Relevant bibliographies by topics / Euclidean Distance Measure

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Euclidean Distance Measure'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Euclidean Distance Measure"

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Cubukcu, K. Mert, and Hatcha Taha. "Are Euclidean Distance and Network Distance Related ?" Environment-Behaviour Proceedings Journal 1, no. 4 (2016): 167. http://dx.doi.org/10.21834/e-bpj.v1i4.137.

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Although spatial distance is a very important concept for a wide variety of disciplines including social, natural, and information sciences, the methods used to measure spatial distance are not directly expressed and fully explained. In this study, we calculate and compare Euclidean distances and network distances for 10 randomly selected European cities. On the contrary to the findings reported in past research, we find that there is not a global straight forward relation between the Euclidian distance and network distance.© 2016. The Authors. Published for AMER ABRA by e-International Publis
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O'Brien, Frank. "A Nonmetric Measure of Euclidean Distance." Perceptual and Motor Skills 78, no. 2 (1994): 561–62. http://dx.doi.org/10.2466/pms.1994.78.2.561.

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The results of a large scale simulation study to test the fundamental properties of the author's finite population spatial density model are reported. A conjecture is given relating metric distance to a nonmetric estimate in two-dimensional space.
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Sinha, Hari Om Sharan. "Enhancement of “Technique for Order Preference by Similarity to Ideal Solution” Approach for Evaluating the Web Sources to Select as External Source for Web Warehousing." International Journal of Natural Computing Research 6, no. 1 (2017): 1–16. http://dx.doi.org/10.4018/ijncr.2017010101.

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The main concern of this paper is to evaluate the web sources, which are to be selected as external data sources for web warehousing. In order to identify the web sources, they are evaluated on the ground of their multiple features. For it, Multi Criteria Decision Making (MCDM) approach has been used. Here, among all the MCDM approach, the focus is on “Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) approach and proposing an enhancement in this method. The conventional TOPSIS approach uses Euclidean Distance to measure the similarity. Here, Jeffrey Divergence has been
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Alcantud, José C. R. "A measure of utility levels by Euclidean distance." Decisions in Economics and Finance 25, no. 1 (2002): 65–69. http://dx.doi.org/10.1007/s102030200004.

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Cowen, N. M., and K. J. Frey. "Relationships between three measures of genetic distance and breeding behaviour in oats (Avena sativa L.)." Genome 29, no. 1 (1987): 97–106. http://dx.doi.org/10.1139/g87-017.

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Nine oat cultivars and experimental lines from four diverse germ plasm sources were crossed in a diallel mating design, without reciprocals. Heterosis for grain yield was evaluated in two experiments and 48 F2-derived lines from each of the 36 matings were evaluated for bundle weight, grain yield, straw yield, harvest index, and heading date in two experiments. The number of transgressive segregates per trait and generalized genetic variances were calculated for each mating. Euclidean distance between parents was calculated by using the first five principal components of the parental correlati
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Mukhopadhaya, Sayan, Anil Kumar та Alfred Stein. "FCM Approach of Similarity and Dissimilarity Measures with α-Cut for Handling Mixed Pixels". Remote Sensing 10, № 11 (2018): 1707. http://dx.doi.org/10.3390/rs10111707.

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In this paper, the fuzzy c-means (FCM) classifier has been studied with 12 similarity and dissimilarity measures: Manhattan distance, chessboard distance, Bray–Curtis distance, Canberra, Cosine distance, correlation distance, mean absolute difference, median absolute difference, Euclidean, Mahalanobis, diagonal Mahalanobis and normalised squared Euclidean distance. Both single and composite modes were used with a varying weight constant (m*) and also at different α-cuts. The two best single measures obtained were combined to study the effect of composite measures on the datasets used. An image
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Liu, Hong Liang, Wei Song, Peng Yu Na, Ming Li, and Pei Yang. "The Analysis of Similarity Measure Function in Image Matching Algorithms." Advanced Materials Research 842 (November 2013): 649–53. http://dx.doi.org/10.4028/www.scientific.net/amr.842.649.

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Similarity measure function is one of the most important factors influencing the matching precision in the field of computer vision. In this paper, a survey is done on the application frequency of distance similarity measure methods and related similarity measure methods, also the statistic characteristic is been given. The significance of Measure functions variable parameters in image matching is showed. In the real time processing aspect, drawn the conclusion that Manhattan distance measure is the fastest, Euclidean distance take second place, correlation coefficient is worst. However, in th
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Liu, Cong, Qianqian Chen, Yingxia Chen, and Jie Liu. "A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination." Mathematical Problems in Engineering 2019 (January 17, 2019): 1–21. http://dx.doi.org/10.1155/2019/3821025.

