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Journal articles on the topic 'The hierarchical model'

Author: Grafiati

Published: 30 June 2021

Last updated: 5 May 2022

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1

Sheng-Guo Wang, Sheng-Guo Wang, Yong-Gang Liu Sheng-Guo Wang, and Tian-Wei Bai Yong-Gang Liu. "Dynamic Node Link Model of Hierarchical Edge Computing." 電腦學刊 32, no. 5 (October 2021): 222–32. http://dx.doi.org/10.53106/199115992021103205019.

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With the rise of the Internet of Things, edge computing has become one of the key technologies in Internet of Things solutions. In the context of the Industrial Internet of Things, hierarchical edge computing shows its advantages. This article focuses on hierarchical edge computing in the industrial Internet of Things scene, and studies the dynamic resource allocation of hierarchical edge computing networks. When using a hierarchical edge computing network with existing equipment, it is difficult to make changes to existing equipment. Therefore, this article uses queuing theory modeling analysis and proposes Dynamic Link Model based on Nodes Relation. Aiming at the hierarchical edge computing network, this model uses a method based on node connection relationship transfer to achieve load balancing of task flow and completes the dynamic allocation of computing resources in the network, and proposes a time experienced priority queue offloading strategy. The paper uses Java to achieve a dynamic link model experiment based on the connection relationship between nodes. The results show that this scheme has significant advantages in the global average delay of the system, and ensure the loss probability is reasonable within a certain limit.

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Zhi-Bo Wang, Zhi-Bo Wang. "Node Resource Management Model of Hierarchical Edge Computing." 電腦學刊 32, no. 5 (October 2021): 233–44. http://dx.doi.org/10.53106/199115992021103205020.

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This article focuses on hierarchical edge computing in the industrial Internet of Things scenario, and studies the static resource deployment of hierarchical edge computing networks. When deploying a hierarchical edge computing network with new equipment, the allocation of computing capacity between layers is one of the hot is-sues. This paper proposes a method for the allocation of edge computing node capacity between layers based on the M/M/1/c queue model, delay and call loss are performance indicators, and the optimal inter-layer capacity allocation algorithm is designed and implemented. This algorithm can reduce the global average delay of the sys-tem under the premise of meeting the requirement of call loss rate. Simulation verification shows that the optimal inter-layer capacity allocation algorithm can effectively reduce the system’s global average delay and call loss rate under the condition of a certain total system cost.

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3

Tashiro, Tohru. "Hierarchical Bass model." Journal of Physics: Conference Series 490 (March 11, 2014): 012181. http://dx.doi.org/10.1088/1742-6596/490/1/012181.

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4

SONG, CHEE-YANG, and DOO-KWON BAIK. "A LAYERED METAMODEL FOR HIERARCHICAL MODELING IN UML." International Journal of Software Engineering and Knowledge Engineering 13, no. 02 (April 2003): 191–214. http://dx.doi.org/10.1142/s0218194003001263.

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As software is becoming larger and more complex, it is increasingly important to use the hierarchical modeling approach. Unfortunately, however, UML does not specify each metamodel with hierarchy for model by modeling phase. Thus, most UML-based methodologies do not address the hierarchical modeling for model. As a method for supporting hierarchical modeling on UML, this paper proposes a layered metamodel which defines hierarchically modeling elements of model according to the modeling phase. We describe each metamodel with hierarchy for models in UML, then present the hierarchical integrated metamodel combined with each metamodel by three modeling phases (conceptual phase, specific phase, and concrete phase). Therefore, designers are able to construct the hierarchical model by applying the metamodel with hierarchy. Using the hierarchical metamodel enables designers to improve the usability of UML and reusability of application model.

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Aly, S., and I. Vrana. "Multiple parallel fuzzy expert systems utilizing a hierarchical fuzz model." Agricultural Economics (Zemědělská ekonomika) 53, No. 2 (January 7, 2008): 89–93. http://dx.doi.org/10.17221/1425-agricecon.

