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Journal articles on the topic 'Input modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Pearson, Ronald K. "Nonlinear Input/Output Modeling." IFAC Proceedings Volumes 27, no. 2 (1994): 1–15. http://dx.doi.org/10.1016/s1474-6670(17)48122-5.

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2

Johnson, Mark E., and Mansooreh Mollaghasemi. "Simulation input data modeling." Annals of Operations Research 53, no. 1 (1994): 47–75. http://dx.doi.org/10.1007/bf02136826.

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3

Biller, Bahar, and Canan Gunes Corlu. "Copula-based multivariate input modeling." Surveys in Operations Research and Management Science 17, no. 2 (2012): 69–84. http://dx.doi.org/10.1016/j.sorms.2012.04.001.

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4

FUKUYAMA, HIROFUMI, and WILLIAM L. WEBER. "MODELING OUTPUT GAINS AND EARNINGS' GAINS." International Journal of Information Technology & Decision Making 04, no. 03 (2005): 433–54. http://dx.doi.org/10.1142/s0219622005001659.

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In this paper, we examine the potential gains in physical outputs or earnings on outputs from an optimal reallocation of inputs. When some decision-making units (DMUs) face higher input prices than other DMUs, the Farrell decomposition of cost efficiency can potentially indicate that a firm with lower overall costs of production is less efficient than a firm that uses fewer physical inputs, but has higher costs. We extend our gain functions accounting for cases where DMUs face different input prices. An empirical illustration of our method is provided using data on Japanese banks operating dur

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5

SHIEBER, STUART M., and RANI NELKEN. "Abbreviated text input using language modeling." Natural Language Engineering 13, no. 2 (2006): 165–83. http://dx.doi.org/10.1017/s1351324906004311.

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We address the problem of improving the efficiency of natural language text input under degraded conditions (for instance, on mobile computing devices or by disabled users), by taking advantage of the informational redundancy in natural language. Previous approaches to this problem have been based on the idea of prediction of the text, but these require the user to take overt action to verify or select the system's predictions. We propose taking advantage of the duality between prediction and compression. We allow the user to enter text in compressed form, in particular, using a simple stipula

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6

Martínez-Pinedo, Gabriel. "Nuclear structure input for supernova modeling." Nuclear Physics A 746 (December 2004): 323–29. http://dx.doi.org/10.1016/j.nuclphysa.2004.09.069.

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7

Jackson, Randall W. "Probabilistic input-output analysis: Modeling directions." Socio-Economic Planning Sciences 23, no. 1-2 (1989): 87–95. http://dx.doi.org/10.1016/0038-0121(89)90049-9.

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8

Rai, Amit, and Kazuki Saito. "Omics data input for metabolic modeling." Current Opinion in Biotechnology 37 (February 2016): 127–34. http://dx.doi.org/10.1016/j.copbio.2015.10.010.

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9

Kim, Sanghong, Manabu Kano, Hiroshi Nakagawa, and Shinji Hasebe. "Input variable scaling for statistical modeling." Computers & Chemical Engineering 74 (March 2015): 59–65. http://dx.doi.org/10.1016/j.compchemeng.2014.12.016.

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10

Su, Yi, and Kooresh I. Shoghi. "Single-Input–Dual-Output Modeling of Image-Based Input Function Estimation." Molecular Imaging and Biology 12, no. 3 (2009): 286–94. http://dx.doi.org/10.1007/s11307-009-0273-5.

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11

Osarobo, Ighodaro, and Akaeze Chika. "Neural Network Modeling for Monitoring Petroleum Pipelines." International Journal of Engineering Research in Africa 26 (October 2016): 122–31. http://dx.doi.org/10.4028/www.scientific.net/jera.26.122.