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Most of the existing clustering algorithms are often based on Euclidean distance measure. However, only using Euclidean distance measure may not be sufficient enough to partition a dataset with different structures. Thus, it is necessary to combine multiple distance measures into clustering. However, the weights for different distance measures are hard to set. Accordingly, it appears natural to keep multiple distance measures separately and to optimize them simultaneously by applying a multiobjective optimization technique. Recently a new clustering algorithm called ‘multiobjective evolutionar
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Naranjo, Rodrigo, Matilde Santos, and Luis Garmendia. "A convolution-based distance measure for fuzzy singletons and its application in a pattern recognition problem." Integrated Computer-Aided Engineering 28, no. 1 (2020): 51–63. http://dx.doi.org/10.3233/ica-200629.

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A new method to measure the distance between fuzzy singletons (FSNs) is presented. It first fuzzifies a crisp number to a generalized trapezoidal fuzzy number (GTFN) using the Mamdani fuzzification method. It then treats an FSN as an impulse signal and transforms the FSN into a new GTFN by convoluting it with the original GTFN. In so doing, an existing distance measure for GTFNs can be used to measure distance between FSNs. It is shown that the new measure offers a desirable behavior over the Euclidean and weighted distance measures in the following sense: Under the new measure, the distance b
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Li, Dan, and Xin Bao Li. "A Modified Version of the K-Means Algorithm Based on the Shape Similarity Distance." Applied Mechanics and Materials 457-458 (October 2013): 1064–68. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.1064.

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K-means Algorithm is a popular method in cluster analysis, and it is most based on the Euclidean distance. In this paper, a modified version of the K-means algorithm based on the shape similarity distance (SSD-K-means) is presented. The shape similarity distance is one kind of non-metric distance measure for similarity estimation based on the characteristic of differences. To demonstrate the effectiveness of the method we proposed, this new algorithm has been tested on three shape data datasets. Experiment results prove that the performance of the SSD-K-means is better than those of the classi
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Dissertations / Theses on the topic "Euclidean Distance Measure"

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Marshall, Scott. "An Empirical Approach to Evaluating Sufficient Similarity: Utilization of Euclidean Distance As A Similarity Measure." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/102.

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Individuals are exposed to chemical mixtures while carrying out everyday tasks, with unknown risk associated with exposure. Given the number of resulting mixtures it is not economically feasible to identify or characterize all possible mixtures. When complete dose-response data are not available on a (candidate) mixture of concern, EPA guidelines define a similar mixture based on chemical composition, component proportions and expert biological judgment (EPA, 1986, 2000). Current work in this literature is by Feder et al. (2009), evaluating sufficient similarity in exposure to disinfectio
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Payal, Yalçin. "Identification of Push-to-Talk Transmitters Using Wavelets." Thesis, Monterey, California. Naval Postgraduate School, 1995. http://hdl.handle.net/10945/30740.

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The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government.<br>The main objective of this study is to find a wavelet-based, feature extracting algorithm for push-to-talk transmitter identification. A distance-measure algorithm is introduced to classify signals belonging to one of four transmitters. The signals are first preprocessed to put them into a form suitable for wavelet analysis. The preprocessing scheme includes taking the envelopes and differentials. Median filtering is also applied
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Ranjitkar, Hari Sagar, and Sudip Karki. "Comparison of A*, Euclidean and Manhattan distance using Influence map in MS. Pac-Man." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-11800.

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Context An influence map and potential fields are used for finding path in domain of Robotics and Gaming in AI. Various distance measures can be used to find influence maps and potential fields. However, these distance measures have not been compared yet. ObjectivesIn this paper, we have proposed a new algorithm suitable to find an optimal point in parameters space from random parameter spaces. Finally, comparisons are made among three popular distance measures to find the most efficient. Methodology For our RQ1 and RQ2, we have implemented a mix of qualitative and quantitative approach and fo
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Wesolkowski, Slawomir. "Color Image Edge Detection and Segmentation: A Comparison of the Vector Angle and the Euclidean Distance Color Similarity Measures." Thesis, University of Waterloo, 1999. http://hdl.handle.net/10012/937.

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This work is based on Shafer's Dichromatic Reflection Model as applied to color image formation. The color spaces RGB, XYZ, CIELAB, CIELUV, rgb, l1l2l3, and the new h1h2h3 color space are discussed from this perspective. Two color similarity measures are studied: the Euclidean distance and the vector angle. The work in this thesis is motivated from a practical point of view by several shortcomings of current methods. The first problem is the inability of all known methods to properly segment objects from the background without interference from object shadows and highlights. The
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Book chapters on the topic "Euclidean Distance Measure"

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Mao, Jianyu, Yalei Guo, Jie Ren, and Weizhong Guo. "A New Euclidian Distance Based Approach to Measure Closeness to Singularity for Parallel Manipulators." In Intelligent Robotics and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40849-6_4.