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Business, economic, and agricultural YES-or-NO decision making problems often require multiple, different and specific expertises. This is due to the nature of such problems in which decisions may be influenced by multiple different, relevant aspects, and accordingly multiple corresponding expertises are required. Fuzzy expert systems (FESs) are widely used to model expertises due to its capability to model real world values, which are not always exact, but frequently vague or uncertain. In this research, different expertises, relevant to the decision solution, are modeled using several corresponding FESs. Every FES produces a crisp numerical output expressing the degree of bias toward “Yes” or “No“ decision. A unified scale is standardized for numerical outputs of all FESs. This scale ranges from 0 to 10, where the value 0 represents a complete bias ”No“ decision and the value 10 represents a complete bias to ”Yes“ decision. Intermediate values reflect the degree of bias either to ”Yes“ or ”No“ decision. These systems are then integrated to comprehensibly judge the binary decision problem, which requires all such expertises. Practically, the main reasons for independency among the multiple FESs can be related to maintainability, decision responsibility, analyzability, knowledge cohesion and modularity, context flexibility, sensitivity of aggregate knowledge, decision consistency, etc. The proposed mechanism for realizing integration is a hierarchical fuzzy system (HFS) based model, which allows the utilization of the existing If-then knowledge about how to combine/aggregate the outputs of FESs.

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Ndung’u, A. W., S. Mwalili, and L. Odongo. "Hierarchical Penalized Mixed Model." Open Journal of Statistics 09, no. 06 (2019): 657–63. http://dx.doi.org/10.4236/ojs.2019.96042.

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7

Mozetič, Igor. "Hierarchical model-based diagnosis." International Journal of Man-Machine Studies 35, no. 3 (September 1991): 329–62. http://dx.doi.org/10.1016/s0020-7373(05)80132-4.

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8

Lin, Zhifang, and Ruibao Tao. "Hierarchical quantum Ising model." Physical Review B 41, no. 16 (June 1, 1990): 11597–99. http://dx.doi.org/10.1103/physrevb.41.11597.

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9

Paluch, R., K. Suchecki, and J. A. Hołyst. "Hierarchical Cont-Bouchaud Model." Acta Physica Polonica A 127, no. 3a (March 2015): A—108—A—112. http://dx.doi.org/10.12693/aphyspola.127.a-108.

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10

Lohrey, Markus. "Model-checking hierarchical structures." Journal of Computer and System Sciences 78, no. 2 (March 2012): 461–90. http://dx.doi.org/10.1016/j.jcss.2011.05.006.

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11

Peshcherenko, S. N. "Hierarchical model of grinding." Theoretical Foundations of Chemical Engineering 34, no. 4 (July 2000): 360–65. http://dx.doi.org/10.1007/bf02758685.

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12

Dotsenko, Viktor S. "Hierarchical model of memory." Physica A: Statistical Mechanics and its Applications 140, no. 1-2 (December 1986): 410–15. http://dx.doi.org/10.1016/0378-4371(86)90248-7.

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13

Sinharay, Sandip, and Hal S. Stern. "Posterior predictive model checking in hierarchical models." Journal of Statistical Planning and Inference 111, no. 1-2 (February 2003): 209–21. http://dx.doi.org/10.1016/s0378-3758(02)00303-8.

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14

Zhang, Gang Lin, Guang Can Liu, Wei Bing Chen, and Cheng Yang. "Optimal Power Consumption Analysis of Two-Level Hierarchical Model and Non-Hierarchical Model." Applied Mechanics and Materials 347-350 (August 2013): 1732–37. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.1732.

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Energy scarcity is one of the most critical problems that occur in wireless sensor networks compared to traditional networks. However, the problem has been partly solved by building the power consumption model of WSN. This paper is based on a simple wireless sensor network model, it gives the optimal location of the CH nodes in a cluster and the best parameter about how to divided the cluster amongst hierarchy networks. As shown last, the proposed scheme can save up to 95% of power consumption.

