A method for Improving the Efficiency of Pear Tree Crossbreeding

In view of the short blooming period of pear tree crossbreeding and the complexity of pollination process, a method that can improve the efficiency of crossbreeding of pear trees was provided. Meanwhile, this method can also be applied to the study of pollen xenia effect, pollination tree selection and pure pollen collection in pear tree cultivation.

A Light and Simplified Branch Bending Method for Young Pear Trees

Aiming at high cost and low efficiency of conventional branch bending method in the modern intensive planting and labor-saving cultivation mode of young pear trees,this paper provides a new branch bending method with wide source of raw materials,cheap price and simple operation,which is also suitable for the management of low-age branches in the process of high grafting and upgrading of traditional big trees.

基于关联分析FP-Tree算法的企业风险信息数据在线挖掘方法

现阶段的数据挖掘方法缺少对数据关联分析的过程,挖掘效果较差,故文章提出基于关联分析频繁模式树(FrequentPattern Tree, FP-Tree)算法的企业风险信息数据在线挖掘方法。选取与企业风险相关的信息指标,收集有关数据并进行预处理操作后,设计一种考虑关联分析的FP-Tree算法,生成FP-Tree节点的条件模式树挖掘频繁项集,计算满足最小置信度的频繁项集,实现企业风险信息数据在线挖掘。实验结果表明,所用方法挖掘量和挖掘效率较高。

Method and standard for selecting elite nut trees of Korean pine

The fruit-bearing quantities of nut Korean pines (Pinus Koraiensis) of natural stands in Changbai Mountain, Xiaoxing'an Mountain, and Wanda Mountain and of artificial forest in Hegang area of Heilongjiang Province were investigated and measured by seed collection of singletree during 1988–1998. In order to evaluate the elite nut tree of fructification, the characteristics of fructification of Korena pine, including, the fruit-bearing quantity, output of seed, quantity of cone, cone size, seed size, the ratio of null seed by solid seed, seed percentage of cone, rate of the cones infested with pest, and fruit-bearing index, etc., were analyzed with the variance analysis, multiple comparison and stepwise regression to obtain the contribution ratio of each fruit-bearing factor to fruit-bearing quantity. The multiple correlation factors and the partial correlation factors for fruit-bearing quantities of Korean pine were determined for different geographical areas, and the cone length, thousand-grain-weight, and the seed percentage of cone were considered as important indices for selection of elite trees. The method of modified weighted coefficients was adopted to select the elite nut trees of Korean pine. Standards for selecting elite nut trees from the natural stands and artificial forest of Korean pine were established. This study could provde selection method and standard of elite nut trees for setting up seed orchard of Korean Pine.

Estimating canopy closure density and above-ground tree biomass using partial least square methods in Chinese boreal forests

Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used partial least square regression （PLSR） models to relate forest parameters, i.e. canopy closure density and above ground tree biomass, to Landsat ETM＋ data. The established models were optimized according to the variable importance for projection （VIP） criterion and the bootstrap method, and their performance was compared using several statistical indices. All variables selected by the VIP criterion passed the bootstrap test （p〈0.05）. The simplified models without insignificant variables （VIP 〈1） performed as well as the full model but with less computation time. The relative root mean square error （RMSE%） was 29% for canopy closure density, and 58% for above ground tree biomass. We conclude that PLSR can be an effective method for estimating canopy closure density and above ground biomass.

Improved EEMD-based standardization method for developing long tree-ring chronologies

Long tree-ring chronologies can be developed by overlapping data from living trees with data from fossil trees through cross-dating.However,low-frequency climate signals are lost when standardizing tree-ring series due to the"segment length curse".To alleviate the segment length curse and thus improve the standardization method for developing long tree-ring chronologies,here we first calculated a mean value for all the tree ring series by overlapping all of the tree ring series.The growth trend of the mean tree ring width(i.e.,cumulated average growth trend of all the series)was determined using ensemble empirical mode decomposition.Then the chronology was developed by dividing the mean value by the growth trend of the mean value.Our improved method alleviated the problem of trend distortion.Long-term signals were better preserved using the improved method than in previous detrending methods.The chronologies developed using the improved method were better correlated with climate than those developed using conservative methods.The improved standardization method alleviates trend distortion and retains more of the low-frequency climate signals.

