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Journal articles on the topic 'Similarity model'

Author: Grafiati

Published: 28 June 2021

Last updated: 9 February 2022

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1

Murugan, N. Senthil Vel, Dr V. Vallinayagam Dr. V.Vallinayagam, and Dr K. Senthamarai Kannan. "Multiple Regression Model and Similarity Analysis – A Comparison Study." Indian Journal of Applied Research 4, no. 8 (October 1, 2011): 430–32. http://dx.doi.org/10.15373/2249555x/august2014/109.

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2

Bouza, Amancio, and Abraham Bernstein. "(Partial) user preference similarity as classification-based model similarity." Semantic Web 5, no. 1 (2014): 47–64. http://dx.doi.org/10.3233/sw-130099.

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3

Ma, Wenchao, Charles Iaconangelo, and Jimmy de la Torre. "Model Similarity, Model Selection, and Attribute Classification." Applied Psychological Measurement 40, no. 3 (January 18, 2016): 200–217. http://dx.doi.org/10.1177/0146621615621717.

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4

Ohba, Masaaki, Takashi Kurabuchi, Endo Tomoyuki, Yoshihiko Akamine, Motoyasu Kamata, and Aya Kurahashi. "Local Dynamic Similarity Model of Cross-Ventilation Part 2 - Application of Local Dynamic Similarity Model." International Journal of Ventilation 2, no. 4 (April 2004): 383–94. http://dx.doi.org/10.1080/14733315.2004.11683680.

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5

Znamenskij, Sergej. "Model and axioms for similarity metrics." Program Systems: Theory and Applications 8, no. 4 (2017): 347–57. http://dx.doi.org/10.25209/2079-3316-2017-8-4-347-357.

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6

Kovács, Balázs. "A generalized model of relational similarity." Social Networks 32, no. 3 (July 2010): 197–211. http://dx.doi.org/10.1016/j.socnet.2010.02.001.

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7

Tesař, Václav, and Jozef Kordík. "Quasi-similarity model of synthetic jets." Sensors and Actuators A: Physical 149, no. 2 (February 2009): 255–65. http://dx.doi.org/10.1016/j.sna.2008.11.017.

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8

Akishin, P. G., M. V. Altaisky, I. Antoniou, A. D. Budnik, and V. V. Ivanov. "Burridge–Knopoff model and self-similarity." Chaos, Solitons & Fractals 11, no. 1-3 (January 2000): 207–22. http://dx.doi.org/10.1016/s0960-0779(98)00285-9.

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9

Pothos, Emmanuel M., Jerome R. Busemeyer, and Jennifer S. Trueblood. "A quantum geometric model of similarity." Psychological Review 120, no. 3 (2013): 679–96. http://dx.doi.org/10.1037/a0033142.

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10

Shapiro, A. M., and G. S. Benton. "A similarity model of axisymmetric convection." Physics of Fluids A: Fluid Dynamics 2, no. 6 (June 1990): 928–36. http://dx.doi.org/10.1063/1.857653.

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11

Assa, Akbar, and Konstantinos N. Plataniotis. "Similarity-Based Multiple Model Adaptive Estimation." IEEE Access 6 (2018): 36632–44. http://dx.doi.org/10.1109/access.2018.2853572.

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12

Murdock, Bennet B. "Similarity in a Distributed Memory Model." Journal of Mathematical Psychology 39, no. 3 (September 1995): 251–64. http://dx.doi.org/10.1006/jmps.1995.1026.

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13

McCullagh, Penny. "Model Similarity Effects on Motor Performance." Journal of Sport Psychology 9, no. 3 (September 1987): 249–60. http://dx.doi.org/10.1123/jsp.9.3.249.