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15

Szczęśniak, Bartosz. "Hierarchical EPC models – review of model linking concepts." Multidisciplinary Aspects of Production Engineering 2, no. 1 (September 1, 2019): 476–86. http://dx.doi.org/10.2478/mape-2019-0048.

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Abstract EPC models are currently among the leading standard business process modelling solutions. As long as the rules for creating flat EPC models are explicit and raise no significant doubts, when it comes to linking models of different types, the literature of the subject delivers diversified solutions. Four such alternative concepts that can be applied in this respect have been identified and described in this article. They have been used to link EPC models for four cases proposed. These cases differ as to the degree of complexity, and they represent different kinds of potential links between processes being modelled. Using the concepts in question with reference to the cases defined has made it possible to identify situations in which it is impossible to link models, or when it raises certain doubts. What has also been proposed is that one of the concepts identified in the paper can be modified in such a manner that using it to link EPC models does not pose any problem in any of the cases discussed.

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16

Zha, Z., J. Jiang, and X. Zhou. "HIERARCHICAL OPTIMIZATION MODEL ON GEONETWORK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B4 (July 31, 2012): 261–64. http://dx.doi.org/10.5194/isprsarchives-xxxix-b4-261-2012.

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17

Carpinteri, Alberto, Pietro Cornetti, Nicola Maria Pugno, and Alberto Sapora. "Fractals to Model Hierarchical Biomaterials." Advances in Science and Technology 58 (September 2008): 54–59. http://dx.doi.org/10.4028/www.scientific.net/ast.58.54.

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Many biological materials exhibit a hierarchical structure over more than one length scale. Understanding how hierarchy affects their mechanical properties emerges as a primary concern, since it can guide the synthesis of new materials to be tailored for specific applications. In this paper the strength and stiffness of hierarchical materials are investigated by means of a fractal approach. A new model is proposed, based both on geometric and material considerations and involving simple recursive formulas.

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18

Ma, Chuoxin, Maozai Tian, and Jianxin Pan. "Semiparametric hierarchical model with heteroscedasticity." Statistics and Its Interface 10, no. 3 (2017): 413–24. http://dx.doi.org/10.4310/sii.2017.v10.n3.a6.

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19

Sannikova, Olha. "Continuum-hierarchical model of personality." PSIHOLOGÌÂ Ì SUSPÌLʹSTVO 73-74, no. 3-4 (September 1, 2018): 166–77. http://dx.doi.org/10.35774/pis2018.03.166.

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20

Law, Iain. "The Hierarchical Model of Autonomy." Cogito 12, no. 1 (1998): 51–57. http://dx.doi.org/10.5840/cogito199812135.

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21

Atabati, Omid, and Babak Farzad. "A hierarchical network formation model." Electronic Notes in Discrete Mathematics 50 (December 2015): 379–84. http://dx.doi.org/10.1016/j.endm.2015.07.063.

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22

Yan, Li. "HKad: A Hierarchical Kademlia Model." Applied Mechanics and Materials 457-458 (October 2013): 834–38. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.834.

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This paper proposed a new structural overlay model HKad. This model employs layered structure, and when constructing the network logical topology of peer nodes, it can detect rock-bottom physical network logical topology fast and efficiently and make it suitable to the rock-bottom network environment, and make the former be consistent to the latter as much as possible and then improve the whole performance of the systems. The goal is to construct a topology-aware overlay network, and improve the finding speed of information, and reduce unnecessary data transmission in networks.

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23

Zaliapin, Ilya, Henry Wong, and Andrei Gabrielov. "Hierarchical aggregation in percolation model." Tectonophysics 413, no. 1-2 (February 2006): 93–107. http://dx.doi.org/10.1016/j.tecto.2005.10.010.

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24

Madura, Izabela. "Hierarchical model of molecular crystals." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C549. http://dx.doi.org/10.1107/s2053273314094509.

