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Estimating wheat fractional vegetation cover using a density peak K-means algorithm based on hyperspectral image data

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Journal of Integrative Agriculture 2021年第11期20卷 2880-2891页

作者： LIU Da-zhong YANG Fei-fei LIU Sheng-ping key Laboratory of Agricultural Information Service Technology Ministry of Agriculture and Rural Affairs/Intelligent Agriculture Research OfficeAgricultural Information InstituteChinese Academy of Agricultural SciencesBeijing 100081P.R.China

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 v

关键词： fractional vegetation cover K-means algorithm NDVI vegetation index wheat

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A Hybrid Method Combining Improved K-means algorithm with BADA Model for Generating Nominal Flight Profiles

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Transactions of Nanjing University of Aeronautics and Astronautics 2016年第4期33卷 414-424页

作者： Tang Xinmin Gu Junwei Shen Zhiyuan Chen Ping Li Bo College of Civil Aviation Nanjing University of Aeronautics and Astronautics The 28th Research Institute of China Electronic Technology Group Corporation

A high-precision nominal flight profile,involving controllers′intentions is critical for 4Dtrajectory estimation in modern automatic air traffic control systems.We proposed a novel method to effectively improve the accuracy of the nominal flight profile,including the nominal altitude profile and the speed profile.First,considering the characteristics of trajectory data,we developed an improved K-means algorithm.The approach was to measure the similarity between different altitude profiles by integrating the space warp edit distance algorithm,thereby to acquire several fitted nominal flight altitude profiles.This approach breaks the constraints of traditional K-means algorithms.Second,to eliminate the influence of meteorological factors,we introduced historical gridded binary data to determine the en-route wind speed and temperature via inverse distance weighted interpolation.Finally,we facilitated the true airspeed determined by speed triangle relationships and the calibrated airspeed determined by aircraft data model to extract a more accurate nominal speed profile from each cluster,therefore we could describe the airspeed profiles above and below the airspeed transition altitude,respectively.Our experimental results showed that the proposed method could obtain a highly accurate nominal flight profile,which reflects the actual aircraft flight status.

关键词： air transportation flight profile K-means algorithm space warp edit distance(SWED)algorithm trajectory prediction

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Research on fault diagnosis method of control moment gyroscope based on K-means algorithm

Research on fault diagnosis method of control moment gyrosco...

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第三十九届中国控制会议

作者： Wenjing Liu Li Yuan Shuyi Wang Chengrui Liu Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering

A fault diagnosis method of control moment gyroscope（CMG） based on K-means algorithm is proposed to improve the fault diagnosis accuracy by considering the hidden correlation between multidimensional telemetry data. Principal component analysis（PCA） and t-distribution random neighborhood embedding（t-SNE） are used to extract features of CMG digital and physical data. On this basis, the fault diagnosis process of CMG based on K-means algorithm is supplied. Finally, the effectiveness of the proposed method is verified by using the actual in-orbit CMG data.

关键词： Control moment gyroscope Fault diagnosis PCA t-SNE K-means algorithm

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An Improved K-means algorithm Based on Initial Clustering Center Optimization

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ZTE Communications 2017年第B12期15卷 43-46页

作者： LI Taihao NAREN Tuya ZHOU Jianshe REN Fuji LIU Shupeng Beijing Advanced Innovation Center for Imaging Technology Capital Normal UniversityBeijing 100048China Flatley Discovery Lab Boston 02129USA Department of Information Science&Intelligent Systems University of TokushimaTokushima 7708506Japan School of Communication and Information Engineering Shanghai UniversityShanghai 200072China

The K-means algorithm is widely known for its simplicity and fastness in text clustering.However,the selection of the initial clus?tering center with the traditional K-means algorithm is some random,and therefore,the fluctuations and instability of the clustering results are strongly affected by the initial clustering center.This paper proposed an algorithm to select the initial clustering center to eliminate the uncertainty of central point selection.The experiment results show that the improved k-means clustering algorithm is superior to the traditional algorithm.

关键词： clustering K-means algorithm initial clustering center

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UAV path planning based on K-means algorithm and simulated annealing algorithm

UAV path planning based on K-means algorithm and simulated a...

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第37届中国控制会议

作者： Xiu Yue Wei Zhang Shanghai key Lab of Modern Optical System and Engineering Research Center of Optical Instrument and System Ministry of EducationUniversity of Shanghai for Science and Technology

This paper proposed a new method for unmanned aerial vehicle（UAV） path planning based on K-means algorithm and simulated annealing（SA） algorithm, which solves the problem for multi-UAVs with multi-mission under complicated constraints. Firstly, the model is established for the no-fly zone, the target zone and the valid zone for cruise within it in the mission area. Then, the decomposition technique decomposes the valid area into multiple sub-target points reasonably. Secondly, the K-means algorithm is used to cluster the target points of UAV cruise, which solves the problem for UAV cruise range and scheduling issues. Combining the SA algorithm for the similar sub-target route planning, this technique increases the coverage of the UAVs in the sub-target area of cruise valid area. Finally, taking the real data of UAVs in earthquake relief as an example, the effectiveness and robustness of the proposed method is validated by simulation experiments.

关键词： UAV model area decomposition technique K-means algorithm SA algorithm

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A k-means Optimized Clustering algorithm Based on Improved Genetic algorithm

A K-means Optimized Clustering Algorithm Based on Improved G...

