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Journal articles on the topic 'Nearest neighbor analysis (Statistics)'

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Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

Cao, Shuchao, Feiyang Sun, Mohcine Chraibi, and Rui Jiang. "Spatial analysis for crowds in multi-directional flows based on large-scale experiments." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 11 (November 1, 2021): 113407. http://dx.doi.org/10.1088/1742-5468/ac3660.

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Abstract In this paper, spatial analysis for the nearest neighbors is performed in the unidirectional, bidirectional and crossing flows. Based on the intended direction given in the experiment, different types of neighbors such as U-ped (neighbor with the same intended direction), B-ped (neighbor with the opposite intended direction) and C-ped (neighbor with the intersecting intended direction) are defined. The preferable positions of these neighbors during movement are investigated under various conditions. The spatial relation is quantified by calculating the distance and angle between the reference pedestrian and neighbors. The results indicate that the distribution of neighbors is closely related to the neighbor’s order, crowd density, neighbor type and flow type. Furthermore, the reasons that result in these distributions for different neighbors are explored. Finally neighbor distributions for different flows are compared and the implications of this research are discussed. The spatial analysis sheds new light on the study of pedestrian dynamics in a different perspective, which can help to develop and validate crowd models in the future.

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2

Ogita, Toshiro, Hidetomo Ichihashi, Akira Notsu, and Katsuhiro Honda. "Improvement of PCA-Based Approximate Nearest Neighbor Search Using Distance Statistics." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 4 (July 20, 2014): 658–64. http://dx.doi.org/10.20965/jaciii.2014.p0658.

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In many computer vision applications, nearest neighbor searching in high-dimensional spaces is often the most time consuming component and we have few algorithms for solving these high-dimensional nearest neighbor search problems that are faster than linear search. Approximately nearest neighbor search algorithms can play an important role in achieving significantly faster running times with relatively small errors. This paper considers the improvement of the PCA-tree nearest neighbor search algorithm [1] by employing nearest neighbor distance statistics. During the preprocessing phase of the PCA-tree nearest neighbor search algorithm, a data set is partitioned into clusters by successive use of Principal Component Analysis (PCA). The search performance is significantly improved if the data points are sorted by leaf node, and the threshold value is updated each time a smaller distance is found. The threshold is updated by the ε-approximate nearest neighbor approach together with the fixed-threshold approach. Performance can be further improved by the annulus bound approach. Moreover, nearest neighbor distance statistics is employed for further improving the efficiency of the search algorithm and the several experimental results are shown for demonstrating how its efficiency is improved.

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3

Biau, Gérard, Benoît Cadre, and Laurent Rouvière. "Statistical analysis of k-nearest neighbor collaborative recommendation." Annals of Statistics 38, no. 3 (June 2010): 1568–92. http://dx.doi.org/10.1214/09-aos759.

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4

Stute, Winfried, and Wenceslao González Manteiga. "Nearest neighbor smoothing in linear regression." Journal of Multivariate Analysis 34, no. 1 (July 1990): 61–74. http://dx.doi.org/10.1016/0047-259x(90)90061-l.

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5

Tran, L. T., and S. Yakowitz. "Nearest Neighbor Estimators for Random Fields." Journal of Multivariate Analysis 44, no. 1 (January 1993): 23–46. http://dx.doi.org/10.1006/jmva.1993.1002.

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6

Guerre, Emmanuel. "Design Adaptive Nearest Neighbor Regression Estimation." Journal of Multivariate Analysis 75, no. 2 (November 2000): 219–44. http://dx.doi.org/10.1006/jmva.2000.1901.

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7

Puchkin, Nikita, and Vladimir Spokoiny. "An adaptive multiclass nearest neighbor classifier." ESAIM: Probability and Statistics 24 (2020): 69–99. http://dx.doi.org/10.1051/ps/2019021.

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We consider a problem of multiclass classification, where the training sample S_n={(Xi,Yi)}ni=1 is generated from the model ℙ(Y = m|X = x) = ηm(x), 1 ≤ m ≤ M, and η1(x), …, ηM(x) are unknown α-Holder continuous functions. Given a test point X, our goal is to predict its label. A widely used k-nearest-neighbors classifier constructs estimates of η1(X), …, ηM(X) and uses a plug-in rule for the prediction. However, it requires a proper choice of the smoothing parameter k, which may become tricky in some situations. We fix several integers n1, …, nK, compute corresponding nk-nearest-neighbor estimates for each m and each nk and apply an aggregation procedure. We study an algorithm, which constructs a convex combination of these estimates such that the aggregated estimate behaves approximately as well as an oracle choice. We also provide a non-asymptotic analysis of the procedure, prove its adaptation to the unknown smoothness parameter α and to the margin and establish rates of convergence under mild assumptions.