A decision tree based decomposition method for oil refinery scheduling

Refinery scheduling attracts increasing concerns in both academic and industrial communities in recent years.However, due to the complexity of refinery processes, little has been reported for success use in real world refineries. In academic studies, refinery scheduling is usually treated as an integrated, large-scale optimization problem,though such complex optimization problems are extremely difficult to solve. In this paper, we proposed a way to exploit the prior knowledge existing in refineries, and developed a decision making system to guide the scheduling process. For a real world fuel oil oriented refinery, ten adjusting process scales are predetermined. A C4.5 decision tree works based on the finished oil demand plan to classify the corresponding category(i.e. adjusting scale). Then,a specific sub-scheduling problem with respect to the determined adjusting scale is solved. The proposed strategy is demonstrated with a scheduling case originated from a real world refinery.

A Study on Priority Based ZigBee Network Performance Analysis with Tree Routing Method

The Wireless Sensor Network (WSN) is spatially distributed autonomous sensor to sense special task. WSN like ZigBee network forms simple interconnecting, low power, and low processing capability wireless devices. The ZigBee devices facilitate numerous applications such as pervasive computing, security monitoring and control. ZigBee end devices collect sensing data and send them to ZigBee Coordinator. The Coordinator processes end device requests. The effect of a large number of random unsynchronized requests may degrade the overall network performance. An effective technique is particularly needed for synchronizing available node’s request processing to design a reliable ZigBee network. In this paper, region based priority mechanism is implemented to synchronize request with Tree Routing Method. Riverbed is used to simulate and analyze overall ZigBee network performance. The results show that the performance of the overall priority based ZigBee network model is better than without a priority based model. This research paves the way for further designing and modeling a large scale ZigBee network.

NUMERICAL ANALYSIS ON BINOMIAL TREE METHODS FOR AMERICAN LOOKBACK OPTIONS

Lookback options are path-dependent options. In general, the binomial tree methods, as the most popular approaches to pricing options, involve a path dependent variable as well as the underlying asset price for lookback options. However, for floating strike lookback options, a single-state variable binomial tree method can be constructed. This paper is devoted to the convergence analysis of the single-state binomial tree methods both for discretely and continuously monitored American floating strike lookback options. We also investigate some properties of such options, including effects of expiration date, interest rate and dividend yield on options prices, properties of optimal exercise boundaries and so

Automatic Sleep Spindle Detection with EEG Based on Complex Demodulation Method and Decision Tree Model

Sleep spindle is the characteristic waveform of electroencephalogram (EEG) which is important for clinical diagnosis. In this study, an automatic sleep spindle detection method was developed. The EEG signals were recorded based on the standard polysomnogram (PSG) measurement. A preprocessing procedure is introduced to exclude the unnecessary data segments and normalized the necessary data segments. Complex demodulation method is adopted to detect the candidate sleep spindle waveforms and calculate the features. The sleep spindles are recognized based on a decision tree model. Finally, the detected sleep spindles were utilized to amend the sleep stage recognition results. The sleep EEG data from 3 patients with sleep disorders were analyzed. The obtained results showed that the detected sleep spindles in EEG signal improved the accuracy of sleep stage recognition.

Effects of Different Density-reducing Methods on Canopy Microenvironment, Tree Growth and Fruit Quality in Closed Apple Orchard

In order to optimize and transform closed mature apple orchards with standard rootstocks and improve the quality of fruit,taking a closed Red Fuji apple orchard as the test object,the effects of different density-reducing methods(deinterlacing,removing every other plant in each row,removing every other plant every other row)on the canopy microenvironment,tree structure,leaf photosynthesis and fruit quality were studied.The results showed that different density-reducing methods significantly reduced the orchard coverage and increased the crown transmittance.Among them,the deinterlacing treatment was the best in improving the population structure of the closed orchard,as it reduced the orchard coverage rate by 55.68 percentage points and the canopy transmittance by 82.38 percentage points,compared with the control(CK).Different density-reducing methods all could significantly reduce the branch amount in the closed orchard and optimized the branch composition.The three density-reducing methods decreased the number of branches per plant by 18.96%,12.41%and 19.58%,respectively,compared with the CK.And compared with the CK,the proportion of short branches and leafy branches to the total branches was increased by 17.13%,14.27%and 7.37%,respectively,and the proportion of long branches and developmental branches to the total branches was decreased by 24.47%,18.04%and 10.79%,respectively.The effects of the different density-reducing methods on the temperature,relative light intensity,SPAD and leaf photosynthetic rate in canopies all followed an order of deinterlacing>removing every other plant in each row>removing every other plant every other row>CK,while those on the relative humidity showed an order of deinterlacing>removing every other plant in each row>removing every other plant every other row>CK,while those on the relative humidity showed an order of deinterlacing<removing every other plant in each row<removing every other plant every other row<CK.The average single fruit weight(238.3 g),coloring index(89.2),smoothness index(83.2),soluble solid content(15.1%)and high quality fruit rate(82.4%)of the deinterlacing treatment were higher than those of other treatments,and the values were 18.2%,11.4%,5.85%,26.9%and 25.2%higher than the CK,respectively.The use of dein￣terlacing to reduce density is the best for improving the microenvironment of closed apple orchards and improving the photosynthetic efficiency and fruit quality.

