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Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables
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作者 Liang Chen Jingbo Zhang +2 位作者 Linjie Wu Xingjuan Cai Yubin Xu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第7期363-383,共21页
The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the intera... The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage. 展开更多
关键词 Decision variable grouping large-scale multi-objective optimization algorithms weighted overlapping grouping direction-guided evolution
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Gaussian Distance Weighted Algorithm for Geometric Characteristics of Three-Dimensional Discrete Curves
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作者 Liyan Zhang Haiyi Ai +3 位作者 Shaohong Yan Haili Chen Jiali Zou Junqing Zhang 《Journal of Applied Mathematics and Physics》 2024年第10期3599-3612,共14页
Discrete curves are composed of a set of ordered discrete points distributed at the intersection of the scanning plane and the surface of the object. In order to accurately calculate the geometric characteristics of a... Discrete curves are composed of a set of ordered discrete points distributed at the intersection of the scanning plane and the surface of the object. In order to accurately calculate the geometric characteristics of any point on the discrete curve, a distance-based Gaussian weighted algorithm is proposed to estimate the geometric characteristics of three-dimensional space discrete curves. According to the definition of discrete derivatives, the algorithm fully considers the relative position difference between a specific point and its neighboring points, introduces the distance weighting idea, and integrates the smoothing strategy. The experiment uses two spatial discrete curves for uniform and non-uniform sampling, and compares them with two commonly used estimation algorithms. The comparative analysis is carried out in terms of sampling density, neighborhood radius and noise resistance. The experimental results show that the Gaussian distance weighted algorithm is effective and provides an efficient algorithm for underground pipeline safety detection. 展开更多
关键词 Discrete Curve Angle weight algorithm Comparison Underground Pipeline Inspection
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基于K-Means聚类与熵权TOPSIS法的岩石可爆性评价研究
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作者 叶海旺 雷丙响 +5 位作者 周汉红 余梦豪 雷涛 王其洲 李宁 Doumbouya Sekou 《爆破》 CSCD 北大核心 2024年第2期112-119,共8页
露天矿山的爆破块度分布,直接影响到后续的采装、运输和破碎工作。为了控制石墨矿山不同区域爆破块度分布,基于K-means无监督聚类学习法与熵权TOPSIS评价法建立了一种新的岩石可爆性评价模型,选取岩石密度、动力能量耗散率、动态抗压强... 露天矿山的爆破块度分布,直接影响到后续的采装、运输和破碎工作。为了控制石墨矿山不同区域爆破块度分布,基于K-means无监督聚类学习法与熵权TOPSIS评价法建立了一种新的岩石可爆性评价模型,选取岩石密度、动力能量耗散率、动态抗压强度、平均应变率、脆性指数作为评价指标,通过熵权计算,发现岩石破碎程度受脆性指数影响最大,受平均应变率影响最小。将此模型应用于实际石墨矿山,可爆性分为10个等级,统计不同分级下的岩石平均破碎粒径,发现可爆性分级等级越高平均粒径越大,有明显的分级特征,验证了模型的有效性。从爆破石墨矿石岩体类型看,岩石可爆性从易到难排序为:片岩、片麻岩、变粒岩、混合岩。结合石墨矿石微观观测结果分析可知:岩性从片岩向混合岩转变,岩石内部石墨晶质呈下降趋势,石墨矿石可爆性等级也随之越来越高。岩石密度、能量耗散率、动态抗压强度之间呈线性正相关,岩石可爆性与平均应变率、脆性指数存在负相关性。研究成果为矿山矿岩可爆性评价提供了一条新思路,对露天矿山爆破块度优化具有一定的理论和实践指导意义。 展开更多
关键词 岩体爆破 可爆性评价 岩石力学 k-means算法 熵权TOPSIS评价
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Weighted PageRank Algorithm Search Engine Ranking Model for Web Pages 被引量:1
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作者 S.Samsudeen Shaffi I.Muthulakshmi 《Intelligent Automation & Soft Computing》 SCIE 2023年第4期183-192,共10页
As data grows in size,search engines face new challenges in extracting more relevant content for users’searches.As a result,a number of retrieval and ranking algorithms have been employed to ensure that the results a... As data grows in size,search engines face new challenges in extracting more relevant content for users’searches.As a result,a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s requirements.Unfortunately,most existing indexes and ranking algo-rithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations,making it impossible to deliver exceptionally accurate results.As a result,this study investigates and analyses how search engines work,as well as the elements