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8

Cutler, Colleen D., and Donald A. Dawson. "Nearest-Neighbor Analysis of a Family of Fractal Distributions." Annals of Probability 18, no. 1 (January 1990): 256–71. http://dx.doi.org/10.1214/aop/1176990948.

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9

Dikta, Gerhard. "Bootstrap approximation of nearest neighbor regression function estimates." Journal of Multivariate Analysis 32, no. 2 (February 1990): 213–29. http://dx.doi.org/10.1016/0047-259x(90)90082-s.

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10

Burman, P., and D. Nolan. "Location-adaptive density estimation and nearest-neighbor distance." Journal of Multivariate Analysis 40, no. 1 (January 1992): 132–57. http://dx.doi.org/10.1016/0047-259x(92)90095-w.

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11

Sancetta, Alessio. "Nearest neighbor conditional estimation for Harris recurrent Markov chains." Journal of Multivariate Analysis 100, no. 10 (November 2009): 2224–36. http://dx.doi.org/10.1016/j.jmva.2009.06.013.

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12

Voorrips, Albertus, and John M. O'Shea. "Conditional Spatial Patterning: Beyond the Nearest Neighbor." American Antiquity 52, no. 3 (July 1987): 500–521. http://dx.doi.org/10.2307/281596.

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This paper discusses a method for the analysis of spatial patterning which is based on aspects of the study of spatial autocorrelation. By means of computer simulation it is shown that the join count statistic (Cliff and Ord 1973; Moran 1950) has a wider validity than was presumed originally. Some archaeological applications from the analysis of a late Mesolithic cemetery in Karelia, USSR, are presented.

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13

Leggoe, Jeremy W. "Nth-nearest neighbor statistics for analysis of particle distribution data derived from micrographs." Scripta Materialia 53, no. 11 (December 2005): 1263–68. http://dx.doi.org/10.1016/j.scriptamat.2005.07.041.

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14

Yang, Tae Young, and Jae Chang Lee. "Bayesian nearest-neighbor analysis via record value statistics and nonhomogeneous spatial Poisson processes." Computational Statistics & Data Analysis 51, no. 9 (May 2007): 4438–49. http://dx.doi.org/10.1016/j.csda.2006.07.001.

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15

Wang, Shubin, Yukun Tian, Xiaogang Deng, Qianlei Cao, Lei Wang, and Pengxiang Sun. "Disturbance Detection of a Power Transmission System Based on the Enhanced Canonical Variate Analysis Method." Machines 9, no. 11 (November 6, 2021): 272. http://dx.doi.org/10.3390/machines9110272.

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Aiming at the characteristics of dynamic correlation, periodic oscillation, and weak disturbance symptom of power transmission system data, this paper proposes an enhanced canonical variate analysis (CVA) method, called SLCVAkNN, for monitoring the disturbances of power transmission systems. In the proposed method, CVA is first used to extract the dynamic features by analyzing the data correlation and establish a statistical model with two monitoring statistics T2 and Q. Then, in order to handling the periodic oscillation of power data, the two statistics are reconstructed in phase space, and the k-nearest neighbor (kNN) technique is applied to design the statistics nearest neighbor distance DT2 and DQ as the enhanced monitoring indices. Further considering the detection difficulty of weak disturbances with the insignificant symptoms, statistical local analysis (SLA) is integrated to construct the primary and improved residual vectors of the CVA dynamic features, which are capable to prompt the disturbance detection sensitivity. The verification results on the real industrial data show that the SLCVAkNN method can detect the occurrence of power system disturbance more effectively than the traditional data-driven monitoring methods.

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16

Okabe, A., T. Yoshikawa, A. Fujii, and K. Oikawa. "The Statistical Analysis of a Distribution of Activity Points in Relation to Surface-Like Elements." Environment and Planning A: Economy and Space 20, no. 5 (May 1988): 609–20. http://dx.doi.org/10.1068/a200609.

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The objective of this paper is to formulate a statistical method of testing the hypothesis that the distribution of activity points (such as retail stores) is independent of location of ‘surface-like’ infrastructural elements (such as parks). In order to do this, first, the probability density function of a distance from a random point to the nearest surface-like element is derived. Second, through the use of this function, a measure, R, of spatial dependency on the surface-like elements is defined as the ratio of the average nearest-neighbor distance to the expected average nearest-neighbor distance. This measure is an extension of the ordinary nearest-neighbor distance measure frequently referred to in geography and ecology. Third, the statistical use of measure R is shown. Fourth, as this measure is difficult to compute geometrically, the computational method of calculating the value of R is developed. Last, by use of this method, a test is conducted to decide whether or not the distribution of high-class apartment buildings in Setagaya, Tokyo, is affected by the location of big parks.

