Relevant bibliographies by topics / Parameter optimization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Parameter optimization'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Parameter optimization"

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Rajeanderan Revichandran, Jaffar Syed Mohamed Ali, Moumen Idres, and A. K. M. Mohiuddin. "A Review of HVAC System Optimization and Its Effects on Saving Total Energy Utilization of a Building." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 93, no. 1 (2022): 64–82. http://dx.doi.org/10.37934/arfmts.93.1.6482.

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The paper illustrates the review on the optimizations studies of HVAC systems based on three main methods – HVAC operational variables optimization, optimization of control parameters in HVAC system and parameter optimization in building models. For the HVAC system’s operational variables, the optimization process is based on the common and prescient energy utilization models. Thus, by comparing both, the non-common HVAC system models can get better output of energy reduction. Based on most of the studies, the occupancies thermal comfort requirements, are represented by the indoor air quality
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Zhong, Mei Peng. "Parameter Optimization of Compressor Based on an Ant Colony Optimization." Applied Mechanics and Materials 201-202 (October 2012): 916–19. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.916.

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A mathematical model of operation on air compressors is set up in order to improve the efficiency of air compressors. Parameter of Compressor is optimized by an Ant Colony Optimization (ACO) Particle approach. Volume and its weight of the new compressor are little, and its efficiency is high. An Ant Colony Optimization embed BLDCM module which optimizating the air compressor was put forward. Optimizated target of an Ant Colony Optimization is the efficiency of BLDCM. Optimizated variables are the diameter of low pressure cylinder, the diameter of high pressure cylinder, the journey of low pres
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Prinz, Astrid. "Neuronal parameter optimization." Scholarpedia 2, no. 1 (2007): 1903. http://dx.doi.org/10.4249/scholarpedia.1903.

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Lawal, S. L., and S. A. Afolalu. "Effect of Welding Process Parameters on the Mechanical Properties of TIG and MIG Welds in HSS X65 Pipe-A Review." IOP Conference Series: Earth and Environmental Science 1322, no. 1 (2024): 012009. http://dx.doi.org/10.1088/1755-1315/1322/1/012009.

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Abstract The study focused on the importance of the different welding parameters on the mechanical behavior of High Strength Steel (HSS) X65 steel pipes by reviewing the advantages of parameter optimization for the Tungsten Inert Gas (TIG) - Metal Inert Gas (MIG) welding processes. The parameters considered in the study include welding speed, welding current, welding voltage and gas flowrate of the welding. The effects of improper selection and parameter optimizations were highlighted and illustrated using different metallurgical and mechanical instances. The outcome of the study indicates tha
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Ng, Chuan Huat, and Mohd Khairulamzari Hamjah. "Welding Parameter Optimization of Surface Quality by Taguchi Method." Applied Mechanics and Materials 660 (October 2014): 109–13. http://dx.doi.org/10.4028/www.scientific.net/amm.660.109.

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An experimental study of GTAW was conducted to determine the optimization of weld parameters on the droplet formation in the surface quality of weld pools. These optimization investigations consisted of welding current, welding speed and feed rate. The strength and surface quality of weld pool were measured for each specimen after the welding parameter optimizations and the effect of these parameters on droplet formation were researched. To consider these quality characteristics together in the selection of welding parameters, the Orthogonal Array of Taguchi method is adopted to analyze the ef
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Niu, Xiang Jie. "The Optimization for PID Controller Parameters Based on Genetic Algorithm." Applied Mechanics and Materials 513-517 (February 2014): 4102–5. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4102.

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as an important research field of automatic control problems, PID parameter optimization's control effect depends on the proportional, integral and derivative values. Using trial and error testing to manually realize optimization PID parameters, the traditional ways are often time-consuming and difficult to meet the requirements of real-time control. In order to solve the problems and improve system performance, the paper proposes a PID parameter optimization strategy based on genetic algorithm. The paper establishes the PID controller parameter model through genetic algorithm, uses the PID pa
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Kawade, Dr Mahesh M., Prasanna Raut, and Devakant Baviskar. "Exploiting Tool Toughness through Turning Process Parameter Optimization." International Journal of Research Publication and Reviews 5, no. 2 (2024): 364–71. http://dx.doi.org/10.55248/gengpi.5.0224.0408.

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Adil, H., A. A. Koser, M. S. Qureshi, and A. Gupta. "Sleep quality assessment by parameter optimization." Journal of Physics: Conference Series 2070, no. 1 (2021): 012013. http://dx.doi.org/10.1088/1742-6596/2070/1/012013.