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It is common occurrence that the transportation of petroleum products via pipelines is susceptible to failure either naturally or intentionally. The paper is a diagnostic problem having continuous inputs of pattern recognition used in predicting pipeline failures. Our problem is to design a neural network that will recognize failure events in pipelines when fed with an input pattern denoting such a scenario. A neural network paradigm is selected, and encoding of input is done to obtain the input pattern. The selected model is simulated and trained to recognize the output pattern, which in our

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12

Morley, S. K., D. T. Welling, and J. R. Woodroffe. "Perturbed Input Ensemble Modeling With the Space Weather Modeling Framework." Space Weather 16, no. 9 (2018): 1330–47. http://dx.doi.org/10.1029/2018sw002000.

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13

Astatkie, T., and W. E. Watt. "Multiple-input transfer function modeling of daily streamflow series using nonlinear inputs." Water Resources Research 34, no. 10 (1998): 2717–25. http://dx.doi.org/10.1029/98wr01473.

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14

Hester, Patrick T., and Kevin MacG Adams. "Determining Stakeholder Influence Using Input-output Modeling." Procedia Computer Science 20 (2013): 337–41. http://dx.doi.org/10.1016/j.procs.2013.09.282.

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15

Ilic, I., R. Scarmozzino, R. M. Osgood, J. T. Yardley, K. W. Beeson, and M. J. McFarland. "Modeling multimode-input star couplers in polymers." Journal of Lightwave Technology 12, no. 6 (1994): 996–1003. http://dx.doi.org/10.1109/50.296190.

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16

Haberman, Robert C. "Improved power input methods in SEA modeling." Journal of the Acoustical Society of America 107, no. 5 (2000): 2884. http://dx.doi.org/10.1121/1.428718.

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17

Statsenko, V., A. Sukhorada, and V. Lelyukhin. "Modeling heat input when friction stir welding." IOP Conference Series: Materials Science and Engineering 681 (November 21, 2019): 012042. http://dx.doi.org/10.1088/1757-899x/681/1/012042.

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18

Risuleo, Riccardo Sven, Giulio Bottegal, and Håkan Hjalmarsson. "Modeling and identification of uncertain-input systems." Automatica 105 (July 2019): 130–41. http://dx.doi.org/10.1016/j.automatica.2019.03.014.

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19

Minoglou, K., G. Halkias, E. D. Kyriakis-Bitzaros, and D. Syvridis. "Input and intrinsic device modeling of VCSELs." Journal of Computational Electronics 6, no. 1-3 (2007): 309–12. http://dx.doi.org/10.1007/s10825-006-0118-2.

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20

Gallagher, Mark A., Anthony W. Snodgrass, and Gregory J. Ehlers. "Input-Output Modeling for Assessing Cascading Effects." Military Operations Research 10, no. 2 (2005): 5–17. http://dx.doi.org/10.5711/morj.10.2.5.

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21

An, C. K., E. J. Powers, and C. P. Ritz. "A digital method of modeling two-input quadratic systems with general random inputs." IEEE Transactions on Signal Processing 39, no. 10 (1991): 2320–23. http://dx.doi.org/10.1109/78.91187.

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22

Safarian, Sahar, Seyed Mohammad Ebrahimi Saryazdi, Runar Unnthorsson, and Christiaan Richter. "Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach." Fermentation 7, no. 2 (2021): 71. http://dx.doi.org/10.3390/fermentation7020071.

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In order to accurately anticipate the proficiency of downdraft biomass gasification linked with a water–gas shift unit to produce biohydrogen, a model based on an artificial neural network (ANN) approach is established to estimate the specific mass flow rate of the biohydrogen output of the plant based on different types of biomasses and diverse operating parameters. The factors considered as inputs to the models are elemental and proximate analysis compositions as well as the operating parameters. The model structure includes one layer for input, a hidden layer and output layer. One thousand

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23

Zhang, Wei, Xue Fei Zhang, and Chao Yan. "Unified Modeling for Input Forces and Input Vectors for Two Scissor Lifting Mechanisms." Advanced Materials Research 819 (September 2013): 362–67. http://dx.doi.org/10.4028/www.scientific.net/amr.819.362.