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17

MURAI, KEI-ICHIRO, YASUHIRO AKUNE, YOHEI SUZUKI, TOSHIHIRO MORIGA, and ICHIRO NAKABAYASHI. "THERMAL VIBRATION ANALYSIS OF RuO2 BY EXAFS." International Journal of Modern Physics B 20, no. 25n27 (October 30, 2006): 4111–16. http://dx.doi.org/10.1142/s0217979206040945.

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Ruthenium dioxide which has a rutile-type structure is an important material in a viewpoint of electronic and magnetic properties. RuO 2 has a negative thermal expansion along c-axis in the high temperature region above room temperature. In this study, we could obtain detailed information about the thermal vibration of atoms by the analysis of EXAFS Debye-Waller factors. EXAFS analysis provides an effective pair potential with temperature dependent shape with Debye-Waller factor. The distance between second-nearest neighbor atoms ( Ru - Ru ) are equal to the length of c-axis in unit cell. It has become apparent that Ru - O bonds in RuO 6 octahedron are much stronger than the interaction between the second-nearest neighbor atoms ( Ru - Ru ) as same as FeF 2 in fluorides. Those results suggest that the negative thermal expansion along c-axis is caused by those weak interactions between second-nearest neighbor atoms ( Ru - Ru ).

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18

Yaguchi, Hirotake. "Entropy Analysis of a Nearest-Neighbor Attractive/Repulsive Exclusion Process on One-Dimensional Lattices." Annals of Probability 18, no. 2 (April 1990): 556–80. http://dx.doi.org/10.1214/aop/1176990845.

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19

Wang, Qiang, Yong Bao Liu, Xing He, Shu Yong Liu, and Jian Hua Liu. "Fault Diagnosis of Bearing Based on KPCA and KNN Method." Advanced Materials Research 986-987 (July 2014): 1491–96. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1491.

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Selection of secondary variables is an effective way to reduce redundant information and to improve efficiency in nonlinear system modeling. The combination of Kernel Principal Component Analysis (KPCA) and K-Nearest Neighbor (KNN) is applied to fault diagnosis of bearing. In this approach, the integral operator kernel functions is used to realize the nonlinear map from the raw feature space of vibration signals to high dimensional feature space, and structure and statistics in the feature space to extract the feature vector from the fault signal with the principal component analytic method. Assessment method using the feature vector of the Kernel Principal Component Analysis, and then enter the sensitive features to K-Nearest Neighbor classification. The experimental results indicated that this method has good accuracy.

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20

Kohler, Michael, Adam Krzyżak, and Harro Walk. "Rates of convergence for partitioning and nearest neighbor regression estimates with unbounded data." Journal of Multivariate Analysis 97, no. 2 (February 2006): 311–23. http://dx.doi.org/10.1016/j.jmva.2005.03.006.

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21

Ebner, Bruno, Norbert Henze, and Joseph E. Yukich. "Multivariate goodness-of-fit on flat and curved spaces via nearest neighbor distances." Journal of Multivariate Analysis 165 (May 2018): 231–42. http://dx.doi.org/10.1016/j.jmva.2017.12.009.

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22

Zhao, L. C. "Exponential bounds of mean error for the nearest neighbor estimates of regression functions." Journal of Multivariate Analysis 21, no. 1 (February 1987): 168–78. http://dx.doi.org/10.1016/0047-259x(87)90105-9.

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23

Ralescu, S. S. "The Law of the Iterated Logarithm for the Multivariate Nearest Neighbor Density Estimators." Journal of Multivariate Analysis 53, no. 1 (April 1995): 159–79. http://dx.doi.org/10.1006/jmva.1995.1030.

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24

Pithuncharurnlap, M., K. E. Basford, and B. R. Cullis. "NEAREST NEIGHBOUR ANALYSIS OF UNEQUALLY REPLICATED TRIALS." Australian Journal of Statistics 34, no. 1 (March 1992): 1–9. http://dx.doi.org/10.1111/j.1467-842x.1992.tb01037.x.

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25

Yakowitz, S. "NEAREST-NEIGHBOUR METHODS FOR TIME SERIES ANALYSIS." Journal of Time Series Analysis 8, no. 2 (March 1987): 235–47. http://dx.doi.org/10.1111/j.1467-9892.1987.tb00435.x.

