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Journal articles on the topic 'Multidimensional Hypersphere'

Author: Grafiati
Published: 6 September 2023
Last updated: 19 July 2025

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1

XU, JINQUAN, ERIC S. CARLSON, and VISHAL V. VORA. "Multidimensional Finite Differencing (MDFD) with Hypersphere-Close-Pack Grids." Chemical Engineering Communications 192, no. 8 (2005): 984–1016. http://dx.doi.org/10.1080/009864490517296.

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2

Sokolov, E. N. "Four-dimensional color space." Behavioral and Brain Sciences 20, no. 2 (1997): 207–8. http://dx.doi.org/10.1017/s0140525x9747142x.

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Multidimensional scaling of subjective color differences has shown that color stimuli are located on a hypersphere in four-dimensional space. The semantic space of color names is isomorphic with perceptual color space. A spherical four-dimensional space revealed in monkeys and fish suggests the primacy of common neuronal basis.
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3

Terada, Yoshikazu, and Hiroshi Yadohisa. "Multidimensional scaling with the nested hypersphere model for percentile dissimilarities." Procedia Computer Science 6 (2011): 364–69. http://dx.doi.org/10.1016/j.procs.2011.08.067.

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4

Reddy, Ravula Muralidhar, and N. Naveen Kumar. "Network Based Feature Extraction Method for Fraud Detection Using Label Propagation." International Journal for Research in Applied Science and Engineering Technology 12, no. 7 (2024): 71–82. http://dx.doi.org/10.22214/ijraset.2024.63525.

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Abstract: Nowadays, judging the current transaction based on user history transactions is an important detection method. However, different users have different transaction behaviors, when all users use the same limit to judge whether the transaction is abnormal, it will result in higher misjudgment for some users. Aiming at the above problems, this paper proposes an individual behavior transaction detection method based on hypersphere model. In this model, considering multiple dimensions of normal historical transaction records, the characteristics of user’s transaction behavior is generated
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5

Kovalov, Sergiy, and Oleksandr Mostovenko. "SOME PROPERTIES OF THE HYPERSPHERE IN N-DIMENSIONAL SPACE." APPLIED GEOMETRY AND ENGINEERING GRAPHICS, no. 100 (May 24, 2021): 153–61. http://dx.doi.org/10.32347/0131-579x.2021.100.153-161.

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The study of the properties of surfaces contributes to the expansion of their use in solving various practical problems, especially if such properties can be generalized to manifolds of n-dimensional space. The most thoroughly studied are the properties of the simplest surfaces, including the properties of a sphere. That is why the simplest surfaces are most often used in practice. Each property not covered in the existing literature expands the indicated possibilities. Therefore, the purpose of this article is to identify the properties of the hypersphere unknown from the literature.
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6

Maeda, Satoshi, Yu Watanabe, and Koichi Ohno. "A scaled hypersphere interpolation technique for efficient construction of multidimensional potential energy surfaces." Chemical Physics Letters 414, no. 4-6 (2005): 265–70. http://dx.doi.org/10.1016/j.cplett.2005.08.063.

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7

Unver, Mustafa, and Nihal Erginel. "Clustering applications of IFDBSCAN algorithm with comparative analysis." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 6099–108. http://dx.doi.org/10.3233/jifs-189082.

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Density Based Spatial Clustering of Application with Noise (DBSCAN) is one of the mostly preferred algorithm among density based clustering approaches in unsupervised machine learning, which uses epsilon neighborhood construction strategy in order to discover arbitrary shaped clusters. DBSCAN separates dense regions from low density regions and simultaneously assigns points that lie alone as outliers to unearth the hidden cluster patterns in the datasets. DBSCAN identifies dense regions by means of core point definition, detection of which are strictly dependent on input parameter definitions:
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8

Wu, Guangjun, Bingqing Zhu, Jun Li, Yong Wang, and Yungang Jia. "H2SA-ALSH: A Privacy-Preserved Indexing and Searching Schema for IoT Data Collection and Mining." Wireless Communications and Mobile Computing 2022 (April 18, 2022): 1–12. http://dx.doi.org/10.1155/2022/9990193.