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Spatial arrangement of molecules in molecular crystals depends on properties of molecules building up the crystal, and in particular on the nature of interactions occurring between them. The knowledge about primary and subsequent interactions building up the 3D structure seems to be important in many aspects, just to mention crystal engineering and crystallization processes. If the only interactions between molecules are isotropic van der Waals interactions, the observed structure will resemble a close-packing arrangement. The presence of any directional interactions leads, in accordance to Kitaigorodsky's principles,[1] to the symmetry breaking of the close-packing structure, and resulting crystal exhibits hierarchical organization. The presentation will discuss consequences of directional intermolecular interactions and their impact on generation and organization of successive levels of the hierarchical architecture in crystals. The strategy for identification, analysis and hierarchization of weak intermolecular interactions will also be presented. Selected examples will serve to illustrate usefulness of the proposed model for the discussion on molecular symmetry, supramolecular synthons' equivalency, polymorphism, isomorphism or packing.

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25

Okuzono, Tohru, Hirohisa Shibuya, and Masao Doi. "Hierarchical model in multiphase flow." Physical Review E 61, no. 4 (April 1, 2000): 4100–4106. http://dx.doi.org/10.1103/physreve.61.4100.

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26

MacKay, David J. C., and Linda C. Bauman Peto. "A hierarchical Dirichlet language model." Natural Language Engineering 1, no. 3 (September 1995): 289–308. http://dx.doi.org/10.1017/s1351324900000218.

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AbstractWe discuss a hierarchical probabilistic model whose predictions are similar to those of the popular language modelling procedure known as ‘smoothing’. A number of interesting differences from smoothing emerge. The insights gained from a probabilistic view of this problem point towards new directions for language modelling. The ideas of this paper are also applicable to other problems such as the modelling of triphomes in speech, and DNA and protein sequences in molecular biology. The new algorithm is compared with smoothing on a two million word corpus. The methods prove to be about equally accurate, with the hierarchical model using fewer computational resources.

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27

Yang, Ching-Wen, Pau-Choo Chung, Chein-I. Chang, San-Kan Lee, and Ling-Yang Kung. "A hierarchical model for pacs." Computerized Medical Imaging and Graphics 21, no. 1 (January 1997): 29–37. http://dx.doi.org/10.1016/s0895-6111(96)00059-6.

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28

Rouder, Jeffrey N., Jun Lu, Richard D. Morey, Dongchu Sun, and Paul L. Speckman. "A hierarchical process-dissociation model." Journal of Experimental Psychology: General 137, no. 2 (2008): 370–89. http://dx.doi.org/10.1037/0096-3445.137.2.370.

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29

Maier, Kimberly S. "A Rasch Hierarchical Measurement Model." Journal of Educational and Behavioral Statistics 26, no. 3 (September 2001): 307–30. http://dx.doi.org/10.3102/10769986026003307.

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In this article, a hierarchical measurement model is developed that enables researchers to measure a latent trait variable and model the error variance corresponding to multiple levels. The Rasch hierarchical measurement model (HMM) results when a Rasch IRT model and a one-way ANOVA with random effects are combined ( Bryk & Raudenbush, 1992 ; Goldstein, 1987 ; Rasch, 1960 ). This model is appropriate for modeling dichotomous response strings nested within a contextual level. Examples of this type of structure include responses from students nested within schools and multiple response strings nested within people. Model parameter estimates of the Rasch HMM were obtained using the Bayesian data analysis methods of Gibbs sampling and the Metropolis-Hastings algorithm ( Gelfand, Hills, Racine-Poon, & Smith, 1990 ; Hastings, 1970 ; Metropolis, Rosenbluth, Rosenbluth, Teller, & Teller, 1953 ). The model is illustrated with two simulated data sets and data from the Sloan Study of Youth and Social Development. The results are discussed and parameter estimates for the simulated data sets are compared to parameter estimates obtained using a two-step estimation approach.

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30

Yan, Li, and Ying Fang Li. "HChord: A Hierarchical Chord Model." Advanced Materials Research 756-759 (September 2013): 1916–20. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1916.