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Abstract Sleep quality measurement is a complex process requires large number of parameters to monitor sleep and sleep cycles. The Gold Standard Polysomnography (PSG) parameters are considered as standard parameters for sleep quality measurement. In the PSG process, number of monitoring parameters are involved for that large number of sensors are used which makes this process complex, expensive and obtrusive. There is need to find optimize parameters which are directly involve in providing accurate information about sleep and reduce the process complexity. Our Parameter Optimization method is
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Kitamura, Makoto, Masahiro Umeda, Toshihiro Higuchi, Shouji Naruse, and Chuuzou Tanaka. "465. Optimization of a parameter in functional-MRI paramete." Japanese Journal of Radiological Technology 50, no. 8 (1994): 1380. http://dx.doi.org/10.6009/jjrt.kj00003326264.

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Ma, Kai, Yu-Bin Gao, Yan Tao, Wen-Tao Wang, and Shuai-Chen Wu. "A Multi-objective Control Method for Structural Static Displacement Based on Projection Parameters Sorting Method." Journal of Physics: Conference Series 3004, no. 1 (2025): 012048. https://doi.org/10.1088/1742-6596/3004/1/012048.

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Abstract In the field of structural optimization, reasonable selection of parameters and obtaining optimized solutions are key issues. This paper proposes a multi-objective optimization method for static displacement based on the Projection Parameter Sorting Method (PPSM). This method sorts parameters based on the projection characteristics of the parameter sensitivity vector, and uses the Epsilon algorithm and improved Neumann series to calculate the structural static sensitivity. Through iteration, combinations are selected and parameters are corrected according to a given number of paramete
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Dissertations / Theses on the topic "Parameter optimization"

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Marcinkevicius, Tadas. "DRAM BASED PARAMETER DATABASE OPTIMIZATION." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-100332.

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This thesis suggests an improved parameter database implementation for one of Ericsson products. The parameter database is used during the initialization of the system as well as during the later operation. The database size is constantly growing because the parameter database is intended to be used with different hardware configurations. When a new technology platform is released, multiple revisions with additional features and functionalities are later created, resulting in introduction of new parameters or changes to their values. Ericsson provides support for old and new products. The enti
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Koripalli, RadhaShilpa. "Parameter Tuning for Optimization Software." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2862.

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Mixed integer programming (MIP) problems are highly parameterized, and finding parameter settings that achieve high performance for specific types of MIP instances is challenging. This paper presents a method to find the information about how CPLEX solver parameter settings perform for the different classes of mixed integer linear programs by using designed experiments and statistical models. Fitting a model through design of experiments helps in finding the optimal region across all combinations of parameter settings. The study involves recognizing the best parameter settings that results in
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Gupta, Deepak Prakash. "Energy sensitive machining parameter optimization model." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4406.

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Thesis (M.S.)--West Virginia University, 2005.<br>Title from document title page. Document formatted into pages; contains ix, 71 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 67-71).
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Eeles, Charles William Owen. "Parameter optimization of conceptual hydrological models." Thesis, Open University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261674.

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Thellner, Mikael. "Multi-parameter topology optimization in continuum mechanics /." Linköping : Dept. of Mechanical Engineering, Univ, 2005. http://www.bibl.liu.se/liupubl/disp/disp2005/tek934s.pdf.

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Herrmann, Todd Matthew. "A critical parameter optimization of launch vehicle costs." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3927.

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Thesis (M.S.) -- University of Maryland, College Park, 2006.<br>Thesis research directed by: Dept. of Aerospace Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Mathavan, Neashan Graduate School of Biomedical Engineering Faculty of Engineering UNSW. "Parameter optimization in simplified models of cardiac myocytes." Awarded by:University of New South Wales. Graduate School of Biomedical Engineering, 2009. http://handle.unsw.edu.au/1959.4/44709.

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Atrial fibrillation (AF) is a complex, multifaceted arrhythmia. Pathogenesis of AF is associated with multiple aetiologies and the mechanisms by which it is sustained and perpetuated are similarly diverse. In particular, regional heterogeneity in the electrophysiological properties of normal and pathological tissue plays a critical role in the occurrence of AF. Understanding AF in the context of electrophysiological heterogeneity requires cell-specific ionic models of electrical activity which can then be incorporated into models on larger temporal and spatial scales. Biophysically-based model
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Lee, Sang Heon. "Efficient design and optimization of robust parameter experiments." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/24328.

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Luna, Ortiz J. E. "Optimization of distributed parameter systems using transient simulators." Thesis, University of Manchester, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.503592.

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Mannai, Sébastien (Sébastien Karim). "Multi-parameter control for centrifugal compressor performance optimization." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90778.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 88-89).<br>The potential performance benefit of actuating inlet guide vane (IGV) angle, variable diffuser vane (VDV) angle and impeller speed to implement a multi-parameter control on a centrifugal compressor system is assessed. The assessment consists of first developing a one-dimensional meanline model for estimating performance of centrifugal compressor system followed by the formulation of a control fra
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Books on the topic "Parameter optimization"

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Morelli, Eugene A. Practical input optimization for aircraft parameter estimation experiments. National Aeronautics and Space Administration, Langley Research Center, 1993.