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Scissor lifting mechanisms are widely equipped on airport ground support facilities, in this paper, we propose a unified modeling process for two special SLMs by establishing the expression of input motion vectors and input force vectors and test the results by three proposed cases. A characteristic triangle and corresponding parameters are introduced for any configuration. The presented method provides a reference mode for studying scissor lifting mechanism.

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24

Lehr, W., R. Overstreet, R. Jones, L. Eclipse, and D. Simecek-Beatty. "THE NEXT GENERATION IN OIL WEATHERING MODELING." International Oil Spill Conference Proceedings 1997, no. 1 (1997): 986–87. http://dx.doi.org/10.7901/2169-3358-1997-1-986.

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ABSTRACT Through a project jointly funded by the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Minerals Management Service (MMS), the Hazardous Material Response and Assessment Division of NOAA (NOAA/HAZMAT) has developed the next generation of the widely used oil weathering model, ADIOS. The algorithms for spreading, emulsification, dispersion, and evaporation have been modified to accommodate new research results. Also, the new version contains a source strength module that estimates leak rates from damaged vessels and new user inputs that allow decisions on the applica

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25

Shah, Muhammad Izhar, Taher Abunama, Muhammad Faisal Javed, et al. "Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization." Sustainability 13, no. 8 (2021): 4576. http://dx.doi.org/10.3390/su13084576.

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Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and inconsistent datasets is still a challenge. Therefore, further research is needed to improve the performance of the predictive models. This study presents a methodology for dataset pre-processing and input optimization for reducing the modeling complexity. The objective of this study was achieved by employing a two-sided detection approach for outlier r

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26

Williams, Peter M. "Using Neural Networks to Model Conditional Multivariate Densities." Neural Computation 8, no. 4 (1996): 843–54. http://dx.doi.org/10.1162/neco.1996.8.4.843.

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Neural network outputs are interpreted as parameters of statistical distributions. This allows us to fit conditional distributions in which the parameters depend on the inputs to the network. We exploit this in modeling multivariate data, including the univariate case, in which there may be input-dependent (e.g., time-dependent) correlations between output components. This provides a novel way of modeling conditional correlation that extends existing techniques for determining input-dependent (local) error bars.

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27

Mandelblatt, Jeanne S., Aimee M. Near, Diana L. Miglioretti, et al. "Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling." Medical Decision Making 38, no. 1_suppl (2018): 9S—23S. http://dx.doi.org/10.1177/0272989x17700624.

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Background. Since their inception in 2000, the Cancer Intervention and Surveillance Network (CISNET) breast cancer models have collaborated to use a nationally representative core of common input parameters to represent key components of breast cancer control in each model. Employment of common inputs permits greater ability to compare model output than when each model begins with different input parameters. The use of common inputs also enhances inferences about the results, and provides a range of reasonable results based on variations in model structure, assumptions, and methods of use of t

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28

Allawi, Mohammed Falah, Faridah Binti Othman, Haitham Abdulmohsin Afan, et al. "Reservoir Evaporation Prediction Modeling Based on Artificial Intelligence Methods." Water 11, no. 6 (2019): 1226. http://dx.doi.org/10.3390/w11061226.

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The current study explored the impact of climatic conditions on predicting evaporation from a reservoir. Several models have been developed for evaporation prediction under different scenarios, with artificial intelligence (AI) methods being the most popular. However, the existing models rely on several climatic parameters as inputs to achieve an acceptable accuracy level, some of which have been unavailable in certain case studies. In addition, the existing AI-based models for evaporation prediction have paid less attention to the influence of the time increment rate on the prediction accurac

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29

Yang, Jin Dian, Qin He Zhang, Dong Ding, and Hai Jiao Wang. "Design and Development of Automated Tire FE Modeling Procedure." Applied Mechanics and Materials 635-637 (September 2014): 493–96. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.493.

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The paper addresses a modelling procedure to automatically generate 2D finite element meshes of tires, together with the assignment of material property. A program is developed based on the secondary development of HyperMesh with TCL/TK language, thus achieving the automatic FE modelling starting from the AutoCAD tire cross-section profile. The model created as input file can be exported into ABAQUS directly. By automatizing the modeling process, the program can significantly reduce the modeling time and improve modeling efficiency.