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26

Mashford, John. "Stochastic Temporal Data Upscaling Using the Generalized k-Nearest Neighbor Algorithm." International Journal of Stochastic Analysis 2018 (September 24, 2018): 1–8. http://dx.doi.org/10.1155/2018/2487947.

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Three methods of temporal data upscaling, which may collectively be called the generalized k-nearest neighbor (GkNN) method, are considered. The accuracy of the GkNN simulation of month by month yield is considered (where the term yield denotes the dependent variable). The notion of an eventually well-distributed time series is introduced and on the basis of this assumption some properties of the average annual yield and its variance for a GkNN simulation are computed. The total yield over a planning period is determined and a general framework for considering the GkNN algorithm based on the notion of stochastically dependent time series is described and it is shown that for a sufficiently large training set the GkNN simulation has the same statistical properties as the training data. An example of the application of the methodology is given in the problem of simulating yield of a rainwater tank given monthly climatic data.

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27

Evans, Dafydd. "A law of large numbers for nearest neighbour statistics." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 464, no. 2100 (August 12, 2008): 3175–92. http://dx.doi.org/10.1098/rspa.2008.0235.

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In practical data analysis, methods based on proximity (near-neighbour) relationships between sample points are important because these relations can be computed in time ( n log n ) as the number of points n →∞. Associated with such methods are a class of random variables defined to be functions of a given point and its nearest neighbours in the sample. If the sample points are independent and identically distributed, the associated random variables will also be identically distributed but not independent. Despite this, we show that random variables of this type satisfy a strong law of large numbers, in the sense that their sample means converge to their expected values almost surely as the number of sample points n →∞.

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28

Mastuti, Winda Chairani, Anik Djuraidah, and Erfiani Erfiani. "ROBUST SPATIAL REGRESSION MODEL ON ORIGINAL LOCAL GOVERNMENT REVENUE IN JAVA 2017." Indonesian Journal of Statistics and Its Applications 4, no. 1 (February 28, 2020): 68–79. http://dx.doi.org/10.29244/ijsa.v4i1.573.

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Spatial regression measures the relationship between response and explanatory variables in the regression model considering spatial effects. Detecting and accommodating outliers is an important step in the regression analysis. Several methods can detect outliers in spatial regression. One of these methods is generating a score test statistics to identify outliers in the spatial autoregressive (SAR) model. This research applies a robust spatial autoregressive (RSAR) model with S- estimator to the Original Local Government Revenue (OLGR) data. The RSAR model with the 4-nearest neighbor weighting matrix is the best model produced in this study. The coefficient of the RSAR model gives a more relevant result. Median absolute deviation (MdAD) and median absolute percentage error (MdAPE) values in the RSAR model with 4-nearest neighbor give smaller results than the SAR model.

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29

Saura, X., J. Suñé, S. Monaghan, P. K. Hurley, and E. Miranda. "Analysis of the breakdown spot spatial distribution in Pt/HfO2/Pt capacitors using nearest neighbor statistics." Journal of Applied Physics 114, no. 15 (October 21, 2013): 154112. http://dx.doi.org/10.1063/1.4825321.

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30

Almanjahie, Ibrahim M., Salim Bouzebda, Zouaoui Chikr Elmezouar, and Ali Laksaci. "The functional kNN estimator of the conditional expectile: Uniform consistency in number of neighbors." Statistics & Risk Modeling 38, no. 3-4 (July 1, 2021): 47–63. http://dx.doi.org/10.1515/strm-2019-0029.

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Abstract The main purpose of the present paper is to investigate the problem of the nonparametric estimation of the expectile regression in which the response variable is scalar while the covariate is a random function. More precisely, an estimator is constructed by using the k Nearest Neighbor procedures (kNN). The main contribution of this study is the establishment of the Uniform consistency in Number of Neighbors (UNN) of the constructed estimator. The usefulness of our result for the smoothing parameter automatic selection is discussed. Short simulation results show that the finite sample performance of the proposed estimator is satisfactory in moderate sample sizes. We finally examine the implementation of this model in practice with a real data in financial risk analysis.

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31

Zhu, Siyu, Chongnan He, Mingjuan Song, and Linna Li. "Two-parameter KNN algorithm and its application in recognition of brand rice." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 1837–43. http://dx.doi.org/10.3233/jifs-210584.