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2021中国智能自动化大会(CIAC2021)

作者： Qiu-Mei Pu Qiong Wu Qian Li Minzu University of China

The K-means algorithm is highly sensitive to the initial clustering centers and easily get trapped in a local optimum. To avoid such problems, this paper proposes an improved crossover operator of chromosomes in the genetic algorithm, redefines the calculation method of genetic probability and the natural selection rules, introduces different individual selection mechanisms for the two adjacent generations of chromosomes, and integrates the K-means algorithm into the improved genetic algorithm. Experiment results demonstrate that the improved K-means algorithm is better than the original genetic algorithm and kmeans algorithm in clustering performance, far better than the bisecting K-means algorithm.

关键词： Clustering K-means algorithm Genetic algorithm

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An Effective algorithm to Solve Optimal k Value of K-means algorithm

An Effective Algorithm to Solve Optimal k Value of K-means A...

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2006年国际电子、工程及科学领域的分布式计算和应用学...

作者： Taoying Li Yan Chen School of Economics and Management Dalian Maritime University Dalian Liaoning China

＜正＞In spatial clustering,constructing appropriate validity clustering function is crucial to solve optimal cluster number k.The classical K-means algorithm required the k value to be pre-designated.However,it was difficult to designate precise k value in actual applications,which restricted the extensive application of K-means algorithm.An effective algorithm to solve optimal k value,using distance expense function based on Euclidean distance for verifying validity of optimal cluster number,was given in this paper.At the same time,the upper limit of k value was given and the experience rule was reasonably proved.Above all,computed the inside distance and outside distance of clustering,and computed respective distance expense with different k,and then the k value with minimum distance expense was the optimal cluster number.Finally,the algorithm is applied in practice.

关键词： Distance Expense Inside Distance K-means algorithm Outside Distance Spatial Clustering

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Using Machine Learning to Identify and Optimize Sensitive Parameters in Urban Flood Model Considering Subsurface Characteristics

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International Journal of Disaster Risk Science 2024年第1期15卷 116-133页

作者： Hengxu Jin Yu Zhao Pengcheng Lu Shuliang Zhang Yiwen Chen Shanghua Zheng Zhizhou Zhu key Laboratory of VGE of Ministry of Education Nanjing Normal UniversityNanjing 210023China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China

This study presents a novel method for optimizing parameters in urban flood models,aiming to address the tedious and complex issues associated with parameter optimization.First,a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model.Next,a principle for dividing urban hydrological response units was introduced,incorporating surface attribute features.The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model,and an artificial neural network(ANN)was employed to identify the sensitive parameters.Finally,a genetic algorithm(GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model.The results demonstrate that the parameter optimization method based on k-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient(NSE) of 0.81.Compared to the ANN-GA and k-means-deep neural networks(DNN) methods,the proposed method better characterizes the runoff generation and flow processes.This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.

关键词： Artifcial neural network Coupled urban fooding model Genetic algorithm K-means algorithm Subcatchment delineation Uncertain parameters

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A Tradeoff Between Accuracy and Speed for k-means Seed Determination

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Computer Systems Science & Engineering 2022年第3期40卷 1085-1098页

作者： Farzaneh khorasani Morteza Mohammadi Zanjireh Mahdi Bahaghighat Qin Xin Computer Engineering Department Imam Khomeini International UniversityQazvinIran Faculty of Science and Technology University of the Faroe IslandsTorshavnFaroe Islands

With a sharp increase in the information volume,analyzing and retrieving this vast data volume is much more essential than ever.One of the main techniques that would be beneficial in this regard is called the Clustering method.Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible.One of the most widely used clustering algorithms with the well and approved performance in different applications is the K-means algorithm.The main problem of the K-means algorithm is its performance which can be directly affected by the selection in the primary clusters.Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time.Besides,the selection of appropriate initial seeds can reduce the cluster’s inconsistency.In this paper,we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm.For this purpose,a new method is proposed considering the average distance between objects to determine the initial seeds.Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm.The experimental results showed that our proposed approach outperforms the Chithra with 1.7%and 2.1%in terms of clustering accuracy for Wine and Abalone detection data,respectively.Furthermore,achieved results indicate that comparing with the Reverse Nearest Neighbor(RNN)search approach,the proposed method has a higher convergence speed.

关键词： Data clustering K-means algorithm information retrieval outlier detection clustering accuracy unsupervised learning

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Joint Energy Predication and Gathering Data in Wireless Rechargeable Sensor Network

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Computer Systems Science & Engineering 2023年第3期44卷 2349-2360页

作者： I.Vallirathi S.Ebenezer Juliet Department of Computer Science and Engineering PET Engineering CollegeVallioor627117India Department of Computer Science and Engineering VV College of EngineeringThisayanvilai627657India

Wireless Sensor Network(WSNs)is an infrastructure-less wireless net-work deployed in an increasing number of wireless sensors in an ad-hoc manner.As the sensor nodes could be powered using batteries,the development of WSN energy constraints is considered to be a key issue.In wireless sensor networks(WSNs),wireless mobile chargers(MCs)conquer such issues mainly,energy shortages.The proposed work is to produce an energy-efficient recharge method for Wireless Rechargeable Sensor Network(WRSN),which results in a longer lifespan of the network by reducing charging delay and maintaining the residual energy of the sensor.In this algorithm,each node gets sorted using the k-means technique,in which the data gets distributed into various clusters.The mobile charges execute a Short Hamiltonian cycle opposite direction to reach each cluster’s anchor point.The position of the anchor points is calculated based on the energy distribution using the base station.In this case,the network will act as a spare MC,so that one of the two MCs will run out of energy before reaching the BS.After the current tours of the two MCs terminate,regression analysis for energy prediction initiates,enabling the updating of anchor points in the upcoming round.Based on thefindings of the regression-based energy prediction model,the recommended algorithm could effectively refill network energy.

关键词： WSNs MCs WRSN K-means algorithm shortest hamiltonian cycle regression analysis

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