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Classification of brain tumor using devernay sub-pixel edge detection and k-nearest neighbours methodology
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作者 Ayush Arora Ritesh Kumar +3 位作者 Shubham Tiwari Mysore Shwetha Selvam Venkatesan Ramesh Babu 《Neuroimmunology and Neuroinflammation》 2018年第6期29-36,共8页
Any disease can be treated only once it is imaged,detected and classified.This paper proposes a set of algorithms for classification of a brain tumor with better accuracy and efficiency.The proposal uses a JPEG format... Any disease can be treated only once it is imaged,detected and classified.This paper proposes a set of algorithms for classification of a brain tumor with better accuracy and efficiency.The proposal uses a JPEG format of the DICOM image fed into three stages namely pre-processing,segmentation using sub-pixel edge detection method and using the nearest neighbor methodology for the detection and differentiation of benign and malignant tumors. 展开更多
关键词 Brain tumor magnetic resonance imaging k-nearest NEIGHBOR SUB-PIXEL edge detection contrast enhancement MALIGNANT BENIGN CLASSIFICATION medical image processing
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基于不规则区域划分方法的k-Nearest Neighbor查询算法 被引量:1
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作者 张清清 李长云 +3 位作者 李旭 周玲芳 胡淑新 邹豪杰 《计算机系统应用》 2015年第9期186-190,共5页
随着越来越多的数据累积,对数据处理能力和分析能力的要求也越来越高.传统k-Nearest Neighbor(k NN)查询算法由于其容易导致计算负载整体不均衡的规则区域划分方法及其单个进程或单台计算机运行环境的较低数据处理能力.本文提出并详细... 随着越来越多的数据累积,对数据处理能力和分析能力的要求也越来越高.传统k-Nearest Neighbor(k NN)查询算法由于其容易导致计算负载整体不均衡的规则区域划分方法及其单个进程或单台计算机运行环境的较低数据处理能力.本文提出并详细介绍了一种基于不规则区域划分方法的改进型k NN查询算法,并利用对大规模数据集进行分布式并行计算的模型Map Reduce对该算法加以实现.实验结果与分析表明,Map Reduce框架下基于不规则区域划分方法的k NN查询算法可以获得较高的数据处理效率,并可以较好的支持大数据环境下数据的高效查询. 展开更多
关键词 k-nearest Neighbor(k NN)查询算法 不规则区域划分方法 MAP REDUCE 大数据
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Entanglement and quantum phase transition in alternating XY spin chain with next-nearest neighbouring interactions 被引量:1
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作者 单传家 程维文 +2 位作者 刘堂昆 黄燕霞 李宏 《Chinese Physics B》 SCIE EI CAS CSCD 2008年第11期4002-4008,共7页
By using the method of density-matrix renormalization-group to solve the different spin spin correlation functions, the nearest-neighbouring entanglement (NNE) and the next-nearest-neighbouring entanglement (NNNE)... By using the method of density-matrix renormalization-group to solve the different spin spin correlation functions, the nearest-neighbouring entanglement (NNE) and the next-nearest-neighbouring entanglement (NNNE) of one-dimensional alternating Heisenberg XY spin chain are investigated in the presence of alternating the-nearestneighbouring interaction of exchange couplings, external magnetic fields and the next-nearest neighbouring interaction. For a dimerised ferromagnetic spin chain, the NNNE appears only above a critical dimerized interaction, meanwhile, the dimerized interaction a effects a quantum phase transition point and improves the NNNE to a large extent. We also study the effect of ferromagnetic or antiferromagnetic next-nearest neighbouring (NNN) interaction on the dynamics of NNE and NNNE. The ferromagnetic NNN interaction increases and shrinks the NNE below and above a critical frustrated interaction respectively, while the antiferromagnetic NNN interaction always reduces the NNE. The antiferromagnetic NNN interaction results in a large value of NNNE compared with the case where the NNN interaction is ferromagnetic. 展开更多
关键词 the entanglement alternating XY spin chain the next-nearest neighbouring interactions
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The Background of Regional Lead Isotopic Compositions and Its Application in the Lower and Middle Reaches of the Yangtze River and Neighbouring Areas 被引量:1
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作者 Ma Zhendong and Shan Guangxiang Geochemistry Institute, China University of Geosciences, Wuhan, Hubei 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 1997年第2期158-170,共13页
The paper systematically deals with the background of regional isotopic compo-sitions in the lower and middle reaches of the Yangtze River and neighbouring areas. It isshown that the lead isotopic compositions of diff... The paper systematically deals with the background of regional isotopic compo-sitions in the lower and middle reaches of the Yangtze River and neighbouring areas. It isshown that the lead isotopic compositions of different geological formations and units are con-trolled by the primary mantle heterogeneity, dynamic process of crust-mantle interchange,abundances of uraninm, thorium and lead of various layers of the earth and timing. Studies onthe background of regional isotopic compositions may offer significant information forgeochemical regionalization, tracing of sources of ore-forming materials, and regionalprognosis of ore deposits. 展开更多
关键词 background of regional lead isotopic compositions tracing action lower and middle reaches of the Yangtze River and neighbouring areas
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Real-Time Spreading Thickness Monitoring of High-core Rockfill Dam Based on K-nearest Neighbor Algorithm 被引量:4