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Currently, smart devices of Internet of Things generate massive amount of data for different applications. However, it will expose sensitive information to external users in the process of IoT data collection, transmission, and mining. In this paper, we propose a novel indexing and searching schema based on homocentric hypersphere and similarity-aware asymmetric LSH (H2SA-ALSH) for privacy-preserved data collection and mining over IoT environments. The H2SA-ALSH collects multidimensional data objects and indexes their features according to the Euclidean norm and cosine similarity. Additionally
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9

Karbauskaitė, Rasa, and Gintautas Dzemyda. "Optimization of the Maximum Likelihood Estimator for Determining the Intrinsic Dimensionality of High–Dimensional Data." International Journal of Applied Mathematics and Computer Science 25, no. 4 (2015): 895–913. http://dx.doi.org/10.1515/amcs-2015-0064.

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AbstractOne of the problems in the analysis of the set of images of a moving object is to evaluate the degree of freedom of motion and the angle of rotation. Here the intrinsic dimensionality of multidimensional data, characterizing the set of images, can be used. Usually, the image may be represented by a high-dimensional point whose dimensionality depends on the number of pixels in the image. The knowledge of the intrinsic dimensionality of a data set is very useful information in exploratory data analysis, because it is possible to reduce the dimensionality of the data without losing much i
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10

Sharmin, Dmitrii V., Tamara N. Sharmina, and Valentin G. Sharmin. "Curvature Tensor of the n-Surface and Its Spherical Image in En+k." UNIVERSITY NEWS. NORTH-CAUCASIAN REGION. NATURAL SCIENCES SERIES, no. 1 (217) (March 31, 2023): 29–34. http://dx.doi.org/10.18522/1026-2237-2023-1-29-34.

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The main task of classical multidimensional differential geometry is to study the properties of various n-surfaces. Often these studies use torsion coefficients that are defined for any n-surface with codimension k > 1 in (n + k)-dimensional Euclidean space. For hypersurfaces, the torsion coefficients are not defined.Another important concept used to study the properties of n-surfaces is the spherical Gaussian mapping. The Gaussian mapping defined on submanifolds of Euclidean and pseudo-Euclidean spaces allows one to study the external properties of a submanifold immersed in a Eucli
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11

Stoyan, Yuriy, Georgiy Yaskov, Tatiana Romanova, Igor Litvinchev, Sergey Yakovlev, and José Manuel Velarde Cantú. "Optimized packing multidimensional hyperspheres: a unified approach." Mathematical Biosciences and Engineering 17, no. 6 (2020): 6601–30. http://dx.doi.org/10.3934/mbe.2020344.

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12

Vazquez-Leal, H., V. M. Jimenez-Fernandez, B. Benhammouda, et al. "Modified Hyperspheres Algorithm to Trace Homotopy Curves of Nonlinear Circuits Composed by Piecewise Linear Modelled Devices." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/938598.

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We present a homotopy continuation method (HCM) for finding multiple operating points of nonlinear circuits composed of devices modelled by using piecewise linear (PWL) representations. We propose an adaptation of the modified spheres path tracking algorithm to trace the homotopy trajectories of PWL circuits. In order to assess the benefits of this proposal, four nonlinear circuits composed of piecewise linear modelled devices are analysed to determine their multiple operating points. The results show that HCM can find multiple solutions within a single homotopy trajectory. Furthermore, we tak
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13

AKHMETYANOV, R. F., and E. S. SHIKHOVTSEVA. "REPRESENTATION OF THE PAIRED INTERACTION POTENTIAL IN THE FORM OF MULTIDIMENSIONAL RATIONAL SERIES IN JACOBI VARIABLES FOR MANY-BODY PROBLEMS." Izvestia Ufimskogo Nauchnogo Tsentra RAN, no. 4 (December 13, 2021): 9–15. http://dx.doi.org/10.31040/2222-8349-2021-0-4-9-15.

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Scalar power functions of the form x1 + + xN -v Î are in some cases found in physical problems and applications, especially in many-body problems with paired interactions. There are known decompositions for two vectors in three-dimensional space. In this paper, we consider analogous decompositions with any number of N arbitrary M-dimensional vectors in Euclidean space as a product of a multidimensional rational series with respect to spatial variables and hyperspheric functions on the unit sphere SM-1. Such an advantage of expansion arises in three-body problems when solving the Faddeev equati
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14

Yang , Luojing, Xiaoping Yang, and Yongdao Zhou. "Construction of Uniform Designs over a Domain with Linear Constraints." Mathematics 13, no. 3 (2025): 438. https://doi.org/10.3390/math13030438.

