Table Operation Method for Optimal Spanning Tree Problem

As far as the weight digraph is considered, based on the table instead of the weightdigraph, an optimal spanning tree method called the Table Operations Method (TOM) is proposed.And the optimality is proved and a numerical example is demonstrated.

A Multi-Objective Optimal Evolutionary Algorithm Based on Tree-Ranking

Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has some shortcoming s, in this paper, we proposed a new method using tree structure to express the relationship of solutions. Experiments prove that the method can reach the Pare-to front, retain the diversity of the population, and use less time.

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.

基于LSM-Tree的键值存储系统的读写性能优化

在写密集型工作环境中,日志结构合并树(LSM-Tree)已逐渐成为主流存储系统,LSM-Tree存在读操作速度慢、写操作成本高、范围查询操作效率低等问题;针对这些问题,为提升LSM-Tree的性能进行了研究,提出了一种基于LSM-Tree的键值存储系统的读写性能优化策略,通过键值分离策略设计vTree结构,并提出层内归并与消极的层间合并相结合的方法,以及范围查询优化合并的策略,从而优化系统的范围查询性能,在LSM-Tree和vTree采用不同的压缩结构,以实现系统读写性能的提升;实验结果表明,与RocksDB相比读性能提升30%,与RocksDB-vTree相比范围查询性能提升10%。

Grasshopper KUWAHARA and Gradient Boosting Tree for Optimal Features Classifications

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’performance.The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance.The work of this paper consists of two parts.The first part is based on collecting data of employees to calculate and illustrate the performance of each employee.The second part is based on the classification and prediction techniques of the employee performance.This model is designed to help companies in their decisions about the employees’performance.The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features.Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years.Results also show that the Grasshopper Optimization,followed by“KF”with the Gradient Boosting Tree as classifier and predictor,is characterized by a high accuracy.The proposed algorithm is compared with other known techniques where our results are fund to be superior.

Optimization models of stand structure and selective cutting cycle for large diameter trees of broadleaved forest in Changbai Mountain

The optimum models of harvesting yield and net profits of large diameter trees for broadleaved forest were developed, of which include matrix growth sub-model, harvesting cost and wood price sub-models, based on the data from Hongshi Forestry Bureau, in Changbai Mountain region, Jilin Province, China. The data were measured in 232 permanent sample plots. With the data of permanent sample plots, the parameters of transition probability and ingrowth models were estimated, and some models were compared and partly modified. During the simulation of stand structure, four factors such as largest diameter residual tree （LDT）, the ratio of the number of trees in a given diameter class to those in the next larger diameter class （q）, residual basal area （RBA） and selective cutting cycle （C） were considered. The simulation results showed that the optimum stand structure parameters for large diameter trees are as follows： q is 1.2, LDT is 46cm, RBA is larger than 26 m^2 and selective cutting cycle time （C） is between 10 and 20 years.

OPTIMAL ALGORITHM FOR NO TOOl-RETRACTIONS CONTOUR-PARALLEL OFFSET TOOL-PATH LINKING

A contour-parallel offset （CPO） tool-path linking algorithm is derived without toolretractions and with the largest practicability. The concept of ＂tool-path loop tree＂ （TPL-tree） providing the information on the parent/child relationships among the tool-path loops （TPLs） is presented. The direction, tool-path loop, leaf/branch, layer number, and the corresponding points of the TPL-tree are introduced. By defining TPL as a vector, and by traveling throughout the tree, a CPO tool-path without tool-retractions can be derived.

Application of optimal harvesting decision model to the analysis of Chinese forestry economic policy

This paper firstly extends the single period forest optimal harvesting decision model to infinite periods,in order to indicate how to determine the optimal rotation period aimed at maximizing forest revenue in all directions when repeat planting and harvesting trees on the same plot of earth till infinite future.The study also analyzes the influence of discounted rates,timber price,harvesting costs,planting costs,and tax on the determination of optimal rotation period;and how the optimal rotation period will change when we introduce the factors of continuously rising timber price and ecological revenue.Secondly,the authors introduce the intergenerational equity principle into the above model to design a resource-exploiting mode which satisfies bom the dynamic efficiency principle and the intergenerational equity principle.Last but not least,the research applies the above model to the analysis of Chinese forestry economic policy and explains the economic theory of institutions such as Government Purchasing Ecological Forest,Tree Compensation,and Forestry Subsidization,which provides a necessary theoretical foundation for future application of these new institutions.Besides,in regard to mis theoretical framework,the authors analyze the necessity of the Natural Forest Protection and Grain for Green projects which are currently being implemented in China.We also point out the emphasis of work to insure the project sustainable and successful.Finally,the research discusses the enterprise's incentive to over-the-quota harvesting and the government's means of restricting such behavior,which highlights the fact mat improved supervision and higher penalties are helpful in restricting over-the-quota harvesting.