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This experiment extended previous findings on whether model characteristics affect attentional subprocesses and mediate performance changes in an observational learning setting. College women (N = 75) were randomly assigned to groups in a 2 x 2 (Cueing by Model Similarity) factorial design or to a no-model control group. To assess attentional differences, subjects were cued either prior to or subsequent to a demonstration as to model characteristics, and model similarity was manipulated by having subjects view a similar or dissimilar model. All subjects saw the same videotaped demonstration, only their perceptions of model characteristics were manipulated. Subjects performed 20 performance trials in Phase 1 with outcome knowledge of results (KR) and, after a 1-rnin rest period, were given 10 more performance trials without KR in Phase 2. The results from Phase 1 indicated that subjects performed better after watching a model they perceived to be similar than after one they perceived as dissimilar. Phase 2 data indicated that all subjects were performing similarly in terms of outcome scores but that the control group was using a different strategy than the demonstration groups. The investigation found support for performance differences dependent on model characteristics and pointed to the need to examine more than outcome scores when assessing observational learning effects.

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14

Fan, Xiaoping, Zhijie Chen, Liangkun Zhu, Zhifang Liao, and Bencai Fu. "A Novel Hybrid Similarity Calculation Model." Scientific Programming 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/4379141.

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This paper addresses the problems of similarity calculation in the traditional recommendation algorithms of nearest neighbor collaborative filtering, especially the failure in describing dynamic user preference. Proceeding from the perspective of solving the problem of user interest drift, a new hybrid similarity calculation model is proposed in this paper. This model consists of two parts, on the one hand the model uses the function fitting to describe users’ rating behaviors and their rating preferences, and on the other hand it employs the Random Forest algorithm to take user attribute features into account. Furthermore, the paper combines the two parts to build a new hybrid similarity calculation model for user recommendation. Experimental results show that, for data sets of different size, the model’s prediction precision is higher than the traditional recommendation algorithms.

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15

Wang, Jiaxing, Shuang Li, and Bin Yang. "Combustion kinetic model development using surrogate model similarity method." Combustion Theory and Modelling 22, no. 4 (April 9, 2018): 777–94. http://dx.doi.org/10.1080/13647830.2018.1454607.

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16

Kanamori, Takafumi, and Naoya Osugi. "Model Description of Similarity-Based Recommendation Systems." Entropy 21, no. 7 (July 17, 2019): 702. http://dx.doi.org/10.3390/e21070702.

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The quality of online services highly depends on the accuracy of the recommendations they can provide to users. Researchers have proposed various similarity measures based on the assumption that similar people like or dislike similar items or people, in order to improve the accuracy of their services. Additionally, statistical models, such as the stochastic block models, have been used to understand network structures. In this paper, we discuss the relationship between similarity-based methods and statistical models using the Bernoulli mixture models and the expectation-maximization (EM) algorithm. The Bernoulli mixture model naturally leads to a completely positive matrix as the similarity matrix. We prove that most of the commonly used similarity measures yield completely positive matrices as the similarity matrix. Based on this relationship, we propose an algorithm to transform the similarity matrix to the Bernoulli mixture model. Such a correspondence provides a statistical interpretation to similarity-based methods. Using this algorithm, we conduct numerical experiments using synthetic data and real-world data provided from an online dating site, and report the efficiency of the recommendation system based on the Bernoulli mixture models.

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17

Yang, Xian Feng, Zhou Yu, Pei Ying Zhang, and Guo Hong Gao. "A Model for Chinese Sentence Similarity Computing." Advanced Materials Research 143-144 (October 2010): 668–71. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.668.

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Sentence similarity computation is very important in the field of case-based machine translation. Through the in-depth analysis of sentence and the sentence similarity computing method based on the similarity computation of the word form feature, the word order feature and the semantic feature, we propose a sentence similarity computing model based on the multi-featured weight. By fusing the three features, giving different feature different weight to adapt the contribution of each feature to the sentence similarity computation, make sentence similarity computation more accurate. Experiment result shows that this approach has better accuracy in sentence similarity computation than the others.

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18

Keane, Jonathan, Zed Sevcikova Sehyr, Karen Emmorey, and Diane Brentari. "A theory-driven model of handshape similarity." Phonology 34, no. 2 (August 2017): 221–41. http://dx.doi.org/10.1017/s0952675717000124.