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This paper proposed a new structural overlay model HChord. This model employs layered structure, and when constructing the network logical topology of peer nodes, it can detect rock-bottom physical network logical topology fast and efficiently and make it suitable to the rock-bottom network environment, and make the former be consistent to the latter as much as possible and then improve the whole performance of the systems. The goal is to construct a topology-aware overlay network, and improve the finding speed of information, and reduce unnecessary data transmission in networks.

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31

Xue, Fengchang. "Hierarchical Geographically Weighted Regression Model." Journal of Quantum Computing 1, no. 1 (2019): 9–20. http://dx.doi.org/10.32604/jqc.2019.05954.

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32

Tzu-Mu Lin and C. A. Mead. "A Hierarchical Timing Simulation Model." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 5, no. 1 (January 1986): 188–97. http://dx.doi.org/10.1109/tcad.1986.1270186.

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Vigouroux, Yves, and Denis Couvet. "The hierarchical island model revisited." Genetics Selection Evolution 32, no. 4 (2000): 395. http://dx.doi.org/10.1186/1297-9686-32-4-395.

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Zhuang, Haoxin, Liqun Diao, and Grace Y. Yi. "A Bayesian hierarchical copula model." Electronic Journal of Statistics 14, no. 2 (2020): 4457–88. http://dx.doi.org/10.1214/20-ejs1784.

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Otoshi, Tatsuya, Yuichi Ohsita, Masayuki Murata, Yousuke Takahashi, Keisuke Ishibashi, Kohei Shiomoto, and Tomoaki Hashimoto. "Hierarchical Model Predictive Traffic Engineering." IEEE/ACM Transactions on Networking 26, no. 4 (August 2018): 1754–67. http://dx.doi.org/10.1109/tnet.2018.2850377.

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Geppert, U., H. Rieger, and M. Schreckenberg. "A hierarchical model for ageing." Journal of Physics A: Mathematical and General 30, no. 12 (June 21, 1997): L393—L400. http://dx.doi.org/10.1088/0305-4470/30/12/001.

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37

Shaik, Saleem, and Sanjoy Bhattacharjee. "Hierarchical crop yield linear model." Letters in Spatial and Resource Sciences 9, no. 2 (June 24, 2015): 219–31. http://dx.doi.org/10.1007/s12076-015-0153-3.

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Yi, M. R., and T. H. Cho. "Hierarchical simulation model with animation." Engineering with Computers 19, no. 2-3 (August 1, 2003): 203–12. http://dx.doi.org/10.1007/s00366-003-0257-z.

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39

Hammell, Robert J., and Thomas Sudkamp. "An adaptive hierarchical fuzzy model." Expert Systems with Applications 11, no. 2 (1996): 125–36. http://dx.doi.org/10.1016/0957-4174(96)00040-1.

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40

Wang, Yashen, Huanhuan Zhang, Zhirun Liu, and Qiang Zhou. "Hierarchical Concept-Driven Language Model." ACM Transactions on Knowledge Discovery from Data 15, no. 6 (May 19, 2021): 1–22. http://dx.doi.org/10.1145/3451167.

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For guiding natural language generation, many semantic-driven methods have been proposed. While clearly improving the performance of the end-to-end training task, these existing semantic-driven methods still have clear limitations: for example, (i) they only utilize shallow semantic signals (e.g., from topic models) with only a single stochastic hidden layer in their data generation process, which suffer easily from noise (especially adapted for short-text etc.) and lack of interpretation; (ii) they ignore the sentence order and document context, as they treat each document as a bag of sentences, and fail to capture the long-distance dependencies and global semantic meaning of a document. To overcome these problems, we propose a novel semantic-driven language modeling framework, which is a method to learn a Hierarchical Language Model and a Recurrent Conceptualization-enhanced Gamma Belief Network, simultaneously. For scalable inference, we develop the auto-encoding Variational Recurrent Inference, allowing efficient end-to-end training and simultaneously capturing global semantics from a text corpus. Especially, this article introduces concept information derived from high-quality lexical knowledge graph Probase, which leverages strong interpretability and anti-nose capability for the proposed model. Moreover, the proposed model captures not only intra-sentence word dependencies, but also temporal transitions between sentences and inter-sentence concept dependence. Experiments conducted on several NLP tasks validate the superiority of the proposed approach, which could effectively infer meaningful hierarchical concept structure of document and hierarchical multi-scale structures of sequences, even compared with latest state-of-the-art Transformer-based models.

