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Статті в журналах з теми "Accuracy of model"

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Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Aubakirov, S. S., P. Trigo, and D. Zh Ahmed-zaki. "BUILDING A MODEL TO PREDICT CLASSIFIER ACCURACY." Eurasian Journal of Mathematical and Computer Applications 5, no. 3 (2017): 4–14. http://dx.doi.org/10.32523/2306-3172-2017-5-3-4-14.

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2

Fan, Mingyu, Daisuke Tanaka, and Seiichi Serikawa. "Proposal of Triggered Touch Switch Model with High Accuracy." Journal of the Institute of Industrial Applications Engineers 4, no. 2 (April 25, 2016): 94–99. http://dx.doi.org/10.12792/jiiae.4.94.

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3

Weld, Daniel S. "Reasoning about model accuracy." Artificial Intelligence 56, no. 2-3 (August 1992): 255–300. http://dx.doi.org/10.1016/0004-3702(92)90029-w.

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4

Ravibabu, Mandla V., and Kamal Jain. "Digital Elevation Model Accuracy Aspects." Journal of Applied Sciences 8, no. 1 (December 15, 2007): 134–39. http://dx.doi.org/10.3923/jas.2008.134.139.

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5

Ljung, L. "Model Accuracy in System Identification." IFAC Proceedings Volumes 24, no. 1 (January 1991): 277–81. http://dx.doi.org/10.1016/s1474-6670(17)51332-4.

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6

Nash, Murray, Michael Anstis, and Michael Bradbury. "Testing Corporate Model Prediction Accuracy." Australian Journal of Management 14, no. 2 (December 1989): 211–21. http://dx.doi.org/10.1177/031289628901400205.

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7

TOLSTOY, A. "PROPAGATION MODEL ACCURACY FOR MFP." Journal of Computational Acoustics 08, no. 03 (September 2000): 389–99. http://dx.doi.org/10.1142/s0218396x00000315.

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Анотація:

The selection of a propagation model for use in many underwater acoustic applications has been understood to be highly important for quite some time. However, it has not previously been understood how various models might actually degrade Matched Field Processing (MFP) performance even when input parameters are known exactly. That is, acoustic propagation models have not previously been benchmarked within the context of MFP where acoustic amplitudes and phases need to be highly accurate depending on the nature of the processor of interest, such as for a high resolution Capon processor. This paper discusses the SCOOTER, ORCA, and KRAKEN models, and a high angle PE model within the context of MFP. These models are compared to the SAFARI data generated for the Workshop97 geoacoustic inversion CAL case. In general it seems that all the abovementioned models show excellent accuracy for use with the Linear Processor. However, it is found that KRAKEN may experience unexpected difficulties at unpredictable frequencies where these errors can affect high resolution processing (as demonstrated by geoacoustic inversions via the RIGS method),14 that SCOOTER and PE require relatively long CPU times for high accuracy, and that how accurately parameters are interpolated, e.g., sound-speed profiles, can also be important. It should be noted that all of the models discussed in this work may be considered to be very accurate in the context of most applications, particularly those involving data.

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8

Molin, Bengt-Arne. "On MOS transistor model accuracy." Microelectronics Journal 18, no. 1 (January 1987): 5–11. http://dx.doi.org/10.1016/s0026-2692(87)80359-2.

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9

Chatfield, Chris. "Model uncertainty and forecast accuracy." Journal of Forecasting 15, no. 7 (December 1996): 495–508. http://dx.doi.org/10.1002/(sici)1099-131x(199612)15:7<495::aid-for640>3.0.co;2-o.

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10

Galkin, V. S., and S. V. Rusakov. "Accuracy of model kinetic equations." Fluid Dynamics 51, no. 1 (January 2016): 109–19. http://dx.doi.org/10.1134/s0015462816010130.

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11

Hjalmarsson, Håkan, and Jonas Mårtensson. "Finite model order accuracy in Hammerstein model estimation." Automatica 48, no. 10 (October 2012): 2640–46. http://dx.doi.org/10.1016/j.automatica.2012.06.067.

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12

Montoya, Luis Humberto, and Patrick Lynett. "QUANTIFYING NUMERICAL MODEL ACCURACY AND VARIABILITY." Coastal Engineering Proceedings, no. 35 (June 23, 2017): 12. http://dx.doi.org/10.9753/icce.v35.currents.12.