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Following the Articulatory Model of Handshape (Keane 2014), which mathematically defines handshapes on the basis of joint angles, we propose two methods for calculating phonetic similarity: a contour difference method, which assesses the amount of change between handshapes within a fingerspelled word, and a positional similarity method, which compares similarity between pairs of letters in the same position across two fingerspelled words. Both methods are validated with psycholinguistic evidence based on similarity ratings by deaf signers. The results indicate that the positional similarity method more reliably predicts native signer intuition judgements about handshape similarity. This new similarity metric fills a gap in the literature (the lack of a theory-driven similarity metric) that has been empty since effectively the beginning of sign-language linguistics.

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19

Yin, Jie, and Ai Lun Wang. "Research on Functional Similarity and Structural Similarity of Mechanical Components." Applied Mechanics and Materials 541-542 (March 2014): 603–7. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.603.

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The similarity theory is in essence a method based on equation expression. In the application, the model is regarded to be similar to the prototype only if the similarity theorem can be met. But different physical objects have the same equation expression, so the model is always not similar to the prototype. To solve this problem, a new method which studies the similarity of mechanical components from the perspective of function and structure is put forward in this paper, based on the similarity theory and the ‘black box’ concept. The functional similarity criteria and structural similarity criteria derived from this method can accurately describe the similar properties of mechanical components under different similar requirements and can be used to set up the experimental model. Finally, a series of basic mechanical components are taken for example to verify the feasibility and superiority of this method. The method put forward in this paper can be applied to model test design and engineering test, etc. under different similar requirements.

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20

Jović, Mladen, Yutaka Hatakeyama, and Kaoru Hirota. "Cross-Resolution Image Similarity Modeling." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 3 (March 20, 2007): 301–7. http://dx.doi.org/10.20965/jaciii.2007.p0301.

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A cross-resolution image similarity model employing probabilistic interpretations of the similarity values turned into fuzzy values combined with suitable merge functions is presented. Masking the negative particularities of individual region-based image similarity models, five region-based image similarity models are used at the same time when calculating the overall image similarity. By employing aggregation operators, capturing of a variety of conjunctive, disjunctive, and other non-linear combinations of similarity criteria is allowed. Empirical evaluation of the proposed model on four test databases, containing 4,444 images in 150 semantic categories was carried out. The results obtained from the evaluation revealed that cross-resolution image similarity modeling results in optimal retrieval performance. Compared to two well-known image retrieval systems, SIMPLicity and WBIIS, the proposed model brings an increase of 1.7% and 22% respectively in average retrieval precision. The experimental evaluation presented may thus be helpful and suggest possible further improvements can be achieved along the same line of research directions in various computer vision tasks.

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21

Tran, Chi-Kien. "Face Recognition Based on similarity Feature-Based Selection and Classification Algorithms and Wrapper Model." International Journal of Machine Learning and Computing 9, no. 3 (June 2019): 357–62. http://dx.doi.org/10.18178/ijmlc.2019.9.3.810.

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22

MORRY, MARIAN M., MIE KITO, and LINDSEY ORTIZ. "The attraction-similarity model and dating couples: Projection, perceived similarity, and psychological benefits." Personal Relationships 18, no. 1 (October 6, 2010): 125–43. http://dx.doi.org/10.1111/j.1475-6811.2010.01293.x.

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23

Wei, Hongchen, and Xiaobin Li. "Agent Simulation Model Based on Similarity Measure." Advances in Modelling and Analysis B 60, no. 4 (December 30, 2017): 657–72. http://dx.doi.org/10.18280/ama_b.600403.

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24

Phogat, Jyoti, and Atul Kumar. "EmpiricalAnalysis of Document Similarity Using Statistical Model." International Journal of Engineering Research and Applications 07, no. 06 (July 2017): 45–50. http://dx.doi.org/10.9790/9622-0706074650.

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25

Huang, Yu-An, Xing Chen, Zhu-Hong You, De-Shuang Huang, and Keith C. C. Chan. "ILNCSIM: improved lncRNA functional similarity calculation model." Oncotarget 7, no. 18 (March 23, 2016): 25902–14. http://dx.doi.org/10.18632/oncotarget.8296.

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26

Sato, Mika, and Yoshiharu Sato. "STRUCTURAL MODEL OF SIMILARITY FOR FUZZY CLUSTERING." Journal of the Japanese Society of Computational Statistics 7, no. 1 (1994): 27–46. http://dx.doi.org/10.5183/jjscs1988.7.27.