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Sudha, N., and E. P. Vivek. "A High-Speed VLSI Array Architecture for Euclidean Metric-Based Hausdorff Distance Measures Between Images." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11602569_22.

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Taşabat, Semra Erpolat, and Tuğba Kıral Özkan. "Modified TOPSIS Method With Banking Case Study." In Multi-Criteria Decision Analysis in Management. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2216-5.ch009.

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In this chapter, an alternative measure to Euclidean distance measurement is proposed which is used to calculate positive and negative ideal solutions in the traditional TOPSIS method. Lp Minkowski family and L1 family distance measures were used for this purpose. By taking the averages of the distance measurements in the Lq and L1 families, more general and accurate level units were tried to be obtained. Thus, it was shown that TOPSIS method can give different results according to the distance measure used. The importance of the distance measurement unit was emphasized to rank the alternatives correctly. The implementation and evaluation of the proposed method was carried out through the financial performance of the deposit bank operating in the Turkish Banking Sector. It was seen that the rankings of the alternatives changed according to the distance measurements used. By referring to the distance measurements that can be used in the TOPSIS method, it was shown that the rank of the alternatives can vary according to the preferred distance measure.
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"Minkowski Distances for Face Recognition." In Similarity Measures for Face Recognition, edited by Enrico Vezzetti and Federica Marcolin. BENTHAM SCIENCE PUBLISHERS, 2015. http://dx.doi.org/10.2174/9781681080444115010004.

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Minkowski distances really deserve a whole chapter for theirselves. Depending on the value choice of parameter p, explained here below in the introduction, the concept of Minkowski distance is split up in different distance measures, which are typically known as taxicab (p=1), Euclidean (p=2), and Chebyshev distances (􀝌 = ∞). These measures have been widely employed in the 2D face recognition context, as the section dealing with performances outlines.
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"Mahalanobis Distance for Face Recognition." In Similarity Measures for Face Recognition, edited by Enrico Vezzetti and Federica Marcolin. BENTHAM SCIENCE PUBLISHERS, 2015. http://dx.doi.org/10.2174/9781681080444115010005.

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If two vectors originate from the same underlying distribution, the distance between them could be computed with the Mahalanobis distance, a generalization of the Euclidean one. Also, it can be defined as the Euclidean distance computed in the Mahalanobis space. Moreover, there exist also the city block-based Mahalanobis distance and other versions including the angle- and cosine-based ones. Largely employed for face recognition with bi-dimensional facial data, Mahalanobis gains very good performances with PCA algorithms.
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Li, Deng-Feng, and Jiang-Xia Nan. "Extension of the TOPSIS for Multi-Attribute Group Decision Making under Atanassov IFS Environments." In Contemporary Theory and Pragmatic Approaches in Fuzzy Computing Utilization. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-1870-1.ch017.

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This paper extends the technique for order preference by similarity to ideal solution (TOPSIS) for solving multi-attribute group decision making (MAGDM) problems under Atanassov intuitionistic fuzzy set (IFS) environments. In this methodology, weights of attributes and ratings of alternatives on attributes are extracted from fuzziness inherent in decision data and making process and described using Atanassov IFSs. An Euclidean distance measure is developed to calculate the differences between alternatives for each decision maker and an Atanassov IFS positive ideal solution (IFSPIS) as well as an Atanassov IFS negative ideal-solution (IFSNIS). Degrees of relative closeness to the Atanassov IFSPIS for all alternatives with respect to each decision maker in the group are calculated. Then all decision makers in the group may be regarded as “attributes” and a corresponding classical MADM problem is generated and hereby solved by the TOPSIS. The proposed methodology is validated and compared with other similar methods. A numerical example is examined to demonstrate the implementation process of the methodology proposed in this paper.
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Zinn-Justin, Jean. "Critical phenomena: General considerations. Mean-field theory (MFT)." In Quantum Field Theory and Critical Phenomena. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198834625.003.0014.