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作者 Denghua Zhong Rongxiang Du +2 位作者 Bo Cui Binping Wu Tao Guan 《Transactions of Tianjin University》 EI CAS 2018年第3期282-289,共8页
During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and... During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and the overallquality of the entire dam. Currently, the method used to monitor and controlspreading thickness during the dam construction process is artificialsampling check after spreading, which makes it difficult to monitor the entire dam storehouse surface. In this paper, we present an in-depth study based on real-time monitoring and controltheory of storehouse surface rolling construction and obtain the rolling compaction thickness by analyzing the construction track of the rolling machine. Comparatively, the traditionalmethod can only analyze the rolling thickness of the dam storehouse surface after it has been compacted and cannot determine the thickness of the dam storehouse surface in realtime. To solve these problems, our system monitors the construction progress of the leveling machine and employs a real-time spreading thickness monitoring modelbased on the K-nearest neighbor algorithm. Taking the LHK core rockfilldam in Southwest China as an example, we performed real-time monitoring for the spreading thickness and conducted real-time interactive queries regarding the spreading thickness. This approach provides a new method for controlling the spreading thickness of the core rockfilldam storehouse surface. 展开更多
关键词 Core rockfill dam Dam storehouse surface construction Spreading thickness k-nearest neighbor algorithm Real-time monitor
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Pruned fuzzy K-nearest neighbor classifier for beat classification 被引量:2
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作者 Muhammad Arif Muhammad Usman Akram Fayyaz-ul-Afsar Amir Minhas 《Journal of Biomedical Science and Engineering》 2010年第4期380-389,共10页
Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats... Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data. 展开更多
关键词 ARRHYTHMIA ECG k-nearest NEIGHBOR PRUNING FUZZY Classification
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A Short-Term Traffic Flow Forecasting Method Based on a Three-Layer K-Nearest Neighbor Non-Parametric Regression Algorithm 被引量:7
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作者 Xiyu Pang Cheng Wang Guolin Huang 《Journal of Transportation Technologies》 2016年第4期200-206,共7页
Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting... Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting method based on a three-layer K-nearest neighbor non-parametric regression algorithm is proposed. Specifically, two screening layers based on shape similarity were introduced in K-nearest neighbor non-parametric regression method, and the forecasting results were output using the weighted averaging on the reciprocal values of the shape similarity distances and the most-similar-point distance adjustment method. According to the experimental results, the proposed algorithm has improved the predictive ability of the traditional K-nearest neighbor non-parametric regression method, and greatly enhanced the accuracy and real-time performance of short-term traffic flow forecasting. 展开更多
关键词 Three-Layer Traffic Flow Forecasting k-nearest Neighbor Non-Parametric Regression
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Effects of local neighbourhood diversity on crown structure and productivity of individual trees in mature mixed-species forests 被引量:1
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作者 Louis Georgi Matthias Kunz +4 位作者 Andreas Fichtner Karl Friedrich Reich Anne Bienert Hans-Gerd Maas Goddert von Oheimb 《Forest Ecosystems》 SCIE CSCD 2021年第2期345-356,共12页
Background:Species-specific genotypic features,local neighbourhood interactions and resource supply strongly influence the tree stature and growth rate.In mixed-species forests,diversity-mediated biomass allocation ha... Background:Species-specific genotypic features,local neighbourhood interactions and resource supply strongly influence the tree stature and growth rate.In mixed-species forests,diversity-mediated biomass allocation has been suggested to be a fundamental mechanism underlying the positive biodiversity-productivity relationships.Empirical evidence,however,is rare about the impact of local neighbourhood diversity on tree characteristics analysed at a very high level of detail.To address this issue we analysed these effects on the individual-tree crown architecture and tree productivity in a mature mixed forest in northern Germany.Methods:Our analysis considers multiple target tree species across a local neighbourhood species richness gradient ranging from 1 to 4.We applied terrestrial laser scanning to quantify a large number of individual mature trees(N=920)at very high accuracy.We evaluated two different neighbour inclusion approaches by analysing both a fixed radius selection procedure and a selection based on overlapping crowns.Results and conclusions:We show that local neighbourhood