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27

He, Ming, Zhen Zhen Wang, and Yong Ping Du. "Document Similarity Measure Based on Topic Model." Applied Mechanics and Materials 513-517 (February 2014): 1280–84. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1280.

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Document similarity computation is an exciting research topic in information retrieval (IR) and it is a key issue for automatic document categorization, clustering analysis, fuzzy query and question answering. Topic model is an emerging field in natural language processing (NLP), IR and machine learning (ML). In this paper, we apply a latent Dirichlet allocation (LDA) topic model-based method to compute similarity between documents. By mapping a document with term space representation into a topic space, a distribution over topics derived for computing document similarity. An empirical study using real data set demonstrates the efficiency of our method.

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28

Song, Guoli, Shuhui Wang, Qingming Huang, and Qi Tian. "Multimodal Similarity Gaussian Process Latent Variable Model." IEEE Transactions on Image Processing 26, no. 9 (September 2017): 4168–81. http://dx.doi.org/10.1109/tip.2017.2713045.

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29

Gayer, Gabi, Offer Lieberman, and Omer Yaffe. "Similarity-based model for ordered categorical data." Econometric Reviews 38, no. 3 (May 16, 2017): 263–78. http://dx.doi.org/10.1080/07474938.2017.1308054.

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30

Wang, Lidong, and Xiaodong Liu. "A new model of evaluating concept similarity." Knowledge-Based Systems 21, no. 8 (December 2008): 842–46. http://dx.doi.org/10.1016/j.knosys.2008.03.042.

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31

Chen, Ding-Yun, Xiao-Pei Tian, Yu-Te Shen, and Ming Ouhyoung. "On Visual Similarity Based 3D Model Retrieval." Computer Graphics Forum 22, no. 3 (September 2003): 223–32. http://dx.doi.org/10.1111/1467-8659.00669.

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32

Mijangos, Victor, Gerardo Sierra, and Abel Herrera. "A Word Embeddings Model for Sentence Similarity." Research in Computing Science 117, no. 1 (December 31, 2016): 63–74. http://dx.doi.org/10.13053/rcs-117-1-5.

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33

TAKASU, Atsuhiro. "Statistical string similarity model for information linkage." Progress in Informatics, no. 6 (March 2009): 57. http://dx.doi.org/10.2201/niipi.2009.6.7.

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34

Lai, Keke, Samuel B. Green, Roy Levy, Ray E. Reichenberg, Yuning Xu, Marilyn S. Thompson, Nedim Yel, Natalie D. Eggum-Wilkens, Katie L. Kunze, and Masumi Iida. "Assessing Model Similarity in Structural Equation Modeling." Structural Equation Modeling: A Multidisciplinary Journal 23, no. 4 (April 8, 2016): 491–506. http://dx.doi.org/10.1080/10705511.2016.1154464.

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35

Lai, Keke, Samuel B. Green, and Roy Levy. "Graphical Displays for Understanding SEM Model Similarity." Structural Equation Modeling: A Multidisciplinary Journal 24, no. 6 (July 12, 2017): 803–18. http://dx.doi.org/10.1080/10705511.2017.1334206.

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36

WANG, XIAOYU, HONGWEI WU, LI WEI, and AOYING ZHOU. "A SIMILARITY-BASED MODEL FOR TOPIC DISTILLATION." International Journal of Computational Intelligence and Applications 02, no. 03 (September 2002): 267–75. http://dx.doi.org/10.1142/s1469026802000592.

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Topic distillation is the process of finding representative pages relevant to a given query. Well-known topic distillation approaches such as the HITS algorithm have shown to be useful for topic distillation. Many succeeding researchers focus on augmenting HITS with further content analysis to alleviate the steady deterioration of distillation quality suffered by HITS. In this paper, we attempt to revisit the behavior of HITS from a different point of view. Namely, a similarity-based analysis model is applied to observing the distillation procedure. By defining a generalized similarity, an algorithm is proposed, which can improve the quality of distillation only using the information of hyperlinks. The experimental results reveal that the new algorithm improves distillation quality without utilizing any content information of pages.

