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Journal articles on the topic 'Optimizing parameters'

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Published: 30 May 2022
Last updated: 31 July 2025

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1

Lysenko, L. L., and L. V. Gorodilov. "Optimizing publsed-machine parameters." Journal of Mining Science 33, no. 4 (1997): 356–62. http://dx.doi.org/10.1007/bf02765855.

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2

El-Fishawy, Nawal, Mohammad M. Zahra, and Mostafa El-gamala. "OPTIMIZING WIMAX MAC LAYER PARAMETERS." JES. Journal of Engineering Sciences 39, no. 6 (2011): 1403–15. http://dx.doi.org/10.21608/jesaun.2011.129431.

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3

Ríos-Fachal, Matilde, Javier Tarrío-Saavedra, Jorge López-Beceiro, Salvador Naya, and Ramón Artiaga. "Optimizing fitting parameters in thermogravimetry." Journal of Thermal Analysis and Calorimetry 116, no. 3 (2014): 1141–51. http://dx.doi.org/10.1007/s10973-013-3623-0.

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4

Bitar, Nabil N., and Carol Y. Espy‐Wilson. "Optimizing acoustic parameters for phonetic features." Journal of the Acoustical Society of America 100, no. 4 (1996): 2756. http://dx.doi.org/10.1121/1.416323.

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5

Huey, Gary, Susan Stair, and Robert Stern. "Hyaluronic Acid Determinations: Optimizing Assay Parameters." Matrix 10, no. 2 (1990): 67–74. http://dx.doi.org/10.1016/s0934-8832(11)80172-1.

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6

Lanciano, Rachelle. "Optimizing radiation parameters for cervical cancer." Seminars in Radiation Oncology 10, no. 1 (2000): 36–43. http://dx.doi.org/10.1016/s1053-4296(00)80019-3.

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7

Tamvakis, Androniki, Vasilis Trygonis, John Miritzis, George Tsirtsis, and Sofie Spatharis. "Optimizing biodiversity prediction from abiotic parameters." Environmental Modelling & Software 53 (March 2014): 112–20. http://dx.doi.org/10.1016/j.envsoft.2013.12.001.

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8

Bemmerl, Thomas, and Thomas Bonk. "Optimizing cache parameters using CAE workstations." Microprocessing and Microprogramming 24, no. 1-5 (1988): 541–46. http://dx.doi.org/10.1016/0165-6074(88)90107-x.

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9

Galiani, Silvia, Benjamin Harke, Giuseppe Vicidomini, et al. "Optimizing Parameters for Wll STED Imaging." Biophysical Journal 102, no. 3 (2012): 725a. http://dx.doi.org/10.1016/j.bpj.2011.11.3932.

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10

Ristovska, Bojana, Elena Papazoska, and Valentina Gecevska. "IMPROVING MANUFACTURING PROCESS BY OPTIMIZING TIME PARAMETERS." Journal of Production Engineering 22, no. 1 (2019): 42–46. http://dx.doi.org/10.24867/jpe-2019-01-042.

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11

de la Garza-Gutiérrez, H., J. P. Muñoz-Mendoza, O. Chimal-Valencia, et al. "Optimizing Milling Parameters for Refining Portland Cement." Journal of Metastable and Nanocrystalline Materials 15-16 (April 2003): 395–400. http://dx.doi.org/10.4028/www.scientific.net/jmnm.15-16.395.

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12

Prokopyev, V. N., and K. V. Gavrilov. "Optimizing parameters of complexly loaded friction bearings." Journal of Machinery Manufacture and Reliability 36, no. 5 (2007): 461–66. http://dx.doi.org/10.3103/s1052618807050135.

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13

Hu, Jian Hua, and Yuan Hua Shuang. "A Reverse Approach in Optimizing Pass Parameters." Advanced Materials Research 113-116 (June 2010): 1707–11. http://dx.doi.org/10.4028/www.scientific.net/amr.113-116.1707.

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A method combines a back propagation neural networks (BPNN) with the data obtained using finite element method (FEM) is introduced in this paper as an approach to solve reverse problems. This paper presents the feasibility of this approach. FEM results are used to train the BPNN. Inputs of the network are associated with dimension deviation values of the steel pipe, and outputs correspond to its pass parameters. Training of the network ensures low error and good convergence of the learning process. At last, a group of optimal pass parameters are obtained, and reliability and accuracy of the pa
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14

Xu, Guang Shen, Gen Yang, and Jing Gong. "Optimizing Build Parameters for Integral Stereolithography System." Advanced Materials Research 424-425 (January 2012): 52–55. http://dx.doi.org/10.4028/www.scientific.net/amr.424-425.52.