species diversity significantly increases crown dimension and wood volume of target trees.Moreover,we found a size-dependency of diversity effects on tree productivity(basal area and wood volume increment)with positive effects for large-sized trees(diameter at breast height(DBH)>40 cm)and negative effects for small-sized(DBH<40 cm)trees.In our analysis,the neighbour inclusion approach has a significant impact on the outcome.For scientific studies and the validation of growth models we recommend a neighbour selection by overlapping crowns,because this seems to be the relevant scale at which local neighbourhood interactions occur.Because local neighbourhood diversity promotes individual-tree productivity in mature European mixed-species forests,we conclude that a small-scale species mixture should be considered in management plans. 展开更多
关键词 BIODIVERSITY Tree growth Crown architecture Quantitative structure models Terrestrial laser scanning neighbour classification
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Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico 被引量:3
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作者 Carlos A AGUIRRE-SALADO Eduardo J TREVIO-GARZA +7 位作者 Oscar A AGUIRRE-CALDERóN Javier JIMNEZ-PREZ Marco A GONZLEZ-TAGLE José R VALDZ-LAZALDE Guillermo SNCHEZ-DíAZ Reija HAAPANEN Alejandro I AGUIRRE-SALADO Liliana MIRANDA-ARAGóN 《Journal of Arid Land》 SCIE CSCD 2014年第1期80-96,共17页
As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring s... As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring strategies using geospatial technology.Among statistical methods for mapping biomass,there is a nonparametric approach called k-nearest neighbor(kNN).We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone.Satellite derived,climatic,and topographic predictor variables were combined with the Mexican National Forest Inventory(NFI)data to accomplish the purpose.Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique.The results indicate that the Most Similar Neighbor(MSN)approach maximizes the correlation between predictor and response variables(r=0.9).Our results are in agreement with those reported in the literature.These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation(REDD+). 展开更多
关键词 k-nearest neighbor Mahalanobis most similar neighbor MODIS BRDF-adjusted reflectance forest inventory the policy of Reducing Emission from Deforestation and Forest Degradation
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Computational Intelligence Prediction Model Integrating Empirical Mode Decomposition,Principal Component Analysis,and Weighted k-Nearest Neighbor 被引量:2
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作者 Li Tang He-Ping Pan Yi-Yong Yao 《Journal of Electronic Science and Technology》 CAS CSCD 2020年第4期341-349,共9页
On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feat... On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate. 展开更多
关键词 Empirical mode decomposition(EMD) k-nearest neighbor(KNN) principal component analysis(PCA) time series
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A research on the geothermal structure in Yanqing-Huailai Basin and its neighbouring region
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作者 祖金华 吴乾蕃 廉雨方 《Acta Seismologica Sinica(English Edition)》 CSCD 1997年第4期115-118,共4页
AresearchonthegeothermalstructureinYanqing┐HuailaiBasinanditsneighbouringregionJIN-HUAZU(祖金华)QIAN-FANWU(吴乾蕃)... AresearchonthegeothermalstructureinYanqing┐HuailaiBasinanditsneighbouringregionJIN-HUAZU(祖金华)QIAN-FANWU(吴乾蕃)YU-FANGLIAN(廉雨方)... 展开更多
关键词 terrestrial heat flow two dimensional temperature structure Yanqing Huailai Basin and its neighbouring regions
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A natural neighbour method based on Fraeijs de Veubeke variational principle for materially non-linear problems
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作者 Xiang Li Serge Cescotto Barbara Rossi 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2009年第1期83-93,共11页
The natural neighbour method can be considered as one of many variants of the meshless methods. In the present paper, a new approach based on the Fraeijs de Veubeke (FdV) functional, which is initially developed for... The natural neighbour method can be considered as one of many variants of the meshless methods. In the present paper, a new approach based on the Fraeijs de Veubeke (FdV) functional, which is initially developed for linear elasticity, is extended to the case of geometrically linear but materially non-linear solids. The new approach provides an original treatment to two classical problems: the numerical evaluation of the integrals over the domain A and the enforcement of boundary conditions of the type ui = hi on Su. In the absence of body forces (Fi = 0), it will be shown that the calculation of integrals of the type fA .dA can be avoided and that boundary conditions of the type ui = hi on Su can be imposed in the average sense in general and exactly if hi is linear between two contour nodes, which is obviously the case for tTi = O. 展开更多
关键词 Natural neighbour method Meshless methodFraeijs de Veubeke variational principle DISCRETIZATION Voronoi cell