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On March 11, 2011 the Tohoku tsunami event caused the death of thousands of people and generated billions of dollars in damages. Following this and previous tsunami events, there has been an effort to improve tsunami risk mitigation for coastal communities. Currently, numerical models are being used in a state-of-the-art methodology to estimate tsunami risk from near and far field sources. Model predictions are essential for the development of Tsunami Hazard Assessments (THA). By better understanding model bias and uncertainties and if possible minimizing them, a more accurate and reliable THA will result. In this study we compare runup height, inundation lines and flow velocity field measurements between GeoClaw and the Method Of Splitting Tsunami (MOST) predictions in the Sendai plain. Runup elevation and average inundation distance was in general over predicted by the models. However, both models agree relatively well with each other when predicting maximum sea surface elevation and maximum flow velocities. Model predictions show that the flow velocity increases as the tsunami wave front reaches the shoreline and makes its way inland for a couple of kilometers, contrary to what it is generally assumed. The tsunami models used in this study show much more variability when predicting flow velocity than predicting runup elevations and inundation lines. The results provided in this study will help understand the uncertainties in model predictions and locate possible sources of errors within a model.

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13

Sasagawa, Keisuke, Satoshi Watanabe, Koichi Kobayashi, Makoto Sakamoto, Takashi Sato, and Yoshio Koga. "OS7-2-5 Accuracy of Three-Dimensional Bone Surface Model Reconstructed Using MRI." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2007.6 (2007): _OS7–2–5–1—_OS7–2–5–4. http://dx.doi.org/10.1299/jsmeatem.2007.6._os7-2-5-1.

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14

An, Bong-Rak, Sae-Bom Lee, In-Sung Roh, and Yung-Ho Suh. "Development of Prediction Model for Churn Agents -Comparing Prediction Accuracy Between Pattern Model and Matrix Model-." Journal of the Korean society for quality management 42, no. 2 (June 30, 2014): 221–34. http://dx.doi.org/10.7469/jksqm.2014.42.2.221.

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15

Keseler, I. M., M. Skrzypek, D. Weerasinghe, A. Y. Chen, C. Fulcher, G. W. Li, K. C. Lemmer, et al. "Curation accuracy of model organism databases." Database 2014 (June 12, 2014): bau058. http://dx.doi.org/10.1093/database/bau058.

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16

Daganzo, Carlos F. "Increasing Model Precision Can Reduce Accuracy." Transportation Science 21, no. 2 (May 1987): 100–105. http://dx.doi.org/10.1287/trsc.21.2.100.

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17

Cheng, Jianlin, Myong‐Ho Choe, Arne Elofsson, Kun‐Sop Han, Jie Hou, Ali H. A. Maghrabi, Liam J. McGuffin, et al. "Estimation of model accuracy in CASP13." Proteins: Structure, Function, and Bioinformatics 87, no. 12 (July 16, 2019): 1361–77. http://dx.doi.org/10.1002/prot.25767.

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18

Efron, Bradley. "Estimation and Accuracy After Model Selection." Journal of the American Statistical Association 109, no. 507 (July 3, 2014): 991–1007. http://dx.doi.org/10.1080/01621459.2013.823775.

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19

CAKANYILDIRIM, METIN, and ROBIN O. ROUNDY. "SeDFAM: semiconductor demand forecast accuracy model." IIE Transactions 34, no. 5 (May 2002): 449–65. http://dx.doi.org/10.1080/07408170208928882.

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20

Storz, Mark F., Bruce R. Bowman, Major James I. Branson, Stephen J. Casali, and W. Kent Tobiska. "High accuracy satellite drag model (HASDM)." Advances in Space Research 36, no. 12 (January 2005): 2497–505. http://dx.doi.org/10.1016/j.asr.2004.02.020.

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21

Konrad, Almudena, Ben Y. Zhao, and Anthony D. Joseph. "Determining model accuracy of network traces." Journal of Computer and System Sciences 72, no. 7 (November 2006): 1156–71. http://dx.doi.org/10.1016/j.jcss.2005.12.009.

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22

Kryshtafovych, Andriy, Alessandro Barbato, Bohdan Monastyrskyy, Krzysztof Fidelis, Torsten Schwede, and Anna Tramontano. "Methods of model accuracy estimation can help selecting the best models from decoy sets: Assessment of model accuracy estimations in CASP11." Proteins: Structure, Function, and Bioinformatics 84 (September 28, 2015): 349–69. http://dx.doi.org/10.1002/prot.24919.