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In response to the frequent counterfeiting of Wuchang rice in the market, an effective method to identify brand rice is proposed. Taking the near-infrared spectroscopy data of a total of 373 grains of rice from the four origins (Wuchang, Shangzhi, Yanshou, and Fangzheng) as the observations, kernel principal component analysis(KPCA) was employed to reduce the dimensionality, and Fisher discriminant analysis(FDA) and k-nearest neighbor algorithm (KNN) were used to identify brand rice respectively. The effects of the two recognition methods are very good, and that of KNN is relatively better. Howerver the shortcomings of KNN are obvious. For instance, it has only one test dimension and its test of samples is not delicate enough. In order to further improve the recognition accuracy, fuzzy k-nearest neighbor set is defined and fuzzy probability theory is employed to get a new recognition method –Two-Parameter KNN discrimination method. Compared with KNN algorithm, this method increases the examination dimension. It not only examines the proportion of the number of samples in each pattern class in the k-nearest neighbor set, but also examines the degree of similarity between the center of each pattern class and the sample to be identified. Therefore, the recognition process is more delicate and the recognition accuracy is higher. In the identification of brand rice, the discriminant accuracy of Two-Parameter KNN algorithm is significantly higher than that of FDA and that of KNN algorithm.

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32

Yaguchi, Hirotake. "Acknowledgment of Priority: Entropy Analysis of a Nearest-Neighbor Attractive/Repulsive Exclusion Process on One-Dimensional Lattices." Annals of Probability 19, no. 4 (October 1991): 1822. http://dx.doi.org/10.1214/aop/1176990238.

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33

Aini, Delvi Nur, Bella Oktavianti, Muhammad Jalal Husain, Dian Ayu Sabillah, Said Thaufik Rizaldi, and Mustakim Mustakim. "Seleksi Fitur untuk Prediksi Hasil Produksi Agrikultur pada Algoritma K-Nearest Neighbor (KNN)." Jurnal Sistem Komputer dan Informatika (JSON) 4, no. 1 (September 30, 2022): 140. http://dx.doi.org/10.30865/json.v4i1.4813.

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Agriculture is one of the largest economic driving sectors in Indonesia. The Central Statistics Agency (BPS) in 2021 recorded that 37.02% of Indonesia's population worked in the agricultural sector. The problem faced by farmers today is the decline in yields, both in quantity and quality due to unpredictable weather, making it difficult for farmers to choose the types of plants that are suitable for planting. The application of data mining techniques has problems related to the complexity of weather parameters and natural conditions that support agricultural production, so it is very important to do feature selection, namely to form the most relevant features. This study conducted an experiment to determine the effect of implementing the Principal Component Analysis (PCA) selection feature on the performance of the K-Nearest Neighbor (KNN) algorithm which produces the highest accuracy of 99.64% in this study.

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34

Bin, Zhang. "Regional enterprise economic development dimensions based on k-means cluster analysis and nearest neighbor discriminant." Journal of Intelligent & Fuzzy Systems 38, no. 6 (June 25, 2020): 7365–75. http://dx.doi.org/10.3233/jifs-179810.

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35

Cs�rgo, M., and P. R�v�sz. "A nearest neighbour-estimator for the score function." Probability Theory and Related Fields 71, no. 2 (January 1986): 293–305. http://dx.doi.org/10.1007/bf00332313.

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36

BRYNILDSEN, MIKKEL H., and HORIA D. CORNEAN. "ON THE VERDET CONSTANT AND FARADAY ROTATION FOR GRAPHENE-LIKE MATERIALS." Reviews in Mathematical Physics 25, no. 04 (May 2013): 1350007. http://dx.doi.org/10.1142/s0129055x13500074.

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We present a rigorous and rather self-contained analysis of the Verdet constant in graphene-like materials. We apply the gauge-invariant magnetic perturbation theory to a nearest-neighbor tight-binding model and obtain a relatively simple and exactly computable formula for the Verdet constant, at all temperatures and all frequencies of sufficiently large absolute value. Moreover, for the standard nearest-neighbor tight-binding model of graphene we show that the transverse component of the conductivity tensor has an asymptotic Taylor expansion in the external magnetic field where all the coefficients of even powers are zero.

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37

Rida, S. Z., Alaa Hassan Noreldeen, and Faten R. Karar. "Applications of the Topological Data Analysis in Real Life." WSEAS TRANSACTIONS ON MATHEMATICS 22 (November 28, 2022): 22–34. http://dx.doi.org/10.37394/23206.2023.22.3.