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Taranti, Pier-Giovanni, Carlos Alberto Nunes Cosenza, Leonardo Antonio Monteiro Pessôa, and Rodrigo Abrunhosa Collazo. "coppeCosenzaR: A hierarchical decision model." SoftwareX 17 (January 2022): 100899. http://dx.doi.org/10.1016/j.softx.2021.100899.

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42

Hosseini, Seyyed Ahmad, Paolo Moretti, Dimitrios Konstantinidis, and Michael Zaiser. "Beam network model for fracture of materials with hierarchical microstructure." International Journal of Fracture 227, no. 2 (January 19, 2021): 243–57. http://dx.doi.org/10.1007/s10704-020-00511-w.

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AbstractWe introduce a beam network model for hierarchically patterned materials. In these materials, load-parallel gaps intercept stress transmission in the load perpendicular direction in such a manner that damage is confined within hierarchically nested, load-carrying ‘modules’. We describe the morphological characteristics of such materials in terms of deterministically constructed, hierarchical beam network (DHBN) models and randomized variants thereof. We then use these models to analyse the process of damage accumulation (characterized by the locations and timings of beam breakages prior to global failures, and the concomitant avalanche statistics) and of global failure. We demonstrate that, irrespective of the degree of local disorder, failure of hierarchically (micro)structured materials is characterized by diffuse local damage nucleation which ultimately percolates on the network, but never by stress-driven propagation of a critical crack. Failure of non hierarchical reference networks, on the other hand, is characterized by the sequence of damage nucleation, crack formation and crack propagation. These differences are apparent at low and intermediate degrees of material disorder but disappear in very strongly disordered materials where the local failure strengths exhibit extreme scatter. We furthermore demonstrate that, independent of material disorder, the different modes of failure lead to significant differences in fracture surface morphology.

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43

Iwasaki, Atsushi. "OS08-2-4 Improvement of Delamination Identification via the Hierarchical Bayes Model." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2011.10 (2011): _OS08–2–4—. http://dx.doi.org/10.1299/jsmeatem.2011.10._os08-2-4-.

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44

Smith, Lucian P., Michael Hucka, Stefan Hoops, Andrew Finney, Martin Ginkel, Chris J. Myers, Ion Moraru, and Wolfram Liebermeister. "SBML Level 3 package: Hierarchical Model Composition, Version 1 Release 3." Journal of Integrative Bioinformatics 12, no. 2 (June 1, 2015): 603–59. http://dx.doi.org/10.1515/jib-2015-268.

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Summary Constructing a model in a hierarchical fashion is a natural approach to managing model complexity, and offers additional opportunities such as the potential to re-use model components. The SBML Level 3 Version 1 Core specification does not directly provide a mechanism for defining hierarchical models, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactical constructs. The SBML Hierarchical Model Composition package for SBML Level 3 adds the necessary features to SBML to support hierarchical modeling. The package enables a modeler to include submodels within an enclosing SBML model, delete unneeded or redundant elements of that submodel, replace elements of that submodel with element of the containing model, and replace elements of the containing model with elements of the submodel. In addition, the package defines an optional “port” construct, allowing a model to be defined with suggested interfaces between hierarchical components; modelers can chose to use these interfaces, but they are not required to do so and can still interact directly with model elements if they so chose. Finally, the SBML Hierarchical Model Composition package is defined in such a way that a hierarchical model can be “flattened” to an equivalent, non-hierarchical version that uses only plain SBML constructs, thus enabling software tools that do not yet support hierarchy to nevertheless work with SBML hierarchical models.