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23

Hwang, Gyojun, Chang Wan Oh, Sukh Que Park, Seung Hun Sheen, Jae Seung Bang, and Hyun-Seung Kang. "Comparison of Different Microanastomosis Training Models : Model Accuracy and Practicality." Journal of Korean Neurosurgical Society 47, no. 4 (2010): 287. http://dx.doi.org/10.3340/jkns.2010.47.4.287.

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24

Wang, Qianqian, Jiao Wang, Pengju Zhao, Jianqiang Kang, Few Yan, and Changqing Du. "Correlation between the model accuracy and model-based SOC estimation." Electrochimica Acta 228 (February 2017): 146–59. http://dx.doi.org/10.1016/j.electacta.2017.01.057.

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25

Borjon, Amandine, Jérôme Belledent, Yorick Trouiller, Kyle Patterson, Kevin Lucas, Christian Gardin, Christophe Couderc, et al. "Critical failure ORC: Improving model accuracy through enhanced model generation." Microelectronic Engineering 83, no. 4-9 (April 2006): 1017–22. http://dx.doi.org/10.1016/j.mee.2006.01.034.

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26

Zhang, Y., L. Wang, R. Cheng, Q. Liu, and X. Shi. "OPC Model Calibration Accuracy Improvement by Using Mask 3D Model." ECS Transactions 60, no. 1 (February 27, 2014): 187–90. http://dx.doi.org/10.1149/06001.0187ecst.

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27

Aubakirov, S. S., P. Trigo, and D. Zh Ahmed-zaki. "BUILDING A MODEL TO PREDICT CLASSIFIER ACCURACY." Eurasian Journal of Mathematical and Computer Applications 5, no. 3 (2017): 4–14. http://dx.doi.org/10.32523/2306-6172-2017-5-3-4-14.

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28

Ninness, Brett, and Stuart Gibson. "Quantifying the Accuracy of Hammerstein Model Estimation." IFAC Proceedings Volumes 33, no. 15 (June 2000): 959–64. http://dx.doi.org/10.1016/s1474-6670(17)39877-4.

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29

Mostyn, Vladimir, and Jiri Skarupa. "Improving mechanical model accuracy for simulation purposes." Mechatronics 14, no. 7 (September 2004): 777–87. http://dx.doi.org/10.1016/j.mechatronics.2004.01.002.

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30

Sjölund, J., A. Karakoç, and J. Freund. "Accuracy of regular wood cell structure model." Mechanics of Materials 76 (September 2014): 35–44. http://dx.doi.org/10.1016/j.mechmat.2014.06.003.

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31

Heagerty, Patrick J., and Yingye Zheng. "Survival Model Predictive Accuracy and ROC Curves." Biometrics 61, no. 1 (March 2005): 92–105. http://dx.doi.org/10.1111/j.0006-341x.2005.030814.x.

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32

Ninness, Brett, and Stuart Gibson. "Quantifying the accuracy of Hammerstein model estimation." Automatica 38, no. 12 (December 2002): 2037–51. http://dx.doi.org/10.1016/s0005-1098(02)00101-2.

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33

Baer, Ferdinand, and Yuejian Zhu. "Forecast Accuracy with Optimum Vertical Model Truncation." Monthly Weather Review 120, no. 11 (November 1992): 2579–91. http://dx.doi.org/10.1175/1520-0493(1992)120<2579:fawovm>2.0.co;2.

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34

Kryshtafovych, Andriy, Bohdan Monastyrskyy, Krzysztof Fidelis, Torsten Schwede, and Anna Tramontano. "Assessment of model accuracy estimations in CASP12." Proteins: Structure, Function, and Bioinformatics 86 (September 8, 2017): 345–60. http://dx.doi.org/10.1002/prot.25371.

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35

Flemming, L., J. Haueisen, U. Tenner, F. Gießler, and M. Eiselt. "Source Localization accuracy in an animal model." Biomedizinische Technik/Biomedical Engineering 46, s2 (2001): 138–40. http://dx.doi.org/10.1515/bmte.2001.46.s2.138.

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36

Mahmoud, Yousef, and Ehab El-Saadany. "Accuracy Improvement of the Ideal PV Model." IEEE Transactions on Sustainable Energy 6, no. 3 (July 2015): 909–11. http://dx.doi.org/10.1109/tste.2015.2412694.

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37

Abdullah, Nor Arymaswati, Azura Che Soh, Samsul Bahari Mohd Noor, Ribhan Zafira Abd. Rahman, and Julia Abd Karim. "TRIGA PUSPATI reactor: model analysis and accuracy." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 2 (November 1, 2020): 788. http://dx.doi.org/10.11591/ijeecs.v20.i2.pp788-797.