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Statistical topology inference is a branch of algebraic topology that analyzes the geometric structure's global topological properties underlying a point cloud dataset. There is an increasing need to analyze massive data sets and screen large databases to address real-world problems. A central challenge to modern applied mathematics is the need to generate tools to simplify the data in high dimensional order to extract the important features or the relationships while performing the analysis. A growing field of study at the intersection of algebraic topology, computational geometry, and statistics is topological data analysis (TDA) inference. This study applies TDA tools to test hypothesis between two high-dimensional data sets. Hypothesis testing is one of the most important topics of statistical topology inference. A proposed test was created, which was built on the nearest-neighbor function. Three tests such as (Hypothesis testing based on persistent homology, hypothesis testing based on persistent landscapes, and hypothesis testing based on density estimation) based on TDA, are discussed. Moreover, a modification of these tests was proposed. Monte Carlo simulation was conducted to compare the power of the previous tests. We displayed the use of TDA tools in hypothesis testing. It was proposed that this test might be established based on the nearest neighbor distance function. Furthermore, a suggested modification for the present tests based on TDA was introduced. Finally, the tests specified in the vignette were enabled by two empirical applications within the biology field. We demonstrated the efficacy of the above tests on the heart disease dataset from Statlog and the Wisconsin breast cancer dataset.

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38

Handayani, Irma, and Ikrimach Ikrimach. "Accuracy Analysis of K-Nearest Neighbor and Naïve Bayes Algorithm in the Diagnosis of Breast Cancer." JURNAL INFOTEL 12, no. 4 (November 29, 2020): 151–59. http://dx.doi.org/10.20895/infotel.v12i4.547.

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In the medical field, there are many records of disease sufferers, one of which is data on breast cancer. An extraction process to fine information in previously unknown data is known as data mining. Data mining uses pattern recognition techniques such as statistics and mathematics to find patterns from old data or cases. One of the main roles of data mining is classification. In the classification dataset, there is one objective attribute or it can be called the label attribute. This attribute will be searched from new data on the basis of other attributes in the past. The number of attributes can affect the performance of an algorithm. This results in if the classification process is inaccurate, the researcher needs to double-check at each previous stage to look for errors. The best algorithm for one data type is not necessarily good for another data type. For this reason, the K-Nearest Neighbor and Naïve Bayes algorithms will be used as a solution to this problem. The research method used was to prepare data from the breast cancer dataset, conduct training and test the data, then perform a comparative analysis. The research target is to produce the best algorithm in classifying breast cancer, so that patients with existing parameters can be predicted which ones are malignant and benign breast cancer. This pattern can be used as a diagnostic measure so that it can be detected earlier and is expected to reduce the mortality rate from breast cancer. By making comparisons, this method produces 95.79% for K-Nearest Neighbor and 93.39% for Naïve Bayes

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39

Huang, Kun, and Jianhu Zheng. "Simulation and modeling traffic flow based on Division K Nearest Neighbor." Modern Physics Letters B 33, no. 32 (November 20, 2019): 1950407. http://dx.doi.org/10.1142/s0217984919504074.

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A new traffic flow model is proposed based on cellular automata and Division K Nearest Neighbor for the predication problem of traffic flow state change trend. The model firstly gives the update rules of vehicle state evolution and lane change rules of a vehicle, and establishes the state prediction model based on Division K Nearest Neighbor. Finally, the simulation analysis is conducted by the use of experimental platform, and the relationship between the factors such as average traffic flow speed, average flow rate, traffic flow density and lane change frequency, etc. is deeply studied. The results show that the prediction model has great advantages in the medium and low density area, and lower lane change rate has limited effect on the improvement of traffic flow.

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40

Banerjee, Arka, and Tom Abel. "Nearest neighbour distributions: New statistical measures for cosmological clustering." Monthly Notices of the Royal Astronomical Society 500, no. 4 (November 20, 2020): 5479–99. http://dx.doi.org/10.1093/mnras/staa3604.

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ABSTRACT The use of summary statistics beyond the two-point correlation function to analyse the non-Gaussian clustering on small scales, and thereby, increasing the sensitivity to the underlying cosmological parameters, is an active field of research in cosmology. In this paper, we explore a set of new summary statistics – the k-Nearest Neighbour Cumulative Distribution Functions (kNN-CDF). This is the empirical cumulative distribution function of distances from a set of volume-filling, Poisson distributed random points to the k-nearest data points, and is sensitive to all connected N-point correlations in the data. The kNN-CDF can be used to measure counts in cell, void probability distributions, and higher N-point correlation functions, all using the same formalism exploiting fast searches with spatial tree data structures. We demonstrate how it can be computed efficiently from various data sets – both discrete points, and the generalization for continuous fields. We use data from a large suite of N-body simulations to explore the sensitivity of this new statistic to various cosmological parameters, compared to the two-point correlation function, while using the same range of scales. We demonstrate that the use of kNN-CDF improves the constraints on the cosmological parameters by more than a factor of 2 when applied to the clustering of dark matter in the range of scales between 10 and $40\, h^{-1}\, {\rm Mpc}$. We also show that relative improvement is even greater when applied on the same scales to the clustering of haloes in the simulations at a fixed number density, both in real space, as well as in redshift space. Since the kNN-CDF are sensitive to all higher order connected correlation functions in the data, the gains over traditional two-point analyses are expected to grow as progressively smaller scales are included in the analysis of cosmological data, provided the higher order correlation functions are sensitive to cosmology on the scales of interest.