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37

Attia, Ahmed Hamed, Ahmed Sobhy Sherif, and Ghada Samy El-Tawel. "Maximal limited similarity-based rough set model." Soft Computing 20, no. 8 (June 30, 2016): 3153–61. http://dx.doi.org/10.1007/s00500-016-2243-6.

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38

Ya-Jun, Liu, and Zhao Yun. "Integrated semantic similarity model based on ontology." Wuhan University Journal of Natural Sciences 9, no. 5 (September 2004): 601–5. http://dx.doi.org/10.1007/bf02831649.

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39

Huang, Tony Cheng-Kui, Yen-Liang Chen, and Min-Chun Chen. "A novel recommendation model with Google similarity." Decision Support Systems 89 (September 2016): 17–27. http://dx.doi.org/10.1016/j.dss.2016.06.005.

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40

Chen, Xi, Xiao Zhang, Yong Xie, and Wei Li. "Opinion Dynamics of Social-Similarity-Based Hegselmann–Krause Model." Complexity 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/1820257.

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The existing opinion dynamics models mainly concentrate on the impact of opinions on other opinions and ignore the effect of the social similarity between individuals. Social similarity between an individual and their neighbors will also affect their opinions in real life. Therefore, an opinion evolution model considering social similarity (social-similarity-based HK model, SSHK model for short) is introduced in this paper. Social similarity is calculated using individual properties and is used to measure the social relationship between individuals. By considering the joint effect of confidence bounds and social similarity in this model, the role of neighbors’ selection is changed significantly in the process of the evolution of opinions. Numerical results demonstrate that the new model can not only obtain the salient features of the opinion result, namely, fragmentation, polarization, and consensus, but also achieve consensus more easily under the appropriate similarity threshold. In addition, the improved model with heterogeneous and homogeneous confidence bounds and similarity thresholds are also discussed. We found that the improved heterogeneous SSHK model could acquire opinion consensus results more easily than the homogeneous SSHK model and the classical models when the confidence bound was related to the similarity threshold. This finding provides a new way of thinking and a theoretical basis for the guidance of public opinion in real life.

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41

Galloway, Mack L., and Craig A. Nolder. "Subordination, Self-Similarity, and Option Pricing." Journal of Applied Mathematics and Decision Sciences 2008 (March 25, 2008): 1–30. http://dx.doi.org/10.1155/2008/397028.

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We use additive processes to price options on the Standard and Poor's 500 index (SPX) for the sake of comparison of pricing performance across both model class and family of time-one distribution. Each of the additive processes in this study is defined using one of the following: subordination, Sato's (2002) construction of self-similar additive processes from self-decomposable distributions, or both. We find that during the year 2005: (1) for a given family of time-one distributions, four-parameter self-similar additive models consistently yielded lower pricing errors than those of four-parameter subordinated, and time-inhomogeneous additive models, (2) for a given class of additive models, the time-one marginal given by the normal inverse Gaussian distribution consistently yielded lower pricing errors than those of the variance gamma distribution. Market and model benchmarks for the additive models under consideration are obtained via the bid-ask spreads of the options and Lévy stochastic volatility model prices, respectively.

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42

Cahyapratama, Afrianda, Kelly Rosa Sungkono, and Riyanarto Sarno. "Gap analysis business process model by using structural similarity." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (April 1, 2020): 124. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp124-134.

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<span>Gap analysis process model is a study that can help an institution to determine differences between business process models, such as a model of Standard Operating Procedure and a model of activities in an event log. Gap analysis is used for finding incomplete processes and can be obtained by using structural similarity. Structural similarity measures the similarity of activities and relationships depicting in the models. This research introduces a graph-matching algorithm as the structural similarity algorithm and compares it with dice coefficient algorithms. Graph-matching algorithm notices parallel relationships and invisible tasks, on the contrary dice coefficient algorithms only measure closeness between activities and relationships. The evaluation shows that the graph-matching algorithm produces 76.76 percent similarity between an SOP model and a process model generating from an event log; while, dice coefficient algorithms produces 70 percent similarity. The ability in detecting parallel relationships and invisible tasks causes the graph-matching algorithm produces a higher similarity value than dice coefficient algorithms.</span>

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43

Zhang, L. L., L. M. Zhang, and W. H. Tang. "Similarity of soil variability in centrifuge models." Canadian Geotechnical Journal 45, no. 8 (August 2008): 1118–29. http://dx.doi.org/10.1139/t08-066.