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Uniform design is a powerful and robust experimental methodology that is particularly advantageous for multidimensional numerical integration and high-level experiments. As its applications expand across diverse disciplines, the theoretical foundation of uniform design continues to evolve. In real-world scenarios, experimental factors are often subject to one or more linear constraints, which pose challenges in constructing efficient designs within constrained high-dimensional experimental spaces. These challenges typically require sophisticated algorithms, which may compromise uniformity and
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15

Huang, Mingfeng, Jianping Sun, Kang Cai, and Qiang Li. "MEMD-Based Hybrid Modal Identification for High-Rise Structures with Multi-Sensor Vibration Measurements." Applied Sciences 12, no. 16 (2022): 8345. http://dx.doi.org/10.3390/app12168345.

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Although widely used in various fields due to its powerful capability of signal processing, empirical mode decomposition has to decompose signals separately, which limits its application for multivariate data such as the structural monitoring data recorded by multiple sensors. In order to avoid this shortcoming, a multivariate extension of empirical mode decomposition is proposed to deal with the multidimensional signals synchronously by employing a real-valued projection on hyperspheres. This study presents a hybrid modal identification method combining the multivariate empirical mode decompo
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16

Moosavi, Forough, Hamid Shiri, Govind Vashishtha, Sumika Chauhan, Agnieszka Wylomanska, and Radoslaw Zimroz. "Novelty detection for long-term diagnostic data with Gaussian and non-Gaussian disturbances using a support vector machine." Measurement Science and Technology, November 11, 2024. http://dx.doi.org/10.1088/1361-6501/ad90fe.

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Abstract In condition monitoring lack of properly balanced data sets with faulty and healthy cases makes proper condition recognition very challenging. In many cases, one may have good condition data only as the machine is unique and there is no other example. This issue is addressed by proposing a support vector machine (SVM) for novelty detection applied to health index (HI) data. In this scheme, the moving window approach has been utilised in which the simple statistical parameterisation of the data is carried out. Then the model in the multidimensional (mD) space is constructed whose shape
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17

Grishentsev, A. Yu. "Numerical solution of multidimensional Thomson Problem for vectors packaging on hypersphere in broadband radiocommunication problems." Scientific and Technical Journal of Information Technologies, Mechanics and Optics, July 1, 2019, 730–39. http://dx.doi.org/10.17586/2226-1494-2019-19-4-730-739.

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18

Goloboff, Pablo A., and Martín E. Morales. "On the effect of measures for comparing trees on the representation of treespace." Cladistics, April 5, 2025. https://doi.org/10.1111/cla.12614.

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AbstractThe multidimensional space determined by distances between trees (as measured with various methods) is often reduced and projected with multidimensional scaling to visually represent the differences between trees in the possible “treespace”. This paper discusses the influence of 18 alternative measures of distance on mapping the treespace for all possible trees (or a large sample thereof) when trees of different degrees of resolution are included. Measures of distance appropriate for such mapping are expected to produce (hyper)spherical mappings, with resolved trees in the outer layer,
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19

Watanabe, Junya. "Detecting (non)parallel evolution in multidimensional spaces: angles, correlations and eigenanalysis." Biology Letters 18, no. 2 (2022). http://dx.doi.org/10.1098/rsbl.2021.0638.

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Parallelism between evolutionary trajectories in a trait space is often seen as evidence for repeatability of phenotypic evolution, and angles between trajectories play a pivotal role in the analysis of parallelism. However, properties of angles in multidimensional spaces have not been widely appreciated by biologists. To remedy this situation, this study provides a brief overview on geometric and statistical aspects of angles in multidimensional spaces. Under the null hypothesis that trajectory vectors have no preferred directions (i.e. uniform distribution on hypersphere), the angle between
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20

S. Gomes, Antoniel A., Mauricio G. S. Costa, Maxime Louet, Nicolas Floquet, Paulo M. Bisch, and David Perahia. "Extended Sampling of Macromolecular Conformations from Uniformly Distributed Points on Multidimensional Normal Mode Hyperspheres." Journal of Chemical Theory and Computation, December 12, 2024. https://doi.org/10.1021/acs.jctc.4c01054.

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