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30

Morita, Kenji, Kunichika Tsumoto, and Kazuyuki Aihara. "Possible Effects of Depolarizing GABAA Conductance on the Neuronal Input–Output Relationship: A Modeling Study." Journal of Neurophysiology 93, no. 6 (2005): 3504–23. http://dx.doi.org/10.1152/jn.00988.2004.

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Recent in vitro experiments revealed that the GABAA reversal potential is about 10 mV higher than the resting potential in mature mammalian neocortical pyramidal cells; thus GABAergic inputs could have facilitatory, rather than inhibitory, effects on action potential generation under certain conditions. However, how the relationship between excitatory input conductances and the output firing rate is modulated by such depolarizing GABAergic inputs under in vivo circumstances has not yet been understood. We examine herewith the input–output relationship in a simple conductance-based model of cor

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31

Khavas, Reza Golshan, Bruce Hellinga, and Amir Zarinbal Masouleh. "Identifying Parameters for Microsimulation Modeling of Traffic in Inclement Weather." Transportation Research Record: Journal of the Transportation Research Board 2613, no. 1 (2017): 52–60. http://dx.doi.org/10.3141/2613-07.

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There is often a desire to use microsimulation models to evaluate road improvements or new traffic management strategies under different weather conditions. However, conventional simulation models do not provide the ability to directly specify weather conditions as an input. Instead, it is necessary to determine ( a) the impact that a specific weather condition has on traffic operations, quantified in terms of the traffic stream parameters (i.e., free speed, speed at capacity, capacity, and jam density); and ( b) appropriate values for the microsimulation model input parameters to generate a s

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32

ELSHARKAWY, R. R., M. HINDY, S. EL-RABAIE, and M. I. DESSOUKY. "FET SMALL-SIGNAL MODELING USING MEL-FREQUENCY CEPSTRAL COEFFICIENTS AND THE DISCRETE COSINE TRANSFORM." Journal of Circuits, Systems and Computers 19, no. 08 (2010): 1835–46. http://dx.doi.org/10.1142/s0218126610007158.

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In this paper, a novel neural technique is proposed for FET small-signal modeling. This technique is based on the discrete cosine transform (DCT) and the Mel-frequency cepstral coefficients (MFCCs). The input data to traditional neural systems for FET small-signal modeling are the scattering parameters and the corresponding frequencies in a certain band, and the outputs are the circuit elements. In the proposed technique, the input data are considered random, and the MFCCs are calculated from these inputs and their DCT. The MFCCs are used to give a few features from the input random data seque

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33

Yang, Can, Jun Meng, and Shanan Zhu. "Cluster-Based Input Selection for Transparent Fuzzy Modeling." International Journal of Data Warehousing and Mining 2, no. 3 (2006): 57–75. http://dx.doi.org/10.4018/jdwm.2006070105.

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34

Nke, Y., and J. Lunze. "A fault modeling approach for Input/Output Automata." IFAC Proceedings Volumes 44, no. 1 (2011): 8657–62. http://dx.doi.org/10.3182/20110828-6-it-1002.00624.

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35

Toroptsev, E. L., A. S. Marakhovskii, and R. R. Duszynski. "Equilibrium and input-output modeling of economic environment." Economic Analysis: Theory and Practice 18, no. 4 (2019): 663–80. http://dx.doi.org/10.24891/ea.18.4.663.

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36

Kite, G. W. "Scaling of Input Data for Macroscale Hydrologic Modeling." Water Resources Research 31, no. 11 (1995): 2769–81. http://dx.doi.org/10.1029/95wr02102.

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37

Euntai Kim, Minkee Park, Seungwoo Kim, and Mignon Park. "A transformed input-domain approach to fuzzy modeling." IEEE Transactions on Fuzzy Systems 6, no. 4 (1998): 596–604. http://dx.doi.org/10.1109/91.728458.