Mango Pest Detection Using Entropy-ELM with Whale Optimization Algorithm

Image processing,agricultural production,andfield monitoring are essential studies in the researchfield.Plant diseases have an impact on agricultural production and quality.Agricultural disease detection at a preliminary phase reduces economic losses and improves the quality of crops.Manually identifying the agricultural pests is usually evident in plants;also,it takes more time and is an expensive technique.A drone system has been developed to gather photographs over enormous regions such as farm areas and plantations.An atmosphere generates vast amounts of data as it is monitored closely;the evaluation of this big data would increase the production of agricultural production.This paper aims to identify pests in mango trees such as hoppers,mealybugs,inflorescence midges,fruitflies,and stem borers.Because of the massive volumes of large-scale high-dimensional big data collected,it is necessary to reduce the dimensionality of the input for classify-ing images.The community-based cumulative algorithm was used to classify the pests in the existing system.The proposed method uses the Entropy-ELM method with Whale Optimization to improve the classification in detecting pests in agricul-ture.The Entropy-ELM method with the Whale Optimization Algorithm(WOA)is used for feature selection,enhancing mango pests’classification accuracy.Support Vector Machines(SVMs)are especially effective for classifying while users get var-ious classes in which they are interested.They are created as suitable classifiers to categorize any dataset in Big Data effectively.The proposed Entropy-ELM-WOA is more capable compared to the existing systems.

Optimal Stochastic Pine Stands Harvest Rotation Policies

A new Faustmann optimal rotation harvesting stands’ problem under Brown geometric price and Logistic and Gompertz wood stock, diffusions is presented. The optimal cut policies for the stochastic Faustmann model and the single harvest rotation or Vicksell model are evaluated in the case of a Chilean Radiata pine forest company. The company cut policy validates the Vicksell model, its optimal cut policies overestimate the company policy cut in 1.2%, in the Gompertz case, and underestimate it in 2.3%, in the Logistic case. The Faustmann optimal cut policies present a larger underestimation of the company cut policy in 10.1%, in the Gompertz case, and in 21.5%, in the Logistic case. The preference for shorter evaluation period that the company shows is due to the organizational risk that the forest economic sectors has in Chile.

Tree Model Optimization Criterion without Using Prediction Error

The use of prediction error to optimize the number of splitting rules in a tree model does not control the probability of the emergence of splitting rules with a predictor that has no functional relationship with the target variable. To solve this problem, a new optimization method is proposed. Using this method, the probability that the predictors used in splitting rules in the optimized tree model have no functional relationships with the target variable is confined to less than 0.05. It is fairly convincing that the tree model given by the new method represents knowledge contained in the data.

Global optimization of manipulator base placement by means of rapidly-exploring random tree

Due to the interrelationship between the base placement of the manipulator and its operation object,it is significant to analyze the accessibility and workspace of manipulators for the optimization of their base location.A new method is presented to optimize the base placement of manipulators through motion planning optimization and location optimization in the feasible area for manipulators.Firstly,research problems and contents are outlined.And then the feasible area for the manipulator base installation is discussed.Next,index depended on the joint movements and used to evaluate the kinematic performance of manipulators is defined.Although the mentioned indices in last section are regarded as the cost function of the latter,rapidly-exploring random tree(RRT) and rapidly-exploring random tree*(RRT*) algorithms are analyzed.And then,the proposed optimization method of manipulator base placement is studied by means of simulation research based on kinematic performance criteria.Finally,the conclusions could be proved effective from the simulation results.

Optimal Deployment with Self-Healing Movement Algo-rithm for Particular Region in Wireless Sensor Network

Optimizing deployment of sensors with self-healing ability is an efficient way to solve the problems of cov-erage, connectivity and the dead nodes in WSNs. This work discusses the particular relationship between the monitoring range and the communication range, and proposes an optimal deployment with self-healing movement algorithm for closed or semi-closed area with irregular shape, which can not only satisfy both coverage and connectivity by using as few nodes as possible, but also compensate the failure of nodes by mobility in WSNs. We compute the maximum efficient range of several neighbor sensors based on the dif-ferent relationships between monitoring range and communication range with consideration of the complex boundary or obstacles in the region, and combine it with the Euclidean Minimum Spanning Tree (EMST) algorithm to ensure the coverage and communication of Region of Interest (ROI). Besides, we calculate the location of dead nodes by Geometry Algorithm, and move the higher priority nodes to replace them by an-other Improved Virtual Force Algorithm (IVFA). Eventually, simulation results based-on MATLAB are presented, which do show that this optimal deployment with self-healing movement algorithm can ensure the coverage and communication of an entire region by requiring the least number of nodes and effectively compensate the loss of the networks.

Hybrid Recommender System Using Systolic Tree for Pattern Mining

A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved.