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Fan, X. D., and L. A. Bursill. "Stable Divergence Angles of a Magnetic Dipole Spiral Array." Modern Physics Letters B 11, no. 24 (October 20, 1997): 1069–75. http://dx.doi.org/10.1142/s0217984997001286.

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An analytical model is introduced for the experiment of Douady and Couder [Phys. Rev. Lett.68, 2098 (1992), where phyllotactic patterns appear as a dynamical result of the interaction between magnetic dipoles. The difference equation for the divergence angle (i.e. the angle between successive radial vectors) is obtained by solving the equations of motion with a second nearest neighbor (SNN) approximation. A one-dimensional map analysis as well as a comprehensive analytical proof shows that the divergence angle always converges to a single attractor regardless of the initial conditions. This attractor is approximately the Fibonacci angle(~ 138°) within variations due to a growth factor μ of the pattern. The system is proved to be stable with the SNN approximation. Further analysis with a third nearest neighbor approximation (TNN) shows extra linearly stable attractors may appear around the Lucas angle (~ 99.5°).

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Stephens, A. M., A. G. Fowler, and L. C. L. Hollenberg. "Universal fault tolerant quantum computation on bilinear nearest neighbor arrays." Quantum Information and Computation 8, no. 3&4 (March 2008): 330–44. http://dx.doi.org/10.26421/qic8.3-4-7.

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Assuming an array that consists of two parallel lines of qubits and that permits only nearest neighbor interactions, we construct physical and logical circuitry to enable universal fault tolerant quantum computation under the $[[7,1,3]]$ quantum code. A rigorous lower bound to the fault tolerant threshold for this array is determined in a number of physical settings. Adversarial memory errors, two-qubit gate errors and readout errors are included in our analysis. In the setting where the physical memory failure rate is equal to one-tenth of the physical gate error rate, the physical readout error rate is equal to the physical gate error rate, and the duration of physical readout is ten times the duration of a physical gate, we obtain a lower bound to the asymptotic threshold of $1.96\times10^{-6}$.

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SHAMIM, K. M., P. TÓTH, D. L. BECKER, and J. E. COOK. "Large retinal ganglion cells that form independent, regular mosaics in the bufonoid frogs Bufo marinus and Litoria moorei." Visual Neuroscience 16, no. 5 (September 1999): 861–79. http://dx.doi.org/10.1017/s0952523899165064.

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Population-based methods were used to study labeled retinal ganglion cells from the cane toad Bufo marinus and the treefrog Litoria moorei, two visually competent bufonoid neobatrachians with contrasting habitats. In both, cells with large somata and thick dendrites formed distinct types with independent mosaics. The αa, αab, and αc mosaics of Bufo in all major respects resembled those of ranids, studied previously, and could be provisionally matched to the same functional classes. As in other frogs, some αa cells were displaced and many α-cells of all types were asymmetric, but within each type all variants belonged to one mosaic. Nearest-neighbor analyses and spatial correlograms confirmed that all three mosaics were regular and independent. In Litoria, monostratified αa cells were not found. Instead, two bistratified types were present, distinguished individually by soma size and dendritic caliber and collectively by membership of independent mosaics: the larger (∼0.8% of all ganglion cells) was termed α1ab and the smaller (∼2.2%) α2ab. An αc cell type was also present, although too inconstantly labeled for mosaic analysis. Nearest-neighbor analyses and spatial correlograms confirmed that the two αab mosaics were regular and independent. Densities, proportions, soma sizes, and mosaic statistics are tabulated for each species. The emergence of a consensus pattern of α-cell types in fishes and frogs, from which this treefrog partly diverges, offers new possibilities for studying correlations between function, phylogeny, ecology, and neuronal form.

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Gill, P. S. "A Bibliography of Nearest Neighbour Methods in Design and Analysis of Experiments." Biometrical Journal 33, no. 4 (1991): 455–59. http://dx.doi.org/10.1002/bimj.4710330413.

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Zhan, Wei Ming, and Jia Wang. "Research of Small Current Grounding Fault Classification Based on Phase Plane Method." Applied Mechanics and Materials 599-601 (August 2014): 1174–77. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.1174.

