业效果较优。对优化后的作业参数进行圆整并开展验证试验,结果表明,在前进速度3 km/h、发动机动力输出转速2200 r/min时,碎土率为85.69%,厢面平整度为21.32 mm,秸秆粉碎合格率为89.35%,秸秆铺放均匀性为88.07%,与理论值的偏差分别为0.04个百分点、1.26 mm、0.96和1.26个百分点。研究结果可为油菜机械化播种和一机多用提供新思路。