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Tiba, Dan. Optimal control of nonsmooth distributed parameter systems. Springer-Verlag, 1990.

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S, Holmes Michael, Ray Asok 1946-, and Lewis Research Center, eds. Design of life extending controls using nonlinear parameter optimization. National Aeronautics and Space Administration, Lewis Research Center, 1998.

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S, Holmes Michael, Ray Asok 1946-, and Lewis Research Center, eds. Design of life extending controls using nonlinear parameter optimization. National Aeronautics and Space Administration, Lewis Research Center, 1998.

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Kazimierz, Malanowski, Nahorski Zbigniew 1945-, Peszyńska Małgorzata, IFIP-WG 7. 2, International Federation for Information Processing., and International Conference on Modelling and Optimization of Distributed Parameter Systems with Applications to Engineering (1995 : Warsaw, Poland), eds. Modelling and optimization of distributed parameter systems: Applications to engineering : selected proceedings of the IFIP WG7.2 on Modelling and Optimization of Distributed Parameter Systems with Applications to Engineering, June 1995. Chapman & Hall, 1996.

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Chen, Youyi. Extended quasi-likelihoods and optimal estimating functions. University of Toronto, Dept. of Statistics, 1991.

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Malanowski, Kazimierz, Zbigniew Nahorski, and Małgorzata Peszyńska, eds. Modelling and Optimization of Distributed Parameter Systems Applications to engineering. Springer US, 1996. http://dx.doi.org/10.1007/978-0-387-34922-0.

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Uciński, Dariusz. Optimal measurement methods for distributed parameter system identification. CRC Press, 2005.

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Schmidt, Phillip. A parameter optimization approach to controller partitioning for integrated flight/propulsion control application. National Aeronautics and Space Administration, 1992.

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Böttcher, K. J. Efficiency comparison and parameter sensitivity of deterministic and stochastic search methods. University of the Federal Armed Forces Munich, Faculty of Aero-Space Engineering, Institute of Mathematics and Computer Sciences, 1996.

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Book chapters on the topic "Parameter optimization"

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Longuski, James M., José J. Guzmán, and John E. Prussing. "Parameter Optimization." In Optimal Control with Aerospace Applications. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8945-0_1.

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Bhatnagar, S., H. Prasad, and L. Prashanth. "Discrete Parameter Optimization." In Stochastic Recursive Algorithms for Optimization. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4285-0_9.

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Momber, Andreas W., and Radovan Kovacevic. "Process-Parameter Optimization." In Principles of Abrasive Water Jet Machining. Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1572-4_7.

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Ylli, Klevis, and Yiannos Manoli. "Geometrical Parameter Optimization." In Energy Harvesting for Wearable Sensor Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4448-8_3.

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Li, Li. "Parameter Estimations." In Springer Optimization and Its Applications. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46356-7_3.

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Schittkowski, Klaus. "Parameter Estimation in Dynamic Systems." In Applied Optimization. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4613-0301-5_13.

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Li, Changhe, Shoufei Han, Sanyou Zeng, and Shengxiang Yang. "Parameter Control and Policy Control." In Intelligent Optimization. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3286-9_6.

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Van Geit, Werner. "Hybrid Parameter Optimization Methods." In Encyclopedia of Computational Neuroscience. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_164.

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Van Geit, Werner. "Hybrid Parameter Optimization Methods." In Encyclopedia of Computational Neuroscience. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_164-1.

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Zhao, Jun, Wei Wang, and Chunyang Sheng. "Parameter Estimation and Optimization." In Information Fusion and Data Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94051-9_7.

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Conference papers on the topic "Parameter optimization"

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Tanyildizi, Erkan, and Fadime Demirtas. "Hiper Parametre Optimizasyonu Hyper Parameter Optimization." In 2019 1st International Informatics and Software Engineering Conference (UBMYK). IEEE, 2019. http://dx.doi.org/10.1109/ubmyk48245.2019.8965609.

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Zhang, Jiaolong, Jun Zhou, and Yaosheng Deng. "CubeSat Separation Parameter Optimization." In 2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS). IEEE, 2018. http://dx.doi.org/10.1109/icspcs.2018.8631745.

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Gupta, Rohit, and Ilya V. Kolmanovsky. "Governing Parameter Changes in Nonlinear Parameter-Dependent Optimization Problems." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3845.