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This chapter is devoted to a brief review of general properties of phase transitions in macroscopic physics and, in particular in lattice models. Some of these lattice models actually appear as lattice regularizations of Euclidean (imaginary time) quantum physics theory (QFT). Most of the transitions considered in this work have the following character: spins on the lattice, or macroscopic particles in the continuum, interact through short-range forces, assumed, for simplicity, to decay exponentially. For simple systems, it is possible to find a local observable, called order parameter, whose expectation values depend on the phase in the several phase region, for example, the spin in ferromagnetic systems. In the disordered phase, the connected two-point function decreases exponentially at large distance, at a rate characterized by the correlation length (the inverse of the smallest physical mass in particle physics). In continuous transitions, the correlation length diverges at the critical temperature. Within the mean-field approximation (consistent with Landau's theory of critical phenomena), it can be shown that the singular behaviour of thermodynamic quantities at the critical temperature is universal. These properties can also be reproduced by calculating correlation functions with a perturbed Gaussian measure. It is then shown that the leading corrections to the mean-field approximation, in Ising-like systems, diverge at the critical temperature for dimensions smaller than or equal to $4$.
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Aruldoss, Martin, Miranda Lakshmi Travis, and Prasanna Venkatesan Venkatasamy. "A Study and Estimation of Different Distance Measures in Generalized Fuzzy TOPSIS to Improve Ranking Order." In Advanced Fuzzy Logic Approaches in Engineering Science. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5709-8.ch010.

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Multi criteria decision making (MCDM) is used to solve multiple conflicting criteria. There are different methods available in MCDM out of which TOPSIS is a well- known method to solve precise and imprecise information. In this chapter, triangular fuzzy TOPSIS is considered which has different steps like normalization, weight, finding of positive ideal solution (PIS) and negative ideal solution (NIS), distance between PIS and NIS, calculating relative closeness coefficient (RCC) value and ranking the alternatives. Out of these different steps a distance method is studied. The distance measures are basically used to find the distance between the target alternative and the best and the least alternatives. The most commonly used distance method is Euclidean distance. Many other distance methods are available such as Manhattan, Bit-vector, Hamming, Chebyshev distance, etc. To obtain the appropriate distance, these methods are evaluated. The proposed approach is applied in banking domain to find the suitable user for multi criteria reporting (MCR).
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Siddiquee, Mahfuzur Rahman, Naimul Haider, and Rashedur M. Rahman. "Movie Recommendation System Based on Fuzzy Inference System and Adaptive Neuro Fuzzy Inference System." In Fuzzy Systems. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1908-9.ch026.

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One of most prominent features that social networks or e-commerce sites now provide is recommendation of items. However, the recommendation task is challenging as high degree of accuracy is required. This paper analyzes the improvement in recommendation of movies using Fuzzy Inference System (FIS) and Adaptive Neuro Fuzzy Inference System (ANFIS). Two similarity measures have been used: one by taking account similar users' choice and the other by matching genres of similar movies rated by the user. For similarity calculation, four different techniques, namely Euclidean Distance, Manhattan Distance, Pearson Coefficient and Cosine Similarity are used. FIS and ANFIS system are used in decision making. The experiments have been carried out on Movie Lens dataset and a comparative performance analysis has been reported. Experimental results demonstrate that ANFIS outperforms FIS in most of the cases when Pearson Correlation metric is used for similarity calculation.
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Unger, Sebastian, Cony Theis, Christina Niedermann, and Thomas Ostermann. "IU – A Digital Application for the Graphical Examination of Interpersonal Attachment." In German Medical Data Sciences: Bringing Data to Life. IOS Press, 2021. http://dx.doi.org/10.3233/shti210067.

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Human attachment describes the establishment of contact between two or more people leading to a closer interpersonal relationship. For measuring attachment, the use of nonverbal assessments tools including art and drawing tasks has been shown to be an alternative to conventional assessment approaches. The present study aims at evaluating the internal criterion validity of a new digital drawing tool for measuring interpersonal attachment. 68 participants took part in this pilot study and were separated in groups of two. After completion of a 10-item subscale of Social Orientation (SO) they were asked to sit opposite to the other and to hold eye contact during a three minute drawing period. Moving the pen to the upper section near the partner stood for thoughts about the other, while moving to the lower section closest to the subject implicated thoughts about oneself. The mean distance of the resulting time series of the two subjects were calculated, using the mean Euclidean distance, and compared with the difference in the SO values via linear regression. Taking all differences together a moderate correlation of r = 0.298 was observed, which however slightly missed the level of significance (p = 0.09). We were able to find small evidence for the criterion validity of IU digital drawing tool. For future studies, other measures of similarity in the time series, i.e. the Manhattan Distance are discussed as an extension to foster the present results.
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Conference papers on the topic "Euclidean Distance Measure"

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Malkauthekar, M. D. "Analysis of euclidean distance and manhattan distance measure in face recognition." In Third International Conference on Computational Intelligence and Information Technology (CIIT 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.2636.