that contribute to higher ranks.This paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank(PR)algorithm,which is one of the most widely used page ranking algorithms We pro-pose weighted PageRank(WPR)algorithms to test the relationship between these various measures.The Weighted Page Rank(WPR)model was used in three dis-tinct trials to compare the rankings of documents and pages based on one or more user preferences criteria.Thefindings of utilizing the Weighted Page Rank model showed that using multiple criteria to rankfinal pages is better than using only one,and that some criteria had a greater impact on ranking results than others. 展开更多
关键词 weighted pagerank algorithms search engines web pages web crawlers World Wide Web
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基于改进K-means算法的电力营销档案信息管理系统 被引量:1
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作者 安大炜 邵琳玲 +1 位作者 向黎藜 谭元刚 《自动化技术与应用》 2024年第3期185-188,共4页
为满足档案管理用户需求,实现不同营销档案管理信息分类,提出改进K-means算法的电力营销档案信息管理系统。设计电能信息收编、客户缴费管理、营销报表管理及系统开发等子系统功能;利用改进的K-means方法定义系统算法,引入权重概念,将... 为满足档案管理用户需求,实现不同营销档案管理信息分类,提出改进K-means算法的电力营销档案信息管理系统。设计电能信息收编、客户缴费管理、营销报表管理及系统开发等子系统功能;利用改进的K-means方法定义系统算法,引入权重概念,将元素分配到和中心点距离最近的簇中,实现档案分类统计。仿真实验证明,该系统可实现不同营销档案的准确分类,减少响应延时,提高系统吞吐量,满足电力营销档案管理用户需求。 展开更多
关键词 改进k-means算法 电力营销 档案信息分类 分类权重
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Analysis of the COVID-19, Outbreak in Brazil Using Topological Weighted Centroid: An Intelligent Geographic Information System Approach
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作者 Masoud Asadi-Zeydabadi Marina Mizukoshi +2 位作者 Massimo Buscema Giulia Massini Weldon Lodwick 《Journal of Data Analysis and Information Processing》 2024年第2期248-266,共19页
This study used Topological Weighted Centroid (TWC) to analyze the Coronavirus outbreak in Brazil. This analysis only uses latitude and longitude in formation of the capitals with the confirmed cases on May 24, 2020 t... This study used Topological Weighted Centroid (TWC) to analyze the Coronavirus outbreak in Brazil. This analysis only uses latitude and longitude in formation of the capitals with the confirmed cases on May 24, 2020 to illustrate the usefulness of TWC though any date could have been used. There are three types of TWC analyses, each type having five associated algorithms that produce fifteen maps, TWC-Original, TWC-Frequency and TWC-Windowing. We focus on TWC-Original to illustrate our approach. The TWC method without using the transportation information predicts the network for COVID-19 outbreak that matches very well with the main radial transportation routes network in Brazil. 展开更多
关键词 COVID-19 Topological weighted Centroid (TWC) algorithms TWC-Original TWC-Frequency and TWC-Windowing
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Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation
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作者 Mona Jamjoom Ahmed Elhadad +1 位作者 Hussein Abulkasim Safia Abbas 《Computers, Materials & Continua》 SCIE EI 2023年第7期367-382,共16页
Several pests feed on leaves,stems,bases,and the entire plant,causing plant illnesses.As a result,it is vital to identify and eliminate the disease before causing any damage to plants.Manually detecting plant disease ... Several pests feed on leaves,stems,bases,and the entire plant,causing plant illnesses.As a result,it is vital to identify and eliminate the disease before causing any damage to plants.Manually detecting plant disease and treating it is pretty challenging in this period.Image processing is employed to detect plant disease since it requires much effort and an extended processing period.The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases,including Phytophthora infestans,Fusarium graminearum,Puccinia graminis,tomato yellow leaf curl.Therefore,this work uses the Support vector machine(SVM)classifier to detect and classify the plant disease using various steps like image acquisition,Pre-processing,Segmentation,feature extraction,and classification.The gray level co-occurrence matrix(GLCM)and the local binary pattern features(LBP)are used to identify the disease-affected portion of the plant leaf.According to experimental data,the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy. 展开更多