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To improve the dimension accuracy, an experimental investigation has been conducted to determine the optimum build parameters of integral stereolithography (SL) system with Taguchi method. The build parameters include shrinkage compensation factor of resin (factor A), liquid surface waiting time (factor B), exposure time (factor C) and light intensity (factor D). It was found that factor A, factor B, factor D and the interaction between factor A and factor D significantly affect the dimension accuracy, and the interaction between factor A and factor B, the interaction between factor A and fact
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15

Park, S.-T., and T.-T. Luu. "Techniques for optimizing parameters of negative stiffness." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 221, no. 5 (2007): 505–10. http://dx.doi.org/10.1243/0954406jmes390.

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Passive isolation systems based on springs are often used to resist vibration due to their low cost and reliability. The basic principle of these systems is that they try to decrease the natural frequency as low as possible. Stiffness of the overall spring system must be low to make the isolator achieve the low natural frequency. An approach to obtain the low dynamic stiffness is the combination of a negative stiffness system and a common isolator. There are, however, a lot of negative stiffness systems, and selecting the best one is difficult. In this paper, mathematical analyses are used to
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16

Hovden, G., and Nam Ling. "Optimizing facial animation parameters for MPEG-4." IEEE Transactions on Consumer Electronics 49, no. 4 (2003): 1354–59. http://dx.doi.org/10.1109/tce.2003.1261240.

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17

Nakorchevskii, A. I. "Optimizing the parameters of soil heat accumulators." Thermal Engineering 55, no. 12 (2008): 1026–30. http://dx.doi.org/10.1134/s0040601508120070.

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18

Taheri, Babak, and Daniel K. Molzahn. "Optimizing parameters of the DC power flow." Electric Power Systems Research 235 (October 2024): 110719. http://dx.doi.org/10.1016/j.epsr.2024.110719.

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19

Shahverdi, Kazem. "Optimizing geometric parameters of Archimedean water turbine." Renewable Energy Focus 50 (September 2024): 100601. http://dx.doi.org/10.1016/j.ref.2024.100601.

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20

Nazari, M., R. M. Behbahani, A. Goshtasbi, and M. Ghavipour. "Optimizing the Operating Parameters for DME Production." Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 37, no. 7 (2015): 766–74. http://dx.doi.org/10.1080/15567036.2011.590858.

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21

Troche, C. J., A. W. Anderson, and J. C. Gore. "Optimizing MR-parameters for functional brain experiments." NeuroImage 3, no. 3 (1996): S40. http://dx.doi.org/10.1016/s1053-8119(96)80042-5.

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22

Zhu, Mingde, Roberto Rodriguez, and Tim Wehr. "Optimizing separation parameters in capillary isoelectric focusing." Journal of Chromatography A 559, no. 1-2 (1991): 479–88. http://dx.doi.org/10.1016/0021-9673(91)80095-x.

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23

Potapov, V. D., and A. M. Luk'yanov. "Optimizing the parameters of adhesive-bonded joints." Strength of Materials 20, no. 4 (1988): 518–23. http://dx.doi.org/10.1007/bf01530866.

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24

Tellinghuisen, Joel. "Optimizing Experimental Parameters in Isothermal Titration Calorimetry." Journal of Physical Chemistry B 109, no. 42 (2005): 20027–35. http://dx.doi.org/10.1021/jp053550y.

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25

Franke, Thomas. "Optimizing the Parameters of a Maintenance Station." NIP & Digital Fabrication Conference 15, no. 1 (1999): 56–58. http://dx.doi.org/10.2352/issn.2169-4451.1999.15.1.art00015_1.

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26

Rathnam, Gunja Santhosha, Chanamala Ratnam, and Govada Rambabu. "Optimizing PVD Coating Parameters for Ti6Al4V Alloy." Revue des composites et des matériaux avancés 33, no. 5 (2023): 317–28. http://dx.doi.org/10.18280/rcma.330506.

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27

Turgunov, K. K., N. U. Annaev, and A. A. Umarov. "Optimizing the parameters of leather pressing machines." Journal of Physics: Conference Series 2373, no. 7 (2022): 072006. http://dx.doi.org/10.1088/1742-6596/2373/7/072006.

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Abstract The work is devoted to the optimization of the parameters of leather pressing machines. Regression models of the residual moisture content of leather and the intensity of the pressing process are developed. which depend on the intensity of the load. the radius and the speed of the rollers. A number of patterns were established that allow increasing the efficiency of leather pressing by rollers. An extremum problem was solved to determine the diameter of the rollers that provide the minimum deflection of their working part. the content of residual moisture required during the pressing
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28

Bae, Han-Jin, Jae-Jin Baek, Chae-Suk Lee, et al. "Optimizing Blasting Parameters to Achieve High Yields." Materials Performance 47, no. 1 (2008): 48–52. https://doi.org/10.5006/mp2008_47_1-48.