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Анотація:

<p>There are many challenging issues with research reactor, such as time variation and uncertainty. Since its first criticality in 1982, the biggest changes in TRIGA PUSPATI Reactor system is the replacement of instrumentation and control console system from analogue to digital in 2013. Apart from providing methods of controlling the power reactor via the control rod movement, the Instrumentation and Control Console System also provides monitoring and display for all reactor parameters to protect the reactor from undue influences or abnormal circumstances. Meanwhile, the simulation model of the TRIGA PUSPATI Reactor system has been developed in the Simulink-MATLAB. The simulation model development is based on the research reactor mathematical representatives and the real plant parameters of TRIGA PUSPATI Reactor. However, the performance of this simulation model needs to be evaluated. Since there is no report or paper work found on the performance of the simulation model to represent the real system of RTP, the present study aims to carry out an analysis for more rigorous understanding of the TRIGA PUSPATI Reactor model simulation through validation and verification methods. After analysing the result, it was found that the simulation model has a good representation of a real plant.</p>

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38

Stephenson, C. A., J. K. Dutcher, L. McProud, and S. J. Vinson. "Continual quality improvement: A trayline accuracy model." Journal of the American Dietetic Association 93, no. 9 (September 1993): A45. http://dx.doi.org/10.1016/0002-8223(93)91140-l.

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39

Charles, Amélie, and Olivier Darné. "The accuracy of asymmetric GARCH model estimation." International Economics 157 (May 2019): 179–202. http://dx.doi.org/10.1016/j.inteco.2018.11.001.

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40

Lima, Claudio F., Fernando G. Lobo, Martin Pelikan, and David E. Goldberg. "Model accuracy in the Bayesian optimization algorithm." Soft Computing 15, no. 7 (December 9, 2010): 1351–71. http://dx.doi.org/10.1007/s00500-010-0675-y.

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41

Casolino, G. M., and A. Losi. "Load Area model accuracy in distribution systems." Electric Power Systems Research 143 (February 2017): 321–28. http://dx.doi.org/10.1016/j.epsr.2016.10.044.

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42

Shetty, Dileep Kumar, and B. Ismail. "Hybrid Model Approach for Accuracy in Forecasting." Journal of the Indian Society for Probability and Statistics 19, no. 2 (September 8, 2018): 417–35. http://dx.doi.org/10.1007/s41096-018-0051-2.

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43

Li, J. G., Y. X. Yao, and P. Wang. "Assembly accuracy prediction based on CAD model." International Journal of Advanced Manufacturing Technology 75, no. 5-8 (August 7, 2014): 825–32. http://dx.doi.org/10.1007/s00170-014-6182-z.

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44

Jiang, Min Lan, Xiao Dong Wang, and Xiu Hui He. "Dynamic Accuracy Loss Prediction Model Based on BPNN." Advanced Materials Research 108-111 (May 2010): 795–98. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.795.

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In this paper, study the dynamic accuracy loss prediction model of measurement system, using measurement standard deviation as the systemic accuracy, the change of measurement standard deviation in different time as the systemic accuracy loss. Using BPNN build the dynamic accuracy loss prediction model about the practical measurement, model precision reach , realized real-time prediction of systemic accuracy loss , the establishment of predictive models to the system Real-time error correction and compensation to provide a theoretical basis, and that can cost-effectively improve the system of measurement accuracy.

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45

Widiasmara, Anny, and Henny Catur Rahayu. "PERBEDAAN MODEL OHLSON, MODEL TAFFLER DAN MODEL SPRINGATE DALAM MEMPREDIKSI FINANCIAL DISTRESS." INVENTORY: JURNAL AKUNTANSI 3, no. 2 (October 10, 2019): 141. http://dx.doi.org/10.25273/inventory.v3i2.5242.