关键词 SVM machine learning GLCM algorithm k-means clustering LBP
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Genetic Algorithm Combined with the K-Means Algorithm:A Hybrid Technique for Unsupervised Feature Selection
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作者 Hachemi Bennaceur Meznah Almutairy Norah Alhussain 《Intelligent Automation & Soft Computing》 SCIE 2023年第9期2687-2706,共20页
The dimensionality of data is increasing very rapidly,which creates challenges for most of the current mining and learning algorithms,such as large memory requirements and high computational costs.The literature inclu... The dimensionality of data is increasing very rapidly,which creates challenges for most of the current mining and learning algorithms,such as large memory requirements and high computational costs.The literature includes much research on feature selection for supervised learning.However,feature selection for unsupervised learning has only recently been studied.Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate.This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS,which combines the genetic algorithm(GA)approach with the classical k-Means algorithm.In the proposed algorithm,a new fitness func-tion is designed in addition to new smart crossover and mutation operators.The effectiveness of this algorithm is demonstrated on various datasets.Fur-thermore,the performance of GAk-MEANS has been compared with other genetic algorithms,such as the genetic algorithm using the Sammon Error Function and the genetic algorithm using the Sum of Squared Error Function.Additionally,the performance of GAk-MEANS is compared with the state-of-the-art statistical unsupervised feature selection techniques.Experimental results show that GAk-MEANS consistently selects subsets of features that result in better classification accuracy compared to others.In particular,GAk-MEANS is able to significantly reduce the size of the subset of selected features by an average of 86.35%(72%–96.14%),which leads to an increase of the accuracy by an average of 3.78%(1.05%–6.32%)compared to using all features.When compared with the genetic algorithm using the Sammon Error Function,GAk-MEANS is able to reduce the size of the subset of selected features by 41.29%on average,improve the accuracy by 5.37%,and reduce the time by 70.71%.When compared with the genetic algorithm using the Sum of Squared Error Function,GAk-MEANS on average is able to reduce the size of the subset of selected features by 15.91%,and improve the accuracy by 9.81%,but the time is increased by a factor of 3.When compared with the machine-learning based methods,we observed that GAk-MEANS is able to increase the accuracy by 13.67%on average with an 88.76%average increase in time. 展开更多
关键词 Genetic algorithm unsupervised feature selection k-means clustering
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Blind source separation by weighted K-means clustering 被引量:5
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作者 Yi Qingming 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2008年第5期882-887,共6页
Blind separation of sparse sources (BSSS) is discussed. The BSSS method based on the conventional K-means clustering is very fast and is also easy to implement. However, the accuracy of this method is generally not ... Blind separation of sparse sources (BSSS) is discussed. The BSSS method based on the conventional K-means clustering is very fast and is also easy to implement. However, the accuracy of this method is generally not satisfactory. The contribution of the vector x(t) with different modules is theoretically proved to be unequal, and a weighted K-means clustering method is proposed on this grounds. The proposed algorithm is not only as fast as the conventional K-means clustering method, but can also achieve considerably accurate results, which is demonstrated by numerical experiments. 展开更多
关键词 blind source separation underdetermined mixing sparse representation weighted k-means clustering.