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This article evaluates a quantitative relationship between blasting-related operation parameters and blasting efficiency, as well as the optimum conditions that can improve blasting efficiency. Abrasive size and hardness, nozzle pressure, and abrasive flow rate played important roles in improving coating performance; the effect of abrasive hardness on surface profile was negligible.
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29

Genser, Krzysztof, Soon Yung Jun, Alberto Ribon, Vladimir Uzhinsky, and Julia Yarba. "Optimizing Geant4 Hadronic Models." EPJ Web of Conferences 295 (2024): 03026. http://dx.doi.org/10.1051/epjconf/202429503026.

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Geant4, the leading detector simulation toolkit used in high energy physics, employs a set of physics models to simulate interactions of particles with matter across a wide range of energies. These models, especially the hadronic ones, rely largely on directly measured cross-sections and inclusive characteristics, and use physically motivated parameters. However, they generally aim to cover a broad range of possible simulation tasks and may not always be optimized for a particular process or a given material. The Geant4 collaboration recently made many parameters of the models accessible via a
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30

Li, Lin Na, and Dong Wang Zhong. "Study on the Optimizing Parameters of Underwater Drilling Blasting." Applied Mechanics and Materials 193-194 (August 2012): 614–18. http://dx.doi.org/10.4028/www.scientific.net/amm.193-194.614.

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Optimizing design of parameters is essential for the underwater blasting. The key point of optimizing model is to reflect the relation of blasting parameters, rock fragmentation and operation cost. In this paper, the blasting optimization mathematical model is established, and the optimization of the blasting parameters is obtained using the complex method with computer. Calculation results show that the blasting parameters designed by the optimizing model make the engineering cost lowest.
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31

Gnanaraj, S. Joe Patrick, and M. G. L. Annaamalai. "Enhancing solar still productivity by optimizing operational parameters." DESALINATION AND WATER TREATMENT 254 (2022): 1–14. http://dx.doi.org/10.5004/dwt.2022.28287.

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32

Chi, Wayne, Jagriti Agrawal, Steve Chien, Elyse Fosse, and Usha Guduri. "Optimizing Parameters for Uncertain Execution and Rescheduling Robustness." Proceedings of the International Conference on Automated Planning and Scheduling 29 (May 25, 2021): 501–9. http://dx.doi.org/10.1609/icaps.v29i1.3552.

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We describe use of Monte Carlo simulation to optimize schedule parameters for execution and rescheduling robustness in the face of execution uncertainties. We search in the activity input parameter space where a) the onboard scheduler is a one shot non-backtracking scheduler and b) the activity input priority determines the order in which activities are considered for placement in the schedule. We show that simulation driven search for activity parameters outperforms static priority assignment. Our approach can be viewed as using simulation feedback to determine problem specific heuristics e.g
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33

Bani Mustafa, Ahmed, Ahmed K. Abbas, Mortadha Alsaba, and Mamoon Alameen. "Improving drilling performance through optimizing controllable drilling parameters." Journal of Petroleum Exploration and Production Technology 11, no. 3 (2021): 1223–32. http://dx.doi.org/10.1007/s13202-021-01116-2.

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AbstractThe prediction of the drilling rate of penetration (ROP) is one of the key aspects of drilling optimization due to its significant role in reducing expensive drilling costs. Many variables could affect ROP, which can be classified into two general categories; controllable operational variables and uncontrollable or environmental variables. Minimizing the drilling cost can be achieved through optimizing the controllable drilling parameters. As a direct result, the drilling speed will be increased while maintaining safe practices. The primary purpose of this study is to address the simul
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34

Pakhomov, G. V., I. N. Safronich, D. L. Dorofeev, and B. A. Zon. "Optimizing the parameters of a four-detector polarimeter." Journal of Optical Technology 70, no. 3 (2003): 166. http://dx.doi.org/10.1364/jot.70.000166.

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35

Pena, A. E., F. D. Anania, and C. Mohora. "Methodology for optimizing cutting parameters on milling process." IOP Conference Series: Materials Science and Engineering 400 (September 18, 2018): 022044. http://dx.doi.org/10.1088/1757-899x/400/2/022044.

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36

Šišak, D., C. Baerlocher, and L. B. McCusker. "Optimizing the input parameters for powder charge flipping." Acta Crystallographica Section A Foundations of Crystallography 67, a1 (2011): C798. http://dx.doi.org/10.1107/s0108767311079773.

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37

Machida, Haruhiko, Toshiyuki Yuhara, Takako Mori, Eiko Ueno, Yoshio Moribe, and John M. Sabol. "Optimizing Parameters for Flat-Panel Detector Digital Tomosynthesis." RadioGraphics 30, no. 2 (2010): 549–62. http://dx.doi.org/10.1148/rg.302095097.