应用多时序特征的哨兵系列影像对南方丘陵区树种识别

树种分类是森林资源调查和监测的重要工作,杉木和油茶作为袁州区主要经济树种,准确获取树种空间分布信息,对产量估算和资源管理具有重要意义。以江西省宜春市袁州区为研究区,试验融合时序哨兵-1(Sentinel-1)、哨兵-2(Sentinel-2)等数据,结合中国南方丘陵区树种特点,提取植被指数、红边植被指数、地形特征和纹理特征等构建特征变量组合,分别利用分离阈值法(SEaTH)和特征权重算法(ReliefF)进行特征重要性排序和特征优选,分析各特征对树种分类的影响。结果表明:(1)在使用光谱特征和植被-水体指数的基础上加入不同特征后,树种分类精度均有提升,其中纹理特征的加入更有利于树种分类。(2)结合随机森林算法和特征权重算法(ReliefF)对树种分类的精度最高,总体精度为85.33%,Kappa系数为0.81,优于相同特征组下的支持向量机算法和分类回归树算法。

基于更新热点感知的LSM-Tree查询优化

基于LSM-Tree的键值存储已经得到广泛使用。LSM-Tree通过将更新的数据缓存在内存中、随后批量写入磁盘的优化措施取得极高的写性能。然而,在基于LSM-Tree的键值存储中,被更新键值对的旧数据不会立即从存储系统中清除,导致整个存储系统中积累大量的无效数据,最终会显著降低键值存储的读性能。针对以上问题,提出一种更积极的压缩(compaction)方法,通过记录键值对更新的历史信息,识别出更新热点,在整个LSM-Tree存储系统中寻找无效数据大量聚集的SSTable,尽早实施压缩,清除无效数据,缓解写放大效应,从而提升读性能。实验表明,该方法能够降低LevelDB 65.2%的平均读时延、69.4%的99%读尾时延以及71.4%的写放大。

改进灰狼算法优化GBDT在PM_(2.5)预测中的应用

针对灰狼算法易陷入局部最优解和全局搜索能力不足的问题,通过霍尔顿序列(Halton Sequence)搜索算法初始化狼群位置,避免灰狼算法陷入局部最优解和重复运算;引入莱维飞行和随机游动策略对灰狼算法的寻优过程进行优化,以增加算法的全局搜索能力;利用粒子群算法模拟灰狼种群得出的最佳适应度以用于惩罚项改进灰狼算法中的头狼更新策略。使用改进算法优化的梯度提升树(Gradient Boosting Decision Trees,GBDT)模型对北京市大气污染物监测数据中PM_(2.5)质量浓度进行预测,采用3种评估函数对各模型以及混合模型预测效果得分进行评估。结果显示,本文改进的灰狼算法对梯度提升树的优化效果优于其他算法,均方根误差E RMS为6.65μg/m^(3),平均绝对值误差E MA为3.20μg/m^(3),拟合优度(R^(2))为99%,比传统灰狼算法优化结果的均方根误差减少了19.19μg/m^(3),平均绝对值误差降低了10.03μg/m^(3),拟合优度增加了9百分点;与霍尔顿序列和莱维飞行改进的(Levy Flight-Halton Sequence,LHGWO)相比,改进的灰狼算法预测得分的均方根误差降低了10.39μg/m^(3),平均绝对值误差减小了6.71μg/m^(3),拟合优度提高了5百分点。研究表明了预测模型优化的有效性,为未来城市改善空气质量提供了科学依据和技术支持。

Learned Index和B-Tree在不同分布数据上的性能对比及优化

Learned Index是一种通过训练模型来建立输入数据和存储位置之间映射关系的索引,它能学习到数据间分布的信息,而不同的数据分布将影响模型训练准确率和模型复杂度之间的平衡。为了探索Learned Index适用的场景,使用不同分布、不同数据量的数据对它和加以优化的可更新的自适应学习索引(ALEX)进行性能测试,并与B-Tree进行对比,最终发现Learned Index构建大批量数据的索引时间比B-Tree短,读操作性能、存储空间大小有明显的优势,但写操作性能较差,因此得出Learned Index更适用于大数据情景下的在线分析处理(OLAP)数据库,用于静态数据的存储和查询操作的结论。基于B-Tree的索引结构,对初版Learned Index的结构进行了优化和调整,最终使优化后Learned Index在大批量数据的读写操作性能上有明显提高,其中读操作最高达到原版Learned Index的2倍,写操作最高达到原版的3倍。

基于改进SVM算法的电力工程异常数据检测方法设计

针对传统电力工程数据异常检测过程中存在准确度差且主观性较强的问题,文中提出了一种基于改进支持向量机的电力工程数据异常检测模型。其在传统支持向量机的基础上加入了二叉树多分类算法,从而使模型具备多特征分类能力。同时通过引入AdaBoost分类器,来改善支持向量机弱特征分类能力较差的不足。为进一步提高准确度,还使用鲸鱼算法对模型惩罚项、核函数及迭代次数进行优化。在实验测试中,所提算法的检测准确度相较其他三种对比算法分别提升了5.35%、2.17%和5.35%,说明该算法具备更为理想的性能,并可有效提升电力工程数据检测的准确度,故能为电力基建工程验收与管理提供数据支撑。

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。