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基于改进的Weighted K-Means聚类的外卖员接单区域划分问题研究 被引量:1
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作者 马云卿 张传鑫 《统计学与应用》 2019年第2期203-217,共15页
外卖行业蓬勃发展,人们对于外卖服务质量的要求也不断提升。因外卖员接单区域基本固定,合理划分外卖员负责区域并分配每个区域外卖员人数成为提升效率的关键。本项目基于查询算法模型,分析上海市2017年某时段的外卖数据,试图得到一个对... 外卖行业蓬勃发展,人们对于外卖服务质量的要求也不断提升。因外卖员接单区域基本固定,合理划分外卖员负责区域并分配每个区域外卖员人数成为提升效率的关键。本项目基于查询算法模型,分析上海市2017年某时段的外卖数据,试图得到一个对于外卖接单区域的较为合理的划分标准并给出该划分。K-Means是一种常见的划分聚类算法,是在集中式系统框架无法对海量数据进行处理分析的基础上提出的。然而对于有权重的二维点集无法使用K-Means聚类算法,因此研究一种改进的Weighted K-Means算法显得尤为必要。本项目定义带权质心和带权距离,提出了新的Weighted K-Means算法,并使用改进前后的两种方法处理上海市外卖接单信息,给出合理可行的外卖员接单区域划分。对比两种方法的结果,改进的Weighted K-Means不仅方法可行,区域划分表现也更优秀。与此同时,使用该方法对外卖接单区域进行新的划分,有助于优化现有外卖模式、提升外卖效率以及顾客满意度。 展开更多
关键词 聚类算法 k-means算法 weighted k-means算法 PYTHON
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Improved k-means clustering algorithm 被引量:16
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作者 夏士雄 李文超 +2 位作者 周勇 张磊 牛强 《Journal of Southeast University(English Edition)》 EI CAS 2007年第3期435-438,共4页
In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering a... In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering algorithm is proposed. First, the concept of a silhouette coefficient is introduced, and the optimal clustering number Kopt of a data set with unknown class information is confirmed by calculating the silhouette coefficient of objects in clusters under different K values. Then the distribution of the data set is obtained through hierarchical clustering and the initial clustering-centers are confirmed. Finally, the clustering is completed by the traditional k-means clustering. By the theoretical analysis, it is proved that the improved k-means clustering algorithm has proper computational complexity. The experimental results of IRIS testing data set show that the algorithm can distinguish different clusters reasonably and recognize the outliers efficiently, and the entropy generated by the algorithm is lower. 展开更多
关键词 CLUSTERING k-means algorithm silhouette coefficient
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Improved parallel weighted bit-flipping algorithm 被引量:1
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作者 刘晓健 赵春明 吴晓富 《Journal of Southeast University(English Edition)》 EI CAS 2009年第4期423-426,共4页
An improved parallel weighted bit-flipping(PWBF) algorithm is presented. To accelerate the information exchanges between check nodes and variable nodes, the bit-flipping step and the check node updating step of the ... An improved parallel weighted bit-flipping(PWBF) algorithm is presented. To accelerate the information exchanges between check nodes and variable nodes, the bit-flipping step and the check node updating step of the original algorithm are parallelized. The simulation experiments demonstrate that the improved PWBF algorithm provides about 0. 1 to 0. 3 dB coding gain over the original PWBF algorithm. And the improved algorithm achieves a higher convergence rate. The choice of the threshold is also discussed, which is used to determine whether a bit should be flipped during each iteration. The appropriate threshold can ensure that most error bits be flipped, and keep the right ones untouched at the same time. The improvement is particularly effective for decoding quasi-cyclic low-density paritycheck(QC-LDPC) codes. 展开更多
关键词 low-density parity-check(LDPC) parallel weighted bit-flipping(PWBF) improved modified weighted bit-flipping (IMWBF) algorithm weighted-sum weighted bit-flipping (WSWBF) algorithm
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基于加权密度Canopy的K-means文本聚类 被引量:2
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作者 宋健 李岩芳 陈占芳 《中南民族大学学报(自然科学版)》 CAS 北大核心 2023年第5期636-642,共7页
针对现有文本聚类性能不高的问题,提出了一种改进质心初始化的K-means文本聚类算法.该算法首先利用Canopy算法进行文本预聚类,并且对Canopy算法的阈值选取策略进行改进,定义加权密度进行Canopy中心的选取,得到更准确的聚类数以及初始聚... 针对现有文本聚类性能不高的问题,提出了一种改进质心初始化的K-means文本聚类算法.该算法首先利用Canopy算法进行文本预聚类,并且对Canopy算法的阈值选取策略进行改进,定义加权密度进行Canopy中心的选取,得到更准确的聚类数以及初始聚类中心;然后将所得结果作为K-means算法的初始化参数进行后续迭代聚类,有效解决了传统算法因随机选取初始聚类中心而陷入局部最优解的问题,减少了算法的迭代次数,提高了聚类准确性.实验结果表明:与其他同类型算法相比,该算法在文本聚类分析中更具优势. 展开更多
关键词 文本聚类 k-means算法 加权密度 Canopy算法
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An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization 被引量:1
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作者 Wenchuan Wang Weican Tian +3 位作者 Kwok-wing Chau Yiming Xue Lei Xu Hongfei Zang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第8期1603-1642,共40页
The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search sta... The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems. 展开更多