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38

Šišak, Dubravka, Christian Baerlocher, Lynne B. McCusker, and Christopher J. Gilmore. "Optimizing the input parameters for powder charge flipping." Journal of Applied Crystallography 45, no. 6 (2012): 1125–35. http://dx.doi.org/10.1107/s0021889812040411.

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Over the past few years, the powder charge-flipping algorithm has proved to be a useful one for structure solution from powder diffraction data, so a semi-systematic study of the effect of the different input parameters on its success has been performed. Two data sets were studied in these tests: a zirconium phosphate framework material and D-ribose. TheSuperflipinput parameters tested were the reflection overlap factor, the intensity repartitioning frequency, the isotropic displacement parameter, the threshold for charge flipping and the number of cycles/runs. By varying the values of these p
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39

Bärring, Hans K., Hans F. M. Boelens, Onno E. De Noord, and Age K. Smilde. "Optimizing Meta-Parameters in Continuous Piecewise Direct Standardization." Applied Spectroscopy 55, no. 4 (2001): 458–66. http://dx.doi.org/10.1366/0003702011951975.

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40

Holland, Matthew, Tianqi Wang, Bulent Tavli, Alireza Seyedi, and Wendi Heinzelman. "Optimizing physical-layer parameters for wireless sensor networks." ACM Transactions on Sensor Networks 7, no. 4 (2011): 1–20. http://dx.doi.org/10.1145/1921621.1921622.

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41

Wang, Qiao, Shi-Fa Wu, Xu-Feng Li, and Xiao-Gang Wang. "Optimizing bowtie structure parameters for specific incident light." Chinese Physics B 19, no. 11 (2010): 117304. http://dx.doi.org/10.1088/1674-1056/19/11/117304.

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42

Sabev, Sabi, Konstantin Chukalov, Valeri Bakardzhiev, and Agop Izmirliyan. "OPTIMIZING 3D PRINTING PARAMETERS TO IMPROVE DIMENSIONAL ACCURACY." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 3 (June 22, 2024): 262–67. http://dx.doi.org/10.17770/etr2024vol3.8146.

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The article examines the influence of 3D printing speed and layer thickness on the accuracy of functional dimensions of a part-eight wall prism with 5 holes-one in the centre and 4 in the periphery. For printing the samples, ABS material was used, which is one of the main materials for the production of machines and parts. A portal measuring machine with the highest accuracy was used to effectively perform the task of measuring deviations from the size of the sample. Due to the large volume of experimental results, they were averaged to minimize error and processed using statistical processing
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43

Carolin Mabel, M., D. Suja Darling, and D. Jeraldin Auxilia. "Optimizing wind turbine parameters to enhance power generation." Journal of Renewable and Sustainable Energy 10, no. 1 (2018): 013303. http://dx.doi.org/10.1063/1.5020041.

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44

Daneshjou, K., and M. Ahmadi. "OPTIMIZING THE EFFECTIVE PARAMETERS OF TUNGSTEN – COPPER COMPOSITES." Transactions of the Canadian Society for Mechanical Engineering 30, no. 3 (2006): 321–28. http://dx.doi.org/10.1139/tcsme-2006-0020.

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45

van Leeuwen, F., A. N. Morgan, and D. L. Harrison. "Optimizing point-source parameters for scanning satellite surveys." Monthly Notices of the Royal Astronomical Society 398, no. 4 (2009): 2074–84. http://dx.doi.org/10.1111/j.1365-2966.2009.15253.x.

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46

Javadi-Dashcasan, M., F. Hajiesmaeilbaigi, H. Razzaghi, M. Mahdizadeh, and M. Moghadam. "Optimizing the Yb:YAG thin disc laser design parameters." Optics Communications 281, no. 18 (2008): 4753–57. http://dx.doi.org/10.1016/j.optcom.2008.05.055.

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47

Al-Ahmari, A. M. A., and Javed Aalam. "Optimizing parameters of freeform surface reconstruction using CMM." Measurement 64 (March 2015): 17–28. http://dx.doi.org/10.1016/j.measurement.2014.12.031.

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48

Mane, P., K. Mossi, and C. Green. "Optimizing energy harvesting parameters using response surface methodology." IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 56, no. 3 (2009): 429–36. http://dx.doi.org/10.1109/tuffc.2009.1061.

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49

Dong, H., Y. Liu, Y. Shen, and X. Wang. "Optimizing Machining Parameters of Compound Machining of Inconel718." Procedia CIRP 42 (2016): 51–56. http://dx.doi.org/10.1016/j.procir.2016.02.185.

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50

Gee, S., B. Johnson, and A. L. Smith. "Optimizing electrospinning parameters for piezoelectric PVDF nanofiber membranes." Journal of Membrane Science 563 (October 2018): 804–12. http://dx.doi.org/10.1016/j.memsci.2018.06.050.

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