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The soil specimen in a centrifuge model is subject to spatial variability depending on the method of sample preparation and the stress field induced by the centrifugal acceleration, even though it is intended to be uniformly prepared. In contrast to extensive measurements for studying the variability of in situ soil properties, soil variability in centrifuge models, especially that which is based on data at very close sampling distances, is less understood. In this paper, the variability of soil density in two centrifuge models is presented. Random field theory is adopted to characterize the spatial soil variability in the two centrifuge models. The importance of taking spatial variability parameters as a model similarity requirement in centrifuge model design is illustrated and discussed. It is demonstrated that, although centrifuge models of different sizes can be designed to simulate the same prototype, the prototypes these models actually represent are not identical in terms of soil spatial variability. To achieve similarity in spatial variability between a centrifuge model and its prototype, one may need to control either the point coefficient of variation or the scale of fluctuation of the model soil so that the coefficients of variation of the spatially averaged soil property in the model and the prototype are the same.

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44

Panteli, Maria, Bruno Rocha, Niels Bogaards, and Aline Honingh. "A model for rhythm and timbre similarity in electronic dance music." Musicae Scientiae 21, no. 3 (July 1, 2016): 338–61. http://dx.doi.org/10.1177/1029864916655596.

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Music similarity is a multidimensional concept to which so-called “sub-similarities”, such as timbre and rhythm similarity, contribute. In this study, two models are presented: one for timbre similarity, and one for rhythm similarity. The musical domain for which the models were established is Electronic Dance Music (EDM). The models extract feature values from segments of audio and calculate a distance between two segments based on their feature vectors. The models are evaluated on perceptual data using linear regression. The accuracy of the rhythm similarity model reaches an empirically established upper bound to model performance. The accuracy of the timbre model is moderate, possibly due to insufficient data. From the selection of features and their weights resulting from the regression analysis, periodicity of rhythmic elements turned out to be the most important feature group for rhythm similarity in EDM.

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45

Meaney, Karen S., L. Kent Griffin, and Melanie A. Hart. "The Effect of Model Similarity on Girls’ Motor Performance." Journal of Teaching in Physical Education 24, no. 2 (April 2005): 165–78. http://dx.doi.org/10.1123/jtpe.24.2.165.

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This investigation examined the effect of model similarity on girls’ acquisition, retention, transfer, and transfer strategies of a novel motor task. Forty girls (mean age = 10 years) were randomly assigned to conditions in a 2 (model skill level) ✓ 2 (model sex) factorial design using four treatment groups: (a) male skilled, (b) male learning, (c) female skilled, and (d) female learning. Quantitative data were collected throughout all phases of the investigation. ANOVA results for transfer strategies revealed a significant main effect for model skill level and model sex. Participants observing a female model or a learning model transferred significantly more learning strategies than did participants observing a male or skilled model. After quantitative data collection, qualitative data were obtained via structured interviews and assessed through content analysis. Results from the interview analyses underscored the need to include models of similar sex, as well as learning models when instructing girls in motor skills.

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46

Liu, Nan, Bao Lian Long, Xiao Kun Zheng, Li Fang Han, Tong Qu, and Bao Jiang Cui. "Comprehensive Similarity Measurement Model Based on Three Algorithms." Advanced Materials Research 989-994 (July 2014): 1680–85. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1680.

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Software source code homologous detection is also called software copy or software clone. It is used to detect the homologous in the source code, by which we can easily find the plagiarism in the code. In this paper, it will discuss the homology detection results based on Text, Token and Abstract Syntax Tree. And will compare the three techniques and raise a model to calculate similarity by synthesizing the results. This model is based on the analysis of a large number of experimental results. Comprehensive similarity calculation model can calculate the respective contribution of the three algorithms and realize integrated computation of similarity according to this respective contribution. Finally, we can get a comprehensive similarity by this integrated similarity calculation model to make the homology detection results more accurate and closer to the actual similarity.