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In order to solve the problem of single-phase ground fault classification, phase plane method is used. We use phase plane method to analysis single-phase ground fault waveform data first, so a series of discrete points is getting. The discrete points are divided by region on the phase plane to statistics the numbers in each region, so these numbers constitute the feature vector what we required. Use nearest neighbor classification method to achieve single-phase ground fault data classification. Empirical results show that this method can divided the faults into five to find fault reason, thereby improving the diagnostic capability of fault status and fault reason in distribution network.

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LI, ZHIPENG, YUNCAI LIU, and FUQIANG LIU. "A DYNAMICAL MODEL WITH NEXT-NEAREST-NEIGHBOR INTERACTION IN RELATIVE VELOCITY." International Journal of Modern Physics C 18, no. 05 (May 2007): 819–32. http://dx.doi.org/10.1142/s0129183107010450.

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By introducing the velocity difference between the preceding car and the car before the preceding one into the optimal velocity model (OVM), we present an extended dynamical model which takes into account the next-nearest-neighbor interaction in relative velocity. The stability condition of this model is derived by considering a small perturbation around the uniform flow solution and the validity of our theoretical analysis is also confirmed by direct simulations. The analytic and simulation results indicate that traffic congestion is suppressed efficiently by incorporating the effect of new consideration. Moreover, the effect of the new consideration is investigated by numerical simulation. In particular, the jamming flow, the current-density relation, and the propagation speed of small disturbance are examined in detail by varying various values of the parameter.

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Alghobiri, M. "A Comparative Analysis of Classification Algorithms on Diverse Datasets." Engineering, Technology & Applied Science Research 8, no. 2 (April 19, 2018): 2790–95. http://dx.doi.org/10.48084/etasr.1952.

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Data mining involves the computational process to find patterns from large data sets. Classification, one of the main domains of data mining, involves known structure generalizing to apply to a new dataset and predict its class. There are various classification algorithms being used to classify various data sets. They are based on different methods such as probability, decision tree, neural network, nearest neighbor, boolean and fuzzy logic, kernel-based etc. In this paper, we apply three diverse classification algorithms on ten datasets. The datasets have been selected based on their size and/or number and nature of attributes. Results have been discussed using some performance evaluation measures like precision, accuracy, F-measure, Kappa statistics, mean absolute error, relative absolute error, ROC Area etc. Comparative analysis has been carried out using the performance evaluation measures of accuracy, precision, and F-measure. We specify features and limitations of the classification algorithms for the diverse nature datasets.

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BLÖTE, HENK W. J., LEV N. SHCHUR, and ANDREI L. TALAPOV. "THE CLUSTER PROCESSOR: NEW RESULTS." International Journal of Modern Physics C 10, no. 06 (September 1999): 1137–48. http://dx.doi.org/10.1142/s0129183199000929.

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We present a progress report on the Cluster Processor, a special-purpose computer system for the Wolff simulation of the three-dimensional Ising model, including an analysis of simulation results obtained thus far. These results allow, within narrow error margins, a determination of the parameters describing the phase transition of the simple-cubic Ising model and its universality class. For an improved determination of the correction-to-scaling exponent, we include Monte Carlo data for systems with nearest-neighbor and third-neighbor interactions in the analysis.

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Sofian, Harry Octavianus. "SEBARAN GUA ARKEOLOGIS DI KECAMATAN PALIYAN KABUPATEN GUNUNGKIDUL DENGAN MENGGUNAKAN ANALISIS TETANGGA TERDEKAT (NEAREST NEIGHBOURHOOD ANALYSIS)." Berkala Arkeologi 31, no. 2 (November 21, 2011): 122–34. http://dx.doi.org/10.30883/jba.v31i2.391.

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Gunung Sewu karst area has attracted the attention archaeologists since the Dutch colonial era to the present. One of the karst area is located Paliyan District, Gunungkidul Regency. Based on research conducted by Harry Octavianus Sofian in year 2007, there were at least 11 caves and rockshelter as a potential residential dwelling. This paper will discuss and look for patterns of spatial distribution of caves and archaeological potential rockshelter as an ancient settlement in the District Paliyan using Nearest Neighbor Analysis (Analisis Tetangga Terdekat) manually and use Neighborhood Statistic analysis contained in the Arc View software.

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Binns, M. R., and P. Y. Jui. "A simulation study of the nearest neighbour analysis method of papadakis." Communications in Statistics - Simulation and Computation 14, no. 1 (January 1985): 159–72. http://dx.doi.org/10.1080/03610918508812432.

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