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45

Kamide, Norihiro. "Logical foundations of hierarchical model checking." Data Technologies and Applications 52, no. 4 (September 4, 2018): 539–63. http://dx.doi.org/10.1108/dta-01-2018-0002.

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Purpose The purpose of this paper is to develop new simple logics and translations for hierarchical model checking. Hierarchical model checking is a model-checking paradigm that can appropriately verify systems with hierarchical information and structures. Design/methodology/approach In this study, logics and translations for hierarchical model checking are developed based on linear-time temporal logic (LTL), computation-tree logic (CTL) and full computation-tree logic (CTL*). A sequential linear-time temporal logic (sLTL), a sequential computation-tree logic (sCTL), and a sequential full computation-tree logic (sCTL*), which can suitably represent hierarchical information and structures, are developed by extending LTL, CTL and CTL*, respectively. Translations from sLTL, sCTL and sCTL* into LTL, CTL and CTL*, respectively, are defined, and theorems for embedding sLTL, sCTL and sCTL* into LTL, CTL and CTL*, respectively, are proved using these translations. Findings These embedding theorems allow us to reuse the standard LTL-, CTL-, and CTL*-based model-checking algorithms to verify hierarchical systems that are modeled and specified by sLTL, sCTL and sCTL*. Originality/value The new logics sLTL, sCTL and sCTL* and their translations are developed, and some illustrative examples of hierarchical model checking are presented based on these logics and translations.

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46

Manzocchi, T., L. Zhang, P. W. D. Haughton, and A. Pontén. "Hierarchical parameterization and compression-based object modelling of high net:gross but poorly amalgamated deep-water lobe deposits." Petroleum Geoscience 26, no. 4 (December 5, 2019): 545–67. http://dx.doi.org/10.1144/petgeo2018-078.

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Deep-water lobe deposits are arranged hierarchically and can be characterized by high net:gross ratios but poor sand connectivity due to thin, but laterally extensive, shale layers. This heterogeneity makes them difficult to represent in standard full-field object-based models, since the sands in an object-based model are not stacked compensationally and become connected at a low net:gross ratio. The compression algorithm allows the generation of low-connectivity object-based models at high net:gross ratios, by including the net:gross and amalgamation ratios as independent input parameters. Object-based modelling constrained by the compression algorithm has been included in a recursive workflow, permitting the generation of realistic models of hierarchical lobe deposits. Representative dimensional and stacking parameters collected at four different hierarchical levels have been used to constrain a 250 m-thick, 14 km2 model that includes hierarchical elements ranging from 20 cm-thick sand beds to more than 30 m-thick lobe complexes. Sand beds and the fine-grained units are represented explicitly in the model, and the characteristic facies associations often used to parameterize lobe deposits are emergent from the modelling process. The model is subsequently resampled without loss of accuracy for flow simulation, and results show clearly the influence of the hierarchical heterogeneity on drainage and sweep efficiency during a water-flood simulation.

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Peffley, Mark A., and Jon Hurwitz. "A Hierarchical Model of Attitude Constraint." American Journal of Political Science 29, no. 4 (November 1985): 871. http://dx.doi.org/10.2307/2111185.

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Subhashdas, Shibudas Kattakkalil, Doo-Hyun Choi, Ho-Gun Ha, and Yeong-Ho Ha. "Hierarchical Classification Model for Color Constancy." Journal of Imaging Science and Technology 61, no. 4 (July 1, 2017): 405021–4050213. http://dx.doi.org/10.2352/j.imagingsci.technol.2017.61.4.040502.

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Mykulyak, S. V. "Hierarchical block model for seismic processes." Reports of the National Academy of Sciences of Ukraine, no. 11 (November 20, 2018): 55–62. http://dx.doi.org/10.15407/dopovidi2018.11.055.

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Solomon, Joel, Andrea Casey, and Ozgur Ekmekci. "A Hierarchical Model of Organizational Identification." Academy of Management Proceedings 2016, no. 1 (January 2016): 14560. http://dx.doi.org/10.5465/ambpp.2016.14560abstract.

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