A Statistical Analysis of Textual E-Commerce Reviews Using Tree-Based Methods

With the increasing interest in e-commerce shopping, customer reviews have become one of the most important elements that determine customer satisfaction regarding products. This demonstrates the importance of working with Text Mining. This study is based on The Women’s Clothing E-Commerce Reviews database, which consists of reviews written by real customers. The aim of this paper is to conduct a Text Mining approach on a set of customer reviews. Each review was classified as either a positive or negative review by employing a classification method. Four tree-based methods were applied to solve the classification problem, namely Classification Tree, Random Forest, Gradient Boosting and XGBoost. The dataset was categorized into training and test sets. The results indicate that the Random Forest method displays an overfitting, XGBoost displays an overfitting if the number of trees is too high, Classification Tree is good at detecting negative reviews and bad at detecting positive reviews and the Gradient Boosting shows stable values and quality measures above 77% for the test dataset. A consensus between the applied methods is noted for important classification terms.

Rooted Tree Optimization for Wind Turbine Optimum Control Based on Energy Storage System

The integration of wind turbines(WTs)in variable speed drive systems belongs to the main factors causing lowstability in electrical networks.Therefore,in order to avoid this issue,WTs hybridization with a storage system is a mandatory.This paper investigates WT system operating at variable speed.The system contains of a permanent magnet synchronous generator(PMSG)supported by a battery storage system(BSS).To enhance the quality of active and reactive power injected into the network,direct power control(DPC)scheme utilizing space-vector modulation(SVM)technique based on proportional-integral(PI)control is proposed.Meanwhile,to improve the rendition of this method(DPC-SVM-PI),the rooted tree optimization technique(RTO)algorithm-based controller parameter identification is used to achieve PI optimal gains.To compare the performance ofRTO-based controllers,they were implemented and tested along with some other popular controllers under different working conditions.The obtained results have shown the supremacy of the suggested PIRTO algorithm compared to competing controllers regarding total harmonic distortion(THD),overshoot percentage,settling time,rise time,average active power value,overall efficiency,and active power steadystate error.

Heru Search Method—Unique in the World that Uses Unprecedented Mathematical Formulas and Replaces the Binary Tree Breaking Various Paradigms Like 0(log<i>n</i>)

This paper deals with the efficiency of the search, with a method of organization and storage of the information that allows better results than the research trees or binary trees. No one ever dared to present better results than 0(log n) complexity, and when they wish to improve, they use balanced trees, but they continue to use principles that do not impact the pre-semantic information treatment. The Heru search method has as main characteristic the total or partial substitution of the use of the binary trees, enabling the elimination of the approximate results and informing the user the desired information instead of occurrences by sampling outside the desired information. The breakdown of the 0(log n) paradigm and the refinement of the searches are achieved with the use of a set of unpublished mathematical formulas and concepts called Infinite Series with Multiple Ratios.