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47

Zhang, Ke, Qiupin Zhong, and Yuan Zuo. "Multivariate grey gradient incidence model and its application." Grey Systems: Theory and Application 7, no. 2 (August 7, 2017): 236–46. http://dx.doi.org/10.1108/gs-04-2017-0007.

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Purpose The purpose of this paper is to overcome the shortcomings of existing multivariate grey incidence models that cannot analyze the similarity of behavior matrixes. Design/methodology/approach First, the feasibility of using gradient to measure the similarity of continuous functions is analyzed theoretically and intuitively. Then, a grey incidence degree is constructed for multivariable continuous functions. The model employs the gradient to measure the local similarity, as incidence coefficient function, of two functions, and combines local similarity into global similarity, as grey incidence degree by double integral. Third, the gradient incidence degree model for behavior matrix is proposed by discretizing the continuous models. Furthermore, the properties and satisfaction of grey incidence atom of the proposed model are research, respectively. Finally, a financial case is studied to examine the validity of the model. Findings The proposed model satisfies properties of invariance under mean value transformation, multiple transformation and linear transformation, which proves it is a model constructed from similarity perspective. Meanwhile, the case study shows that proposed model performs effectively. Practical implications The method proposed in the paper could be used in financial multivariable time series clustering, personalized recommendation in e-commerce, etc., when the behavior matrixes need to be analyzed from trend similarity perspective. Originality/value It will promote the accuracy of multivariate grey incidence model.

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48

Hu, Zhi Jian, Yun Yang Wang, and Ting Jiang. "Study on the Similarity Relationship." Advanced Materials Research 255-260 (May 2011): 1326–30. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.1326.

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With the development of bridge structures, it is very necessary to ascertain load-bearing characteristic of complex structures. Model test can directly response complex mechanical characteristics. Based on the model test of 1:5 T-beam, this paper introduces the factors of designing model beam, and gives some similarity relation between model beam and prototype beam, including the deflection and stress similarity relation. So this paper laid a theoretical foundation for the later model test.

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Wang, Jihua, and Huayu Wang. "A study of 3D model similarity based on surface bipartite graph matching." Engineering Computations 34, no. 1 (March 6, 2017): 174–88. http://dx.doi.org/10.1108/ec-10-2015-0315.

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Purpose This study aims to compute 3D model similarity by extracting and comparing shape features from the neutral files. Design/methodology/approach In this work, the clear text encoding document STEP (Standard for The Exchange of Product model data) of 3D models was analysed, and the models were characterized by two-depth trees consisting of both surface and shell nodes. All surfaces in the STEP files can be subdivided into three kinds, namely, free, analytical and loop surfaces. Surface similarity is defined by the variation coefficients of distances between data points on two surfaces, and subsequently, the shell similarity and 3D model similarity are determined using an optimal algorithm for bipartite graph matching. Findings This approach is used to experimentally verify the effectiveness of the 3D model similarity algorithm. Originality/value The novelty of this study research lies in the computation of 3D model similarity by comparison of all surfaces. In addition, the study makes several key observations: surfaces reflect the most information concerning the functions and attributes of a 3D model and so the similarity between surfaces generates more comprehensive content (both external and internal); semantic-based 3D retrieval can be obtained under the premise of comparison of surface semantics; and more accurate similarity of 3D models can be obtained using the optimal algorithm of bipartite graph matching for all surfaces.

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Tao, Song Qiao, and Wei He. "Similarity Assessment for Assembly Model Based on Component Attributed Relational Graph Matching." Applied Mechanics and Materials 215-216 (November 2012): 270–74. http://dx.doi.org/10.4028/www.scientific.net/amm.215-216.270.

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3D CAD model retrieval has received a lot of attentions in the academic community. Most existent methods for 3D model similarity assessment focus on component models instead of assembly models. In this paper, an assembly model similarity assessment method is presented in order to find the similar assemblies model for design reuse. First, assembly model is described as component attributed relational graph. Then, the compatibility matrix between two assemblies is calculated, which serves as the measure of their similarity. Finally, the optimal matching under the measures is calculated using Hungarian Method. Experimental results show that this method is able to support the assembly similarity evaluation.

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