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38

Enns, S. T. "Evaluating shop floor input control using rapid modeling." International Journal of Production Economics 63, no. 3 (2000): 229–41. http://dx.doi.org/10.1016/s0925-5273(99)00024-9.

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39

Kovacevic, Ivana F., Thomas Friedli, Andreas M. Muesing, and Johann W. Kolar. "3-D Electromagnetic Modeling of EMI Input Filters." IEEE Transactions on Industrial Electronics 61, no. 1 (2014): 231–42. http://dx.doi.org/10.1109/tie.2013.2242422.

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40

Dondokov, Z. B. D. "INPUT-OUTPUT MODELING OF ECONOMIC INTERACTION BETWEEN TERRITORIES." Bulletin of the Buryat Scientific Center of the Siberian Branch of the Russian Academy of Sciences, no. 4 (2019): 200–205. http://dx.doi.org/10.31554/2222-9175-2019-36-200-205.

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41

Kozicka, Marta. "A novel approach to stochastic input-output modeling." RAIRO - Operations Research 53, no. 4 (2019): 1155–69. http://dx.doi.org/10.1051/ro/2018046.

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An approach to input-output modeling is proposed in which the inner consumption and the final demand are random. The main aspects of its novelty are: (a) the economy is allowed to be nonproductive with a certain probability ϰ ∈ [0,1); (b) the economy can be open, which means that import of the corresponding commodities is included in the model. In this approach, the production-and-import plan is set to be feasible if the probability of not satisfying the final demand does not exceed a certain value α ∈ (0,1). Then the problem of finding optimal plans consists in minimizing the production and i

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42

Hadjili, M. L., and V. Wertz. "Takagi-Sugeno fuzzy modeling incorporating input variables selection." IEEE Transactions on Fuzzy Systems 10, no. 6 (2002): 728–42. http://dx.doi.org/10.1109/tfuzz.2002.805897.

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43

Carter, E. A. "Challenges in Modeling Materials Properties Without Experimental Input." Science 321, no. 5890 (2008): 800–803. http://dx.doi.org/10.1126/science.1158009.

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44

Zouaoui, Faker, and James R. Wilson. "Accounting for Parameter Uncertainty in Simulation Input Modeling." IIE Transactions 35, no. 9 (2003): 781–92. http://dx.doi.org/10.1080/07408170304413.

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45

Oliveira, Carlos S., Hong Hao, and J. Penzien. "Ground motion modeling for multiple-input structural analysis." Structural Safety 10, no. 1-3 (1991): 79–93. http://dx.doi.org/10.1016/0167-4730(91)90007-v.

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46

De Risi, Raffaele, Katsuichiro Goda, Nobuhito Mori, and Tomohiro Yasuda. "Bayesian tsunami fragility modeling considering input data uncertainty." Stochastic Environmental Research and Risk Assessment 31, no. 5 (2016): 1253–69. http://dx.doi.org/10.1007/s00477-016-1230-x.

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47

Shrestha, Roshan, Yasuto Tachikawa, and Kaoru Takara. "Input data resolution analysis for distributed hydrological modeling." Journal of Hydrology 319, no. 1-4 (2006): 36–50. http://dx.doi.org/10.1016/j.jhydrol.2005.04.025.

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48

Gera, B. S., and Neeraj Saxena. "Sodar data—A useful input for dispersion modeling." Atmospheric Environment 30, no. 21 (1996): 3623–31. http://dx.doi.org/10.1016/1352-2310(96)00062-3.

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49

Su, Xiaofeng, Yingmin Jia, Junping Du, and Jun Zhang. "Modeling and input-output decoupling of hypersonic vehicles." International Journal of Control, Automation and Systems 13, no. 1 (2014): 156–66. http://dx.doi.org/10.1007/s12555-013-0548-0.

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50

Стородубцева and Tamara Storodubtseva. "WOOD POLYMER COMPOSITE - MODELING ITS PROPERTIES." Modeling of systems and processes 8, no. 3 (2016): 82–86. http://dx.doi.org/10.12737/17181.

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