关键词 Bald eagle search algorithm cauchymutation adaptive weight factor CEC2017 benchmark functions engineering optimization problems
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Optimizing Polynomial-Time Solutions to a Network Weighted Vertex Cover Game 被引量:1
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作者 Jie Chen Kaiyi Luo +2 位作者 Changbing Tang Zhao Zhang Xiang Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第2期512-523,共12页
Weighted vertex cover(WVC)is one of the most important combinatorial optimization problems.In this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted n... Weighted vertex cover(WVC)is one of the most important combinatorial optimization problems.In this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted networks.We first model the WVC problem as a general game on weighted networks.Under the framework of a game,we newly define several cover states to describe the WVC problem.Moreover,we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums(SNEs)of the game.Then,we propose a game-based asynchronous algorithm(GAA),which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial time.Subsequently,we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms,termed the improved game-based asynchronous algorithm(IGAA),in which we prove that it can obtain a better solution to the WVC problem than using a the GAA.Finally,numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms. 展开更多
关键词 Game-based asynchronous algorithm(GAA) game optimization polynomial time strict Nash equilibrium(SNE) weighted vertex cover(WVC)
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Photovoltaic Models Parameters Estimation Based on Weighted Mean of Vectors 被引量:1
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作者 Mohamed Elnagi Salah Kamel +1 位作者 Abdelhady Ramadan Mohamed F.Elnaggar 《Computers, Materials & Continua》 SCIE EI 2023年第3期5229-5250,共22页
Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the ... Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the non-linear nature of the photovoltaic cell,modeling solar cells and extracting their parameters is one of the most important challenges in this discipline.As a result,the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate.In this paper,a weIghted meaN oF vectOrs algorithm(INFO)that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way.In each generation,the INFO utilizes three operations to update the vectors’locations:updating rules,vector merging,and local search.The INFO is applied to estimate the parameters of static models such as single and double diodes,as well as dynamic models such as integral and fractional models.The outcomes of all applications are examined and compared to several recent algorithms.As well as the results are evaluated through statistical analysis.The results analyzed supported the proposed algorithm’s efficiency,accuracy,and durability when compared to recent optimization algorithms. 展开更多
关键词 Photovoltaic(PV)modules weighted meaN oF vectOrs algorithm(INFO) renewable energy static PV models dynamic PV models solar energy
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An efficient enhanced k-means clustering algorithm 被引量:30
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作者 FAHIM A.M SALEM A.M +1 位作者 TORKEY F.A RAMADAN M.A 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2006年第10期1626-1633,共8页
In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared dista... In k-means clustering, we are given a set of n data points in d-dimensional space R^d and an integer k and the problem is to determine a set of k points in R^d, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this paper, we present a simple and efficient clustering algorithm based on the k-means algorithm, which we call enhanced k-means algorithm. This algorithm is easy to implement, requiring a simple data structure to keep some information in each iteration to be used in the next iteration. Our experimental results demonstrated that our scheme can improve the computational speed of the k-means algorithm by the magnitude in the total number of distance calculations and the overall time of computation. 展开更多
关键词 Clustering algorithms Cluster analysis k-means algorithm Data analysis
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Estimating wheat fractional vegetation cover using a density peak k-means algorithm based on hyperspectral image data 被引量:4
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作者 LIU Da-zhong YANG Fei-fei LIU Sheng-ping 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2021年第11期2880-2891,共12页
Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction m... Fractional vegetation cover(FVC)is an important parameter to measure crop growth.In studies of crop growth monitoring,it is very important to extract FVC quickly and accurately.As the most widely used FVC extraction method,the photographic method has the advantages of simple operation and high extraction accuracy.However,when soil moisture and acquisition times vary,the extraction results are less accurate.To accommodate various conditions of FVC extraction,this study proposes a new FVC extraction method that extracts FVC from a normalized difference vegetation index(NDVI)greyscale image of wheat by using a density peak k-means(DPK-means)algorithm.In this study,Yangfumai 4(YF4)planted in pots and Yangmai 16(Y16)planted in the field were used as the research materials.With a hyperspectral imaging camera mounted on a tripod,ground hyperspectral images of winter wheat under different soil conditions(dry and wet)were collected at 1 m above the potted wheat canopy.Unmanned aerial vehicle(UAV)hyperspectral images of winter wheat at various stages were collected at 50 m above the field wheat canopy by a UAV equipped with a hyperspectral camera.The pixel dichotomy method and DPK-means algorithm were used to classify vegetation pixels and non-vegetation pixels in NDVI greyscale images of wheat,and the extraction effects of the two methods were compared and analysed.The results showed that extraction by pixel dichotomy was influenced by the acquisition conditions and its error distribution was relatively scattered,while the extraction effect of the DPK-means algorithm was less affected by the acquisition conditions and its error distribution was concentrated.The absolute values of error were 0.042 and 0.044,the root mean square errors(RMSE)were 0.028 and 0.030,and the fitting accuracy R2 of the FVC was 0.87 and 0.93,under dry and wet soil conditions and under various time conditions,respectively.This study found that the DPK-means algorithm was capable of achieving more accurate results than the pixel dichotomy method in various soil and time conditions and was an accurate and robust method for FVC extraction. 展开更多
关键词 fractional vegetation cover k-means algorithm NDVI vegetation index WHEAT
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A Weighted Algorithm for Watermarking Relational Databases 被引量:2
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作者 CUI Xinchun QIN Xiaolin SHENG Gang 《Wuhan University Journal of Natural Sciences》 CAS 2007年第1期79-82,共4页
A weighted algorithm for watermarking relational databases for copyright protection is presented. The possibility of watermarking an attribute is assigned according to its weight decided by the owner of the database. ... A weighted algorithm for watermarking relational databases for copyright protection is presented. The possibility of watermarking an attribute is assigned according to its weight decided by the owner of the database. A one-way hash function and a secret key known only to the owner of the data are used to select tuples and bits to mark. By assigning high weight to significant attributes, the scheme ensures that important attributes take more chance to be marked than less important ones. Experimental results show that the proposed scheme is robust against various forms of attacks, and has perfect immunity to subset attack. 展开更多
关键词 weighted algorithm watermarking relational databases copyright protection
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Concise review of relaxations and approximation algorithms for nonidentical parallel-machine scheduling to minimize total weighted completion times 被引量:1
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作者 Li Kai Yang Shanlin 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2008年第4期827-834,共8页
A class of nonidentical parallel machine scheduling problems are considered in which the goal is to minimize the total weighted completion time. Models and relaxations are collected. Most of these problems are NP-hard... A class of nonidentical parallel machine scheduling problems are considered in which the goal is to minimize the total weighted completion time. Models and relaxations are collected. Most of these problems are NP-hard, in the strong sense, or open problems, therefore approximation algorithms are studied. The review reveals that there exist some potential areas worthy of further research. 展开更多
关键词 parallel machine SCHEDULING REVIEW total weighted completion time RELAXATION algorithm
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