基于优化快速搜索随机树算法的全局路径规划

为了改善传统快速搜索随机树(RRT)算法在全局路径规划中存在的平滑度差、具有潜在碰撞性等问题,提出了一种双重优化的RRT算法。在传统RRT算法基础上,引入自适应目标偏向策略以缩短采样时间,引入角度约束采样策略以适应车辆极限转角。得到初始路径后,建立二项优化函数(即降低路径曲率和远离障碍物),并将其作为基点进行梯度下降二次优化,生成可供车辆行驶、平滑性良好且碰撞概率低的路径,并进行仿真验证。结果表明:优化RRT算法相比于传统RRT算法、RRT-Connect算法和RRT算法,平均曲率分别降低了38.1%、36.4%和24.7%,曲率均方差分别降低了38.4%、38.4%和27.2%。

基于决策树模型的中医特色医保付费方式研究——以结直肠恶性肿瘤为例

目的:以结直肠恶性肿瘤住院患者为例,探索适合中医医疗机构的医保付费方式。方法:使用2021年北京市部分中医及综合医院住院患者病案首页数据,选取结直肠恶性肿瘤病例28083例。采用非参数检验、多元线性回归分析确定住院费用影响因素,以决策树模型构建病组分组方案。结果:以是否手术、其他诊断数量和中医治疗费用占比等8个费用影响因素作为住院费用分类节点纳入决策树模型,形成12个病例组,CV及RIV值显示分组结果较好。结论:将代表中医特色的指标“中医治疗费用占比”作为分类节点纳入分组模型,能够得到体现中医特色的结直肠恶性肿瘤治疗分组方案,为建立适合中医医疗机构的医保付费方式提供参考。

改进人工势场引导的双向扩展随机树路径规划算法

针对地面移动机器人在复杂环境之下要求规划路径实时性强、路线平滑度高、避障精确完备等需求,在快速扩展随机树算法(RRT)的基础之上,提出一种由改进人工势场法(APF)引导的双向扩展随机树算法(APF-Bi-RRT^(*))。首先,在每次迭代的过程之中两棵随机树同时分别从起始点和目标点进行扩展,以加快算法收敛速度;其次,在算法随机树生长方向上,引入目标偏置策略来优化随机子节点的选取,并在随机树和障碍物中加入人工势场分量,限制路径方向选择的随机性,改进算法克服引力和斥力过大导致陷入局部最优值或目标不可达的问题;最后,在形成锯齿型规划路径之上应用一种采样优化和关键节点平滑策略,进一步缩短和平滑原路径的总距离。对比实验结果证明,该算法既克服了传统随机树算法的节点盲目扩展的问题,又兼顾了生成路径的效率和平滑性,与目标偏置RRT算法相比,在规划路径长度上减少了9.7%左右,在运行时间上缩短了65.3%左右,在算法迭代次数上减少了78.2%左右。

物流大通道的形成机理与识别方法

物流大通道是国家综合立体交通网的重要功能系统,是国内国际双循环相互促进的基础。为更加精准识别物流大通道,通过国内外概念辨析进一步明确物流大通道的定义,研究提出物流大通道的形成是以交通运输基础设施网络化发展为根本依托,是网络成分互补的横向整合和节点集聚的纵向整合共同作用的结果,其表征是枢纽点极化、通行量规模化。在此基础上,明确了“先找点、后定线”的识别思路,提出以区位熵定“点”、最大树(边)定“线”为核心的基于城市、交通和经济数据的识别方法,通过多源数据收集、节点识别、线路识别、通道汇集、通道识别,最终提出“八纵七横”的物流大通道布局。相比于传统方法,选点定线的识别方法精度更高,能够进一步展示货物流向、运输结构、货运规模等运行特征,并为开展物流大通道与经济发展相关分析提供了可能。

决策树算法在机械臂碰撞规避方法中的应用

针对机械臂在复杂环境作业时,由于碰撞规避方法的碰撞检测效果差、障碍物规避效果不佳,导致机械臂频繁发生碰撞的问题,提出一种应用决策树算法的机械臂碰撞规避方法。通过分析机械臂运动学结构,获取机械臂运动学方程,结合二次投影法将机械臂碰撞过程,简化为三维视角下包围盒相交情况的判断过程,检测机械臂碰撞情况,通过浓度函数采集三维空间中包围盒预计位移信息,建立基于预计位移信息的决策树模型,获取机械臂碰撞规避行为最优决策信息,实现机械臂规避碰撞。实验结果表明,应用决策树算法的机械臂碰撞规避方法仅需0.29s有效检测出碰撞,在复杂环境下,规划的碰撞规避路线中机械臂与障碍物未发生碰撞,因此,该方法有效提高了机械臂碰撞规避的效果。