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Active learning accelerated Monte-Carlo simulation based on the modified K-nearest neighbors algorithm and its application to reliability estimations
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作者 Zhifeng Xu Jiyin Cao +2 位作者 Gang Zhang Xuyong Chen Yushun Wu 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2023年第10期306-313,共8页
This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a rand... This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability. 展开更多
关键词 Active learning Monte-carlo simulation k-nearest neighbors Reliability estimation CLASSIFICATION
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A Hybrid Approach to Neighbour Discovery in Wireless Sensor Networks
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作者 Sagar Mekala K.Shahu Chatrapati 《Intelligent Automation & Soft Computing》 SCIE 2023年第1期581-593,共13页
In the contemporary era of unprecedented innovations such as Internet of Things(IoT),modern applications cannot be imagined without the presence of Wireless Sensor Network(WSN).Nodes in WSN use neighbour discovery(ND)... In the contemporary era of unprecedented innovations such as Internet of Things(IoT),modern applications cannot be imagined without the presence of Wireless Sensor Network(WSN).Nodes in WSN use neighbour discovery(ND)protocols to have necessary communication among the nodes.Neighbour discovery process is crucial as it is to be done with energy efficiency and minimize discovery latency and maximize percentage of neighbours discovered.The current ND approaches that are indirect in nature are categorized into methods of removal of active slots from wake-up schedules and intelligent addition of new slots.The two methods are found to have certain drawbacks.Thefirst category disturbs original integrity of wake-up schedules leading to reduced chances of discovering new nodes in WSN as neighbours.When second category is followed,it may have inefficient slots in the wake-up schedules leading to performance degradation.Therefore,the motivation behind the work in this paper is that by combining the two categories,it is possible to reap benefits of both and get rid of the limitations of the both.Making a hybrid is achieved by introducing virtual nodes that help maximize performance by ensuring original integrity of wake-up schedules and adding of efficient active slots.Thus a Hybrid Approach to Neighbour Discovery(HAND)protocol is realized in WSN.The simulation study revealed that HAND outperforms the existing indirect ND models. 展开更多
关键词 Wireless sensor networks neighbour discovery hybrid method energy efficiency wake-up schedules
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GHM-FKNN:a generalized Heronian mean based fuzzy k-nearest neighbor classifier for the stock trend prediction
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作者 吴振峰 WANG Mengmeng +1 位作者 LAN Tian ZHANG Anyuan 《High Technology Letters》 EI CAS 2023年第2期122-129,共8页
Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-n... Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-nearest neighbor(KNN), and fuzzy KNN(FKNN), have difficulty in accurately predicting the stock trend(uptrend or downtrend) for a given date, a generalized Heronian mean(GHM) based FKNN predictor named GHM-FKNN was proposed.GHM-FKNN combines GHM aggregation function with the ideas of the classical FKNN approach.After evaluation, the comparison results elucidated that GHM-FKNN outperformed the other best existing methods RF, PRF, KNN and FKNN on independent test datasets corresponding to three stocks, namely AAPL, AMZN and NFLX.Compared with RF, PRF, KNN and FKNN, GHM-FKNN achieved the best performance with accuracy of 62.37% for AAPL, 58.25% for AMZN, and 64.10% for NFLX. 展开更多
关键词 stock trend prediction Heronian mean fuzzy k-nearest neighbor(FKNN)
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ID-TIMS U-Pb Geochronology of the Tayatea Dyke Swarm of Australia: Identifying Tasmania's Nearest Neighbours in the Proterozoic
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作者 Charmaine MCGREGOR Steven DENYSZYN +3 位作者 Galen HALVERSON John EVERARD Grace CUMMING Clive CALVER 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 2016年第S1期38-,共1页
The Tayatea Dyke Swarm(also known as the Tayatea Dolerite)comprises well-exposed northeast-trending tholeiitic dykes that intrude the Rocky Cape Group(RCG)of northwest Tasmania,Australia.The dykes commonly
关键词 TIMS PB Identifying Tasmania’s Nearest neighbours in the Proterozoic ID-TIMS U-Pb Geochronology of the Tayatea Dyke Swarm of Australia
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Diagnosis of Disc Space Variation Fault Degree of Transformer Winding Based on K-Nearest Neighbor Algorithm
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作者 Song Wang Fei Xie +3 位作者 Fengye Yang Shengxuan Qiu Chuang Liu Tong Li 《Energy Engineering》 EI 2023年第10期2273-2285,共13页
Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose t... Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose the disc space variation(DSV)fault degree of transformer winding,this paper presents a diagnostic method of winding fault based on the K-Nearest Neighbor(KNN)algorithmand the frequency response analysis(FRA)method.First,a laboratory winding model is used,and DSV faults with four different degrees are achieved by changing disc space of the discs in the winding.Then,a series of FRA tests are conducted to obtain the FRA results and set up the FRA dataset.Second,ten different numerical indices are utilized to obtain features of FRA curves of faulted winding.Third,the 10-fold cross-validation method is employed to determine the optimal k-value of KNN.In addition,to improve the accuracy of the KNN model,a comparative analysis is made between the accuracy of the KNN algorithm and k-value under four distance functions.After getting the most appropriate distance metric and kvalue,the fault classificationmodel based on theKNN and FRA is constructed and it is used to classify the degrees of DSV faults.The identification accuracy rate of the proposed model is up to 98.30%.Finally,the performance of the model is presented by comparing with the support vector machine(SVM),SVM optimized by the particle swarmoptimization(PSO-SVM)method,and randomforest(RF).The results show that the diagnosis accuracy of the proposed model is the highest and the model can be used to accurately diagnose the DSV fault degrees of the winding. 展开更多
关键词 Transformer winding frequency response analysis(FRA)method k-nearest Neighbor(KNN) disc space variation(DSV)
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Propagation Path Loss Models at 28 GHz Using K-Nearest Neighbor Algorithm
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作者 Vu Thanh Quang Dinh Van Linh To Thi Thao 《通讯和计算机(中英文版)》 2022年第1期1-8,共8页
In this paper,we develop and apply K-Nearest Neighbor algorithm to propagation pathloss regression.The path loss models present the dependency of attenuation value on distance using machine learning algorithms based o... In this paper,we develop and apply K-Nearest Neighbor algorithm to propagation pathloss regression.The path loss models present the dependency of attenuation value on distance using machine learning algorithms based on the experimental data.The algorithm is performed by choosing k nearest points and training dataset to find the optimal k value.The proposed method is applied to impove and adjust pathloss model at 28 GHz in Keangnam area,Hanoi,Vietnam.The experiments in both line-of-sight and non-line-of-sight scenarios used many combinations of transmit and receive antennas at different transmit antenna heights and random locations of receive antenna have been carried out using Wireless Insite Software.The results have been compared with 3GPP and NYU Wireless Path Loss Models in order to verify the performance of the proposed approach. 展开更多
关键词 k-nearest neighbor regression 5G millimeter waves path loss
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Enhancement of next-nearest-neighbouring entanglement in quantum Ising spin chain
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作者 单传家 刘继兵 +3 位作者 程维文 刘堂昆 黄燕霞 李宏 《Chinese Physics B》 SCIE EI CAS CSCD 2009年第9期3687-3692,共6页
Using the method of the Jordan-Wigner transformation for solving different spin-spin correlation functions, we have investigated the generation of next-nearest-neighbouring entanglement in a one-dimensional quantum Is... Using the method of the Jordan-Wigner transformation for solving different spin-spin correlation functions, we have investigated the generation of next-nearest-neighbouring entanglement in a one-dimensional quantum Ising spin chain with the Gaussian distribution impurities of exchange couplings and external magnetic fields taken into account. The maximal value of entanglement between the next-nearest-neighbouring qubits in the transverse Ising model was analysed in detail by varying the effectively controlled parameters such as interchange coupling, magnetic field and the system impurity. For such systems, where both exchange couplings and external magnetic field disorder appear, we show that it is possible to achieve next-nearest-neighbouring entanglement better than the previously discussed pure Ising spin chain case. We also show that the Gaussian distribution impurity can induce next-nearest-neighbouring entanglement, which can be used as a means to characterize quantum phase transition. 展开更多
关键词 next-nearest-neighbouring entanglement lsing spin chain Jordan Wigner transformation
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Condition Monitoring of Roller Bearing by K-star Classifier andK-nearest Neighborhood Classifier Using Sound Signal
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作者 Rahul Kumar Sharma V.Sugumaran +1 位作者 Hemantha Kumar M.Amarnath 《Structural Durability & Health Monitoring》 EI 2017年第1期1-17,共17页
Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is v... Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared. 展开更多
关键词 K-star k-nearest neighborhood K-NN machine learning approach conditionmonitoring fault diagnosis roller bearing decision tree algorithm J-48 random treealgorithm decision making two-layer feature selection sound signal statistical features
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