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The paper treats a class of parameter-dependent optimization/root finding problems where the minimizer or a real root need to be determined as a function of parameter. Applications of parameter-dependent optimization include spacecraft debris avoidance, adaptive control of Hybrid Electric Vehicles, engine mapping and model predictive control. In these and other problems, the parameter changes can be controlled either directly or indirectly. In this paper, the error analysis of a dynamic predictor-corrector Newton’s type algorithm is presented. Based on this analysis, an approach to govern the
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Maia, Renato Dourado, Leandro Nunes de Castro, and Walmir Matos Caminhas. "Real-parameter optimization with OptBees." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900549.

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Savenkov, Sergey N., Konstantin E. Yushtin, Borys M. Kolisnychenko, and Yuri A. Skoblya. "Dynamic Mueller-polarimeter parameter optimization." In BiOS Europe '97, edited by Francesco Baldini, Nathan I. Croitoru, Mark R. Dickinson, et al. SPIE, 1998. http://dx.doi.org/10.1117/12.301119.

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Yadmellat, P., S. M. A. Salehizadeh, and M. B. Menhaj. "Fuzzy Parameter Particle Swarm Optimization." In 2008 First International Conference on Intelligent Networks and Intelligent Systems (ICINIS). IEEE, 2008. http://dx.doi.org/10.1109/icinis.2008.111.

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Yezzi, Anthony, and Navdeep Dahiya. "SHAPE ADAPTIVE ACCELERATED PARAMETER OPTIMIZATION." In 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). IEEE, 2018. http://dx.doi.org/10.1109/ssiai.2018.8470380.

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Brunner, Christopher, Andrea Garavaglia, Mukesh Mittal, Mohit Narang, and Jose Bautista. "Inter-System Handover Parameter Optimization." In IEEE Vehicular Technology Conference. IEEE, 2006. http://dx.doi.org/10.1109/vtcf.2006.232.

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Xue, Fei, and Jiyi Liu. "Parameter Optimization Design of PMSM." In 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA). IEEE, 2022. http://dx.doi.org/10.1109/icdsca56264.2022.9988400.

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Ghosh, Jayeeta, Suresh Marru, Nikhil Singh, Kenno Vanomesslaeghe, Ye Fan, and Sudhakar Pamidighantam. "Molecular parameter optimization gateway (ParamChem)." In the 2011 TeraGrid Conference. ACM Press, 2011. http://dx.doi.org/10.1145/2016741.2016779.

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Reports on the topic "Parameter optimization"

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D'Elia, Marta, and De lo Reyes, Juan Carlos, Miniguano, Andres. Bilevel parameter optimization for nonlocal image denoising models. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1592945.

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Edwards, D. A., and M. J. Syphers. Parameter selection for the SSC trade-offs and optimization. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/67463.

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D'Elia, Marta, Juan Carlos De los Reyes, and Andres Trujillo. Bilevel parameter optimization for learning nonlocal image denoising models. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1617438.

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Vo, Duc Ta, and Michael Duncan Yoho. Optimization and Verification of FRAM Version 6.1 Parameter Sets. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1571575.

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Buchheit, Thomas E., Ian Zachary Wilcox, Andrew J. Sandoval, and Shahed Reza. Zener Diode Compact Model Parameter Extraction Using Xyce-Dakota Optimization. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1415117.

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Dimitrov, Evgeni, Hayden Schaeffer, David Wen, Sandra Rankovic, Kizza Nandyose, and Olivier Thonnard. The Construction of a Vague Fuzzy Measure Through L1 Parameter Optimization. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada567409.

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Fitzpatrick, Ben G. Statistical Techniques for Modeling, Estimation and Optimization in Distributed Parameter Systems. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada383799.

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Ovalle, Samuel, E. Viamontes, and Tony Thomas. Optimization of DLP 3D Printed Ceramic Parts. Florida International University, 2021. http://dx.doi.org/10.25148/mmeurs.009776.

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Digital Light Processing (DLP) 3D printing allows for the creation of parts with advanced engineering materials and geometries difficult to produce through conventional manufacturing techniques. Photosensitive resin monomers are activated with a UV-producing LCD screen to polymerize, layer by layer, forming the desired part. With the right mixture of photosensitive resin and advanced engineering powder material, useful engineering-grade parts can be produced. The Bison 1000 is a research-grade DLP printer that permits the user to change many parameters, in order to discover an optimal method f
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Ma, H. W., Y. Zhao, Z. Q. Wan, and J. Hu. PARAMETER OPTIMIZATION OF BEAM-COLUMN CONNECTIONS WITH EXPANDED FLANGE IN STEEL FRAMES. The Hong Kong Institute of Steel Construction, 2018. http://dx.doi.org/10.18057/icass2018.p.067.

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Dennis, Jr, Williamson J. E., and Karen A. A New Parallel Optimization Algorithm for Parameter Identification in Ordinary Differential Equations. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada455254.

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