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Mohd, Wan Rosanisah Wan, and Lazim Abdullah. "Similarity measures of Pythagorean fuzzy sets based on combination of cosine similarity measure and Euclidean distance measure." In PROCEEDING OF THE 25TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM25): Mathematical Sciences as the Core of Intellectual Excellence. Author(s), 2018. http://dx.doi.org/10.1063/1.5041661.

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Greche, Latifa, Maha Jazouli, Najia Es-Sbai, Aicha Majda, and Arsalane Zarghili. "Comparison between Euclidean and Manhattan distance measure for facial expressions classification." In 2017 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS). IEEE, 2017. http://dx.doi.org/10.1109/wits.2017.7934618.

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Zajc, Matej, and Emil Plesnik. "Euclidean Distance Measure for the Electrocardiogram Fiducial Points Detection in the Phase-Space Formed with the Derivative Rule." In 5th EAI International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies". ICST, 2015. http://dx.doi.org/10.4108/eai.14-10-2015.2261633.

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Magalhães, Dimmy, Aurora Pozo, and Roberto Santana. "An empirical comparison of distance/similarity measures for Natural Language Processing." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/eniac.2019.9328.

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Text Classification is one of the tasks of Natural Language Processing (NLP). In this area, Graph Convolutional Networks (GCN) has achieved values higher than CNN's and other related models. For GCN, the metric that defines the correlation between words in a vector space plays a crucial role in the classification because it determines the weight of the edges between two words (represented by nodes in the graph). In this study, we empirically investigated the impact of thirteen measures of distance/similarity. A representation was built for each document using word embedding from word2vec model
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Santhanam, T., and K. Chithra. "A new decision based unsymmetric trimmed median filter using Euclidean distance measure for removal of high density Salt and Pepper noise from images." In 2014 International Conference on Information Communication and Embedded Systems (ICICES). IEEE, 2014. http://dx.doi.org/10.1109/icices.2014.7033825.

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Larochelle, Pierre M., and Andrew P. Murray. "Projection Metrics for Rigid-Body Displacements." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84698.

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An open research question is how to define a useful metric on SE(n) with respect to (1) the choice of coordinate frames and (2) the units used to measure linear and angular distances. We present two techniques for approximating elements of the special Euclidean group SE(n) with elements of the special orthogonal group SO(n+1). These techniques are based on the singular value and polar decompositions (denoted as SVD and PD respectively) of the homogeneous transform representation of the elements of SE(n). The projection of the elements of SE(n) onto SO(n+1) yields hyperdimensional rotations tha
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Patil, Nishad, Sandeep Menon, Diganta Das, and Michael Pecht. "Evaluation of Robust Covariance Estimation Techniques for Anomaly Detection of Insulated Gate Bipolar Transistors (IGBT)." In ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2010. http://dx.doi.org/10.1115/smasis2010-3861.

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An approach to detect anomalies in IGBTs is to monitor the collector-emitter current and voltage in application. These current and voltage parameters can then be reduced to a univariate distance measure called the Mahalanobis Distance (MD). The MD values with the use of an appropriate threshold enable anomaly detection of these devices. Mahalanobis distances (MD) are weighted Euclidean distances; the distance of each point from the center of the distribution is weighted by the inverse of the sample variance-covariance matrix. The presence of outliers in the monitored data can lead to the overe
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Li, Jian, Xianglin Zhan, Shili Chen, et al. "Study of Feature Extraction Method in Ultrasonic Phased Array Testing for Long-Distance Pipeline." In 2008 7th International Pipeline Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/ipc2008-64181.

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Though ultrasonic phased array technology is more efficient than traditional manual ultrasonic testing method, automatic flaw classification is a challenge and still hasn’t been well solved. Whether the representative features can be extracted from each type of ultrasonic flaw signal is a key to influencing the accuracy rate of automatic flaw classification. In this paper, second generation wavelet transform (SGWT) is proposed as a flaw feature extraction method, having the advantages of high computation speed, simple structure and occupying less memory. After introducing the principle of SGWT
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Huang, Weinan, Shanshan Tao, Qiang Bai, and Sheng Dong. "Bivariate Distribution Modelling for Wave Height and Period in Jiaozhou Bay." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77395.

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Ocean waves with one-peaked and double-peaked spectra are the main wave patterns in Jiaozhou Bay, China. This paper discusses the joint distribution of the wave heights and the periods of the two wave patterns. Maximum entropy distribution, lognormal distribution, GEV distribution and Weibull distribution are adopted to construct bivariate distribution using conditional probability formula. In order to compare the four joint distribution models, squared Euclidean distance and AIC criterion are applied to measure the goodness of fit. The result shows that bivariate distribution constructed by G
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