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<p><em>The purpose of this study is to provide empirical evidence that there are differences in the Ohlson, the Taffler and the Springate Model in predicting financial distress for companies listed on the Indonesia Stock Exchange in 2015-2017. The population in this study are all nine industrial sector companies listed. This study uses secondary data from annual financial reports / annual report in 2015-2017. Testing the hypothesis in this study using the normality test and the Kruskal Wallis test (different test). The results showed that the Taffler model is the model that has the highest level of accuracy in predicting financial distress conditions of companies listed on the Stock Exchange with an accuracy rate of 83.93%, then the Springate Model with an accuracy rate of 54.91% and the Ohlson Model which is a model with the lowest accuracy rate is 6.70%.</em></p><strong><em>Keywords:</em></strong><em> Ohlson Model, Taffler Model, Springate Model, Financial Distress</em>

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46

Muhammad Noor Mathivanan, Norsyela, Nor Azura Md.Ghani, and Roziah Mohd Janor. "Improving Classification Accuracy Using Clustering Technique." Bulletin of Electrical Engineering and Informatics 7, no. 3 (September 1, 2018): 465–70. http://dx.doi.org/10.11591/eei.v7i3.1272.

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Product classification is the key issue in e-commerce domains. Many products are released to the market rapidly and to select the correct category in taxonomy for each product has become a challenging task. The application of classification model is useful to precisely classify the products. The study proposed a method to apply clustering prior to classification. This study has used a large-scale real-world data set to identify the efficiency of clustering technique to improve the classification model. The conventional text classification procedures are used in the study such as preprocessing, feature extraction and feature selection before applying the clustering technique. Results show that clustering technique improves the accuracy of the classification model. The best classification model for all three approaches which are classification model only, classification with hierarchical clustering and classification with K-means clustering is K-Nearest Neighbor (KNN) model. Even though the accuracy of the KNN models are the same across different approaches but the KNN model with K-means clustering had the shortest time of execution. Hence, applying K-means clustering prior to KNN model helps in reducing the computation time.

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47

Ortiz, Riz Rupert L. "The Accuracy Rate of Holt-Winters Model with Particle Swarm Optimization in Forecasting Exchange Rates." Journal of Computers 11, no. 3 (May 2016): 216–24. http://dx.doi.org/10.17706/jcp.11.3.216-224.

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48

van Eijnatten, Maureen, Ferco Henricus Berger, Pim de Graaf, Juha Koivisto, Tymour Forouzanfar, and Jan Wolff. "Influence of CT parameters on STL model accuracy." Rapid Prototyping Journal 23, no. 4 (June 20, 2017): 678–85. http://dx.doi.org/10.1108/rpj-07-2015-0092.

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Purpose Additive manufactured (AM) skull models are increasingly used to plan complex surgical cases and design custom implants. The accuracy of such constructs depends on the standard tessellation language (STL) model, which is commonly obtained from computed tomography (CT) data. The aims of this study were to assess the image quality and the accuracy of STL models acquired using different CT scanners and acquisition parameters. Design/methodology/approach Images of three dry human skulls were acquired using two multi-detector row computed tomography (MDCT) scanners, a dual energy computed tomography (DECT) scanner and one cone beam computed tomography (CBCT) scanner. Different scanning protocols were used on each scanner. All images were ranked according to their image quality and converted into STL models. The STL models were compared to gold standard models. Findings Image quality differed between the MDCT, DECT and CBCT scanners. Images acquired using low-dose MDCT protocols were preferred over images acquired using routine protocols. All CT-based STL models demonstrated non-uniform geometrical deviations of up to +0.9 mm. The largest deviations were observed in CBCT-derived STL models. Practical implications While patient-specific AM constructs can be fabricated with great accuracy using AM technologies, their design is more challenging because it is dictated by the correctness of the STL model. Inaccurate STL models can lead to ill-fitting implants that can cause complications after surgery. Originality/value This paper suggests that CT imaging technologies and their acquisition parameters affect the accuracy of medical AM constructs.

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49

Hung, Lai-Fa. "A Negative Binomial Regression Model for Accuracy Tests." Applied Psychological Measurement 36, no. 2 (January 24, 2012): 88–103. http://dx.doi.org/10.1177/0146621611429548.

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Анотація:

Rasch used a Poisson model to analyze errors and speed in reading tests. An important property of the Poisson distribution is that the mean and variance are equal. However, in social science research, it is very common for the variance to be greater than the mean (i.e., the data are overdispersed). This study embeds the Rasch model within an overdispersion framework and proposes new estimation methods. The parameters in the proposed model can be estimated using the Markov chain Monte Carlo method implemented in WinBUGS and the marginal maximum likelihood method implemented in SAS. An empirical example based on models generated by the results of empirical data, which are fitted and discussed, is examined.

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50

Denoyer, Keith K., and Lee D. Peterson. "Comparing Model Update Error Residuals and Effects on Model Predictive Accuracy." AIAA Journal 36, no. 10 (October 1998): 1894–900. http://dx.doi.org/10.2514/2.283.

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